CoDA: Towards Effective Cross-domain Knowledge Transfer via CoT-guided Domain Adaptation

2026-04-21 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, quick

Summary

Large language models (LLMs) often struggle with logical reasoning tasks in expertise-scarce domains due to a lack of high-quality in-domain exemplars for in-context learning. While cross-domain sample retrieval has been explored, its effectiveness is limited by significant domain shifts that hinder LLMs from identifying shared reasoning patterns. To overcome this, CoDA (CoT-guided Domain Adaptation) introduces a lightweight adapter that intervenes in the LLM's intermediate hidden states. CoDA combines feature-based distillation of Chain-of-Thought (CoT)-enriched reference representations with Maximum Mean Discrepancy (MMD) for kernelized distribution matching. This approach aligns the latent reasoning representations between source and target domains, enabling more robust cross-domain knowledge transfer. Experimental results across multiple logical reasoning tasks and various model families demonstrate CoDA's significant outperformance compared to prior state-of-the-art baselines.

Key takeaway

For research scientists developing LLMs for specialized or low-resource domains, CoDA offers a robust method to improve logical reasoning performance. You should consider implementing CoDA's lightweight adapter and CoT-guided distillation to effectively transfer knowledge across domains, especially when high-quality in-domain data is scarce, thereby enhancing model generalization and accuracy.

Key insights

CoDA enhances LLM cross-domain reasoning by aligning latent representations via CoT-guided distillation and MMD.

Principles

• Domain shift impedes cross-domain knowledge transfer.
• Aligning latent reasoning representations improves transfer.

Method

CoDA uses a lightweight adapter to intervene in LLM hidden states, combining CoT-enriched feature distillation with MMD for kernelized distribution matching to align source and target domain latent reasoning representations.

In practice

• Apply CoDA for LLM reasoning in low-resource domains.
• Use CoT-enriched data for feature distillation.

Topics

• Cross-domain Knowledge Transfer
• Large Language Models
• Chain-of-Thought
• Domain Adaptation
• Maximum Mean Discrepancy

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

Related on AIssential

• CrossTrace: A Cross-Domain Dataset of Grounded Scientific Reasoning Traces for Hypothesis Generation — Structured, grounded reasoning traces encode transferable, domain-general scientific reasoning primitives for hypothesis generation.
• Context-Mediated Domain Adaptation in Multi-Agent Sensemaking Systems — User edits to AI-generated content can implicitly teach multi-agent systems domain expertise, enabling continuous adaptation.
• Beyond KV-Cache: Test-Time Training & Invariant Latent Topologies for ICL — New methods enable LLMs to learn during inference and generalize across diverse domains by modifying internal weight structures or identifying invariant neural subspaces.
• Rethinking Generalization in Reasoning SFT: A Conditional Analysis on Optimization, Data, and Model Capability — Reasoning SFT can generalize cross-domain, but it is conditional on optimization, data quality, and model capability.
• MixSD: Mixed Contextual Self-Distillation for Knowledge Injection — Aligning supervision targets with an LLM's native generation distribution significantly mitigates catastrophic forgetting during knowledge injection.
• Towards Automated Knowledge Transfer in Evolutionary Multitasking via Large Language Models — LLMs can autonomously design efficient and effective knowledge transfer models for multi-task optimization.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.