Papers24

#LLM agents

Reinforcement World Model Learning for LLM-based Agents

Large language models are great at words, but they struggle to predict what will happen after they act in a changing world.

#Reinforcement World Model Learning#world modeling#LLM agents

Spider-Sense: Intrinsic Risk Sensing for Efficient Agent Defense with Hierarchical Adaptive Screening

Intermediate

Zhenxiong Yu, Zhi Yang et al.Feb 5arXiv

Before this work, AI agents often stopped to run safety checks at every single step, which made them slow and still easy to trick in sneaky ways.

#Intrinsic Risk Sensing#Event-driven defense#Hierarchical Adaptive Screening

Towards Reducible Uncertainty Modeling for Reliable Large Language Model Agents

Intermediate

Changdae Oh, Seongheon Park et al.Feb 4arXiv

This paper says we should measure an AI agent’s uncertainty across its whole conversation, not just on one final answer.

#uncertainty quantification#LLM agents#interactive AI

Agent-Omit: Training Efficient LLM Agents for Adaptive Thought and Observation Omission via Agentic Reinforcement Learning

Intermediate

Yansong Ning, Jun Fang et al.Feb 4arXiv

Agent-Omit teaches AI agents to skip unneeded thinking and old observations, cutting tokens while keeping accuracy high.

#LLM agents#reinforcement learning#agentic RL

MemSkill: Learning and Evolving Memory Skills for Self-Evolving Agents

Intermediate

Haozhen Zhang, Quanyu Long et al.Feb 2arXiv

MemSkill turns memory operations for AI agents into learnable skills instead of fixed, hand-made rules.

#memory skills#LLM agents#skill bank

TIDE: Trajectory-based Diagnostic Evaluation of Test-Time Improvement in LLM Agents

Intermediate

Hang Yan, Xinyu Che et al.Feb 2arXiv

This paper studies how AI agents get better while they are working, not just whether they finish the job.

#Test-Time Improvement#LLM agents#trajectory analysis

LRAgent: Efficient KV Cache Sharing for Multi-LoRA LLM Agents

Intermediate

Hyesung Jeon, Hyeongju Ha et al.Feb 1arXiv

Multi-agent LLM systems often use LoRA adapters so each agent has a special role, but they all rebuild almost the same KV cache, wasting memory and time.

#LoRA#Multi-LoRA#KV cache

CAR-bench: Evaluating the Consistency and Limit-Awareness of LLM Agents under Real-World Uncertainty

Intermediate

Johannes Kirmayr, Lukas Stappen et al.Jan 29arXiv

CAR-bench is a new 'driving test' for AI assistants that checks if they can stay careful, honest, and consistent during real back-and-forth conversations in a car.

#LLM agents#benchmarking#consistency

Idea2Story: An Automated Pipeline for Transforming Research Concepts into Complete Scientific Narratives

Intermediate

Tengyue Xu, Zhuoyang Qian et al.Jan 28arXiv

Idea2Story is a two-stage system that first studies many accepted research papers offline and then uses that knowledge online to turn a vague idea into a full scientific plan.

#autonomous scientific discovery#knowledge graph#method unit extraction

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

Intermediate

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

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