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arXiv:2602.22227v1 Announce Type: new Abstract: Despite their impressive capabilities, Multimodal Large Language Models (MLLMs) exhibit perceptual fragility when confronted with visually complex scenes. This weakness …
arXiv:2602.22249v1 Announce Type: new Abstract: In energy system analysis, coupling models with mismatched spatial resolutions is a significant challenge. A common solution is assigning weights …
arXiv:2602.22254v1 Announce Type: new Abstract: We introduce Causal Computational Asymmetry (CCA), a principle for causal direction identification based on optimization dynamics in which one neural …
arXiv:2602.22255v1 Announce Type: new Abstract: We introduce a sequence modeling framework in which the latent state is a complex-valued wave function evolving on a finite-dimensional …
arXiv:2602.22259v1 Announce Type: new Abstract: Recognizing the substantial computational cost of backpropagation (BP), non-BP methods have emerged as attractive alternatives for efficient learning on emerging …
arXiv:2602.22260v1 Announce Type: new Abstract: Can an LLM learn how an optimizer behaves -- and use that knowledge to control it? We extend Code World …
arXiv:2602.22261v1 Announce Type: new Abstract: Large language models have become central to many AI applications, but their growing energy consumption raises serious sustainability concerns. A …
arXiv:2602.22265v1 Announce Type: new Abstract: Modern vision generators transport a base distribution to data through time-indexed measures, implemented as deterministic flows (ODEs) or stochastic diffusions …
arXiv:2602.22266v1 Announce Type: new Abstract: State-space models (SSMs) have emerged as a powerful foundation for long-range sequence modeling, with the HiPPO framework showing that continuous-time …
arXiv:2602.22267v1 Announce Type: new Abstract: The real-time supervision of production processes is a common challenge across several industries. It targets process component monitoring and its …
arXiv:2602.22268v1 Announce Type: new Abstract: Quantization followed by parameter-efficient fine-tuning has emerged as a promising paradigm for downstream adaptation under tight GPU memory constraints. However, …
arXiv:2602.22269v1 Announce Type: new Abstract: Federated Learning (FL) enables collaborative model training without sharing raw data. However, shared local model updates remain vulnerable to inference …