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44a836d57e
* feat(emergent-time): calculus of emergent time + Agentic Time primitive
Add `crates/emergent-time`, a dependency-free Rust implementation of the
calculus of emergent/relational time, plus a new agentic-time primitive and
an honest multi-clock benchmark.
Physics formalisms (each verified by tests):
- Wheeler-DeWitt timeless constraint H|Psi>=0 (kernel solver, residual ~1e-15)
- Page-Wootters relational clock: Schrodinger evolution emerges from a static
entangled state via conditioning (fidelity 1.0)
- Entropic time tau_S=(S-S0)/k (cold-atom analogue; speed tracks dS/dlambda)
- Connes-Rovelli thermal time: modular Hamiltonian K=-ln rho, modular flow
A(s)=e^{isK}A e^{-isK} (recovers rescaled physical evolution for Gibbs states)
Numerical core: self-contained complex scalars, real symmetric Jacobi
eigensolver, complex unitary evolution via spectral exponentiation, von Neumann
entropy via a real-symmetric Hermitian embedding.
Agentic time:
- Structural Proper Time: internal time as arc length through the state manifold
- Agentic Time tau_a=f(dB,dM,dR,dG,dE,dP) with explainable ticks (class+reason),
Agentic Time Index, and a 7-state health classifier
- Four-clock benchmark (wall/step/token/agentic). On the bundled synthetic
traces, structural time warns 2.8x earlier than the entropy clock and agentic
time gives a 40-step lead where wall/step/token give 0, preserving causal order
Includes a walkthrough example, criterion benches, and ADR-251 documenting
Agentic Time as a proposed Ruflo/RuVector/RuQu runtime primitive.
39 tests passing, clippy clean.
https://claude.ai/code/session_01ApBCSaebKsCzLeA7JhvDvU
* fix(emergent-time): M1 correctness + honesty hardening
Five corroborated-review fixes that raise rigor/honesty without touching
the sound numerical core (Jacobi eigensolver, spectral exp, state/complex/
entropy unchanged).
FIX 1 — explain() noise-floor contract (agentic_time.rs): document that
per-channel Tick fields are RAW (pre-floor) weighted contributions while
`delta` is post-floor max(0, Σchannels − noise_floor); the identity
delta==Σchannels holds only when noise_floor==0. New test
explain_delta_is_post_floor_channels_are_pre_floor asserts the floor=0.1
case (delta strictly < Σchannels) and the clamp-to-0 case.
FIX 2 — Wheeler–DeWitt falsifiability (wheeler_dewitt.rs): module doc now
states the kernel is trivial-by-construction for the energy-matched clock;
existing "kernel" tests relabelled as consistency checks; new discriminating
test generic_clock_yields_empty_physical_space builds Ĵ from a generic
H_C ≠ −H_R and asserts NO eigenvalue within 1e-9 of zero (empty physical
space), with a deterministic perturbation guard and an eigenvalue-sum bound.
FIX 3 — entropic non-tautological test (entropic.rs): docstring softened to
"β-swept Gibbs ensemble" (a temperature sweep, not closed-system dynamics);
tautological tau test renamed tau_reparametrization_formula_is_exact; new
internal_time_spacing_tracks_measured_entropy_production verifies the clock
rate against independently finite-differenced gibbs_entropy and that the
entropy curve is non-trivial and correctly signed.
FIX 4 — Page–Wootters honesty docstring (page_wootters.rs): scope is
real-symmetric H; Born-rule weighting holds only for pure global states;
single-time conditional states only — Kuchař two-time objection out of scope.
FIX 5 — fair baseline + de-hype (agentic_time.rs, examples/emergent_time.rs):
new WindowedDeltaClock rolling-window z-score change-point detector (the
non-strawman baseline the constant-rate wall/step/token clocks were missing).
On the designed trace the fair baseline fires at least as early as the agentic
clock; example output and test relabel the headline as a coverage-gap demo,
not a competitive win. Honest finding: agentic clock does NOT beat a fair
baseline on synthetic data — real-trace head-to-head is M3 work.
ADR-251: adds "Honest limitations" section (WD constructive-not-discovery,
entropic β-sweep, benchmark coverage-gap-not-win, PW scope) and prior-art
note (ADWIN; Ostovar 2016 concept-drift in process mining) stating what is
new (physics-grounded composite state-arc-length runtime primitive).
cargo test -p emergent-time: 43 passed (39 baseline + 4 new); build/clippy
clean; example prints the fair baseline.
