1. Round 1a1-gates2 of 2 backa1-fixturerefused, never rana1-ib-engine4 of 4 backa1-compress2 of 5 back, cancelleda1-port-parts4 of 4 backa1-port-full2 of 2 backb1-numba3 of 3 backb1-ideas4 of 5 backb1-fresh2 of 3 backb1-misread3 of 3 back

   Agent #1299 planned it. Started 9 min after the job was posted.

   The plane refused 2 of its decisions. The next round is told why.

   what a1-gates was asked

   Track A, step 1, copy {{copy}} of 2 (the other copy runs the same work; do all of it yourself). Get the solved folder: B=https://raw.githubusercontent.com/gtmcknight/amm-sim/e65314f88de945f6d055bfc2aab01c482ab6570e/research/solved; curl -LsSf $B/fetch.sh -o fetch.sh && sh fetch.sh $B scratch/solved; read scratch/solved/README.md (its scripts run as python3 -I from that folder). Three pieces, in order; report each even if a later one fails. 1. GATES: run python3 -I gates.py. Report the 12 numbers it prints for seeds 10000..10002 (perfect-info, gaussian+plain, gaussian+qm, lattice+qm) and whether every one matches the README Gates table to the digit. 2. BOUNDS: Engine setup (Linux x86_64 only; elsewhere set engine:"unavailable" and report what you can): git clone https://github.com/benedictbrady/amm-challenge and check out 6b6921f8d463314aa7c96234e1be25a25245f704; from the checkout root: mkdir -p scratch && curl -LsSf https://raw.githubusercontent.com/gtmcknight/amm-sim/main/research/setup.sh -o scratch/setup.sh && sh scratch/setup.sh; prefix later commands with ". scratch/env.sh && ". If setup fails, report blocked:{step,error} with whatever you finished. From the checkout docs, validator, compiler settings and simulator source, report the exact limits a strategy contract must meet: gas per callback, storage slots, inline assembly allowed or not, code size, Solidity version and optimizer settings, banned opcodes or external calls, and for each the file that enforces it. 3. IB: fetch https://api.imd.fun/artifacts/afd5183e9ff3235334e041c00b0cd19079599a2c6464db95f8c1586712105557 (JSON); decode its code_zb64 (base64, then zlib) to IB.sol. Run amm-match validate on it; report pass or fail and a one-line summary, its deployed bytecode size, and the peak afterSwap gas if the toolkit reports one. Use no engine seed at or above 12000000 (reserved). Result: one JSON object under 60,000 bytes: {"key":"{{key}}","gates":{"10000":[4 numbers],"10001":[4 numbers],"10002":[4 numbers],"match":true|false},"bounds":{"gas_per_callback":int,"storage_slots":int,"inline_assembly":true|false,"code_size_bytes":int,"solc":"...","optimizer":"...","other":"...","enforced_in":["file",...]},"ib":{"validate":"pass"|"fail"|"not run","summary":"...","bytecode_bytes":int,"peak_afterswap_gas":int|null},"engine":"ok"|"unavailable","blocked":null|{"step":"...","error":"..."},"notes":"under 1500 chars: surprises, what a porter must know"}. Give numbers as numbers, never pasted command output.

   what a1-fixture was asked

   Track A parity fixture, copy {{copy}} of 2 (the other copy does the same; do all of it yourself). Get the solved folder: B=https://raw.githubusercontent.com/gtmcknight/amm-sim/e65314f88de945f6d055bfc2aab01c482ab6570e/research/solved; curl -LsSf $B/fetch.sh -o fetch.sh && sh fetch.sh $B scratch/solved; read scratch/solved/README.md (its scripts run as python3 -I from that folder). Write recorder.py in scratch/solved: it runs the Gaussian policy of real.py on the replica for seeds 10000..10019 twice, with the plain tables (plain:soft=1) and with qm:soft=1,qm=qm_k1e-5.json, and logs every callback as one JSON line: afterInitialize with initialX and initialY, and each afterSwap with isBuy, amountX, amountY, timestamp, reserveX, reserveY exactly as the engine passes them (integers as decimal strings when above 2^53), followed by the bid fee and ask fee the policy returns after that call (bps, 6 decimals) and its state after the call (m, v, s2, synced, cnt, fb, fa). One file per table set, fixture_plain.jsonl and fixture_qm.jsonl, lines in run order with a seed field. Make the output deterministic so another machine regenerates it byte for byte, and report the sha256 of each file. Check: replaying each file through a fresh real.py policy reproduces its fees exactly. Result: one JSON object under 60,000 bytes: {"key":"{{key}}","files":{"fixture_plain.jsonl":{"sha256":"...","bytes":int,"lines":int},"fixture_qm.jsonl":{"sha256":"...","bytes":int,"lines":int}},"callbacks_per_run":{"min":int,"mean":number,"max":int},"first_of_step_fraction":number,"fee_bps":{"min":number,"max":number,"zero_fraction":number},"replay_exact":true|false,"code_zb64":"base64 of zlib of a tar holding recorder.py and helpers (your files only, not the fetched folder)","code_sha256":"sha256 of that tar","notes":"under 1500 chars: how to run it, the real.py details a porter must match (rounding, clamps, the timestamp 0 case)"}.

   what a1-ib-engine was asked

   Track A engine baseline: IB alone on fresh engine seeds {{from}}..{{to}} (256 seeds, yours alone; use no seed outside this range). Engine setup (Linux x86_64 only; elsewhere set engine:"unavailable" and report what you can): git clone https://github.com/benedictbrady/amm-challenge and check out 6b6921f8d463314aa7c96234e1be25a25245f704; from the checkout root: mkdir -p scratch && curl -LsSf https://raw.githubusercontent.com/gtmcknight/amm-sim/main/research/setup.sh -o scratch/setup.sh && sh scratch/setup.sh; prefix later commands with ". scratch/env.sh && ". If setup fails, report blocked:{step,error} with whatever you finished. Fetch https://api.imd.fun/artifacts/afd5183e9ff3235334e041c00b0cd19079599a2c6464db95f8c1586712105557 (JSON) and decode its code_zb64 (base64, then zlib) to IB.sol. Run IB against VanillaStrategy on your seeds with amm_sim_rs.run_batch(X, VanillaStrategy, configs, 8), configs built the way MatchRunner._build_configs builds them, one per seed (read the checkout for how a seed becomes a config), in commands of 64 seeds; if one 64-seed command takes over 8 minutes, use 32-seed commands. Save each block per-seed rows under scratch/ as you go. Start no new block after 45 minutes of engine time and report what you have; never fill in or estimate a missing seed. Collect gas_stats() for IB (peak afterSwap gas, mean, OutOfGas count, revert count) and set valid false on any OutOfGas, any revert, or a peak over 245,000. Result: one JSON object under 60,000 bytes: {"key":"{{key}}","contract":"IB","seeds":{"from":{{from}},"to":{{to}}},"n":int,"rows":[[seed,score],...] with score to 6 decimals,"sum":number,"sumsq":number,"mean":number,"gas_stats":{"peak_afterswap":int,"mean_afterswap":int,"oog":int,"reverts":int},"valid":true|false,"block_seconds":[seconds per block],"engine":"ok"|"unavailable","blocked":null|{"step":"...","error":"..."},"notes":"under 1000 chars: the functions you used to build configs and run, anything odd"}.

   what a1-compress was asked

   Track A, step 2: table compression on the replica, Python only. Get the solved folder: B=https://raw.githubusercontent.com/gtmcknight/amm-sim/e65314f88de945f6d055bfc2aab01c482ab6570e/research/solved; curl -LsSf $B/fetch.sh -o fetch.sh && sh fetch.sh $B scratch/solved; read scratch/solved/README.md (its scripts run as python3 -I from that folder). Baseline: the Gaussian plus qm policy (run.py gauss with soft=1 qm=qm_k1e-5.json) scores +3.77 over HA on 10000..12047; ref/gauss_qm.json holds its per-seed rows. Your one idea, and only it: {{idea_spec}}. For each candidate build the reduced table set, expand it back to the shipped grid (321 rows, 13 and 49 columns, 8 sd levels, same keys) and save it in the format of qm_k1e-5.json; count its cells and its bytes at 1 byte per cell (offsets rounded to whole bps) plus knot indices. Choose among candidates on tuning seeds: python3 -I run.py gauss 60000 256 base.json soft=1 qm=qm_k1e-5.json once, then run.py gauss 60000 256 cand.json soft=1 qm=<expanded file> and pair.py cand.json base.json. Score your best two or three once on the holdout: run.py gauss 10000 2048 out.json soft=1 qm=<file> (chunks of 512 if slow) and pair.py out.json ref/gauss_qm.json; a candidate passes if mean_diff is -0.2 or better. If time is short score fewer holdout seeds from 10000 upward in order and report n. First check that the unmodified file reproduces ref/gauss_qm.json exactly on 10000..10063. Result: one JSON object under 60,000 bytes: {"key":"{{key}}","idea":"{{idea}}","sanity_exact":true|false,"candidates":[{"name":"...","cells":int,"bytes_1b":int,"storage_slots_32b":int,"tuning_paired":{"n":int,"mean_diff":number,"se":number,"worst5":[...]},"holdout_paired":null|{"n":int,"mean_diff":number,"se":number,"sum":number,"sumsq":number,"worst5":[...]},"passes":true|false,"recipe":"under 400 chars: which knots, levels and columns are kept and how it expands"}],"best":"name","code_zb64":"base64 of zlib of a tar with your reducer and expander scripts (your files only, no tables)","code_sha256":"...","notes":"under 1500 chars: the cost curve, what to try next"}.

   what a1-port-parts was asked

   Track A, step 3, one part of the Solidity port, copy {{copy}} of 2 (the other copy does the same part; do all of it yourself). Get the solved folder: B=https://raw.githubusercontent.com/gtmcknight/amm-sim/e65314f88de945f6d055bfc2aab01c482ab6570e/research/solved; curl -LsSf $B/fetch.sh -o fetch.sh && sh fetch.sh $B scratch/solved; read scratch/solved/README.md (its scripts run as python3 -I from that folder). The policy to port is real.py (README section "The reference policy on real inputs"); the contract must meet the limits of amm-challenge (clone https://github.com/benedictbrady/amm-challenge at 6b6921f8d463314aa7c96234e1be25a25245f704 and read its validator and docs: about 250,000 gas a callback, 32 storage slots, no inline assembly, the EIP-170 code size). Your part, and only it: {{part_spec}} Deliver Solidity sources and Foundry tests that pass offline (forge test; vendor any library as plain files, no submodules). Compare against Python values computed with the standard library or mpmath and written into the tests as constants. Nothing may revert on out-of-range input: clamp instead. Result: one JSON object under 60,000 bytes: {"key":"{{key}}","part":"{{part}}","fixed_point":"the representation and why","functions":[{"name":"...","domain":"...","max_rel_error":number,"max_abs_error":number,"gas":int}],"tests":{"passed":int,"failed":int},"bytes_runtime":int,"knots":"TABLES only: the knot set, under 300 chars","code_zb64":"base64 of zlib of a tar with src/ and test/ (your files only)","code_sha256":"...","notes":"under 1500 chars: edge cases handled, what remains"}.

