R1 결론: trap 깨지지 않음. 자기참조 회로는 충분조건이 아니라 trap 강화
요인일 뿐. opponent를 fixed external로 바꾸자 모델이 zero-pit으로
collapse — 핵심 결함은 model 자체의 credit assignment 실패임이 확정.
R2 방향 = curriculum (작은 게임 → 큰 게임)을 후속 후보로 등록.
Replace the three explicit Cython-generated paths with src/**/*.c so
new .pyx files anywhere under src/ are automatically covered (the
recent bots/heuristic_cy.pyx surfaced this gap; its generated
heuristic_cy.c was showing up as untracked).
Also ignore .compute.lock (local flock file used by training/benchmarks
to coordinate GPU access) and .claude/ (Claude Code session metadata
directory). Both are local-only artifacts and should never be checked
in.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Three coordinated hygiene changes; none target the diagnosed
selection-bias bottleneck. They make the codebase honestly reflect the
pure-self-play stance and reduce dashboard noise.
Bot rename (drop the unhelpful safe_ prefix; suffixes describe behaviour):
- safe_heuristic_loose -> heuristic_aggressive
- safe_heuristic -> heuristic_balanced
- safe_heuristic_strict -> heuristic_cautious
- noisy_safe -> heuristic_noisy
- passive_discard -> discard_only
Class renames in bots/: SafeHeuristicBot -> HeuristicBot,
SafeHeuristicParams -> HeuristicParams, PassiveDiscardBot -> DiscardOnlyBot,
plus loose/strict parameter constants. Backwards compatibility was dropped
intentionally per user instruction; no aliases. Active configs, docs,
scripts, tests updated. Archive directories (configs/archive,
docs/archive, runs/archive) left intact and may still reference old
names per their read-only policy. The src/.../bots/passive.py module was
renamed to discard_only.py via git mv.
Analyze plot curation (deep_cfr/analyze.py):
- Added analysis_00_core.png as the canonical daily dashboard with 10
heuristic-free metrics (loss/{advantage,strategy}; vs heuristic_cautious:
avg_score_diff0, win_rate0, avg_opened_colors, positive_expedition_rate,
bonus_expedition_rate, score_per_opened_color, policy_entropy; vs random:
win_rate0).
- Removed analysis_05_open_quality.png (bad/weak/good open rates,
recoverable score) and analysis_07_calibration.png (calibration gap,
recoverable mean) - both relied on the heuristic recoverable_score
classifier already dropped from inputs.
- Removed SELECTIVITY_PLOTS and plot_selectivity (heuristic-laden).
- SUMMARY_EVAL_METRICS no longer includes bad_open_rate or
calibration_gap.
- PlotSpec gained an opponents allowlist so the new core section can pin
a specific opponent per panel without restructuring plot_section.
Tiered evaluation cadence (EvaluationConfig):
- Added extended_opponents and extended_eval_every (default 0 = disabled).
- opponents_for_iteration(iteration) returns the core list every
eval_every and appends extended_opponents (de-duplicated) when
iteration is also a multiple of extended_eval_every.
- default.yaml now uses 3 core opponents (random, discard_only,
heuristic_cautious) every 5 iterations and 3 extended opponents
(heuristic_balanced, heuristic_aggressive, heuristic_noisy) every 50
iterations. random is the floor sanity. discard_only is the
zero-pit detector / absolute-score reference (its score is always 0,
so eval/discard_only/avg_score_diff0 directly equals the model's raw
average score). heuristic_cautious is the ceiling and the
archive-comparable benchmark used in the prior diagnostic sections.
Net eval cost reduction: roughly 50% (3 opponents x every 5 iter, plus
6 opponents x every 50 iter, vs the prior 6 x every 5).
Documented in docs/plans/deep-cfr-selectivity.md section 9.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The interleaved traversal scheduler's _regret_matching was hard-coded to
spread fallback policy uniformly across legal actions, regardless of the
configured regret_matching.all_negative_fallback. default.yaml has
shipped with all_negative_fallback: argmax_tiebreak since 618d5f8 based
on the 20-iter audit + 1000-iter empirical comparison in
docs/archive/deep-cfr-regret-fallback-audit-2026-05-07.md, but the
default scheduler was switched to interleaved in 09bbe7c, after which
the configured fallback mode silently no-op'd.
