arXiv AI
Jul 7
The Changing Role of Symbolic Methods in Artificial Intelligence
arXiv:2607. 05168v1 Announce Type: new Abstract: Why do intelligent systems need to perform explicit symbolic reasoning?
Synthesizing symbolic models of human and machine reasoning at scale
Related stories
arXiv AI
Aug 7
Symbol Grounding in Neuro-Symbolic AI: A Gentle Introduction to Reasoning Shortcuts
arXiv:2510. 14538v3 Announce Type: replace Abstract: Neuro-symbolic (NeSy) AI aims to develop deep neural networks whose predictions comply with prior knowledge encoding, e.
arXiv AI
Jun 24
Neuro-Symbolic Drive: Rule-Grounded Faithful Reasoning for Driving VLAs
arXiv:2606. 23938v1 Announce Type: new Abstract: Driving VLA models incorporating Chain-of-Thought (CoT) reasoning are attractive because they leverage pretrained VLM representations and expose intermediate decisions in natural language, yet current rationales often lack the step-by-step decision semantics needed to keep the rationale causally connected to the planned motion.
arXiv AI
Sep 15
Generative Interpretability via Scalable Neuro-Symbolic Models
The paper argues that as Large Language Models transition from chatbots to agentic systems, the current post-hoc interpretability paradigm is insufficient for safe deployment because it cannot audit or intervene before an output is produced. It proposes a shift to generative interpretability, where a model’s inference process inherently exposes semantically meaningful checkpoints that are human-understandable and can be causally intervened upon. The authors illustrate the advantages of this approach and introduce Neuro‑Symbolic Models as a concrete implementation.
arXiv AI
Aug 6
The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning
arXiv:2608. 04285v1 Announce Type: new Abstract: Neurosymbolic AI systems that integrate machine learning and symbolic reasoning are rapidly gaining attention.
arXiv AI
Sep 1
Stop Before You Fail: Operational Capability Boundaries for Mitigating Unproductive Reasoning in Large Reasoning Models
arXiv:2509.24711v4 Announce Type: replace Abstract: Current answering paradigms for Large Reasoning Models (LRMs) often fail to account for the fact that some questions may lie beyond the model's ope...
arXiv AI
Aug 14
Position: Reasoning is a Learnable Rule-Based Process
arXiv:2608. 12325v1 Announce Type: new Abstract: Autonomous reasoning is among the most scientifically and economically motivating topics in AI today.
arXiv AI
Oct 27
Reasoning Patterns for Efficient AI Coding: An Empirical Study
arXiv:2610.34943v1 Announce Type: cross Abstract: Large Reasoning Models (LRMs) generate long reasoning traces that expose intermediate inference behavior, offering opportunities to study and control...
Hugging Face Trending Papers
Oct 26
Reasoning Patterns for Efficient AI Coding: An Empirical Study
Large Reasoning Models (LRMs) generate long reasoning traces that expose intermediate inference behavior, offering opportunities to study and control reasoning in AI coding. However, the underlying pa...
arXiv Machine Learning
Jun 4
Deliberate Evolution: Agentic Reasoning for Sample-Efficient Symbolic Regression with LLMs
arXiv:2606. 04360v1 Announce Type: cross Abstract: Symbolic regression (SR) discovers compact mathematical expressions from data, yet recent LLM-based evolutionary methods remain sample-inefficient because they rely mainly on scalar feedback such as MSE.
arXiv AI
Jun 11
From Consumption to Reflection: Designing Human-AI Relations for Stable Reasoning
arXiv:2606. 11195v1 Announce Type: cross Abstract: Large language models (LLMs) have transformed how humans access information, but not how we reason with it.
arXiv Computation and Language
Oct 23
Semantic Deception: When Reasoning Models Can't Compute an Addition
The paper introduces the concept of semantic deception, where unfamiliar symbols are used to mislead large language models (LLMs) during simple arithmetic tasks. By redefining digits and operators with novel symbols, the authors test whether LLMs can abstract and manipulate arbitrary symbol systems or fall back on learned semantic associations. Experiments with four LLMs show that misleading semantic cues significantly degrade performance, revealing a reliance on surface-level semantics that can undermine symbolic reasoning.