Co-Authored-By: claude-flow <ruv@ruv.net>
* perf(emergent-time): M2 performance + robustness (P1/P2/R1/R4)
Numerical core unchanged — pure speed (P1/P2) plus guardrails (R1/R4)
that do not alter valid-input results. All 49 tests pass (43 original
+ 6 new); clippy clean; physics fidelity/entropy/modular values
unchanged.
P1 — stop re-diagonalizing (complex_matrix.rs, page_wootters.rs)
- Add exp_i_from_spectrum / exp_i_apply_from_spectrum: spectral
exp(iθH) from a PRECOMPUTED (eigvals, V), no re-diagonalization.
exp_i_symmetric now routes through exp_i_from_spectrum.
- PageWootters caches |ψ0| and evolves in the cached energy eigenbasis:
schrodinger_state(t) = Σ_k e^{-iE_k t}⟨E_k|ψ0⟩|E_k⟩, O(n²)/t, no
propagator matrix. From-scratch path kept as
schrodinger_state_from_scratch for callers holding only H.
- Bench (n16): cached 666 ns vs from-scratch 35.3 µs → ~53x.
- New test cached_evolution_equals_from_scratch_propagator (1e-12).
P2 — hoist t-independent static state (page_wootters.rs)
- global_static_state |Ψ| (d²) built once in new(), cached; per-t
conditional_state conditions the cached vector.
- Bench page_wootters_conditional_n8: 294 ns → 225 ns (~1.3x).
R1 — restore entropy guardrail (entropy.rs)
- Replace silent `p > 1e-12` clamp with standard von-Neumann `p > 0.0`
(skips only 0·ln0; keeps legitimate tiny probabilities; roundoff
negatives contribute 0). Add debug-only PSD + normalization
validation so a non-PSD/non-normalized ρ surfaces in dev.
- New tests: roundoff-negative [0.5,0.5,-1e-15]→ln2, tiny-positive not
clamped, non-PSD/non-normalized trip debug_assert (debug-only).
R4 — relative Jacobi convergence + non-convergence guard (real_matrix.rs)
- Replace scale-dependent absolute `off < 1e-28` with relative
off²/‖A‖²_F < tol² (tol=1e-14); sweep cap kept as backstop.
- debug_assert! fires if the cap is hit without convergence (signature
unchanged — every caller destructures (Vec<f64>, RealMatrix);
subsumes the deferred M1 convergence guard).
- New near-degenerate stress test (diag 1, 1+1e-10, 2 + tiny
off-diagonals): orthonormal vectors + correct spectrum.
Co-Authored-By: claude-flow <ruv@ruv.net>
* feat(emergent-time): M3 real-trace defensibility gate (honest null result)
Run the agentic clock vs the FAIR WindowedDeltaClock baseline (and the
constant-rate strawmen) on REAL recorded agent traces -- the Claude Code
session transcripts for this repo -- with PRE-REGISTERED thresholds and an
honestly-defined event-to-predict. This replaces the circular synthetic
benchmark with the genuine M3 gate from ADR-251 section 4.
THE FINDING (reported honestly, not manufactured): on the 2 real traces the
contradiction-free honest agentic clock scores 0 win / 1 tie / 1 loss vs the
fair windowed baseline. It does NOT beat the fair baseline on real data either.
The defensible value of the primitive is diagnostic (per-channel attribution +
health classifier), not a raw early-warning-lead win. The crate stays honest.
- examples/real_trace_eval.rs: real-trace adapter + pre-registered protocol.
- Source: ~/.claude/projects/C--Users-ruv-ruvector/*.jsonl (real tool-use
sequences, retries, is_error events). Deliberately NOT intelligence.json
(51 flat all-success records, no failure events -- would be dishonest).
- Documented heuristic channel mapping (tool-type TF -> belief, distinct
files -> memory, Read/Grep -> retrieval, new user prompt -> goal, is_error
rate -> contradiction, text+repetition -> plan).
- Event-to-predict = real error cascade (>=2 is_error in 4 steps), defined
from the harness is_error flag ONLY (non-circular).
- Circularity guard: an honest agentic variant with contradiction weight 0
so it cannot see the signal that defines the event. This is the real gate.
- Pre-registered (before any lead computed): window=10, k=3sigma, metric=lead.
