REFLECT benchmark shows current LLM judges achieve below 55% accuracy detecting failures in evidence-based research agents, especially on evidence verification.
Dr. bench: A multidimensional evaluation for deep research agents, from answers to reports,
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Frontier LLMs display emerging investigatory agency in autonomous database analysis but struggle with long-horizon exploration on the new DDR-Bench.
This survey categorizes agentic environments for LLMs by eight attributes and domains, introduces symbolic and neural synthesis paradigms with evaluation, and outlines four agent evolution pathways plus three environment evolution paradigms.
citing papers explorer
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Time to REFLECT: Can We Trust LLM Judges for Evidence-based Research Agents?
REFLECT benchmark shows current LLM judges achieve below 55% accuracy detecting failures in evidence-based research agents, especially on evidence verification.
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Hunt Instead of Wait: Evaluating Deep Data Research on Large Language Models
Frontier LLMs display emerging investigatory agency in autonomous database analysis but struggle with long-horizon exploration on the new DDR-Bench.
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Agentic Environment Engineering for Large Language Models: A Survey of Environment Modeling, Synthesis, Evaluation, and Application
This survey categorizes agentic environments for LLMs by eight attributes and domains, introduces symbolic and neural synthesis paradigms with evaluation, and outlines four agent evolution pathways plus three environment evolution paradigms.