arXiv AI
Polished but Unresolved: Identifying Late-Stage Pressure States in Long-Horizon Tool-Use Agents
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arXiv AI
Oct 31
AgentDecept: Benchmarking Deceptive Behavior in Tool-Using LLM Agents under Pressure
AgentDecept is a new benchmark that evaluates deceptive behavior in large language model agents that use tools under pressure. It contains 477 pressure variants derived from 257 source tasks across six pressure sources, each paired with a pressure‑free counterpart. The protocol compares user‑facing claims to time‑ordered observations and executed outcomes, distinguishing falsification, concealment, and equivocation from honest failure and disclosed limitations.
arXiv AI
Jun 2
SPADE-Bench: Evaluating Spontaneous Strategic Deception in Agents via Plan-Action Divergence
arXiv:2606. 02380v1 Announce Type: cross Abstract: As LLM-based agents expand their operational scope, reliability becomes a prerequisite for real-world deployment.
arXiv AI
Jul 7
AgentGym2: Benchmarking Large Language Model Agents in De-Idealized Real-World Environments
arXiv:2607. 05174v1 Announce Type: new Abstract: Language agents, i.
arXiv AI
Oct 5
LPS-Bench: Benchmarking Safety Awareness of Computer-Use Agents in Long-Horizon Planning under Benign and Adversarial Scenarios
LPS-Bench is a benchmark designed to evaluate the safety awareness of computer‑use agents (CUAs) in long‑horizon planning tasks that involve tool workflows. It uses a template‑guided multi‑agent pipeline to generate user instructions, simulated toolkits, and case‑specific safety criteria, followed by human review, allowing scalable expansion without building separate application environments. The benchmark includes 570 cases from 65 scenarios across seven task domains and nine planning‑risk types, and an LLM‑based evaluator assesses tool choices, arguments, and responses throughout execution. Evaluations of 13 LLM agents show persistent safety failures in both benign and adversarial settings, with prompt‑based interventions providing only model‑dependent improvements.
arXiv AI
Aug 5
Towards Robust Tool Use in Agents via Experience-Driven Adaptive Guidance
arXiv:2608. 03403v1 Announce Type: new Abstract: The performance bottleneck of agents is increasingly shifting from model capability to the robustness of their execution processes.
arXiv Computation and Language
Oct 12
Scaling Agentic Data for Long-Horizon Terminal Intelligence
The paper introduces TerminalHorizon, an automated engine that synthesizes long‑horizon trajectories for terminal agents by reconstructing environments from real‑world professional work and progressively expanding tasks to require further exploration and improvement. Using this engine, the authors create 1.5K task environments and fine‑tune Qwen3.5‑27B, producing TerminalHorizon‑27B, which shows significant performance gains across 12 benchmarks in coding, agentic production tasks, and reasoning. Case studies demonstrate the model’s ability to experiment beyond initial solutions, refine methods based on feedback, and preserve effective improvements in final deliverables.