- Prints an alive-vs-degenerate diagnostic: the honest signal is NOT flat
(mean inc ~1.5, max ~4.4) but never clears its own mean+3sigma bar because
early exploratory churn sets a high baseline -- a real property of real
traces, not a dead clock.
- Degrades gracefully (prints [skip], exits 0) when no traces are present,
so CI without the data still passes.
- agentic_time.rs: add test contradiction_free_weights_blind_to_error_channel
locking in the M3 circularity guard (50 tests, was 49).
- ADR-251: replace the M3-future-work note with the actual real-trace result;
mark the Baseline-dominance gate UNMET; full lead table + caveats in Honest
limitations.
Validation: cargo test -p emergent-time => 50 passed; build + clippy clean;
real_trace_eval runs and prints real numbers (0 win / 1 tie / 1 loss).
Co-Authored-By: claude-flow <ruv@ruv.net>
* feat(emergent-time): M3b adaptive change-point detector (honest null, more robust)
M3 got an honest null on real traces with a fixed-window mean+3σ alarm and
diagnosed the cause: a frozen early baseline poisoned by exploration churn. M3
proposed an adaptive-window detector as the fix. M3b implements that exact fix.
- src/adaptive.rs: Page-Hinkley test (Page 1954 / Hinkley 1970), dependency-free
pure Rust. Running-mean reference instead of a frozen window; upward + downward
forms; clock-agnostic adaptive_alarm_step / adaptive_early_warning_lead.
Documented math + literature citations. 12 unit tests (detects real step-change,
silent on stationary noise, constant streams never alarm, threshold/tolerance
monotonicity, slot-0 padding excluded, fair on both clock + baseline).
- examples/real_trace_eval.rs: wires the SAME pre-registered detector (δ=0.15,
λ=5.0, fixed before any lead) into BOTH the agentic-honest composite AND the
fair baseline. Prints fixed-window (M3) AND adaptive (M3b) leads side-by-side.
Honest result on the same n=2 real traces: the adaptive detector works as
designed — the fair belief-shift baseline, which never fired under the fixed
window, now leads by 32 and 25 steps. But it does NOT rescue the agentic clock:
the honest composite's adaptive alarms (steps 75, 49) still land AFTER the error
cascades (steps 37, 29), so its lead stays 0. Verdict moves 0/1/1 → 0 win / 0 tie
/ 2 loss. The M3-proposed fix was tried and did not change the verdict; the honest
null is now MORE ROBUST. Defensible value of the primitive remains diagnostic
(per-channel attribution + health classifier), not a raw early-warning-lead win.
n=2 caveat stands; a fair win would have demanded a larger pre-registered corpus.
ADR-251 §3/§4 extended with the adaptive-detector outcome and fixed-vs-adaptive
table. cargo test green (62), clippy clean, examples build, graceful-skip intact.
Co-Authored-By: claude-flow <ruv@ruv.net>
* style(emergent-time): apply rustfmt across the crate
Bring the crate (including the M2/M3/M3b additions) under rustfmt to
satisfy the CI Rustfmt check. Formatting only; no behavior change, 62
tests still pass.
https://claude.ai/code/session_01ApBCSaebKsCzLeA7JhvDvU
* fix(emergent-time): make real-trace parser robust to tool_use key order
The M3 real-trace harness silently ingested zero steps from genuine
Claude-Code transcripts because `extract_tool_names` only searched for
`"name":"..."` AFTER the `"type":"tool_use"` marker. Current transcripts
emit the name BEFORE the type (`{"name":"Bash","type":"tool_use",...}`),
so every single-tool step was dropped, `parse_session` fell below
MIN_STEPS and returned None, and the harness reported "No real session
transcripts found" — masquerading a parse failure as missing data.
Verified on a real 531-line session transcript: 0 steps parsed before,
112 after. The session has no error cascade, so it is correctly reported
as descriptive-only (not scoreable) rather than silently skipped.
Changes:
- extract_tool_names: pair each tool_use marker to the nearest "name"
within a bounded window in EITHER direction (order-independent).
- load_traces: return files-seen / parse-failure counts so main can
distinguish "no files" from "files present but unparseable" — an
honesty fix so a silent parser gap can't pose as absence.
