What ?:  

AI reasoning has progressed rapidly, with large language models now supporting multi-step work across many domains. Yet long-horizon reasoning remains unreliable: early errors propagate silently through later steps, without principled local revision. This project replaces the text trace with an editable workspace: a graph based state of claims, evidence, and dependencies. When something changes, the system performs a minimal-change repair on the affected region instead of rerunning the chain.

Why ?:

Multi-step reasoning failures already cause real harm, from fabricated citations to unsafe medical advice. Current methods are limited in a crucial respect: reasoning is free form text with no explicit dependencies or constraints, so revision is not controlled but a byproduct of rerunning the chain. Recent work shows that making intermediate reasoning structured enables much more reliable correction, suggesting reliability comes from how reasoning is represented, not just searched.

How ?

We will implement this by first executing graph-based state construction based on structured belief maintenance. Next, we will develop the dynamics of this state through minimal repair dynamics, utilizing search and learning-based policy development to ensure cost-bounded updates. Finally, evaluation will feature mid-run interventions to test stability under sudden changes, alongside reusable motif distillation to capture and adapt optimized pathways.

Sayambhu Sen

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