Large Language Models (LLMs) generalize across tasks via reusable representations and flexible reasoning, yet remain brittle in real deployment under evolving tasks and continual distribution shift. A common approach is Test-Time Adaptation (TTA), existing ones of which updates models with hand-designed unsupervised objectives over the full parameter space and mostly overlook preserving shared source knowledge and the reliability of adaptation signals. Drawing on molecular signaling cascades of memory updating in Drosophila, we propose Synapse Consolidation (SyCo), a parameter-efficient […]

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arXiv:2601.12150v1 Announce Type: new Abstract: Despite their prominent performance on tasks such as ROI classification and segmentation, many pathology foundation models remain constrained by a specific input size e.g. 224 x 224, creating substantial inefficiencies when applied to whole-slide images (WSIs), which span thousands of resolutions. A naive strategy is to either enlarge inputs or downsample the WSIs. However, enlarging inputs results in prohibitive GPU memory consumption, while downsampling alters the microns-per-pixel resolution and obscures critical morphological details. […]

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Hi everyone, I recently published a paper on arXiv introducing a new ensemble learning framework called EARCP: https://arxiv.org/abs/2603.14651 EARCP is designed for sequential decision-making problems and dynamically combines multiple models based on both their performance and their agreement (coherence). Key ideas: Online adaptation of model weights using a multiplicative weights framework Coherence-aware regularization to stabilize ensemble behavior Sublinear regret guarantees: O(√(T log M)) Tested on time series forecasting, activity recognition, and financial prediction tasks The goal is to […]

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arXiv:2604.09698v1 Announce Type: new Abstract: The growing ubiquity of Extended Reality (XR) is driving Conversational Recommendation Systems (CRS) toward visually immersive experiences. We formalize this paradigm as Immersive CRS (ICRS), where recommended items are highlighted directly in the user’s scene-based visual environment and augmented with in-situ labels. While item recommendation has been widely studied, the problem of how to select and evaluate which information to present as immersive labels remains an open problem. To this end, we introduce […]

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arXiv:2604.04982v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have opened new opportunities for recommender systems by enabling rich semantic understanding and reasoning about user interests and item attributes. However, as privacy regulations tighten, incorporating user data into LLM-based recommendation (LLMRec) introduces significant privacy risks, making unlearning algorithms increasingly crucial for practical deployment. Despite growing interest in LLMRec unlearning, most existing approaches formulate unlearning as a weighted combination of forgetting and retaining objectives while updating […]

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arXiv:2603.05697v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) achieve strong performance on benchmarks that evaluate text, image, or video understanding separately. However, these settings do not assess a critical real-world requirement, which involves retrieving relevant evidence from large, heterogeneous multimodal corpora prior to reasoning. Most existing benchmarks restrict retrieval to small, single-modality candidate sets, substantially simplifying the search space and overstating end-to-end reliability. To address this gap, we introduce MultiHaystack, the first benchmark designed to evaluate […]

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Partition drift is a hidden performance drain where the physical layout of data on disk gradually uncouples from user query patterns, breaking file pruning and forcing expensive full table scans. This structural decay typically happens due to late-arriving data pollution and un-ordered high-cardinality keys. By setting up automated metadata monitoring alerts, forcing pre-sorted write gates in your ingestion pipelines, and creating isolated staging tables for delayed data backlogs, you can restore maximum pruning efficiency and significantly lower cloud […]

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