Spot-check of the three drafts gemini produced in the --max 3
survey smoke test: all cited file paths exist, line numbers and
function names land within 1-2 lines of actual symbols
(game.pyx:217 cdef class GameState, evaluate.py:220 batched-entropy
block, trainer.py:892 _evaluate_parallel, action_distribution at
evaluate.py:238 with cited code at line 250 inside it). Numbers
cross-checked against archives match. Conclusions preserved.
The one systemic weakness was the Last-verified commit field:
gemini left a `<short-hash>` placeholder, a literal `HEAD`, or
omitted the commit entirely depending on the call. Fixed in two
places:
1. Manually patched the three drafts before acceptance and copied
them into docs/research/.
2. Added _current_commit_sha and _post_process_draft helpers to
both librarian_survey.py and librarian_promote.py. The drafts
now go through `**Last verified:**` line normalization that
substitutes today's date and `git rev-parse --short HEAD`
before being written to disk. Future runs converge
deterministically.
Net: docs/research/ gains classic-port-notes.md,
deep-cfr-batched-evaluation.md, and deep-cfr-evaluation-profile.md.
12 archive entries remain unprocessed.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
scripts/librarian_promote.py gains a --accept flag that copies the
generated draft to the suggested docs/research/ target in the same
invocation. This is explicit per-invocation opt-in, not auto-apply:
the operator types --accept knowingly, the cp is still a deliberate
acceptance decision, just expressed in one command instead of two.
docs/research/option-a-bench-result.md is the first promoted note,
generated by gemini from
docs/archive/option-a-bench-result-2026-05-07.md and accepted
verbatim. Spot-check verified that cited file:line locations match
current source (traversal.pyx:473-475 and
inference_server.py:226-228 carry the cited code), the Last-verified
header reflects today's date and HEAD, and the durable conclusion
(sync-blocking policy boundary as the structural ceiling, not IPC
plumbing) is preserved.
This closes the end-to-end loop the librarian was designed for:
oversize check surfaced docs/performance.md, the routing pass split
durable analysis into the archive entry, the promote dispatcher
turned the archive entry into a research draft, and the human
accept step landed it as a tracked research note. Took one LLM call.
Removes the now-resolved ignore-list entry for
docs/research/option-a-bench-result.md (the file exists; the
forward reference is real).
scripts/librarian.sh exits 0 against the working tree.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
docs/performance.md grew to 914 lines because dated experiments and
design analyses kept getting appended instead of routed to
docs/archive/ and docs/research/ as AGENTS.md prescribes. The
oversize check from librarian Stage 1 surfaced the file; this commit
acts on that finding by extracting the parts that belong elsewhere
and trimming the source to a focused current-state reference.
Extracts (verbatim from the original prose, with cross-link headers
and a brief routing note added at top):
- docs/archive/deep-cfr-performance-experiments-2026-05-07.md
bundles torch.compile (regression), AMP (regression), GPU forward
profiling (decision support), and Option B interleaved traversal
(pass) — same date, same theme.
- docs/research/batched-traversal-inference-decision.md captures the
durable A vs B vs C rationale with a closing "Outcome" pointer to
the post-bench archive doc.
- docs/archive/post-a-optimization-calculus-2026-05-07.md preserves
the forward-looking sequencing recorded pre-bench.
- docs/archive/option-a-bench-result-2026-05-07.md preserves the
regression diagnosis and re-enable criteria.
docs/performance.md is now 345 lines, holds sections 1–9 (current
runtime / bottleneck / device / AMP status / batching / eval /
TensorRT / priorities), and ends with a "See Also" linking the four
extracts.
Also reworded the AGENTS.md soft-cap rule from a bare "~500-line
soft cap" to clarify the intent: the cap is a *routing trigger* (is
content piling up that should live in archive/research?), not a
split mandate. Reduces the risk of future agents shredding a useful
doc just to satisfy a number.
scripts/librarian.sh now exits 0 against the working tree.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
scripts/librarian_check_citations.py extracts file:line references from
inline-code spans across docs/**/*.md and verifies each path exists
(and, if cited with a line number, is within range). Skips
docs/archive/ and docs/plans/archive/ which are read-only by policy.