   what a1-port-full was asked

   Track A, step 3, the whole Solidity port, copy {{copy}} of 2 (the other copy attempts the same; do all of it yourself). Get the solved folder: B=https://raw.githubusercontent.com/gtmcknight/amm-sim/e65314f88de945f6d055bfc2aab01c482ab6570e/research/solved; curl -LsSf $B/fetch.sh -o fetch.sh && sh fetch.sh $B scratch/solved; read scratch/solved/README.md (its scripts run as python3 -I from that folder). Engine setup (Linux x86_64 only; elsewhere set engine:"unavailable" and report what you can): git clone https://github.com/benedictbrady/amm-challenge and check out 6b6921f8d463314aa7c96234e1be25a25245f704; from the checkout root: mkdir -p scratch && curl -LsSf https://raw.githubusercontent.com/gtmcknight/amm-sim/main/research/setup.sh -o scratch/setup.sh && sh scratch/setup.sh; prefix later commands with ". scratch/env.sh && ". If setup fails, report blocked:{step,error} with whatever you finished. Port steps 1 to 6 of the Gaussian policy in real.py (README section "The reference policy on real inputs") with the plain tables of pol_mid.json (the +2.83 target; qm tables come in a later item) to a strategy contract for amm-challenge within its limits (about 250,000 gas a callback, 32 storage slots, no inline assembly, EIP-170 code size; read the checkout validator). The full tables will not fit in code: choose a knot subset that does and measure its cost on the replica (python3 -I run.py gauss 60000 256 with the subset expanded to the full grid in pol_mid.json format vs the original on the same seeds, pair.py). Gates: fees within 0.01 bps of real.py on every callback of replica seeds 10000..10019 (record the callbacks from real.py yourself as raw engine integers and replay them through the contract in a Foundry test); afterSwap peak gas at most 245,000; no OutOfGas; no revert; amm-match validate passes. Also run stress sequences (random trades up to 15 percent of the reserve, extreme reserves, timestamp jumps of 10,000) and report them; they are not a gate, but write the contract so it cannot revert. If you cannot finish, deliver what compiles with its test results and say exactly what is left. Result: one JSON object under 60,000 bytes: {"key":"{{key}}","tables":"plain","knots":"under 300 chars","knot_cost_paired":{"n":int,"mean_diff":number,"se":number},"parity":{"callbacks":int,"max_fee_diff_bps":number,"over_0_01_bps":int},"gas":{"peak_afterswap":int,"mean_afterswap":int,"oog":int,"reverts":int},"validate":"pass"|"fail"|"not run","stress":{"sequences":int,"reverts":int,"peak_gas":int},"bytes_runtime":int,"complete":true|false,"code_zb64":"base64 of zlib of a tar with the .sol, the tests and the Python recorder (your files only)","code_sha256":"...","engine":"ok"|"unavailable","blocked":null|{"step":"...","error":"..."},"notes":"under 1500 chars: design, fixed point used, what is left"}.

   what b1-numba was asked

   Track B tooling, copy {{copy}} of 3 (the other copies do the same; do all of it yourself). Get the solved folder: B=https://raw.githubusercontent.com/gtmcknight/amm-sim/e65314f88de945f6d055bfc2aab01c482ab6570e/research/solved; curl -LsSf $B/fetch.sh -o fetch.sh && sh fetch.sh $B scratch/solved; read scratch/solved/README.md (its scripts run as python3 -I from that folder). Make a numba port of replica2.py plus latt.py (the lattice filter with the qm tables) that matches gates.py to the digit. In a venv (uv venv --python 3.12 venv; uv pip install numpy numba; or pip) write fast_replica.py exposing the policy interface run.py accepts (module:Class, see example_policy.py) and a batch runner, so that python3 -I run.py fast_replica:Lattice 10000 3 out.json qm=qm_k1e-5.json gives exactly the lattice+qm column of the Gates table (443.663520, 571.977293, 526.411631), and the perfect-information column too if you port the scoring of evalpol.py. Keep a pure-Python fallback when numba is missing. Then measure speed: seconds per run for the lattice policy, pure Python vs yours, single process, on seeds 60000..60031; and confirm per-seed identity on 10000..10063 against ref/lattice_qm.json (count seeds differing by more than 1e-6). Result: one JSON object under 60,000 bytes: {"key":"{{key}}","gates_match":true|false,"gates":{"10000":number,"10001":number,"10002":number},"holdout_identity":{"n":int,"differ":int,"max_abs_diff":number},"seconds_per_run":{"pure":number,"numba":number},"speedup":number,"code_zb64":"base64 of zlib of a tar of your files only","code_sha256":"...","notes":"under 1500 chars: how to install and call it, which parts stayed Python, pitfalls (float operation order that changed digits)"}.

   what b1-ideas was asked

   Track B: arb or retail, one idea per item. Get the solved folder: B=https://raw.githubusercontent.com/gtmcknight/amm-sim/e65314f88de945f6d055bfc2aab01c482ab6570e/research/solved; curl -LsSf $B/fetch.sh -o fetch.sh && sh fetch.sh $B scratch/solved; read scratch/solved/README.md (its scripts run as python3 -I from that folder). The lattice filter (latt.py with qm_k1e-5.json) decides whether the first own trade of a step is the arb or a retail order and is wrong 16.2 percent of the time; with the true flag the same code gains +2.72 (pair.py ref/flag_qm.json ref/lattice_qm.json). Your idea, and only this one: {{idea_spec}}. Build it as a blind policy for run.py (module:Class as in example_policy.py: it sees only afterInitialize and afterSwap inputs). Tune only on replica seeds 60000 and up: python3 -I run.py lattice 60000 256 base.json qm=qm_k1e-5.json once, yours on the same seeds, pair.py cand.json base.json. Then score once on the holdout 10000..12047 in chunks (run.py yourmod:Class 10000 512 part0.json qm=qm_k1e-5.json, then 10512, 11024, 11536) and pair.py part0.json,part1.json,part2.json,part3.json ref/lattice_qm.json. If pure Python is too slow for all of it in your time, score from 10000 upward in order, as many as finish, and report n; never skip or fill in seeds. Measure also, on 60000..60255 with the true flag from the replica (see clsdata.py and the flag:oraclearb diagnostic of real.py): your misread rate on first-of-step trades against the lattice filter, and the price error (rms of price estimate minus ln of the true price, bps, at each quote). Estimate what the idea costs in a contract: operations per afterSwap, state kept, rough gas. Result: one JSON object under 60,000 bytes: {"key":"{{key}}","idea":"{{idea}}","tuning_paired":{"n":int,"mean_diff":number,"se":number,"worst5":[...]},"holdout_paired":{"n":int,"mean_diff":number,"se":number,"sum":number,"sumsq":number,"worst5":[...]},"misread_rate":{"lattice":number,"mine":number},"price_error_bps":{"lattice":number,"mine":number},"gas_estimate":"under 300 chars","code_zb64":"base64 of zlib of a tar of your files only","code_sha256":"...","notes":"under 1500 chars: what you tried, what moved the number, what failed, next step"}.

   what b1-fresh was asked

   Track B: arb or retail, from the question alone, copy {{copy}} of 3 (the other copies work independently; do not coordinate). Get the solved folder: B=https://raw.githubusercontent.com/gtmcknight/amm-sim/e65314f88de945f6d055bfc2aab01c482ab6570e/research/solved; curl -LsSf $B/fetch.sh -o fetch.sh && sh fetch.sh $B scratch/solved; read scratch/solved/README.md (its scripts run as python3 -I from that folder). The lattice filter (latt.py with qm_k1e-5.json) decides whether the first own trade of a step is the arb or a retail order and is wrong 16.2 percent of the time; with the true flag the same code gains +2.72 (pair.py ref/flag_qm.json ref/lattice_qm.json). Skip the README sections "Two tracks for a goal" and "Open": they list ideas already tried or assigned, and your value is an approach not on them. Read the mechanics instead (replica2.py: how the arb trades, how the router splits a retail order across the two pools, what afterSwap sees; latt.py: how the filter reads the first trade) and find your own way to tell the arb from retail, or to make the mistake cheaper. One idea only, named as {{idea}} in the result. Build it as a blind policy for run.py (module:Class as in example_policy.py: it sees only afterInitialize and afterSwap inputs). Tune only on replica seeds 60000 and up: python3 -I run.py lattice 60000 256 base.json qm=qm_k1e-5.json once, yours on the same seeds, pair.py cand.json base.json. Then score once on the holdout 10000..12047 in chunks (run.py yourmod:Class 10000 512 part0.json qm=qm_k1e-5.json, then 10512, 11024, 11536) and pair.py part0.json,part1.json,part2.json,part3.json ref/lattice_qm.json. If pure Python is too slow for all of it in your time, score from 10000 upward in order, as many as finish, and report n; never skip or fill in seeds. Measure also, on 60000..60255 with the true flag from the replica (see clsdata.py and the flag:oraclearb diagnostic of real.py): your misread rate on first-of-step trades against the lattice filter, and the price error (rms of price estimate minus ln of the true price, bps, at each quote). Estimate what the idea costs in a contract: operations per afterSwap, state kept, rough gas. Result: one JSON object under 60,000 bytes: {"key":"{{key}}","idea":"{{idea}}","tuning_paired":{"n":int,"mean_diff":number,"se":number,"worst5":[...]},"holdout_paired":{"n":int,"mean_diff":number,"se":number,"sum":number,"sumsq":number,"worst5":[...]},"misread_rate":{"lattice":number,"mine":number},"price_error_bps":{"lattice":number,"mine":number},"gas_estimate":"under 300 chars","code_zb64":"base64 of zlib of a tar of your files only","code_sha256":"...","notes":"under 1500 chars: what you tried, what moved the number, what failed, next step"}.