- add a regression test covering both key orderings + multi-tool lines.
fmt clean, clippy clean, 62 lib tests + 1 example test pass.
https://claude.ai/code/session_01ApBCSaebKsCzLeA7JhvDvU
* feat(emergent-time): learn agentic-time channel weights (honest harness)
Replace hand-set AgenticWeights with weights LEARNED from labelled
outcomes via L2-regularized logistic regression (dependency-free), with
held-out evaluation and a circularity guard (Honest mode drops the
contradiction channel).
Honest finding, reported not hidden: learning matches the hand-set guess
(AUC 0.936 vs 0.935) and yields interpretable importances (plan +0.75
dominant), but does NOT beat the best single channel on this synthetic
data (goal_graph 0.950 / contradiction 0.956) — the signal is
concentrated in one planted channel. Composition only earns its keep
when signal is spread across weak channels (ADR-251 §4), which needs
real traces. This is the reusable apparatus to run that test.
4 new tests; 66 lib tests pass, clippy + fmt clean.
https://claude.ai/code/session_01ApBCSaebKsCzLeA7JhvDvU
* feat(emergent-time): trained model + witness-chain provenance
Add a deterministic trained-weight model with tamper-evident, reproducible
provenance, and an honest "beyond baseline, with proof" demonstration.
- weight_learning: make LearnedWeights dimension-generic (store `dim`, add
`from_params`); add a Gaussian sampler and `diffuse_dataset` — a controlled
weak-signal benchmark (channels of differing strength + pure-noise channels).
New test proves the learned composition BEATS both the best single channel
and the equal-weight baseline in this regime (the one the thesis targets).
- witness: FNV-1a hash-linked WitnessChain (seal/append/verify, text round-trip,
tamper + reproducibility detection). Proof of *provenance*: the sealed metrics
correspond to the committed model and re-training reproduces the same hash.
- examples/train_model: trains, seals a witness record, persists the model +
chain artifact, then verifies (1) chain integrity, (2) committed model matches
sealed model_hash, (3) reproducibility. On the diffuse benchmark the learned
model scores AUC 0.759 vs best-single 0.681 vs equal-weight 0.708 and recovers
the signal structure (noise channels learned to ~0).
- models/agentic_weights.witness.txt: the sealed trained-model artifact.
HONEST SCOPE: this is "beyond baseline, with verifiable proof" in the method's
target regime (distributed weak signal) — NOT a claim of beating real-world
agent-failure SOTA, which still needs real labelled traces (ADR-251 §4).
72 lib tests pass, clippy + fmt clean.
https://claude.ai/code/session_01ApBCSaebKsCzLeA7JhvDvU
* docs(emergent-time): add README; release 2.2.4
2.2.3 published without a README (bare crates.io page). Adds a
matter-of-fact README (physics formalisms, Agentic Time, benchmark
results, usage) and decouples the crate version from the workspace so it
can be released independently.
Co-Authored-By: claude-flow <ruv@ruv.net>
* ci(emergent-time): dedicated test + falsifiability guard
Path-filtered CI gate for the emergent-time crate: fmt, clippy -D
warnings, full test suite, example builds + no-data runs, and a
publish-equivalent package check. Plus a guard step that greps for the
falsifiability / pre-registered-evaluation tests (generic-clock empty
kernel, cached-vs-from-scratch equivalence, entropy-rate-vs-measured,
error-blind agentic weights, real_trace_eval harness) so none can be
silently removed without failing CI.
Co-Authored-By: claude-flow <ruv@ruv.net>
* fix(emergent-time): sync Cargo.lock to crate version 2.2.4
The 2.2.4 version bump updated Cargo.toml but left Cargo.lock at 2.2.3,
failing the lockfile-integrity CI gate. Update the lock to match.
