Inductive Reasoning in Language Models

Latest papers

12 papers1 paper in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Cognitively Decoupled Meta-Induction for Out-of-Distribution Generalization
Oct 19, 2026Yuqian Wang, Yuanzheng Wang, Chaojun Xiao +7Inductive Reasoning in Language Models OOD Generalization
Beijing Institute of Technology · University of Chinese Academy of Sciences · Department of Computer Science and Technology, Tsinghua University +4
Latent Taxonomies: How Property Induction Develops in Language Model Pre-training
Oct 11, 2026Jan Sobotka, Ekaterina Goliakova, Marcin Detyniecki +2Inductive Reasoning in Language Models Inductive Inference
EPFL · AXA · Sorbonne University, CNRS, LIP6 +1
The IOL-AI Challenge: An Open Challenge towards Advancing Linguistic Reasoning
Aug 18, 2026Eduardo Sánchez, Rita Berrada, Dan-Mircea Mirea +8Inductive Reasoning in Language Models LLM Reasoning
University College London & Meta · CentraleSupélec & McGill University · Cohere Labs +6
Invariant Learning Dynamics of Transformers in Inductive Reasoning Tasks
Jul 13, 2026Tiberiu Musat, Tiago Pimentel, Nicolas Zucchet +1Transformer Training Dynamics In-Context Learning
ETH Zurich · Stanford University
Think Through a Bottleneck: Hourglass Reasoning for Rigorous Induction
Jul 13, 2026Huan ZhuStructured Reasoning LLM Self-Refinement
Peking University
Words as Difference Makers: How Large Language Models Determine Causal Structure in Text
Jun 21, 2026Wolfgang PietschCausal World Models LLM Interpretability
tum
FALSIFYBENCH: Evaluating Inductive Reasoning in LLMs with Rule Discovery Games
Jun 3, 2026Leonardo Bertolazzi, Katya Tentori, Raffaella BernardiLLM Evaluation AI Agent Benchmarks
University of Trento · Free University of Bozen-Bolzano
Using Probabilistic Programs to Train Inductive Reasoning in Large Language Models
May 26, 2026Liyi Zhang, Akshay K. Jagadish, Brenden M. Lake +1Language Model Calibration LLM Uncertainty Estimation
Department of Computer Science Princeton University · Princeton AI Lab Princeton University · Department of Psychology and Computer Science Princeton University
Heuristic Classification of Thoughts Prompting (HCoT): Integrating Expert System Heuristics for Structured Reasoning into Large Language Models
Apr 14, 2026Lei Lin, Jizhao Zhu, Yong Liu +3Structured Reasoning in Language Models Constraint-Guided Reasoning in Language Models
Computer Network Information Center, Chinese Academy of Sciences, Beijing, China · Shenyang Aerospace University · ZGC LAB, Beijing,China +1
Watson & Holmes: A Naturalistic Benchmark for Comparing Human and LLM Reasoning
Feb 23, 2026Thatchawin Leelawat, Lewis D GriffinInductive Reasoning in Language Models Reasoning Evaluation
Computer Science, University College London, UK
PBEBench: A Multi-Step Programming by Examples Reasoning Benchmark inspired by Historical Linguistics
May 29, 2025Atharva Naik, Prakam, Yash Mathur +6Program Synthesis Inductive Inference
Carnegie Mellon University · Independent Researcher · Ohio State University
An Incomplete Loop: Deductive, Inductive, and Abductive Reasoning in Language Models
Apr 3, 2024Emmy Liu, Graham Neubig, Jacob AndreasLLM Prompting Abductive Reasoning in Language Models
Language Technologies Institute Carnegie Mellon University · CSAIL Massachusetts Institute of Technology

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Cognitively Decoupled Meta-Induction for Out-of-Distribution Generalization

Yuqian Wang, Yuanzheng Wang, Chaojun Xiao, Ke Xu+6

Beijing Institute of Technology · University of Chinese Academy of Sciences · Department of Computer Science and Technology, Tsinghua University+4

Inductive Reasoning in Language Models OOD Generalization In-Context Learning Meta-Learning

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In-context learning (ICL) tackles downstream tasks conditioned on demonstrations. This paradigm relies heavily on neighbor-based matching between queries and demonstrations, limiting out-of-distribution (OOD) performance. For better generalization, rule-based inductive reasoning derives task-level regularities, which we term task-level inductive representations (TIR), before directly answering. However, existing methods lack generalizable inductive ability across diverse tasks. Inspired by meta-learning, we propose Cognitive-Decoupling meta-Induction (CoDI) for TIR-based inductive reasoning on general tasks. CoDI comprises a Cognitive-Decoupling Framework for query-blind TIR induction, and a Dual-Loop Meta-Inductive Learning method for cross-task inducer optimization. Empirically, CoDI achieves substantial performance gains on five ID and OOD benchmarks, and preserves OOD gains without explicit induction at inference. Code is available at https://github.com/Yuqian-Wang2277/codi-meta-induction.