Implicit Reasoning in Language Models

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5 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

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SanSi: A Looped Typed Decision Model for System 1.5 Thinking
Oct 6, 2026Shuyu Gan, Young-Jun Lee, Dongyeop KangEfficient Language Model Reasoning Implicit Reasoning in Language Models
University of Minnesota
CALR: Continuous Anchored Latent Reasoning via Render-of-Thought Compression
Oct 5, 2026Zhaoyang Wei, Bowen Jiang, Yanchao Hao +5Efficient Language Model Reasoning Latent Visual Reasoning
University of the Chinese Academy of Sciences · Tencent
Base Models Can Reason By Taking a Cue From Training Data
Oct 5, 2026Sophie L. Wang, Amil Dravid, Rulin Shao +3LLM Prompting RL for Language Model Reasoning
MIT · UC Berkeley · University of Washington +1
AURAL: Adaptive Latent Reasoning with Joint Chunk for Speech Language Models
Oct 1, 2026Yuxiang Wang, Kunyu Feng, Yuancheng Wang +12RL for Language Model Reasoning Audio Reasoning
The Chinese University of Hong Kong, Shenzhen · Tencent Hunyuan · Tsinghua University +2
Learning Steganography Is Easy, Learning Steganographic Reasoning Is Hard
Sep 30, 2026Julian Schulz, Lukas Fülle, Rieke FruengelLLM Security Implicit Reasoning in Language Models
Meridian Cambridge · SAIGE
DatalogBench: Evaluating Large Language Models on Text-to-Datalog Synthesis
Sep 29, 2026Yuan Li, Hanyun Jiang, Guowei Tian +2LLM Evaluation LLM-Based Program Synthesis
The State Key Laboratory of Blockchain and Data Security, Zhejiang University · National University of Singapore
Capable yet Parsimonious: Extracting and Characterizing Hidden Chain-of-Thought in Frontier Models
Sep 22, 2026Xiaoyu Luo, Tao Ren, Wenrui Yu +3LLM Interpretability CoT Reasoning
Department of Computer Science Aalborg University, Copenhagen, Denmark · Department of Electronic Systems Aalborg University, Copenhagen, Denmark · Seafill
How Should Reasoning Be Organized in a Transformer's Latent Space?
Sep 12, 2026Hongyu Gu, Chang Liu, Jingwen FuTransformer Attention Implicit Reasoning in Language Models
University of Science and Technology of China Hefei, China · Zhongguancun Academy Beijing, China
Wrong Prediction, Right Answer: Recovering Evidence from Collapsed LLM Sequence Scores
Aug 31, 2026Qiyao Yan, Chenpeng Wang, Liangming PanLLM Evaluation Language Model Calibration
State Key Laboratory of Multimedia Information Processing, Peking University · School of Computer Science, Peking University · YiXin-AILab, YIXIN, Beijing, China +1
Detecting Hidden Chain-of-Thought in Large Language Models with Linguistic, Behavioral, and Mechanistic Indicators
Aug 30, 2026Armaan Singh, Ryan Trinh Le, Jasmine Kaur +5LLM Evaluation LLM Interpretability
1Stanford University · 2York University · 3Facebook
Benchmarking the Benchmarks: Testing the Predictive Validity of Commonsense Benchmarks
Aug 4, 2026Ine Gevers, Walter DaelemansLLM Evaluation Reasoning Evaluation
CLiPS, University of Antwerp
Recursive Vision Language Models for General Symbolic Reasoning
Aug 2, 2026Omid Nejati Manzari, Guillaume Lajoie, Hassan RivazSymbolic Reasoning Implicit Reasoning in Language Models
Concordia University, Montreal, Canada · Mila — Quebec AI Institute · Université de Montréal
Unified Multi-Dimensional Benchmark for Complex Graph Reasoning in Large Language Models
Aug 2, 2026Fali Wang, Ali Al-Lawati, Iliyas Bektas +5LLM Evaluation LLM Reasoning with Graphs
The Pennsylvania State University, University Park, PA, USA · Rensselaer Polytechnic Institute, Troy, NY, US
Would You Walk to the Car Wash? Revealing the Salience Bias of Large Language Models in Commonsense Reasoning
Jul 30, 2026Zheng Wu, Chenhao Xue, Shijie Zheng +3Implicit Reasoning in Language Models LLM Reasoning
School of Computer Science, Shanghai Jiao Tong University · ByteDance Inc
Not All LLM Reasoning is Visible in the Chain-of-Thought
Jul 24, 2026Vatsal Baherwani, Tom Goldstein, Ashwinee PandaCoT Faithfulness LLM Interpretability
New York University · University of Maryland · TogetherAI
Uncovering Latent Reasoning Strategies in Language Models
Jul 20, 2026Awni Altabaa, John LaffertyPosterior Collapse Implicit Reasoning in Language Models
Yale University
Implicit Reasoning Steering via Concept Chaining
Jul 15, 2026Xiao Ye, Sanika Chavan, Yuxi Huang +4Adversarial Attacks on LLMs Reasoning Consistency in Language Models
School of Computing and Augmented Intelligence, Arizona State University · Bellini College of AI, Cybersecurity, and Computing, University of South Florida, Tampa, Florida, USA · Department of Computer Science, University of California, Davis
Reading Between the Dots: Decoding Hidden Computation across Filler Tokens
Jul 3, 2026Kaley Brauer, Claudio Mayrink Verdun, Samuel MarksLLM Auditing LLM Interpretability
Harvard University · 4Anthropic
Invariant Reasoning Directions in Latent Trajectories of Language Models
Jun 28, 2026Arun Vignesh Malarkkan, Manan Roy Choudhury, Utkarsh Byahut +3Reasoning Consistency in Language Models Implicit Reasoning in Language Models
Arizona State University, Tempe, Arizona.
Learning to Refine Hidden States for Reliable LLM Reasoning
Jun 16, 2026Chia-Hsuan Hsu, Jui-Ming YaoRL for Language Model Reasoning Efficient Language Model Reasoning
Implicit Reasoning for Large Language Model-based Generative Recommendation
Jun 12, 2026Yinhan He, Liam Collins, Bhuvesh Kumar +3Implicit Reasoning in Language Models Large Language Model-Based Recommendation
University of Virginia, Charlottesville, VA, USA · Snap Inc., Santa Monica, CA, USA
Multi-Hop Knowledge Composition is Bound by Pretraining Exposure
Jun 8, 2026Yannis Karmim, Luis Marti, Djamé Seddah +1Language Model Pretraining Implicit Reasoning in Language Models
Inria, Paris, France · Inria Chile, Las Condes, Chile · Dept. of Computer Science, Univ. de Chile +1
LoRi: Low-Rank Distillation for Implicit Reasoning
Jun 3, 2026Ryan Solgi, Jiayi Tian, Zheng ZhangCoT Distillation Mathematical Reasoning
University of California-Santa Barbara, USA
Unlocking the Working Memory of Large Language Models for Latent Reasoning
May 28, 2026Lukas Aichberger, Sepp HochreiterMemory-Augmented Language Models Efficient Language Model Reasoning
ELLIS Unit Linz and LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Austria · NXAI GmbH, Linz, Austria
CIRF: Tokenizing Chain-of-Thoughts into Reusable Functional Units for Efficient Latent Reasoning in Large Language Models
May 27, 2026Yukyung Lee, Yumeng Shen, Jinhyeong Park +2CoT Reasoning Efficient Language Model Reasoning
Boston University · Hankuk University of Foreign Studies
MixRea: Benchmarking Explicit-Implicit Reasoning in Large Language Models
May 19, 2026Yuanqing Cai, Ziyi Huang, Minhao Liu +3LLM Evaluation LLM Prompting
Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China
One Refiner to Unlock Them All: Inference-Time Reasoning Elicitation via Reinforcement Query Refinement
Apr 28, 2026Yixiao Zhou, Dongzhou Cheng, zhiliang wu +3LLM Alignment RL for Language Model Reasoning
Zhejiang University · Shanghai Innovation Institute · 1Zhejiang University, 2Shanghai Innovation Institute +9
Thinking Without Words: Efficient Latent Reasoning with Abstract Chain-of-Thought
Apr 24, 2026Keshav Ramji, Tahira Naseem, Ramón Fernandez AstudilloEfficient Language Model Reasoning Implicit Reasoning in Language Models
IBM Research AI
Comparing Human and Large Language Model Interpretation of Implicit Information
Apr 18, 2026Antonio De Santis, Tommaso Bonetti, Andrea Tocchetti +1Implicit Reasoning in Language Models LLM Information Extraction
Politecnico di Milano, Italy
Bypassing the Rationale: Causal Auditing of Implicit Reasoning in Language Models
Feb 3, 2026Anish Sathyanarayanan, Aditya Nagarsekar, Aarush RathoreCoT Faithfulness Faithfulness of Language Model Explanations
BITS Pilani, K. K. Birla Goa Campus

