EVA-Bench supplies a simulation engine for bot-to-bot voice dialogues plus two composite metrics (EVA-A for accuracy, EVA-X for experience) evaluated on 213 enterprise scenarios, showing no tested system exceeds 0.5 on both pass@1 scores.
Beyond task completion: Revealing corrupt success in LLM agents through procedure-aware evaluation
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A closed-loop trading benchmark with a five-axis capability scorecard shows that LLM agents' returns rank poorly against diagnostic measures of their reasoning, with the nominal Sharpe winner exposed as a reliability artifact.
False success occurs in 3-76% of LLM agent failures; LLM judges reach at most 0.65 AUROC while TF-IDF detectors reach 0.83-0.95 and recover 4-8x more cases at equal flag rate.
Survey framing LLM agents as model-plus-harness systems, decomposing harness responsibilities, mapping them to tasks, and highlighting open challenges in evaluation, safety, and co-evolution.
citing papers explorer
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EVA-Bench: A New End-to-end Framework for Evaluating Voice Agents
EVA-Bench supplies a simulation engine for bot-to-bot voice dialogues plus two composite metrics (EVA-A for accuracy, EVA-X for experience) evaluated on 213 enterprise scenarios, showing no tested system exceeds 0.5 on both pass@1 scores.
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CLQT: A Closed-Loop, Cost-Aware, Strategy-Consistent Benchmark for Diagnostic Evaluation of LLM Portfolio-Management Agents
A closed-loop trading benchmark with a five-axis capability scorecard shows that LLM agents' returns rank poorly against diagnostic measures of their reasoning, with the nominal Sharpe winner exposed as a reliability artifact.
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From Confident Closing to Silent Failure: Characterizing False Success in LLM Agents
False success occurs in 3-76% of LLM agent failures; LLM judges reach at most 0.65 AUROC while TF-IDF detectors reach 0.83-0.95 and recover 4-8x more cases at equal flag rate.
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From Question Answering to Task Completion: A Survey on Agent System and Harness Design
Survey framing LLM agents as model-plus-harness systems, decomposing harness responsibilities, mapping them to tasks, and highlighting open challenges in evaluation, safety, and co-evolution.