Algorithmic Reasoning

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8 papers in the last four weeks, against 2 the four weeks before. 0.1% of all new papers.

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Finding the Evidence: Discovering Decision-Supporting Tokens for On-Policy Reasoning Distillation
Jun 22, 2026Jinwei Xiao, Zhuowen Han, Yueqing Sun +6CoT Distillation On-Policy Distillation
Meituan Longcat Team · TJUNLP Lab, College of Intelligence and Computing, Tianjin University · Nanjing University
A Verifiable Search Is Not a Learnable Chain-of-Thought
Jun 20, 2026Harsh PatelInference-Time Search Memorization in Language Models
Independent Researcher
RACL: Reasoning-Agent Control Layers for Continuous Metaheuristic Learning
Jun 18, 2026Antón Asla ManzárragaMetaheuristic Optimization Algorithmic Reasoning
Independent Researcher
Efficiently Representing Algorithms With Chain-of-Thought Transformers
Jun 18, 2026Yanhong Li, Anej Svete, Ashish Sabharwal +1Transformer CoT Reasoning
Allen Institute for AI · ETH Zürich
Fixed-Point Reasoners: Stable and Adaptive Deep Looped Transformers
Jun 16, 2026Sajad Movahedi, Vera Milovanović, Shlomo Libo Feigin +5Recurrent Transformers Algorithmic Reasoning
ELLIS Institute Tübingen, Max Planck Institute for Intelligent Systems, Tübingen AI Center · ETH Zurich · Swiss Institute of Bioinformatics +2
Is Code Better Than Language for Algorithmic Reasoning
Jun 14, 2026Terry Tong, Yu Feng, Surbhi Goel +1LLM Evaluation LLM Tool Use
University of Pennsylvania · 2Oracle AI
Structural Grid Descriptors Predict Within-Task Solver Success on ARC-AGI
Jun 8, 2026Ayan PendharkarARC-AGI Algorithmic Reasoning
Step-by-Step Optimization-like Reasoning in LLMs over Expanding Search Spaces
Jun 3, 2026Nicolás Astorga, Nabeel Seedat, Mihaela van der SchaarInference-Time Search Large Language Model-Guided Optimization
University of Cambridge
Contrastive Neural Algorithmic Reasoning for Graph Coloring
Jun 2, 2026Thien Le, Tianyu Zhao, Melanie WeberGraph Neural Networks Graph Representation Learning
Harvard University SEAS · Harvard University T.H. Chan School of Public Health
CRAFTQA: A Code-Driven Adaptive Framework for Complex Structured Data Reasoning
Jun 1, 2026Chengtao Gan, Zhiqiang Liu, Long Jin +3Table QA LLM-Based Program Synthesis
Zhejiang University · JIUTIAN Research, Beijing, China · Ant Group +1
Richer Representations for Neural Algorithmic Reasoning via Auxiliary Reconstruction
May 30, 2026Jiafu Huang, Chao Peng, Chenyang Xu +7Neural Algorithmic Reasoning State Representation Learning
Shanghai Key Laboratory of Trustworthy Computing, East China Normal University, China · Overseas Education College, Xiamen University, China · Dept. of Electrical and Electronic Engineering, The University of Melbourne, Australia
LinTree: Improving LLM Reasoning with Explicitly Structured Search Histories
May 29, 2026Liwei Kang, Yee Whye Teh, Wee Sun LeeInference-Time Search Tree of Thoughts
National University of Singapore · University of Oxford
Neuro-symbolic Syntactic Parsing: Shaping a Neural Network with the CYK Algorithm
May 29, 2026Fabio Massimo Zanzotto, Federico Ranaldi, Giorgio SattaNeural Algorithmic Reasoning Recurrent Neural Networks
Human-centric ART, University of Rome Tor Vergata, Italy · DEI, University of Padua, Italy
Discovering a Zeta Map Algorithm on Dyck Paths via Mechanistic Interpretability
May 28, 2026Xiaoyu Huang, Blake Jackson, Kyu-Hwan LeeAutomated Algorithm Discovery Algorithmic Reasoning
Department of Mathematics, Temple University, Philadelphia, PA, USA · Institute for Computer-Aided Reasoning in Mathematics, Carnegie Mellon University, Pittsburgh, PA, USA · Department of Mathematics, University of Connecticut, Storrs, CT, USA +1
Open Problem: Separating Geometric and Algorithmic Compression via Cayley-Table Completion
May 28, 2026Dongsung HuhStatistical Learning Theory Algorithmic Reasoning
Revealing Algorithmic Deductive Circuits for Logical Reasoning
May 27, 2026Phuong Minh Nguyen, Tien Huu Dang, Naoya InoueAttention Head Analysis LLM Interpretability
Japan Advanced Institute of Science and Technology
Boosting Inference with Guided Reasoning: Stochastic Exploration for Recursive Models
May 24, 2026Andrew Corbett, Archit Sood, Anna Tzatzopoulou +2Inference-Time Search Structured Reasoning
digiLab, UK · University of Bristol, UK
Represented Is Not Computed: A Causal Test of Candidate Algorithmic Intermediates in a Transformer
May 21, 2026Ishita Darade, Sushrut ThoratNumerical Reasoning in Language Models Transformer Interpretability
MKSSS’s Cummins College of Engineering for Women, Pune 411052, India · Institute of Cognitive Science, Osnabrück University, Osnabrück 49069, Germany
Equilibrium Reasoners: Learning Attractors Enables Scalable Reasoning
May 20, 2026Benhao Huang, Zhengyang Geng, Zico KolterDynamical Systems Test-Time Scaling
CMU
Generative Recursive Reasoning
May 19, 2026Junyeob Baek, Mingyu Jo, Minsu Kim +3Latent Variable Models Generative Modeling
KAIST · Mila – Québec AI Institute · New York University +1
Adaptive Generate-Rank-Verify: Inference-Time Search with Costly Verification
May 17, 2026Shaddin Dughmi, Mahdi Haghifam, Yusuf Hakan KalayciAdaptive Inference Inference-Time Search
University of Southern California · Data Science Institute at the University of Chicago · Northwestern University +2
Algorithmic Constitutionalism
May 16, 2026Oren Perez, Nurit WimerContent Moderation AI Governance
Process Rewards with Learned Reliability
May 15, 2026Jinyuan Li, Langlin Huang, Chengsong Huang +5Reward Modeling Process Reward Models
Washington University in St. Louis · Singapore University of Technology and Design
Reasoning Models Don't Just Think Longer, They Move Differently
May 14, 2026Anders Gjølbye, Lars Kai Hansen, Sanmi KoyejoLLM Interpretability Representation Geometry in Language Models
Technical University of Denmark · Stanford University
OpenDeepThink: Parallel Reasoning via Bradley-Terry Aggregation
May 14, 2026Shang Zhou, Wenhao Chai, Kaiyuan Liu +3Pairwise Comparison Test-Time Optimization
1UC San Diego · 2Princeton University · University of Washington +1
Transformers Linearly Represent Highly Structured World Models
May 13, 2026Roman Kniazev, Nathanaël FijalkowCircuit Discovery World Models
LaBRI, CNRS · University of Bordeaux · France
First-Passage Prediction of Grokking Delay: ACalibrated Law under AdamW with Causal Validation
May 13, 2026Truong Xuan Khanh, Truong Quynh Hoa, Luu Duc Trung +1Neural Network Memorization Neural Network Training Dynamics
H&K Research Studio, Clevix LLC, Hanoi, Vietnam · Banking Academy of Vietnam, Hanoi, Vietnam
Retrieval-Augmented Tutoring for Algorithm Tracing and Problem-Solving in AI Education
May 13, 2026Mragisha Jain, Tirth Bhatt, Griffin Pitts +6Retrieval-Augmented Generation Intelligent Tutoring Systems
North Carolina State University · University of Pittsburgh · University of California, Berkeley +1
Evolutionary Task Discovery: Advancing Reasoning Frontiers via Skill Composition and Complexity Scaling
May 12, 2026Liqin Ye, Yanbin Yin, Michael Galarnyk +3Evolutionary Optimization Curriculum Learning
Georgia Institute of Technology
MAS-Algorithm: A Workflow for Solving Algorithmic Programming Problems with a Multi-Agent System
May 7, 2026Yuliang Xu, Xiang Xu, Yao Wan +2Competitive Programming Multi-Agent Coding
1Peking University · 2Alibaba Group · 3Huazhong University of Science and Technology

