LLM Self-Correction

LLM: Large Language Model

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8 papers in the last four weeks, up 167% on the four weeks before. 0.1% of all new papers.

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Remask, Don't Replace: Token-to-Mask Refinement in Diffusion Large Language Models
Apr 20, 2026Lin YaoLLM Self-Correction Multi-Token Prediction
School of Computer Science, Shanghai Jiao Tong University, Shanghai, 200240, China · Zhongguancun Academy, Beijing, 100097, China
Latent Phase-Shift Rollback: Inference-Time Error Correction via Residual Stream Monitoring and KV-Cache Steering
Apr 20, 2026Manan Gupta, Dhruv KumarLLM Self-Correction Language Model Error Detection
1BITS Pilani, Pilani Campus, India
SPREG: Structured Plan Repair with Entropy-Guided Test-Time Intervention for Large Language Model Reasoning
Apr 20, 2026Xuan Wang, Yu Ming, Xinhao Zhong +4LLM Self-Correction Classifier-Free Guidance
1Meituan
Improving Retrieval-Augmented Generation without Taxonomy-based Error Categorization
Apr 16, 2026Gongbo Zhang, Yifan Peng, Chunhua WengLLM Self-Correction Retrieval-Augmented Generation
Columbia University · Weill Cornell Medicine
Vibe-Coding: Feedback-Based Automated Verification with no Human Code Inspection, a Feasibility Study
Apr 16, 2026Michal Töpfer, František Plášil, Tomáš Bureš +1LLM Self-Correction Formal Verification
Charles University Prague, Czech Republic
REGREACT: Self-Correcting Multi-Agent Pipelines for Structured Regulatory Information Extraction
Apr 13, 2026Mohammed Ali, Abdelrahman Abdallah, Adam JatowtLLM Self-Correction Document Information Extraction
University of Innsbruck, Austria
Cross-Context Review: Improving LLM Output Quality by Separating Production and Review Sessions
Mar 12, 2026Tae-Eun SongLLM Self-Correction LLM Evaluation
Daejeon Jungang Cheonggua Co., Ltd.
ErrorLLM: Modeling SQL Errors for Text-to-SQL Refinement
Mar 4, 2026Zijin Hong, Hao Chen, Zheng Yuan +6LLM Self-Correction Text-to-SQL
The Hong Kong Polytechnic University Kowloon, Hong Kong · City University of Macau Taipa, Macau · Jilin University Changchun, China +3
When Actions Go Off-Task: Detecting and Correcting Misaligned Actions in Computer-Use Agents
Feb 9, 2026Yuting Ning, Jaylen Jones, Zhehao Zhang +5LLM Self-Correction Computer-Use Agents
The Ohio State University · Amazon AGI
Agentic AI for Commercial Insurance Underwriting with Adversarial Self-Critique
Jan 21, 2026Joyjit Roy, Samaresh Kumar SinghLLM Self-Correction AI Agent Safety
Austin, Texas, USA · Leander, Texas, USA
Stepwise Think-Critique: Interleaved Reasoning and Self-Critique in a Single LLM
Dec 17, 2025Jiaqi Xu, Cuiling Lan, Xuejin Chen +1LLM Self-Correction CoT Reasoning
University of Science and Technology of China · ⋆This work was done when Jiaqi was an intern at MSRA. · Microsoft Research Asia
Superficial Reflection or Genuine Thought? A Fine-Grained Cognitive Analysis of Large Reasoning Models
Nov 30, 2025Yuxiang Chen, Zuohan Wu, Ziwei Wang +6LLM Self-Correction Cognitive Modeling
University College London · AI Lab, the Yangtze River Delta, China · The Hong Kong University of Science and Technology (Guangzhou) +4
Knowing When to Critique: Task-Adaptive Metacognitive Regulation for Reliable LLM Reasoning
Jul 20, 2025Xinmeng Hou, Ziting Chang, Zhouquan Lu +5LLM Self-Correction Multi-Agent LLMs
Nanyang Technological University · Unicorn Verse · Shanghai Jiao Tong University +3
SG-FSM: A Self-Guiding Zero-Shot Prompting Paradigm for Multi-Hop Question Answering Based on Finite State Machine
Oct 22, 2024Xiaochen Wang, Liang Chen, Reza Haf Zhe Yang +5Multi-Hop QA LLM Self-Correction
School of Software & Microelectronics, Peking University · International Digital Economy Academy · National Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University +1
Causal Episodic Memory for Feedback-Driven Agent Repair
Date pendingKhang Nhat Hoang Vo, Tam Minh Chu, Anh Trac Duc Dinh +2LLM Self-Correction LLM Agent Memory
Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates · Faculty of Computer Science and Engineering, Ho Chi Minh City University of Technology (HCMUT), VNU-HCM, Ho Chi Minh City, Vietnam

Apr 20, 2026, cs.CLJ/K move · Enter open · S save

Remask, Don't Replace: Token-to-Mask Refinement in Diffusion Large Language Models

Lin Yao

School of Computer Science, Shanghai Jiao Tong University, Shanghai, 200240, China · Zhongguancun Academy, Beijing, 100097, China

LLM Self-Correction Multi-Token Prediction LLM Reliability Diffusion Language Model Decoding Diffusion Language Models

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Diffusion large language models (dLLMs) gain speed by committing multiple tokens in parallel at each denoising step, but any erroneous commitment persists as conditioning context and biases every subsequent prediction. LLaDA2.1 repairs such errors with Token-to-Token (T2T) editing, which re-examines previously unmasked tokens and overwrites them when an alternative becomes sufficiently confident. We argue that this replacement action is itself the limiting factor: under polluted context, a confident replacement can propagate the error, while under a multimodal posterior no alternative may be confident enough to trigger an edit. We propose Token-to-Mask (T2M) remasking, a training-free rule that revokes suspicious commitments by resetting them to [M] and lets the subsequent mask-filling steps re-predict them from a cleaner context. T2M improves accuracy by +13.33 points on AIME 2025 and +8.56 points on CMATH. These results suggest that, for parallel discrete generators, remasking suspect tokens rather than overwriting them is a more reliable self-correction primitive.

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