LLM Judges Verify Presence, Not Absence: Omission Blindness in AI Clinical Notes and What Recovers It

arXiv:2608.31016 · cs.CL, cs.AI · Submitted 2026-08-31 · Read on arXiv

cs.CL, cs.AI

Submitted: 2026-08-31

Updated: 2026-08-31

Code: https://github.com/composo-ai/omission-bench

Terminology

• Trace — 91× in this paper · explained in 2 episodes
• Benchmark — 81× in this paper · explained in 2 episodes
• Verification — 36× in this paper · explained in 2 episodes
• Calibration — 26× in this paper · explained in 7 episodes
• Ablation — 18× in this paper · explained in 1 episode
• Abstention — 13× in this paper · explained in 1 episode
• Core — 12× in this paper · explained in 2 episodes
• Fidelity — 9× in this paper · explained in 2 episodes
• Provenance — 8× in this paper · explained in 3 episodes
• AUC — 7× in this paper · explained in 2 episodes
• LLM judge — 6× in this paper · explained in 2 episodes
• Actor — 5× in this paper · explained in 3 episodes
• Accuracy — 4× in this paper · explained in 1 episode
• Hallucination — 4× in this paper · explained in 3 episodes
• Redundancy — 4× in this paper · explained in 2 episodes
• Clustering — 3× in this paper · explained in 2 episodes
• Consistency — 3× in this paper · explained in 3 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Adam — 2× in this paper · explained in 2 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Aggregation — 2× in this paper · explained in 1 episode
• Transformer — 2× in this paper · explained in 2 episodes
• Verifier — 2× in this paper · explained in 2 episodes
• Alignment — 1× in this paper · explained in 1 episode
• Allocator — 1× in this paper · explained in 1 episode
• Anti-correlation — 1× in this paper · explained in 3 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• LLM-as-a-Judge — 1× in this paper · explained in 3 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes

Sources

• GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning
• CARE: A Conformal Safety Layer for Medical Summarization
• Judging Is Not Enumerating: Silent Omissions in LLM-Authored Acceptable Sets
• Same Verdict, Different Reasons: LLM-as-a-Judge and Clinician Disagreement on Medical Chatbot Completeness
• AbsenceBench: Language Models Can't Tell What's Missing
• Blending Human and LLM Expertise to Detect Hallucinations and Omissions in Mental Health Chatbot Responses
• Extrinsically-Focused Evaluation of Omissions in Medical Summarization
• Beyond Literal Summarization: Redefining Hallucination for Medical SOAP Note Evaluation

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