Cognos Session Memory

CognOS Session Memory

mcp-name: io.github.base76-research-lab/cognos-session-memory

Verified context injection via epistemic trust scoring for LLMs.

Solves session fragmentation by maintaining verified, high-confidence session context between conversations.

Problem

Large language models suffer from session fragmentation: each new conversation starts without verified context of previous work. This forces repeated explanations, loses decision history, and breaks long-running workflows.

Existing solutions (persistent memory systems, vector retrieval) either:

• Lack trust scores before injection → hallucinations propagate
• Don't audit which context was injected → compliance gaps
• Treat all past information equally → noise overwhelms signal

Solution

A plan-mode gateway that:

1. Extracts structured context from 3-5 recent traces
2. Scores context quality via CognOS epistemic formula: C = p · (1 − Ue − Ua)
3. Injects as system prompt only if C > threshold
4. Flags for manual review if C < threshold
5. Audits every context injection with trace IDs → EU AI Act compliance

Architecture

recent_traces (n=5)

↓ extract_context() → ContextField + coverage ↓ compute_trust_score(p, ue, ua) → C, R, decision ↓ if C > threshold: system_prompt ← inject else: flagged_reason ← manual review

Core Formula

C = p · (1 − Ue − Ua)

R = 1 − C

where: p = prediction confidence (coverage of required fields) Ue = epistemic uncertainty (divergence between traces) Ua = aleatoric uncertainty (mean risk in traces)

Action Gate

R < 0.25       → PASS      (inject without review)

0.25 ≤ R < 0.60 → REFINE (inject with caution) R ≥ 0.60 → ESCALATE (flag for manual review)

API

POST /v1/plan

Extract and score context.

Request:

{

"n": 5, "trust_threshold": 0.75, "mode": "auto" }

Response (if injected):

{

"status": "injected", "trust_score": 0.82, "confidence": 0.82, "risk": 0.18, "decision": "PASS", "context": { "active_project": "CognOS mHC research", "last_decision": "Verify P1 hypothesis", "open_questions": ["How does routing entropy scale?"], "current_output": "exp_008 complete", "recent_models": ["gpt-4", "claude-3", "mistral"] }, "system_prompt": "## CognOS Context...", "trace_ids": ["uuid-1", "uuid-2", ...] }

Response (if flagged):

{

"status": "flagged", "trust_score": 0.45, "decision": "REFINE", "flagged_reason": "Trust score 0.45 below threshold 0.75. Manual review recommended.", "trace_ids": [...] }

Modes

• auto (default) — inject if trust_score ≥ threshold, else flag
• force — always inject (for testing)
• dry_run — compute score but never inject

Claude Code Integration

As a /compact replacement

# In any Claude Code session:

/save

Claude writes a structured summary, trust-scores it, and persists it to SQLite. Next session: automatically injected as SESSION_CONTEXT before your first prompt.

See docs/COMPACT_ALTERNATIVE.md for a full comparison.

As an MCP server

Add to ~/.claude/settings.json:

{

"mcpServers": { "cognos-session-memory": { "command": "python3", "args": ["/path/to/cognos-session-memory/mcp_server.py"] } } }

Tools exposed:

---

Quick Start

Installation

git clone https://github.com/base76-research-lab/cognos-session-memory

cd cognos-session-memory pip install -e .

Run Gateway

python3 -m uvicorn --app-dir src main:app --port 8788

Test /v1/plan (dry_run)

curl -X POST http://127.0.0.1:8788/v1/plan \

-H 'Content-Type: application/json' \ -d '{"n": 5, "mode": "dry_run"}'

Test /v1/plan (auto)

curl -X POST http://127.0.0.1:8788/v1/plan \

-H 'Content-Type: application/json' \ -d '{"n": 5, "trust_threshold": 0.75, "mode": "auto"}'

Modules

• trust.py — CognOS confidence formula, action gate, signal extractors
• trace_store.py — SQLite persistence (write/read/purge)
• plan.py — Context extraction, trust scoring, system prompt building
• main.py — FastAPI gateway + middleware
• mcp_server.py — MCP stdio server (save_session, load_session)

Testing

pytest tests/ -v --cov=src

Documentation

• COMPACT_ALTERNATIVE.md — Why this beats /compact
• PAPER.md — Research paper

Research Paper

See docs/PAPER.md — "Verified Context Injection: Epistemically Scored Session Memory for Large Language Models"

Status: Independent research — Base76 Research Lab, 2026 Authors: Björn André Wikström (Base76)

Citation

@software{wikstrom2026cognos,

author = {Wikström, Björn André}, title = {{CognOS Session Memory}: Verified Context Injection via Epistemic Trust Scoring}, year = {2026}, url = {https://github.com/base76-research-lab/cognos-session-memory} }

License

MIT

Contact

• Author: Björn André Wikström
• Email: bjorn@base76.se
• ORCID: 0009-0000-4015-2357
• GitHub: base76-research-lab