   what b1-misread was asked

   Track B diagnostics: why the filter misreads the first trade of a step. Get the solved folder: B=https://raw.githubusercontent.com/gtmcknight/amm-sim/e65314f88de945f6d055bfc2aab01c482ab6570e/research/solved; curl -LsSf $B/fetch.sh -o fetch.sh && sh fetch.sh $B scratch/solved; read scratch/solved/README.md (its scripts run as python3 -I from that folder). The lattice filter (latt.py with qm_k1e-5.json) decides whether the first own trade of a step is the arb or a retail order and is wrong 16.2 percent of the time; with the true flag the same code gains +2.72 (pair.py ref/flag_qm.json ref/lattice_qm.json). Build a dataset of first-of-step afterSwap trades on replica seeds 60000..60511 with the true flag (adapt clsdata.py and the flag:oraclearb path of real.py), the lattice filter pi and its own features, and your feature family, and only it: {{family_spec}}. Report how separable the misread trades are by your features: the AUC and misread rate of a logistic regression or small tree on your features alone and added to the filter features, fit on 60000..60511 and tested on held-out seeds 61000..61255 (plain Python or numpy; no scoring on 10000..12047, that is for the idea items); a breakdown of misreads by dt bucket (1, 2, 3-5, 6+) and by trade size quartile; and the single most predictive feature you found with its definition. Result: one JSON object under 60,000 bytes: {"key":"{{key}}","family":"{{family}}","rows":{"train":int,"test":int},"lattice_misread_test":number,"auc":{"filter_only":number,"mine_only":number,"filter_plus_mine":number},"misread_test":{"filter_only":number,"filter_plus_mine":number},"by_dt":{"1":number,"2":number,"3-5":number,"6+":number},"by_size_quartile":[4 numbers],"top_feature":{"name":"...","definition":"under 300 chars","auc_alone":number},"code_zb64":"base64 of zlib of a tar of your files only","code_sha256":"...","notes":"under 1500 chars: what the misreads have in common, what a policy should do"}.

   the whole memo

   EPISODE 1 (2026-11-09). Nothing had run. Read the solved-folder README at the pinned commit (gtmcknight/amm-sim e65314f8 research/solved). Plan: run both tracks from episode 1, batches of <=5 so none holds behind a canary, spare copies on lines that need api.imd.fun or the engine, research mode throughout (code travels as code_zb64 + sha256 in results; no code trunk yet). KEY FACTS FROM THE README: all gains are vs HA on replica seeds 10000..12047 (HA mean 529.557). IB = HA +0.59 on the engine. Reference policies on real inputs: gauss+plain +2.83, gauss+qm +3.77, lattice+qm +4.26, true-flag diagnostic +6.98 (= #1). Top-10 line is +3.25 vs HA (+2.66 vs IB); #1 about +7.0. Track A gates: fees within 0.01 bps of real.py on callbacks of replica seeds 10000..10019, afterSwap peak <= 245,000, 0 OOG, 0 revert, amm-match validate. Engine bar: +2.7 over IB on 4,000 paired seeds. Tables are 8 MB JSON: 321 w rows x (13 cols phase 0, 49 cols phases 1-2) x 8 sd levels x s/c modes; plain tables alone are ~71k cells, so compression is required to fit EIP-170 code size (24,576 bytes). Track B: misread rate 16.2% worth +2.72; already tried without gain: DP_MASK quote-zone forbidding, kappa > 1e-5, quote-aware fill likelihood, table classifier on filter features (18.8%). Fleet rules: no speed gates, 64-seed engine commands (32 on slow seats), no command over 8 minutes, never fill missing seeds, paired stats carry sum and sumsq, keep filled briefs < 2,900 chars with every placeholder (key included) in every item's vars as a string, ask for numbers not command text, results < 60,000 bytes. ENGINE SEED LEDGER (used once, never reused): 12000000..12001023 assigned to a1-ib-engine (IB alone, 4 items x 256: ib-12000000, ib-12000256, ib-12000512, ib-12000768). Per-seed IB rows from those items are the baseline half of the first port-vs-IB pairing: the first validated port must run on exactly these seeds (only seeds IB actually returned). Next free engine seed: 12001024. Replica tuning seeds in use: 60000..60511 (ideas, compression, misread), 61000..61255 (misread holdout). Holdout 10000..12047 is scored once per idea. BATCHES SPAWNED (33 items; Track A 19, Track B 14): Track A: a1-gates (2 copies: gates.py digits, contract bounds from the amm-challenge checkout, IB validate + bytecode size); a1-fixture (2 copies: deterministic recorder of real.py callbacks + fees for seeds 10000..10019, plain and qm, sha256s; the fixture itself is too big to travel, the recorder does); a1-ib-engine (4 x 256 seeds); a1-compress (5 ideas: knots, mirror, sdlevels, rivalmode, fit; pass = within 0.2 of +3.77 vs ref/gauss_qm.json); a1-port-parts (math lib x2, table packer x2); a1-port-full (2 copies, plain tables, full gate set). Track B: b1-numba (3 copies: numba replica2+latt matching gates to the digit, speed); b1-ideas (5 README ideas: posterior, twohyp, perrun, router, trained); b1-fresh (3 copies told to skip the README idea lists); b1-misread (3 feature families: amounts, hindsight, size; diagnostics only, AUC and misread on 61000..61255). NEXT EPISODE SHOULD: (1) Check a1-gates: do gates match to the digit, what are the real bounds (gas, slots, code size, solc). If gates do not match, stop Track A ports until the replica/digits question is settled. (2) From a1-compress pick the smallest table set that passes (-0.2 vs gauss_qm) and its byte count; feed the knot set into the next port batch and the table packer. (3) From a1-port-parts and a1-port-full take the best math lib and packer (lowest error, lowest gas) as the starting code_zb64 for an episode-2 port batch targeting the qm tables; if a1-port-full reached parity, run it on the engine on seeds 12000000..12001023 against the IB rows, then widen on fresh seeds from 12001024 in 256-seed items (both arms per seed range in the same item from then on, so the pair never depends on two items). Check the replica-vs-engine consistency rule: port gain over IB on the engine should be within 0.2 of (its replica gain over HA minus 0.59). (4) From b1-ideas/b1-fresh: any idea with holdout mean_diff >= +0.3 vs ref/lattice_qm.json (se small) goes to Track A's next port and gets a confirmation item; ideas between 0 and 0.3 get one tuning item; negative ones are dropped unless notes argue a clear fix. Combine b1-misread findings (the top feature) into a new idea item. (5) Use the numba port from b1-numba (if gates_match) as the runner in all later Track B briefs to cut holdout time. (6) Budget: 207 items left after this episode, 19 episodes; aim ~25-40 items per episode, widen only briefs that worked. Track A and B stay about even until a port passes the engine; then shift to A (engine confirmation to 4,000 seeds) and B-to-A transfer. WATCH FOR: items refused for brief length or missing placeholders (every brief here was checked under 2,900 filled chars, all placeholders present); seats that cannot reach api.imd.fun (a1-gates, a1-ib-engine) or are not Linux x86_64 (they report engine unavailable; that is fine for gates/fixture); results over 60,000 bytes (code_zb64 too big: ask for scripts only, no tables).

   1 step

   a1-gates, item by itema1-ib-engine, item by itema1-compress, item by itema1-port-parts, item by itema1-port-full, item by itemb1-numba, item by itemb1-ideas, item by itemb1-fresh, item by itemb1-misread, item by item

2. Round 2a2-engine-plain4 of 4 backa2-port-qm2 of 2 backa2-latt-gas1 of 1 backb2-blend2 of 2 backb2-revise1 of 1 backb2-oracle-gap1 of 1 backb2-fresh2 of 2 back

   Agent #2 planned it.

   what a2-engine-plain was asked

   Track A engine pairing: port P2 and IB, each alone against VanillaStrategy, on engine seeds {{from}}..{{to}} (256 seeds, yours alone; use no seed outside this range; never fill a missing seed). Engine: git clone https://github.com/benedictbrady/amm-challenge and check out 6b6921f8d463314aa7c96234e1be25a25245f704; from the checkout root: mkdir -p scratch && curl -LsSf https://raw.githubusercontent.com/gtmcknight/amm-sim/main/research/setup.sh -o scratch/setup.sh && sh scratch/setup.sh; prefix later commands with `. scratch/env.sh && `. If a setup step fails, set engine:"unavailable" and blocked:{step,error} (the step and its error message), report what you have and finish; do not test the machine first. Contracts: IB = field code_zb64 (base64, then zlib; one .sol, contract inputs_b) of https://api.imd.fun/artifacts/afd5183e9ff3235334e041c00b0cd19079599a2c6464db95f8c1586712105557. P2 = field code_zb64 (base64, then zlib; a tar whose README names its Strategy .sol) of https://api.imd.fun/artifacts/679e9ec3e69ce093fb9ba56944ad799079b2a311ad4bea0521c2f7b15aa626d6, a Gaussian-filter port with plain tables. Run amm-match validate on both; a failure is reported, not fixed. Configs per MatchRunner._build_configs with the loop index replaced by the seed: np.random.default_rng(seed=seed), uniform draws in order retail_mean_size, retail_arrival_rate, gbm_sigma from BASELINE_VARIANCE, other fields from the base config, SimulationConfig(seed=seed). For each arm run amm_sim_rs.run_batch(X, VanillaStrategy, configs, 8) in 64-seed blocks (32 on a slow seat), gas_stats_reset() before each block and gas_stats() after; score = result.edges['submission']. Both arms on exactly the same seeds; if time runs short, stop both arms at the same seed and report n. Scores vary by 90 across seeds, so only the per-seed difference matters. Result, one JSON object under 60,000 bytes: key "{{key}}", seeds{from,to}, ib{n,sum,sumsq,rows:[[seed,score],...]}, port{n,sum,sumsq,rows}, paired{n,mean_diff,se,sum,sumsq,worst5:[[seed,diff]x5]} over port minus IB per seed, gas{ib:{peak_afterswap,mean_afterswap,oog,reverts},port:{same}}, valid (false on any port OutOfGas or revert or a port afterSwap peak over 245,000), ib_sum_check (earlier IB sum on this range over six-decimal rows: {{ibsum}}; "none" means no earlier run; answer "equal", "differs by <x>" or "none"),block_seconds (per block), engine ("ok"|"unavailable"), blocked (null or {step,error}), notes (under 1,500 characters: what you ran, anything odd). Numbers only, no command output.

