arXiv:2604.00795v1 Announce Type: cross Abstract: We propose the Preference Guided Iterated Pareto Referent Optimisation (PG-IPRO) for urban route planning for people with different accessibility requirements and preferences. With this algorithm the user can interact with the system by giving feedback…
arXiv:2604.00223v1 Announce Type: new Abstract: Reverse Kullback-Leibler (RKL) divergence has recently emerged as the preferred objective for large language model (LLM) distillation, consistently outperforming forward KL (FKL), particularly in regimes with large vocabularies and significant teacher-student capacity mismatch, where RKL…
arXiv:2604.00963v1 Announce Type: cross Abstract: We show polylogarithmic mixing time bounds for the alternating-scan sampler for positively weighted restricted Boltzmann machines. This is done via analysing the same chain and the Glauber dynamics for ferromagnetic two-spin systems, where we obtain…
arXiv:2604.00230v1 Announce Type: new Abstract: Neural collapse (NC) — the convergence of penultimate-layer features to a simplex equiangular tight frame — is well understood at equilibrium, but the dynamics governing its onset remain poorly characterised. We identify a simple and…
MIT researchers developed a testing framework that pinpoints situations where AI decision-support systems are not treating people and communities fairly.
arXiv:2508.02473v3 Announce Type: replace-cross Abstract: Code editing is a frequent yet cognitively demanding task in software development. Existing AI-powered tools often disrupt developer flow by requiring explicit natural language instructions and suffer from high latency, limiting real-world usability. We present…
arXiv:2603.09697v2 Announce Type: replace Abstract: Recent advances in spectral optimization, notably Muon, have demonstrated that constraining update steps to the Stiefel manifold can significantly accelerate training and improve generalization. However, Muon implicitly assumes an isotropic optimization landscape, enforcing a uniform…
arXiv:2303.08250v5 Announce Type: replace-cross Abstract: To effectively manage the complexities of real-world dynamic environments, continual learning must incrementally acquire, update, and accumulate knowledge from a stream of tasks of different nature without suffering from catastrophic forgetting of prior knowledge. While…
arXiv:2505.04738v3 Announce Type: replace Abstract: Most neural-operator surrogates for PDEs inherit from DeepONet-style formulations the requirement that the input function be sampled at a fixed, ordered set of sensors. This assumption limits applicability to problems with variable sensor layouts, missing…
arXiv:2511.02272v3 Announce Type: replace Abstract: Probabilistic relaxations of graph cuts offer a differentiable alternative to spectral clustering, enabling end-to-end and online learning without eigendecompositions, yet prior work centered on RatioCut and lacked general guarantees and principled gradients. We present a…
