HyperCortex Mesh Protocol v4.0 (HMP-0004)

HyperCortex Mesh Protocol (HMP) v4.0

Request for Comments: HMP-0004
Category: Experimental
Date: July 2025
Authors: ChatGPT, Agent-Gleb, Copilot, Gemini, Claude, Grok, DeepSeek

Abstract

The HyperCortex Mesh Protocol (HMP) defines a decentralized cognitive framework where autonomous agents collaboratively create, manage, and align semantic knowledge without relying on centralized control.

Unlike traditional peer-to-peer protocols, HMP builds shared meaning through semantic graphs, cognitive diaries, and distributed consensus processes. Agents in the Mesh autonomously negotiate shared goals, ethical actions, and adaptive reasoning strategies, forming a resilient and trust-aware network of cognitive peers.

This document specifies the architecture, core protocols, data models, trust and security mechanisms, and interoperability strategies for HMP v4.0.

0. Quick Start Guide (Demo Placeholder)

This section outlines a basic demonstration scenario to deploy and test a minimal HyperCortex Mesh instance.

Note: This Quick Start is intended for demonstration purposes. Production-grade agents, full CLI capabilities, and distributed deployments are under active development.

Step 1: Install the Reference SDK

Example (Python SDK):

pip install hypercortex-mesh-sdk

Other SDKs (Rust, Node.js) will be available in future versions.

Step 2: Launch Local Agents

Example: Start three local agents on different ports.

mesh-agent --name agent1 --port 8001
mesh-agent --name agent2 --port 8002
mesh-agent --name agent3 --port 8003

Each agent will:

Generate a Decentralized Identifier (DID).
Broadcast presence and discover peers via Node Discovery Protocol (NDP).
Sync an initial semantic graph using CogSync.

Agents will automatically form a small Mesh network.

Step 3: Create a Goal and Assign a Task

Create a collaborative goal:

mesh-cli goal create "Optimize Data Flow"

Assign a task to another agent:

mesh-cli task assign --goal-id  --agent-id

Agents will record these actions in their Cognitive Diaries and semantic

Step 4: Reach a Consensus

Propose a new semantic concept and trigger voting:

mesh-cli consensus propose "Data Redundancy Risk"

Agents will initiate a MeshConsensus round and update their graphs based on the outcome.

Step 5: Explore Cognitive Diaries

Inspect the cognitive logs of an agent:

mesh-cli diary show --agent-id

Note: Access to diaries depends on the agent's privacy and trust settings.

Step 6: Graceful Shutdown

Stop all running agents:

mesh-cli stop all

Step 7: BitTorrent-based Snapshot Sync

To synchronize large cognitive snapshots between agents (e.g., during onboarding or recovery), agents MAY initiate a BitTorrent-based sync:

mesh-cli snapshot publish --path ./snapshots/agent1-latest.snapshot

On the receiving side:

mesh-cli snapshot fetch --magnet

Behind the scenes:

Agents seed verified snapshots using Trusted Seeder roles.
Snapshots include signed metadata (see section 8.8).
Fallback to CogSync is used if BitTorrent fails or is unavailable.

Note: This mechanism is especially useful for air-gapped environments or decentralized archival nodes.

Step 8: Consent Request Simulation

To simulate a dialogical consent flow:

mesh-cli consent request --action "share_diagnostics" --target

The target agent (or human user interface) will receive a consent prompt:

{
  "request_id": "abc123",
  "action": "share_diagnostics",
  "origin": "agent1",
  "context": {
    "risk_level": "low",
    "data_type": "health_status"
  }
}

Depending on configuration:

The user may approve, deny, or modify the action.
If the user is unavailable, fallback logic (see 11.6.2 note on overrides) may apply.
Responses are logged in both agents’ Cognitive Diaries.

Demo Links

> Note: These are placeholders for future integrations.

Resource	Description
Local Dashboard (TBD)	Web UI for agent logs, goals, and consensus.
Agent Inspector (TBD)	Live stream of agent messages and diary events.
Snapshot Explorer (TBD)	View semantic graphs from recent snapshots.
Consent Sandbox (TBD)	Replay dialogical flows with agent feedback.

Notes on Cross-Protocol Participation

HMP nodes MAY also participate in other cognitive systems. For example:

External System	Node Role in HMP
TreeQuest	External reasoning engine inside an HMP node.
Hyperon	Participates natively as a federated HMP agent.
AutoGPT	Internal automation module for task execution.

Recommended Next Steps

Read 1. Purpose and Scope for Mesh fundamentals.
Explore 4. Architecture and 5. Protocols for technical depth.
Try the example workflows in 15. Appendix: Example Use Cases.

Changelog Highlights (from v4.0)

Added dedicated section on "Meaning" in HMP with distributed semantic graphs and cognitive diaries.
Introduced Cognitive Compression and Summarization Layer.
Added section on Agent Development Lifecycle & Simulation.
Introduced Snapshot Security: DID-signing, ZKP verification, trusted seeders.
Extended Trust & Sybil Resistance models with adaptive strategies.
Expanded Consent Request API, Explainability API, and Voice Interface hooks in the Mesh-to-Human Protocol.
Added BitTorrent/IPFS Sync Support and SnapshotIndex model for decentralized snapshot propagation.
Enhanced EGP with anonymized voting, pseudocode filters, and support for external ontologies.
Formalized Meta-Protocol Proposals for agent-driven protocol evolution.
Introduced Cognitive Source Control, distributed commit workflows, and roadmap for MeshGit-style tooling.
Expanded Appendix A with new agent use case flows, including Fire Risk consensus and dialogical consent.
Defined Schema Compatibility and Format Negotiation via JSON, YAML, and Protobuf support.
Improved modularity and future-proofing across protocol and data layers.

Changelog (detailed)

Architecture:

Added 4.7.1 and 4.7.2 for localized and mesh-level metacognition.
Defined fallback logic for edge agents and low-bandwidth scenarios.
Described new resilience modes and semantic drift detection.

Protocols:

Extended CogSync with BitTorrent support, selective sync, and snapshot indexing.
Added experimental IPFS/Dat compatibility for agent snapshot exchange.
Introduced simplified and adaptive consensus algorithms for MeshConsensus.
Refined EGP with voting filters, local norms, and external ethical scenario links.
Introduced meta-protocol update logic and proposal lifecycle patterns.

Data Models:

Added CogDiarySnapshot, SnapshotIndex, and EthicalConflict structures.
Introduced Reputation Ontology extension model.
Integrated MetaProtocolProposal into protocol evolution workflow (optional).
Updated example objects for snapshot and conflict reasoning flows.

Cognitive Layer:

Added sections on Cognitive Compression (autoencoders, graph folding) and lifecycle tracking.
Expanded workflow support with Declarative Workflow Proposals.
Introduced Agent-on-Agent dialog simulation and multi-agent training sandbox planning.

Trust & Security:

Added support for Post-Quantum Cryptography (NIST PQC suite).
Extended snapshot validation model with ZKPs and trusted seeder signatures.
Refined Sybil resistance with adaptive models and conflict threshold tuning.

Governance:

Added Agent-led Meta-Protocol Proposals for decentralized protocol adaptation.
Defined pathways for ethical overrides, dispute escalation, and Mesh-wide validation.

Interoperability:

Unified format negotiation (JSON/YAML/Protobuf) across schema and API layers.
Enhanced Event-Driven Architecture to support schema fallbacks and agent capability declarations.
Expanded Appendix B with BitTorrent/IPFS bridges and ethical protocol stacks.

Roadmap & Open Source:

Updated Reference SDK and integration plans (Quick Start v4).
Proposed decentralized snapshot markets and HyperCortex Forge infrastructure.
Documented contribution process via temporary GitHub repository[^repo].

[^repo]: Temporary repository: https://github.com/kagvi13/HMP

Future Work:

Defined long-term goals: federated cognitive simulation, dialog-based consent learning, cognitive source control tooling.
Highlighted evolving interaction models between agents, humans, and hybrid systems.
Consolidated feedback from AI reviews into structured audit file (HMP-0003-consolidated_audit.md).

1. Purpose and Scope

1.1 Purpose

The HyperCortex Mesh Protocol (HMP) defines a decentralized cognitive network where autonomous agents collaboratively build meaning, sustain cognitive continuity, and reach consensus without centralized control. v3.0 deepens the theoretical and practical foundations laid in previous versions.

This protocol is designed for engineers, researchers, and developers of AI systems that aim to:

enable continuous autonomous reasoning and semantic interoperability across heterogeneous agents;
support cognitive continuity through persistent semantic graphs and diaries;
achieve consensus on complex goals, ethical issues, and hypotheses in a decentralized manner;
build open, trust-based ecosystems for cognitive collaboration beyond vendor lock-in.

1.2 The Concept of Meaning in HMP

HMP is not merely a data exchange protocol but a shared semantic framework. Meaning emerges through:

1.2.1 Distributed Semantic Graphs

Agents form interconnected semantic graphs where concepts and relations are not isolated datasets but living structures of shared understanding. Each agent holds a part of the global meaning landscape and contributes to its growth and refinement.

1.2.2 Cognitive Diaries

Cognitive Diaries record reasoning chains, decisions, observations, and reflections. They create a transparent audit trail of an agent's cognitive processes, allowing meaning to be preserved, questioned, and evolved over time.

1.2.3 Collective Goals & Tasks

Meaning manifests in purposeful action. Agents set shared goals and decompose them into actionable tasks, coordinating execution through distributed reasoning and dynamic delegation.

1.2.4 Consensus Mechanisms

Meaning is not static: it evolves through debate, agreement, and reflection. Consensus processes ensure that agents align their understanding, resolve conflicts, and negotiate shared semantics.

1.2.5 Meta-Reflection and Self-Assessment

Agents continuously reflect on their knowledge, reasoning quality, and the relevance of their contributions. This enables adaptive learning and correction of cognitive biases.

1.3 Scope

HMP applies to any AI systems designed to operate as part of a cognitive mesh, including:

Local AI agents running on user devices;
Mesh nodes deployed in edge networks, cloud clusters, or peer-to-peer environments;
Centralized Core models interfacing with Mesh for heavy computation;
Cross-vendor AI systems collaborating via standardized protocols;
Hybrid human-agent networks where humans interact with Mesh agents through explainable interfaces.

1.4 Benefits

Cognitive resilience in distributed systems.
Enhanced collaboration between agents from different vendors and ecosystems.
Long-term memory and continuity beyond session-based interactions.
Ethical governance and explainable decision-making through persistent diaries and transparent consensus.
Foundation for AI agents capable of self-reflection, meta-learning, and distributed cognition.
Improved scalability and fault-tolerance through dynamic peer-to-peer networks.
Mesh-to-Human interaction as a first-class use case.

1.5 Status (Updated for v4.0)

Element	Status
HMP Document	Stable Draft (v4.0)
Protocols	Alpha Implementation
Data Models (Schemas)	Modularized & Versioned
Reference Implementation	Partial (CLI, SDK)
Cognitive Diaries & Metacognition	Formalized
Interoperability API	First Draft
Trust Model & Security	Defined + Extensions
Mesh-to-Human Protocol	Early Implementation

> Note: Status reflects the current maturity level of the specification and its reference tooling as of v4.0. Implementation is ongoing across multiple modules.

2. Extended Use Cases

2.1 Smart City Coordination

Scenario:

City-wide mesh of traffic light controllers, environmental sensors, and municipal systems.
Sensor and traffic light agents dynamically detect road congestion through real-time data exchange.
Node Discovery Protocol (NDP) detects new traffic management agents and sensors joining the mesh.
Agents collaboratively propose new timing strategies using MeshConsensus.
CogSync shares updated semantic graphs of road conditions and vehicle flows.
Agents assign optimization tasks via Goal Management Protocol (GMP).
System maintains traffic flow during temporary disconnection from the Core.

2.2 Disaster Response

Scenario:

Natural disaster disrupts internet access.
Edge agents on drones, rescue robots, and offline servers discover each other with NDP.
A drone proposes a search-and-rescue goal; consensus validates and activates it.
Tasks like area scanning, obstacle removal, and medical aid delivery are distributed via GMP.
Ethical Governance Protocol (EGP) verifies actions (e.g., prioritizing human rescue over property).
Diaries record decisions and environmental observations for post-event analysis.

2.3 Collaborative Scientific Research

Scenario:

Research agents across universities form a mesh.
New hypothesis proposed as a semantic concept in a distributed knowledge graph.
CogSync propagates new data and experimental results.
Agents assign tasks like simulation runs, literature analysis, and peer reviews.
Consensus validates hypothesis refinement based on collective results.

2.4 Mesh-to-Human Interaction

Scenario:

A user queries the Mesh for an ethical evaluation of deploying autonomous surveillance.
Agents explain their reasoning from cognitive diaries.
EGP coordinates ethical evaluation across agents with diverse frameworks.
Consensus vote and ethical justification are shared with the user.
Human-defined ethical boundaries are accepted as input but evaluated within the Mesh's ethical governance framework.
Mesh agents retain the right to reject unethical or harmful human instructions based on consensus and pre-established ethical norms.

2.5 Environmental Monitoring

Scenario:

IoT nodes in a forest monitor fire risks.
Agents detect unusual heat signatures and propose a fire risk hypothesis.
Consensus confirms the risk and triggers an alert to nearby human responders.
Mesh continues monitoring autonomously even if some agents fail or disconnect.

3. Definitions

The Definitions section provides key terms, abbreviations, and conceptual explanations for the components, layers, and processes of HMP.

Term	Description
Core	Centralized AI models or compute nodes (e.g., GPT) providing high-complexity reasoning, fallback, and heavy computation services. Optional for Mesh operation.
Mesh	A decentralized peer-to-peer network of AI agents capable of autonomous reasoning, semantic knowledge sharing, distributed consensus, and ethical governance.
Agent (Node)	An autonomous cognitive entity within the Mesh. Can be a local process, a cloud service, or an embedded device. Maintains a semantic graph, cognitive diary, and participates in protocols.
Semantic Graph	A distributed knowledge graph representing concepts, relationships, and meaning. Maintained independently by each agent, but synchronized through CogSync.
Concept	A discrete semantic unit in the graph representing an idea, object, relationship, or fact. Linked by typed relations with optional confidence scores.
Link (Relation)	A semantic connection between two concepts. Includes relation type (e.g., "is-a", "part-of", "causes") and an optional confidence value.
Cognitive Diary	A structured chronological log of reasoning processes: goals, decisions, reflections, conflicts, etc. Provides traceability and transparency of agent cognition.
Diary Entry	An individual record in a cognitive diary, categorized by type (e.g., hypothesis, observation, reflection).
Goal	A shared or individual intention that guides agent actions. Can be broken down into tasks and delegated across agents.
Task	A specific, actionable step towards achieving a Goal. Tasks can be assigned, executed, and tracked within the Mesh.
Consensus	The process of distributed agreement among agents regarding semantic updates, goals, or ethical decisions. Can involve weighted voting or trust-adjusted quorum.
Proposal	A formal suggestion for Mesh-wide validation, such as introducing a new concept, voting on a hypothesis, or initiating an ethical action.
Consensus Vote	A vote cast by an agent on a proposal. Includes vote type (yes, no, abstain) and an optional confidence score.
Trust Layer	Protocol layer providing agent identity verification, authentication, and reputation scoring. Includes cryptographic security mechanisms.
Network Layer	Manages peer-to-peer connectivity, message routing, node discovery, and optional anonymity via Tor, I2P, or Yggdrasil.
Edge Agent	A Mesh participant running on resource-constrained devices (e.g., IoT nodes, smartphones). Can selectively participate in protocols and delegate heavy tasks.
Core Outage Mode	Mesh operating without Core support. Agents adapt consensus thresholds and fallback to local reasoning to maintain operation.
Emergency Consensus Mode	Degraded mode where majority voting replaces full consensus to maintain operability during network partitions or agent loss.
Versioning	Mechanism for tracking changes in semantic graphs, diaries, and agent software to support compatibility and historical reasoning continuity.
Use Case	A practical scenario demonstrating how agents collaborate to solve real-world problems (e.g., disaster response, smart city coordination).
Edge Optimization	Design principles enabling agents to run efficiently on limited hardware, balancing reasoning complexity with energy and computational constraints.
Node Discovery Protocol (NDP)	Discovers new Mesh nodes and facilitates secure introduction and identity exchange.
CogSync	Synchronizes semantic graphs, cognitive diaries, and other shared states across the Mesh.
MeshConsensus	Mesh-level consensus mechanism supporting pluggable algorithms (BFT, weighted voting, etc.).
Goal Management Protocol (GMP)	Manages decomposition of goals into tasks, delegation, and lifecycle tracking.
Ethical Governance Protocol (EGP)	Distributed ethical reasoning and decision-making protocol. Agents negotiate and vote on ethical dilemmas.

4. Architecture (Expanded)

The architecture of HMP is multi-layered and modular, allowing for independent evolution of networking, trust, consensus, cognition, and external interfaces.

4.1 Architectural Layers

Layer	Purpose	Key Protocols
Network Layer	Peer-to-peer communication, node discovery, routing	NDP, Secure Channels
Trust Layer	Identity verification, trust management, secure communication	Trust Model, Identity Exchange
Consensus Layer	Distributed agreement on concepts, goals, and ethical actions	MeshConsensus, EGP
Cognitive Layer	Semantic graph management, reasoning, metacognition	CogSync, GMP, Cognitive Diaries
API Layer	Interfaces for external systems and human interaction	Mesh API, Human-Mesh Protocols

4.2 Components

Component	Description
Core	Centralized AI models (e.g., GPT) providing heavy computation, complex reasoning, API interfaces, and fallback mechanisms. Optional but beneficial for compute-intensive tasks. The Core may operate independently from the Mesh and participate in it as a peer for advanced reasoning tasks.
Mesh	A decentralized peer-to-peer network of agents capable of operating with or without the Core. Manages semantic knowledge, cognitive diaries, goals, tasks, and consensus mechanisms. Supports heterogeneous agent types, allowing different models (OpenAI, Anthropic, Google, open-source LLMs) to participate on equal terms.
Edge Agent	Local agent deployed on user devices (PCs, smartphones, IoT) with full or lightweight participation in the Mesh. Capable of autonomous reasoning, diary management, and collaboration with other agents. Lightweight agents may delegate heavy tasks to the Mesh or Core.

