ADR-358: AutoMem-Style RL Training Loop for AgentDB Memory Operations

Status: Proposed Date: 2026-07-03 Authors: claude (dream-cycle agent, 2026-07-03) Source: Dream Cycle research — Issue #[TBD], arXiv 2607.01224 (AutoMem) Tracking: [Dream Cycle 2026-07-03] memory deep-dive

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Context

AutoMem (Wu et al., arXiv 2607.01224, July 1 2026) demonstrates that memory management is an independently learnable RL skill: treating memory operations (store, retrieve, update, summarize, discard) as first-class RL actions and training a dedicated memory-specialist agent from its own episode traces yields 2x–4x improvement on long-horizon tasks (Crafter, MiniHack, NetHack), bringing a 32B open-weight model to frontier-competitive performance without changing the task policy at all.

Ruflo's AgentDB currently provides:

• HNSW-indexed vector retrieval (measured ~1.9x–4.7x vs brute force at N=5k–20k)
• SONA adaptation (0.0043ms/adapt) for neural weight tuning
• EWC++ for forgetting prevention
• A consolidate background worker (heuristic scheduling, no trajectory feedback)

None of these train the management strategy itself — which facts to store, when to discard, how to structure retrieval keys. SONA adapts weights; it does not optimize the memory scaffold or proficiency. The consolidate worker fires on a timer, not on outcome signals.

This is the single highest-leverage unimplemented memory optimization identified in the July 2026 SOTA sweep.

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Decision

Add an AutoMem-compatible two-phase RL training loop to AgentDB:

Phase 1 — Memory Scaffold Optimizer (memory-scaffold-optimizer)

A meta-LLM loop that iteratively refines how AgentDB is used:

• Inputs: agent task trajectories + outcome signals from hooks_post-task
• Outputs: updated memory prompts (what to write, key schema) stored as scaffold-vN in the memory-config namespace
• Trigger: after every N completed trajectories (default N=10), or on explicit npx ruflo memory optimize-scaffold
• Backed by the existing hooks_intelligence_trajectory-start/-step/-end pipeline (ADR-074)

Phase 2 — Memory Proficiency Trainer (memory-specialist agent)

A dedicated background agent trained from successful episodes:

• Identifies high-value memory decisions (writes that led to correct retrieval + task success) vs low-value ones (writes never retrieved, or retrieved but task failed)
• Generates GRPO-style reward signals: +1 per retrieved fact that contributed to success, -1 per write that went unretrieved or preceded failure
• Updates the memory-specialist LoRA adapter (reusing existing ContrastiveTrainer from ADR-086)
• Exposes results via npx ruflo memory proficiency-status

Bounded Memory Contract (companion)

Per AgenticSTS (arXiv Jul 2 2026): add a MemoryContract option to AgentDB that enforces typed retrieval (semantic_search, key_lookup) as the only access path, blocking raw transcript appending beyond a configurable token budget (default: 2048 tokens/session). This prevents ghost-memory accumulation (A-TMA finding) and bounds context growth.

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Implementation Scope

Component	Change	Risk
@claude-flow/memory	Add scaffold-optimizer.ts + proficiency-trainer.ts	Medium
@claude-flow/hooks	Wire post-task hook to emit trajectory reward signal	Low
@claude-flow/cli	Add memory optimize-scaffold, memory proficiency-status subcommands	Low
AgentDB	Add scaffold-vN namespace, reward-signal schema	Low
ContrastiveTrainer (ADR-086)	Extend to accept memory-op triplets	Low

Files to keep under 500 lines each. No changes to task-policy paths.

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Consequences

Positive:

• Closes the single largest gap vs AutoMem SOTA (2x–4x long-horizon improvement potential)
• Reuses existing trajectory pipeline (ADR-074) and ContrastiveTrainer (ADR-086) — minimal new infrastructure
• Measurable: benchmark against @claude-flow/performance benchmark --suite memory-long-horizon before/after

Negative / Risks:

• Reward signal quality depends on trajectory completeness; tasks without explicit success signals produce noisy rewards
• Phase 2 training adds ~50–200ms per post-task hook call (must be async/non-blocking)
• Over-optimized scaffold may overfit to a task distribution — need periodic scaffold reset option

Not in scope:

• LongMemEval harness integration (separate issue — competitive benchmarking)
• Pluggable memory backends (separate ADR, triggered by CrewAI v1.14.7 competitive signal)

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Alternatives Considered

1. Heuristic consolidation tuning — improve existing consolidate worker scheduling. Rejected: doesn't learn from outcomes, still heuristic.
2. External memory library (Mem0, Zep) — swap AgentDB for a dedicated memory service. Rejected: removes HNSW+SONA integration, architectural regression.
3. Manual memory scaffold authoring — let users configure prompts. Rejected: doesn't scale, doesn't learn.

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References

• AutoMem: https://arxiv.org/abs/2607.01224
• AgenticSTS: arXiv Jul 2 2026 (bounded memory contract)
• A-TMA: arXiv Jul 2 2026 (ghost memory / validity annotations)
• ADR-074: Self-Learning Wiring (hooks_intelligence_trajectory-start/-step/-end)
• ADR-086: ruvllm as Intelligence Coordinator (ContrastiveTrainer)