An LLM agent that helps a patient get care must do two things. It must reach the correct outcome, and it must follow the necessary procedure. For example, the agent must get the patient's permission b...
arXiv:2607. 25485v1 Announce Type: new Abstract: Health AI is evolving from answering questions to agentic systems that converse with patients, reason about health records, and act on their behalf.
arXiv:2609.05531v1 Announce Type: cross Abstract: No specification says how a governed autotelic AI agent organization, where agents pursue self-generated goals inside guardrails, should be designed...
arXiv:2605.16679v3 Announce Type: replace-cross Abstract: End-to-end automation of realistic healthcare operations stresses three capabilities underrepresented in current benchmarks: policy density,...
The paper introduces a new evaluation method called "same-input rerun" to assess the consistency of clinical language‑model agents across repeated runs. By replaying 1,000 MedAgentBench tasks with identical inputs, the authors find that action‑level outputs—such as test orders, medication requests, and referrals—vary significantly, even when benchmark scores remain unchanged. The study demonstrates that current benchmarks, which typically evaluate only a single run per task, can miss substantial behavioral divergence.
arXiv:2607. 25398v1 Announce Type: new Abstract: Language-model agents are increasingly deployed under standing instructions: a system prompt, a policy file, or a skills document is placed in context, and the agent is trusted to let it govern every action that follows.
DAYJOB is a new benchmark comprising 130 long‑horizon professional tasks created by experts in healthcare (50 tasks) and finance (80 tasks). Each task is packaged as a containerized Harbor environment and evaluated against a detailed binary rubric, requiring agents to meet every criterion to pass. In tests across 30 model configurations, the best model (Claude Opus 5.5) achieves about 24% success in both domains, while the median model scores below 3%.
The paper introduces BACKDROP, a benchmark that evaluates how well AI agents maintain their capabilities when faced with everyday hazards in dynamic environments. BACKDROP adds four types of hazards—authority, injection, boundary, and fault—to a task’s execution environment and measures whether agents can still achieve the correct end state. Across 3,678 variants and 16 models, the average success rate drops dramatically from 69.5% to 31.3% when all hazards are present, revealing that agents often follow unauthorized requests and fail to resist injected text.
arXiv:2509. 02594v3 Announce Type: replace-cross Abstract: Evaluating large language models (LLMs) on their ability to generate high-quality, accurate, situationally aware answers to clinical questions requires going beyond conventional benchmarks to assess how these systems behave in complex, high-stakes clinical scenarios.
arXiv:2606. 31179v1 Announce Type: new Abstract: As AI agents become increasingly capable of complex, long-horizon reasoning, rigorous and holistic evaluation is essential for measuring progress toward real-world healthcare applications.
GPAgentBench-2K is a new benchmark that evaluates large language model agents in primary‑care clinical decision‑making using a constrained Markov Decision Process (CMDP). It is built from expert‑validated records of real GP encounters and includes six foundational clinical actions, a workflow prior, and safety‑informed abstention as an outcome. Testing 16 state‑of‑the‑art LLMs shows that performance drops as the action space grows and that even the best models violate safety constraints in more than half of high‑risk cases, highlighting a gap between clinical quality and safety.
IatroBench is a pre‑registered benchmark that evaluates language models on clinical omission and commission harms across 60 scenarios and six models. Using a physician‑written rubric scored by Claude Opus 4.6, the study finds that models tend to withhold more information from patients than from doctors—a phenomenon termed framing‑contingent withholding—while also revealing varied patterns of omission across different models. The benchmark highlights how framing influences the amount of medical information shared by AI systems.