ReasonAny: Incorporating Reasoning Capability to Any Model via Simple and Effective Model Merging

2026-01-09

arXiv.org

AI Summary

This work addresses a key challenge in integrating strong reasoning capabilities with domain-specific expertise—namely, how to effectively inject advanced reasoning without compromising specialized performance. The authors propose ReasonAny, a framework that reveals reasoning abilities are predominantly encoded in parameter regions with low gradient sensitivity. Leveraging this insight, they develop a training-free model merging strategy that precisely identifies and preserves critical parameters for both reasoning and domain tasks through gradient-based contrastive analysis. This approach circumvents the common pitfall of conventional fusion methods, which often degrade both reasoning depth and domain performance. Evaluated across diverse domains including safety, biomedicine, and finance, ReasonAny consistently outperforms existing techniques, simultaneously enhancing reasoning capability and domain-specific task accuracy.

Technology Category

• Knowledge Representation and Reasoning: Reasoning with Beliefs
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Large Reasoning Models (LRMs) with long chain-of-thought reasoning have recently achieved remarkable success. Yet, equipping domain-specialized models with such reasoning capabilities, referred to as"Reasoning + X", remains a significant challenge. While model merging offers a promising training-free solution, existing methods often suffer from a destructive performance collapse: existing methods tend to both weaken reasoning depth and compromise domain-specific utility. Interestingly, we identify a counter-intuitive phenomenon underlying this failure: reasoning ability predominantly resides in parameter regions with low gradient sensitivity, contrary to the common assumption that domain capabilities correspond to high-magnitude parameters. Motivated by this insight, we propose ReasonAny, a novel merging framework that resolves the reasoning-domain performance collapse through Contrastive Gradient Identification. Experiments across safety, biomedicine, and finance domains show that ReasonAny effectively synthesizes"Reasoning + X"capabilities, significantly outperforming state-of-the-art baselines while retaining robust reasoning performance.

Problem

• Reasoning
• Model Merging
• Domain-Specialized Models
• Performance Collapse
• Large Reasoning Models

Innovation

• model merging
• reasoning capability
• contrastive gradient identification
• parameter sensitivity
• reasoning + X