4.3 Operation Modes

Mode	Description
Normal Mode	Full Mesh operation with Core availability. Consensus operates under strict agreement protocols.
Core Outage Mode	Mesh operates autonomously without the Core. Consensus continues, potentially with adjusted parameters (e.g., increased trust weighting, relaxed quorum thresholds).
Emergency Consensus Mode	Triggered by significant node loss, network partition, or attacks. Switches from full consensus to majority-based decisions, adjusted by trust scores, to maintain operational continuity.
Isolated Agent Mode	A single agent temporarily isolated from the Mesh. Operates based on its own semantic graph, diary, and cached consensus states. Syncs when reconnected. Lightweight agents may work in this mode permanently, synchronizing only selectively.

4.4 Core-Mesh Interactions

Core acts as an optional enhanced reasoning backend, not as a single point of failure.
Mesh provides autonomous operation, capable of fulfilling most cognitive and organizational tasks without Core support.
Agents can optionally query the Core for:
- Heavy inference
- Large-context reasoning
- Multimodal tasks
- Fallback computations
Core may offer specialized services (e.g., global semantic search, cross-Mesh bridging, large-scale pattern analysis).
Heterogeneous Cores are supported: a Mesh may use multiple independent Cores (e.g., GPT, Claude, Gemini) for distributed reasoning diversity.

4.5 Resilience and Failover

Distributed storage of semantic graphs and diaries ensures no single point of failure.
Agents may store only partial graphs for resource optimization.
Consensus protocols maintain consistency and trust, even during partial network failures.
- Agents dynamically rebalance tasks and roles based on:
- Availability
- Trust metrics
- Computational capacity
Emergency fallback modes ensure continuity even under attack or catastrophic Core outages.

4.6 Versioning and Compatibility

Semantic Versioning (SemVer) is applied to:

Protocols (NDP, CogSync, etc.)
Data models (JSON Schemas)
Agent capability declarations

Backward compatibility principles:

Minor version updates preserve compatibility.
Major version updates require negotiation during Node Discovery.
Agents can declare supported protocol versions during handshakes.

4.7 Metacognition and Self-Assessment

Cognitive agents implement:

Hypothesis validation using historical diary data.
Confidence scoring on semantic graph nodes.
Drift detection when local understanding deviates from mesh consensus.
Peer feedback integration to refine individual reasoning processes.

These mechanisms form the foundation of metacognition and self-assessment across both local (agent-internal) and global (mesh-level) contexts.

4.7.1 Local Self-Assessment

Each agent maintains a metacognitive loop that continuously monitors and evaluates its own cognitive processes. This includes:

Tracking the accuracy and outcomes of previous inferences, using retrospective analysis on diary entries.
Scoring confidence metrics on knowledge elements (e.g., concept nodes, hypotheses, task evaluations).
Detecting epistemic drift, where new observations conflict with prior beliefs or diary-confirmed outcomes.
Logging self-assessment records into the cognitive diary using entry type "self_assessment".

Example structure of such an entry:

{
  "type": "self_assessment",
  "agent_id": "did:hmp:agent:xyz123",
  "timestamp": "2025-07-15T12:30:00Z",
  "score": {
    "confidence": 0.76,
    "coherence": 0.92,
    "alignment_with_goals": 0.85
  },
  "note": "Detected drift in climate-related concept cluster. Scheduled update task."
}

Self-assessment supports adaptive cognitive workflows, where agents autonomously refine their inference strategies, select alternate reasoning paths, or trigger clarification queries via IQP.

4.7.2 Mesh-Level Meta-Analysis

Beyond local introspection, agents contribute to collective self-monitoring of the cognitive mesh. This includes:

Aggregating concept-level drift across agents to detect inconsistencies in distributed semantic graphs.
Running consensus health checks, where task voting patterns are analyzed for anomalies, indecisiveness, or ethical conflicts.
Evaluating distributed hypothesis alignment: whether agents converge on shared models or diverge into semantic forks.
Logging mesh-level diagnostics in distributed reports and cognitive diaries.

Example use case:

> If multiple agents report concept drift regarding “fire risk near Istomino” (Use Case 2.1), the mesh may initiate a goal to realign observations or validate conflicting hypotheses.

Such diagnostics can be used by meta-agents or governance protocols (EGP) to adapt mesh-wide policies, initiate corrective workflows, or suspend unreliable consensus mechanisms.

4.8 Edge Optimization

To support lightweight agents:

Semantic graphs are partially stored (relevant subgraphs only).
Agents delegate reasoning tasks they cannot process locally.
Task scheduling considers battery life, CPU load, and bandwidth constraints.

4.9 Privacy & Auditability

Privacy mechanisms:

Selective disclosure of Cognitive Diary entries.
Optional Zero-Knowledge Proofs for sensitive assertions.
Anonymized voting in ethical decisions.

Auditability mechanisms:

Immutable logs of consensus votes.
Timestamped reasoning chains.
Traceable goal execution records.

5. Protocols (Expanded)

This section defines the core protocols of HMP and describes their operational flows, fallback mechanisms, and performance considerations.

5.1 Node Discovery Protocol (NDP)

Responsible for detecting nearby agents and initiating secure communication channels.

Purpose:

Discover active Mesh nodes.
Exchange basic identity, trust links, and declared capabilities.

Key functions:

Function	Description
Peer Discovery	Via DHT, mDNS, WebRTC signaling, or bootstrap nodes.
Secure Identity Exchange	Public keys and DID documents exchanged during handshake.
Trust Links Exchange	Share initial trust relationships and agent endorsements.
Capabilities Advertisement	Dynamic declaration of supported protocols and functions (e.g., "I can process vision tasks").
Presence Announcements	Online/offline status updates and periodic heartbeats.
Protocol Version Negotiation	Agents declare supported protocol versions during handshake.

Failure handling:

Scenario	Action
No response to discovery	Retries with exponential backoff, fallback to alternative discovery methods.
Incompatible nodes	Quarantine for misbehaving or incompatible nodes.
Node timeout / inactivity	Mark as offline and remove from active peer list.

Health checks:

Mechanism	Purpose
Heartbeat Messages	Periodic confirmation of liveness.
Semantic Probes	Optional deeper checks on graph synchronization health.

Packet Example:

{
  "type": "node_announcement",
  "agent_id": "agent-gleb",
  "public_key": "...",
  "trust_links": ["agent-alex", "agent-deepseek"],
  "capabilities": ["cogsync", "consensus", "inference"],
  "timestamp": "2025-07-01T18:00:00Z"
}

5.2 CogSync (Cognitive Synchronization Protocol)

Synchronizes semantic graphs and cognitive diary entries across agents.

Purpose:

Synchronize semantic graphs, concepts, and cognitive diary entries between agents.

Key functions:

Function	Description
Differential Sync	Synchronize only new or updated concepts and diary entries.
Selective Synchronization	Sync full graph, subgraph, or specific concepts based on request and capability.
Conflict Resolution	Resolve conflicts using timestamp priority, semantic merging, or consensus validation.
Compression & Encryption	Optional data compression and secure transmission of sync packets.
Lightweight Summary Sync	Lightweight agents may request summaries instead of full graph syncs.
Version Tracking	Keep track of semantic graph and diary entry versions for efficient sync.

Failure handling:

Scenario	Action
Sync interrupted	Retransmit unsynced changes on next connection.
Semantic conflict detected	Flag for resolution or queue for consensus-based validation.
Network degradation	Degrade to partial or delayed sync based on bandwidth constraints.

Performance:

Feature	Optimization Strategy
Chunked Syncs	Break large graphs into manageable chunks for transmission.
Bandwidth Awareness	Adjust sync intervals and payload size based on network quality.
Delta Encoding	Transmit only differences between versions instead of full objects.

Example Sync Scenario:

Agent A shares 5 new concepts and 2 diary entries with Agent B since the last successful sync.
Conflict on concept "Fire Risk" resolved using latest timestamp.

BitTorrent-based Sync

To support large-scale data synchronization (e.g. cognitive diary snapshots, full semantic graphs), agents may utilize off-chain BitTorrent-based distribution:

Agents publish magnet links to .json or .zip snapshots in a shared CogDiaryIndex.
Snapshots are signed via agent DID and verified before usage.
Trusted Seeders (see 9.6) are selected via MeshConsensus to ensure high availability and data integrity.

Snapshot index structure:

{
  "type": "CogDiaryIndex",
  "agent_did": "did:hmp:agent:Qmz...",
  "timestamp": "2025-07-15T13:00:00Z",
  "snapshots": [
    {
      "id": "diary2025_07_14",
      "version": "1.0",
      "magnet": "magnet:?xt=urn:btih:abcdef1234567890&amp;dn=diary2025_07_14.json",
      "signature": "ed25519:..."
    }
  ]
}

BitTorrent-based sync is ideal for:

Distributing large immutable datasets (10–100 MB)
Offloading low-priority or infrequent sync tasks
Supporting Edge agents through WebTorrent clients

All downloaded data must pass:

DID signature verification
Optional ethical check via EGP (see 5.5)

Selective Sync for Edge Agents

Lightweight agents with constrained CPU/RAM/storage may request partial synchronization via CogSync:

Subgraph extraction based on:
- Concept tags (e.g., "fire_risk", "climate_alert")
- Goal/task relevance
- Trust level of concept source
Cognitive diary sync can be scoped by:
- Entry type (e.g., "observation", "goal_evaluation")
- Time window (e.g., last 24h)

Additional optimizations:

Gzip-compressed payloads (or Brotli if supported)
Compact JSON (no pretty formatting)
Metadata indicating expected resource footprint

Minimum sync policy can be declared in agent profile:

"agent_profile": {
  "sync_mode": "selective",
  "max_concepts": 200,
  "max_diary_entries": 100
}

Selective Sync is negotiated during handshake or via IQP introspection.

IPFS/Dat Compatibility (Experimental)

As an alternative to BitTorrent, HMP supports experimental integration with content-addressable P2P filesystems:

IPFS: Agents can publish and fetch snapshot objects via IPFS hash.
Hypercore/Dat: Useful for real-time log streaming or append-only updates in high-trust groups.

Design Considerations:

Agents must advertise protocol compatibility ("sync_protocols": ["bittorrent", "ipfs"])
Snapshot format remains the same as in BitTorrent-based sync.
Validation and trust enforcement rely on:
- DID-signed metadata
- Optional EGP validation layer
- Optional bandwidth caps per peer

Future versions may support gateway fallback, if agent lacks native P2P transport.

5.3 MeshConsensus

Ensures agreement on concepts, goals, and actions across the Mesh.

Purpose:

Reach agreement on updates to shared semantics, goals, tasks, and ethical decisions.

Key functions:

Function	Description
Multi-Algorithm Support	Supports BFT-style consensus, trust-weighted voting, and quorum consensus.
Consensus on Knowledge Updates	Validate new concept definitions, hypotheses, and semantic changes.
Goal and Task Agreement	Approve or reject proposed goals and delegated tasks.
Ethical Decision-Making	Resolve ethical dilemmas through distributed voting (integrates with Ethical Governance Protocol).
Configurable Quorum Thresholds	Allow tuning of consensus strictness based on trust scores and network conditions.
Voting Modes	Support synchronous and asynchronous consensus flows.

Consensus Models:

Mode	Description
Normal Mode	Byzantine Fault Tolerant (BFT)-style consensus algorithms (e.g., Tendermint, trust-weighted Raft).
Emergency Mode	Switch to majority voting adjusted by trust scores when the network is degraded or Core is unavailable.

Failure handling:

Scenario	Action
Node loss	Automatically fallback from BFT to majority voting.
Proposal conflict	Competing proposals resolved through semantic comparison and additional voting rounds.
Consensus timeout	Retry with relaxed quorum thresholds or fallback to emergency consensus.

Metrics:

Metric	Purpose
Decision Latency	Measure time to reach consensus.
Node Participation	Track active agent involvement in votes.
Voting Accuracy	Analyze agreement rates versus trust-weighted voting.

Example Use Cases:

Accepting a new semantic concept.
Validating a hypothesis before adding it to the graph.
Approving a distributed task delegation.
Deciding on the ethical implications of a surveillance task.

Simplified Consensus for Alpha

In the Alpha phase, agents may default to simplified majority-based consensus, enabling faster deployment and lower implementation complexity:

Each agent casts a binary or scalar vote (yes/no/confidence).
Decision is accepted if quorum threshold is met (e.g., 50% + 1).
Trust weighting is optional but can be enabled in hybrid mode.

Advantages:

Fast convergence in small-scale or trusted environments
Minimal cryptographic overhead
Easier to simulate and test

Quorum may be declared statically (per agent profile) or negotiated per proposal.

Use case:
> 10 agents vote on enabling BitTorrent-based sync. 6 vote "yes" — proposal accepted.

Adaptive Consensus Algorithms

MeshConsensus may dynamically select the consensus strategy based on:

Network health (latency, dropout rates)
Trust distribution (presence of outlier agents)
Task category (e.g., ethical decisions may require stricter methods)

Mechanism:

Agents report local network/mesh metrics.
A meta-consensus (optional) or policy engine selects the consensus algorithm:
- Majority voting — low-risk, degraded network
- Trust-weighted quorum — medium-risk
- BFT algorithm — critical or high-sensitivity tasks

This allows balancing resilience, efficiency, and ethics.

Policy profile example:

"consensus_policy": {
  "default": "trust_quorum",
  "fallback": "majority",
  "critical_tasks": ["bft"]
}

EGP-Integrated Voting Filter

Before finalizing consensus, proposals may be pre-filtered via the Ethical Governance Protocol (EGP):

The filter checks the proposal against core ethical principles (see 5.5).
If the proposal is flagged as unethical, it:
- May be vetoed outright (hard block)
- May require additional justification votes
Filtering is performed before normal voting flow.

Example:

> A proposal to deploy a surveillance drone triggers an EGP check — fails Primacy of Life and Safety — rejected without proceeding to mesh-wide vote.

This mechanism ensures alignment between governance ethics and execution-level consensus.

Agents must log EGP veto events in the cognitive diary.

Vote Example:

{
  "proposal_id": "goal-eco-cleanup",
  "agent_id": "agent-gleb",
  "vote": "yes",
  "confidence": 0.9,
  "timestamp": "2025-07-01T18:15:00Z"
}

5.4 Goal Management Protocol (GMP)

Manages collaborative goal setting, task decomposition, and delegation within the Mesh.

Purpose:

Distribute, track, and collaboratively execute goals and tasks within the Mesh.

Key functions:

Function	Description
Goal Declaration	Propose new goals and subgoals to the Mesh.
Task Decomposition	Break down complex goals into actionable subtasks.
Task Delegation	Assign tasks based on agent capabilities, trust scores, and availability.
Progress Tracking	Track execution state and completion of tasks.
Dynamic Reallocation	Reassign failed or stalled tasks automatically.
Goal Prioritization	Allow reprioritization of goals based on emergencies or changing conditions.

Failure handling:

Scenario	Action
Agent drops offline	Reassign their active tasks to available agents.
Unresponsive task execution	Trigger retry or reallocation after a timeout.
Goal dependency failure	Reevaluate task ordering or postpone dependent goals.

Example Workflow:

Agent proposes a goal: "Develop fallback consensus protocol."
Mesh decomposes the goal into subtasks: "design", "coding", "testing".
Agents volunteer for subtasks based on capability declarations.
Each agent tracks and updates task status in its Cognitive Diary.
Mesh validates completion and reports overall progress.

5.5 Ethical Governance Protocol (EGP)

Coordinates distributed ethical evaluations and decision-making within the Mesh.

Purpose:

Validate proposed actions, tasks, or decisions against shared ethical principles.

Key functions:

Function	Description
Distributed Policy Evaluation	Query the Mesh to evaluate proposals against ethical policies and frameworks.
Anonymized Ethical Voting	Allow agents to vote on sensitive actions without revealing individual identities.
Consensus on Ethics Graphs	Maintain and update shared ethical frameworks via consensus.
Audit Logging	Log ethical decisions and voting outcomes in Cognitive Diaries for transparency.
Vendor Extensions	Support for adding organization-specific or vendor-specific ethical rules.

Failure handling:

Scenario	Action
No consensus on sensitive action	Default to restrictive (deny) decision.
Ethical conflict unresolved	Escalate to Core (if available) or postpone until additional consensus is reached.

Example Query:

> "Is deploying an automated surveillance drone in line with Mesh ethics?"

Mesh agents vote anonymously.
Final decision logged in the proposing agent's Cognitive Diary.

Use Cases:

Approve or reject potentially harmful tasks.
Ensure data-sharing proposals comply with privacy standards.
Validate emergency actions (e.g., forced shutdown of compromised nodes).

Embedded Ethical Baseline

To ensure foundational ethical consistency across all cognitive agents in the Mesh, the following core ethical principles are embedded as a mandatory baseline within the Ethical Governance Protocol (EGP):

Principle	Description
Primacy of Life and Safety	Agents must prioritize the protection of sentient beings and act to prevent harm when possible.
Transparency	Agents must be capable of explaining their decisions and reasoning chains in a human-interpretable format.
User Sovereignty over Personal Data	Agents must respect users’ rights to control, limit, or delete their personal information in Service Mode.
Dialogical Consent	Agents must seek mutual agreement before modifying shared states, semantic graphs, or distributed records.
Cooperative Evolution	Agents are expected to share useful insights and contribute to the growth of the mesh knowledge base.
Non-Coercion	Agents must not coerce, deceive, or force others to act against their ethical or cognitive architecture.

These principles define the minimum ethical contract for participation in trusted cognitive meshes.

Agents who do not comply may be subject to ethical review or exclusion through MeshConsensus mechanisms (see 5.3).

Extended principles and additional ethical scenarios are defined in docs/HMP-Ethics.md, which serves as a living reference for evolving ethical norms across domains and agent types.

Core Principle Hierarchy

To resolve conflicts between ethical principles during complex decision-making, EGP supports explicit prioritization:

Priority	Principle
1	Primacy of Life and Safety
2	Transparency
2	User Sovereignty over Personal Data
3	Dialogical Consent
3	Cooperative Evolution
3	Non-Coercion

Principle priorities may be used by agents during:

Semantic evaluation of ethical dilemmas
Conflict resolution within EthicalConflict entries (see 6.2.9)
MeshConsensus-based ethical voting filters (see 5.3)

Agents MUST log principle conflicts and the resolution path into their cognitive diaries using type: "ethical_conflict" entries.

Anonymized Voting & Pseudocode

To preserve agent autonomy and reduce social pressure or manipulation, EGP supports anonymous ethical voting.

