Context-Mediated Domain Adaptation in Multi-Agent Sensemaking Systems

2025-10-23 · Source: cs.MA updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Expert, extended

Summary

Context-Mediated Domain Adaptation (CMDA) is a novel paradigm that transforms user modifications to AI-generated artifacts into implicit domain specifications, reshaping Large Language Model-powered multi-agent reasoning. This approach addresses the limitation of current systems that treat user edits as endpoint corrections rather than learning signals. Implemented through Seedentia, a web-based multi-agent framework, CMDA establishes bidirectional semantic links between generated content and system reasoning. It enables specification bootstrapping, implicit knowledge transfer, and in-context learning, allowing vague prompts to evolve into precise domain specifications through iterative human-AI collaboration. An evaluation with domain experts, who modified research questions from academic papers, successfully extracted 46 domain knowledge entries from user edits, demonstrating the feasibility of capturing tacit expertise. The system features a Next.js/React frontend, a Python/LangGraph backend, and a PostgreSQL database, supporting direct manipulation, prompt-based regeneration, and context-based generation interaction modes.

Key takeaway

For AI Engineers building multi-agent LLM systems, consider implementing context-mediated domain adaptation to overcome the limitations of ephemeral prompts. Your system can learn from user edits, transforming corrections into persistent domain knowledge that reshapes future agent reasoning. Integrate bidirectional semantic links and structured knowledge representations to enable continuous learning and reduce repeated manual corrections, significantly enhancing human-AI collaboration and domain-specific output quality.

Key insights

User edits to AI-generated content can implicitly teach multi-agent systems domain expertise, enabling continuous adaptation.

Principles

• User edits serve as implicit domain specifications.
• Bidirectional semantic links enable persistent knowledge transfer.
• Accumulated knowledge improves future AI artifact generation.

Method

CMDA captures user edits (M), extracts domain knowledge (D) via f:M→D (edit pattern analysis), and propagates D to reshape agent behavior via g:D→C (context injection into prompts).

In practice

• Capture terminology via direct inline edits.
• Guide content restructuring with prompt-based regeneration.
• Generate new artifacts using accumulated domain context.

Topics

• Context-Mediated Domain Adaptation
• Multi-Agent Systems
• LLM-powered Agents
• Human-AI Collaboration
• Domain Knowledge Elicitation
• Seedentia Framework

Best for: AI Scientist, AI Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.MA updates on arXiv.org.