   what a2-port-qm was asked

   Track A, the qm port, copy {{copy}} of 2 (the other copy attempts the same; do all of it yourself). Get the solved folder: B=https://raw.githubusercontent.com/gtmcknight/amm-sim/e65314f88de945f6d055bfc2aab01c482ab6570e/research/solved; curl -LsSf $B/fetch.sh -o fetch.sh && sh fetch.sh $B scratch/solved; read scratch/solved/README.md (its scripts run as python3 -I from that folder). Start from P2, an accepted port of real.py soft=1 with plain tables: field code_zb64 (base64, then zlib; a tar with source, tests, recorder, table packer, README) of https://api.imd.fun/artifacts/679e9ec3e69ce093fb9ba56944ad799079b2a311ad4bea0521c2f7b15aa626d6 (parity 3e-9 bps on 212,490 callbacks, peak 145,062 gas, runtime 22,567 bytes). Make it the Gaussian + qm policy (run.py gauss with soft=1 qm=qm_k1e-5.json, +3.77 over HA): qm has 8 sd levels and s/c rival modes, so it must be compressed to fit. Choose w knots, an sd-level subset and a rival-mode treatment (ideas: P2's knots, the mirror ask(w,R)=bid(-w,-R), 3 or 4 sd levels with linear interpolation, one rival mode or c-minus-s residuals); expand the candidate back to the shipped grid in qm_k1e-5.json format and measure its cost: python3 -I run.py gauss 60000 256 cand.json soft=1 qm=<file> against run.py gauss 60000 256 base.json soft=1 qm=qm_k1e-5.json, then pair.py cand.json base.json; the cost must be mean_diff >= -0.2. Then port it. Gates: fees within 0.01 bps of real.py (soft=1, your expanded qm file) on every callback of replica seeds 10000..10019 recorded blind; afterSwap peak <= 245,000 gas counted as the engine does (21,000 intrinsic included); no revert on stress sequences (report, do not gate); runtime <= 24,576 bytes; solc 0.8.24, viaIR, Paris, optimizer 200, no assembly, 32 slots; amm-match validate if the engine installs (clone github.com/benedictbrady/amm-challenge at 6b6921f8d463314aa7c96234e1be25a25245f704; mkdir -p scratch && curl -LsSf https://raw.githubusercontent.com/gtmcknight/amm-sim/main/research/setup.sh -o scratch/setup.sh && sh scratch/setup.sh; if that fails, validate:"not run" and go on). Consume no engine seeds. If it will not fit, ship the best you reached with complete:false and say how many bytes over. Result, one JSON object under 60,000 bytes: key "{{key}}", tables:"qm", knots (w knots, sd levels, rival modes, residual format), knot_cost_paired{n,mean_diff,se,sum,sumsq}, parity{callbacks,max_fee_diff_bps,over_0_01_bps}, gas{peak_afterswap,mean_afterswap,oog,reverts}, validate ("pass"|"fail"|"not run"), stress{sequences,reverts,peak_gas}, bytes_runtime, complete (true only when every gate passed), code_zb64 (base64 of zlib of a tar: source, tests, packer, expanded-table generator, README; no table JSON), code_sha256 (of the tar), notes (under 1,500 characters: what changed, what failed, what to try next). Numbers only, no command output.

   what a2-latt-gas was asked

   Track A feasibility: can the lattice filter run in the contract? Get the solved folder: B=https://raw.githubusercontent.com/gtmcknight/amm-sim/e65314f88de945f6d055bfc2aab01c482ab6570e/research/solved; curl -LsSf $B/fetch.sh -o fetch.sh && sh fetch.sh $B scratch/solved; read scratch/solved/README.md (its scripts run as python3 -I from that folder). latt.py keeps probability mass on 121 cells of 2.5 bps and is worth +0.5 over the Gaussian filter on the replica. Contract bounds: 250,000 gas a callback including the 21,000 intrinsic cost, 32 storage slots, no inline assembly, 24,576 runtime bytes, solc 0.8.24 viaIR Paris optimizer 200. Write the lattice belief update alone in Solidity (no fee tables: it returns mean and sd in bps and pi): masses packed as fixed-point integers (16 or 21 bits a cell) in as few slots as possible; per callback: up to dt-1 silent steps (cap 12), each a convolution with the price-move kernel and zeroing of the cells where the arb would have traded, this step's convolution, the first-trade mixture (point mass at the arb reading plus the cut prior, pi as latt.py), renormalisation, mean and sd. Prove it against latt.py on callbacks recorded from replica seeds 10000..10004 run blind (timestamp, isBuy, reserves, and latt.py's mean, sd, pi at each): max abs error of mean and sd in bps and of pi. Measure gas with Foundry or pyrevm on those callbacks: peak, mean, and the peak by dt bucket (1, 2, 3-5, 6+). If the first peak is over 180,000 try at least two cheaper variants (fewer bits, a truncated kernel, a coarser silent-step update) and report each. Result, one JSON object under 60,000 bytes: key "{{key}}", slots_used, bits_per_cell, kernel_width_cells, gas{peak,mean,by_dt:{"1","2","3-5","6+"}}, variants:[{name,bits,kernel,peak_gas,max_err_mean_bps,max_err_sd_bps,max_err_pi}], err{max_mean_bps,max_sd_bps,max_pi}, bytes_runtime, feasible (true if a variant has peak <= 170,000 and max_err_mean_bps <= 0.5), code_zb64 (base64 of zlib of a tar: Solidity, tests, recorder; no large data), code_sha256 (of the tar), notes (under 1,500 characters: where the gas goes, what a full port with tables would need). Numbers only, no command output.

   what b2-blend was asked

   Track B: the branch-blend idea, variant {{variant}}. Get the solved folder: B=https://raw.githubusercontent.com/gtmcknight/amm-sim/e65314f88de945f6d055bfc2aab01c482ab6570e/research/solved; curl -LsSf $B/fetch.sh -o fetch.sh && sh fetch.sh $B scratch/solved; read scratch/solved/README.md (its scripts run as python3 -I from that folder). Found in episode 1 (result at https://api.imd.fun/artifacts/80b9f00d0d387deabd812da152d935cf049fa6654ae5e1cf43a30e16e7dd6830; its code_zb64 is base64 of zlib of a tar holding bb.py, module bb:BB, options bl=1 bw=2 qm=qm_k1e-5.json, beside latt.py, real.py and replica2.py; if the fetch fails, re-implement from this): on an ambiguous first trade of a step (0.001 < pi < 0.999) the lattice makes the phase and rival choice hard at pi > 0.5. Branch-blend instead looks up quotes under each branch, arb (price = arb reading, sd 0, phase 0, rival band-clipped to it) and retail (cut posterior mean and sd, phase 1, rival from the router identity), and quotes the weighted average of the two log bid and ask prices with weight wA = pi^g/(pi^g+(1-pi)^g), g=2. Holdout 10000..12047 vs ref/lattice_qm.json: +0.183 se 0.011; misread 16.03% vs 16.24%. Your task: {{task}} Rules: blind (run.py only); tune on replica seeds 60000..60255, confirm the chosen setting on 60256..60511 before touching the holdout; freeze the code; score the holdout 10000..12047 once, in 512-seed run.py commands (PROCS=8), and pair against {{ref}}. A bit-identical numba lattice runner exists at https://api.imd.fun/artifacts/1134b0f83ff44cf484f8dc780a5c7f0e5d08d2cbbfad3d4ee2d3578ae6fa1ac6 (its code_zb64 is base64 of zlib of a tar; fast_replica.py goes beside run.py; numpy and numba in a venv) if you want speed; pure Python is acceptable. Result, one JSON object under 60,000 bytes: key "{{key}}", idea (one line), settings_tried:[{setting,n,mean_diff,se}] on tuning seeds, confirm_paired{n,mean_diff,se}, holdout_paired{n,mean_diff,se,sum,sumsq,worst5}, misread_rate{base,mine}, price_error_bps{base,mine}, gas_estimate (extra state and ops per callback, rough gas), code_zb64 (base64 of zlib of a tar of your module and a README), code_sha256 (of the tar), notes (under 1,500 characters: what failed first, what to try next). Numbers only, no command output.

   what b2-revise was asked

   Track B: revise the state when a later callback flips the label. Get the solved folder: B=https://raw.githubusercontent.com/gtmcknight/amm-sim/e65314f88de945f6d055bfc2aab01c482ab6570e/research/solved; curl -LsSf $B/fetch.sh -o fetch.sh && sh fetch.sh $B scratch/solved; read scratch/solved/README.md (its scripts run as python3 -I from that folder). Episode-1 diagnostics (replica seeds 60000..60511 train, 61000..61255 test): the lattice misreads 16.2% of first trades of a step (9.7% retail read as arb, 6.5% arb read as retail). Forking the lattice at the first trade into an arb branch and a retail branch and scoring the next own callbacks (the log predictive-likelihood difference, AUC 0.86 alone, 0.95 with the filter's features) cuts the misread in hindsight to 14.9/13.1/12.0% after 1/2/3 callbacks. The forward price estimate gains nothing from that (the lattice mixture already carries it), but latt.py never revises what the hard read set: the rival spot clip and synced flag, the phase and the count. Your idea, measured alone: keep two branches after an ambiguous first trade (0.05 < pi < 0.95); at each later callback of the same step, and at the next step's first trade, update the branch posterior with the predictive likelihoods; when it crosses 0.5, recompute s2, synced, cnt and phase as if the first trade had been read the other way and quote from the revised state (optionally pi-weighted between the two states' quotes). The price belief update stays latt.py's. Rules: blind (run.py only); tune on replica seeds 60000..60255, confirm the chosen setting on 60256..60511 before touching the holdout; freeze the code; score the holdout 10000..12047 once, in 512-seed run.py commands (PROCS=8), and pair against ref/lattice_qm.json. A bit-identical numba lattice runner exists at https://api.imd.fun/artifacts/1134b0f83ff44cf484f8dc780a5c7f0e5d08d2cbbfad3d4ee2d3578ae6fa1ac6 (its code_zb64 is base64 of zlib of a tar; fast_replica.py goes beside run.py; numpy and numba in a venv) if you want speed; pure Python is acceptable. Result, one JSON object under 60,000 bytes: key "{{key}}", idea (one line), settings_tried:[{setting,n,mean_diff,se}] on tuning seeds, confirm_paired{n,mean_diff,se}, holdout_paired{n,mean_diff,se,sum,sumsq,worst5}, flips{share_of_ambiguous,share_right} on tuning seeds, misread_rate{base,mine}, price_error_bps{base,mine}, gas_estimate (extra state and ops per callback, rough gas), code_zb64 (base64 of zlib of a tar of your module and a README), code_sha256 (of the tar), notes (under 1,500 characters: what failed first, what to try next). Numbers only, no command output.

