Symbolic Reasoning in Language Models

Latest papers

18 papers2 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Probing for Long-Horizon Deductive Reasoning Capabilities in Language Models with Prolog
Oct 8, 2026Hadeel Al-Negheimish, Jasna Ilieva, Yoon KimLLM Reasoning Long-Horizon LLM Reasoning
King Saud University · Massachusetts Institute of Technology
In-Context Learning Amplifies a Latent Symbolic Circuit
Sep 28, 2026Melissa WesselLLM Interpretability In-Context Learning
Independent Researcher.
A Verifier-Guided Explainable Reasoning Framework with Gold-Anchored QLoRA, Task-Aware Mixture-of-Experts, and Group-Relative RLVR
Sep 4, 2026Thi Kim Trang Vo, Nam Tien Le, Thi Kim Nguyet Vo +2Symbolic Reasoning in Language Models Question Answering
University of Information Technology (UIT), Ho Chi Minh City, Vietnam · Vietnam National University, Ho Chi Minh City, Vietnam · Ho Chi Minh City University of Technology (HCMUT), Vietnam +2
A Pre-Specified Construction-Confirmation Test of Operation-Level Causal Transfer Across Finite Isomorphic Symbolic Domains
Aug 16, 2026Xinyi ShanSymbolic Reasoning in Language Models Causal Reasoning in Language Models
Independent Researcher
Reason Popper-ly: Patching In-Context Reasoning with Inductive Logic Programming
Jul 25, 2026Zirong Chen, Meiyi MaReasoning Trace Analysis Reasoning Consistency in Language Models
Department of Computer Science, Vanderbilt University, USA
Are you Talking Logic to Me? Assessing Language Models Syllogistic Reasoning Capabilities
Jul 22, 2026Hanna Abi Akl, Fabien Gandon, Catherine Faron +1Knowledge Representation Structured Reasoning
Université Côte d’Azur, Inria, CNRS, I3S, Sophia Antipolis, France · Data ScienceTech Institute, Paris, France
When LLMs Develop Languages: Symbolic Communication for Efficient Multi-Agent Reasoning
Jun 28, 2026Zhengqi Pei, Qingming Huang, Shuhui WangMulti-Agent LLM Systems Efficient Language Model Reasoning
State Key Lab of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China · School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China
HOLMES: Evaluating Higher-Order Logical Reasoning in LLMs
Jun 22, 2026Yucheng Wu, Jundong Xu, Mingzhen Ju +4Logical Reasoning LLM Evaluation
State Key Laboratory of Multimedia Information Processing, Peking University · School of Computer Science, Peking University · School of Computing, National University of Singapore +3
SEF-CLGC at SemEval-2026 Task 11: Logical Notation Impact on Language Model Performance
Jun 8, 2026Hanna Abi Akl, Fabien Gandon, Catherine Faron +1Logical Reasoning Symbolic Reasoning
Université Côte d’Azur, Inria, CNRS, I3S, Sophia Antipolis, France · Data ScienceTech Institute, Paris, France
Imbuing Large Language Models with Bidirectional Logic for Robust Chain Repair
Jun 3, 2026Zehua Cheng, Wei Dai, Jiahao Sun +1LLM Reasoning Efficient Language Model Inference
Department of Computer Science, University of Oxford, UK · Institute of Logic and Computation, TU Wien, Austria
From Early Encoding to Late Suppression: Interpreting LLMs on Character Counting Tasks
Apr 1, 2026Ayan Datta, Mounika Marreddy, Alexander Mehler +2Symbolic Reasoning in Language Models LLM Interpretability
IIIT Hyderabad · Goethe University, Frankfurt am Main, Germany · University of Sheffield, UK
Unveiling the Reasoning Process of Large Language Models
Mar 31, 2026Junjie Zhang, Zhen Shen, Xisong Dong +1Symbolic Reasoning in Language Models LLM Interpretability
Institute of Automation, Chinese Academy of Sciences · School of Artificial Intelligence, University of Chinese Academy of Sciences
The Lattice Representation Hypothesis of Large Language Models
Mar 1, 2026Bo XiongSymbolic Reasoning in Language Models Representation Geometry in Language Models
Stanford University, United States
Neurosymbolic Language Reasoning as Satisfiability Modulo Theory
Feb 20, 2026Hyunseok Oh, Sam Stern, Youngki Lee +1Constraint-Guided Reasoning in Language Models Neuro-Symbolic Reasoning
Seoul National University · U. Mass. Amherst · Microsoft
Neuro-Symbolic Synergy for World Modeling
Feb 11, 2026Hongyu Zhao, Siyu Zhou, Haolin Yang +2LLM World Models World Model Learning
Department of Computer Science University of Maryland College Park, MD 20742, USA · Faculty of Engineering and IT Australian AI Institute University of Technology Sydney Sydney, Australia · Department of Statistics University of Chicago Chicago, IL 60637, USA +2
ChaosBench-Logic: A Benchmark for Logical and Symbolic Reasoning on Chaotic Dynamical Systems
Jan 5, 2026Noel ThomasSymbolic Reasoning in Language Models LLM Reasoning
Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)
Training Language Models to Use Prolog as a Tool
Dec 8, 2025Niklas Mellgren, Peter Schneider-Kamp, Lukas Galke PoechReward Hacking Logical Reasoning
University of Southern Denmark
Thought-Like-Pro: Enhancing Reasoning of Large Language Models through Self-Bootstrapped Prolog-based Chain-of-Thought
Jul 18, 2024Xihe Qiu, Yongxin Deng, Xiaoyu Tan +2Symbolic Reasoning in Language Models LLM Training
Shanghai University of Engineering Science · National University of Singapore · University of Technology Sydney

Oct 8, 2026J/K move · Enter open · S save

Probing for Long-Horizon Deductive Reasoning Capabilities in Language Models with Prolog

Hadeel Al-Negheimish, Jasna Ilieva, Yoon Kim

King Saud University · Massachusetts Institute of Technology

LLM Reasoning Long-Horizon LLM Reasoning Long-Horizon Reasoning Long-Context Reasoning in Language Models Symbolic Reasoning in Language Models Long-Context Language Model Inference+2

Open paper AskPDF arXiv

Current frontier LLMs can theoretically process long contexts with 1M tokens or more. But to what extent can they go beyond simple retrieval and perform deeper reasoning over such long contexts? We empirically investigate long-horizon reasoning capabilities of LLMs, focusing on deductive logic expressed in Prolog. We construct ProloNg, a synthetic testbed to probe Prolog Long Reasoning, which systematically varies the complexity (reasoning depth) of problems, where the hardest case has a reasoning depth of 22 and 62k context length. We study 8 reasoning models across 5 families of frontier LLMs, and find that performance degrades substantially as reasoning depth grows, with the majority of models approaching chance beyond depth 10.