ADR-360 — Cross-Agent Shared KV Pool (PolyKV Architecture)

• Status: Proposed
• Date: 2026-07-05
• Authors: claude (dream-cycle agent, 2026-07-05)
• Related: ADR-174 (memory distillation), ADR-006 (unified memory), ADR-009 (hybrid memory backend)
• Source: Dream Cycle 2026-07-05 — performance deep-dive; arXiv:2604.24971 (PolyKV)

Context

Ruflo currently maintains per-agent KV caches with no cross-agent sharing or compression. At 15+ concurrent agents, the per-agent approach accumulates memory linearly — a 4K-token context per agent on Llama-3-8B scale requires ~19.8 GB of KV memory for 15 agents under the default (unshared) model.

PolyKV (arXiv:2604.24971, Patel & Joshi, Apr 2026) proves that a shared asymmetrically-compressed KV pool collapses this to 0.45 GB (97.7% reduction) with 2.91x compression via int8 key quantization + FWHT + 3-bit Lloyd-Max value quantization, while preserving BERTScore F1 of 0.928 and perplexity degradation of only +0.57%. This is a Grade A finding (two model scales tested with specified baselines).

Two complementary findings reinforce the case for serving-layer changes:

• ConServe (arXiv Jun 2026): disaggregated prefill/decode scheduling cuts p95 TTFT by 51.08% (Grade A)
• AsymCache (arXiv Jun 2026): asymmetric segment-based KV eviction cuts TTFT 1.90–2.03x (Grade A)

The @claude-flow/memory package owns AgentDB + HNSW vector indexing but does not manage KV caches for LLM inference contexts. Adding a shared compressed pool requires a new coordination layer between the memory package and the CLI's agent execution path.

Decision

Implement a SharedKVPoolManager in @claude-flow/memory that:

1. Maintains a single process-shared compressed KV pool keyed by (model, context_hash)
2. Applies asymmetric compression: int8 quantization for keys (3.84x measured, reconstruction cosine 0.99999); FWHT + 3-bit Lloyd-Max for values (target 2.91x per PolyKV)
3. Exposes a kvpool:// pseudo-backend selectable via CLAUDE_FLOW_KV_BACKEND=shared-pool
4. Integrates with the existing hybrid memory backend — pool lives alongside SQLite + AgentDB
5. Exports metrics (pool_hit_rate, pool_memory_bytes, compression_ratio) to the performance monitoring hook

Consequences

Positive:

• 97.7% KV memory reduction at 15 concurrent agents (Grade A, Llama-3-8B, 4K ctx)
• Enables higher agent concurrency on same hardware without OOM
• Compression reuses existing int8 quantization path (3.84x measured in @claude-flow/memory)
• Orthogonal to HNSW vector search — both can be active simultaneously

Negative / Risks:

• FWHT + Lloyd-Max value compression is not yet implemented in the codebase; requires new codec module
• Shared pool introduces cross-agent state — requires eviction policy and LRU accounting
• Perplexity +0.57% and BERTScore F1 0.928 are acceptable but must be verified against Ruflo's specific models
• Pool requires a benchmark gate (compare vs current per-agent baseline) before enabling by default

Neutral:

• Speculative decoding (4.48x throughput, IBM arXiv:2606.18502) and disaggregated scheduling (ConServe, 51% TTFT) are related optimizations but scoped to separate ADRs — this ADR covers the KV pool only

Implementation Path

1. v3/@claude-flow/memory/src/kv-pool/ — new module: SharedKVPoolManager, AsymmetricKVCodec (int8 keys + FWHT+Lloyd-Max values), KVEvictionPolicy (LRU)
2. v3/@claude-flow/memory/src/index.ts — export SharedKVPoolManager; register kvpool:// backend
3. v3/@claude-flow/cli/src/commands/performance/ — add kv-pool subcommand (status, flush, benchmark)
4. scripts/benchmark-kv-pool.mjs — measure pool vs per-agent baseline at N=5,10,15 agents; gate merge on ≥90% memory reduction and BERTScore F1 ≥0.90
5. CLAUDE.md performance table — add row once benchmark validates

Alternatives Considered

• Per-agent int8 quantization only (no sharing): Captures ~3.84x compression but not the cross-agent sharing benefit; still linear with agent count. Rejected — leaves the primary gap open.
• External KV cache service (Redis/Memcached): Introduces network hop; adds operational dependency. Rejected for initial implementation — in-process shared pool first.
• Wait for upstream LLM serving framework support: vLLM and SGLang are moving toward cross-request KV sharing; Ruflo could delegate. Acceptable future path but blocks on external timeline. Implement own first.