Symbolic coverage.

Towards Reliable and Robust LLM Planning: Symbolic Feedback-Driven Iterative Self-Refinement Framework

arXiv:2606.27757v1 Announce Type: new Abstract: Large language models (LLMs) have attracted widespread attention from academia and industry, yet their deployment raises critical se…

Senate adopts House-passed Iran resolution in symbolic rebuke of Trump

The Senate approved a House-passed resolution aimed at reining in President Trump on Iran, marking the first time such a measure has made it through both chambers.…

AUDITFLOW: Executable Symbolic Environments for Structured Financial Reporting Verification

Structured financial audit verification is difficult for language-model agents because correctness depends on structured evidence rather than text alone. A model must link reported…

Distilling Answer-Set Programming Rules from LLMs for Neurosymbolic Visual Question Answering

Visual Question Answering (VQA) is the task of answering questions about images, requiring the integration of multimodal input and reasoning. Modular approaches that incorporate lo…

I’m building a free/open-source, LINUX-FIRST, symbolic calculator for desktop — looking for calculator-user feedback

Beyond the Numbers: How Ada Lovelace Envisioned the Dawn of Symbolic Computation (1833–1834)

In the early 1830s, London was a city defined by the clatter of industrial machinery and the soot of...…

Neuro-Symbolic Verification of LLM Outputs for Data-Sensitive Domains (extended preprint)

LLMs deployed in high-stakes domains face fundamental reliability challenges: hallucinations, inconsistencies, and privacy vulnerabilities introduce unacceptable risks where errors…

Query Symbolically or Retrieve Semantically? A Dataset and Method for Semi-Structured Question Answering

Retrieval-Augmented Generation (RAG) systems for question answering typically retrieve evidence by semantic similarity between the query and document chunks. While effective for un…

Astrum Verum – A Vector Symbolic cognitive memory that beats RAG

Composition-episodic cognitive memory for AI agents (VSA + lattice geometry) - vitaliyfedotovpro-art/astrum-verum…

Expert.ai and Fincons Group Join Forces to Bring Neuro-Symbolic AI to Data-Driven Businesses - Morningstar

Comprehensive up-to-date news coverage, aggregated from sources all over the world by Google News.…

NeuroNL2LTL: A Neurosymbolic Framework for Natural Language Translation of Linear Temporal Logic

Effectively translating between natural language (NL) and formal logics like Linear Temporal Logic (LTL) requires expertise that limits formal verification's reach in safety-critic…

ImProver 2: Iteratively Self-Improving LMs for Neurosymbolic Proof Optimization

Formal mathematics libraries are rapidly expanding, creating a growing need to refactor verified proofs for maintainability and to improve training data quality for neural provers.…

When Good Equations Get Bad Scores: Improving Symbolic Regression Through Better Parameter Optimization

Symbolic Regression (SR) plays a central role in scientific knowledge discovery by distilling mathematical equations from observational data. Most existing SR methods function with…

Peru's Catholic Church holds a symbolic ceremony in apology for Indigenous land dispossession

The highest Catholic authorities in Peru have held a symbolic reparation ceremony for an Indigenous community affected by land dispossession…

Symbolic Constant Conundrum

There are a few different ways to define symbolic constants in C and C++. Which you should use when?…

Macron backs symbolic repeal of France's slavery laws, warns against 'false promises' on reparations

Show HN: Modernizing my old PhD work in an evening with little Qwen3.6 MoE

Jax implementation of the PGE algorithm (Prioritized Grammar Enumeration) - verdverm/pge-jax…

How Hint App uses symbolic systems to help users process emotional complexity

No longer centered on horoscopes or predictions, Hint App uses astrology to transform today’s understanding of emotional literacy.…

Neurosymbolic Learning for Inference-Time Argumentation

Claim verification is an important problem in high-stakes settings, including health and finance. When information underpinning claims is incomplete or conflicting, uncertain answe…

ANNEAL: Adapting LLM Agents via Governed Symbolic Patch Learning

LLM-based agents can recover from individual execution errors, yet they repeatedly fail on the same fault when the underlying process knowledge--operator schemas, preconditions, an…

NeuSymMS: A Hybrid Neuro-Symbolic Memory System for Persistent, Self-Curating LLM Agents

We present NeuSymMS, an adaptive memory system that enables large language model (LLM) agents to learn, remember, and reason about users across sessions via a hybrid neuro-symbolic…

Learning Bilevel Policies over Symbolic World Models for Long-Horizon Planning

We tackle the challenge of building embodied AI agents that can reliably solve long-horizon planning problems. Imitation learning from demonstrations has shown itself to be effecti…

Reasoners or Translators? Contamination-aware Evaluation and Neuro-Symbolic Robustness in Tax Law

Recent advances in large language models (LLMs) have significantly enhanced automated legal reasoning. Yet, it remains unclear whether their performance reflects genuine legal reas…