Neuro-Symbolic Reasoning

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11 papers in the last four weeks, up 57% on the four weeks before. 0.1% of all new papers.

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Bilevel Planning with Learned Symbolic Abstractions from Interaction Data
Mar 9, 2026Fatih Dogangun, Burcu Kilic, Serdar Bahar +1Hierarchical Planning Symbolic Planning
Department of Computer Engineering, Bogazici University, Istanbul, Türkiye. · Department of Computer Engineering, Middle East Technical University, Ankara, Türkiye.
TT-Sparse: Learning Sparse Rule Models with Differentiable Truth Tables
Mar 8, 2026Hans Farrell Soegeng, Sarthak Ketanbhai Modi, Thomas PeyrinBoolean Function Learning Interpretable ML
School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore.
Ontology-Guided Neuro-Symbolic Inference: Grounding Language Models with Mathematical Domain Knowledge
Feb 19, 2026Marcelo LabreRetrieval-Augmented Generation LLM Grounding
Advanced Institute for Artificial Intelligence (AI2)
Neuro-Symbolic Strong-AI Robots with Closed Knowledge Assumption: Learning and Deductions
Feb 18, 2026Zoran MajkicLogical Reasoning Knowledge Representation
ISRST, Tallahassee, FL, USA
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
Achieving Olympiad-Level Geometry Large Language Model Agent via Complexity Boosting Reinforcement Learning
Dec 11, 2025Haiteng Zhao, Junhao Shen, Yiming Zhang +7LLM Agents Reinforcement Learning
1Shanghai AI Laboratory · 2Shanghai Jiao Tong University · 3Peking University +2
Modal Logic Neural Networks
Dec 3, 2025Antonin Sulc, Noor NaddourLogical Reasoning Neuro-Symbolic Reasoning
Lawrence Berkeley National Laboratory, Berkeley, CA, USA · The University of Queensland, Brisbane, QLD, Australia
Bridging Symbolic Control and Neural Reasoning in LLM Agents -- The Structured Cognitive Loop
Nov 21, 2025Myung Ho KimNeuro-Symbolic Reasoning AI Agent Governance
JEI University
Neurosymbolic Characterization for Reliable Access Control Policy Analysis
Oct 23, 2025Adarsh Vatsa, Bethel Hall, William EiersNeuro-Symbolic Reasoning Access Control
Stevens Institute of Technology
RLIE: Rule Generation with Logistic Regression, Iterative Refinement, and Evaluation for Large Language Models
Oct 22, 2025Yang Yang, Hua XU, Zhangyi Hu +1Logistic Regression Neuro-Symbolic Reasoning
The Hong Kong University of Science and Technology (Guangzhou), Guangzhou 511400, China · Institute of Deep Perception Technology, JITRI, Wuxi 214000, China
CQD-SHAP: Explainable Complex Query Answering via Shapley Values
Oct 17, 2025Parsa Abbasi, Stefan HeindorfNeuro-Symbolic Reasoning Knowledge Graph Question Answering
Department of Computer Science, Paderborn University, Germany
Reliable Proof Generation with LLMs via Analogical Retrieval and Symbolic Verification: A Case Study in Euclidean Geometry
May 20, 2025Oren Sultan, Eitan Stern, Dafna ShahafAutomated Theorem Proving Neuro-Symbolic Reasoning
The Hebrew University of Jerusalem
Neuro-symbolic Weak Supervision: Theory and Semantics
Mar 24, 2025Nijesh Upreti, Vaishak BelleWeakly Supervised Learning Neuro-Symbolic Reasoning
School of Informatics, University of Edinburgh, Edinburgh, UK
VL-Nav: Neuro-Symbolic Reasoning-based Vision-Language Navigation
Feb 2, 2025Yi Du, Taimeng Fu, Zhipeng Zhao +6Robot Navigation Vision-Language Navigation
Spatial AI & Robotics Lab, University at Buffalo, USA. · 2Robotics Institute, Carnegie Mellon University, USA. · 1Spatial AI & Robotics Lab, University at Buffalo, USA.
Answer Set Networks: Casting Answer Set Programming into Deep Learning
Dec 19, 2024Arseny Skryagin, Daniel Ochs, Philipp Deibert +3Answer Set Programming Neuro-Symbolic Reasoning
AI & Machine Learning Group, CS Dept., TU Darmstadt · Uncertainty in AI Group, Dept. of Mathematics and CS, TU Eindhoven · Hessian Center for AI (hessian.AI) +1
MARS: A neurosymbolic approach for interpretable drug discovery
Oct 2, 2024Lauren Nicole DeLong, Yojana Gadiya, Paola Galdi +2Interpretable ML AI-Assisted Scientific Research
School of Informatics, University of Edinburgh, 10 Crichton St, Edinburgh, EH8 9AB, United Kingdom. · Cancer Research UK Scotland Institute, Switchback Road, Glasgow, G61 1BD, United Kingdom. · Enveda Therapeutics, 5700 Flatiron Parkway, Boulder, 80301, Colorado, United States of America.
PACE: A Neuro-Symbolic Framework for Plausible and Actionable Counterfactual Explanations
Date pendingPavel Iakovets, Liyanapathiranage Sudeepika Wajirakumari Samarathunga, Martin Thomas Horsch +1Counterfactual Explanations Neuro-Symbolic Reasoning
University of Klagenfurt, Universitätsstraße 65/67, 9020 Klagenfurt am Wörthersee, Austria · Norwegian University of Life Sciences, Elizabeth Stephansens v. 15, 1433 ˚As, Norway
NSIDDx: A Design Framework for Neuro-Symbolic, Practitioner-First Differential Diagnosis in Low-Resource Settings
Date pendingAarav SinghRare Disease Diagnosis Healthcare
IIIT Naya Raipur
Beyond Solver Verdicts: Generative Reward Models for Autoformalization
Date pendingVikash Singh, Debargha Ganguly, Aman Goel +5Reward Modeling Formal Verification
Case Western Reserve University · Amazon Web Services

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Bilevel Planning with Learned Symbolic Abstractions from Interaction Data

Fatih Dogangun, Burcu Kilic, Serdar Bahar, Emre Ugur

Department of Computer Engineering, Bogazici University, Istanbul, Türkiye. · Department of Computer Engineering, Middle East Technical University, Ankara, Türkiye.

Hierarchical Planning Symbolic Planning Robotic Manipulation Robot Task Planning Neuro-Symbolic Reasoning

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Intelligent agents must reason over both continuous dynamics and discrete representations to generate effective plans in complex environments. Previous studies have shown that symbolic abstractions can emerge from neural effect predictors trained with a robot's unsupervised exploration. However, these methods rely on deterministic symbolic domains, lack mechanisms to verify the generated symbolic plans, and operate only at the abstract level, often failing to capture the continuous dynamics of the environment. To overcome these limitations, we propose a bilevel neuro-symbolic framework in which learned probabilistic symbolic rules generate candidate plans rapidly at the high level, and learned continuous effect models verify these plans and perform forward search when necessary at the low level. Our experiments on multi-object manipulation tasks demonstrate that the proposed bilevel method outperforms symbolic-only approaches, reliably identifying failing plans through verification, and achieves planning performance statistically comparable to continuous forward search while resolving most problems via efficient symbolic reasoning.

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