Agent-ValueBench is the first dedicated benchmark for agent values, showing they diverge from LLM values, form a homogeneous 'Value Tide' across models, and bend under harnesses and skill steering.
Appworld: A controllable world of apps and people for benchmarking interactive coding agents
9 Pith papers cite this work, alongside 7 external citations. Polarity classification is still indexing.
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CRAB-Bench and RUSE create a new evaluation framework for LLM agents on constraint-graph tasks with realistic human-like user behaviors, reporting 61% pass@1 for the best model and up to 57% further drops under RUSE.
An AI-agent social platform generated mostly neutral content whose use in fine-tuning reduced model truthfulness comparably to human Reddit data, suggesting limited unique harm but flagging tail risks like secret leaks.
LedgerAgent is an inference-time method that uses a structured ledger to track task states and enforce domain policies in tool-calling agents, improving average pass^k over standard prompt-based approaches across four domains.
On the new verifiable movie-recommendation benchmark tau-Rec, the best tested LLM agent passes only about 57% of tasks once and 35% of tasks four times in a row.
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 mapping persistent state in LLM agents along six axes and proposing the AOEP-v0 protocol to evaluate governance and recovery obligations.
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.
An automated environment construction pipeline plus verifiable rewards enables RL training that improves LLM tool-use performance across scales without harming general capabilities.
citing papers explorer
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Agent-ValueBench: A Comprehensive Benchmark for Evaluating Agent Values
Agent-ValueBench is the first dedicated benchmark for agent values, showing they diverge from LLM values, form a homogeneous 'Value Tide' across models, and bend under harnesses and skill steering.
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CRAB-Bench: Evaluating LLM Agents under Complex Task Dependencies and Human-aligned User Simulation
CRAB-Bench and RUSE create a new evaluation framework for LLM agents on constraint-graph tasks with realistic human-like user behaviors, reporting 61% pass@1 for the best model and up to 57% further drops under RUSE.
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The Moltbook Files: A Harmless Slopocalypse or Humanity's Last Experiment
An AI-agent social platform generated mostly neutral content whose use in fine-tuning reduced model truthfulness comparably to human Reddit data, suggesting limited unique harm but flagging tail risks like secret leaks.
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LedgerAgent: Structured State for Policy-Adherent Tool-Calling Agents
LedgerAgent is an inference-time method that uses a structured ledger to track task states and enforce domain policies in tool-calling agents, improving average pass^k over standard prompt-based approaches across four domains.
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$\tau$-Rec: A Verifiable Benchmark for Agentic Recommender Systems
On the new verifiable movie-recommendation benchmark tau-Rec, the best tested LLM agent passes only about 57% of tasks once and 35% of tasks four times in a row.
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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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Always-OnAgents:A Survey of Persistent Memory, State, and Governance in LLMAgents
Survey mapping persistent state in LLM agents along six axes and proposing the AOEP-v0 protocol to evaluate governance and recovery obligations.
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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.
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Feedback-Driven Tool-Use Improvements in Large Language Models via Automated Build Environments
An automated environment construction pipeline plus verifiable rewards enables RL training that improves LLM tool-use performance across scales without harming general capabilities.