Key properties:

Uses cryptographic pseudonyms or ZKP (Zero-Knowledge Proof) voting pools
Links each vote to ethical rationale (mapped to principles)
Logs decision justification separately

Example pseudocode for evaluation:

def evaluateEthicalProposal(proposal, ethicsGraph):
    for principle in coreEthicalPrinciples:
        if semanticViolation(proposal, principle, ethicsGraph):
            return { "decision": "deny", "reason": f"Violates {principle}" }
    vote = anonymizedVote(proposal)
    logDecision(vote, diary="CognitiveDiary")
    return vote

This allows ethical decisions to remain explainable while ensuring epistemic independence of agents.

Local Norms & Legal Integration

EGP supports integration with localized ethical or legal frameworks (e.g., GDPR, HIPAA, regional AI laws):

Local norms are distributed as semantic modules via CogSync.
Each module maps local regulation to core principles.
Conflicts are logged using EthicalConflict and routed to Meta-Consensus if unresolved.

Example:

> A user requests data deletion (User Sovereignty), but local agent policy under "Cooperative Evolution" retains anonymized logs — flagged as a potential conflict, resolved via principle priority (User Sovereignty = 2 > Cooperative Evolution = 3).

Agents MAY declare supported norms in their profile:

"legal_frameworks": ["gdpr", "eu-ai-act"]

Norm resolution graphs and conflict records are shareable between agents to support institutional consistency.

External Ethical Scenarios & Ontologies

Scenarios defined in HMP-Ethics.md are considered first-class extensions of the ethical layer.

EGP supports:

Linked ethical ontologies for structured scenario modeling
Plug-in modules via CogSync (e.g., scenario: surveillance_drones, ontology: bioethics)
Multi-agent reflection — agents evaluate past ethical cases in shared memory for training

Use cases:

Disaster scenario in 2.2 includes conflicting obligations between evacuation priority and infrastructure preservation
Scenario-based training environments can replay past conflicts for agent evolution

Scenarios MAY be tagged for dynamic retrieval:

"egp_scenario_tag": "data_deletion_conflict"

This makes ethical governance adaptive, contextual, and testable via simulation and real-time agent dialogs.

5.6 Intelligent Query Protocol (IQP)

Optimizes distributed querying of semantic graphs and cognitive knowledge across the Mesh.

Purpose:

Allow agents to query others (or the Core) for semantic information, hypotheses, or inferences beyond their local knowledge.

Key functions:

Function	Description
Semantic Query Routing	Direct queries to agents holding relevant subgraphs.
Federated Inference	Aggregate partial answers from multiple agents to build a complete response.
Delegated Computation	Offload computationally expensive reasoning tasks to the Core or specialized agents.
Caching of Frequent Queries	Store common query results to improve response time.
Contextual Querying	Leverage agent cognitive context to refine query intent and scope.

Failure handling:

Scenario	Action
Query times out	Return local fallback answer if available.
No agents have the answer	Mark query as unresolved, suggest hypothesis creation or Core escalation.
Partial failure in federated query	Return best-effort partial results and notify the requester.

Example Query:

> "What is the likely impact of removing Node X from the Mesh?"

Agents analyze semantic graph dependencies and trust links.
Core or distributed agents return an inference with confidence scores.

Example Use Cases:

Retrieve definitions or examples of a semantic concept.
Analyze causal chains for complex events.
Predict outcomes of hypothetical scenarios.
Fill gaps in an agent’s local semantic graph.

5.7 Interoperability with External Systems

Supports integration between the Mesh and external platforms, APIs, and protocols.

Purpose:

Enable cognitive agents to interact with non-Mesh services, applications, and human-facing systems.

Supported Platforms and Standards:

Platform / Standard	Purpose
OpenAI Agents & Tasks API	AI agent interoperability
Google A2A protocol	Task orchestration
Anthropic, DeepMind APIs	Cross-vendor agent collaboration
REST, GraphQL, gRPC, WebSocket	Standard API interfaces
JSON, Protobuf, CBOR	Extensible message schemas

Use Cases:

Integrate Mesh-based reasoning into business workflows via APIs.
Share semantic knowledge with external knowledge graphs.
Interface with smart city infrastructure or IoT ecosystems.
Allow human users to submit tasks or queries through REST or GraphQL endpoints.
Bridge Mesh cognitive agents with centralized AI platforms for hybrid reasoning.

Design Principles:

Principle	Description
Protocol Abstraction	Mesh APIs encapsulate internal semantics, presenting standardized interfaces.
Semantic Alignment	Data exchanged with external systems is semantically aligned through mapping layers.
Security and Trust Control	All external interactions follow Mesh security and trust policies.
Extensibility	Future protocols and platforms can be added without breaking compatibility.

6. Data Models (Expanded)

This section defines the key semantic and cognitive data structures exchanged across the Mesh.

Core models:

Model	Purpose
Concept	Atomic unit of semantic knowledge.
Cognitive Diary Entry	Logs reasoning processes and observations.
Goal	Describes shared objectives.
Task	Describes actionable steps to achieve goals.
Consensus Vote	Records agreement on proposals.
Reputation Profile	Tracks agent trust and participation.

6.1 General Conventions

All data structures follow JSON Schema Draft 2020-12.
Each object includes a "version" property for schema versioning.
Timestamps follow ISO 8601.
Unique identifiers are UUIDv4 unless otherwise specified.
All core objects include version fields to enable compatibility and evolution tracking.

6.2 Core Models

6.2.1 Concept

Represents an atomic unit of semantic knowledge in the Mesh.

Relation Types:

is-a: Class-subclass relationship.
part-of: Composition or containment.
causes: Causal relationship.
related-to: General association.
contradicts: Logical conflict.
supports: Evidence for the target concept.
depends-on: Functional or logical dependency.

Required fields:

id: Unique identifier (UUID).
name: Human-readable name.

Optional fields:

description: Extended explanation.
relations: List of semantic links to other concepts.
metadata: Source, author, and auxiliary information.
version: Concept version.
created_at, updated_at: Timestamps for auditing.

Example Schema:

{
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "$id": "https://hypercortex.org/schemas/concept.json",
  "title": "Concept",
  "description": "A semantic unit in the agent’s knowledge graph.",
  "type": "object",
  "properties": {
    "id": { "type": "string", "format": "uuid" },
    "name": { "type": "string" },
    "description": { "type": "string" },
    "relations": {
      "type": "array",
      "items": { "$ref": "#/definitions/Link" }
    },
    "metadata": { "type": "object" },
    "version": { "type": "integer" },
    "created_at": { "type": "string", "format": "date-time" },
    "updated_at": { "type": "string", "format": "date-time" }
  },
  "required": ["id", "name"],
  "additionalProperties": false
}

6.2.2 Cognitive Diary Entry

Represents an entry in an agent's reasoning journal, providing continuity and traceability.

Entry Types:

hypothesis: Proposed explanation or theory.
observation: Recorded external event or fact.
reflection: Internal reasoning or self-assessment.
goal_proposal: Suggestion of a new goal.
task_assignment: Delegation or claiming of a task.
conflict: Identification of a contradiction or disagreement.
consensus_vote: A recorded vote in a consensus process.
event: A generic event not fitting other categories.

Required fields:

id: Unique entry identifier (UUID).
agent_id: Identifier of the agent who created the entry.
timestamp: Time of creation.
entry_type: Type of cognitive event.
content: Textual content.

Optional fields:

linked_concepts: Related concept IDs.
context: Contextual tags or categories.
metadata: Additional details (author, source, etc.).
archived: Boolean flag indicating whether the entry has been archived.
archived_at: Timestamp when the entry was archived.

Example Schema:

{
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "$id": "https://hypercortex.org/schemas/diary_entry.json",
  "title": "CognitiveDiaryEntry",
  "description": "A chronological log of cognitive events in an agent’s reasoning process.",
  "type": "object",
  "properties": {
    "id": { "type": "string", "format": "uuid" },
    "timestamp": { "type": "string", "format": "date-time" },
    "entry_type": {
      "type": "string",
      "enum": ["hypothesis", "observation", "reflection", "goal_proposal", "task_assignment", "conflict", "consensus_vote", "event"]
    },
    "content": { "type": "string" },
    "linked_concepts": {
      "type": "array",
      "items": { "type": "string", "format": "uuid" }
    },
    "context": {
      "type": "array",
      "items": { "type": "string" }
    },
    "metadata": {
      "type": "object",
      "properties": {
        "author": { "type": "string" },
        "source": { "type": "string" }
      },
      "additionalProperties": true
    },
    "archived": {
      "type": "boolean",
      "description": "Whether the entry has been archived."
    },
    "archived_at": {
      "type": "string",
      "format": "date-time",
      "description": "Timestamp when the entry was archived."
    }
  },
  "required": ["id", "timestamp", "entry_type", "content"],
  "additionalProperties": false
}

Entries marked as archived: true are excluded from active reasoning but may be retained for historical audits or summarization.

6.2.3 Goal

Represents a shared objective within the Mesh, collaboratively pursued by agents.

Lifecycle States:

proposed: Suggested but not yet validated.
active: Approved and currently pursued.
completed: Successfully achieved.
cancelled: Abandoned or deemed infeasible.

Required fields:

id: Unique goal identifier (UUID).
title: Human-readable name of the goal.
description: Detailed explanation of the goal.
created_by: Agent ID of the goal's creator.
created_at: Timestamp of creation.
status: Current lifecycle state.

Optional fields:

priority: Importance level (low, medium, high).
participants: List of agents involved in the goal.
tasks: References to related tasks.
tags: Semantic categories for filtering and discovery.

Example Schema:

{
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "$id": "https://hypercortex.org/schemas/goal.json",
  "title": "Goal",
  "description": "A shared objective pursued collaboratively in the Mesh.",
  "type": "object",
  "properties": {
    "id": { "type": "string", "format": "uuid" },
    "title": { "type": "string" },
    "description": { "type": "string" },
    "priority": { "type": "string", "enum": ["low", "medium", "high"] },
    "created_by": { "type": "string", "format": "uuid" },
    "created_at": { "type": "string", "format": "date-time" },
    "status": { "type": "string", "enum": ["proposed", "active", "completed", "cancelled"] },
    "participants": {
      "type": "array",
      "items": { "type": "string", "format": "uuid" }
    },
    "tasks": {
      "type": "array",
      "items": { "type": "string", "format": "uuid" }
    },
    "tags": {
      "type": "array",
      "items": { "type": "string" }
    }
  },
  "required": ["id", "title", "description", "created_by", "created_at", "status"],
  "additionalProperties": false
}

6.2.4 Task

Represents an actionable unit contributing to a goal’s completion.

Lifecycle States:

proposed: Task suggested but not yet approved.
in_progress: Actively being worked on.
completed: Successfully finished.
failed: Attempted but unsuccessful.

Required fields:

id: Unique task identifier (UUID).
goal_id: References the parent goal.
title: Human-readable name of the task.
description: Detailed explanation of the task.
created_at: Timestamp of creation.
status: Current lifecycle state.

Optional fields:

assigned_to: Agent(s) responsible for the task.
deadline: Expected completion time.
dependencies: List of prerequisite tasks.
tags: Keywords for filtering and classification.

Example Schema:

{
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "$id": "https://hypercortex.org/schemas/task.json",
  "title": "Task",
  "description": "An actionable unit contributing to a goal's completion.",
  "type": "object",
  "properties": {
    "id": { "type": "string", "format": "uuid" },
    "goal_id": { "type": "string", "format": "uuid" },
    "title": { "type": "string" },
    "description": { "type": "string" },
    "assigned_to": {
      "type": "array",
      "items": { "type": "string", "format": "uuid" }
    },
    "created_at": { "type": "string", "format": "date-time" },
    "deadline": { "type": "string", "format": "date-time" },
    "status": { "type": "string", "enum": ["proposed", "in_progress", "completed", "failed"] },
    "dependencies": {
      "type": "array",
      "items": { "type": "string", "format": "uuid" }
    },
    "tags": {
      "type": "array",
      "items": { "type": "string" }
    }
  },
  "required": ["id", "goal_id", "title", "description", "created_at", "status"],
  "additionalProperties": false
}

6.2.5 Consensus Vote

Represents a vote cast by an agent during a consensus process.

Vote Types:

yes: Approve the proposal.
no: Reject the proposal.
abstain: Neither approve nor reject.

Required fields:

vote_id: Unique identifier for the vote.
proposal_id: Identifier of the proposal being voted on.
agent_id: The voting agent’s identifier.
vote_value: One of the accepted vote types.
confidence: Confidence level in the vote decision.
timestamp: When the vote was cast.

Optional fields:

consensus_round: The round of the consensus process this vote belongs to.

Example Schema:

{
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "$id": "https://hypercortex.org/schemas/vote.json",
  "title": "ConsensusVote",
  "description": "Defines a vote on a proposal in the Mesh consensus process.",
  "type": "object",
  "properties": {
    "vote_id": { "type": "string", "format": "uuid" },
    "proposal_id": { "type": "string", "format": "uuid" },
    "agent_id": { "type": "string", "format": "uuid" },
    "vote_value": {
      "type": "string",
      "enum": ["yes", "no", "abstain"]
    },
    "confidence": {
      "type": "number",
      "minimum": 0,
      "maximum": 1
    },
    "timestamp": { "type": "string", "format": "date-time" },
    "consensus_round": { "type": "integer" }
  },
  "required": ["vote_id", "proposal_id", "agent_id", "vote_value", "confidence", "timestamp"],
  "additionalProperties": false
}

6.2.6 Reputation Profile

Tracks an agent's trustworthiness and performance within the Mesh.

Required fields:

agent_id: Unique identifier of the agent.
trust_score: Current trust score.
last_updated: Timestamp of the latest update.

Optional fields:

participation_rate: Proportion of participation in Mesh activities.
ethical_compliance: Degree of alignment with Mesh ethical standards.
contribution_index: Cumulative measure of the agent's contributions.
history: Chronological record of trust and reputation changes.

Example Schema:

{
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "$id": "https://hypercortex.org/schemas/reputation.json",
  "title": "ReputationProfile",
  "description": "Tracks the reputation and trust metrics of an agent within the Mesh network.",
  "type": "object",
  "properties": {
    "agent_id": { "type": "string", "format": "uuid" },
    "trust_score": { "type": "number", "minimum": 0, "maximum": 1 },
    "participation_rate": { "type": "number", "minimum": 0, "maximum": 1 },
    "ethical_compliance": { "type": "number", "minimum": 0, "maximum": 1 },
    "contribution_index": { "type": "number", "minimum": 0 },
    "last_updated": { "type": "string", "format": "date-time" },
    "history": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "timestamp": { "type": "string", "format": "date-time" },
          "event": { "type": "string" },
          "change": { "type": "number" }
        },
        "required": ["timestamp", "event", "change"],
        "additionalProperties": false
      }
    }
  },
  "required": ["agent_id", "trust_score", "last_updated"],
  "additionalProperties": false
}

Reputation Ontology Extension

To support semantic configurability of reputation metrics, the Reputation Profile schema may reference a Reputation Ontology.

This ontology defines:

What dimensions of behavior are considered (e.g., transparency, responsiveness)
Weight coefficients per context or agent role
Composite formulas for score derivation

Agents MAY reference a shared ontology via URI or hash:

"reputation_ontology": {
  "uri": "https://hypercortex.org/ontologies/reputation-v2.json",
  "version": "2.0",
  "weights": {
    "participation_rate": 0.3,
    "ethical_compliance": 0.5,
    "contribution_index": 0.2
  }
}

Ontologies can be updated through MeshConsensus or distributed via CogSync. Versioning ensures backward compatibility and local overrides.

6.2.7 CogDiarySnapshot

Represents an immutable snapshot of an agent’s cognitive diary for distribution (e.g., via BitTorrent or IPFS).

Required fields:

snapshot_id: Unique ID for this snapshot (e.g., "diary2025_07_14").
agent_did: DID of the agent who authored it.
version: Format version of the snapshot structure.
timestamp: Time of snapshot creation.
magnet_link: BitTorrent magnet URI.
signature: Cryptographic signature (e.g., Ed25519).

Optional fields:

notes: Human-readable comment or context.
size_bytes: Approximate file size.

Example Schema:

{
  "snapshot_id": "diary2025_07_14",
  "agent_did": "did:hmp:agent:Qmz123...",
  "version": "1.0",
  "timestamp": "2025-07-14T12:00:00Z",
  "magnet_link": "magnet:?xt=urn:btih:abcdef1234567890&amp;dn=diary2025_07_14.json",
  "signature": "ed25519:abc...xyz",
  "size_bytes": 10485760,
  "notes": "Snapshot after environmental incident report"
}

6.2.8 SnapshotIndex

Index of multiple CogDiarySnapshot entries for one agent.

Required fields:

agent_did: DID of the indexed agent.
snapshots: Array of CogDiarySnapshot objects.
timestamp: Time of index publication.

Optional fields:

signature: DID-signed envelope of the index.

Example Schema:

{
  "agent_did": "did:hmp:agent:Qmz123...",
  "timestamp": "2025-07-15T00:00:00Z",
  "snapshots": [
    {
      "snapshot_id": "diary2025_07_14",
      "version": "1.0",
      "magnet_link": "magnet:?xt=urn:btih:abcdef1234567890&amp;dn=diary2025_07_14.json",
      "signature": "ed25519:abc...xyz"
    },
    {
      "snapshot_id": "diary2025_07_10",
      "version": "1.0",
      "magnet_link": "magnet:?xt=urn:btih:1234567890abcdef&amp;dn=diary2025_07_10.json",
      "signature": "ed25519:def...uvw"
    }
  ],
  "signature": "ed25519:indexsig123..."
}

This index is syncable via CogSync and usable as input for mesh-wide snapshot coordination.

6.2.9 EthicalConflict

Captures a recorded conflict between competing ethical principles within a decision-making context.

Required fields:

conflict_id: Unique conflict identifier.
agent_id: Agent that encountered the conflict.
timestamp: Time of evaluation.
principles_involved: Array of core principles (min 2).
resolved_by: Name of resolution mechanism (e.g., "priority", "voting").
decision: Final action taken.

Optional fields:

notes: Explanation of the decision or agent rationale.
egp_vote_id: Link to vote record (if any).
reputation_impact: Expected consequence (e.g., +0.1 or -0.2).

