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Mulberry: Empowering MLLM with o1-like Reasoning and Reflection via Collective Monte Carlo Tree Search
Paper • 2412.18319 • Published • 39 -
Token-Budget-Aware LLM Reasoning
Paper • 2412.18547 • Published • 46 -
Efficiently Serving LLM Reasoning Programs with Certaindex
Paper • 2412.20993 • Published • 37 -
B-STaR: Monitoring and Balancing Exploration and Exploitation in Self-Taught Reasoners
Paper • 2412.17256 • Published • 47
Collections
Discover the best community collections!
Collections including paper arxiv:2501.09891
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Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems
Paper • 2504.01990 • Published • 299 -
InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models
Paper • 2504.10479 • Published • 303 -
What, How, Where, and How Well? A Survey on Test-Time Scaling in Large Language Models
Paper • 2503.24235 • Published • 54 -
Seedream 3.0 Technical Report
Paper • 2504.11346 • Published • 70
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Internal Consistency and Self-Feedback in Large Language Models: A Survey
Paper • 2407.14507 • Published • 46 -
Large Language Models are Zero-Shot Reasoners
Paper • 2205.11916 • Published • 3 -
Let's Verify Step by Step
Paper • 2305.20050 • Published • 11 -
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
Paper • 2201.11903 • Published • 14
-
Evolving Deeper LLM Thinking
Paper • 2501.09891 • Published • 115 -
rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking
Paper • 2501.04519 • Published • 286 -
Towards System 2 Reasoning in LLMs: Learning How to Think With Meta Chain-of-Though
Paper • 2501.04682 • Published • 99
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DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
Paper • 2501.12948 • Published • 429 -
Evolving Deeper LLM Thinking
Paper • 2501.09891 • Published • 115 -
rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking
Paper • 2501.04519 • Published • 286
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Describe What You See with Multimodal Large Language Models to Enhance Video Recommendations
Paper • 2508.09789 • Published • 5 -
MM-BrowseComp: A Comprehensive Benchmark for Multimodal Browsing Agents
Paper • 2508.13186 • Published • 18 -
ZARA: Zero-shot Motion Time-Series Analysis via Knowledge and Retrieval Driven LLM Agents
Paper • 2508.04038 • Published • 1 -
Prompt Orchestration Markup Language
Paper • 2508.13948 • Published • 48
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Visual-RFT: Visual Reinforcement Fine-Tuning
Paper • 2503.01785 • Published • 85 -
When an LLM is apprehensive about its answers -- and when its uncertainty is justified
Paper • 2503.01688 • Published • 21 -
Predictive Data Selection: The Data That Predicts Is the Data That Teaches
Paper • 2503.00808 • Published • 56 -
Chain of Draft: Thinking Faster by Writing Less
Paper • 2502.18600 • Published • 50
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Evolving Deeper LLM Thinking
Paper • 2501.09891 • Published • 115 -
PaSa: An LLM Agent for Comprehensive Academic Paper Search
Paper • 2501.10120 • Published • 53 -
Multiple Choice Questions: Reasoning Makes Large Language Models (LLMs) More Self-Confident Even When They Are Wrong
Paper • 2501.09775 • Published • 33 -
ComplexFuncBench: Exploring Multi-Step and Constrained Function Calling under Long-Context Scenario
Paper • 2501.10132 • Published • 22
-
Evolving Deeper LLM Thinking
Paper • 2501.09891 • Published • 115 -
Reasoning Language Models: A Blueprint
Paper • 2501.11223 • Published • 33 -
Multiple Choice Questions: Reasoning Makes Large Language Models (LLMs) More Self-Confident Even When They Are Wrong
Paper • 2501.09775 • Published • 33 -
Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models
Paper • 2501.09686 • Published • 41
-
Mulberry: Empowering MLLM with o1-like Reasoning and Reflection via Collective Monte Carlo Tree Search
Paper • 2412.18319 • Published • 39 -
Token-Budget-Aware LLM Reasoning
Paper • 2412.18547 • Published • 46 -
Efficiently Serving LLM Reasoning Programs with Certaindex
Paper • 2412.20993 • Published • 37 -
B-STaR: Monitoring and Balancing Exploration and Exploitation in Self-Taught Reasoners
Paper • 2412.17256 • Published • 47
-
Describe What You See with Multimodal Large Language Models to Enhance Video Recommendations
Paper • 2508.09789 • Published • 5 -
MM-BrowseComp: A Comprehensive Benchmark for Multimodal Browsing Agents
Paper • 2508.13186 • Published • 18 -
ZARA: Zero-shot Motion Time-Series Analysis via Knowledge and Retrieval Driven LLM Agents
Paper • 2508.04038 • Published • 1 -
Prompt Orchestration Markup Language
Paper • 2508.13948 • Published • 48
-
Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems
Paper • 2504.01990 • Published • 299 -
InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models
Paper • 2504.10479 • Published • 303 -
What, How, Where, and How Well? A Survey on Test-Time Scaling in Large Language Models
Paper • 2503.24235 • Published • 54 -
Seedream 3.0 Technical Report
Paper • 2504.11346 • Published • 70
-
Visual-RFT: Visual Reinforcement Fine-Tuning
Paper • 2503.01785 • Published • 85 -
When an LLM is apprehensive about its answers -- and when its uncertainty is justified
Paper • 2503.01688 • Published • 21 -
Predictive Data Selection: The Data That Predicts Is the Data That Teaches
Paper • 2503.00808 • Published • 56 -
Chain of Draft: Thinking Faster by Writing Less
Paper • 2502.18600 • Published • 50
-
Internal Consistency and Self-Feedback in Large Language Models: A Survey
Paper • 2407.14507 • Published • 46 -
Large Language Models are Zero-Shot Reasoners
Paper • 2205.11916 • Published • 3 -
Let's Verify Step by Step
Paper • 2305.20050 • Published • 11 -
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
Paper • 2201.11903 • Published • 14
-
Evolving Deeper LLM Thinking
Paper • 2501.09891 • Published • 115 -
PaSa: An LLM Agent for Comprehensive Academic Paper Search
Paper • 2501.10120 • Published • 53 -
Multiple Choice Questions: Reasoning Makes Large Language Models (LLMs) More Self-Confident Even When They Are Wrong
Paper • 2501.09775 • Published • 33 -
ComplexFuncBench: Exploring Multi-Step and Constrained Function Calling under Long-Context Scenario
Paper • 2501.10132 • Published • 22
-
Evolving Deeper LLM Thinking
Paper • 2501.09891 • Published • 115 -
rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking
Paper • 2501.04519 • Published • 286 -
Towards System 2 Reasoning in LLMs: Learning How to Think With Meta Chain-of-Though
Paper • 2501.04682 • Published • 99
-
Evolving Deeper LLM Thinking
Paper • 2501.09891 • Published • 115 -
Reasoning Language Models: A Blueprint
Paper • 2501.11223 • Published • 33 -
Multiple Choice Questions: Reasoning Makes Large Language Models (LLMs) More Self-Confident Even When They Are Wrong
Paper • 2501.09775 • Published • 33 -
Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models
Paper • 2501.09686 • Published • 41
-
DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
Paper • 2501.12948 • Published • 429 -
Evolving Deeper LLM Thinking
Paper • 2501.09891 • Published • 115 -
rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking
Paper • 2501.04519 • Published • 286