arXiv:2601.08025v1 Announce Type: new Abstract: The deployment of deep neural networks (DNNs) on resource-constrained edge devices is frequently hindered by their significant computational and memory requirements. While partitioning and distributing a DNN across multiple devices is a well-established strategy to mitigate this challenge, prior research has largely focused on single-objective optimization, such as minimizing latency or maximizing throughput. This paper challenges that view by reframing DNN partitioning as a multi-objective optimization problem. We argue that in real-world scenarios, […]

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arXiv:2603.24639v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have enabled the development of autonomous agents capable of complex reasoning and multi-step problem solving. However, these agents struggle to adapt to specialized environments and do not leverage past interactions, approaching each new task from scratch regardless of their accumulated experience. We introduce Experiential Reflective Learning (ERL), a simple self-improvement framework that enables rapid environment adaptation through experiential learning. ERL reflects on task trajectories and outcomes […]

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Networks of interdependent industrial assets (clients) are tightly coupled through physical processes and control inputs, raising a key question: how would the output of one client change if another client were operated differently? This is difficult to answer because client-specific data are high-dimensional and private, making centralization of raw data infeasible. Each client also maintains proprietary local models that cannot be modified. We propose a federated framework for causal representation learning in state-space systems that captures interdependencies among […]

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In computational cytology, detecting malignancy on whole-slide images is difficult because malignant cells are morphologically diverse yet vanishingly rare amid a vast background of normal cells. Accurate detection of these extremely rare malignant cells remains challenging due to large class imbalance and limited annotations. Conventional weakly supervised approaches, such as multiple instance learning (MIL), often fail to generalize at the instance level, especially when the fraction of malignant cells (witness rate) is exceedingly low. In this study, we […]

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arXiv:2604.05068v1 Announce Type: new Abstract: Compute-optimal scaling laws are relatively well studied for NLP and CV, where objectives are typically single-step and targets are comparatively homogeneous. Weather forecasting is harder to characterize in the same framework: autoregressive rollouts compound errors over long horizons, outputs couple many physical channels with disparate scales and predictability, and globally pooled test metrics can disagree sharply with per-channel, late-lead behavior implied by short-horizon training. We extend neural scaling analysis for autoregressive weather forecasting […]

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You’re not bad at planning. Your calendar is packed with appointments, meal schedules, and activities that look perfectly organized. But somehow, those carefully laid plans vanish between your phone screen and real life. The truth is, most households struggle with execution, not planning. Everyone knows what needs to happen. Yet nobody takes action because the information lives in scattered places. A calendar app here, a sticky note there, and verbal reminders everywhere create chaos. This isn’t about downloading […]

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arXiv:2602.20379v1 Announce Type: new Abstract: Enterprise Retrieval-Augmented Generation (RAG) assistants operate in multi-turn, case-based workflows such as technical support and IT operations, where evaluation must reflect operational constraints, structured identifiers (e.g., error codes, versions), and resolution workflows. Existing RAG evaluation frameworks are primarily designed for benchmark-style or single-turn settings and often fail to capture enterprise-specific failure modes such as case misidentification, workflow misalignment, and partial resolution across turns. We present a case-aware LLM-as-a-Judge evaluation framework for enterprise multi-turn […]

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arXiv:2601.20038v1 Announce Type: new Abstract: Kawarabayashi and Sidiropoulos [KS22] obtained an $O(log^2 n)$-approximation algorithm for Multicut in planar digraphs via a natural LP relaxation, which also establishes a corresponding upper bound on the multicommodity flow-cut gap. Their result is in contrast to a lower bound of $tilde{Omega}(n^{1/7})$ on the flow-cut gap for general digraphs due to Chuzhoy and Khanna [CK09]. We extend the algorithm and analysis in [KS22] to the node-weighted Multicut problem. Unlike in general digraphs, node-weighted […]

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In 2025, the pressure on business leaders to adopt AI and demonstrate immediate financial returns has reached unprecedented levels. With technology budgets steadily climbing to a projected 14% of total revenue a significant increase from previous years the C-suite has definitively shifted from speculative experimentation to demanding a rigorous quantification of AI’s financial impact. But the true path to generating sustainable value from AI is filled with counter-intuitive realities and hidden complexities that extend far beyond the mainstream […]

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