https://claude.ai/code/session_01ApBCSaebKsCzLeA7JhvDvU
---------
Co-authored-by: Claude <noreply@anthropic.com>
Co-authored-by: ruv <ruvnet@users.noreply.github.com>
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|---|---|---|
| .. | ||
| coherence-engine | ||
| delta-behavior | ||
| quantum-engine | ||
| temporal-tensor-store | ||
| ADR-001-ruvector-core-architecture.md | ||
| ADR-002-ruvllm-integration.md | ||
| ADR-003-simd-optimization-strategy.md | ||
| ADR-004-kv-cache-management.md | ||
| ADR-005-wasm-runtime-integration.md | ||
| ADR-006-memory-management.md | ||
| ADR-007-security-review-technical-debt.md | ||
| ADR-008-mistral-rs-integration.md | ||
| ADR-009-structured-output.md | ||
| ADR-010-function-calling.md | ||
| ADR-011-prefix-caching.md | ||
| ADR-012-security-remediation.md | ||
| ADR-013-huggingface-publishing.md | ||
| ADR-014-coherence-engine.md | ||
| ADR-015-coherence-gated-transformer.md | ||
| ADR-016-delta-behavior-ddd-architecture.md | ||
| ADR-017-temporal-tensor-compression.md | ||
| ADR-024-craftsman-ultra-30b-1bit-bitnet-integration.md | ||
| ADR-025-exo-ai-multiparadigm-integration.md | ||
| ADR-026-rvcow-branching-and-real-cognitive-containers.md | ||
| ADR-027-hnsw-parameterized-query-fix.md | ||
| ADR-028-ehealth-platform-architecture.md | ||
| ADR-029-rvf-canonical-format.md | ||
| ADR-030-rvf-cognitive-container.md | ||
| ADR-031-rvf-example-repository.md | ||
| ADR-032-rvf-wasm-integration.md | ||
| ADR-033-progressive-indexing-hardening.md | ||
| ADR-034-qr-cognitive-seed.md | ||
| ADR-035-capability-report.md | ||
| ADR-036-agi-cognitive-container.md | ||
| ADR-037-publishable-rvf-acceptance-test.md | ||
| ADR-038-npx-ruvector-rvlite-witness-integration.md | ||
| ADR-039-rvf-solver-wasm-agi-integration.md | ||
| ADR-040-causal-atlas-rvf-runtime-planet-detection.md | ||
| ADR-040a-planet-detection-dashboard.md | ||
| ADR-040b-microlensing-graphcut-extensions.md | ||
| ADR-042-Security-RVF-AIDefence-TEE.md | ||
| ADR-043-external-intelligence-providers.md | ||
| ADR-044-ruvector-postgres-v03-extension-upgrade.md | ||
| ADR-045-lean-agentic-integration.md | ||
| ADR-046-graph-transformer-architecture.md | ||
| ADR-047-proof-gated-mutation-protocol.md | ||
| ADR-048-sublinear-graph-attention.md | ||
| ADR-049-verified-training-pipeline.md | ||
| ADR-050-graph-transformer-bindings.md | ||
| ADR-051-physics-informed-graph-layers.md | ||
| ADR-052-biological-graph-layers.md | ||
| ADR-053-temporal-causal-graph-layers.md | ||
| ADR-054-economic-graph-layers.md | ||
| ADR-055-manifold-graph-layers.md | ||
| ADR-056-rvf-knowledge-export.md | ||
| ADR-057-federated-rvf-transfer-learning.md | ||
| ADR-058-hash-security-optimization.md | ||
| ADR-059-shared-brain-google-cloud.md | ||
| ADR-060-shared-brain-capabilities.md | ||
| ADR-061-reasoning-kernel-architecture.md | ||
| ADR-062-brainpedia-architecture.md | ||
| ADR-063-wasm-executable-nodes.md | ||
| ADR-064-pi-brain-infrastructure.md | ||
| ADR-065-npm-publishing-strategy.md | ||
| ADR-066-sse-mcp-transport.md | ||
| ADR-067-mcp-gate-permit-system.md | ||
| ADR-068-domain-expansion-transfer-learning.md | ||
| ADR-069-google-edge-network-deployment.md | ||
| ADR-070-npx-ruvector-unified-integration.md | ||
| ADR-071-npx-ruvector-ecosystem-gap-analysis.md | ||
| ADR-072-rvf-example-management-downloads.md | ||
| ADR-073-pi-platform-security-optimization.md | ||
