arXiv:2608. 07796v1 Announce Type: new Abstract: Large language models perform strongly on medical knowledge benchmarks, but reliable clinical deployment requires agents to conduct defensible investigations over heterogeneous, longitudinal records: determining what evidence is needed, retrieving and reconciling structured and free-text data, grounding conclusions in verifiable evidence, and deferring cases that cannot be resolved reliably.
By Veronica Chatrath, Bryan Zhu, George Pu, Jingxuan Fan, Apaar Shanker, Varun Ursekar, Anahita Sharma, Jason Qin, Keqi Han, Soham Dinesh Tiwari, Soham Dan, Vijay Kalmath, Yuan Li, Daniel Yue Zhang, Chenguang Wang, Zainab Doctor, Zhijun Yin, Nigam H. Shah, Yuan Xue
The paper introduces ToxicBench, a benchmark designed to evaluate how tool‑augmented data agents handle incorrect tool outputs. By pairing clean and poisoned observations across numerical, label, schema, and retrieval errors, the authors assess both the checking process and the final answer adoption. In a 118‑task GPT evaluation, poisoning reduces task success by 26–39 percentage points, revealing that repeated poisoning leads to wrong-answer adoption even after checking, while ordinary retries help only under one‑shot poisoning. Human annotations on 200 trajectories confirm the scoring system’s reliability, showing 96% agreement with task success and supporting the benefits of retries and audit‑based adoption.
By Zifu Tao, Changqing Yin
The study evaluates an auditable patient‑timeline reconstruction system that tracks provenance, records revisions, and refuses to answer when evidence is missing. Using a synthetic corpus of 1,000 patients and 3,353 notes, two provenance‑aware Evidence Graph operators reduced graph size by 33–37% while preserving all answers across 6,813 query points; a fixed‑window baseline failed to answer over half of the points. The system’s evidence‑gating mechanisms (BioClinicalBERT and a zero‑shot LLM) responded appropriately to evidence‑unavailable controls, but performance varied on marker‑free controls, with BERT maintaining high accuracy but the LLM’s coverage dropping sharply.
"whyItMatters":"The results demonstrate that provenance‑aware evidence graphs can significantly reduce data complexity while maintaining answer integrity, highlighting a practical approach to building auditable clinical NLP systems."
TruthInsightBench is a new benchmark designed to evaluate automated scientific discovery agents by presenting them with 40 blind tasks drawn from peer‑reviewed studies across ten domains. Each task provides only a neutral objective and frozen data, withholding source conclusions, expected values, and analysis paths, forcing agents to determine which claim the data support. A fixed LLM‑based judge scores agents on evidentiary maturity across six dimensions, using 29 artifact‑grounded items, enabling fully automated, repeatable evaluation without human grading.
By Zhibo Yang, Chen Zhang, Yuewei Zhang, Hao Wang
arXiv:2608. 03744v1 Announce Type: new Abstract: Clinical decision support is moving toward committees of language-model agents deliberating on a shared workspace.
By Sebasti\'an Andr\'es Cajas Ord\'o\~nez, Agastya Munnangi, Aldo Marzullo, Felipe Ocampo Osorio, Quang Bui, Mohammad Shahin, Armaan Grewal, Emmanuel Paul Kwesiga, Anqi Peter Li, Josephine Nanyonjo, Aaditya Panchal, Arshnoor Bhutani, Nikhil Jaiswal, Milit S. Patel, Maximin Lange, Leo Anthony Celi
arXiv:2608.31016v1 Announce Type: cross
Abstract: Ambient AI scribes draft clinical notes, and published audits find their dominant error is omission: information the encounter established that the n...
By Sebastian Fox, Luke Markham, Ryan Lail, Michael Karotsieris
arXiv:2610.29812v1 Announce Type: cross
Abstract: Literature-searching language agents are becoming a standard component of scientific assistants for the life sciences, yet there is little controlled...
By Mohammed Saqlain
arXiv:2610.02588v1 Announce Type: new
Abstract: Agents are increasingly given open-ended research tasks: discovering an empirical law from self-designed experiments, improving a heuristic whose optim...
By Chengyang Shi, Xianglin Ji, Jintao Huang, Jicheng Wang, Yifeng He, Jiachen Liu
arXiv:2609.33357v2 Announce Type: replace
Abstract: Reliable automated research requires agents to vet data, verify analyses, and generate hypotheses grounded in trustworthy evidence, potentially red...
By Nan Huang, Mario Tapia-Pacheco, Kun Zhou, Yiming Huang, Kevin Jos\'e Barrientos D\'iaz, Tiffany Amariuta, Jingbo Shang
The paper introduces a joint fact‑verification score that evaluates both answers and the evidence submitted with them. On the FEVEROUS dataset, replacing the DCUF evidence with UnifEE evidence improves the strict score by about 9.6 percentage points, while answer accuracy rises only 1.96 points. The study also shows that increasing context length for large language models yields modest evidence‑gain improvements, and that detailed answer‑evidence analyses uncover patterns missed by aggregate metrics.
By Han Chen, Yingrui Li
The paper examines how AI‑written research papers often contain misattributed numerical claims. By re‑analyzing 540 judgments from a benchmark of nine AI agents on fifteen research problems, the authors manually labeled 256 claims and found that most failures stem from misalignment between the paper and the underlying work rather than outright cheating. They propose a deterministic claim‑to‑evidence checker that achieves over 90% precision and, when combined with an LLM verifier, offers a more reliable audit of AI‑generated research.
By Mosab Alfaqeeh, Omar Hourani, Muath Hourani, Hashem Alfaqeeh
arXiv:2610.41368v1 Announce Type: new
Abstract: Overly restrictive eligibility criteria contribute to widespread enrollment failure in clinical trials, yet published recommendations for how specific...
By Russell Ro, Reza Abbasi-Asl