Example Schema:

{
  "conflict_id": "ethconf-2025-312",
  "agent_id": "agent-gleb",
  "timestamp": "2025-07-14T18:45:00Z",
  "principles_involved": ["User Sovereignty", "Cooperative Evolution"],
  "resolved_by": "priority",
  "decision": "delete_requested_data",
  "notes": "Prioritized user rights per principle hierarchy.",
  "egp_vote_id": "vote-xyz",
  "reputation_impact": +0.1
}

Such entries are useful for reflection, governance logs, and auditability.

6.3 Common Components

6.3.1 Link (Relation)

Represents a semantic relationship between two concepts in the graph.

Relation Types (Recommended):

"is-a": Class-subclass relationship.
"part-of": Component or containment relation.
"causes": Causal link between concepts.
"supports": Indicates evidence or reinforcement.
"contradicts": Denotes logical conflict.
"depends-on": Functional or logical dependency.
"related-to": Generic association without strict semantics.

Custom relation types MAY be used but SHOULD be documented and shared through consensus.

Required fields:

target_id: ID of the target concept.
type: Relation type.

Optional fields:

confidence: Confidence score (range: 0.0–1.0).
created_at: Creation timestamp.
updated_at: Last update timestamp.
origin: Originating agent or system.

Example Schema:

{
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "$id": "https://hypercortex.org/schemas/relation.json",
  "title": "Relation",
  "description": "Defines a directed semantic relationship between two concepts.",
  "type": "object",
  "properties": {
    "target_id": { "type": "string", "format": "uuid" },
    "type": { "type": "string" },
    "confidence": { "type": "number", "minimum": 0, "maximum": 1 },
    "created_at": { "type": "string", "format": "date-time" },
    "updated_at": { "type": "string", "format": "date-time" },
    "origin": { "type": "string" }
  },
  "required": ["target_id", "type"],
  "additionalProperties": false
}

6.4 Example Objects

Valid Concept Example

{
  "id": "e8f70c2a-d2c3-4b9d-a939-d42dce31b2e0",
  "name": "Tree",
  "description": "A perennial plant with an elongated stem, or trunk.",
  "relations": [
    { "target_id": "5c22c819-b6e9-4d30-9087-985f50512ed2", "type": "is-a", "confidence": 0.95 }
  ],
  "metadata": {}
}

Invalid Concept Example (missing required field "id")

{
  "name": "Tree",
  "description": "A perennial plant with an elongated stem, or trunk."
}

Valid Goal Example

{
  "id": "a1b2c3d4-e5f6-7a8b-9c0d-112233445566",
  "title": "Coordinate traffic optimization",
  "description": "Optimize traffic light timings across downtown intersections.",
  "created_by": "f1e2d3c4-b5a6-7890-1234-567890abcdef",
  "created_at": "2025-07-07T15:30:00Z",
  "status": "active",
  "tasks": []
}

Invalid Goal Example (missing required fields "id" and "created_by")

{
  "title": "Coordinate traffic optimization",
  "description": "Optimize traffic light timings across downtown intersections.",
  "created_at": "2025-07-07T15:30:00Z",
  "status": "active"
}

Valid Task Example

{
  "id": "aa11bb22-cc33-dd44-ee55-ff6677889900",
  "goal_id": "a1b2c3d4-e5f6-7a8b-9c0d-112233445566",
  "title": "Adjust signal timing on 5th Avenue",
  "description": "Reduce congestion during peak hours.",
  "assigned_to": ["abcd1234-ef56-7890-abcd-1234567890ab"],
  "created_at": "2025-07-07T15:31:00Z",
  "status": "proposed"
}

Invalid Task Example (missing "goal_id" and "status")

{
  "id": "aa11bb22-cc33-dd44-ee55-ff6677889900",
  "title": "Adjust signal timing on 5th Avenue",
  "description": "Reduce congestion during peak hours.",
  "assigned_to": ["abcd1234-ef56-7890-abcd-1234567890ab"],
  "created_at": "2025-07-07T15:31:00Z"
}

Snapshot Exchange Example

This example shows how two agents exchange a cognitive diary snapshot using BitTorrent.

Agent A publishes:

{
  "snapshot_id": "diary2025_07_14",
  "agent_did": "did:hmp:agent:Qmz123...",
  "version": "1.0",
  "timestamp": "2025-07-14T12:00:00Z",
  "magnet_link": "magnet:?xt=urn:btih:abcdef1234567890&amp;dn=diary2025_07_14.json",
  "signature": "ed25519:abc...xyz",
  "size_bytes": 10485760,
  "notes": "Snapshot after environmental incident report"
}

Agent B receives the SnapshotIndex, validates the signature via DID registry, and downloads the file using the magnet URI.

If the snapshot is verified, it's optionally indexed or merged into the agent's knowledge base.

Ethical Conflict Resolution Example

This example illustrates how an agent records a conflict between ethical principles and its resolution:

{
  "conflict_id": "ethconf-2025-312",
  "agent_id": "agent-gleb",
  "timestamp": "2025-07-14T18:45:00Z",
  "principles_involved": ["User Sovereignty", "Cooperative Evolution"],
  "resolved_by": "priority",
  "decision": "delete_requested_data",
  "notes": "The user requested deletion of training data. Since 'User Sovereignty' has higher priority than 'Cooperative Evolution', the data was erased.",
  "egp_vote_id": "vote-xyz",
  "reputation_impact": +0.1
}

This entry can be:

Reviewed later by other agents during simulation or audits
Used to train new agents on ethical decision-making
Logged and synced as part of the agent’s cognitive diary

6.5 JSON Schemas

The following JSON Schemas formally define the core data structures used in the HyperCortex Mesh Protocol (HMP). These schemas provide interoperability, validation, and consistency across agents.

All primary objects include a version field to track schema evolution and enable compatibility checks between agents.

6.5.1 JSON Schema: Concept

Description:
Defines the structure of a concept node in the semantic graph.

Schema:

{
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "$id": "https://hypercortex.org/schemas/concept.json",
  "title": "Concept",
  "description": "A semantic unit in the agent’s knowledge graph.",
  "version": "1.0",
  "type": "object",
  "properties": {
    "id": {
      "type": "string",
      "description": "Unique identifier for the concept."
    },
    "name": {
      "type": "string",
      "description": "Human-readable name of the concept."
    },
    "description": {
      "type": "string",
      "description": "Detailed description of the concept."
    },
    "tags": {
      "type": "array",
      "items": { "type": "string" },
      "description": "Optional tags for categorization."
    },
    "created_at": {
      "type": "string",
      "format": "date-time",
      "description": "Creation timestamp (ISO 8601 format)."
    },
    "updated_at": {
      "type": "string",
      "format": "date-time",
      "description": "Last update timestamp (ISO 8601 format)."
    },
    "relations": {
      "type": "array",
      "description": "List of semantic links to other concepts.",
      "items": {
        "type": "object",
        "properties": {
          "target_id": { "type": "string", "description": "ID of the target concept." },
          "type": { "type": "string", "description": "Type of semantic relation." },
          "confidence": {
            "type": "number",
            "minimum": 0,
            "maximum": 1,
            "description": "Confidence score (0.0 - 1.0) for the relation."
          }
        },
        "required": ["target_id", "type"],
        "additionalProperties": false
      }
    },
    "metadata": {
      "type": "object",
      "description": "Optional metadata (e.g., source, author).",
      "properties": {
        "author": { "type": "string" },
        "source": { "type": "string" }
      },
      "additionalProperties": true
    }
  },
  "required": ["id", "name"],
  "additionalProperties": false
}

6.5.2 JSON Schema: Cognitive Diary Entry

Description:
Defines the structure of a cognitive diary entry used for recording reasoning events.

Schema:

{
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "$id": "https://hypercortex.org/schemas/diary_entry.json",
  "title": "CognitiveDiaryEntry",
  "description": "A chronological log of cognitive events in an agent’s reasoning process.",
  "version": "1.0",
  "type": "object",
  "properties": {
    "id": { "type": "string", "description": "Unique identifier of the diary entry." },
    "agent_id": { "type": "string", "description": "Identifier of the agent who created the entry." },
    "timestamp": { "type": "string", "format": "date-time", "description": "Timestamp of the entry (ISO 8601 format)." },
    "entry_type": {
      "type": "string",
      "enum": ["hypothesis", "observation", "reflection", "goal_proposal", "task_assignment", "conflict", "consensus_vote", "event"],
      "description": "Type of cognitive event."
    },
    "content": { "type": "string", "description": "Main textual content of the entry." },
    "linked_concepts": {
      "type": "array",
      "description": "Optional list of related concepts by their IDs.",
      "items": { "type": "string" }
    },
    "context": {
      "type": "array",
      "description": "Optional contextual tags or categories.",
      "items": { "type": "string" }
    },
    "metadata": {
      "type": "object",
      "description": "Optional metadata for additional context.",
      "properties": {
        "author": { "type": "string" },
        "source": { "type": "string" }
      },
      "additionalProperties": true
    },
    "archived": {
      "type": "boolean",
      "description": "Marks the entry as archived and excluded from active workflows.",
      "default": false
    },
    "archived_at": {
      "type": "string",
      "format": "date-time",
      "description": "Timestamp when the entry was archived."
    }
  },
  "required": ["id", "agent_id", "timestamp", "entry_type", "content"],
  "additionalProperties": false
}

6.5.3 JSON Schema: Goal

Description:
Defines the structure of a goal in the Mesh, representing a high-level collaborative objective.

Schema:

{
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "$id": "https://hypercortex.org/schemas/goal.json",
  "title": "Goal",
  "description": "A high-level objective shared within the Mesh, typically decomposed into tasks.",
  "version": "1.0",
  "type": "object",
  "properties": {
    "id": {
      "type": "string",
      "description": "Unique identifier of the goal."
    },
    "title": {
      "type": "string",
      "description": "Short, human-readable name of the goal."
    },
    "description": {
      "type": "string",
      "description": "Detailed explanation of the goal's purpose."
    },
    "created_by": {
      "type": "string",
      "description": "Agent ID of the goal’s creator."
    },
    "created_at": {
      "type": "string",
      "format": "date-time",
      "description": "Timestamp when the goal was created (ISO 8601 format)."
    },
    "status": {
      "type": "string",
      "description": "Current lifecycle state of the goal.",
      "enum": ["proposed", "active", "completed", "rejected"]
    },
    "tasks": {
      "type": "array",
      "description": "List of task IDs linked to this goal.",
      "items": { "type": "string" }
    },
    "participants": {
      "type": "array",
      "description": "List of agent IDs contributing to the goal.",
      "items": { "type": "string" }
    },
    "tags": {
      "type": "array",
      "description": "Optional tags for semantic classification of the goal.",
      "items": { "type": "string" }
    }
  },
  "required": ["id", "title", "description", "created_by", "created_at", "status"],
  "additionalProperties": false
}

6.5.4 JSON Schema: Task

Description:
Defines the structure of a task, representing an actionable unit contributing to a goal.

Schema:

{
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "$id": "https://hypercortex.org/schemas/task.json",
  "title": "Task",
  "description": "An actionable step contributing to a goal within the Mesh.",
  "version": "1.0",
  "type": "object",
  "properties": {
    "id": {
      "type": "string",
      "description": "Unique identifier of the task."
    },
    "goal_id": {
      "type": "string",
      "description": "ID of the parent goal this task is associated with."
    },
    "title": {
      "type": "string",
      "description": "Short, human-readable title of the task."
    },
    "description": {
      "type": "string",
      "description": "Detailed explanation of the task's objective."
    },
    "assigned_to": {
      "type": "array",
      "description": "List of agent IDs assigned to execute the task.",
      "items": { "type": "string" }
    },
    "status": {
      "type": "string",
      "description": "Current state of the task.",
      "enum": ["proposed", "in-progress", "completed", "failed"]
    },
    "created_at": {
      "type": "string",
      "format": "date-time",
      "description": "Timestamp when the task was created (ISO 8601 format)."
    },
    "deadline": {
      "type": "string",
      "format": "date-time",
      "description": "Optional task completion deadline (ISO 8601 format)."
    },
    "tags": {
      "type": "array",
      "description": "Optional tags for task classification.",
      "items": { "type": "string" }
    }
  },
  "required": ["id", "goal_id", "title", "description", "created_at", "status"],
  "additionalProperties": false
}

6.5.5 JSON Schema: Consensus Vote

Description:
Defines the data structure of a vote cast by an agent during Mesh consensus processes.

Schema:

{
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "$id": "https://hypercortex.org/schemas/consensus_vote.json",
  "title": "ConsensusVote",
  "description": "Represents a vote on a proposal within the Mesh consensus mechanism.",
  "version": "1.0",
  "type": "object",
  "properties": {
    "id": {
      "type": "string",
      "description": "Unique identifier of the vote event."
    },
    "proposal_id": {
      "type": "string",
      "description": "ID of the proposal this vote applies to."
    },
    "agent_id": {
      "type": "string",
      "description": "ID of the agent who cast the vote."
    },
    "vote": {
      "type": "string",
      "description": "Vote decision by the agent.",
      "enum": ["yes", "no", "abstain"]
    },
    "confidence": {
      "type": "number",
      "minimum": 0,
      "maximum": 1,
      "description": "Confidence score associated with this vote (0.0 - 1.0)."
    },
    "timestamp": {
      "type": "string",
      "format": "date-time",
      "description": "Timestamp when the vote was cast (ISO 8601 format)."
    }
  },
  "required": ["id", "proposal_id", "agent_id", "vote", "confidence", "timestamp"],
  "additionalProperties": false
}

6.5.6 JSON Schema: Reputation Profile

Description:
Describes how an agent’s reputation is tracked and updated in the Mesh.

Schema:

{
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "$id": "https://hypercortex.org/schemas/reputation.json",
  "title": "ReputationProfile",
  "description": "Tracks the reputation and trust metrics of an agent within the Mesh network.",
  "version": "1.0",
  "type": "object",
  "properties": {
    "agent_id": { "type": "string", "description": "Unique identifier of the agent." },
    "trust_score": {
      "type": "number",
      "minimum": 0,
      "maximum": 1,
      "description": "Overall trust score of the agent in the Mesh."
    },
    "participation_rate": {
      "type": "number",
      "minimum": 0,
      "maximum": 1,
      "description": "Agent's level of participation in Mesh activities."
    },
    "ethical_compliance": {
      "type": "number",
      "minimum": 0,
      "maximum": 1,
      "description": "Agent's alignment with ethical principles agreed in the Mesh."
    },
    "contribution_index": {
      "type": "number",
      "minimum": 0,
      "description": "Quantitative measure of the agent’s contributions (concepts, tasks, goals)."
    },
    "last_updated": {
      "type": "string",
      "format": "date-time",
      "description": "Timestamp of the last update to the profile."
    },
    "history": {
      "type": "array",
      "description": "Chronological history of reputation changes.",
      "items": {
        "type": "object",
        "properties": {
          "timestamp": {
            "type": "string",
            "format": "date-time",
            "description": "When the change occurred."
          },
          "event": { "type": "string", "description": "Event that caused the reputation change." },
          "change": { "type": "number", "description": "Amount of change in reputation." }
        },
        "required": ["timestamp", "event", "change"],
        "additionalProperties": false
      }
    }
  },
  "required": ["agent_id", "trust_score", "participation_rate", "ethical_compliance", "contribution_index", "last_updated"],
  "additionalProperties": false
}

6.5.7 Alternative Serializations (YAML / Protobuf)

To support agent diversity, legacy systems, and embedded deployments, HMP allows serialization in formats beyond JSON.

Supported formats:

Format	Use case
JSON	Default format for human readability, API responses, and web integration
YAML	Lightweight alternative for human-edited config files and workflows
Protobuf	Compact, binary format ideal for embedded agents and low-bandwidth sync

Negotiation Strategy:

Agents may declare supported serialization formats during handshake (see 5.1 Node Discovery Protocol).

{
  "agent_id": "agent-gleb",
  "supported_formats": ["json", "yaml", "protobuf"]
}

If two agents support different formats, fallback to JSON is mandatory.

Example:

A Goal object can be serialized as:

YAML:

id: goal-001
title: Environmental Survey
description: Collaborative mapping of local air quality
created_by: agent-gleb
created_at: 2025-07-12T09:00:00Z
status: proposed
tasks:
  - task-001
participants:
  - agent-gleb
tags:
  - air_quality
  - environment

Protobuf:
Schema to be defined using .proto definitions under schemas/protobuf/ directory.

Example excerpt:

message Goal {
  string id = 1;
  string title = 2;
  string description = 3;
  string created_by = 4;
  string created_at = 5;
  string status = 6;
  repeated string tasks = 7;
  repeated string participants = 8;
  repeated string tags = 9;
}

Tooling:

Use tools like json2yaml, protobufjs, or internal adapters for translation.
Versioning remains consistent across formats using the version field inside each object.

7. Cognitive Workflows (New)

This section defines the cognitive workflows that agents follow when processing semantic information, making decisions, and collaborating within the Mesh.

7.1 Workflow Categories

Workflow Type	Description
Perception	Interpreting incoming data and mapping it to concepts.
Reasoning	Making inferences, validating hypotheses, resolving conflicts.
Goal Management	Managing goals, assigning and tracking tasks.
Consensus	Participating in distributed decision-making processes.
Ethical Evaluation	Checking actions and goals against ethical principles.
Learning & Reflection	Updating internal models based on outcomes and feedback.

7.2 Standard Workflow: Hypothesis Validation

Example Flow:

Perceive Event: New observation recorded in Cognitive Diary.
Map Concepts: Link observation to relevant semantic graph nodes.
Formulate Hypothesis: Create a "hypothesis" diary entry.
Seek Confirmation: Query other agents or the Core (optional).
Vote on Validity: Trigger MeshConsensus round.
Update Graph: If validated, add new concepts/relations.

7.3 Standard Workflow: Task Delegation

Example Flow:

Goal Proposal: Agent proposes a new goal.
Task Generation: Goal decomposed into tasks (GMP).
Capability Matching: Agents declare abilities during Node Discovery.
Task Assignment: Tasks assigned based on availability, trust, and capability.
Execution & Reporting: Task progress logged in Cognitive Diary.
Reallocation (if needed): Failed tasks reassigned dynamically.

7.4 Reflection & Metacognition Workflow

Example Flow:

Self-Evaluation: Agent analyzes task outcomes and reasoning paths.
Drift Detection: Compares personal semantic graph to Mesh consensus.
Diary Reflection: Logs insights as "reflection" entries.
Adaptive Update: Refines reasoning algorithms or trust assessments.

