Memory-Induced Inference-Time Adaptation for continual learning with small language models
Read the original at arxiv.org→arXiv:2607.22556v1 Announce Type: new Abstract: Continual learning (CL) is essential for small language models (SLMs) to adapt to evolving real-world needs in resource-constrained deployments. However, directly...
Original headline: "MIITA: Memory-Induced Inference-Time Adaptation for Continual Learning with Small Language Models"
Coverage timeline
- Jul 28, 04:00 UTC arXiv cs.AI lead source MIITA: Memory-Induced Inference-Time Adaptation for Continual Learning with Small Language Models
- Jul 28, 04:00 UTC arXiv cs.AI Temporal Context Reinstatement Drives Episodic-Like Order Memory in Long-Context Language Models
- Jul 29, 04:00 UTC arXiv cs.CL Neuromorphic Diffusion Language Models: Addressing Compute and Memory Bottlenecks via Sparsity and Block Denoising
- Jul 29, 04:00 UTC arXiv cs.CL Memory for Large Language Models
- Jul 30, 04:00 UTC arXiv cs.CL ForgetBench: Benchmarking Forgetting Dynamics of Long-Term Parametric Memory in Language Models
- Jul 31, 04:00 UTC arXiv cs.CL ChronoMem: Version Control and Semantic Rollback for Large Language Model Agent Memory
- Jul 31, 04:00 UTC arXiv cs.LG Regularizing modality contribution drift in multimodal continual learning