# Disentangling Reasoning Logic to Resolve Explicit Knowledge Conflicts

arXiv: https://arxiv.org/abs/2508.01273 · HTML: https://mlsanity.com/paper/2508.01273/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 0 of its 29 references that are in the map.
- First posted on arXiv 2 Aug 2025.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)

## Built on by (most cited first, 2 of 2)

- [Do Reasoning LLMs Refuse What They Infer in Long Contexts?](https://mlsanity.com/paper/2602.08874/) (2026)
- [HyPER: Bridging Exploration and Exploitation for Scalable LLM Reasoning with Hypothesis Path Expansion and Reduction](https://mlsanity.com/paper/2602.06527/) (2026)

## Abstract (arXiv)

Explicit knowledge conflicts, occurring when retrieved contexts contain contradictory information, pose a fundamental challenge for Large Language Models (LLMs) as they integrate increasingly diverse data sources. The core difficulty lies in the complexity of entangled narratives and heterogeneous conflict patterns, which frequently exceeds the reasoning capacity of standard backbone architectures. We propose textbftextscKcr (Knowledge Conflict Reasoning), a framework that adjudicates contradictions by systematically structuring their underlying logic. textscKcr disentangles conflicting contexts into discrete sets of reasoning traces, utilizing a hybrid representation of text and graphs to facilitate systematic comprehension. It then employs a Reinforcement Learning with Verifiable Rewards (RLVR) paradigm to instill a reasoning policy that maximizes logical consistency while suppressing spurious paths derived from contradictory evidence. Extensive evaluations demonstrate that textscKcr yields substantial performance gains. Notably, a 7B model enhanced by textscKcr achieves adjudication capabilities that significantly outperform leading proprietary models, including GPT-4o and GPT-5.1, on complex tasks. Code is available at https://github.com/zhengxianda/KCR.