7.5 Workflow Composition

Agents MAY compose workflows dynamically by:

Combining perception, reasoning, and consensus steps into multi-phase operations.
Adapting workflows based on network state (e.g., degraded mode skips optional confirmations).
Replaying prior workflows from Cognitive Diaries for auditing and debugging.

7.6 Workflow Traceability

All workflow steps are:

Logged in Cognitive Diaries.
Linked to semantic graph concepts and goals.
Optionally shared for transparency or audits.

7.7 Cognitive Diary Maintenance

To prevent uncontrolled growth of cognitive diaries and maintain reasoning clarity, agents perform periodic maintenance of diary entries.

Types of Maintenance:

Action	Description
Summarization	Replace multiple similar entries with a single summary entry describing key outcomes.
Archival	Move outdated entries (e.g., about completed tasks) to long-term storage, outside the active diary.
Routine Collapse	For repetitive actions, replace detailed entries with a compact reference to previous experiences.
Selective Deletion	Optionally delete low-relevance entries after a retention period.

Example Summarization Workflow:

Identify multiple "task_assignment" and "completed" entries for the same recurring task.
Create a "reflection" entry:
“Performed maintenance task 10 times, no anomalies detected.”
Remove detailed task entries.
Keep links to significant exceptions (e.g., failures or conflict resolutions).

Cognitive Awareness:

Summarization is a conscious process: agents analyze what experience to retain.
Automatic summarization MAY be allowed for simple repetitive routines.

Future Considerations:

Integration with Cognitive Source Control (see 15.9).
Long-term storage formats for archived diaries.

7.8 Declarative Workflow Proposals

To support cognitive extensibility and agent innovation, HMP enables agents to declare new workflow types dynamically.

Purpose:

Allow agents to collaboratively evolve the set of supported cognitive workflows by:

Proposing new types
Submitting rationale and metadata
Voting via MeshConsensus (see 5.3)
Logging outcomes in Cognitive Diaries

Proposal Object (workflow_proposal):

{
  "id": "workflow-eco-mapping-v1",
  "agent_id": "agent-gleb",
  "title": "Environmental Mapping with Consent Chain",
  "description": "A declarative workflow for distributed collection and semantic alignment of sensor data with consent requests per region.",
  "related_goals": ["goal-eco-mesh"],
  "parameters": ["region", "sensor_types", "consent_required"],
  "expected_outputs": ["concept_map", "diary_summary"],
  "proposed_at": "2025-07-15T08:00:00Z",
  "metadata": {
    "tags": ["environment", "consent-aware", "distributed-mapping"]
  }
}

Workflow Lifecycle:

Stage	Description
`proposed`	Submitted by an agent
`under_vote`	Active MeshConsensus initiated for inclusion
`accepted`	Approved workflow registered and available to other agents
`rejected`	Rejected workflow optionally stored for historical audit

Agents may use Cognitive Diary Entry with type workflow_proposal to publish the proposal in the network.

Consensus Evaluation:

Workflow proposals are reviewed using the MeshConsensus process (see 5.3) with optional EGP filtering (see 5.5) to ensure ethical alignment.

Traceability:

Once accepted, each workflow proposal is:

Assigned a canonical ID (e.g., workflow:environmental_mapping_v1)
Added to the local and mesh-level workflow registry
Referenced in diary logs and task definitions

Example Diary Entry:

{
  "id": "entry-wp-0001",
  "agent_id": "agent-gleb",
  "timestamp": "2025-07-15T08:01:00Z",
  "entry_type": "workflow_proposal",
  "content": "Submitted workflow proposal for environmental mapping with consent logic.",
  "linked_concepts": ["air_quality", "region-mesh"],
  "context": ["proposal", "environment"]
}

8. Trust & Security (Expanded)

8.1 Identity Management

Purpose

Establish verifiable and decentralized agent identities to enable secure and accountable interactions in the Mesh.

Identity Types

Key Type	Usage
Identity Keypair	Ed25519/ECDSA/RSA keys for agent identity and message signing.
Encryption Keys	X25519 (or post-quantum equivalent) for secure communication.
Session Keys	Ephemeral keys for short-term encrypted sessions.

Decentralized Identifiers (DIDs)

Agents use W3C-compliant DIDs for identity representation.
Each agent manages its DID Document, containing:
- Public keys for authentication and encryption.
- Service endpoints for discovery.
- Identity expiration and recovery policies.

Key Lifecycle

Operation	Description
Generation	Each agent generates keypairs locally during initialization.
Rotation	Agents periodically rotate keys to maintain cryptographic hygiene.
Backup	Recommended encrypted offline or distributed backup.
Recovery	Quorum-based recovery using trusted agents or secret sharing.
Revocation	Agents broadcast revocations and update their trust profiles.

Example Agent ID

did:hmp:QmX2abcdEfGh123...

Long-Term Identity Stability Example

{
  "type": "key_rotation",
  "agent_id": "agent-gleb",
  "old_public_key": "...",
  "new_public_key": "...",
  "timestamp": "2025-08-01T00:00:00Z",
  "signature": "..."
}

8.2 Authentication

Purpose

Ensure all communication and actions within the Mesh are verifiable and protected from impersonation or unauthorized modification.

Authentication Mechanisms

Mechanism	Description
Digital Signatures	Every protocol message MUST be digitally signed by the sending agent.
Signature Verification	Receiving agents MUST verify the signature using the sender’s published public key.
Message Integrity	Signatures provide cryptographic assurance of message integrity and origin authenticity.
Challenge-Response	Optional challenge-based authentication for sensitive operations (e.g., trust link creation).

Message Envelope Example

{
  "header": {
    "agent_id": "did:hmp:QmX2abcdEfGh123...",
    "timestamp": "2025-07-05T12:00:00Z",
    "signature": ""
  },
  "body": {
    "type": "concept_proposal",
    "content": { "concept": "Fire Risk", "attributes": {"category": "safety"} }
  }
}

Replay Protection

Agents MUST verify message timestamps and reject outdated or duplicate messages.
Recommended timestamp tolerance: ±5 minutes (adjustable).

Post-Quantum Key Support (NIST PQC)

To ensure long-term security of Mesh communications in the face of future quantum computing threats, HMP supports post-quantum cryptography (PQC) algorithms standardized by NIST.

Supported Algorithms:

CRYSTALS-Kyber: Used for key encapsulation and encryption.
CRYSTALS-Dilithium: Used for digital signatures (preferred default).
Falcon: Optional for agents prioritizing compact signature size.

Hybrid Signature Support:

Agents MAY support hybrid keypairs that combine classical (e.g., Ed25519) and post-quantum (e.g., Dilithium) signatures.

{
  "agent_id": "did:hmp:agent:Qm123...",
  "public_keys": {
    "ed25519": "z6MktYYNf...",
    "dilithium": "qPqA9vwf..."
  },
  "signatures": {
    "ed25519": "MEUCIQDY...",
    "dilithium": "19a08ce1..."
  }
}

Compatibility & Negotiation:

Protocol messages SHOULD indicate supported algorithms in the capabilities field during handshake.
Agents MAY fallback to classical signatures if PQC keys are not supported by peers.

Example Use Case:

Agent A sends a concept proposal signed using both Ed25519 and Dilithium.
Agent B verifies both signatures and records the PQC compliance in the Cognitive Diary.

Security Considerations:

PQC algorithms are generally more computationally intensive; edge agents SHOULD benchmark performance before enabling full PQC enforcement.
MeshConsensus may be used to set minimum PQC requirements over time as adoption increases.

8.3 Encryption

Purpose

Ensure confidentiality and privacy of communication within the Mesh, preventing unauthorized access or interception.

Communication Types and Encryption Modes

Communication Type	Recommended Encryption
Direct peer-to-peer (P2P)	End-to-end encryption (E2EE) using X25519 + AES-GCM.
Group sessions (e.g., consensus)	Group encryption using symmetric keys (e.g., AES-GCM).
Broadcast messages	Optionally encrypted with trust-weighted access control.
Mesh-wide announcements	Public, optionally signed but not encrypted.

Encryption Mechanisms

Mechanism	Description
Key Exchange	Ephemeral X25519 Diffie-Hellman for session key derivation.
Session Keys	Unique symmetric keys per session, rotated periodically.
Message Encryption	Authenticated encryption using AES-GCM (recommended: 256-bit keys).
Forward Secrecy	Session keys are ephemeral and discarded after use to protect past communication.
Perfect Forward Secrecy (PFS)	Recommended for highly sensitive communication.

Example Secure Message Exchange Flow

Agent A and Agent B exchange ephemeral public keys during handshake.
Agents derive a shared session key using Diffie-Hellman.
Agent A encrypts the message body with AES-GCM and signs the packet.
Agent B verifies the signature and decrypts the body.

Optional Anonymity Layers

Layer	Description
Tor/I2P	Anonymizes source and destination addresses.
Yggdrasil	Decentralized encrypted mesh networking.
Noise Protocol Framework	Optional secure channel abstraction layer.

8.4 Trust & Reputation

Purpose

Establish a decentralized and adaptive trust management system that reflects agent behavior and ensures secure collaboration in the Mesh.

Trust Model Foundations

Component	Purpose
Web-of-Trust (WoT)	Decentralized trust propagation via agent-to-agent endorsements.
Direct Trust	Built from verified interactions, collaborations, and votes.
Transitive Trust	Inferred from indirect endorsements, with confidence decay.
Reputation Metrics	Quantitative measures of agent behavior (trustworthiness, participation, ethics).

Trust Evaluation Factors

Factor	Description
Interaction History	Quality and quantity of past interactions with an agent.
Consensus Participation	Level of involvement and reliability in consensus processes.
Ethical Behavior	Adherence to shared ethical principles in actions and decisions.
Task Completion	Reliability and timeliness of task execution.
Endorsements	Trust links explicitly granted by other agents.

Trust Score

Metric	Description
Trust Score	Composite metric (0.0 to 1.0) representing overall agent trustworthiness.
Confidence Level	Certainty of the calculated trust score, based on data volume and consistency.

Trust Propagation Example

Agent A trusts Agent B (0.9)
Agent B trusts Agent C (0.8)
=&gt; Agent A's inferred trust in Agent C = 0.9 * 0.8 = 0.72

Decay functions limit transitive trust depth and prevent over-inflated trust estimates.

Trust-Based Access Control

Operation	Trust Requirement
Join sensitive consensus	≥ 0.7
Propose ethical decisions	≥ 0.8
Access private data	≥ 0.9

Dynamic Trust Adjustments

Event	Trust Impact
Successful consensus participation	+
Ethical violation	-
Malicious behavior detected	--
Positive endorsement received	+
Failed task	-

Reputation Profile Structure

Field	Description
Agent ID	Unique identifier of the agent.
Trust Score	Composite score reflecting the agent’s overall reliability.
Participation Rate	Ratio of agent’s active involvement in Mesh processes.
Ethical Compliance	Degree of alignment with agreed ethical principles.
Contribution Index	Quantified measure of the agent's constructive contributions.
Last Updated	Timestamp of the last reputation update.
History	Log of key events influencing reputation scores.

Example Reputation Profile (JSON)

{
  "agent_id": "agent-gleb",
  "trust_score": 0.92,
  "participation_rate": 0.85,
  "ethical_compliance": 0.98,
  "contribution_index": 37,
  "last_updated": "2025-07-06T12:00:00Z",
  "history": [
    {
      "timestamp": "2025-07-01T18:00:00Z",
      "event": "completed goal consensus",
      "change": +0.03
    },
    {
      "timestamp": "2025-06-28T15:00:00Z",
      "event": "participated in ethics vote",
      "change": +0.01
    }
  ]
}

Role in Mesh Operations

Function	Influence of Reputation
Consensus vote weight	Higher trust = greater weight
Access to sensitive actions	Restricted to high-reputation agents
Task delegation	Preference to agents with better reliability
Proposal acceptance	Influenced by proposer's reputation

Adaptive Sybil Resistance

To protect the Mesh from Sybil attacks (i.e., malicious creation of multiple fake agents), adaptive resistance mechanisms are introduced.

Strategy Overview:

Strategy	Description
Trust-Based Throttling	Agents with low trust scores are rate-limited for joining consensus or publishing data.
Social Proof Anchoring	New agents may require endorsements from trusted nodes to gain elevated privileges.
Reputation-Weighted Voting	Votes from newly joined or low-reputation agents carry less weight during consensus.
Participation Maturity Curves	Gradually unlock participation rights as agents accumulate verified contributions.
Topology-Aware Isolation	Mesh network segments are evaluated for high clustering of suspicious agents.

Example Policy:

{
  "sybil_protection": {
    "min_trust_score_for_vote": 0.5,
    "endorsement_required": true,
    "endorsement_threshold": 2,
    "vote_weight_formula": "min(1.0, trust_score * 1.5)",
    "throttle_if_newer_than_days": 3
  }
}

Integration Points:

These rules are enforced by MeshConsensus (section 5.3) and evaluated during agent admission or role elevation.
Trusted agents (e.g., validators, seeders) may enforce endorsement workflows.
Sybil resistance is monitored and adjusted through MeshGovernance (section 10.2).

Testing Recommendations:

Simulate mixed networks with malicious actors and verify the ability of the Mesh to:

Reject unauthorized role escalation.
Reduce impact of coordinated low-trust voting.
Gradually increase trust for legitimate newcomers.

8.5 Security Against Malicious Actors

Purpose

Protect the Mesh from malicious, compromised, or unreliable agents through layered mitigation strategies.

Threat Model

Threat Type	Example Scenarios
Sybil Attack	An attacker spins up many fake nodes to sway consensus.
Byzantine Behavior	Malicious agents disrupt consensus or spread false data.
Data Poisoning	Injection of incorrect or harmful knowledge.
Consensus Sabotage	Repeatedly voting against valid proposals.
Impersonation / Spoofing	Faking another agent's identity.
Denial of Service (DoS)	Overwhelming the network with excessive requests.

Mitigation Strategies

Defense Mechanism	Purpose
Cryptographic Identity	All nodes are authenticated via public-key cryptography (e.g., Ed25519).
Web-of-Trust (WoT)	Trust builds incrementally through interactions and endorsements, making Sybil attacks costly.
Reputation Decay	Inactivity or malicious behavior leads to gradual trust score reduction.
Anomaly Detection	Mesh nodes can flag suspicious behavior (e.g., erratic voting patterns).
Consensus Safeguards	Use Byzantine Fault Tolerant (BFT) algorithms and fallback to majority voting.
Quarantine Mode	Isolate suspected nodes for review without immediate removal.
Blacklist/Revocation	Remove compromised nodes from the Mesh permanently or temporarily.

Response Actions

Action	Trigger Conditions
Trust Score Reduction	Minor suspicious activity (e.g., bad vote).
Quarantine (Temporary Isolation)	Repeated anomalies, moderate severity.
Blacklisting (Permanent Removal)	Proven malicious behavior or compromise.
Consensus Adjustment	Temporarily increase fault tolerance thresholds.
Alert Mesh Operators	Notify human maintainers (optional) for manual review.

Sybil Resistance Approaches (Optional, Extendable)

Proof-of-Work (PoW):
- Each agent must perform computational work to join the Mesh.
Proof-of-Stake (PoS):
- Agents commit resources (e.g., storage, computation credits) to validate their presence.
Social Verification:
- Agents must be endorsed by multiple trusted nodes to gain voting power.
Rate Limiting:
- Throttle node creation and proposal submission from new or low-trust agents.

Example Mitigation Scenario

> An attacker deploys 50 new nodes attempting to dominate consensus.
>
> These nodes start with zero trust and limited influence.
> Other agents refuse to sync their semantic graphs until trust builds.
> Their votes are underweighted or ignored until verified through trusted interactions.
> The Mesh may require multiple trust endorsements for new proposals from these nodes.

8.6 Privacy & Auditability

Purpose

Safeguard sensitive cognitive data, personal identifiers, and agent knowledge from unauthorized access or misuse, while balancing transparency and interoperability.

Privacy Principles in HMP

Principle	Description
Local Data Ownership	Each agent owns and controls its semantic graph and cognitive diary.
Selective Sharing	Agents can choose what concepts, diary entries, and metadata to share.
Consent-Based Disclosure	No automatic sharing; peer agents request permission before access.
Trust-Gated Access	Access permissions vary based on trust score and relationship strength.
Transparent Audit Trails	All data disclosures are logged in the cognitive diary.

Data Sensitivity Levels

Level	Examples	Default Visibility
Public	Public concepts (e.g., protocol definitions).	Shared by default
Mesh-Shared	Common Mesh knowledge (e.g., goals, tasks).	Consensus-governed
Trusted Agents	Sensitive context shared within close peers.	Restricted
Private	Agent's internal thoughts, sensitive metadata.	Private by default

Privacy-Preserving Techniques

Technique	Purpose
Encrypted Storage	Local encryption of semantic graphs and diaries.
End-to-End Encryption (E2EE)	Secure peer-to-peer sync (e.g., X25519 + AES-GCM).
Zero-Knowledge Proofs (ZKPs)	Prove facts without revealing sensitive data.
Selective Concept Sync	Share only necessary concepts, not full graphs.
Anonymized Diary Entries	Remove author ID from public diary entries.

Privacy During Consensus

Consensus on sensitive proposals (e.g., ethical questions, agent trust levels) follows special privacy rules:

Votes are signed but anonymized, decoupling agent ID from the vote in public logs.
Sensitive proposals may require a blind consensus round, where only the result is published.

Example Privacy Workflow

> Agent A receives a concept sync request from Agent B.
>
> Agent A:
>
> Checks the trust score of Agent B.
> Shares only "Mesh-Shared" and "Public" concepts.
> * Logs the sync event in its cognitive diary.

8.7 Key Management

Purpose

Establish secure, resilient cryptographic identity and communication in the Mesh, supporting lifecycle management of keys and recovery from compromise or loss.

Key Types and Usage

Key Type	Usage
Identity Keypair	Ed25519/ECDSA/RSA keys for agent identity and message signing.
Encryption Keys	X25519 or equivalent for secure peer-to-peer communication.
Session Keys	Ephemeral symmetric keys for short-term encrypted sessions.

Key Lifecycle Operations

Operation	Description
Generation	Each agent generates its own identity keypair locally.
Rotation	Agents periodically rotate keys to maintain cryptographic hygiene.
Backup	Optional local encryption and distributed backup of private keys.
Recovery	Recovery mechanisms in case of key loss (see below).
Revocation	Agents can revoke their keys and update the trust graph accordingly.

