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This paper proposes a probabilistic extension to neuro-symbolic AGI robots using Belnap's typed intensional first-order logic. It introduces global and local symmetry transformations to preserve knowledge and enable real-time decisions, with neural networks computing probability density based on maximum information entropy.
Introduces CANDI-QA, a dataset to evaluate LLMs on contextual alignment in niche domains, comprising information assistance and applied inference questions. Systematic evaluations across ten LLMs show challenges, and a lightweight neuro-symbolic framework MTSS-Net is proposed as baseline.
This paper presents KGRL, a neuro-symbolic algorithm that uses domain knowledge expressed in Datalog to prune actions and constrain parameters in Parametrized Action Markov Decision Processes, improving sample efficiency and episodic return over state-of-the-art baselines.
This paper proposes a neuro-symbolic approach that integrates a MaxSAT oracle as a consistency validator to guide Vision-Language Models (VLMs) in solving Sudoku puzzles, improving logical consistency and the number of solved instances.
This paper proposes a theoretical framework for intelligence as atomic compression and compositional reuse, introducing the Compression Calculus and the Compounding Cascade thesis.
Introduces ZendoWorld, a controlled interactive environment for evaluating AI agents on active visual concept induction, where agents must perceive scenes, infer hidden logical rules, and design informative experiments. Experiments with various agent classes reveal that high prediction accuracy does not guarantee rule recovery, and VLM-based agents struggle with informative experimentation, highlighting gaps compared to human inductive reasoning.
RimRule proposes a neuro-symbolic method that distills compact, interpretable rules from failure traces using the Minimum Description Length principle, improving LLM tool-use performance without modifying weights, and demonstrating rule portability across models.
This paper proposes a ReAct-style agentic setup that combines LLM reasoning with verifiable feedback from SageMath, evaluating it on research-level mathematical problems. Results show substantial performance gains across models, with GPT-5.5 achieving the highest solve rate of 75.2%.
InvWeaver is a neuro-symbolic framework that uses LLMs and deductive feedback to synthesize loop invariants for programs with multiple interacting loops, outperforming existing methods on a benchmark suite.
This paper introduces Agentic SABRE, an uncertainty-aware neuro-symbolic multi-agent framework for adaptive ransomware detection that fuses semantic and behavioral evidence with Monte Carlo Dropout and interpretable risk-uncertainty triage, demonstrating improved robustness and explainability.
This paper introduces G-RRM, a neuro-symbolic approach that uses recurrent reasoning models to guide symbolic solvers for constraint satisfaction problems, showing significant speedups in certain conditions.
Nankai University and Lenovo collaborated to propose Graph of States (GoS), a neuro-symbolic framework for general abductive reasoning, which uses explicit belief states and state machines to control multi-agent collaboration, achieving significant improvements in medical diagnosis and system fault diagnosis tasks. This work was accepted at ICML 2026.
This paper presents PASE, a neuro-symbolic framework that uses LLMs to generate structured recovery plans for cloud systems and verifies them via a neural-symbolic world model, achieving over 40% reduction in recovery time.
This paper introduces PACE, a modular neuro-symbolic framework that combines a neural predictive model with symbolic reasoning to generate counterfactual explanations that respect domain-specific feasibility constraints. A case study on the Adult Income dataset demonstrates that incorporating symbolic rules yields more plausible and actionable explanations.
This paper introduces SD-GPS, a solver-driven framework for geometry problem solving that uses autoformalization guided by solver feedback and verified theorem proposing to overcome bottlenecks in neuro-symbolic systems.
A new neuro-symbolic world model is claimed to be the first of its kind for stock market prediction, offering zero-shot capabilities.
KG-TRACE is a neuro-symbolic framework that integrates a WHO mutation knowledge graph with a neural genomic model for antimicrobial resistance prediction, achieving high accuracy and introducing a Biological Grounding Ratio metric to ensure alignment with established biological knowledge.
Introduces Neuro-Symbolic Drive, a framework that uses rule-grounded reasoning traces from classical planners to fine-tune a driving VLA (Qwen3.5-4B), achieving significant reductions in average displacement error and miss rate compared to standard CoT reasoning.
This paper formalizes the 'Impedance Mismatch' between foundation models and knowledge graphs, and proposes a theoretical roadmap for neuro-symbolic fusion using structured residual streams, vector symbolic architectures, and orthogonal subspace editing.
The author describes building a Minsky brain: a runtime of 40+ LLM agents wired in a connectome and staged phylogenetically to simulate a brain. They ask for advice on where to post this project on Reddit.