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This paper proposes OSPDIM, a source-free online unsupervised domain adaptation framework for EEG-based BCIs that corrects geometric misalignment caused by class-imbalanced label shifts on the Riemannian manifold, outperforming standard alignment methods in online scenarios.
The paper introduces Σ-Mem, an online reliability memory for LLM-based multi-agent systems that tracks historical competence of peers and peer relationships, enabling stable adaptation via spectral bounds and improving coordination through residual steering, routing, and weighted voting.
This paper studies online adaptation strategies for discrete diffusion models in molecular optimization, identifying complementary components like acquisition, reward shaping, debiasing, replay, and validity control that improve feedback efficiency on small-molecule and protein tasks.
This paper proposes ORCA, a method for black-box online adaptation of time series foundation models by learning the context of predictive errors. It demonstrates effectiveness across five TSFMs and eight datasets, addressing the challenge of adapting closed-source API-based models.
Researchers from Alibaba/Qwen and Peking University introduce TMEM, a self-evolving parametric memory framework that uses online LoRA weight updates to let LLM agents genuinely learn from experience within a single episode, rather than relying solely on prompt-space memory. TMEM outperforms summary-based and retrieval-based baselines across multiple benchmarks including LoCoMo, LongMemEval-S, and CL-Bench.
This paper introduces Spectral Souping, a framework for efficiently aligning LLMs with individual user preferences by discovering a universal spectral representation that enables merging of specialized policies at inference time without costly retraining.
OjaKV introduces a context-aware online low-rank KV cache compression framework that uses hybrid storage and Oja's algorithm for incremental subspace adaptation to reduce GPU memory bottlenecks in long-context LLM inference without model fine-tuning.