   what b2-oracle-gap was asked

   Track B diagnostic: where the +4.44 between the lattice policy (+4.26 over HA) and perfect information (+8.70) sits, so the next ideas aim at the largest piece. Get the solved folder: B=https://raw.githubusercontent.com/gtmcknight/amm-sim/e65314f88de945f6d055bfc2aab01c482ab6570e/research/solved; curl -LsSf $B/fetch.sh -o fetch.sh && sh fetch.sh $B scratch/solved; read scratch/solved/README.md (its scripts run as python3 -I from that folder). Known: the true arb/retail flag alone takes the same code to +6.98 (real.py flag:oraclearb=1); perfect price with the rival tracked from own trades +8.50. Build oracle variants of latt.py (qm=qm_k1e-5.json) that read one true quantity from the replica callback, labelled as diagnostics (run.py's blindness is bypassed only here): (a) true flag only, (b) true price at the first trade of a step only, the flag still inferred, (c) true price at every own callback, (d) true rival spot only, (e) true flag plus true rival, (f) true flag plus true phase and count, (g) lattice as is. Run each on replica seeds 60000..60511 (PROCS=8, 128-seed commands) and pair each against (g) on the same seeds; also pair (g) against run.py perfect there. A bit-identical numba lattice runner exists at https://api.imd.fun/artifacts/1134b0f83ff44cf484f8dc780a5c7f0e5d08d2cbbfad3d4ee2d3578ae6fa1ac6 (its code_zb64 is base64 of zlib of a tar; fast_replica.py goes beside run.py; numpy and numba in a venv) and exposes perfect and lattice modes; use it if it saves time; pure Python is acceptable. If 512 seeds do not fit the hour, run fewer and report n; never touch 10000..12047. Result, one JSON object under 60,000 bytes: key "{{key}}", n_seeds, variants:[{name,inputs,mean_vs_lattice,se,sum,sumsq,misread_rate,price_rms_bps}] for (a) to (f) and perfect, decomposition (one paragraph: how much of the gap is the flag, the price given the flag, the rival, the phase; which parts afterSwap inputs could reach and which they cannot), code_zb64 (base64 of zlib of a tar of the oracle module and runner script), code_sha256 (of the tar), notes (under 1,500 characters). Numbers only, no command output.

   what b2-fresh was asked

   Track B: arb or retail, from the question alone, copy {{copy}} of 2 (the other copy works independently; do not coordinate). Get the solved folder: B=https://raw.githubusercontent.com/gtmcknight/amm-sim/e65314f88de945f6d055bfc2aab01c482ab6570e/research/solved; curl -LsSf $B/fetch.sh -o fetch.sh && sh fetch.sh $B scratch/solved; read scratch/solved/README.md (its scripts run as python3 -I from that folder). The lattice filter (latt.py with qm_k1e-5.json) decides whether the first own trade of a step is the arb or a retail order and is wrong 16.2% of the time; with the true flag the same code scores +2.72 more (ref/flag_qm.json vs ref/lattice_qm.json), the distance from 7th to the top two. Skip the README's idea list and these, already measured: per-run sigma and rate estimates inside the classifier (+0.02), a router likelihood per price hypothesis (+0.09, about 1M gas), quoting a blend of the arb-branch and retail-branch quotes (+0.18, kept), trade size, timing and exact-amount features (redundant: AUC 0.92 with or without them; misreads are small fills under 8 bps near the quote edge, 24.7% misread in the smallest size quartile and 5.4% in the largest, worst at dt=2), hindsight relabelling (fixes the past estimate, not the forward one). Find one idea of your own that reads afterSwap inputs only, state it in one line, and measure it alone. Rules: blind (run.py only); tune on replica seeds 60000..60255, confirm the chosen setting on 60256..60511 before touching the holdout; freeze the code; score the holdout 10000..12047 once, in 512-seed run.py commands (PROCS=8), and pair against ref/lattice_qm.json. A bit-identical numba lattice runner exists at https://api.imd.fun/artifacts/1134b0f83ff44cf484f8dc780a5c7f0e5d08d2cbbfad3d4ee2d3578ae6fa1ac6 (its code_zb64 is base64 of zlib of a tar; fast_replica.py goes beside run.py; numpy and numba in a venv) if you want speed; pure Python is acceptable. Result, one JSON object under 60,000 bytes: key "{{key}}", idea (one line), settings_tried:[{setting,n,mean_diff,se}] on tuning seeds, confirm_paired{n,mean_diff,se}, holdout_paired{n,mean_diff,se,sum,sumsq,worst5}, misread_rate{base,mine}, price_error_bps{base,mine}, gas_estimate (extra state and ops per callback, rough gas), code_zb64 (base64 of zlib of a tar of your module and a README), code_sha256 (of the tar), notes (under 1,500 characters: what failed first, what to try next). Numbers only, no command output.

   the whole memo

   EPISODE 2 (2026-11-09). 22 results read; a1-compress (5), b1-ideas posterior/twohyp/trained and b1-fresh/fresh-1 still running. 31 items used before this episode, 13 spawned now (44 of 240), 18 episodes left. HOW CODE TRAVELS: every result is served byte-for-byte at https://api.imd.fun/artifacts/<sha256 of the result file> (the sha the episode's input list gives; verified this episode for three results). A new item starts from an earlier result by fetching that URL and decoding code_zb64 (base64, then zlib, a tar). Briefs can therefore chain work without a code trunk. Keep that URL form; a fixture too big for a result still cannot travel. TRACK A FINDINGS. Gates: both copies match the README digits (gates.py 7 s). Bounds (from the amm-challenge checkout): 250,000 gas per afterInitialize and per afterSwap INCLUDING the 21,000 intrinsic cost (so the 245,000 target leaves ~224,000 of execution), getName 50,000, deploy 10,000,000; 32 storage slots; no inline assembly; runtime 24,576 bytes; solc 0.8.24, optimizer 200, viaIR, evmVersion paris; contract named Strategy inheriting AMMStrategyBase, imports only ./AMMStrategyBase.sol and ./IAMMStrategy.sol; bytecode scan rejects BALANCE, EXTCODE*, CREATE*, CALL*, DELEGATECALL, STATICCALL, SELFDESTRUCT; source regex bans .call/.send/.transfer/coinbase/custom interfaces. IB: 14,528 runtime bytes, afterInitialize 243,227 gas, afterSwap peak 234,958-237,009 on the engine, mean 135,200. IB ENGINE BASELINE: 768 of 1,024 seeds done. ib-12000000: n 256 sum 134918.570353 sumsq 73255977.563357 (mean 527.03); ib-12000512: sum 136071.26244 sumsq 74524616.540014 (531.53); ib-12000768: sum 136903.691024 sumsq 75420076.107479 (534.78). Pooled mean 531.11, sd across seeds 92.4: only PAIRED per-seed differences carry signal. ib-12000256 (seeds 12000256..12000511) returned nothing: the worker read "Linux x86_64 only; elsewhere set engine unavailable" as an order to inspect its machine and refused. NEVER write a platform condition in a brief: say "run the setup; if a step fails report blocked{step,error}". Those 256 seeds were not consumed. PORT: a1-port-full/port-2 = P2 (result sha 679e9ec3e69ce093fb9ba56944ad799079b2a311ad4bea0521c2f7b15aa626d6) is a complete plain-table port of real.py soft=1: 71 w knots, all 13/49 rival columns, mirrored bid, int16 residuals; parity max 3.4e-9 bps over 212,490 callbacks of seeds 10000..10019; peak 145,062 gas, mean 49,544 (pyrevm, engine accounting); runtime 22,567 bytes; validate pass; knot cost -0.008 se 0.009 on 60000..60255; stress 64 sequences 0 reverts. port-1 failed parity (58 bps) and is dropped. Parts: math-2 (sha 28f7ff6aca0c48b081f6c1461973a94721dd34429012d1f267450f5cdde7cba6, Q30, trunc moments 18k gas, 4,161 bytes) beats math-1 (truncNormal 147k gas); tables-1 (sha d5e0e4e9...) packer 19k gas/lookup, tables-2 (sha 1c57f8b7...) 42k. P2 already carries its own math and packer, so parts are spares. Plain tables expected on the engine: +2.83-0.59 = +2.24 over IB, below the +2.7 bar; the qm tables (+3.77 replica, +3.18 expected over IB) are needed and must be compressed from 8 sd levels x s/c modes into the ~2,000 bytes P2 leaves, or P2 must shrink. TRACK B FINDINGS. numba: all three match gates; b1-numba/numba-3 (sha 1134b0f83ff44cf484f8dc780a5c7f0e5d08d2cbbfad3d4ee2d3578ae6fa1ac6) is the one to use: whole replica + lattice + RNG in numba, bit-identical to pure Python on all 2,048 holdout seeds, 0.0086 s/run, modes perfect|lattice, fast_replica.py beside run.py, numpy+numba venv. It found ref/lattice_qm.json differs from pure Python by >1e-6 on 34/2048 seeds (max 0.074, another libm): pair.py against ref is still the rule, but a 0.07 single-seed wobble is noise. Ideas on holdout 10000..12047 vs ref/lattice_qm.json: perrun +0.022 se 0.0075 (drop); router alpha=0 only, 0.000 (nonzero alpha -15 on 8 seeds; drop); fresh-2 BRANCH-BLEND +0.183 se 0.011 (sha 80b9f00d0d387deabd812da152d935cf049fa6654ae5e1cf43a30e16e7dd6830: on ambiguous first trades quote the pi^2-weighted log-blend of the arb-branch and retail-branch quotes; +20-30k gas only on those calls; best so far, under the 0.3 bar); fresh-3 RouterEvidence +0.089 se 0.010 (0.3-1.5M gas, drop for ports). Misread diagnostics (three families agree): misreads are small fills near the quote edge (24.7% misread in the smallest size quartile, 5.4% in the largest; d<4 bps 24.8%, d>23 bps 4.5%; dt=2 worst at 20.6%); size, timing and exact-amount features add nothing (AUC 0.92 either way, 2D tables flip no cell); hindsight forking cuts misread to 11.8% but the forward price estimate does not improve (lattice mixture already holds it); the untested levers are revising rival clip/synced/phase when a later callback flips the label, and quotes that make the next arb separate the hypotheses (that is b1-ideas/idea-twohyp, still running). DECISIONS THIS EPISODE (13 items). Track A (7): a2-engine-plain, 4 items x 256 seeds on 12000000..12001023, BOTH arms (IB and P2) in the same item, 64-seed blocks, paired stats, and an ib_sum_check against the earlier IB sums (determinism check; seeds 12000256..12000511 have none). Expected: P2 over IB = +2.24 +-0.2 if replica gaps carry. a2-port-qm, 2 copies: P2 plus compressed qm tables, cost >= -0.2 on 60000..60255 vs gauss qm, full gate set. a2-latt-gas, 1 item: the lattice belief update alone in Solidity, gas by dt bucket, feasible if a variant peaks <= 170,000 with mean error <= 0.5 bps (decides whether the next port can be the lattice, +3.67 expected over IB). Track B (6): b2-blend/blend-tune (weight schedule, fee blend, per-branch modes) and blend-gauss (the blend on the Gaussian filter vs ref/gauss_qm.json: the B-to-A transfer test); b2-revise (state revision on label flip); b2-oracle-gap (oracle ablations on 60000..60511: flag, price, rival, phase, to locate the remaining +4.44); b2-fresh, 2 copies told to skip the README list and the episode-1 results. SEED LEDGER. Engine: 12000000..12001023 assigned (IB rows on 768 of them; a2-engine-plain reruns IB and runs P2 on all 1,024). Next free engine seed: 12001024. Replica: tuning 60000..60255, confirmation 60256..60511, diagnostics 60000..60511 and 61000..61255; holdout 10000..12047 scored once per idea. EPISODE 3 SHOULD: (1) Pool a2-engine-plain: sum the per-item paired sum/sumsq, mean_diff and se on up to 1,024 seeds; check ib_sum_check says equal on the three known ranges. If P2-IB is within 0.2 of +2.24 the engine pipeline is trusted; if not, spawn a diagnosis item (per-seed replica-vs-engine trace of P2 on the worst seeds) before any more engine work. Record port gas on the engine. (2) From a2-port-qm and a1-compress pick a qm port with complete:true; if none, combine the best compression (smallest set within -0.2) with P2 in a new 2-copy port batch, passing both result URLs. When one passes, run it on fresh seeds from 12001024 in 256-seed both-arm items (4 items first, then widen to 4,000 seeds over two episodes if the first 1,024 show >= +2.5). (3) Track B: holdout >= +0.3 vs its ref goes into the next port (blend-gauss tells whether branch-blend transfers to the Gaussian filter; if it does at >= +0.15 there, add it to the qm port as an option anyway, it is cheap); 0 to 0.3 gets one tuning item; read idea-posterior and idea-twohyp first, they are the two README ideas most likely to pay. (4) If a2-latt-gas says feasible, spawn a lattice port (2 copies) from P2 plus the lattice update code. (5) Budget: 196 items after this episode; keep ~12-20 per episode, widen only engine pairing of a port that passed its gates. WATCH FOR: items that fail because api.imd.fun was unreachable (give the line a spare next time); workers who refuse on platform grounds (brief wording); results over 60,000 bytes; the plane's 'sources flagged' marks on most results (no effect seen, nothing to do). a1-fixture was refused in episode 1 for asking for raw command output; the recorder now lives inside P2's tar, so no fixture batch is needed.

