[논문리뷰] LawThinker: A Deep Research Legal Agent in Dynamic EnvironmentsarXiv에 게시된 'LawThinker: A Deep Research Legal Agent in Dynamic Environments' 논문에 대한 자세한 리뷰입니다.#Review#Legal Reasoning#AI Agent#Large Language Models#Verification#Knowledge Management#Dynamic Environments#Procedural Compliance#Tool Use2026년 2월 12일댓글 수 로딩 중
[논문리뷰] LOCA-bench: Benchmarking Language Agents Under Controllable and Extreme Context GrowtharXiv에 게시된 'LOCA-bench: Benchmarking Language Agents Under Controllable and Extreme Context Growth' 논문에 대한 자세한 리뷰입니다.#Review#Large Language Models#Language Agents#Long Context#Context Rot#Benchmarking#Context Management#Tool Use#Agent Evaluation#Dynamic Environments2026년 2월 9일댓글 수 로딩 중
[논문리뷰] MemGUI-Bench: Benchmarking Memory of Mobile GUI Agents in Dynamic EnvironmentsarXiv에 게시된 'MemGUI-Bench: Benchmarking Memory of Mobile GUI Agents in Dynamic Environments' 논문에 대한 자세한 리뷰입니다.#Review#Mobile GUI Agents#Memory Benchmarking#Short-Term Memory#Long-Term Memory#LLM-as-Judge#Dynamic Environments#Evaluation Metrics#Task Automation2026년 2월 8일댓글 수 로딩 중
[논문리뷰] Real-Time Reasoning Agents in Evolving EnvironmentsarXiv에 게시된 'Real-Time Reasoning Agents in Evolving Environments' 논문에 대한 자세한 리뷰입니다.#Review#Real-time Reasoning#LLM Agents#Dynamic Environments#Dual-System AI#AgileThinker#Reactive Planning#Cognitive Load#Time Pressure2025년 11월 9일댓글 수 로딩 중
[논문리뷰] CostBench: Evaluating Multi-Turn Cost-Optimal Planning and Adaptation in Dynamic Environments for LLM Tool-Use AgentsShijue Huang이 arXiv에 게시한 'CostBench: Evaluating Multi-Turn Cost-Optimal Planning and Adaptation in Dynamic Environments for LLM Tool-Use Agents' 논문에 대한 자세한 리뷰입니다.#Review#LLM Agents#Tool Use#Cost-Optimal Planning#Dynamic Environments#Benchmarking#Multi-Turn Interaction#Economic Reasoning2025년 11월 9일댓글 수 로딩 중
[논문리뷰] The Landscape of Agentic Reinforcement Learning for LLMs: A SurveyHejia Geng이 arXiv에 게시한 'The Landscape of Agentic Reinforcement Learning for LLMs: A Survey' 논문에 대한 자세한 리뷰입니다.#Review#Agentic Reinforcement Learning#Large Language Models#LLM Agents#Sequential Decision Making#Policy Optimization#Tool Use#Dynamic Environments#Autonomous AI2025년 9월 3일댓글 수 로딩 중
[논문리뷰] How Can Input Reformulation Improve Tool Usage Accuracy in a Complex Dynamic Environment? A Study on τ-benchJayanth Srinivasa이 arXiv에 게시한 'How Can Input Reformulation Improve Tool Usage Accuracy in a Complex Dynamic Environment? A Study on τ-bench' 논문에 대한 자세한 리뷰입니다.#Review#LLM Agents#Tool Use#Function Calling#Input Reformulation#Dynamic Environments#τ-bench#Context Engineering#Multi-Agent Framework2025년 9월 2일댓글 수 로딩 중