ADR-367: Epistemic Working Memory in the Intelligence Pipeline

Context

Two July 2026 papers reveal actionable gaps in Ruflo's intelligence pipeline:

SLEUTH (arXiv:2607.12267, Ning Liu, Jul 2026, grade A): Introduces a structured epistemic working memory with three named lists per reasoning task — confirmedFacts, activeHypotheses, openQuestions — maintained across reasoning steps. Result: +11 points on multi-hop benchmarks. Ruflo's AgentDB stores long-term embeddings but has no per-task structured epistemic buffer.

GRADE (arXiv:2607.10836, Ghosh & Chakraborty, Jul 2026, grade A): Hierarchical multi-agent routing with gated depth control — learned gates govern agent selection, hierarchy depth, inter-agent communication, and branch pruning via collaborative policy optimization. Result: 44% less inference runtime vs mixture-of-agents baseline, 8.15% accuracy gain. Ruflo's SONA MoE gates token routing across 8 experts but does not gate agent-selection depth or prune reasoning branches.

Additionally, ArXiv:2607.13034 shows that a Cognitive Redundancy Ratio (CRR) gate can reduce cost by 85% on simple tasks by estimating minimum-sufficient execution scope before routing.

The existing intelligence pipeline (RETRIEVE → JUDGE → DISTILL → CONSOLIDATE) has no per-task working memory layer and no branch-level gate.

Decision

Add an EpistemicWorkingMemory (EWM) layer to the intelligence pipeline, inserted between RETRIEVE and JUDGE:

RETRIEVE → [EWM] → JUDGE → DISTILL → CONSOLIDATE

EWM Schema (per task)

interface EpistemicWorkingMemory {
  taskId: string;
  confirmedFacts: string[];      // facts verified during this task
  activeHypotheses: string[];    // hypotheses being tested
  openQuestions: string[];       // unresolved sub-questions
  stepCount: number;
  createdAt: number;
  flushedAt?: number;
}

At each intelligence step, the agent appends to the appropriate list. At task completion, EWM is flushed: a summary is written to AgentDB (long-term) and the in-memory buffer is released.

CRR Pre-Routing Gate

Before task routing, compute:

CRR = (estimated_unique_reasoning_steps) / (total_input_tokens / 500)

If CRR < 0.3 → route to Haiku with scope-bounded system prompt suffix. If CRR ≥ 0.3 → standard 3-tier routing (ADR-026).

Agent-Selection Depth Gate (GRADE-inspired)

Extend swarm coordinator to emit a depthBudget per task (default 3). Agents that exhaust their depth budget without resolution escalate to the next tier rather than continuing horizontally. This limits runaway agent fan-out on hard tasks and prunes solved branches early on easy tasks.

Implementation Scope

Consequences

Positive:

• Expected +8-11 points on multi-hop reasoning (grade-A baseline, arXiv:2607.12267)
• Expected 44% runtime reduction on complex multi-agent tasks via depth gating
• Expected 60-85% cost reduction on simple tasks via CRR gate (grade-A baseline, arXiv:2607.13034)
• Competitive with LangGraph's HITL checkpoints and AG2 Beta's typed tools

Negative:

• EWM adds ~3-12 KB of in-memory state per concurrent task; at 100 concurrent tasks, ~1.2 MB overhead
• CRR threshold (0.3) requires calibration against real task distribution
• GRADE's collaborative policy optimization requires offline training — depth gate here uses a simpler heuristic (no policy gradient), so gains may be partial

Neutral:

• EWM flush to AgentDB is async; tasks complete without waiting for flush
• Existing ADR-026 3-tier routing is preserved; CRR gate is a pre-filter, not a replacement

Alternatives Considered

1. No EWM, extend AgentDB session scope — Cheaper but doesn't expose structured fact/hypothesis/question lists to the agent, so the +11 pt gain is unlikely.
2. Full GRADE collaborative policy optimization — Higher ceiling but requires training data; deferred to a future ADR.
3. RAG-only — Already implemented; SLEUTH shows structured EWM is orthogonal and additive to retrieval.

Validation

Run npx claude-flow@v3alpha performance benchmark --suite multi-hop before and after EWM integration. Gate merge on ≥5 pt improvement on an internal multi-hop eval set. Evaluate CRR gate savings by comparing cost per task on the existing regression test suite.