Abductive Reasoning in Language Models

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Thinking effort aligns between humans and reasoning models in abductive reasoning
Sep 1, 2026Henry ArthurLLM Alignment Cognitive Modeling
Center for Mind/Brain Sciences, University of Trento, Italy
When the Canonical Completion Is Wrong: Formalizing and Measuring the Jump in Large Language Models
Aug 22, 2026Dai Shi, Xiaoyu Li, José Miguel Hernández-LobatoAbductive Reasoning in Language Models LLM Evaluation
University of Cambridge · University of New South Wales
CEDAR-GRPO: Process-Aware Reinforcement Learning for General Abductive Reasoning in LLMs
Aug 14, 2026Moein Salimi, Danial Parnian, Shaygan Adim +6Abductive Reasoning in Language Models Abductive Reasoning
Sharif University of Technology · University of Tehran
LLMs Don't Pay for the Jump
Aug 14, 2026Paras Balani, Subhrakanta PandaAbductive Reasoning in Language Models Abductive Reasoning
Department of Mathematics and Department of Computer Science, Birla Institute of Technology and Science, Pilani, Hyderabad Campus, Jawahar Nagar, Kapra Mandal, Medchal District, Telangana 500078, India · Department of Computer Science, Birla Institute of Technology and Science, Pilani, Hyderabad Campus, Jawahar Nagar, Kapra Mandal, Medchal District, Telangana 500078, India
Don't Let Me Ask for It: LLMs Show Deficiencies in Active Multi-Turn Information Acquisition for Abductive Inference
Aug 4, 2026Shahrukh Mohiuddin, Chalamalasetti Kranti, Sherzod Hakimov +1LLM Evaluation Multi-Turn Language Model Reasoning
Computational Linguistics, Department of Linguistics University of Potsdam, Germany · German Research Center for Artificial Intelligence (DFKI), Berlin, Germany
LLMs Can See the Smoke but not the Fire: Evaluating Abductive Reasoning with Elenchos
Jul 14, 2026Julius Steiglechner, Lucas Mahler, Gabriele LohmannLLM Evaluation Abductive Reasoning in Language Models
Dept. of Biomedical Magnetic Resonance, University Hospital Tübingen, Tübingen, Germany · Max-Planck-Institute for Biological Cybernetics, Magnetic Resonance Center, Tübingen, Germany
Wiring the 'Why': A Unified Taxonomy and Survey of Abductive Reasoning in LLMs
Apr 9, 2026Moein Salimi, Shaygan Adim, Danial Parnian +3Abductive Reasoning in Language Models Abductive Reasoning
Department of Computer Engineering, Sharif University of Technology · Department of Mathematical Sciences, Sharif University of Technology
MolQuest: A Benchmark for Agentic Evaluation of Abductive Reasoning in Chemical Structure Elucidation
Mar 26, 2026Taolin Han, Shuang Wu, Jinghang Wang +4Abductive Reasoning in Language Models Scientific Reasoning in Language Models
Alibaba Group, Hangzhou, China
Abductive Reasoning with Syllogistic Forms in Large Language Models
Mar 6, 2026Hirohiko Abe, Risako Ando, Takanobu Morishita Kentaro Ozeki +2Abductive Reasoning in Language Models Abductive Reasoning
Keio University, Tokyo, Japan · The University of Tokyo, Tokyo, Japan
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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Thinking effort aligns between humans and reasoning models in abductive reasoning

Henry Arthur

Center for Mind/Brain Sciences, University of Trento, Italy

LLM Alignment Cognitive Modeling Abductive Reasoning in Language Models Large Reasoning Models

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A major question in cognitive modeling concerns the behavioral alignment between large language models and humans across linguistic and non-linguistic tasks. Unlike standard LLMs, large reasoning models (LRMs) are optimized with reinforcement learning from verifiable rewards, encouraging correct solutions to reasoning tasks rather than preference-aligned responses. Recent work (de Varda et al., 2025) investigates the cost of thinking in humans and LRMs by comparing human reaction times with model reasoning traces across a range of reasoning tasks. We isolate this alignment by turning to abductive reasoning: unlike deductive tasks, its difficulty cannot be inferred from formal structure and offers no shortcuts a model could exploit to mimic effort without genuine search, providing firmer ground for empirical claims of shared effort. We find further evidence of alignment between LRM and human reasoning effort, as well as evidence that models and humans tend to make similar errors. Finally, we show that decoding methods that let models explore multiple reasoning paths increase alignment in reasoning cost between humans and LRMs across the three models tested.