ResiMind

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292
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Equip AI agents with verified memory, neuro-symbolic self-correction, and symbolic math verification tools.

ResiMind is an open-source neuro-symbolic agent framework that integrates independent domain verification, residual-guided self-correction, and reusable rule memory. When models or tools propose actions, deterministic domain checks ensure only sound steps become committed facts, while failed checks supply precise residual feedback to drive targeted repairs. Checked solutions can be synthesized into independently admitted rule schemas, allowing knowledge to accumulate outside model weights and execute future algebraic tasks with zero model calls. Beyond its core Model Context Protocol (MCP) tools for bounded rational algebra, ResiMind supports domain-specific verifiers for Lean 4 formal proofs, continuous bridge engineering, constrained optimization, and open-ended itinerary planning.

主要功能

01292 GitHub stars
02Model Context Protocol (MCP) server providing exact tools for bounded rational polynomial algebra and identity verification
03Verified memory architecture that extracts, admits, and rechecks reusable rule schemas across tasks without retraining models
04Residual-guided self-correction loops that deliver explicit error feedback to steer targeted agent repair steps
05Multi-domain verification engine with support for Lean 4 theorem proving, structural engineering constraints, and complex task planning
06Zero-model-call execution for cached symbolic derivations and local rewrites within explicit resource bounds

使用案例

01Preventing mathematical and algebraic hallucinations in LLM agents via external, exact polynomial verification tools
02Self-correcting constrained planning tasks like multi-stop itineraries or customer refund policy calculations using residual feedback
03Accumulating verified symbolic rules across tasks to accelerate problem-solving while reducing API costs and inference overhead