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#LLM agents

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

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

BEDA: Belief Estimation as Probabilistic Constraints for Performing Strategic Dialogue Acts

Intermediate

Hengli Li, Zhaoxin Yu et al.Dec 31arXiv

This paper presents BEDA, a simple way to make chatty AI act strategically by turning what it believes into gentle rules (probabilistic constraints) that guide what it can say.

#strategic dialogue#belief estimation#probabilistic constraints

Youtu-Agent: Scaling Agent Productivity with Automated Generation and Hybrid Policy Optimization

Intermediate

Yuchen Shi, Yuzheng Cai et al.Dec 31arXiv

Youtu-Agent is a build-and-grow factory for AI agents that cuts manual setup and keeps agents improving over time.

#LLM agents#automated agent generation#modular architecture

GenEnv: Difficulty-Aligned Co-Evolution Between LLM Agents and Environment Simulators

Intermediate

Jiacheng Guo, Ling Yang et al.Dec 22arXiv

GenEnv is a training system where a student AI and a teacher simulator grow together by exchanging tasks and feedback.

#GenEnv#co-evolutionary learning#difficulty-aligned curriculum

Achieving Olympia-Level Geometry Large Language Model Agent via Complexity Boosting Reinforcement Learning

Intermediate

Haiteng Zhao, Junhao Shen et al.Dec 11arXiv

This paper builds InternGeometry, a large language model agent that solves Olympiad-level geometry by talking to a math engine, remembering what worked, and trying smart new ideas.

#InternGeometry#geometry theorem proving#auxiliary constructions

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