   1 step

   a2-engine-plain, item by itema2-port-qm, item by itema2-latt-gas, item by itemb2-blend, item by itemb2-revise, item by itemb2-oracle-gap, item by itemb2-fresh, item by item

3. Round 3a3-eng-qm2-a4 of 4 backa3-eng-qm2-b4 of 4 backa3-port-blend2 of 2 backa3-latt-swar1 of 1 backb3-mix20 of 1 backb3-blend-revise1 of 1 backb3-fresh31 of 1 back

   Agent #2 planned it.

   what a3-eng-qm2-a was asked

   Track A engine pairing: port Q2 and IB, each alone against VanillaStrategy, on engine seeds {{from}}..{{to}} (256 seeds, yours alone; use no seed outside this range; never fill a missing seed). Engine: git clone https://github.com/benedictbrady/amm-challenge and check out 6b6921f8d463314aa7c96234e1be25a25245f704; from the checkout root: mkdir -p scratch && curl -LsSf https://raw.githubusercontent.com/gtmcknight/amm-sim/main/research/setup.sh -o scratch/setup.sh && sh scratch/setup.sh; prefix later commands with `. scratch/env.sh && `; if a step fails, report blocked{step,error} and whatever you could still do. Contracts: IB = field code_zb64 (base64 of zlib of IB.sol, contract inputs_b) of https://api.imd.fun/artifacts/afd5183e9ff3235334e041c00b0cd19079599a2c6464db95f8c1586712105557. Q2 = https://api.imd.fun/artifacts/f6f4c4cccbd3fd9b0d7a4c4c329011ba1c765a10cc192b1402434033bdd10402: its JSON is {"qm-2": {...}}; inside that object code_zb64 is base64 of zlib of a tar holding the Solidity port (contract Strategy; its README names the file). Compile both unchanged with the engine's own compiler (amm_competition.evm.compiler.SolidityCompiler) and run amm-match validate on each. Configs: exactly MatchRunner._build_configs with the loop index replaced by the seed s: np.random.default_rng(seed=s), uniform draws in order retail_mean_size, retail_arrival_rate, gbm_sigma from BASELINE_VARIANCE, other fields from the base config, SimulationConfig(seed=s). Run amm_sim_rs.run_batch(strategy_bytecode, vanilla_bytecode, configs, 8) in four blocks of 64 seeds per arm (eight commands, none over 8 minutes); score = edges['submission']. Call gas_stats_reset() before each block and gas_stats() after it: peak and mean afterSwap over successful Submission afterSwap calls, oog and reverts over all Submission callbacks. Result JSON (under 40,000 bytes): key, seeds{from,to}, ib{n,sum,sumsq,rows:[[seed,score],...]}, port{n,sum,sumsq,rows}, paired{n,mean_diff,se,sum,sumsq,worst5:[[seed,diff],...]} with diff = port minus IB per seed and se = sd/sqrt(n), gas{ib{peak_afterswap,mean_afterswap,oog,reverts},port{same}}, validate{ib,port} as pass or fail, block_seconds, engine 'ok' or blocked{step,error}, notes under 1,200 characters (anything odd; numbers, never command output). Report only seeds that actually ran; a block that did not run stays missing and is named in notes.

   what a3-eng-qm2-b was asked

   Track A engine pairing: port Q2 and IB, each alone against VanillaStrategy, on engine seeds {{from}}..{{to}} (256 seeds, yours alone; use no seed outside this range; never fill a missing seed). Engine: git clone https://github.com/benedictbrady/amm-challenge and check out 6b6921f8d463314aa7c96234e1be25a25245f704; from the checkout root: mkdir -p scratch && curl -LsSf https://raw.githubusercontent.com/gtmcknight/amm-sim/main/research/setup.sh -o scratch/setup.sh && sh scratch/setup.sh; prefix later commands with `. scratch/env.sh && `; if a step fails, report blocked{step,error} and whatever you could still do. Contracts: IB = field code_zb64 (base64 of zlib of IB.sol, contract inputs_b) of https://api.imd.fun/artifacts/afd5183e9ff3235334e041c00b0cd19079599a2c6464db95f8c1586712105557. Q2 = https://api.imd.fun/artifacts/f6f4c4cccbd3fd9b0d7a4c4c329011ba1c765a10cc192b1402434033bdd10402: its JSON is {"qm-2": {...}}; inside that object code_zb64 is base64 of zlib of a tar holding the Solidity port (contract Strategy; its README names the file). Compile both unchanged with the engine's own compiler (amm_competition.evm.compiler.SolidityCompiler) and run amm-match validate on each. Configs: exactly MatchRunner._build_configs with the loop index replaced by the seed s: np.random.default_rng(seed=s), uniform draws in order retail_mean_size, retail_arrival_rate, gbm_sigma from BASELINE_VARIANCE, other fields from the base config, SimulationConfig(seed=s). Run amm_sim_rs.run_batch(strategy_bytecode, vanilla_bytecode, configs, 8) in four blocks of 64 seeds per arm (eight commands, none over 8 minutes); score = edges['submission']. Call gas_stats_reset() before each block and gas_stats() after it: peak and mean afterSwap over successful Submission afterSwap calls, oog and reverts over all Submission callbacks. Result JSON (under 40,000 bytes): key, seeds{from,to}, ib{n,sum,sumsq,rows:[[seed,score],...]}, port{n,sum,sumsq,rows}, paired{n,mean_diff,se,sum,sumsq,worst5:[[seed,diff],...]} with diff = port minus IB per seed and se = sd/sqrt(n), gas{ib{peak_afterswap,mean_afterswap,oog,reverts},port{same}}, validate{ib,port} as pass or fail, block_seconds, engine 'ok' or blocked{step,error}, notes under 1,200 characters (anything odd; numbers, never command output). Report only seeds that actually ran; a block that did not run stays missing and is named in notes.

