arXiv AI
BABA-Bench: Evaluating Long-Horizon Patient-Advocacy Agents
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Oct 29
BABA-Bench: Evaluating Long-Horizon Patient-Advocacy Agents
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 AI
Jul 29
PatientAgentBench: A Benchmark Framework for Evaluating Patient-Facing Health AI Agents
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 AI
Sep 10
When Agent Governance Helps
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 AI
Oct 1
CHI-Bench: Can AI Agents Automate End-to-End, Long-Horizon, Policy-Rich Healthcare Workflows?
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,...
arXiv AI
Sep 15
Same Patient, Different Order: Action-Level Reliability of Clinical LLM Agents Under Repeated Runs
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 AI
Jul 29
HANDBOOK.md: A Benchmark for Long-Context Agentic Instruction Following
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.