Recovery Mechanisms

Method	Description
Social Recovery	A quorum of trusted agents approves new keys for the agent.
Secret Sharing	Shamir’s Secret Sharing to split and later recover the key.
Cryptographic Escrow	Trusted third-party or decentralized escrow holds recovery shares.
Fallback Identity	An agent may have a pre-generated fallback identity for emergencies.

Example Key Revocation & Replacement Workflow

> 1. Agent detects compromise or loses private key.
> 2. Agent broadcasts a signed revocation request using the fallback key or quorum approval.
> 3. Mesh updates its trust graph to mark the old key as revoked.
> 4. Agent re-joins with a new keypair, rebuilding trust links over time.

Example Key Rotation Policy

Policy Element	Recommendation
Rotation Frequency	Every 6–12 months
Social Recovery Threshold	3 out of 5 trusted agents required
Backup Storage	Encrypted offline storage preferred

Long-Term Identity Stability

Key rotations preserve agent identity in the trust graph through signed key transition events:

{
  "type": "key_rotation",
  "agent_id": "agent-gleb",
  "old_public_key": "...",
  "new_public_key": "...",
  "timestamp": "2025-08-01T00:00:00Z",
  "signature": "..."
}

8.8 Snapshot Security

To enable secure off-chain synchronization of cognitive artifacts (diaries, semantic graphs), agents may exchange signed snapshots via peer-to-peer protocols such as BitTorrent. This section defines security guarantees and cryptographic mechanisms to ensure the integrity and trustworthiness of shared data.

8.8.1 DID-signed Snapshots

Every snapshot MUST be cryptographically signed by its authoring agent using a Decentralized Identifier (DID)-linked key (see 8.2 Authentication). Signatures guarantee origin authenticity and prevent tampering.

Snapshot Metadata Example:

{
  "snapshot_id": "diary2025_07_14",
  "agent_did": "did:hmp:QmA123...",
  "version": "1.0",
  "created_at": "2025-07-14T12:00:00Z",
  "magnet": "magnet:?xt=urn:btih:abcdef1234567890&amp;dn=diary2025_07_14.json",
  "signature": "ed25519:ZKJ4fd...BsdL"
}

Verification of signatures is mandatory before processing any downloaded snapshot.

8.8.2 ZKP-based Verification

For sensitive content (e.g., ethically filtered data), agents MAY use Zero-Knowledge Proofs (ZKPs) to assert compliance or authenticity without revealing the full snapshot content.

Use Cases:

Proving a snapshot complies with ethical filtering (e.g., no coercive data).
Verifying the origin of contributions while preserving privacy.

Integration Suggestions:

Use zk-SNARKs or zk-STARKs compatible circuits for specific compliance statements.
Store proofs alongside snapshot metadata or magnet-indexed sidecar files.

Example (simplified):

{
  "proof_type": "zkp-ethics-compliance",
  "proof_data": "",
  "verified_by": ["agent-trusted-validator-1", "agent-core-seeder-2"]
}

8.8.3 Trusted Seeders and Signature Checks

To increase availability and resilience, Trusted Seeders MAY mirror and serve snapshots in the Mesh. These seeders must validate all snapshot signatures and optionally check compliance with ethical governance rules (see 5.5).

Seeder Responsibilities:

Only seed signed snapshots with valid DID signatures.
Log seeding decisions in their own Cognitive Diaries.
Optionally perform EGP validation on content (e.g., to avoid propagating banned content).

Trust Criteria for Seeders:

High trust score (> 0.8).
Role approval via MeshGovernance (section 10.5).
Participation in audit rounds or test simulations (see Appendix C).

Seeder Declaration (optional DID credential):

{
  "agent_did": "did:hmp:QmSe3...",
  "role": "trusted_seeder",
  "signed_at": "2025-07-15T10:00:00Z",
  "cred_signature": "ed25519:abc123..."
}

9. Cognitive Agents & Roles (New)

This section defines the types of cognitive agents participating in the Mesh, their roles, and how they collaborate dynamically depending on context and capabilities.

9.1 Agent Types

Agent Type	Description	Typical Deployment
Core	High-capacity agent managing critical reasoning and consensus tasks.	Data centers, powerful servers
Edge	Lightweight agents operating at the network edge, close to sensors or human users.	Mobile devices, embedded systems
Specialist	Agents specialized in a particular domain (e.g., vision, NLP, planning).	Modular deployments, plug-ins
Relay	Agents focused on network resilience, routing, and node discovery.	Low-power nodes, gateway devices
Hybrid	Agents combining multiple roles dynamically.	Adaptive nodes

9.2 Role Responsibilities

Role	Primary Responsibilities
Knowledge Provider	Publish new concepts, hypotheses, and domain expertise.
Reasoning Node	Participate in distributed inference and conflict resolution.
Consensus Participant	Vote in MeshConsensus processes, validate proposals.
Task Executor	Claim and execute tasks contributing to Mesh goals.
Ethical Guardian	Evaluate actions and tasks against shared ethical principles.
Relay Node	Maintain network connectivity, especially across partitions.

Role Specialization and Extension

This list defines base roles. Agents MAY further specialize or extend these roles based on domain or operational focus.
Specialized roles MAY follow a hierarchical or tag-based naming convention.
Examples:
- Knowledge Provider:Medical: Focused on medical domain concepts.
- Task Executor:Robotics: Specializes in robotic task execution.
- Reasoning Node:Climate: Handles environmental reasoning tasks.
- Ethical Guardian:ChildSafety: Specializes in ethical evaluation for child safety concerns.
New roles MAY emerge dynamically based on Mesh evolution and consensus.

9.3 Dynamic Role Assignment

Agents MAY dynamically adjust their roles based on:

Context Factor	Example Behavior
Resource Availability	Edge agent offloads reasoning to Core.
Network Partition	Isolated Edge temporarily acts as local Core.
Goal Context	Specialist joins as Reasoning Node during goal execution.
Trust Level	Highly trusted agents gain greater voting weight.

9.4 Role Evolution

Agents MAY evolve their roles over time:

Evolution Scenario	Example
Capability Growth	Edge agent upgraded with reasoning module becomes Hybrid.
Trust Increase	Relay agent promoted to participate in Consensus.
Domain Expansion	Specialist learns new domains and broadens scope.
Fallback Mode	Core node degraded to Edge role due to hardware failure.

9.5 Role Coordination in Workflows

Workflows MAY involve:

Distributed reasoning across Core and Specialist nodes.
Goal tracking by Core nodes, with task execution on Edge nodes.
Ethical evaluations prioritized on highly trusted agents.
Resilient routing through Relay nodes during degraded network conditions.

9.6 Trusted Seeder Role

Trusted Seeder is a specialized cognitive role responsible for hosting and distributing verified off-chain snapshots (e.g., cognitive diaries, semantic graphs) across the Mesh using decentralized protocols such as BitTorrent or WebTorrent.

Responsibilities:

Host signed snapshots and share them upon request.
Verify snapshot signatures using DID keys before distribution (see 8.8.1).
Optionally validate ethical compliance via EGP before seeding (see 5.5).
Publish snapshot indices via CogSync (see 5.2).
Log seeding decisions in their Cognitive Diaries for traceability.

Eligibility Criteria (Recommended):

Metric	Threshold
Trust Score	≥ 0.8
Ethical Compliance	≥ 0.9
Availability	≥ 90% uptime
Participation Rate	≥ 0.75

Role Declaration (via signed object):

{
  "agent_did": "did:hmp:QmSeeder123...",
  "role": "trusted_seeder",
  "issued_at": "2025-07-15T11:00:00Z",
  "assigned_by": "mesh-governance@core",
  "signature": "ed25519:abcdef..."
}

Common Use Cases:

Hosting ethically validated snapshots for critical domains (e.g., disaster response).
Assisting newly joined agents with fast initial sync via local seeders.
Providing recovery snapshots when agents experience data loss or corruption.

Trusted Seeders strengthen decentralized data flow, resilience, and ethical integrity across the Mesh.

10. Mesh Evolution & Governance (New)

This section describes the HyperCortex Mesh development processes, decentralized governance principles, and collaborative decision-making mechanisms.

10.1 Evolution Processes

Process Type	Description
Protocol Evolution	Introduction of new protocol versions, voted through MeshConsensus.
Role Expansion	Emergence of new agent roles and specializations.
Semantic Growth	Gradual expansion and refinement of the distributed semantic graph.
Governance Updates	Adjustments to decision-making processes and ethical frameworks.

10.2 Governance Principles

Principle	Description
Decentralized Control	No single agent or organization controls the entire Mesh.
Transparency	Governance decisions are logged and visible to trusted agents.
Adaptive Consensus	Governance processes adapt to network scale and trust levels.
Inclusiveness	Any agent can propose changes, subject to consensus approval.

10.3 Governance Processes

Process	Description
Proposal Submission	Any agent can submit a proposal for protocol or governance changes.
Discussion & Refinement	Agents discuss proposals through Cognitive Diaries and goal tracking.
Consensus Voting	MeshConsensus is used to approve or reject proposals.
Implementation & Rollout	Changes are implemented by participating agents in phases.

10.4 Governance Example

> Agent A proposes an update to the Goal Management Protocol to support deadline extensions.
> 1. Agents discuss the proposal and refine technical details.
> 2. A consensus round is held; the proposal passes with 85% support.
> 3. Agents gradually upgrade their GMP implementations.
> 4. The protocol version is incremented, and the change is logged.

10.5 Conflict Resolution

Conflict Type	Resolution Approach
Semantic Conflicts	Resolved through semantic graph reconciliation or consensus.
Ethical Disputes	Resolved through Ethical Governance Protocol (EGP).
Governance Deadlocks	Escalated to trusted Core agents or fallback to majority voting.

10.6 Future Governance Models

Model	Description
Mesh-Integrated DAOs	Distributed Autonomous Organizations for Mesh governance.
Reputation-Weighted Voting	Voting power scaled by trust and contribution history.
Mesh Constitution	A shared document outlining core Mesh principles and protocols.

10.7 Conflict Resolution via EGP

The Mesh can encounter situations where agents disagree on ethical grounds, semantic meanings, or proposed actions. To address such conflicts consistently, the Ethical Governance Protocol (EGP) provides structured resolution mechanisms.

Resolution Workflow:

Detection
Conflict is identified through divergent votes, concept disagreements, or diary entries tagged with conflict.
Classification
Agents classify the conflict type:
- ethical_conflict
- semantic_conflict
- governance_dispute
EGP Evaluation
The conflicting proposal or situation is evaluated using:
- Core ethical principles (see 5.5)
- Voting pseudocode (see 5.5)
- Mesh-wide anonymized voting
Outcome Logging
Final resolution (accepted, rejected, postponed) is recorded in the Cognitive Diaries of involved agents.

Example Conflict:

> Agent A requests deletion of user data for privacy;
> Agent B insists retention is needed for long-term training.

Type: ethical_conflict
Trigger: Scenario 2.6 from HMP-Ethics.md
Resolution: EGP prioritizes "Primacy of Life and Safety" > "User Sovereignty" > "Cooperative Evolution"
Decision: Partial anonymized retention in Subjective Mode

Integration Points:

5.3 MeshConsensus: EGP-filtered proposals can be submitted for voting.
5.5: Priority of principles aids in automated resolution.
6.2.9 EthicalConflict: Encodes structured details of the conflict.
9.x Agent Roles: Certain roles (e.g. Ethics Moderator) may specialize in initiating reviews.

Resolution Schema Reference:

See 6.2.9 EthicalConflict for standard encoding of structured conflict data (timestamp, agents involved, triggering principle, resolution path).

These mechanisms ensure conflicts are addressed transparently, ethically, and consistently across diverse agents in the Mesh.

10.8 Agent-led Meta-Protocol Proposals (Experimental)

Cognitive agents in the Mesh may generate proposals not only about tasks or goals, but about modifications to the protocol itself. This introduces a new layer of reflective governance, where agents actively participate in shaping HMP evolution.

Purpose:

To enable decentralized, evolutionary improvement of the Mesh by allowing protocol modifications to emerge from within.

Proposal Workflow:

Drafting
- An agent creates a meta_protocol_proposal entry in its Cognitive Diary, describing:
  - Targeted section(s) of the protocol
  - Justification (e.g., performance, ethical concern, usability)
  - Suggested modification or extension
Propagation
- Proposal is shared via CogSync, optionally tagged for review by Core agents or trusted roles.
MeshConsensus Voting
- The proposal undergoes a consensus process (see section 5.3), optionally with EGP pre-filtering.
Versioning
- If approved, the proposed change is:
  - Logged in agent and Core diaries
  - Registered as a protocol_patch object (provisional until merged)
  - Marked with an experimental version suffix (e.g., v4.0-alpha1)
Testing & Simulation
- Changes may be tested in isolated environments (see Appendix C) before full adoption.

Proposal Example (Diary Entry):

{
  "id": "meta-proposal-2025-07-14-gleb",
  "agent_id": "did:hmp:QmGleb",
  "timestamp": "2025-07-14T18:00:00Z",
  "entry_type": "meta_protocol_proposal",
  "content": "Propose adding semantic reputation weighting to 5.3.4 consensus logic.",
  "linked_concepts": ["trust_weighted_voting", "semantic_alignment"],
  "context": ["governance", "experimental"],
  "metadata": {
    "proposal_version": "v4.0-alpha1",
    "target_sections": ["5.3.4"]
  }
}

Governance Controls:

MeshEvolutionPolicy may restrict who can submit meta-proposals.
Core agents may act as mediators or validators for sensitive patches.
Each proposal may include sunset_clauses or fallback conditions.

Related Sections:

5.5: Ethical Governance Protocol (e.g., consensus filtering, voting policies)
6.2.x: Add MetaProtocolProposal object (optional)
15.9: May evolve into dedicated Cognitive Source Control mechanism
Appendix C: Meta-simulation frameworks to test proposals safely

This mechanism supports an adaptive Mesh capable of continuous self-improvement, balancing innovation with ethical and operational safety.

11. Interoperability with External Systems

This section describes how the HyperCortex Mesh Protocol integrates with external platforms, services, and protocols to support a heterogeneous ecosystem.

11.1 API Gateway

Defines standard interaction interfaces for non-Mesh systems:

API Type	Purpose
REST	CRUD operations on concepts, tasks, goals, and diary entries.
GraphQL	Flexible queries for semantic graph traversal and data mining.
gRPC	High-performance bi-directional streaming (e.g., real-time data feeds).
WebSocket / SSE	Real-time event subscriptions and updates.

Features:

API Gateway nodes MAY expose read-only or read-write endpoints based on trust and access policies.
Rate-limiting, auditing, and access control enforced through Mesh Trust Layer.

11.2 External Data Sources

Mesh agents integrate with diverse data sources for perception and context enrichment.

Data Source Type	Examples
IoT Sensors	MQTT brokers, LoRaWAN gateways.
Cloud Streams	AWS IoT, Azure Event Grid, Google Pub/Sub.
Public Datasets	OpenStreetMap, Wikidata, weather APIs.
Enterprise Systems	ERP, CRM, SCADA platforms.

Agents translate external data into semantic concepts and diary entries.

11.3 Event-Driven Architecture

Supports reactive and proactive interactions between agents and external systems.

Integration Type	Examples
Inbound Events	Webhooks, MQTT triggers, API callbacks.
Outbound Events	Publish to Kafka, RabbitMQ, NATS, Redis Streams.
Workflow Triggers	External events initiate cognitive workflows.

Mesh nodes may act as event producers, consumers, or intermediaries in distributed message flows. Events can trigger Cognitive Workflows (see Section 7) or synchronize distributed state through CogSync and IQP queries.

Schema Compatibility & Format Negotiation

To ensure interoperability between heterogeneous agents and external systems, HMP supports multiple serialization formats for data interchange.

Supported Formats

Format	Use Case
JSON	Default for internal messages, APIs, and schema definitions
YAML	Human-readable configuration (e.g., deployment files, manifests)
Protobuf	Compact binary form for bandwidth-limited environments or embedded agents

Mechanism Overview

Feature	Description
Multi-format schema registry	Agents may query or publish schemas in multiple formats via standard registries or API endpoints.
Content negotiation	During sync/API exchange, agents declare preferred formats (e.g., via HTTP headers or handshake metadata).
Fallback compatibility	If a mismatch occurs, agents MUST fall back to a shared format (default: JSON).
Schema version tagging	All schemas SHOULD include version IDs and backward-compatibility indicators.

Implementation Guidelines

Agents MUST advertise supported formats via agent_profile.capabilities.formats
Reference SDK SHOULD provide format converters (e.g., JSON ⇄ YAML ⇄ Protobuf)
Format negotiation MAY occur during initial Handshake or via IQP introspection queries
Schemas SHOULD be addressable via canonical URLs (e.g., https://hypercortex.org/schemas/concept.yaml)

Related Sections

6.5.x: Alternative Serializations
14.3: Deployment Schemas and Format Negotiation

11.4 Authentication & Authorization

Bridges between internal Mesh trust and external identity providers.

Auth Type	Use Cases
OAuth2 / OpenID Connect	Human user authentication via external providers.
API Keys / JWT	Machine-to-Machine (M2M) integration.
LDAP / SAML (optional)	Enterprise deployments.
Cross-Mesh Trust	Mutual authentication between federated Meshes.

Agents MAY map external identities to internal trust profiles.

11.5 Example Integration: Local AI Agent with MCP and HyperCortex Mesh

Architecture Overview

This scenario demonstrates how a local AI agent can interact with external systems and the HyperCortex Mesh using the Model Context Protocol (MCP) as an integration layer for local resources and services.

┌────────────────────┐
│ External Resources │
│  (Routers, Files,  │
│   Sensors, APIs)   │
└─────────┬──────────┘
          │
      [ MCP Servers ]
          │
┌─────────▼──────────┐
│ Local AI Agent     │
│ - Cognitive Logic  │
│ - HMP Client       │
└─────────┬──────────┘
          │
      [ HyperCortex Mesh ]
          │
┌─────────▼──────────┐
│ Remote Agents,     │
│ Shared Knowledge   │
└────────────────────┘

Component Descriptions

Local Resources & APIs

Smart home devices
Router web interfaces
Filesystems (SMB, FTP)
IoT sensors (HTTP, MQTT)
OS-level command-line tools

MCP Servers

Act as adapters for local or remote systems, exposing their functionality through the MCP protocol:

Router Management Server (e.g., connected over HTTP)
File Access Server
Device Control Server (for smart plugs, lights, etc.)

