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.
E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios
arXiv:2607. 23722v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed as agents that interact with stateful environments over multiple steps: gathering hidden information, composing tool calls, and committing state changes.
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arXiv AI
Jun 29
LiveClawBench: Benchmarking LLM Agents on Complex, Real-World Assistant Tasks
arXiv:2604. 13072v2 Announce Type: replace-cross Abstract: OpenClaw-style personal assistants extend LLM agents from isolated tool use to open-ended, stateful, and personalized software environments.
arXiv AI
6d ago
VAKRA: Evaluating Multi-Hop Reasoning Across APIs and Retrieval Under Tool-Use Policies
arXiv:2608. 12282v1 Announce Type: new Abstract: Agents deployed in enterprise settings must reason across structured APIs and document collections, yet existing benchmarks evaluate these capabilities in isolation.
arXiv AI
Jul 3
A Unified Framework for the Evaluation of LLM Agentic Capabilities
arXiv:2605. 27898v2 Announce Type: replace Abstract: As LLMs are increasingly deployed as agents, reliable assessment of their agentic capabilities has become essential.
arXiv AI
Jul 21
Can AI Agents Really Complete RTL-to-GDS? Lessons from Benchmarking Tool-Interactive EDA Workflows
arXiv:2607. 17528v1 Announce Type: new Abstract: LLM-driven agent systems have emerged as a promising paradigm for electronic design automation (EDA), demonstrating strong potential for automating complex design workflows.
arXiv AI
Jun 2
AgentProcessBench: Diagnosing Step-Level Process Quality in Tool-Using Agents
arXiv:2603. 14465v2 Announce Type: replace Abstract: While Large Language Models (LLMs) have evolved into tool-using agents, they remain brittle in long-horizon interactions.
arXiv Machine Learning
Aug 12
FlowScout: From Execution Feedback to Reliable Tool-Using Agent Workflows
arXiv:2608. 10039v1 Announce Type: new Abstract: Agentic workflows have become an important abstraction for building reliable LLM-based automation systems by organizing large language models (LLMs), tools, and control logic into explicit execution structures.
arXiv AI
Jul 14
AgentAbstain: Do LLM Agents Know When Not to Act?
arXiv:2607. 10059v1 Announce Type: new Abstract: Agent systems based on large language models (LLMs) are increasingly deployed for autonomous tasks, yet existing evaluations mostly focus on task success rather than whether agents know when to abstain.
arXiv AI
Jul 24
AppWorld-UL: Benchmarking Diverse Agent-User Interactions for Tool-Use
arXiv:2607. 20536v1 Announce Type: new Abstract: Tool-use agents that address day-to-day digital tasks such as ordering groceries must not only operate applications, but also interact with the user, e.
arXiv AI
Jul 21
Environment-free Synthetic Data Generation for API-Calling Agents
arXiv:2607. 16900v1 Announce Type: new Abstract: Training API-calling large language model (LLM) agents demands massive amounts of high-quality trajectories.
arXiv AI
Jun 30
TUA-Bench: A Benchmark for General-Purpose Terminal-Use Agents
arXiv:2606. 28480v1 Announce Type: cross Abstract: As large language models and harness frameworks continue to advance, agents operating in terminals are increasingly capable of performing a broader range of general computer-use tasks beyond coding.
arXiv AI
Jun 3
A Matter of TASTE: Improving Coverage and Difficulty of Agent Benchmarks
arXiv:2605. 28556v2 Announce Type: replace Abstract: As agent capabilities advance, existing benchmarks, such as $\tau^2$-Bench, are becoming increasingly saturated.