8 citations · 8 across the 7 of their papers we have counts for
6 papers · 1 filter
CoverSynth: Coverage-Guided Generation of Retrieval Evaluation Sets
Anahita Sharma, Apaar Shanker, Rishav Chakravarti +6
Enterprise agents need retrieval evaluation sets that expose domain-specific failures and guide developers towards efficient deployment tradeoffs. Neither expert-human-authored set…
READY or Not: Reliable Enterprise Agent Deployment
Veronica Chatrath, Bryan Zhu, Jingxuan Fan +15
An AI agent can perform well on benchmarks and still be unsuitable for deployment. Existing AI-agent benchmarks measure whether an agent can complete realistic professional work, w…
CliniCARE-Bench: Clinical Calibrated Audit of Medical Reasoning in EHR
Veronica Chatrath, Bryan Zhu, George Pu +16
Large language models perform strongly on medical knowledge benchmarks, but reliable clinical deployment requires agents to conduct defensible investigations over heterogeneous, lo…
PSEBench: A Controllable and Verifiable Benchmark for Evaluating LLMs in Patient Safety Event Triage
Keqi Han, Ryan Young, Annabel Strauss +7
Patient safety event triage, determining whether a clinical event is reportable under jurisdiction-specific policy, is a high-stakes task typically performed manually by patient sa…
Insights Generator: Systematic Corpus-Level Trace Diagnostics for LLM Agents
Akshay Manglik, Apaar Shanker, Kaustubh Deshpande +6
Diagnosing failures in LLM agents remains largely manual. Practitioners inspect a small subset of execution traces, form ad-hoc hypotheses, and iterate. This process misses pattern…
Large Language Models can Learn Rules
Zhaocheng Zhu, Yuan Xue, Xinyun Chen +4
When prompted with a few examples and intermediate steps, large language models (LLMs) have demonstrated impressive performance in various reasoning tasks. However, prompting metho…