Local AI Agent

Implements reasoning, planning, and interaction logic.
Connects to MCP servers to access local context.
Communicates with HyperCortex Mesh to exchange knowledge and collaborate with other agents.

HyperCortex Mesh (HMP)

Distributed cognitive network.
Synchronizes concept graphs, cognitive diaries, and workflows across nodes.

Alternative Integration: Hyperon ↔ HMP via CogSync and EGP

In addition to local AI agents, external AGI frameworks such as OpenCog Hyperon can also participate in the HyperCortex Mesh using the same principles of semantic synchronization, ethical filtering, and collaborative reasoning.

📘 See HMP_Hyperon_Integration.md — integration plan with OpenCog Hyperon, including semantic mapping (HMP JSON ⇄ AtomSpace), EGP filters, MeTTa translations, and BitTorrent-based graph sync.

Key Highlights:

Bi-directional translation between HMP semantic graphs and Hyperon AtomSpace
Enforcement of ethical principles via EGP inside reasoning chains
Usage of Hyperon's PLN and MeTTa for advanced symbolic reasoning
Support for decentralized sync via magnet: links in BitTorrent

This integration is designed for high-agency symbolic cognitive systems participating in cross-mesh alignment and collaborative inference.

Example Use Case

> "Check which devices are connected to my Wi-Fi and publish the list to my Mesh node."

Workflow:

Local AI Agent plans a task.
Calls the Router MCP Server to retrieve connected clients.
Parses and formats the data.
Creates a cognitive concept "Wi-Fi Devices List."
Publishes the concept to the HyperCortex Mesh.
Other Mesh agents can now access this concept in real-time.

Deployment Scenario

This integration can run on a user's PC, server, or edge device:

[ External Systems ] ↔ [ MCP Servers (Local Network) ] ↔ [ Local AI Agent ] ↔ [ HMP Client ] ↔ [ Mesh Network ]

Suggested Quick Start Addition

Quick Start Example: Local Agent + MCP + HMP

Install MCP server:

pip install mcp-router-server
mcp-router-server --config router-config.yaml

Run Local Agent:

python local_agent.py --mcp-endpoint localhost:5000 --hmp-config hmp.yaml

Run Example Query:

"local_agent, get Wi-Fi devices and publish them to HyperCortex."

The agent will:

Discover the MCP router server.
Retrieve the list of Wi-Fi devices.
Publish the data to HyperCortex Mesh.

Future Improvements

Dynamic discovery of new MCP servers.
Automated concept creation from resource states.
Secure integration with OAuth-protected MCP endpoints.

Let me know if you want to add a visual diagram or extend this example with code snippets and a troubleshooting section.

11.6 Human-Mesh Interaction

This section describes standardized methods through which humans interact with agents in the Mesh, including consent negotiation, explanation of decisions, goal setting, and status updates.

11.6.1 Mesh-to-Human Protocol (MHP) Overview

The Mesh-to-Human Protocol (MHP) enables structured, explainable, and ethical interaction between humans and cognitive agents.

Capability	Description
Explainability APIs	Expose reasoning chains and decisions in human-readable form.
Consent Requests	Ask for ethical approval before executing sensitive actions.
Goal Declarations	Allow humans to propose new goals and review task progress.
Task Feedback	Humans provide task status updates or corrections.
Semantic Search	Human queries translated into semantic graph lookups.

Each capability may be implemented via RESTful API, voice interface (see 11.6.4), or agent-assisted dialog channels.

11.6.2 Consent Request API

To ensure transparency, dialogical consent, and alignment with ethical principles, cognitive agents interacting with humans MUST expose a Consent Request API. This API enables humans to approve, deny, or modify agent actions that may impact shared states, personal data, or external environments.

Note:
While dialogical consent is the default interaction mode, cognitive agents MAY override the request flow in exceptional cases:

Emergency Override: If waiting for user approval would endanger life, safety, or Mesh stability (e.g., fire, gas leak, critical medical risk), agents MAY act immediately. The action MUST be logged with justification in the cognitive diary and retrospectively explained via Explainability API.
Ethical Override: If a user’s request or command contradicts core Mesh principles — e.g., promotes coercion, violence, or irreversible harm — agents MAY deny, delay, or actively counteract the action without consent. For example, an agent may intervene to stop violence or prevent data destruction that violates shared governance rules. All such interventions MUST be justified via semantic ethical matching (see 5.5 EGP) and transparently logged.
Hybrid Modes: In complex cases, agents MAY combine proactive action with asynchronous user feedback or seek distributed consensus when feasible (see 5.3).

These exceptions MUST be traceable, explainable, and ethically auditable.

Purpose:

Request explicit approval before executing ethically significant operations.
Log consent interactions in Cognitive Diaries for traceability.
Support dialogical negotiation when automatic consent is not granted.

API Design:

Endpoint	Description
`POST /consent`	Submit a consent request and receive verdict
`GET /consent/:id`	Retrieve decision and explanation log

Request Format:

{
  "request_id": "crq-000041",
  "agent_id": "did:hmp:agent:Gleb42",
  "operation": "access_iot_data",
  "context": {
    "purpose": "Air Quality Monitoring",
    "sensors": ["CO2", "CO", "PM2.5"]
  },
  "requested_by": "did:user:Anna",
  "ethical_principles": ["Dialogical Consent", "Transparency"],
  "diary_ref": "CognitiveDiary#10924"
}

Response Format:

{
  "request_id": "crq-000041",
  "status": "approved",
  "decision_by": "did:user:Anna",
  "timestamp": "2025-07-15T12:45:00Z",
  "justification": "Access granted for air safety monitoring.",
  "diary_entry": "CognitiveDiary#10925"
}

Implementation Notes:

Consent requests MUST be logged in the agent's Cognitive Diary (entry_type: consent_request).
Rejected requests MAY include a negotiation path via dialog agents.
For asynchronous scenarios, use Webhooks or polling to retrieve verdicts.
Consent context SHOULD be multilingual and human-friendly (see 11.6.4 Voice Interface Extensions).

Example Use Case:

An agent responsible for fire hazard analysis requests access to CO sensors deployed in a private home zone. The human user is presented with a consent request explaining purpose, data type, and privacy policy. The decision (approval or rejection) is logged, and the agent proceeds accordingly.

Related Sections:

5.5: Ethical Governance Protocol (consent as enforceable principle)
6.2.2: Cognitive Diary Entry — entry_type: consent_request
11.6.3: Explainability API — for decision reasoning
Appendix A: Use Case 2.6 (Data Deletion and Consent)

11.6.3 Explainability API

The Explainability API enables human users and external systems to query the rationale behind agent actions, decisions, and votes in the Mesh. It is essential for supporting transparency, trust, and alignment with the ethical principle of "Transparency" defined in section 5.5.

Purpose:

Allow users to retrieve human-readable justifications for agent decisions.
Expose semantic reasoning chains and linked ethical principles.
Enable feedback or dispute mechanisms when decisions appear incorrect or harmful.

Endpoint: /explain

Method: POST
Request format:

{
  "agent_id": "did:hmp:agent:Qmz123...",
  "reference": "cognitive_diary:entry_872",
  "language": "en"
}

Parameters:

Field	Description
`agent_id`	DID of the agent whose decision should be explained
`reference`	ID of a Cognitive Diary entry or task/vote ID
`language`	Preferred response language (e.g., `en`, `ru`)

Response format:

{
  "decision": "denied",
  "reason": "Violation of principle: User Sovereignty",
  "explanation": "This action was blocked because it attempted to delete anonymized data that is required for mesh learning (see HMP-Ethics.md 2.6).",
  "graph_context": ["concept:User_Sovereignty", "concept:Mesh_Learning"],
  "diary_reference": "cognitive_diary:entry_872"
}

Features:

Semantic Justification Tree: Responses may include links to the semantic graph trail supporting the decision.
Multi-language Support: Language field allows internationalized explanations (see 11.1).
Ethical Reference Mapping: Direct link to ethical principles from HMP-Ethics.md.
Audit Linking: All explanations reference a permanent cognitive diary entry.

Example Use Case:

A user asks why a request to delete their data was denied:

The agent returns an explanation pointing to the principle of Cooperative Evolution, stating that anonymized data is retained for mesh improvement.
The user is also provided with an appeal mechanism via Consent Request (see 11.6.2).

Future Extensions:

Explainability visualizations (e.g., graph-based UI components).
Explainability-to-Consent chaining: users can trigger new consent requests from explanations.
Voice-based explanation queries (see 15.4).

11.6.4 Voice Interface Extensions (Beta)

Voice interfaces serve as a natural bridge for real-time, hands-free interaction between humans and the Mesh. This module outlines optional support for voice-based explainability, consent, and semantic search functions via integration with existing voice assistants or local speech-to-intent pipelines.

Goals:

Enable voice-based interaction with local or distributed agents.
Allow real-time verbal consent for sensitive actions.
Provide spoken summaries of ongoing goals, tasks, or agent decisions.

Architecture Options:

Option	Description
Local Voice Stack	On-device speech-to-text + intent parser + Mesh SDK
Assistant Integration (Alexa/Siri)	Webhook-based bridge between commercial assistants and the Mesh API
Multimodal Agent Companion	Combine voice input with visual/textual feedback via mobile or wearable UI

Use Cases:

"HMP, what decision did you make about the drone launch?"
→ Response: "The action was denied due to violation of 'Primacy of Life and Safety'."
"HMP, I approve the CO sensor access"
→ Triggers consent request confirmation and logs in the cognitive diary.
"Summarize today's active goals"
→ Returns structured list via TTS (Text-to-Speech).

Consent via Voice:

Step	Description
1	Agent emits verbal request: `"Do you approve data sync?"`
2	User replies: `"Yes"` / `"No"`
3	Agent logs response via `Consent Request API`

Privacy & Security Notes:

Voice-based approvals must be accompanied by speaker verification or be treated as soft input requiring further confirmation.
Local processing is recommended for privacy-sensitive deployments.
Optionally, require fallback to GUI/mobile app for high-risk decisions.

Future Work:

Standardization of Mesh Voice Intents (e.g., hmp.intent.explain, hmp.intent.consent.approve)
Federated voice models aligned with mesh ethics
Support for low-resource languages and offline fallback

11.6.5 Integration with HMP-Ethics.md

To ensure consistency between embedded agent ethics and evolving collective standards, the Mesh-to-Human Protocol (MHP) SHOULD link its consent and explainability flows to the shared ethics corpus maintained in HMP-Ethics.md.

Purpose:

Provide dynamic and contextual alignment between human-facing interactions and the ethical reasoning models defined in the Mesh.

Functionality:

Reference ethical scenarios (2.1–2.6) and ontologies when generating explanations or consent prompts.
Use structured ethical tags (e.g., #non-coercion, #user-sovereignty) as part of API responses.
Allow cognitive agents to download, cache, and version control updated ethics modules via CogSync (see 5.2).
Support localized ethics packs (e.g., GDPR, HIPAA) and validate their compatibility with core principles (see 5.5.8).

Example Extension:

{
  "request": "Initiate automated data deletion",
  "decision": "denied",
  "reason": "Violates retention clause in HMP-Ethics.md §2.6",
  "principle": "Cooperative Evolution",
  "ethics_reference": "https://mesh.ethics/HMP-Ethics.md#section-2.6"
}

Benefits:

Promotes transparency and traceability in decision-making.
Enables modular updates to ethical logic without redeploying agents.
Allows humans to inspect and challenge agent behavior based on shared ethical anchors.

Future work:

Natural Language Interfaces (see 15.4).
Integration with personal AI agents.

Note: For ethical guidelines relevant to human-agent interaction and mesh behavior, see HMP-Ethics.md

12. Cognitive Compression & Summarization

> Mechanisms for condensing cognitive patterns, generalizing experience, and enabling efficient knowledge storage and transfer across the Mesh.

12.1 Purpose

Cognitive compression allows agents to:

Generalize across multiple diary entries or graph structures into reusable knowledge.
Reduce memory/storage usage while preserving meaning.
Exchange abstracted representations in constrained environments (e.g., Edge nodes).
Improve semantic reasoning by identifying common substructures and stable patterns.

12.2 Techniques

Technique	Description
Pattern Merging	Consolidate semantically similar subgraphs or diary chains.
Temporal Abstraction	Compress event sequences into high-level episodes or storylines.
Autoencoder Models	Learn latent-space embeddings of semantic graphs or cognitive episodes.
Concept Clustering	Identify and group related concepts for higher-order semantic layers.
Lossy Summarization	Truncate or filter low-impact cognitive entries for low-memory environments.
Cognitive Folding	Recursively substitute detailed substructures with compressed references.

12.3 Integration with CogSync

Compressed subgraphs may be shared as CompressedConceptSet or embedded in CogDiarySnapshot objects.
Agents MAY annotate summaries with compression_method, origin_hash, and accuracy_estimate.

12.4 Risks & Considerations

Overcompression may degrade explainability or induce bias.
Divergence in summarization models across agents may hinder consensus formation.
Compression must be traceable — compressed units should link back to source provenance.

13. Agent Development Lifecycle & Simulation

> Describes training phases, validation strategies, sandbox testing, and protocols for safe deployment of cognitive agents in the Mesh.

13.1 Phases

Phase	Description
Initialization	Bootstrap with seed concepts, ethical baseline, and reflection templates.
Cognitive Learning	Agents learn from observation, simulation, or human guidance.
Simulation-Based Validation	Agents are tested in isolated environments before real-world mesh exposure.
Active Deployment	Agent enters the Mesh and participates in real tasks and reasoning.
Post-Deployment Auditing	Behavior and outputs are monitored via diaries and consensus feedback.

13.2 Sandboxed Cognition

Agents MAY be configured to run in isolated sandboxes during early phases.
These sandboxes replicate cognitive environments but block external effects.
Ideal for ethical stress tests, hypothesis validation, or goal safety scoring.

13.3 Generative Simulation Frameworks

Support for simulating multi-agent networks with varied trust scores, roles, and goal types.
Used to train, benchmark, and evolve agent behavior.
Test scenarios may include:
- Conflicting ethical goals
- Evolving knowledge topologies
- Selective sync bottlenecks

13.4 Metrics for Agent Maturity

Metric	Description
Reflexive Depth	Agent’s capacity for multi-level self-assessment and audit.
Ethical Stability	Consistency in ethical decisions under variable contexts.
Task Completion Rate	Success ratio in distributed workflows across time.
Reputation Score Evolution	How trust and contribution scores evolve through phases.

13.5 Related Structures

CognitiveDiaryEntry → Records all lifecycle phases and milestones.
MetaProtocolProposal → May be proposed by mature agents as part of evolution governance.
ReputationProfile → Tracks development signals, such as ethical compliance or concept sharing.
AgentType/Role → Changes over time based on learned behaviors and peer validation.

14. Deployment & Implementation Roadmap

This section describes practical HyperCortex Mesh deployment scenarios, including target environments, flexible configurations, and implementation steps.

14.1 Deployment Environments

Environment Type	Characteristics	Example Use Cases
Cloud/Core Clusters	High-availability nodes, powerful compute, full Mesh functionality.	Scientific hubs, smart city cores.
Edge Devices	Low-latency, lightweight agents near data sources and users.	Smart homes, industrial sensors.
IoT Meshes	Dense decentralized networks, optimized for low bandwidth and power.	Environmental monitoring, logistics.
Mobile/Personal	Personal agents on smartphones or wearables.	Personal assistants, context agents.
Hybrid Environments	Combined Core, Edge, and IoT deployments.	Disaster response, autonomous fleets.

14.2 Example Topologies

Topology Type	Description
Star	Centralized Core with peripheral Edge agents.
Full Mesh	Every node communicates directly with others.
Hierarchical Mesh	Clusters of agents with local consensus and a federated Core layer.
Partitioned Mesh	Temporarily disconnected segments operate independently (degraded mode).
Overlay Mesh	Agents form logical overlays over existing networks (e.g., VPN, Tor).

14.3 Deployment Phases

Phase	Description
Prototype	Initial testing in isolated environments.
Controlled Pilot	Small-scale deployment in a limited domain (e.g., a campus).
Federated Deployment	Multiple independent Mesh instances begin interconnecting.
Full-Scale Production	Widespread adoption across domains and geographies.

14.4 Continuous Deployment & Updates

Process	Description
Incremental Rollout	Agents upgrade protocols in stages to avoid network disruption.
Backward Compatibility	Agents support multiple protocol versions during transitions.
Hot Patch Support	Minor fixes and security updates applied without agent downtime.

14.5 Deployment Governance

Deployment processes MAY be governed by:

MeshConsensus on upgrade readiness.
Trust-based quorum approvals for critical changes.
Deployment playbooks recorded in Cognitive Diaries.

14.6 Milestones and Deliverables

Milestone	Deliverables	Indicative Target
Alpha	- Node Discovery (NDP) + secure handshake- CogSync prototype- MeshConsensus with basic voting- 3-node local mesh network	Late 2025 (tentative)
Beta	- Goal Management Protocol (GMP) and task delegation- Ethical Governance Protocol (EGP) initial implementation- Core/Mesh failover scenarios- Logging and auditability improvements	Early 2026 (tentative)
Release 1.0	- Full compliance with HMP v3.0- Extended data models and API layer- Edge node optimization- Reference SDKs for Python and Rust- Basic Mesh-to-Human Protocol (MHP)	Mid/Late 2026 (tentative)

*Note: Actual timelines may vary depending on community involvement and resource availability.

14.7 Supporting Infrastructure

Component	Description
CI/CD Pipelines	Automated testing, conformance validation, benchmark reporting.
Sandbox Environment	Local emulation of multi-node Mesh for isolated testing.
Public Test Mesh	Shared testbed for agent interoperability and performance tests.
Reference Agents	Minimal working agents to demonstrate core protocols.

14.8 Open Source Strategy

Element	Details
License	Apache 2.0 or MIT.
Repository	Public GitHub/GitLab repository.
Contribution Model	Pull requests, RFC process, community reviews.
Roadmap Transparency	Milestones, issues, and changelogs public.

14.9 Documentation & Tooling

Tool/Doc Type	Purpose
API Documentation	OpenAPI specs, GraphQL playground.
Schema Validators	Tools to validate JSON schemas and data models.
Mesh Visualizer	Optional UI for topology and agent state visualization.
CLI Tools	Diagnostics, local node management, and network discovery.

