MIITA: Memory-Induced Inference-Time Adaptation for Continual Learning with Small Language Models
MIITA is a continual learning framework designed for small language models (SLMs) under resource-constrained deployments. It stores experiences as compact correction-direction prototypes with semantic anchors and retrieves them at inference using semantic and uncertainty cues, enabling non-destructive reuse of past supervision without backbone updates. Experiments show consistent improvement in final performance and mitigation of forgetting under fixed memory budgets.
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[Submitted on 20 May 2026]
Title:MIITA: Memory-Induced Inference-Time Adaptation for Continual Learning with Small Language Models
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Abstract:Continual learning (CL) is essential for small language models (SLMs) to adapt to evolving real-world needs in resource-constrained deployments. However, directly updating their limited parameter space causes catastrophic forgetting. While memory-based methods naturally address this by decoupling knowledge retention from parameters, existing approaches designed for large language models (LLMs) rely on abundant storage and strong in-context reasoning that SLMs lack. To address these challenges, we propose MIITA, a Memory-Induced Inference-Time Adaptation framework for supervised CL under constrained storage. MIITA stores supervised experiences as compact correction-direction prototypes with semantic anchors, and retrieves them at inference time using semantic and uncertainty-based cues. The retrieved directions are applied through gated temporary hidden-state adaptation, enabling non-destructive reuse of past supervision without backbone updates, prompt extensions, or test-time backpropagation. A local theoretical analysis links this design to first-order loss reduction, uncertainty-guided retrieval, and directional coverage for retaining old-stage knowledge. Extensive experiments across diverse supervised CL settings show that MIITA consistently improves final performance and mitigates forgetting under fixed memory budgets.
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.22556 [cs.AI]
(or arXiv:2607.22556v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.22556
arXiv-issued DOI via DataCite
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From: Dong Li [view email] [v1] Wed, 20 May 2026 03:03:04 UTC (148 KB)
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