arXiv:2509.22458v2 Announce Type: replace Abstract: Physics-informed graph neural networks (PIGNNs) have emerged as fast AC power-flow solvers that can replace the classic NewtonRaphson (NR) solvers, especially when thousands of scenarios must be evaluated. However, current PIGNNs still need accuracy improvements…
arXiv:2602.17751v1 Announce Type: new Abstract: Biodiversity loss poses a significant threat to humanity, making wildlife monitoring essential for assessing ecosystem health. Avian species are ideal subjects for this due to their popularity and the ease of identifying them through their…
arXiv:2511.02872v3 Announce Type: replace Abstract: Recent advances in large language models (LLMs) have demonstrated impressive capabilities in formal theorem proving, particularly on contest-based mathematical benchmarks like the IMO. However, these contests do not reflect the depth, breadth, and abstraction of…
arXiv:2602.17778v1 Announce Type: new Abstract: Multi-turn interaction length is a dominant factor in the operational costs of conversational LLMs. In this work, we present a new failure mode in conversational LLMs: turn amplification, in which a model consistently prolongs multi-turn…
arXiv:2512.08217v2 Announce Type: replace Abstract: Decoupled weight decay, solely responsible for the performance advantage of AdamW over Adam, has long been set to proportional to learning rate $gamma$ without questioning. Some researchers have recently challenged such assumption and argued that…
arXiv:2601.09282v2 Announce Type: replace Abstract: Cluster workload allocation often requires complex configurations, creating a usability gap. This paper introduces a semantic, intent-driven scheduling paradigm for cluster systems using Natural Language Processing. The system employs a Large Language Model (LLM) integrated…
arXiv:2508.04581v2 Announce Type: replace-cross Abstract: Large language models have revolutionized AI applications, yet their high computational and memory demands hinder their widespread deployment. Existing compression techniques focus on intra-block optimizations (e.g., low-rank approximation or attention pruning), while the repetitive layered…
arXiv:2510.08570v2 Announce Type: replace Abstract: Neural networks are famously nonlinear. However, linearity is defined relative to a pair of vector spaces, $f:X to Y$. Leveraging the algebraic concept of transport of structure, we propose a method to explicitly identify non-standard…
arXiv:2601.11924v2 Announce Type: replace Abstract: We study cooperative stochastic multi-armed bandits with vector-valued rewards under adversarial corruption and limited verification. In each of $T$ rounds, each of $N$ agents selects an arm, the environment generates a clean reward vector, and…
arXiv:1803.09319v2 Announce Type: replace Abstract: Deep neural networks are often used to implement powerful generative models for real-world data. Notable applications include image denoising, as well as other classical inverse problems like compressed sensing and super-resolution. To provide a rigorous…
