arXiv:2603.10100v1 Announce Type: new Abstract: Modern CNNs’ high computational demands hinder edge deployment, as traditional “hard” sparsity (skipping mathematical zeros) loses effectiveness in deep layers or with smooth activations like Tanh. We propose a “soft sparsity” paradigm using a hardware…
arXiv:2603.10753v1 Announce Type: cross Abstract: More and more companies’ Intellectual Property (IP) is being integrated into Neural Network (NN) models. This IP has considerable value for companies and, therefore, requires adequate protection. For example, an attacker might replicate a production…
arXiv:2603.10101v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has significantly advanced the reasoning capacity of Large Language Models (LLMs). However, RLVR solely relies on final answers as outcome rewards, neglecting the correctness of intermediate reasoning steps. Training…
arXiv:2603.11042v1 Announce Type: cross Abstract: Generating music that temporally aligns with video events is challenging for existing text-to-music models, which lack fine-grained temporal control. We introduce V2M-Zero, a zero-pair video-to-music generation approach that outputs time-aligned music for video. Our method…
arXiv:2603.10123v1 Announce Type: new Abstract: The “Lost in the Middle” phenomenon — a U-shaped performance curve where LLMs retrieve well from the beginning and end of a context but fail in the middle — is widely attributed to learned Softmax…
arXiv:2410.02113v3 Announce Type: replace Abstract: Partial differential equations (PDEs) are widely used to model complex physical systems, but solving them efficiently remains a significant challenge. Recently, Transformers have emerged as the preferred architecture for PDEs due to their ability to…
arXiv:2603.10149v1 Announce Type: new Abstract: In the design of engineered components, rigorous vibration testing is essential for performance validation and identification of resonant frequencies and amplitudes encountered during operation. Performing this evaluation numerically via machine learning has great potential to…
arXiv:2506.10918v2 Announce Type: replace Abstract: Modern neural sequence models are designed to meet the dual mandate of parallelizable training and fast sequential inference. Recent developments have given rise to various models, such as Gated Linear Attention (GLA) and Mamba, that…
arXiv:2603.10156v1 Announce Type: new Abstract: Finetuning on domain-specific data is a well-established method for enhancing LLM performance on downstream tasks. Training on each dataset produces a new set of model weights, resulting in a multitude of checkpoints saved in-house or…
MIT astronomers are developing a new way to detect, monitor, and mitigate the threats posed by smaller asteroids to our critical space infrastructure.
