arXiv:2604. 16742v2 Announce Type: replace Abstract: Scientists have long sought to accurately predict outcomes of real-world events before they happen.
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We evaluate whether AI agents can match the reported findings of published biomedical studies using public data. Existing evaluations do not consistently separate analysis from prior knowledge or retr...
BioStudyBench is a new benchmark that tests AI agents on 25 long‑horizon analysis tasks derived from biomedical studies published between July and September 2026, after the models’ knowledge cutoffs. Each task gives the agent a neutral research question but no data files, requiring it to locate and download relevant public data, search literature using tools limited to pre‑cutoff records, and perform data analysis to report findings. The benchmark shows that providing data and tools improves model performance by an average of 47 percentage points, with closed‑weight models outperforming open‑weight ones (best open‑weight 81.3% vs. best closed‑weight 94.7%).
arXiv:2608.23248v1 Announce Type: cross Abstract: Traditional clinical prediction models rely on task-specific pipelines and curated, structured data, which scale poorly and underutilize unstructured...
arXiv:2607. 28788v1 Announce Type: new Abstract: Clinical diagnosis at hospital admission must be made rapidly from limited, incomplete evidence.
ClinAgent is a conversational system that uses a ReAct-based LLM agent to retrieve and synthesize clinical trial information from multiple sources such as ClinicalTrials.gov, PubMed, and a local dataset. The agent iteratively reasons over user queries, selects appropriate tools, and refines its actions to provide grounded, up-to-date responses in natural language across multi-turn interactions. Evaluation across three phases shows that DeepSeek (thinking mode) excels in planning quality while Gemini 3.0 Flash delivers the highest overall performance and expert ratings, demonstrating the promise of agentic AI for improving clinical trial data access.
The paper introduces Tasks over Application Manuals (TAM), a benchmark designed to test long‑horizon procedural reasoning in large language models. TAM uses real‑world tasks from ICD‑10‑CM clinical coding and U.S. federal sentencing, requiring models to follow extensive, rule‑based manuals and perform interdependent steps to produce exact answers. Experiments with GPT‑5 and various prompting strategies show very low exact‑match accuracy—1% for coding and 15.5% for sentencing—highlighting a gap between current benchmarks and the ability to reliably follow complex procedures.