What it shows
A graph-based framework optimizes chain-of-thought reasoning in large language models by identifying and eliminating redundant thinking patterns through structured pruning and reinforcement learning techniques.
By Hongyuan Yuan, Xinran He, Run Shao and 6 more · arXiv 2604.05643 · PDF
UnverifiedHugging Face's summary; not yet checked by hand.
More from Baidu
All 16FlashMask-3: Efficient and Expressive Mask-Aware Distributed AttentionFlashMask-3 accelerates long-context attention via a Hopper-optimized sparse-mask kernel and distributed context-parallel extensions that eliminate mask-communication overhead and improve training throughput.
Baidu11 upvotesRead →Virtual Head AttentionVirtual Head Attention improves long-context model efficiency by reducing KV cache and compute while preserving quality through lightweight linear query and output transformations.
Baidu17 upvotesRead →Unlimited OCR WorksUnlimited OCR introduces Reference Sliding Window Attention to eliminate growing memory consumption during long-sequence OCR tasks, enabling efficient transcription of multiple pages in a single forward pass.
Baidu9 citations90 upvotesRead →Memento: Reconstruct to Remember for Consistent Long Video GenerationMemento is a subject-reconstruction-guided framework that improves long-form video generation by preserving recurring subjects through memory-based reconstruction and dual-query mechanisms.
Baidu6 citations18 upvotesRead →DuMate-DeepResearch: An Auditable Multi-Agent System with Recursive Search and Rubric-Grounded ReasoningA multi-agent framework for deep research tasks that addresses planning, evidence acquisition, and report synthesis through decoupled components and dynamic optimization mechanisms.
Baidu2 citations9 upvotesRead →When Tools Fail: Benchmarking Dynamic Replanning and Anomaly Recovery in LLM AgentsToolMaze benchmark reveals that real-world tool failures significantly degrade TIR performance, with implicit semantic failures causing the most severe drops and dynamic replanning emerging as a key bottleneck.
Baidu28 citations27 upvotesRead →MapAgent: An Industrial-Grade Agentic Framework for City-scale Lane-level Map GenerationMapAgent is an industrial-grade agentic architecture that combines vision-language processing with constraint-aware reasoning to produce specification-compliant lane maps, achieving high automation rates in large-scale urban mapping.
Baidu2 citations18 upvotesRead →Native Audio-Visual Alignment for GenerationNAVA enables joint audio-video generation with improved synchronization and controllability through native audio-visual alignment and context-conditioned denoising.
Baidu8 citations31 upvotesRead →
Other reasoning papers
TopicChain-of-Thought Prompting Elicits Reasoning in Large Language ModelsAsking a model to write out its intermediate steps (chain of thought) sharply improves its math and logic answers.
Google23.8k citations16 upvotesRead →DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language ModelsDeepSeekMath 7B improves mathematical reasoning through enhanced data pre-training and Group Relative Policy Optimization, achieving high scores on MATH benchmark without external tools.
DeepSeek10.6k citations152 upvotesRead →DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement LearningReinforcement learning taught a model long step-by-step reasoning on par with OpenAI o1.
DeepSeek5,972 citations458 upvotesRead →DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code IntelligenceDeepSeek-Coder-V2, a Mixture-of-Experts language model, excels in code-specific tasks by enhancing coding and mathematical reasoning capabilities while expanding language support and context length.
DeepSeek530 citations73 upvotesRead →MolmoAct: Action Reasoning Models that can Reason in SpaceAction Reasoning Models (ARMs) integrate perception, planning, and control to enable adaptable and explainable robotic behavior, achieving superior performance across various tasks and settings.
Ai2232 citations45 upvotesRead →ThinkAct: Vision-Language-Action Reasoning via Reinforced Visual Latent PlanningThinkAct, a dual-system framework, uses reinforced visual latent planning to enable few-shot adaptation, long-horizon planning, and self-correction in embodied AI tasks by bridging high-level reasoning with low-level action execution.
NVIDIA192 citations43 upvotesRead →
About this paper
- Venue
- Annual Meeting of the Association for Computational Linguistics
- Citations
- 2, 0 influential · Semantic Scholar
- Upvotes
- 12 · Hugging Face
- Lab
- Baidu · on Companies · on Quarterly
Changes
A first count of 0 influential citations.
A first count of 2 citations.
Found by the weekly Hugging Face scan; not yet checked by hand.