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Large Language Models Orchestrating Structured Reasoning Achieve Kaggle Grandmaster Level
Paper • 2411.03562 • Published • 69 -
Training Language Models for Social Deduction with Multi-Agent Reinforcement Learning
Paper • 2502.06060 • Published • 38 -
MLGym: A New Framework and Benchmark for Advancing AI Research Agents
Paper • 2502.14499 • Published • 195 -
SurveyX: Academic Survey Automation via Large Language Models
Paper • 2502.14776 • Published • 100
Collections
Discover the best community collections!
Collections including paper arxiv:2604.27660
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MultiWorld: Scalable Multi-Agent Multi-View Video World Models
Paper • 2604.18564 • Published • 45 -
From Context to Skills: Can Language Models Learn from Context Skillfully?
Paper • 2604.27660 • Published • 136 -
MolmoAct2: Action Reasoning Models for Real-world Deployment
Paper • 2605.02881 • Published • 198
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Natural-Language Agent Harnesses
Paper • 2603.25723 • Published • 25 -
From Reasoning to Agentic: Credit Assignment in Reinforcement Learning for Large Language Models
Paper • 2604.09459 • Published • 13 -
From Context to Skills: Can Language Models Learn from Context Skillfully?
Paper • 2604.27660 • Published • 136
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Self-Supervised Prompt Optimization
Paper • 2502.06855 • Published • 18 -
Context Learning for Multi-Agent Discussion
Paper • 2602.02350 • Published • 4 -
XSkill: Continual Learning from Experience and Skills in Multimodal Agents
Paper • 2603.12056 • Published • 33 -
Online Experiential Learning for Language Models
Paper • 2603.16856 • Published • 59
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ShotStream: Streaming Multi-Shot Video Generation for Interactive Storytelling
Paper • 2603.25746 • Published • 155 -
TAPS: Task Aware Proposal Distributions for Speculative Sampling
Paper • 2603.27027 • Published • 143 -
Out of Sight but Not Out of Mind: Hybrid Memory for Dynamic Video World Models
Paper • 2603.25716 • Published • 156 -
LongCat-Next: Lexicalizing Modalities as Discrete Tokens
Paper • 2603.27538 • Published • 145
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XSkill: Continual Learning from Experience and Skills in Multimodal Agents
Paper • 2603.12056 • Published • 33 -
Memento-Skills: Let Agents Design Agents
Paper • 2603.18743 • Published • 58 -
SWE-Skills-Bench: Do Agent Skills Actually Help in Real-World Software Engineering?
Paper • 2603.15401 • Published • 19 -
Trace2Skill: Distill Trajectory-Local Lessons into Transferable Agent Skills
Paper • 2603.25158 • Published • 52
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lusxvr/nanoVLM-222M
Image-Text-to-Text • 0.2B • Updated • 403 • 99 -
Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning
Paper • 2503.09516 • Published • 39 -
AlphaOne: Reasoning Models Thinking Slow and Fast at Test Time
Paper • 2505.24863 • Published • 97 -
QwenLong-L1: Towards Long-Context Large Reasoning Models with Reinforcement Learning
Paper • 2505.17667 • Published • 88
-
Large Language Models Orchestrating Structured Reasoning Achieve Kaggle Grandmaster Level
Paper • 2411.03562 • Published • 69 -
Training Language Models for Social Deduction with Multi-Agent Reinforcement Learning
Paper • 2502.06060 • Published • 38 -
MLGym: A New Framework and Benchmark for Advancing AI Research Agents
Paper • 2502.14499 • Published • 195 -
SurveyX: Academic Survey Automation via Large Language Models
Paper • 2502.14776 • Published • 100
-
MultiWorld: Scalable Multi-Agent Multi-View Video World Models
Paper • 2604.18564 • Published • 45 -
From Context to Skills: Can Language Models Learn from Context Skillfully?
Paper • 2604.27660 • Published • 136 -
MolmoAct2: Action Reasoning Models for Real-world Deployment
Paper • 2605.02881 • Published • 198
-
ShotStream: Streaming Multi-Shot Video Generation for Interactive Storytelling
Paper • 2603.25746 • Published • 155 -
TAPS: Task Aware Proposal Distributions for Speculative Sampling
Paper • 2603.27027 • Published • 143 -
Out of Sight but Not Out of Mind: Hybrid Memory for Dynamic Video World Models
Paper • 2603.25716 • Published • 156 -
LongCat-Next: Lexicalizing Modalities as Discrete Tokens
Paper • 2603.27538 • Published • 145
-
Natural-Language Agent Harnesses
Paper • 2603.25723 • Published • 25 -
From Reasoning to Agentic: Credit Assignment in Reinforcement Learning for Large Language Models
Paper • 2604.09459 • Published • 13 -
From Context to Skills: Can Language Models Learn from Context Skillfully?
Paper • 2604.27660 • Published • 136
-
XSkill: Continual Learning from Experience and Skills in Multimodal Agents
Paper • 2603.12056 • Published • 33 -
Memento-Skills: Let Agents Design Agents
Paper • 2603.18743 • Published • 58 -
SWE-Skills-Bench: Do Agent Skills Actually Help in Real-World Software Engineering?
Paper • 2603.15401 • Published • 19 -
Trace2Skill: Distill Trajectory-Local Lessons into Transferable Agent Skills
Paper • 2603.25158 • Published • 52
-
Self-Supervised Prompt Optimization
Paper • 2502.06855 • Published • 18 -
Context Learning for Multi-Agent Discussion
Paper • 2602.02350 • Published • 4 -
XSkill: Continual Learning from Experience and Skills in Multimodal Agents
Paper • 2603.12056 • Published • 33 -
Online Experiential Learning for Language Models
Paper • 2603.16856 • Published • 59
-
lusxvr/nanoVLM-222M
Image-Text-to-Text • 0.2B • Updated • 403 • 99 -
Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning
Paper • 2503.09516 • Published • 39 -
AlphaOne: Reasoning Models Thinking Slow and Fast at Test Time
Paper • 2505.24863 • Published • 97 -
QwenLong-L1: Towards Long-Context Large Reasoning Models with Reinforcement Learning
Paper • 2505.17667 • Published • 88