arXiv:2510.02457v1 Announce Type: new Abstract: Post-training quantization (PTQ) has recently emerged as an effective tool for reducing the computational complexity and memory usage of a neural network by representing its weights and activations with lower precision. While this paradigm has…
arXiv:2510.02470v1 Announce Type: new Abstract: Training modern neural networks on large datasets is computationally and energy intensive. We present SAGE, a streaming data-subset selection method that maintains a compact Frequent Directions (FD) sketch of gradient geometry in $O(ell D)$ memory…
arXiv:2510.02456v1 Announce Type: new Abstract: Selecting a small yet useful subset of training data is hard because signals of example utility (uncertainty, rarity, diversity, etc.) are heterogeneous and typically combined with ad hoc weights. We propose a market-based selector that…
arXiv:2510.02453v1 Announce Type: new Abstract: Foundation models are increasingly deployed as black-box services, where model weights cannot be modified and customization is limited to prompting. While static prompt optimization has shown promise, it produces a single fixed prompt that fails…
arXiv:2510.02414v1 Announce Type: new Abstract: Reconstructing high-resolution rainfall fields is essential for flood forecasting, hydrological modeling, and climate analysis. However, existing spatial interpolation methods-whether based on automatic weather station (AWS) measurements or enhanced with satellite/radar observations often over-smooth critical structures,…
arXiv:2510.00476v2 Announce Type: replace-cross Abstract: Interpreting the internal behavior of large language models trained on code remains a critical challenge, particularly for applications demanding trust, transparency, and semantic robustness. We propose Code Concept Analysis (CoCoA): a global post-hoc interpretability framework…
arXiv:2510.02483v1 Announce Type: new Abstract: Training Large Language Models (LLMs) is plagued by long training times and massive energy consumption, with modern models requiring months of computation and gigawatt-hours of electricity. In light of these challenges,we introduce Litespark, a novel…
arXiv:2510.02795v1 Announce Type: cross Abstract: Kleinberg and Mullainathan (2024) recently proposed an interesting model for language generation in the limit: Given a countable collection of languages, and an adversary enumerating the strings of some language $L$ from the collection, the…
arXiv:2510.02484v1 Announce Type: new Abstract: Algorithms that exploit factored Markov decision processes are far more sample-efficient than factor-agnostic methods, yet they assume a factored representation is known a priori — a requirement that breaks down when the agent sees only…
arXiv:2510.03209v1 Announce Type: cross Abstract: As renewable energy integration increases supply variability, battery energy storage systems (BESS) present a viable solution for balancing supply and demand. This paper proposes a novel approach for optimizing battery BESS participation in multiple electricity…
