When Agents Choose What to Observe: BEATS – Answer-Free Test-Time Selection for Verifiable-Reward Agents

Verifiable-reward methods typically evaluate an output against a fixed problem instance or an externally supplied evidence record. In partially observable agent environments, however, the policy may also control which information is acquired. Verification is therefore conditional on a policy-constructed record: a certificate can be valid relative to that record while its decision is wrong under the complete record. We study this failure surface in LegalEvidenceGym, a synthetic executable testbed that separates latent facts, available sources, the agent's acquired information state, record-entailed decisions, and structured certificates. Across six registered cost-penalized RL runs, optimized reward, certificate validity, and joint decision–certificate success increase, while decisive-evidence recall (DER) and joint correctness on controlled Evidence-Twin pairs (ETC) decrease. We call this longitudinal signature Evidence Collapse; it is consistent with shortcut learning under the optimized objective but does not identify a unique cause or a verifier error. We introduce Best-of-N Evidence-Aware Trajectory Search (BEATS), an answer-free, training-free test-time procedure. BEATS diversifies candidate information states with bounded public prefixes, ranks them using only replayable public features, and serializes a certificate entailed by the selected record. On 150 held-out episodes (75 Evidence-Twin pairs) evaluated under six frozen Qwen checkpoint conditions, BEATS@4 achieves 92.33% joint success, compared with 86.89% for the strongest same-pool, same-recovery baseline, deepest-prefix Max-Probe@4 (+5.44 points; pair-clustered 95% CI [2.44, 9.00]), while reducing mean environment investigation cost by 23.3%. These results show that terminal verification should account for how an agent constructs its information state, not only whether its final output is internally valid.