arXiv:2309.12032v4 Announce Type: replace Abstract: Causal discovery (CD) is an important component of many scientific applications, yet most techniques produce unreliable point estimates that often contradict expert knowledge. To mitigate this, recent research has focused on ex-ante incorporation of background…
A new approach could help users know whether to trust a model’s predictions in safety-critical applications like health care and autonomous driving.
arXiv:2603.06488v1 Announce Type: cross Abstract: Diffusion-based generative modeling suggests reversing a noising semigroup by adding a score drift. For continuous-variable Gaussian Markov dynamics, complete positivity couples drift and diffusion at the generator level. For a quantum-limited attenuator with thermal parameter…
arXiv:2510.16657v2 Announce Type: replace-cross Abstract: Synthetic data has been increasingly used to train frontier generative models. However, recent studies raise key concerns that iteratively retraining a generative model on its self-generated synthetic data may keep deteriorating model performance, a phenomenon…
arXiv:2602.15423v2 Announce Type: replace-cross Abstract: As the burgeoning power requirements of sophisticated neural architectures escalate, the information retrieval community has recognized ecological sustainability as a pivotal priority that necessitates a fundamental paradigm shift in model design. While contemporary neural rankers…
arXiv:2602.18982v2 Announce Type: replace Abstract: Common deep learning approaches for antibody engineering focus on modeling the marginal distribution of sequences. By treating sequences as independent samples, however, these methods overlook affinity maturation as a rich and largely untapped source of…
arXiv:2505.02614v2 Announce Type: replace-cross Abstract: This paper focuses on applying entropic mirror descent to solve linear systems, where the main challenge for the convergence analysis stems from the unboundedness of the domain. To overcome this without imposing restrictive assumptions, we…
arXiv:2508.10154v2 Announce Type: replace Abstract: Mixture models have attracted significant attention due to practical effectiveness and comprehensive theoretical foundations. A persisting challenge is model misspecification, which occurs when the model to be fitted has more mixture components than those in…
arXiv:2512.05962v2 Announce Type: replace Abstract: Reinforcement Learning (RL) has become the de facto standard for tuning LLMs to solve tasks involving reasoning. However, growing evidence shows that models trained in such way often suffer from a significant loss in diversity.…
arXiv:2603.05566v1 Announce Type: new Abstract: Cross-modal alignment is a crucial task in multimodal learning aimed at achieving semantic consistency between vision and language. This requires that image-text pairs exhibit similar semantics. Traditional algorithms pursue embedding consistency to achieve semantic consistency,…
