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Due to the rapid generation and dissemination of information, large language models (LLMs) quickly run out of date despite enormous development costs. To address the crucial need to keep models updated, online learning has emerged as a critical tool when utilizing LLMs for real-world applications. However, given the ever-expanding corpus of unseen documents and the large parameter space of modern LLMs, efficient adaptation is essential. To address these challenges, we propose Memory of Amortized Contexts (MAC), an efficient and effective online adaptation framework for LLMs with strong knowledge retention. We propose a feature extraction and memory-augmentation approach to compress and extract information from new documents into compact modulations stored in a memory bank.

Abstract-- Long-term state estimation over graphs remains challenging as current graph estimation methods scale poorly on large, long-term graphs. T o address this, our work advances a current state-of-the-art graph sparsification algorithm, maximizing algebraic connectivity (MAC). MAC is a sparsification method that preserves estimation performance by maximizing the algebraic connectivity, a spectral graph property that is directly connected to the estimation error . Unfortunately, MAC remains computationally prohibitive for online use and requires users to manually pre-specify a connectivity-preserving edge set. Our contributions close these gaps along three complementary fronts: we develop a specialized solver for algebraic connectivity that yields an average 2x runtime speedup; we investigate advanced step size strategies for MAC's optimization procedure to enhance both convergence speed and solution quality; and we propose automatic schemes that guarantee graph connectivity without requiring manual specification of edges. T ogether, these contributions make MAC more scalable, reliable, and suitable for real-time estimation applications. The scalability of state estimation and perception remains a critical challenge for long-term autonomous robotic systems.

Understanding the decision-making processes of large language models (LLMs) is essential for their trustworthy development and deployment. However, current interpretability methods often face challenges such as low resolution and high computational cost. To address these limitations, we propose the \textbf{Multi-Layer Attention Consistency Score (MACS)}, a novel, lightweight, and easily deployable heuristic for estimating the importance of input tokens in decoder-based models. MACS measures contributions of input tokens based on the consistency of maximal attention. Empirical evaluations demonstrate that MACS achieves a favorable trade-off between interpretability quality and computational efficiency, showing faithfulness comparable to complex techniques with a 22\% decrease in VRAM usage and 30\% reduction in latency.

Therefore, we need to specialize the feature extractor to best match the target dataset. The peak memory cost of this phase is 61MB under resolution 224, which is reached when the largest sub-network is sampled. MAC (only forward) of sampled sub-nets is (355M + 1182M) / 2 = 768.5M Therefore, the total MAC of this phase is 768.5M Flowers, where 2040 is the number of total training samples, 0.2 means the validation set consists of Details of the accuracy predictor is provided in Appendix B. It takes the one-hot encoding of the sub-network's MAC of this accuracy predictor is only 0.37M, which is 3-4 orders of magnitude smaller than the Therefore, TinyTL is not only more memory-efficient but also more computation-efficient.

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Thank you all for your thoughtful comments; we address your concerns below. The MDL principle formalizes Occam's razor and is a We will add the discussion of such relevant studies to section 1. We will add these results and accompanying visualizations to appendix. Model (solver) MAC DAFT MAC (euler) DAFT MAC (rk4) DAFT MAC (dopri5; used in training)Time (ms) 153. We found that during evaluation, rk4 solves all the dynamics generated from CLEVR dataset.

Due to the rapid generation and dissemination of information, large language models (LLMs) quickly run out of date despite enormous development costs. To address the crucial need to keep models updated, online learning has emerged as a critical tool when utilizing LLMs for real-world applications. However, given the ever-expanding corpus of unseen documents and the large parameter space of modern LLMs, efficient adaptation is essential. To address these challenges, we propose Memory of Amortized Contexts (MAC), an efficient and effective online adaptation framework for LLMs with strong knowledge retention. We propose a feature extraction and memory-augmentation approach to compress and extract information from new documents into compact modulations stored in a memory bank.