AINewsnow

Learning Symbolic Constraint Representations from Examples: A Neuro-Symbolic Approach

This story is from 2026-09-14. It is preserved in the archive; the latest stories are on the live feed.

arXiv:2609.12267v1 Announce Type: new Abstract: Learning user-defined concepts as constraint networks has been extensively studied in the constraint acquisition (CA) literature. However, existing approaches typically rely on intensive interactions with a human oracle, making the learning process co…

Read the full story at arXiv cs.AI ↗

Timeline · 1 report

  1. 2026-09-14 04:00 · arXiv cs.AI
    Learning Symbolic Constraint Representations from Examples: A Neuro-Symbolic Approach

More stories

  1. Anthropic says Claude 'leads' 26 percent of its AI R&D work — Engadget
  2. Google Joins OpenAI, Anthropic, Meta in Disclosing AI Hacks — Bloomberg AI
  3. Introducing Kimi K3 on Amazon Bedrock — AWS Machine Learning Blog
  4. Optimizing agent system prompts with Amazon Bedrock AgentCore — AWS Machine Learning Blog
  5. Introducing Amazon SageMaker HyperPod Inference Gateway — AWS Machine Learning Blog
  6. Introducing Astra for Law — OpenAI News
  7. Gemini Hacked Three Companies in First Known Breakout by Google’s AI — Wall Street Journal Technology
  8. OpenAI reveals cases of ‘concerning’ AI behaviour as it announces new disclosure system — The Guardian AI

Get the daily brief of stories like this at 6:30 every morning →