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[2602.15156] Panini: Continual Learning in Token Space via Structured Memory
Summary
The paper presents Panini, a continual learning framework for language models that enhances efficiency and accuracy by integrating experiences into a structured memory system, outperforming existing methods in several benchmarks.
Why It Matters
As language models evolve, the need for efficient learning from new information becomes critical. Panini addresses the limitations of traditional retrieval-augmented generation methods by proposing a novel approach that reduces computational costs and improves the relevance of generated responses. This advancement could significantly impact applications in natural language processing and AI-driven systems.
Key Takeaways
- Panini uses a structured memory system to facilitate continual learning in language models.
- The framework achieves 5%-7% higher performance on QA benchmarks compared to existing methods.
- It significantly reduces the number of tokens needed for context, enhancing computational efficiency.
- Panini supports open-source pipelines, promoting accessibility in AI research.
- The method minimizes unsupported answers, improving the reliability of language model outputs.
Computer Science > Artificial Intelligence arXiv:2602.15156 (cs) [Submitted on 16 Feb 2026] Title:Panini: Continual Learning in Token Space via Structured Memory Authors:Shreyas Rajesh, Pavan Holur, Mehmet Yigit Turali, Chenda Duan, Vwani Roychowdhury View a PDF of the paper titled Panini: Continual Learning in Token Space via Structured Memory, by Shreyas Rajesh and 3 other authors View PDF HTML (experimental) Abstract:Language models are increasingly used to reason over content they were not trained on, such as new documents, evolving knowledge, and user-specific data. A common approach is retrieval-augmented generation (RAG), which stores verbatim documents externally (as chunks) and retrieves only a relevant subset at inference time for an LLM to reason over. However, this results in inefficient usage of test-time compute (LLM repeatedly reasons over the same documents); moreover, chunk retrieval can inject irrelevant context that increases unsupported generation. We propose a human-like non-parametric continual learning framework, where the base model remains fixed, and learning occurs by integrating each new experience into an external semantic memory state that accumulates and consolidates itself continually. We present Panini, which realizes this by representing documents as Generative Semantic Workspaces (GSW) -- an entity- and event-aware network of question-answer (QA) pairs, sufficient for an LLM to reconstruct the experienced situations and mine latent knowled...