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docs(cluster-rag): remove trailing whitespace

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docs/research/nightly/2026-08-07-hierarchical-cluster-rag/README.md
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@ -20,7 +20,7 @@ Three variants are benchmarked on a deterministic synthetic corpus (n=10,000, di
| ClusterSearch | **330.9** | **304.9** | **447.2** | **3022** | **5.0 MB** | **0.778** |
| CoherenceTree | 336.4 | 309.7 | 469.1 | 2972 | 5.0 MB | 0.775 |
Platform: x86_64 Linux, release build.
Platform: x86_64 Linux, release build.
Config: k_clusters=40, nprobe=20 (50% of clusters), lambda=0.70.
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docs/research/nightly/2026-08-07-hierarchical-cluster-rag/gist.md
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@ -4,8 +4,8 @@
A RAPTOR-inspired approach to fast, practical agent memory retrieval. All code is Rust, all benchmarks are real.
GitHub: https://github.com/ruvnet/ruvector
Research branch: `research/nightly/2026-08-07-hierarchical-cluster-rag`
GitHub: https://github.com/ruvnet/ruvector
Research branch: `research/nightly/2026-08-07-hierarchical-cluster-rag`
Crate: `ruvector-cluster-rag`
Decision record: [ADR-300](../../../adr/ADR-300-hierarchical-cluster-rag.md).
@ -136,7 +136,7 @@ graph LR
## Benchmark results
All numbers from `cargo run --release -p ruvector-cluster-rag --bin benchmark`.
All numbers from `cargo run --release -p ruvector-cluster-rag --bin benchmark`.
Platform: x86_64 Linux, release build, 2026-08-08.
No aspirational values; no invented competitor numbers.
@ -150,9 +150,9 @@ Config: N=10000, DIM=128, NQ=500, K=10, K_CLUSTERS=40, NPROBE=20, LAMBDA=0.70
| ClusterSearch | 10K | 128 | 500 | 330.9 | 304.9 | 447.2 | 3022 | 5.0 MB | 0.778 | ✅ PASS |
| CoherenceTree | 10K | 128 | 500 | 336.4 | 309.7 | 469.1 | 2972 | 5.0 MB | 0.775 | ✅ PASS |
**Hardware**: x86_64 Linux (cloud CI)
**OS**: linux
**Rust**: release profile, `cargo run --release`
**Hardware**: x86_64 Linux (cloud CI)
**OS**: linux
**Rust**: release profile, `cargo run --release`
**Cargo command**: `cargo run --release -p ruvector-cluster-rag --bin benchmark`
**Notes**:
@ -206,7 +206,7 @@ RuVector's differentiator in this crate: Rust, zero dependencies, WASM-ready, co
| RVM coherence domains | Each RVM domain maps to a cluster; cross-domain queries trigger explicit routing | RVM integration, domain coherence metrics | CoherenceTree with domain-gated nprobe | Combinatorial explosion in multi-domain queries |
| Self-healing memory graph | Cohesion decay triggers autonomous re-clustering; stale clusters are evicted | ruFlo loop + cohesion monitoring | CoherenceTree + temporal-coherence + ruFlo | Re-clustering disrupts in-flight queries |
| Bio-signal memory | Physiological sensor embeddings cluster by mental state; memory retrieval conditioned on current state | Multi-modal embedding, hardware sensor input | State-conditioned nprobe selection | Privacy, sensor calibration drift |
| Swarm memory partitioning | Each agent in a swarm owns a cluster; global queries fan out to the relevant subset of agents | Multi-agent coordination protocol | Distributed ClusterTree with agent-scoped inverted lists | Network partition, quorum |
| Swarm memory partitioning | Each agent in a swarm owns a cluster; global queries fan out to the relevant subset of agents | Multi-agent coordination protocol | Distributed ClusterTree with agent-scoped inverted lists | Network partition, quorum |
| Proof-gated cluster insert | New memories require cryptographic witness before entering a cluster | ruvector-proof-gate, witness log | ClusterTree with signed insert | Performance overhead of signature verification |
| Dynamic world model shards | Agent world model partitioned semantically; each shard updated on independent cycle | World model embedding, semantic sharding | CoherenceTree over world-state vectors | Shard boundary ambiguity |
| Space autonomy | Rover accumulates terrain observation embeddings; spatial queries retrieve nearby observations without ground link | Embedded Rust, WASM, no-std | ruvector-cluster-rag in no-std mode | Radiation, limited compute |