Reference Free Evaluation Of Reasoning In Open End

---
name: reference-free-evaluation-of-reasoning-in-open-end
description: 'Skill generated from arXiv paper 2607.19678: Reference-Free Evaluation of Reasoning in Open-Ended Question Answering'
metadata:
  {
    "arxiv": {
      "id": "2607.19678",
      "title": "Reference-Free Evaluation of Reasoning in Open-Ended Question Answering",
      "authors": ['Guneet Singh Kohli', 'Yuxiang Zhou', 'Michael Sejr Schlichtkrull', 'Gregory E Dean', 'Maria Liakata'],
      "published": "2026-07-22",
      "categories": ['cs.CL', 'cs.AI', 'cs.LG'],
      "url": "https://arxiv.org/abs/2607.19678",
      "utility": 1.0
    }
  }
---
# Reference-Free Evaluation of Reasoning in Open-Ended Question Answering

**arXiv:** 2607.19678  
**Published:** 2026-07-22  
**Authors:** Guneet Singh Kohli, Yuxiang Zhou, Michael Sejr Schlichtkrull, Gregory E Dean, Maria Liakata  
**Categories:** cs.CL, cs.AI, cs.LG  
**Utility:** 1.00

## Key Innovation
AI-generated answers in high-stakes domains are often fluent but difficult to verify, especially when they contain multi-step reasoning rather than a single final answer. We propose a reasoning-based, reference-free framework for auditing LLM-generated outputs. The method decomposes a generated reasoning trace into segments, labels local premise-target relations using Natural Language Inference (NLI), and organizes these relations into a hypergraph. A deterministic backward AND-OR search then as...

## Potential Application
This paper presents advancements that could be applied to enhance agent capabilities in the areas of cs.CL, cs.AI, cs.LG.

## References
- arXiv: https://arxiv.org/abs/2607.19678