Papers38

#Chain-of-Thought

No Shortcuts to Culture: Indonesian Multi-hop Question Answering for Complex Cultural Understanding

Vynska Amalia Permadi, Xingwei Tan et al.Feb 3arXiv

This paper builds ID-MoCQA, a new two-step (multi-hop) quiz set about Indonesian culture that makes AI connect clues before answering.

#multi-hop question answering#cultural reasoning#Indonesian culture

ReGuLaR: Variational Latent Reasoning Guided by Rendered Chain-of-Thought

Intermediate

Fanmeng Wang, Haotian Liu et al.Jan 30arXiv

Chain-of-Thought (CoT) makes AI think step by step, but it is slow because it writes many tokens one by one.

#Chain-of-Thought#Latent Reasoning#Variational Auto-Encoder

Render-of-Thought: Rendering Textual Chain-of-Thought as Images for Visual Latent Reasoning

Intermediate

Yifan Wang, Shiyu Li et al.Jan 21arXiv

Render-of-Thought (RoT) turns the model’s step-by-step thinking from long text into slim images so the model can think faster with fewer tokens.

#Render-of-Thought#Chain-of-Thought#Latent Reasoning

Jet-RL: Enabling On-Policy FP8 Reinforcement Learning with Unified Training and Rollout Precision Flow

Intermediate

Haocheng Xi, Charlie Ruan et al.Jan 20arXiv

Reinforcement learning (RL) for large language models is slow because the rollout (text generation) stage can take more than 70% of training time, especially for long, step-by-step answers.

#FP8 quantization#on-policy reinforcement learning#precision flow

FantasyVLN: Unified Multimodal Chain-of-Thought Reasoning for Vision-Language Navigation

Intermediate

Jing Zuo, Lingzhou Mu et al.Jan 20arXiv

FantasyVLN teaches a robot to follow language instructions while looking around, using a smart, step-by-step thinking style during training but not at test time.

#Vision-and-Language Navigation#Chain-of-Thought#Multimodal CoT

Think-Then-Generate: Reasoning-Aware Text-to-Image Diffusion with LLM Encoders

Intermediate

Siqi Kou, Jiachun Jin et al.Jan 15arXiv

Most text-to-image models act like word-to-pixel copy machines and miss the hidden meaning in our prompts.

#think-then-generate#reasoning-aware text-to-image#LLM encoder

PRL: Process Reward Learning Improves LLMs' Reasoning Ability and Broadens the Reasoning Boundary

Intermediate

Jiarui Yao, Ruida Wang et al.Jan 15arXiv

Large language models usually get only a final thumbs-up or thumbs-down at the end of their answer, which is too late to fix mistakes made in the middle.

#Process Reward Learning#PRL#Reasoning LLMs

Patient-Similarity Cohort Reasoning in Clinical Text-to-SQL

Intermediate

Yifei Shen, Yilun Zhao et al.Jan 14arXiv

This paper introduces CLINSQL, a 633-task benchmark that turns real clinician-style questions into SQL challenges over the MIMIC-IV v3.1 hospital database.

#clinical text-to-SQL#EHR#MIMIC-IV

Forest Before Trees: Latent Superposition for Efficient Visual Reasoning

Intermediate

Yubo Wang, Juntian Zhang et al.Jan 11arXiv

This paper introduces Laser, a new way for vision-language models to think in their hidden space before speaking, so they see the whole “forest” before picking out the “trees.”

#Latent reasoning#Dynamic Windowed Alignment Learning#Dynamic Semantic Windows

EpiCaR: Knowing What You Don't Know Matters for Better Reasoning in LLMs

Intermediate

Jewon Yeom, Jaewon Sok et al.Jan 11arXiv

This paper teaches AI models not just how to solve problems but also how to tell when their own answers might be wrong.

#EPICAR#calibration#epistemic uncertainty

Re-Align: Structured Reasoning-guided Alignment for In-Context Image Generation and Editing

Beginner

Runze He, Yiji Cheng et al.Jan 8arXiv

Re-Align is a new way for AI to make and edit pictures by thinking in clear steps before drawing.

#In-Context Image Generation#Reference-based Image Editing#Structured Reasoning

DiffCoT: Diffusion-styled Chain-of-Thought Reasoning in LLMs

Intermediate

Shidong Cao, Hongzhan Lin et al.Jan 7arXiv

DiffCoT treats a model’s step-by-step thinking (Chain-of-Thought) like a messy draft that can be cleaned up over time, not something fixed forever.

#Chain-of-Thought#Diffusion models#Autoregressive decoding

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