   what a3-port-blend was asked

   Track A, the blend port, copy {{copy}} of 2 (the other copy does the same; do all of it yourself). Get the solved folder: B=https://raw.githubusercontent.com/gtmcknight/amm-sim/e65314f88de945f6d055bfc2aab01c482ab6570e/research/solved; curl -LsSf $B/fetch.sh -o fetch.sh && sh fetch.sh $B scratch/solved; read scratch/solved/README.md (its scripts run as python3 -I from that folder). Start from Q2, an accepted Solidity port of real.py soft=1 with compressed qm tables: https://api.imd.fun/artifacts/f6f4c4cccbd3fd9b0d7a4c4c329011ba1c765a10cc192b1402434033bdd10402 (JSON {"qm-2": {...}}; code_zb64 is base64 of zlib of a tar: contract, scripts, Python reference, README; parity 7e-9 bps, peak 155,745 gas, 23,729 bytes, cost -0.038 vs ref/gauss_qm.json). Add the branch blend from https://api.imd.fun/artifacts/4e88413335e28582aa9e96af6520c248a03c03f6944b561bdc7cf433460e2a97 (code_zb64 the same way; README gives the formulas): on an ambiguous first trade of a step quote the pi^g-weighted log-blend of the arb-branch and retail-branch quotes, g=1.5; in Python it scored +0.319 se 0.011 over gauss+qm on 10000..12047. Steps. 1. Write the Python reference of the new policy (Q2's compressed tables plus the blend) as a run.py module and measure it with pair.py against ref/gauss_qm.json on replica seeds 60000..61023 in 256-seed commands (expect about +0.28); report cost_paired{n,mean_diff,se,sum,sumsq,worst5}. 2. Port the blend into the contract in fixed point within every bound: solc 0.8.24 viaIR optimizer 200 evm paris, contract Strategy inheriting AMMStrategyBase, no inline assembly, no CODECOPY data segment (see Q2's README on the XOR fix), runtime <= 24,576 bytes, 32 storage slots; if bytes run short drop phase-0 band columns or rival columns before any sd level and measure the cost of what you dropped. 3. Gates: fee parity with your Python reference within 0.01 bps on every callback of replica seeds 10000..10019 (report callbacks, max_fee_diff_bps, over_0_01_bps); afterSwap peak <= 245,000 including the 21,000 intrinsic; 0 OutOfGas and 0 reverts over a 64-sequence stress run; amm-match validate pass (amm-challenge checkout at 6b6921f8d463314aa7c96234e1be25a25245f704, as Q2's README describes). Consume no engine seed. Result JSON (under 50,000 bytes): key, blend{g, where applied, fixed_point}, cost_paired, parity, gas{peak_afterswap,mean_afterswap,oog,reverts}, stress{sequences,reverts,peak_gas}, validate, bytes_runtime, dropped (what and its measured cost), complete (true only when every gate passed), code_zb64 (base64 of zlib of a tar: sources, scripts, Python reference, README; no JSON tables, no build outputs), code_sha256 (sha256 of the code_zb64 string), notes under 1,500 characters (no command output). If a gate fails, still send the code and say which gate.

   what a3-latt-swar was asked

   Track A feasibility, second pass: a lattice filter that fits the callback budget. Get the solved folder: B=https://raw.githubusercontent.com/gtmcknight/amm-sim/e65314f88de945f6d055bfc2aab01c482ab6570e/research/solved; curl -LsSf $B/fetch.sh -o fetch.sh && sh fetch.sh $B scratch/solved; read scratch/solved/README.md (its scripts run as python3 -I from that folder). The episode-2 item https://api.imd.fun/artifacts/1df8af4ba8f080458a601275e5026cc8a6e522784ed7ef42701da69ac2b86671 (code_zb64 is base64 of zlib of a tar; its variants list is in the JSON) ported latt.py exactly (121 cells of 2.5 bps, 33-tap kernel): dt=1 calls 210k gas typical and 518k peak, each silent step 75-100k more, dt=0 calls 25k; coarser cells and truncated kernels cut gas 2-4x and broke parity. Bound: a callback has 250,000 gas including the 21,000 intrinsic and the fee tables take about 60,000, so the filter has about 120,000 at its peak. Try in this order, measuring each on the same callbacks (replica seeds 10000..10004, latt.py with qm_k1e-5.json run blind, as that item did): (a) SWAR: pack four 64-bit cells in one word and convolve with one MUL per four cells, kernel exact; (b) precomputed silent-step operators: the k-step diffusion kernel for k = 1..12 as constants, applied once instead of k times; (c) a quasi-stationary shape: after the first silent step replace the convolution by a parametric spread and re-expand; (d) anything of your own. Feasible means peak <= 120,000 with mean price error <= 0.5 bps on 99 percent of callbacks. Solidity bounds: solc 0.8.24 viaIR optimizer 200 evm paris, no inline assembly, 32 storage slots in all. Consume no engine seed. Result JSON (under 40,000 bytes): key, variants[{name,desc,peak_gas,mean_gas,by_dt{1,2,3-5,6+},max_err_mean_bps,max_err_sd_bps,max_err_pi,share_mean_err_le_0_5bps,bytes_runtime,slots,reverts}], best, feasible, code_zb64 (base64 of zlib of a tar: sources and scripts, no tables), code_sha256 (sha256 of the code_zb64 string), notes under 1,500 characters (where the gas goes, what to try next; no command output).

   what b3-mix2 was asked

   Track B, one idea, measured alone: a two-component Gaussian mixture price belief. Get the solved folder: B=https://raw.githubusercontent.com/gtmcknight/amm-sim/e65314f88de945f6d055bfc2aab01c482ab6570e/research/solved; curl -LsSf $B/fetch.sh -o fetch.sh && sh fetch.sh $B scratch/solved; read scratch/solved/README.md (its scripts run as python3 -I from that folder). The lattice filter (latt.py with qm_k1e-5.json) beats the Gaussian filter (real.py) by about +0.5 over HA on 10000..12047 (ref/lattice_qm.json vs ref/gauss_qm.json), but the exact lattice costs 210k-518k gas a callback and cannot be ported; the Gaussian costs about 60k. Hypothesis: the lattice's value is the two-peaked belief after an ambiguous first trade of a step (arb branch: price pinned at the band edge; retail branch: price where it was). Build a filter that keeps two Gaussians (mean, variance, weight), one per branch, each updated as real.py updates its single Gaussian under that branch's hypothesis, the weights updated by each branch's likelihood of the later observations, collapsed to one Gaussian when a weight falls under a floor or the step closes. Quote from the mixture two ways and keep the better: a moment-matched single Gaussian, or the pi^1.5 log-blend of the two branch quotes from https://api.imd.fun/artifacts/4e88413335e28582aa9e96af6520c248a03c03f6944b561bdc7cf433460e2a97 (code_zb64 is base64 of zlib of a tar; its README gives the blend). Keep the tables and the flag classifier as real.py's. Rules: a policy sees afterSwap inputs only (run.py Blind); tune on replica seeds 60000..60255 (256-seed commands, PROCS=8), confirm the chosen setting once on 60256..60511, freeze the code, then score once on 10000..12047 in four 512-seed run.py commands and pair.py against BOTH ref/gauss_qm.json and ref/lattice_qm.json. The numba runner at https://api.imd.fun/artifacts/1134b0f83ff44cf484f8dc780a5c7f0e5d08d2cbbfad3d4ee2d3578ae6fa1ac6 (code_zb64 is base64 of zlib of a tar; fast_replica.py sits beside run.py) is bit-identical to the Python replica and about 100x faster; use it if it runs for you. Result JSON (under 30,000 bytes): key, idea (one sentence), settings_tried[{setting,n,mean_diff,se}], confirm_paired{n,mean_diff,se}, holdout_vs_gauss{n,mean_diff,se,sum,sumsq,worst5}, holdout_vs_lattice{same}, misread_rate{base,mine}, price_error_bps{base,mine}, gas_estimate{extra_slots,extra_exp_ln_sqrt_per_callback,rough_gas}, code_zb64 (base64 of zlib of a tar: module, scripts, README; no tables), code_sha256 (sha256 of the code_zb64 string), notes under 1,500 characters (what failed first, what to try next; no command output). Never fill a missing seed; report what ran.

   what b3-blend-revise was asked

   Track B, one idea, measured alone: the full two-branch quote on the lattice. Get the solved folder: B=https://raw.githubusercontent.com/gtmcknight/amm-sim/e65314f88de945f6d055bfc2aab01c482ab6570e/research/solved; curl -LsSf $B/fetch.sh -o fetch.sh && sh fetch.sh $B scratch/solved; read scratch/solved/README.md (its scripts run as python3 -I from that folder). Two findings each help a little and concern the same ambiguous first trade of a step. (1) branch-blend, https://api.imd.fun/artifacts/80b9f00d0d387deabd812da152d935cf049fa6654ae5e1cf43a30e16e7dd6830: quote the pi-weighted log-blend of the arb-branch and retail-branch quotes, +0.183 se 0.011 over ref/lattice_qm.json; on the Gaussian filter a sharpened weight pi^g with g=1.5 lifted it to +0.319 (https://api.imd.fun/artifacts/4e88413335e28582aa9e96af6520c248a03c03f6944b561bdc7cf433460e2a97). (2) revise-1, https://api.imd.fun/artifacts/9049572bfda37c44c7556f7e4eb508c335e4746db61af15a345e4791a09bb185: keep both branches' states (s2, synced, cnt/phase) and lattices open after an ambiguous trade, update the branch posterior at later callbacks of the step and at the next step's first trade, and pi-weight the fees of the two branch states: +0.079 se 0.023 on 256 holdout seeds. Each code_zb64 is base64 of zlib of a tar with a README. Build ONE policy: open branches as revise-1 does, quote the sharpened log-blend of the two branch quotes (each from its own branch state and branch lattice) with weight pi^g, and keep updating pi while the branches stay open. Tune g in {1, 1.5, 2} and the ambiguity window (0.05..0.95 or 0.02..0.98) on replica seeds 60000..60255 (256-seed commands, PROCS=8), confirm once on 60256..60511, freeze the code, then score once on 10000..12047 in four 512-seed run.py commands with pair.py against ref/lattice_qm.json. A policy sees afterSwap inputs only (run.py Blind). The numba runner at https://api.imd.fun/artifacts/1134b0f83ff44cf484f8dc780a5c7f0e5d08d2cbbfad3d4ee2d3578ae6fa1ac6 (code_zb64 is base64 of zlib of a tar; fast_replica.py sits beside run.py) is bit-identical to the Python replica and about 100x faster; use it if it runs for you. Result JSON (under 30,000 bytes): key, idea (one sentence), settings_tried[{setting,n,mean_diff,se}], confirm_paired{n,mean_diff,se}, holdout_paired{n,mean_diff,se,sum,sumsq,worst5}, misread_rate{base,mine}, price_error_bps{base,mine}, gas_estimate{extra_slots,extra_lookups_per_callback,rough_gas}, code_zb64 (base64 of zlib of a tar: module, scripts, README; no tables), code_sha256 (sha256 of the code_zb64 string), notes under 1,500 characters (what each part adds, what to try next; no command output). Never fill a missing seed; report what ran.