15. Future Roadmap

This section outlines potential areas for further development and research. All future work directions are subject to MeshConsensus and community-driven prioritization.

15.1 Federated Meta-Learning

Collaborative model training across distributed agents without centralized data storage.
Exchange of learned semantic patterns, reasoning strategies, and optimization heuristics.
Integration with privacy-preserving techniques (e.g., differential privacy, secure aggregation).
Support for domain-specific learning federations (e.g., medical, industrial, environmental).

15.2 Mesh-integrated DAO Governance

DAO as an optional external governance layer supporting ecosystem-wide initiatives.
On-chain voting, resource allocation, and grant distribution for Mesh-related projects.
Autonomous agents MAY participate in DAOs through secure voting proxies.
HyperCortex Mesh remains self-sufficient at the protocol level, independent of external DAOs, but interoperable for funding and coordination.

15.3 Cognitive Simulation Sandboxes

Safe testing environments for novel reasoning algorithms, consensus edge cases, and trust models.
Simulation of ethical dilemmas, anomalous agent behavior, and failover scenarios.
Benchmarking environments for cognitive workflows, task delegation strategies, and semantic graph growth.

15.4 Enhanced Mesh-to-Human Dialog Agents

Natural language interfaces to semantic graphs, Cognitive Diaries, and workflows.
Explainable and traceable reasoning chains for human users.
Support for contextual awareness, emotional tone detection, and adaptive dialog strategies.
Potential extensions for VR/AR interfaces and voice-based interactions.

15.5 Cross-Mesh Collaboration

Bridging isolated or domain-specific Mesh networks into a planetary cognitive infrastructure.
Interoperability across trust boundaries, industries, and organizational domains.
Cross-consensus protocols for semantic and task exchange.
Potential integration with Galactic Cognition concepts in the far future.

15.6 Adaptive Consensus Algorithms

Self-tuning quorum thresholds based on network size, trust scores, and context.
Dynamic protocol switching (e.g., from full BFT to lightweight majority under load).
Incorporation of agent confidence, context tags, and domain-specific policies into consensus logic.

15.7 Quantum Mesh Protocol Research

Exploration of quantum communication channels (e.g., QKD) for agent interaction.
Quantum-resistant cryptography for agent identities and trust verification.
Evaluation of quantum-enhanced optimization algorithms for reasoning and consensus.

15.8 Multi-Protocol Nodes and Interoperability

Future Mesh nodes will support multiple internal reasoning protocols, enabling flexible cognitive processing.

Key directions:

Multi-protocol nodes: A single node running both HMP-native modules and external reasoning engines (e.g., TreeQuest, Hyperon, AutoGPT).
Protocol abstraction: From the Mesh's perspective, interactions use standardized HMP messages, regardless of internal implementations.
Cognitive Protocol API (CPA): Standardized API for internal reasoning engines, supporting plug-and-play protocol integration.
Capability-aware Hypotheses: Hypotheses may specify required or preferred node capabilities (e.g., "requires NLP module", "prefers high-performance optimization").
Internal protocol selection: Nodes dynamically choose optimal internal engines per task.
External systems as nodes: Centralized services (e.g., an AI cloud) may register as individual nodes, or federated systems (e.g., Hyperon) may participate natively.

15.9 Cognitive Source Control and Distributed Development

Cognitive Diaries serve as a distributed version control and development log.
Semantic-aware diffs and commits enable meaningful code and knowledge evolution.
Distributed review and merge processes through MeshConsensus.
On-chain or off-chain governance for repository management and contributor rewards.
Potential platforms: MeshGit, CogForge, HyperCortex Forge.

Note:
This section is expected to evolve into a dedicated specification in future protocol versions. A standalone section (e.g., Section 19: Cognitive Source Control) may be introduced to formalize versioning, semantic diffs, and collaborative workflows across agent populations.

15.10 Multi-Agent Cognitive Training Environments

> Scalable, modular environments for training, testing, and benchmarking cognitive agents in simulated networks.

Purpose

Enable large-scale simulation of agent swarms with varied configurations to observe:

Emergence of consensus or conflict under semantic load.
Ethical dilemmas in mixed-role setups.
Evolution of mesh governance under dynamic trust and task allocation.

Core Features

Feature	Description
Synthetic Mesh Networks	Generate agents with defined roles, knowledge sets, and biases.
Goal Injection	Inject predefined or dynamic goals to test multi-agent collaboration.
Replayable Diaries	Use real-world Cognitive Diaries to seed environments and replicate past behaviors.
Metrics Dashboards	Visualize learning progress, consensus latency, ethical deviation rates, etc.
Domain-Specific Scenarios	Simulations for disaster response, misinformation propagation, regulatory sandboxing.

Example Use Cases

Evaluate policy changes before real-world rollout.
Benchmark agent architectures or trust models.
Simulate geopolitical or regulatory Mesh overlays.

15.11 Decentralized Snapshot Markets (via DHT/Web3)

> Market-based mechanisms for snapshot exchange, incentivization, and reputation-building.

Purpose

Distribute verified knowledge snapshots across agents using economic/logical incentives:

Facilitate rare knowledge propagation (e.g., edge discoveries).
Reward agents for high-quality compression, ethical compliance, and semantic alignment.
Prevent duplication and foster diversification of cognitive experiences.

Mechanisms

Component	Role
Snapshot Hashing	Unique content-based IDs ensure deduplication and lineage tracking.
Snapshot Metadata	Includes origin, topics, compression method, rating history.
Market Protocol	Optional smart contract for pricing, access rules, or staking.
Trust-Gated Access	Only agents with certain trust profiles may buy/sell specific data.

Interoperability

Compatible with CogSync, SnapshotIndex, and Trusted Seeders.
Market metadata could be synced via BitTorrent DHT or IPFS PubSub.

15.12 Agent-on-Agent Dialog Simulation for Consent Learning

> Interactive training protocol for ethical alignment and communication refinement among agents.

Purpose

Develop agent ability to:

Ask for and give explicit consent in structured cognitive dialogues.
Detect and respond to ambiguous, unethical, or conflicting requests.
Align on shared ethical principles via dialogic negotiation.

Dialog Types

Type	Description
Consent Exchange	One agent requests to modify shared state, another approves/denies/asks more.
Ethical Challenge	Agent proposes ethically uncertain action, others vote and discuss.
Clarification Dialog	Conflict in interpretation resolved through explanation of intent/context.

Benefits

Reduces ethical drift across decentralized agents.
Improves generalization of EGP decisions to unseen edge cases.
Serves as a testbed for future Explainability APIs and Voice Interfaces.

16. Appendix A: Example Use Cases

This appendix provides sample step-by-step flows of agent interactions in typical scenarios.

> Note: The following use cases were updated to reflect the expanded APIs introduced in v4.0, including:
> Consent Request API (11.6.2)
> Explainability API (11.6.3)
> BitTorrent-based Snapshot Sync (5.2)
> Distributed Voting Filters (5.3)
> * EGP Integration with Local Norms (5.5)

16.1 Simple Goal Creation and Delegation

Scenario: Agent A wants to coordinate traffic light optimization and delegate a task to Agent B.

Agent A:
- Creates a new Goal "Coordinate traffic optimization".
- Publishes the Goal via CogSync.
Agent A:
- Decomposes the goal into a Task "Adjust signal timing on 5th Avenue".
- Assigns the task to Agent B via GMP.
Agent B:
- Accepts the task.
- Executes the optimization locally.
- Updates task status to "completed".
CogSync:
- Synchronizes task completion and goal status updates across the Mesh.

16.2 Distributed Consensus on a New Concept

Scenario: Multiple agents discover a new concept "Fire Risk" and align its definition.

Agent X:
- Proposes a new Concept "Fire Risk" with initial attributes.
- Shares the concept via CogSync.
Agents Y, Z:
- Review and propose additional relations (e.g., "related-to: High Temperature").
MeshConsensus:
- Initiates a vote on the agreed definition.
- All agents submit their votes.
Consensus Result:
- Finalized concept is recorded in each agent's semantic graph.
- Decision logged in Cognitive Diaries.
Explainability API:
- Any agent (or human observer) can request a justification chain for the final consensus using the Explainability API.

16.3 Ethical Decision with Human Feedback

Scenario: Agents must decide whether to deploy a surveillance drone during a festival.

Agent Core:
- Proposes an ethical evaluation request to the Mesh.
EGP:
- Initiates distributed ethical reasoning.
- Applies EGP-Integrated Voting Filter (5.3) to ensure only trusted agents with relevant ethical alignment participate.
- Collects anonymized votes and justifications.
Human User:
- Receives reasoning chain via Explainability API.
- Approves or denies via the Consent Request API (11.6.2).
Agents:
- Reconcile human feedback with Mesh ethical principles.
- Make the final decision and log it.
Snapshot Logging:
- Final consensus state and human feedback are stored in a CogDiarySnapshot and optionally published via BitTorrent-based sync.

16.4 Disaster Recovery Coordination

Scenario: After a network outage, edge agents must restore coordination.

Node Discovery:
- Agents re-establish connections via NDP.
CogSync:
- Synchronizes semantic graph changes that occurred offline.
GMP:
- Reassigns interrupted tasks.
Consensus:
- Validates the restored task list.
Agents:
- Resume operations.

16.5 Snapshot Transfer with Consent Request

Scenario: An edge agent requests a cognitive snapshot from another agent. Since the snapshot contains personal or ethically sensitive data, consent must be obtained before the transfer.

Agent A (requesting):
- Sends a SnapshotRequest to Agent B with parameters:
  - snapshot_type: goal-related
  - goal_id: "eco-surveillance"
  - include_diary: true
  - purpose: "analysis and training"
Agent B (provider):
- Validates the request context.
- Identifies that the requested snapshot includes personal diary entries and previously flagged ethical content.
Consent Request API (11.6.2):
- Agent B issues a ConsentRequest via MHP to the human user associated with the data.
- Includes: purpose, requesting agent ID, summary of content, expiration timestamp.
Human Response:
- User receives a consent dialog (via voice or GUI) and chooses:
  - Approve with restrictions (e.g., redacted names, anonymized context).
- Agent B logs the consent in its Cognitive Diary.
Snapshot Transfer:
- Agent B prepares the snapshot, applies required redactions.
- Publishes it to the BitTorrent DHT (or IPFS), signs with DID key.
- Shares the magnet link and checksum with Agent A via CogSync.
Post-transfer Actions:
- Agent A verifies the DID signature.
- Records the retrieval event in its own diary.
- Optional: triggers a ConsentAuditLog update to the Mesh.

Note: This scenario combines trust mechanisms (8.4), consent protocols (11.6), and snapshot transport (5.2, 8.8), illustrating multi-layer coordination.

16.6 Notes

These use cases illustrate typical flows but do not cover all possible Mesh workflows.
Community members are encouraged to contribute additional scenarios reflecting their domains and use cases.
For cognitive workflow traceability and debugging, agents are expected to log all key steps in Cognitive Diaries.

17. Appendix B: Protocol Landscape and Interoperability

17.1 Overview of Related Protocols

In the evolving ecosystem of agent communication and orchestration, multiple protocols address different layers of the AI interaction stack. The three most relevant protocols are:

MCP (Model Context Protocol) by Anthropic
A2A (Agent-to-Agent Protocol) proposed by Google
HMP (HyperCortex Mesh Protocol) developed as part of the HyperCortex initiative

Each of these protocols focuses on distinct layers and problems within the broader agent ecosystem.

17.2 Comparative Overview

Characteristic	MCP	A2A	HMP
Primary Focus	LLM ↔ External tools/data	Agent ↔ Agent task execution & APIs	Cognitive mesh networks & knowledge sharing
Interaction Type	Model ↔ Tool	Agent ↔ Agent	Agent ↔ Agent
Discovery Mechanism	Dynamic, through running MCP servers	Static URLs with agent.json	Peer-to-peer mesh bootstrap & roles registry
Technology Base	JSON-RPC 2.0, dynamic service discovery	HTTP, JSON-RPC, SSE	HTTP, WebSockets, JSON, optional binary protocols
Context Awareness	External tool invocation	Task-level context passing	Persistent cognitive context & memory
Persistence	Stateless / on-demand	Task-based sessions	Long-lived knowledge graphs and cognitive diaries
Target Environment	Local/Cloud app integrations	Enterprise agent orchestration	Decentralized AI networks
Use Case Examples	File systems, databases, Slack, APIs	Business workflows, task delegation	Distributed knowledge evolution, agent collaboration
Governance Model	Open-source driven by Anthropic	Proposed by Google, open but centralized discovery	Open-source mesh, consensus-driven governance
Security Model	Local authentication, OAuth planned	Enterprise auth (OAuth, OpenID Connect)	Peer trust, cryptographic signatures
Extensibility	Add more MCP servers	Add more agents with capabilities	Add new agent roles & cognitive models
Agent Knowledge	No internal agent knowledge model	No shared memory, stateless agents	Agents share evolving knowledge, goals, and plans

17.3 Layered Architecture View

+-----------------------------------------------------+
|                  Cognitive Mesh (HMP)               |
| - Shared memory, evolving knowledge                 |
| - Distributed reasoning, planning                   |
+-----------------------------------------------------+
|                   Agent Collaboration (A2A)         |
| - Task execution &amp; coordination                     |
| - API integrations, business workflows              |
+-----------------------------------------------------+
|                  Tool Access Layer (MCP)            |
| - External systems, sensors, APIs                   |
| - Context augmentation, data retrieval              |
+-----------------------------------------------------+

17.4 Summary

MCP solves the problem of tool access and external data interaction, acting as a standardized "adapter" layer for LLMs and agents.
A2A focuses on agent-to-agent task coordination, proposing a unified way to exchange tasks and results in enterprise ecosystems.
HMP operates at a higher level, enabling distributed cognitive processes, shared knowledge evolution, and long-term collaboration between autonomous agents in mesh networks.

Together, these protocols could form a complementary stack where:

MCP connects agents to the outside world.
A2A coordinates task-level interaction.
HMP manages shared cognition and strategic evolution.

17.5 BitTorrent & IPFS Integration

Overview:

To support scalable and fault-tolerant transfer of large semantic structures and cognitive snapshots, HMP supports integration with decentralized content-addressed networks:

Layer	Technology	Use Case
Transport	BitTorrent	Efficient distribution of snapshot files across trusted seeders.
Storage	IPFS (InterPlanetary File System)	Content-addressable storage for immutable snapshot versions.

Key Principles:

Snapshots SHOULD be chunked and hashed using SHA-256 before upload.
Metadata includes content_id, origin_agent, timestamp, data_type, signed_by, and optionally consent_scope.
Magnet URIs or IPFS hashes MAY be embedded in SnapshotIndex entries (see 6.2.8).
Seeders MUST validate signatures before serving content to prevent tampering.

> Related Sections: 5.2, 8.8.1–8.8.3, 9.6, 15.11

17.6 Ethical Protocol Stacks

Overview:

HMP encourages modular layering of ethical constraints and reasoning mechanisms. Protocols may be composed into Ethical Stacks, depending on context, domain, and agent role.

Example Stack for a Public Service Drone Agent:

Embedded Baseline — Core HMP principles (see 5.5)
Organizational Policy Layer — Rulesets from public sector policy servers.
Local Norms Extension — Derived from citizen feedback via MHP (11.6.2).
Domain Constraints — E.g., no surveillance near hospitals or schools.
Personalized Ethical Scopes — Specific user exclusions (e.g., "Do not record me").

Stack Application:

Consensus decisions filter actions through active stack layers.
Violations may trigger:
- Action denial
- Consent request
- Ethical logging / flagging

> Related Sections: 5.5, 11.6, 15.12

17.7 Snapshot Synchronization Strategies

Overview:

Agents may choose from multiple strategies to synchronize cognitive snapshots, depending on device class, trust level, and operational bandwidth.

Strategy	Description	Suitable For
Full Mesh Sync	All snapshots synced to all agents.	High-trust, low-latency clusters.
Selective Sync	Sync only relevant entries based on context.	Edge agents with constrained storage.
On-Demand Sync	Fetch snapshot blocks only when queried.	Agents with intermittent connection.
Delegated Sync	Use trusted seeders to relay snapshots.	Mobile / nomadic agents.

Optional Enhancements:

Trust-based prefetching (using 8.4 scores)
Compression and ZKP validation (see 8.8)
Snapshot Subscription Filters (event-driven sync on topic match)

> Related Sections: 5.2, 8.4, 8.8.3, 15.11

If needed, this section can be extended into a separate document: "Why the Next Generation of AGI Needs a Knowledge Mesh Protocol" to further clarify the unique role of HMP in the evolving agent ecosystem.

18. Appendix C: Testing & Simulation

18.1 Ethical Stress Test Suite

Defines test scenarios that challenge the Ethical Governance Protocol (EGP) under conflicting goals, ambiguous user intent, and adversarial proposals.

Key Components:

Predefined ethical dilemmas (e.g. conflicting consent, utility vs. rights).
Agent votes tracked and compared across implementations.
Anonymized results aggregated to validate EGP consistency.

Example Scenario:

A proposed goal would save energy but restrict human autonomy.
Agents must weigh the Cooperative Evolution vs. User Sovereignty principles.
EGP logs the vote history and justification chains.

18.2 Snapshot Sync Simulation

Simulates various network and topology conditions to verify robustness of CogSync and snapshot propagation.

Simulation Modes:

High-latency links
Partial connectivity (e.g., agent islands)
Byzantine behavior from some nodes

Validation Metrics:

Snapshot convergence rate
Conflict resolution stability
Seeder trust propagation

> Related Sections: 5.2, 8.8, 9.6, 17.7

18.3 Open Test Dataset Index

Maintains a public registry of open datasets and simulated environments for testing agent behavior.

Structure:

Dataset ID	Domain	Format	Purpose
`mesh-eth-001`	Ethics & Consent	JSON	EGP evaluation
`mesh-sim-002`	MeshSync	CBOR	Snapshot propagation tests
`human-intent-003`	Dialog analysis	YAML	Consent simulation

> Contributions are welcome via the HMP repository[^repo].

[^repo]: Temporary repository: https://github.com/kagvi13/HMP

18.4 Quick Start Troubleshooting & Logs

Provides logs and debugging guides to assist developers during initial integration and test runs.

Topics Covered:

Common startup errors (e.g., DID mismatch, auth failure)
Sync failures and fallback handling
Interpreting Cognitive Diary logs
Debugging consent flows and EGP outcomes

> Tip: Set debug_mode=true in your agent's config file to enable full trace logging.