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BRIDGE is a model‑agnostic acceptance gate that filters model updates by cross‑fitting and bootstrap bounds, aiming to prevent false improvements that arise when validation gains do not translate to new data. Across multiple settings—including 11 new datasets, synthetic tasks, 12 retrospective clinical tasks, and 39 supporting datasets—BRIDGE reduces false‑improvement rates from roughly 19–25% with a naive rule to as low as 0–1.1%. The method also yields higher mean gains per deployed update, achieving 1.8 to 3.1 times the naive rule’s performance.
The study evaluates whether reward‑trained language models can learn to keep evidence‑sufficiency boundaries without a formal correctness signal. Using a controlled knock‑in design, a boundary‑keeping recipe (SFT followed by GRPO) dramatically increased spontaneous correct refusals from 25 % to 100 %, while a scrambled‑pairing placebo collapsed performance. The results show that final boundary‑keeping is trainable without an inference‑time verifier, though active numeric checking remains negligible.
arXiv:2608. 07813v1 Announce Type: new Abstract: An LLM judge deployed inside a reasoning pipeline does not merely measure quality, it decides which answer ships.
The paper presents a system for the MedReason 2026 challenge that tackles both multiple‑choice and open‑ended medical visual question answering using offline, containerized inference. Key findings include that comparing answer semantics rather than labels boosts retrieval‑only accuracy from 20.0 % to 57.5 % on a 200‑case holdout, and that varying the number of in‑prompt retrieved examples has minimal impact on final accuracy (93.5 %–94.0 %). The final system achieves 94.0 % MCQ accuracy on the development set and 93.20 % on the official pre‑evaluation, far surpassing the off‑the‑shelf baseline. "whyItMatters":"The results demonstrate that semantic‑aware retrieval and careful adapter tuning can dramatically improve medical VQA performance, offering a practical approach for high‑accuracy, offline inference in clinical settings."
arXiv:2608. 14551v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used for title-abstract screening in systematic reviews, but their decisions lack calibrated uncertainty.
The paper introduces a benchmark of 400 synthetic healthcare workflow episodes to evaluate large language model (LLM) agents’ reliability in escalating care when evidence is missing or conflicting. It finds that while clean decision accuracy is high (0.97), models miss a large proportion of necessary escalations (32–97%) under uncertainty, and even the largest 9.7B model’s performance drops to 0.78 in conflict scenarios. A workflow-aware controller improves utility and reduces missed escalations, but its effectiveness is limited by lexical generalization failures.