Oct 6, 2026, cs.CLJ/K move · Enter open · S save

SanSi: A Looped Typed Decision Model for System 1.5 Thinking

Shuyu Gan, Young-Jun Lee, Dongyeop Kang

University of Minnesota

Efficient Language Model Reasoning Implicit Reasoning in Language Models LLM Decision-Making Recursive Language Models

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Typed decision models answer a declared question without generating text: a decision head returns a probability for each of the declared options in a single forward pass. A single pass is fast, intuitive System 1 thinking. We study what lies between one pass and generated reasoning: looping, in which the same layers are recursively applied several times before one typed readout. Each loop lets the model revise its hidden state before it commits to an answer, without generating a token; we call this System 1.5 thinking. We propose SanSi, which turns a pre-trained looped language model into a typed decision model. The option probabilities are read after every loop, and every loop is trained with a proper scoring rule, so that one model serves every budget from one loop to eight in a single run. On 10,027 test decisions from 59 sources, SanSi reaches 72.0% accuracy: 13.5 points above a non-looped model of the same shape trained with the same recipe, 5.3 points above a newer non-looped model of its size, and 1.8 points below one with three times the parameters. On two depth-controlled tasks, loops extend the solvable depth beyond the depths seen in training, where the larger single-pass model fails. Used as the judge for policy optimization with reinforcement learning, without gold answers, SanSi raises the generator's F1 by 7.7 points.

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