Papers36

#chain-of-thought

Mind-Brush: Integrating Agentic Cognitive Search and Reasoning into Image Generation

Mind-Brush turns image generation from a one-step 'read the prompt and draw' into a multi-step 'think, research, and create' process.

#agentic image generation#multimodal reasoning#retrieval-augmented generation

THINKSAFE: Self-Generated Safety Alignment for Reasoning Models

Intermediate

Seanie Lee, Sangwoo Park et al.Jan 30arXiv

Large reasoning models got very good at thinking step-by-step, but that sometimes made them too eager to follow harmful instructions.

#THINKSAFE#self-generated safety alignment#refusal steering

Pushing the Boundaries of Natural Reasoning: Interleaved Bonus from Formal-Logic Verification

Intermediate

Chuxue Cao, Jinluan Yang et al.Jan 30arXiv

Large language models sometimes reach the right answer for the wrong reasons, which is risky and confusing.

#formal logic verification#interleaved verification#neuro-symbolic reasoning

MMFineReason: Closing the Multimodal Reasoning Gap via Open Data-Centric Methods

Intermediate

Honglin Lin, Zheng Liu et al.Jan 29arXiv

MMFineReason is a huge, open dataset (1.8 million examples, 5.1 billion solution tokens) that teaches AIs to think step by step about pictures and text together.

#multimodal reasoning#vision-language models#chain-of-thought

Beyond Imitation: Reinforcement Learning for Active Latent Planning

Intermediate

Zhi Zheng, Wee Sun LeeJan 29arXiv

The paper shows how to make AI think faster and smarter by planning in a hidden space instead of writing long step-by-step sentences.

#latent reasoning#chain-of-thought#variational autoencoder

Thinking in Frames: How Visual Context and Test-Time Scaling Empower Video Reasoning

Intermediate

Chengzu Li, Zanyi Wang et al.Jan 28arXiv

This paper shows that making short videos can help AI plan and reason in pictures better than writing out steps in text.

#video reasoning#visual planning#test-time scaling

Innovator-VL: A Multimodal Large Language Model for Scientific Discovery

Intermediate

Zichen Wen, Boxue Yang et al.Jan 27arXiv

Innovator-VL is a new multimodal AI model that understands both pictures and text to help solve science problems without needing mountains of special data.

#Innovator-VL#multimodal large language model#scientific reasoning

PROGRESSLM: Towards Progress Reasoning in Vision-Language Models

Intermediate

Jianshu Zhang, Chengxuan Qian et al.Jan 21arXiv

This paper asks a new question for vision-language models: not just 'What do you see?' but 'How far along is the task right now?'

#progress reasoning#vision-language models#episodic retrieval

Multiplex Thinking: Reasoning via Token-wise Branch-and-Merge

Intermediate

Yao Tang, Li Dong et al.Jan 13arXiv

The paper introduces Multiplex Thinking, a new way for AI to think by sampling several likely next words at once and blending them into a single super-token.

#Multiplex Thinking#chain-of-thought#continuous token

JudgeRLVR: Judge First, Generate Second for Efficient Reasoning

Intermediate

Jiangshan Duo, Hanyu Li et al.Jan 13arXiv

JudgeRLVR teaches a model to be a strict judge of answers before it learns to generate them, which trims bad ideas early.

#RLVR#judge-then-generate#discriminative supervision

X-Coder: Advancing Competitive Programming with Fully Synthetic Tasks, Solutions, and Tests

Intermediate

Jie Wu, Haoling Li et al.Jan 11arXiv

X-Coder shows that models can learn expert-level competitive programming using data that is 100% synthetic—no real contest problems needed.

#competitive programming#synthetic data generation#feature-based synthesis

TourPlanner: A Competitive Consensus Framework with Constraint-Gated Reinforcement Learning for Travel Planning

Intermediate

Yinuo Wang, Mining Tan et al.Jan 8arXiv

TourPlanner is a travel-planning system that first gathers the right places, then lets multiple expert ‘voices’ debate plans, and finally polishes the winner with a learning method that follows rules before style.

#travel planning#multi-agent reasoning#chain-of-thought

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