_regret_matching now takes fallback_mode and concentrates policy mass on
the lowest-index tied action when "argmax_tiebreak". Tiebreak is
deterministic; the Cython recursive traverser randomises ties using its
per-traverser RNG, which the batched policy does not have. Behaviour
matches the spirit of the recursive path (concentrate on best, do not
dilute uniformly).
Plumbed through BatchedPolicy, InterleavedTraversalConfig,
run_interleaved_traversal_batch, trainer.py, workers.py, and the
analyze_first_open_targets.py caller. Two unit tests added.
Also bumps default.yaml outcome_sampling_epsilon 0.2 -> 0.05. The
200-iter sweep in docs/plans/deep-cfr-selectivity.md section 1 showed
0.05 produced the best short-run safe_heuristic_strict score diff
(-40.01 vs -57.87 for 0.20). Recent experiments already used 0.05; the
default now matches actual experimental practice.
Neither change targets the diagnosed selection-bias bottleneck. They
align config intent with scheduler behaviour and make the default config
reproduce known-best knob settings out of the box.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
ColorSharedNetwork previously sliced the input vector into n_colors equal
chunks (input_dim // n_colors). The slice boundaries do not align with the
actual encoding layout: adjacent slices contain phase flags, hand slots,
expedition state, scores, etc. mixed together. The "color-shared" encoder
was therefore sharing weights across semantically unrelated chunks, not
across per-color blocks. The single archived run that exercised this path
(2026-05-07_092137_color_shared_attention_1000iter) was killed at iter 41
and produced no eval data, so we have no measurement of whether a real
per-color architecture would help.
Adds compute_lost_cities_color_layout(input_dim) which returns explicit
per-color and common index lists for the standard Lost Cities encoding
(n_colors=5, hand_size=8, n_ranks=9). It recognises input_dim values
171, 219, 249, 297 across derived_playability and slot_aware_playability
flag combinations.
Per-color block (39 dims when derived_playability is on): both players'
expedition state for that color, discard top, public-histogram row,
pending-discard one-hot bit, legal-action draw-pile bit, and the
derived_playability per-color block. Slot-aware features are slot-major
and stay in common.
ColorSharedNetwork.forward now indexes per-color blocks via the layout
when input_dim matches a known schema. For other dims (unit tests,
non-Lost Cities use), it falls back to chunked slicing with a UserWarning
- preserves backward compatibility for tests but makes the legacy
behaviour visible.
No fair test of the new architecture was run as part of this commit.
Documented in docs/plans/deep-cfr-selectivity.md section 7.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Audit of the Deep CFR information-state encoding identified two tiers of
non-pure features and removed both:
- Tier 3 (judgment): is_bad_open_candidate, open_risk_score,
is_safe_continuation. Same heuristic family used to label bad_open in
evaluation, embedded as model input.
- Tier 2 (projection): recoverable_score_no_bonus,
recoverable_margin_no_bonus, min_needed_to_break_even,
cards_needed_for_bonus, has_bonus_path. Mechanical but assumption-laden
("commit and play all currently-playable cards"). The no_bonus form is
asymmetric: it amplifies the immediate -20 penalty while truncating the
+20 bonus upside, biasing the model toward the same "don't open" basin
the diagnostics already flagged.
Input dim 365 -> 297. DERIVED_PLAYABILITY_PER_COLOR 19 -> 15;
SLOT_AWARE_PLAYABILITY_PER_SLOT 12 -> 6. Test shape assertions updated.
Also adds selectivity diagnostic infrastructure used to reach this point:
- traversal.outcome_unsampled_first_open_prior_alpha config field with
signed-prior overlay on unsampled first-open advantage targets (A1).
- analyze_first_open_counterfactual.py --post-policy to swap the
policy_player rollout policy and isolate selection bias (D1).
