arXiv:2511.02966v1 Announce Type: new Abstract: We study the problem of aligning a generative model’s response with a user’s preferences. Recent works have proposed several different formulations for personalized alignment; however, they either require a large amount of user preference queries…
arXiv:2511.02936v1 Announce Type: new Abstract: Efforts have increased in recent years to identify associations between specific datasets and the scientific literature that incorporates them. Knowing that a given publication cites a given dataset, the next logical step is to explore…
arXiv:2511.02944v1 Announce Type: new Abstract: A common challenge for decision makers is selecting actions whose rewards are unknown and evolve over time based on prior policies. For instance, repeated use may reduce an action’s effectiveness (habituation), while inactivity may restore…
arXiv:2511.02894v1 Announce Type: new Abstract: The widespread integration of wearable sensing devices in Internet of Things (IoT) ecosystems, particularly in healthcare, smart homes, and industrial applications, has required robust human activity recognition (HAR) techniques to improve functionality and user experience.…
arXiv:2511.02887v1 Announce Type: new Abstract: The North Indian Ocean, including the Arabian Sea and the Bay of Bengal, represents a vital source of livelihood for coastal communities, yet fishermen often face uncertainty in locating productive fishing grounds. To address this…
arXiv:2511.02886v1 Announce Type: new Abstract: Prior to the close of the 2025 ARC Prize competition, the leading open source approach – known as TRM, or Tiny Recursive Models – involved training a 7M parameter recursive neural network on augmented variants…
arXiv:2301.11375v4 Announce Type: replace Abstract: In machine learning, there is a long history of trying to build neural networks that can learn from fewer example data by baking in strong geometric priors. However, it is not always clear a priori…
arXiv:2511.02969v1 Announce Type: new Abstract: Efficient exploration in deep reinforcement learning remains a fundamental challenge, especially in environments characterized by high-dimensional states and sparse rewards. Traditional exploration strategies that rely on random local policy noise, such as $epsilon$-greedy and Boltzmann…
arXiv:2511.03012v1 Announce Type: new Abstract: Metamaterials are engineered materials composed of specially designed unit cells that exhibit extraordinary properties beyond those of natural materials. Complex engineering tasks often require heterogeneous unit cells to accommodate spatially varying property requirements. However, designing…
arXiv:2505.20172v2 Announce Type: replace Abstract: We study the dynamics of gradient flow with small weight decay on general training losses $F: mathbb{R}^d to mathbb{R}$. Under mild regularity assumptions and assuming convergence of the unregularised gradient flow, we show that the…