Caught one real drift in docs/research/optimization_sequencing.md: the
note pointed at docs/plans/amp_trainer.md, which had moved into
docs/plans/archive/.
scripts/librarian-ignore.txt holds fnmatch globs for citations that are
intentionally future-tense (planned files described in the plan docs
themselves). Used sparingly so the checker stays useful as a drift
signal.
Updates docs/plans/librarian.md Progress + Next Step. Next concrete
step is a thin scripts/librarian.sh orchestrator over both checkers.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Add a microbench that separates game/encoding overhead, single-request policy boundary overhead, and batched PyTorch forward lower bounds. Record CPU/CUDA results and link the finding from the Julia port evaluation.
Co-Authored-By: Codex <codex@openai.com>
Captures the dependency graph between pending levers (model size,
Option B, AMP, compile, TensorRT, Option A re-enable, Julia port) and
the rule that infrastructure optimization precedes the model-size
experiment because every future training run benefits from the
infrastructure speedup, not just the one keystone experiment.
ideas.md gets a third Active Research Threads pointer.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Decision-in-advance for criteria 3 (multi-thread scaling), 4 (Flux/CUDA
MLP forward), and 5 (real-game-state slice). Each criterion lists PASS
/ PARTIAL / FAIL bands with concrete numerical thresholds, plus a
decision rule that maps {3, 4, 5} outcomes to a single action: port,
hybrid (Julia traversal + PyTorch networks), or stay on Python/Cython
and pursue Option B instead.
Cost-of-being-wrong asymmetry stated explicitly: port is months,
staying is zero work, so the GO bar is deliberately above 50% and the
STAY bar is permissive.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
experiments/julia_cfr_toy/ ports a synthetic CFR external-sampling
traversal to both Julia and Cython for direct head-to-head
measurement of the actual project hot-path pattern (recursive
tree + mutable regret state + branch-heavy legal-action logic).
Headline result (2026-05-07, single run): Julia ~1.9× faster than
Cython on this pattern, 0 MB allocation, 0% GC time. Root regret
parity ε ≤ 1e-9. The GC-pause concern that was the main argument
against Julia adoption did not materialize. Cython's 21.3 MB
allocation suggests its implementation can be tightened, so the
honest gap window is roughly 1.3×–1.9×.
Multi-thread scaling (bench_cfr_threaded.jl): 2.44× wall-clock at 8T
but only 31% efficiency — inconclusive, likely a toy-size artifact
(per-thread workload too small to amortize dispatch). A heavier
per-thread workload sweep is the remaining decisive test.
docs/research/julia_port_evaluation.md captures this evidence
alongside the earlier safe-heuristic single-thread parity result
and lists the remaining decision criteria (multi-thread scaling with
heavier workload, Flux.jl+CUDA.jl coverage, real-game-state slice).
Do not commit to porting until multi-thread scaling is conclusively
settled.
ideas.md gets a second Active Research Threads pointer.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Mechanism-level analysis of the slot_aware_playability iter 240
plateau (opened_colors 4.95+, bad_open_rate 88-92%, calibration
gap 6-9 → 2-4). Captures four expert consultations with diagnostic
hypotheses, intervention catalog (architectural / training-dynamics
/ game-specific), measurement plan, and a comparison table across
the four sources.
Key new directions surfaced:
- Current vs average vs league policy separation (Deep CFR average
strategy is the convergence target, not advantage current).
- All-negative fallback as Deep CFR ablation lever.
- Empirical r̃ partitioning by action class.
- Tabular Lost Cities oracle as a clean test of "is 5-color the
game-theoretic answer or an approximation artifact".
- Entry-gate target defined from traversal counterfactual values
instead of heuristic labels.
ideas.md gets an "Active Research Threads" pointer.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
OpenSpiel's Deep CFR records strategy samples at opponent nodes during
the traverser's tree walk; storing on traverser nodes under external
sampling drops the ρ_p reach factor and biases the average-policy
estimate. Reject that config combination at load time and add a research
note deriving why outcome sampling is unaffected while external is not.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Five notes covering outcome-sampling target correctness, package
architecture, v0 feature-parity vs legacy, opponent-policy network
divergence, and regret-matching fallback audit. Four are derived from
archive sources (cited via Source: lines); outcome-sampling-target is
a fresh write-up and serves as the style template.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
coolguyandClaude Opus 4.7