Papers30

#LLM agents

Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces

Mike A. Merrill, Alexander G. Shaw et al.Jan 17arXiv

Terminal-Bench 2.0 is a tough test that checks how well AI agents can solve real, professional tasks by typing commands in a computer terminal.

#Terminal-Bench#command line interface#Docker containers

PACEvolve: Enabling Long-Horizon Progress-Aware Consistent Evolution

Intermediate

Minghao Yan, Bo Peng et al.Jan 15arXiv

PACEvolve is a new recipe that helps AI agents improve their ideas step by step over long periods without getting stuck.

#evolutionary search#LLM agents#context management

Toward Ultra-Long-Horizon Agentic Science: Cognitive Accumulation for Machine Learning Engineering

Intermediate

Xinyu Zhu, Yuzhu Cai et al.Jan 15arXiv

This paper builds an AI agent, ML-Master 2.0, that can work on machine learning projects for a very long time without forgetting what matters.

#Hierarchical Cognitive Caching#cognitive accumulation#ultra-long-horizon autonomy

ToolSafe: Enhancing Tool Invocation Safety of LLM-based agents via Proactive Step-level Guardrail and Feedback

Intermediate

Yutao Mou, Zhangchi Xue et al.Jan 15arXiv

ToolSafe is a new way to keep AI agents safe when they use external tools, by checking each action before it runs.

#step-level safety#tool invocation#LLM agents

MAXS: Meta-Adaptive Exploration with LLM Agents

Intermediate

Jian Zhang, Zhiyuan Wang et al.Jan 14arXiv

MAXS is a new way for AI agents to think a few steps ahead while using tools like search and code, so they make smarter choices.

#LLM agents#tool-augmented reasoning#lookahead

Imagine-then-Plan: Agent Learning from Adaptive Lookahead with World Models

Intermediate

Youwei Liu, Jian Wang et al.Jan 13arXiv

Agents often act like tourists without a map: they react to what they see now and miss long-term consequences.

#Imagine-then-Plan#world models#adaptive lookahead

Beyond Static Tools: Test-Time Tool Evolution for Scientific Reasoning

Intermediate

Jiaxuan Lu, Ziyu Kong et al.Jan 12arXiv

This paper teaches AI to build and improve its own small computer helpers (tools) while solving science problems, instead of relying only on a fixed toolbox made beforehand.

#Test-Time Tool Evolution#Dynamic tool synthesis#Scientific reasoning

OpenTinker: Separating Concerns in Agentic Reinforcement Learning

Intermediate

Siqi Zhu, Jiaxuan YouJan 12arXiv

OpenTinker is an open-source system that makes training AI agents with reinforcement learning simple, modular, and reusable.

#Reinforcement learning#LLM agents#Agent–environment interaction

The Confidence Dichotomy: Analyzing and Mitigating Miscalibration in Tool-Use Agents

Beginner

Weihao Xuan, Qingcheng Zeng et al.Jan 12arXiv

This paper studies how AI agents that use tools talk about how sure they are and finds a split: some tools make them too sure, others help them be honest.

#LLM agents#calibration#overconfidence

EnvScaler: Scaling Tool-Interactive Environments for LLM Agent via Programmatic Synthesis

Intermediate

Xiaoshuai Song, Haofei Chang et al.Jan 9arXiv

EnvScaler is an automatic factory that builds many safe, rule-following practice worlds where AI agents can talk to users and call tools, just like real apps.

#EnvScaler#tool-interactive environments#programmatic synthesis

Memory Matters More: Event-Centric Memory as a Logic Map for Agent Searching and Reasoning

Intermediate

Yuyang Hu, Jiongnan Liu et al.Jan 8arXiv

This paper turns an AI agent’s memory from a flat list of notes into a logic map of events connected by cause-and-time links.

#event-centric memory#Event Graph#logic-aware retrieval

Evolving Programmatic Skill Networks

Intermediate

Haochen Shi, Xingdi Yuan et al.Jan 7arXiv

This paper teaches a computer agent to grow a toolbox of skills that are real, runnable programs, not just text ideas.

#Programmatic Skill Network#continual learning#symbolic programs

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