- analyze_first_open_followup.py to inspect post-forced-open behavior
(E2): same-color play vs discard counts, other-open rate, terminal
hand composition.
Findings recorded in docs/plans/deep-cfr-selectivity.md sections 3-6.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Second survey processed the remaining 12 archives via gemini after
the first batch of 3 was accepted. 12 drafts, 0 skips, 0 errors.
Every draft carries a deterministic Last-verified header
(2026-05-08, commit 5c221fb) thanks to the post-processing fix
landed in the previous commit. All 12 accepted into docs/research/
verbatim:
deep-cfr-evaluation-profile-plan
deep-cfr-legacy-experiment-reproduction
deep-cfr-legacy-runtime-comparison
deep-cfr-performance-experiments
deep-cfr-profile-advantage-memory-split
deep-cfr-profile
deep-cfr-regret-fallback-audit
deep-cfr-v0-gap-vs-coolrl
deep-cfr-v0-plan
fast-engine-next-optimizations
post-a-optimization-calculus
test-coverage-notes
docs/archive/ is now fully covered: every entry either has a
research counterpart by stem or by tail-match.
Also extracts _dispatch_one and adds --parallel N to
scripts/librarian_survey.py. ThreadPoolExecutor over the per-archive
work is safe because subprocess.run is network-bound (no GIL fight)
and each thread writes to its own output filename. Default stays
1 (sequential); --parallel 4 is the recommended speedup for large
surveys. The two surveys above ran sequentially; future runs can
opt in.
Plan declares librarian closed for new feature work. MEMORY drift
fixup and duplicate-merge modes stay deferred until a real input
surfaces. Stage 1 (5 deterministic checks) and Stage 2 (promote +
survey) remain operational.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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_survey.py walks docs/archive/*.md and dispatches
every entry without a research counterpart through the same prompt
assembly as librarian_promote.py. Outputs land under
runs/tmp/librarian-survey-<timestamp>/, classified into:
<stem>.md draft, ready to copy into docs/research/
<stem>.SKIP.txt LLM's one-line "not promotable" reason
<stem>.ERROR.txt CLI stderr if the call itself failed
Counterpart detection uses exact stem match plus a tail-match
heuristic so research notes that intentionally drop a domain prefix
still suppress their archive. Verified against the current tree:
docs/archive/deep-cfr-opponent-policy-network-divergence-* is
correctly recognized as already covered by
docs/research/opponent-policy-network-divergence.md.
--dry-run lists candidates and suggested research targets without
calling the LLM. --max N caps processed archives per run, useful as
a cost guard. Sequential dispatch; one LLM call per archive.
Plan updated to mark survey mode complete.
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>
scripts/librarian_promote.py is the first Stage 2 piece: a
vendor-agnostic LLM dispatcher that drafts a docs/research/ note
from a given docs/archive/ entry. It assembles the prompt by
stitching scripts/librarian-prompt.md (system) onto the archive
body with a "draft a research note per the rules above" task
instruction, then shells out to the CLI selected by LIBRARIAN_LLM
({claude|codex|gemini}; default claude). The LLM's stdout is
captured to runs/tmp/librarian-promote-<timestamp>-draft.md for
human review — the script never writes into docs/research/ itself.
Refusal cases:
- path not under docs/archive/
- target docs/research/<stem>.md already exists (after stripping any
-YYYY-MM-DD suffix)
- archive missing
If the LLM judges the archive non-promotable, it is instructed to
return a single SKIP: <reason> line instead of fabricating a draft.
--show-prompt prints the assembled prompt without invoking the LLM,
useful for inspecting what would be sent.
Plan updated: Stage 2 v1 marked complete; remaining Stage 2 work
(MEMORY drift fixup, duplicate-merge, survey mode) catalogued.
Next concrete step is a smoke test against one real archive entry.
Adds one ignore-list entry for docs/research/option-a-bench-result.md
which appears in the plan as a hypothetical accept target.