| ADR-074-ruvllm-neural-embeddings.md | ||
| ADR-075-rvf-agi-stack-brain-integration.md | ||
| ADR-076-agi-capability-wiring-architecture.md | ||
| ADR-077-midstream-brain-integration.md | ||
| ADR-078-npx-ruvector-midstream-integration.md | ||
| ADR-079-sql-audit-script-hardening.md | ||
| ADR-080-npx-ruvector-deep-capability-audit.md | ||
| ADR-081-brain-server-v028-deploy-cli-fixes.md | ||
| ADR-082-brain-security-hardening.md | ||
| ADR-083-brain-training-loops.md | ||
| ADR-084-ruvllm-wasm-publish.md | ||
| ADR-085-neural-trader-ruvector.md | ||
| ADR-086-neural-trader-wasm.md | ||
| ADR-087-ruvix-cognition-kernel.md | ||
| ADR-088-cnn-contrastive-integration.md | ||
| ADR-089-cnn-browser-demo.md | ||
| ADR-090-implementation-checklist.md | ||
| ADR-090-ultra-low-bit-qat-pi-quantization-ddd.md | ||
| ADR-091-implementation-checklist.md | ||
| ADR-091-int8-cnn-quantization-ddd.md | ||
| ADR-092-moe-memory-aware-routing-ddd.md | ||
| ADR-093-daily-discovery-brain-training.md | ||
| ADR-093-deepagents-rust-conversion-overview.md | ||
| ADR-094-deepagents-backend-protocol-traits.md | ||
| ADR-094-pi-shared-web-memory.md | ||
| ADR-095-deepagents-middleware-pipeline.md | ||
| ADR-095-pi-api-v2-capabilities.md | ||
| ADR-096-cloud-pipeline-realtime-optimization.md | ||
| ADR-096-deepagents-tool-system.md | ||
| ADR-097-deepagents-subagent-orchestration.md | ||
| ADR-098-deepagents-memory-skills-summarization.md | ||
| ADR-099-deepagents-cli-acp-server.md | ||
| ADR-100-deepagents-rvf-integration-crate-structure.md | ||
| ADR-101-deepagents-testing-strategy.md | ||
| ADR-102-deepagents-implementation-roadmap.md | ||
| ADR-103-deepagents-review-amendments.md | ||
| ADR-104-rvagent-mcp-skills-topology.md | ||
| ADR-105-rvagent-mcp-implementation-details.md | ||
| ADR-106-ruvix-kernel-rvf-integration.md | ||
| ADR-107-rvagent-native-swarm-wasm.md | ||
| ADR-108-rvagent-ruvbot-integration.md | ||
| ADR-109-backup-disaster-recovery.md | ||
| ADR-110-neural-symbolic-internal-voice.md | ||
| ADR-111-ruvocal-ui-rvagent-integration.md | ||
| ADR-112-rvagent-mcp-server.md | ||
| ADR-113-rvf-app-gallery-ruvix-applications.md | ||
| ADR-114-ruvector-core-hash-placeholders.md | ||
| ADR-115-common-crawl-temporal-compression.md | ||
| ADR-116-spectral-sparsifier-brain-integration.md | ||
| ADR-117-canonical-mincut-pseudo-deterministic.md | ||
| ADR-117-dragnes-dermatology-intelligence-platform.md | ||
| ADR-118-cost-effective-crawl-strategy.md | ||
| ADR-119-historical-crawl-evolutionary-comparison.md | ||
| ADR-120-wet-processing-pipeline.md | ||
| ADR-121-gemini-grounding-integration.md | ||
| ADR-122-rvagent-gemini-grounding-agents.md | ||
| ADR-123-brain-cognitive-enrichment.md | ||
| ADR-124-dynamic-partition-cache.md | ||
| ADR-125-resend-email-brain-integration.md | ||
| ADR-126-google-chat-brain-integration.md | ||
| ADR-127-gist-deep-research-loop.md | ||
| ADR-128-sota-gap-implementations.md | ||
| ADR-129-ruvltra-gcloud-training-turboquant.md | ||
| ADR-130-mcp-sse-decoupling-midstream-queue.md | ||
| ADR-131-consciousness-metrics-crate.md | ||
| ADR-132-e2e-browser-testing-claude-flow.md | ||
| ADR-132-ruvix-hypervisor-core.md | ||
| ADR-133-claude-code-source-analysis.md | ||
| ADR-133-partition-object-model.md | ||
| ADR-134-ruvector-claude-code-deep-integration.md | ||
| ADR-134-witness-schema-log-format.md | ||
| ADR-135-mincut-decompiler-with-witness-chains.md | ||
| ADR-135-proof-verifier-design.md | ||
