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This paper presents a framework that augments Large Language Models with geometric vision parsing and symbolic solving to match state-of-the-art multimodal models on complex geometry problems, using a new benchmark from 2025 Chinese Zhongkao exams for evaluation.
This paper introduces Certification-Driven Reinforcement Learning (CDRL), a framework that leverages symbolic reasoning to generate reusable constraints for improving reinforcement learning in combinatorial search spaces, demonstrated in neutrino flavor model discovery with higher valid model rates and efficiency.
This paper introduces the RAIL principles (Reasoning, Assurances, Interfacing, Learning) for designing neurosymbolic AI systems that integrate machine learning with symbolic reasoning, arguing this approach is crucial for reliable, efficient, and trustworthy AI.
Proposes SymCA, an interpretable column annotation framework using LLMs to materialize annotation as a global-to-local symbolic decision process, achieving significant accuracy improvements over baselines.
This paper proposes that AI memory consolidation should recombine knowledge across domains (like dreaming) rather than merely replaying experiences, and demonstrates that cross-domain consolidation improves performance in both neural (LoRA fine-tuning) and symbolic systems.
This paper introduces Inductive Logic Programming to extract symbolic representations of RL policies and proposes novel explainability metrics (activation rate, feature coverage, syntactic and semantic distance) for objective evaluation in single- and multi-agent settings.
GATS introduces a Graph-Augmented Tree Search with a layered world model (symbolic, learned, generative) to eliminate LLM calls during planning, achieving 100% success on synthetic tasks and stress tests, outperforming LATS and ReAct.
This paper presents a novel fuzzy-logic-based extension of Answer Set Programming (ASP) that integrates numerical information with qualitative reasoning using membership functions, demonstrated through a travel recommendation case study.
Introduces RusFinChain, the first Russian-language symbolic benchmark for verifiable chain-of-thought reasoning in finance, spanning 17 domains with 5,280 parameterized examples and enhanced evaluation metrics including fuzzy numeric alignment.
RuleChef is a framework that uses LLMs to generate human-editable, executable rules for NLP tasks, iteratively improving them based on examples and human feedback, resulting in fast, deterministic, and inspectable rule systems.
The paper argues that data-driven machine learning systems, including GPT-5, cannot achieve symbolic-level logical reasoning through scaling alone, due to inherent limitations in distinguishing logical structures from statistical regularities.
This paper presents Process-Verified Reinforcement Learning, using the Lean proof assistant as a process oracle to provide fine-grained tactic-level feedback during training, improving theorem proving performance.
This paper introduces RecurrReason, a difficulty-controlled benchmark of four symbolic logic puzzles to evaluate multi-step reasoning in sequence models. Fine-tuning experiments on T5 and GPT-2 show that architecture determines success more than scale, and that pre-training transfer depends on local transition structure.
Introduces PrologMCP, an open-source server that exposes Prolog as a stateful tool via the Model Context Protocol, enabling LLM agents to delegate reasoning to a symbolic solver. Evaluation shows competitive or superior accuracy on deductive reasoning tasks compared to frontier reasoning LLMs.
This paper proposes Orthogonal Subspace Carving (OSC), a novel memory architecture that enables deep recursive binding in a constant memory footprint by projecting fillers onto the null space of role bases, overcoming the exponential scaling of Tensor Product Representations.
MAVEN is a lightweight symbolic reasoning scaffold that improves generalization in agentic tool calling by using modular verification and adaptive tool orchestration. It achieves significant accuracy gains on a new stress-test benchmark (MAVEN-Bench) and remains competitive with proprietary models at a fraction of the cost.
This paper proposes a symbolic framework that converts redacted police narratives into evidence-linked facts using ontology, semantic parsing (AMR), and reasoning, enabling structured querying of incident details that are typically only available in free text.
Proposes BISON, a system combining learned low-level neural policies with high-level symbolic planning for long-horizon embodied tasks, showing strong generalization and efficiency.
This paper introduces the Neural Rule Inducer (NRI), a foundation model for zero-shot logical rule induction that uses domain-agnostic statistical properties to generalize across tasks without retraining.