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>
Adds the last two deterministic Stage 1 checks and wires them into
scripts/librarian.sh:
- librarian_check_stale_plans.py: per docs/plans/*.md, uses
`git log -1 --format=%cs` to get the last commit date and flags
plans untouched in 60+ days. Plans should be either active or
archived; long silence is drift.
- librarian_check_memory_drift.py: validates the user-memory dir
(~/.claude/projects/<slug>/memory). Each MEMORY.md index line
must point at a real file with frontmatter (name, description,
type ∈ {user, feedback, project, reference}); no orphaned memory
files. Memory dir is derived from repo root for portability.
Both run clean against current state.
Stage 1 declared complete. Promotable-archive and duplicate-prose
detection are moved to Stage 2 in the plan because both need LLM
judgment to avoid false positives — not pattern matching.
Next concrete step is the open Stage 1 finding from yesterday:
docs/performance.md at 914 lines, due for a split into sub-topic
notes.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Third Stage 1 piece: scripts/librarian_check_oversize.py walks
non-archive markdown files and flags any over the 500-line soft cap
declared in AGENTS.md. Wired into scripts/librarian.sh.
Caught one real finding on first run: docs/performance.md at 914
lines. Splitting it into sub-topic notes is a separate cleanup task
— surfaced for the user, not auto-applied.
Updates docs/plans/librarian.md Progress + sets the next concrete
step to a stale-plan checker.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Documents two coordination mechanisms agents need to know about on
every turn:
- Compute Lock: train and speed-benchmark commands grab a shared
.compute.lock so multiple agents on one machine don't trample each
other's GPU/CPU runs. Eval and analyze stay lock-free.
- Librarian: scripts/librarian.sh runs the Stage 1 doc lints (lychee
link integrity + file:line citation parity) and should run before
committing doc changes. Full design in docs/plans/librarian.md.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Single entry point that runs every Stage 1 check in order and
aggregates exit codes — both checks always run so users see all
findings in one pass. Two checks wired up today (lychee link
integrity, file:line citations), more land incrementally as Stage 1
grows.
Removes scripts/librarian.sh from the ignore list now that the file
exists. Updates docs/plans/librarian.md Progress + Next Step.
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>
Captures the full librarian design (two-layer architecture, three-stage
pipeline, vendor-agnostic via LIBRARIAN_LLM env, propose-only / no
auto-apply) in docs/plans/librarian.md. Lands the first concrete Stage 1
piece: scripts/librarian_check_links.py, a lychee --offline wrapper
ported from ~/dev/coolrl/src/coolrl/dev/check_doc_links.py.
Also moves the librarian prompt from .claude/agents/ (Claude Code only)
to scripts/librarian-prompt.md so any CLI can load it as a system
prompt later. Fixes one stale README link the new checker caught:
docs/classic-port-notes.md → docs/archive/classic-port-notes.md.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Add a "Where to write what" table and 5 writing rules so every agent
sees the same doc-placement policy at the top of each session, instead
of the rules living only inside the librarian subagent prompt.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Add an experiment-only explicit-continuation traversal prototype that batches policy requests, records CPU/CUDA parity results, and updates the Option B plan with Phase 0/1 evidence.
Co-Authored-By: Codex <codex@openai.com>
Archive implemented AMP, Option A inference-server, and Cython heuristic plans. Add the active Option B interleaved traversal plan and update model-size/torch.compile plans to reflect the current traversal scheduling conclusion.
Co-Authored-By: Codex <codex@openai.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>
진단 가설/개입/측정 섹션은 docs/research/lost_cities_selectivity.md
에 verbatim으로 보존되어 있어 ideas.md 쪽 사본은 stale 위험 + 중복
유지 비용만 남는다. ideas.md는 brainstorm 인덱스로 축소하고, research/
의 세 thread를 단일 진입점으로 정리.
기존의 "All-negative fallback 가설 — 검증됨, 부분 풀림" 같은 stale
표현도 함께 사라진다 (research doc에선 여전히 open hypothesis로
다뤄지고 있음).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.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>
coolguy
