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83d5975 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 | # Phase 1: deterministic execution diagnostics
Frozen before first scientific run: `configs/phase1.json` (2026-09-16-v1).
CPU only, one numerical-library thread; no GPU and no package installation.
The existing project environment reuses base Conda site packages; each run
records actual package versions and source/config SHA256 hashes.
## Questions and interpretation
1. E1: Can a shrinking coherent model error coexist with increasing error of
the all-A/all-B execution? Evaluate actual KL and model joint TV, output TV,
and the *bound* on uniform head TV. No claim of optimal-policy performance.
2. E2: How does an actual self-generated pilot change the posterior and output?
Compare a single alternative, the two-level manuscript ladder, and a single
alternative matched in initial drift. Same w/Delta does not match KL or
drift. Show actual model statistics and retain every grid point. No expected
direction of improvement is an acceptance test.
3. E3: What does the best randomized adaptive policy attain at small n for a
fixed oracle? Compare to predefined schedules, keeping R fixed across K.
This does not optimize the outer KL neighborhood and is not an exact proof.
## Numerical safeguards
- Pre-round conditional heads are frozen within each batch. No shared latent
mixture component is sampled during execution.
- E2 uses M ~ Bin(c, p0), not target prefixes. Its pilot set is selected uniformly
using private randomness and is not part of the delivered output; this
symmetry is essential for count TV to equal full TV.
- E3 must pass an independent literal-coordinate LP check and the n=1 private
randomization regression. Flow, objective, recovered-policy and dual residuals
are reported. Solver output remains a floating-point diagnostic.
- Main calculations are deterministic finite sums, not histogram TV estimates;
floating-point and normalization checks are reported, not mislabeled exact
arithmetic. There are no Monte Carlo confidence bands in this phase.
- No neural-language-model or practical-context-length claims follow.
- Resource caps: 120 s per LP solve, 250,000 state-actions per case; whole study
45 min timeout, 8 GiB address-space cap. Stop/report failures rather than omit.
- This phase does not include the optional biased model or large-n stress tests.
## Plotting
Visual style reference, accessed 2026-09-16:
https://github.com/ChenLiu-1996/figures4papers/
Use blue #0F4D92, red #B64342, teal #42949E, violet #9A4D8E;
sans-serif labels, frameless legends, no upper/right spines, redundant line styles,
white background, vector PDF and 300-dpi PNG. Scientific calculations and
plotting implementations are independent of the reference repository.
Do not use decorative uncertainty bands for deterministic calculations.
## Outputs and versioning
Each invocation creates a new results/run-* directory. Do not overwrite earlier
results. `status.json` gives actual completion/failure; source hashes identify
the implementation. Results are exploratory finite diagnostics, not a
preregistration or a claim that this is the final paper-ready experiment set.
The manuscript is not modified by this runner.
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