Panini: Continual Learning via GSW Memory

• Architecture: GSW represents documents as directed networks where nodes encode entities and events, with edges capturing semantic relationships and question-answerable connections • Retrieval Mechanism: Instead of retrieving raw text chunks, the system extracts and chains inference paths through the GSW, preserving logical dependencies and reasoning sequences • Non-parametric Design: Continual learning occurs through GSW expansion and refinement without gradient updates to the base LLM, reducing computational overhead and catastrophic forgetting • Benchmark Performance: Evaluated on six QA datasets with consistent 5-7% improvements over RAG baselines, with particular gains on complex reasoning tasks requiring multi-hop inference • Token Optimization: Achieves 2-30x token reduction by transmitting structured inference chains rather than full document passages, critical for cost-sensitive production systems • Hallucination Mitigation: Grounds responses in explicit semantic structures, reducing model tendency to generate unsupported claims on out-of-distribution or unanswerable queries[1]

Panini's GSW framework addresses critical production challenges in LLM deployment: reducing inference costs through token efficiency, improving reliability by mitigating hallucinations, and enabling continual learning without model retraining. This non-parametric approach aligns with industry trends toward modular, interpretable AI systems that separate knowledge representation from model parameters. The open-source release may accelerate adoption of semantic workspace architectures in enterprise knowledge management systems, particularly for domain-specific QA applications where cost and accuracy are paramount. As organizations scale LLM deployments, GSW-style approaches could become standard for managing evolving knowledge bases without expensive fine-tuning cycles, potentially reshaping how enterprises balance model stability with knowledge currency[1][3][5].