ANTE Reviewer 1

Critical Weaknesses:

1. BAPO is not isolated. The "w/o BAPO" condition removes both the decision reward and belief-anchored grouping, so the reported drop supports the combination but not the claimed novelty of grouping by belief. Fixing the prediction loss and decision reward while only changing the advantage construction would settle this.
2. The TTO gain is close to noise and lacks the decisive control. Gains are +1.4/+2.4/+0.6, while the reported error bars come from repeated evaluation of one checkpoint, not training seeds; +0.6 is within noise. More importantly, there is no control at the same trigger positions, candidate count, and budget using random or simple selection, so it is unclear how much of the gain comes from belief ranking rather than extra sampling.
3. "World model" overstates what is modeled. What is predicted is a discrete test outcome and exit status — a result predictor, not a state model of the environment. The out-of-domain +6.2 average (MMLU-Pro +10.4) is presented with a stronger causal reading than a single belief intervention supports.
4. Theory-to-practice gap. Proposition 2 depends on inter-candidate KL, a mutual-information term, and the point-prediction error ζ; none of these are estimated empirically, so validity of the bound does not imply tightness.

Questions For Authors:

1. Can you report a BAPO isolation run: same prediction loss, same decision reward, same data and budget, only the step-level advantage construction changed? How much of the +1.8–3.0 remains?
2. For TTO, can you compare against random/simple candidate selection at identical trigger positions, K, and token budget, with variance over training seeds?
3. Can you estimate the extra terms in Proposition 2 (candidate divergence, ζ) on a small sample, to indicate whether the bound is tight enough to be informative?
4. Would you consider narrowing the "world model" framing to execution-result prediction, and qualifying the cross-domain causal claim accordingly?