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#chain-of-thought

On-Policy Self-Distillation for Reasoning Compression

Beginner

Hejian Sang, Yuanda Xu et al.Mar 5arXiv

Reasoning models often talk too much, and those extra words can actually make them more wrong.

#on-policy self-distillation#reasoning compression#conciseness instruction

MMR-Life: Piecing Together Real-life Scenes for Multimodal Multi-image Reasoning

Beginner

Jiachun Li, Shaoping Huang et al.Mar 2arXiv

MMR-Life is a new test (benchmark) that checks how AI understands everyday situations using several real photos at once.

#multimodal reasoning#multi-image understanding#real-life benchmark

CHIMERA: Compact Synthetic Data for Generalizable LLM Reasoning

Beginner

Xinyu Zhu, Yihao Feng et al.Mar 1arXiv

CHIMERA is a small (about 9,000 examples) but very carefully built synthetic dataset that teaches AI to solve hard problems step by step.

#CHIMERA dataset#synthetic data generation#chain-of-thought

Thinking with Drafting: Optical Decompression via Logical Reconstruction

Beginner

Jingxuan Wei, Honghao He et al.Feb 12arXiv

The paper fixes a common problem in AI: models can read pictures and text well, but they often mess up the logic behind them.

#Thinking with Drafting#optical decompression#visual algebra

Effective Reasoning Chains Reduce Intrinsic Dimensionality

Beginner

Archiki Prasad, Mandar Joshi et al.Feb 9arXiv

The paper asks a simple question: which kind of step-by-step reasoning helps small language models learn best, and why?

#intrinsic dimensionality#chain-of-thought#LoRA

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

Beginner

Zhuoran Yang, Ed Li et al.Jan 28arXiv

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

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

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

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

Large Reasoning Models Are (Not Yet) Multilingual Latent Reasoners

Beginner

Yihong Liu, Raoyuan Zhao et al.Jan 6arXiv

Large reasoning models can often find the right math answer in their “head” before finishing their written steps, but this works best in languages with lots of training data like English and Chinese.

#latent reasoning#chain-of-thought#multilingual LLMs

Fantastic Reasoning Behaviors and Where to Find Them: Unsupervised Discovery of the Reasoning Process

Beginner

Zhenyu Zhang, Shujian Zhang et al.Dec 30arXiv

This paper shows a new way (called RISE) to find and control how AI models think without needing any human-made labels.

#RISE#sparse auto-encoder#reasoning vectors

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