arXiv:2610.09163v1 Announce Type: new Abstract: Task-oriented conversational agents remain fragile under real world conversation scenarios as they rarely follow a predictable script, especially when...
arXiv:2609.38043v1 Announce Type: new Abstract: Interactive agent benchmarks and multi-turn reinforcement learning increasingly place a second language model in the role of the user. This simulated u...
The paper introduces TURTLE, an Unsupervised Environment Design framework that evolves tool‑use tasks for AI assistants by using a proposer, solver, and feedback generator powered by large language models. TURTLE automatically generates self‑tuning, long‑horizon benchmarks that increase task difficulty, diversity, and failure modes across multiple applications. Experiments on AppWorld, τ‑Bench, and AutomationBench demonstrate significant gains in hard task percentages and broader reasoning challenges.
arXiv:2607. 16610v1 Announce Type: new Abstract: Long-horizon AI agents are becoming increasingly capable, yet their interaction with users remains surprisingly thin.
RideWay is a new benchmark that evaluates ride‑hailing language agents not just on task completion but on interaction efficiency. It introduces the Efficiency Utility metric, which penalizes agents for excessive tool calls and user‑facing turns relative to a task‑specific reference effort, with human preferences used to calibrate the penalties. Across 58 tasks and 24 models, the metric shows that extra dialogue is penalized more heavily than extra tool use, and it achieves high accuracy in distinguishing trajectories that differ in turns but struggles when differences are only in tool calls.
AnyAct introduces a universal action layer that consolidates diverse tool capabilities into a self‑evolving action space for AI agents operating in open‑world environments. It tackles the scale dilemma, tool non‑stationarity, and heterogeneous feedback by using hierarchical progressive retrieval and test‑time reliability evolution, while a heterogeneous observation grounding module unifies multi‑modal feedback. Evaluations on LiveMCPBench and the newly created OSMCP benchmark show state‑of‑the‑art performance, with significant gains in task success rate and reduced execution steps, especially for models with limited native capabilities.
MIMESIS is a user simulator designed to train interactive language agents by mimicking real user behavior. It is trained on human conversations with explicit reasoning supervision and incorporates 13 realistic behavioral patterns. Experiments show that agents trained with MIMESIS outperform those trained with GPT-5.5 across multiple unseen simulators, and the proposed Coached On-Policy Self-Distillation further improves agent performance by providing dense, token-level supervision.
arXiv:2605.12894v2 Announce Type: replace-cross Abstract: Large Language Model (LLM) agents are increasingly deployed in settings where they interact with diverse users, including those who are uncle...
arXiv:2510. 04491v3 Announce Type: replace Abstract: Despite rapid progress in building conversational AI agents, robustness is still largely untested.
The paper introduces Test-Time Adaptation through Human‑Agent Interaction (TAHI), a method that uses iterative human feedback to adapt AI agents to individual users’ criteria. By integrating cross‑session interaction data into agent context and weights, and building an evolving rubric module, the authors demonstrate that agents can improve task success by 4.5–20.9% after only a few interactions. The evolving rubric also serves as a scalable annotation tool, detecting 16.0–22.3% more failures than language models or humans alone, and personalized agents can even generalize improvements up to 8.8% across users.
arXiv:2606. 15508v1 Announce Type: new Abstract: Tool-augmented large language model agents increasingly operate over large tool libraries, but existing evaluations often focus on whether a model can call a tool correctly rather than how the visible tool menu shapes reliability, efficiency, and safety-relevant risk exposure.
arXiv:2610.02330v1 Announce Type: new Abstract: Large language models (LLMs) rely on long-horizon tool invocation sequences for complex tasks, where each invocation can alter the task state and condi...