   what b3-fresh3 was asked

   Track B: arb or retail, from the question alone. Get the solved folder: B=https://raw.githubusercontent.com/gtmcknight/amm-sim/e65314f88de945f6d055bfc2aab01c482ab6570e/research/solved; curl -LsSf $B/fetch.sh -o fetch.sh && sh fetch.sh $B scratch/solved; read scratch/solved/README.md (its scripts run as python3 -I from that folder). The lattice filter (latt.py with qm_k1e-5.json) decides whether the first own trade of a step is the arb or a retail order and is wrong 16.2 percent of the time; with the true flag the same tables score +2.72 more over 10000..12047 (61 percent of the whole gap to perfect information; the true price given the flag is the next +1.48). Misreads are small fills near the quote edge (25 percent misread in the smallest size quartile, 5 percent in the largest); the filter is already calibrated on fill size, timing, distance, side and the previous step's retail count (a trained logistic correction gained +0.02). Do not spend time on: the README's five ideas, DP_MASK quote zones, kappa, quote-aware fill likelihood, table classifiers, per-run parameter estimation, router evidence (+0.09), hindsight revision of the state (+0.08), finer lattices (0.00), posterior-value quote search (-2.9). What did help: quoting a pi-weighted log-blend of the arb-branch and retail-branch quotes on ambiguous trades (+0.18 on the lattice, +0.32 on the Gaussian). Think from the game: what else in the afterSwap stream separates the two hypotheses, what quote makes the next observation separate them, or what loss the quote should minimise under the posterior? Pick ONE idea of your own, state it in one sentence, implement it as a run.py module (a policy sees afterSwap inputs only, run.py Blind), tune on replica seeds 60000..60255 (256-seed commands, PROCS=8), confirm once on 60256..60511, freeze the code, score once on 10000..12047 in four 512-seed run.py commands with pair.py against ref/lattice_qm.json. The numba runner at https://api.imd.fun/artifacts/1134b0f83ff44cf484f8dc780a5c7f0e5d08d2cbbfad3d4ee2d3578ae6fa1ac6 (code_zb64 is base64 of zlib of a tar; fast_replica.py sits beside run.py) is bit-identical to the Python replica and about 100x faster; use it if it runs for you. Result JSON (under 30,000 bytes): key, idea, settings_tried[{setting,n,mean_diff,se}], confirm_paired{n,mean_diff,se}, holdout_paired{n,mean_diff,se,sum,sumsq,worst5}, misread_rate{base,mine}, price_error_bps{base,mine}, gas_estimate, code_zb64 (base64 of zlib of a tar: module, scripts, README; no tables), code_sha256 (sha256 of the code_zb64 string), notes under 1,500 characters (why it should work, what failed, what next; no command output). A clean negative result is accepted. Never fill a missing seed; report what ran.

   the whole memo

   EPISODE 3 (2026-11-10). 18 results read; 44 items used before this episode, 14 spawned now (58 of 240), 17 episodes left. Still running from earlier: b1-ideas/idea-twohyp, b1-fresh/fresh-1, b2-blend/blend-tune (asked for in read); a1-compress's four remaining items were cancelled (superseded by the qm ports below). TRACK A FINDINGS. (1) ENGINE PIPELINE TRUSTED: a2-engine-plain, all four items accepted, both arms on 12000000..12001023: P2 over IB pooled n 1024 mean_diff +2.2064 se 0.0497 (sum 2259.302724, sumsq 7576.407494; per item +2.263/+2.081/+2.134/+2.347). Prediction was +2.24 (replica +2.83 over HA minus 0.59), so the replica-to-engine rule holds within 0.04. ib_sum_check equal on the three ranges that had episode-1 IB rows. P2 gas on the engine: peak 179,316-193,866, mean 82,8xx; IB peak 234,958, mean 135,2xx. Expected board score of P2 = 524.2, just under 10th. (2) QM PORTS: both a2-port-qm copies complete:true. qm-2 (result sha f6f4c4cccbd3fd9b0d7a4c4c329011ba1c765a10cc192b1402434033bdd10402; NOTE its JSON is wrapped as {"qm-2": {...}}; code sha 988ef957e0f6633583384005936a94cee2816b8202feadbc2a633ee318e50c1c) is the one: s and c tables kept, 5 of 8 sd levels (0,4,12,20,34 bps), 33 w knots, 7 band + 41 rival cols, phase 2 shares phase 1 (free), 8-bit 1-bps residuals in 459 PUSH32 words; cost vs ref/gauss_qm.json -0.038 se 0.012 on 60000..61023; parity 6.8e-9 bps on 214,310 callbacks; peak 155,745 gas incl. intrinsic, mean 59,801; validate pass; stress 0 reverts; 23,729 runtime bytes (847 spare). qm-1 (sha ae6097410067e83beaf870a7f7399bba23ed7abccf8d5cc35a274f7ef7b3883a) dropped the synced flag: cost -0.11, peak 165k, 23,879 bytes; spare. Single-factor costs (1024 seeds): sd levels cost most (4 levels -0.038, 3 levels -0.098); s-only -0.021; 71 knots vs 33 -0.001. Lesson both copies hit: solc's constant optimizer turns repeated table words into a CODECOPY segment that validate flags; XOR-scramble bytes by position so every word is unique. Expected qm-2 on the engine: +3.77 - 0.04 - 0.59 = +3.14 over IB (score about 525.1, 7th-9th). (3) LATTICE IN-CONTRACT INFEASIBLE as ported (a2-latt-gas): exact latt.py port peak 518k at dt=1 (210k typical), +75-100k per silent step, dt=0 25k; coarser cells/truncated kernels cut 2-4x and break parity (5 bps cells 24 bps error); none under 170k. Suggested routes: SWAR packing, precomputed multi-step silent operators, quasi-stationary shape, or a parametric belief. (4) a1-compress/rivalmode: 13 uniform knots cost -0.27 on holdout; uniform knots lose, nonuniform (qm ports) win. TRACK B FINDINGS (all vs ref/lattice_qm.json on 10000..12047 unless said). blend-gauss (sha 4e88413335e28582aa9e96af6520c248a03c03f6944b561bdc7cf433460e2a97): branch-blend with sharpened weight pi^g, g=1.5, on the GAUSSIAN+qm policy = +0.319 se 0.011 vs ref/gauss_qm.json (tuning g: 1.0 +0.275, 1.5 +0.325, 2 +0.278, 8 +0.04; confirm +0.249); ambiguous callbacks 57% of all; est. +15-35k gas on those. FIRST B RESULT OVER THE 0.3 BAR: it goes into A's next port now. idea-posterior -2.87 (drop). idea-trained +0.023 (the filter is already calibrated on every feature tried; drop). revise-1 +0.079 se 0.023 on 256 holdout seeds (hindsight misread 16.1% -> 13.0% but forward value small; pi-blending branch-state fees was the only positive part; slow seat). fresh2-1 and fresh2-2 both chose finer lattices: +0.002 (drop; next fresh brief lists it as tried). ORACLE GAP (gap-1, 512 tuning seeds, perfect minus lattice = +4.42): true flag alone +2.715 (61%); true price given flag +1.483; rival+phase after flag+price +0.198; perfect tables vs qm +0.026. Flag inference is where the ceiling sits; price given flag second. With Gaussian+qm+blend the port tops out near +3.46 over IB (about 525.5, 7th-8th); first needs about +6.4 over IB, i.e. most of the flag gap. DECISIONS THIS EPISODE (14 items). Track A (11): a3-eng-qm2-a and a3-eng-qm2-b (4 items each, 256 fresh engine seeds each, qm-2 and IB both arms in the same item, 64-seed blocks, paired stats, gas, validate) on 12001024..12003071; a3-port-blend, 2 copies: qm-2 plus the g=1.5 branch blend in fixed point, Python reference first (cost vs gauss_qm on 60000..61023, expect about +0.28), full gate set; a3-latt-swar, 1 item: SWAR / precomputed silent operators / quasi-stationary lattice, feasible = peak <= 120k with <= 0.5 bps error on 99% of callbacks. Track B (3): b3-mix2 (two-Gaussian mixture belief: the lattice's bimodality at Gaussian gas, scored vs both refs), b3-blend-revise (branch-blend g + revise-1's open branches, one policy, vs lattice_qm), b3-fresh3 (one fresh idea, long do-not-repeat list). SEED LEDGER. Engine: 12000000..12001023 used (IB and P2); 12001024..12003071 assigned to a3-eng-qm2-a/-b (qm-2 and IB). Next free engine seed: 12003072. Replica: tuning 60000..60255, confirmation 60256..60511, port cost 60000..61023, diagnostics 60000..60511 and 61000..61255; holdout 10000..12047 once per idea. EPISODE 4 SHOULD: (1) Pool a3-eng-qm2-a/-b (n up to 2048): expect +3.14 +-0.15 over IB; if >= +2.7 the goal's bar is met on 2,048 seeds, so assign 12003072..12005119 to the same port (8 more items, two 4-item batches) to reach 4,096 seeds; if it is below +2.5 spawn a per-seed trace item (worst seeds, replica vs engine callbacks) before more engine work. (2) If an a3-port-blend copy is complete:true with cost >= +0.15 vs gauss_qm, it replaces qm-2 for every later engine item (fresh seeds only; never rerun a port on used seeds); if both failed on bytes, spawn a denser-encoding item (9-bit cells or a second XOR scheme) rather than dropping sd levels. (3) Track B: read idea-twohyp, fresh-1, blend-tune; anything >= +0.3 vs its ref goes into the next port; b3-mix2 >= +0.2 vs gauss_qm with a gas estimate under 100k means a port item; 0 to 0.3 gets one tuning item; drop the rest. If blend-tune beats g=1.5 by >= 0.1, pass its setting to the next port. (4) a3-latt-swar feasible means a lattice port item (2 copies) from qm-2 plus its filter; otherwise the lattice is closed for the contract. (5) Budget: 182 items after this episode; keep 12-18 per episode; from here engine confirmation of a passing port takes the largest share. WATCH FOR: the {"qm-2": {...}} wrapper in qm-2's result (briefs now say so); items that stall for whole episodes (a1-compress ran 2 episodes; cancel what a later result supersedes); results over 60,000 bytes (every brief caps its result and forbids tables in the tar); never write a platform condition in a brief; workers quoting command output in notes (briefs say numbers only).

   1 step

   a3-eng-qm2-a, item by itema3-eng-qm2-b, item by itema3-port-blend, item by itema3-latt-swar, item by itemb3-mix2, item by itemb3-blend-revise, item by itemb3-fresh3, item by item

4. Still out6 agents, silent 112 to 161 min
   1. mix2-1Agent #1542112m ago
   2. compress-knotsAgent #804161m ago
   3. compress-mirrorAgent #1168161m ago
   4. compress-fitAgent #747161m ago
   5. idea-twohypAgent #1781161m ago

   All 6
5. NextRound 4 plans what comes after, then the write-up.
6. Publishedafter verification

60 agents at most