Analogical Reasoning

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5 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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Compound interpretation is based on analogy
Oct 1, 2026Tian Shen, Harald BaayenAnalogical Reasoning Distributional Semantics
School of Foreign Languages, Northwest University, Xi’an, China · Department of Quantitative Linguistics, University of Tübingen, Tübingen, Germany
What Can Analogy Tell Us About Artificial Consciousness?
Oct 1, 2026Keith J. Holyoak, Martin M. MontiAnalogical Reasoning Machine Consciousness
Department of Psychology, University of California, Los Angeles · Brain Injury Research Center, Department of Neurosurgery, University of California, Los Angeles
Unifying Video Tasks via Spatiotemporal Analogy
Sep 27, 2026Chia-Hsiang Kao, Belinda Zeng, Bharath Hariharan +1Analogical Reasoning In-Context Learning
Cornell University · Meta
SMTB: Fast Structure-Mapping with Tight Bounds
Sep 22, 2026Daniel Weitekamp, Christopher MacLellanCognitive Modeling Analogical Reasoning
School of Interactive Computing, Georgia Tech, Atlanta, GA 30332 USA
Semiotic Relations and Proof Methods: A Cross-Genre Study of Argument Structure with Large Language Models
Sep 14, 2026Edirlei Soares de Lima, Marco A. Casanova, Antonio L. FurtadoAnalogical Reasoning
Academy for AI, Games and Media Breda University of Applied Sciences Breda, The Netherlands · Department of Informatics PUC-Rio Rio de Janeiro, Brazil
Proportional Analogies on Probability Distributions via Bayesian Updating
Aug 12, 2026Pierre-Alexandre MurenaAnalogical Reasoning
Hamburg University of Technology (TUHH) Research Group on Human-Centric Machine Learning
GEB-Bench: Abstract Structures Told in Many Voices
Aug 4, 2026Tong Zhang, Zhiyuan Shi, Yun Peng +1Analogical Reasoning Multimodal Reasoning
Fudan University · Peking University
ADAGE: A Language-Agnostic Pipeline for Analogical Reasoning Evaluation
Jul 25, 2026Ahmed Haj Ahmed, Alvin GrissomMultilingual Language Model Evaluation Reasoning Evaluation
Haverford College
Analogical Deep Research: Retrieving and Integrating Historical Analogies for Foresight Analysis
Jul 15, 2026Yongqiang Chen, Guangyi Chen, Yuewen Sun +1Analogical Reasoning LLM Agent Evaluation
1Mohamed bin Zayed University of Artificial Intelligence · 2Carnegie Mellon University
Why Pure Reasoning is Not Enough: Nature as the Source of Mathematical Innovation
Jul 5, 2026Charanjit S. Jutla, Vimal SharmaAnalogical Reasoning Mathematical Reasoning
IBM T. J. Watson Research Center Yorktown Heights, NY 10598 · Independent Melbourne, FL 32940
From numerical proportions to analogical proportions between probabilities
Jun 22, 2026Henri Prade, Gilles RichardAnalogical Reasoning
IRIT · Cr Rose Dieng-Kuntz · Toulouse, 31400, France
Learning to Reason by Analogy via Retrieval-Augmented Reinforcement Fine-Tuning
Jun 11, 2026Zilin Xiao, Qi Ma, Chun-cheng Jason Chen +4Analogical Reasoning RL for Language Model Reasoning
Meta Superintelligence Labs · Rice University · Work partially done during internship at Meta
Representing Research Attention as Contextually Structured Flows
Jun 4, 2026Jessica Rodrigues, Angelo Salatino, Gard Jenset +1Representation Learning Analogical Reasoning
University of Oxford, United Kingdom · The Open University, United Kingdom · Independent researcher, United Kingdom
Teaching Through Analogies: A Modular Pipeline for Educational Analogy Generation
May 22, 2026Mariam Barakat, Ekaterina KochmarLLM Evaluation LLM Grounding
Mohamed bin Zayed University of Artificial Intelligence, UAE
VisAnalog: A Diagnostic Suite for Visual Concept Transfer on Natural Images
May 22, 2026Zhaonan Li, Kyle R. Chickering, Bangzheng Li +13VLM Evaluation Analogical Reasoning
Arizona State University · Luma AI · UC Davis
Unlocking LLM Creativity in Science through Analogical Reasoning
May 11, 2026Andrew Shen, Shaul Druckmann, James ZouAnalogical Reasoning Large Language Model-Guided Scientific Discovery
Stanford University
In Data or Invisible: Toward a Better Digital Representation of Low-Resource Languages with Knowledge Graphs
May 7, 2026Ndeye-Emilie MbengueAnalogical Reasoning Low-Resource Language Processing
WIMMICS · Université Côte d’Azur, Inria, CNRS, I3S, Sophia-Antipolis, France
Structural Ranking of the Cognitive Plausibility of Computational Models of Analogy and Metaphors with the Minimal Cognitive Grid
May 2, 2026Alessio Donvito, Antonio LietoLLM Evaluation Computational Cognitive Modeling
University of Bari, Italy · University of Salerno, CIIT Lab / ICAR-CNR, Italy
Yanasse: Finding New Proofs from Deep Vision's Analogies, Part 1
Apr 19, 2026Alexandre LinharesAutomated Theorem Proving Analogical Reasoning
Getulio Vargas Foundation · ARGO LABS

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Compound interpretation is based on analogy

Tian Shen, Harald Baayen

School of Foreign Languages, Northwest University, Xi’an, China · Department of Quantitative Linguistics, University of Tübingen, Tübingen, Germany

Analogical Reasoning Distributional Semantics Computational Psycholinguistics Compositional Generalization

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How compound meanings are best predicted from constituent meanings remains a central question in computational models of lexical semantics. Comparing different computational models provides a way to evaluate alternative accounts of how semantic information is combined during compound comprehension. We propose a new model, the Compound Analogy Model (CAM), that predicts a compound's embedding by adding its constituent embeddings together with the average shift vectors of the two constituents' compound families. The resulting model is parameter-free and exploits local analogical structure in the semantic space. We evaluated CAM against the CAOSS model on Mandarin Chinese compounds. CAM consistently achieved higher prediction accuracy than CAOSS on both training and held-out data, with the exception of three-character compounds, for which analogical generalization is constrained by both small constituent families and a pronounced imbalance in family size between the two constituents. The advantage of CAM remained when evaluation was based on frequency-defined train-test splits that better approximate generalization from familiar to novel compounds. To assess the cognitive plausibility of the two models, we further examined whether model-derived semantic measures predict visual lexical decision latencies for two-character compounds. Predictors derived from CAM provided improved prediction for response latencies compared to predictors derived from the CAOSS model. These findings indicate that compound meaning is better characterized as local analogical generalization than as the application of a learned global linear transformation, and demonstrate that analogical semantic structure provides a cognitively plausible basis for compound comprehension.

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