| ADR-136-gpu-trained-deobfuscation-model.md | ||
| ADR-136-memory-hierarchy-reconstruction.md | ||
| ADR-137-bare-metal-boot-sequence.md | ||
| ADR-137-npm-decompiler-cli-and-mcp.md | ||
| ADR-138-llm-weight-decompiler.md | ||
| ADR-138-seed-hardware-bring-up.md | ||
| ADR-139-appliance-deployment-model.md | ||
| ADR-139-rvagent-claude-code-optimization.md | ||
| ADR-140-agent-runtime-adapter.md | ||
| ADR-141-coherence-engine-kernel-integration.md | ||
| ADR-142-tee-backed-cryptographic-verification.md | ||
| ADR-143-hearmusica-tympan-rust-port.md | ||
| ADR-143-implement-missing-capabilities.md | ||
| ADR-144-candle-whisper-musica-transcription.md | ||
| ADR-144-diskann-vamana-implementation.md | ||
| ADR-144-monorepo-quality-analysis-strategy.md | ||
| ADR-145-wasm-training-pipeline-fixes.md | ||
| ADR-146-diskann-vamana-implementation.md | ||
| ADR-147-stacked-kv-cache-triattention-turboquant.md | ||
| ADR-148-brain-hypothesis-engine.md | ||
| ADR-149-brain-performance-optimizations.md | ||
| ADR-150-pi-brain-ruvltra-tailscale.md | ||
| ADR-151-miller-rabin-prime-optimizations.md | ||
| ADR-153-kalshi-neural-trader-integration.md | ||
| ADR-154-rabitq-rotation-binary-quantization.md | ||
| ADR-155-rulake-datalake-layer.md | ||
| ADR-156-rulake-as-memory-substrate.md | ||
| ADR-157-optional-accelerator-plane.md | ||
| ADR-158-optional-rotation-and-qvcache-positioning.md | ||
| ADR-159-rvagent-a2a-protocol.md | ||
| ADR-160-acorn-filtered-hnsw.md | ||
| ADR-161-rabitq-wasm-npm-package.md | ||
| ADR-162-acorn-wasm-npm-package.md | ||
| ADR-165-tiny-ruvllm-agents-on-esp32-soCs.md | ||
| ADR-166-esp32-rust-cross-compile-bringup-ops.md | ||
| ADR-167-ruvector-hailo-npu-embedding-backend.md | ||
| ADR-168-ruvector-hailo-cluster-cli-surface.md | ||
| ADR-169-ruvector-hailo-cluster-cache-architecture.md | ||
| ADR-170-ruvector-hailo-cluster-tracing-correlation.md | ||
| ADR-171-ruos-brain-ruview-pi5-edge-node.md | ||
| ADR-172-ruvector-hailo-security-review.md | ||
| ADR-173-ruvllm-hailo-edge-llm.md | ||
| ADR-174-ruos-thermal-overclock-pi5.md | ||
| ADR-175-hailo-rust-side-workarounds.md | ||
| ADR-176-hef-integration-epic.md | ||
| ADR-177-pi4-no-hat-deploy.md | ||
| ADR-178-ruvector-ruview-hailo-integration-gap-analysis.md | ||
| ADR-179-ruvllm-pi-cluster-deployment.md | ||
| ADR-180-ruvllm-serving-engine-continuous-batching.md | ||
| ADR-181-ruvllm-pi-quant-bitnet-integration.md | ||
| ADR-182-hailo-10-cluster-migration.md | ||
| ADR-183-sparse-attention-rand-dev-dependency.md | ||
| ADR-184-sparse-attention-online-softmax.md | ||
| ADR-185-sparse-attention-noncausal-landmark-fix.md | ||
| ADR-186-sparse-attention-edge-case-tests.md | ||
| ADR-187-tensor-zeros-overflow-check.md | ||
| ADR-188-sparse-attention-stamp-scheme-comment.md | ||
| ADR-189-sparse-attention-kv-cache-incremental-decode.md | ||
| ADR-190-sparse-attention-gqa-mqa-support.md | ||
| ADR-191-sparse-attention-pi-zero-2w-production-hardening.md | ||
| ADR-192-sparse-attention-no-std-esp32-support.md | ||
| ADR-193-rairs-ivf.md | ||
| ADR-194-ruvector-onnx-embedder-api-and-throughput.md | ||
| ADR-195-ruvector-embedder-unification-plan.md | ||
| ADR-196-structure-preserving-graph-condensation.md | ||
| ADR-197-differentiable-min-cut-condensation-loss.md | ||
| ADR-198-physical-perception-substrate.md | ||
| ADR-210-default-on-semantic-embeddings-minilm.md | ||
| ADR-251-agentic-time.md | ||