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Finding the Evidence: Discovering Decision-Supporting Tokens for On-Policy Reasoning Distillation

Jinwei Xiao, Zhuowen Han, Yueqing Sun, Zhengxi Lu+5

Meituan Longcat Team · TJUNLP Lab, College of Intelligence and Computing, Tianjin University · Nanjing University

CoT Distillation On-Policy Distillation Teacher-Student Learning Knowledge Distillation Algorithmic Reasoning LLM Reasoning

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On-policy distillation transfers reasoning ability through dense token-level supervision, yet the nature of the transferable signal remains unclear. We discover that reasoning chains contain two types of knowledge that require different discovery mechanisms: decisions (where to branch), which surface through student uncertainty, and evidence (intermediate steps that justify decisions), which hides in positions where the student is confident yet wrong. Current methods capture only decisions; the substantive knowledge in evidence tokens remains untransferred. We propose DEAR(Decision-Evidence Aware Reasoning Distillation), which first identifies decisions via student entropy, then discovers their supporting evidence through hidden-state cosine similarity to decision anchors, boosted by teacher-student divergence to prioritize the largest knowledge gaps. Across three student-teacher configurations on math and code benchmarks, DEAR consistently outperforms standard OPD, with up to +2.5pp on competition math and +5.7pp on code generation.

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Latest papers 86