Papers38

#chain-of-thought

Llama-3.1-FoundationAI-SecurityLLM-Reasoning-8B Technical Report

This paper introduces Foundation-Sec-8B-Reasoning, a small (8 billion parameter) AI model that is trained to “think out loud” before answering cybersecurity questions.

#native reasoning#cybersecurity LLM#chain-of-thought

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

Enhancing Sentiment Classification and Irony Detection in Large Language Models through Advanced Prompt Engineering Techniques

Beginner

Marvin Schmitt, Anne Schwerk et al.Jan 13arXiv

Giving large language models a few good examples and step-by-step instructions can make them much better at spotting feelings in text.

#prompt engineering#few-shot learning#chain-of-thought

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

Illusions of Confidence? Diagnosing LLM Truthfulness via Neighborhood Consistency

Beginner

Haoming Xu, Ningyuan Zhao et al.Jan 9arXiv

LLMs can look confident but still change their answers when the surrounding text nudges them, showing that confidence alone isn’t real truthfulness.

#Neighbor-Consistency Belief#belief robustness#self-consistency

GlimpRouter: Efficient Collaborative Inference by Glimpsing One Token of Thoughts

Beginner

Wenhao Zeng, Xuteng Zhang et al.Jan 8arXiv

Big reasoning AIs think in many steps, which is slow and costly.

#collaborative inference#initial token entropy#step-level routing

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

ThinkRL-Edit: Thinking in Reinforcement Learning for Reasoning-Centric Image Editing

Beginner

Hengjia Li, Liming Jiang et al.Jan 6arXiv

ThinkRL-Edit teaches an image editor to think first and draw second, which makes tricky, reasoning-heavy edits much more accurate.

#reasoning-centric image editing#reinforcement learning#chain-of-thought

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