arXiv:2601.09042v1 Announce Type: new Abstract: This work addresses the fundamental problem of unbounded metric movement costs in bandit online convex optimization, by considering high-dimensional dynamic quadratic hitting costs and $ell_2$-norm switching costs in a noisy bandit feedback model. For a…
arXiv:2404.03471v5 Announce Type: replace-cross Abstract: Bias in large language models (LLMs) has many forms, from overt discrimination to implicit stereotypes. Counterfactual bias evaluation is a widely used approach to quantifying bias and often relies on template-based probes that explicitly state…
arXiv:2601.09051v1 Announce Type: new Abstract: Incomplete multi-view clustering (IMVC) aims to discover shared cluster structures from multi-view data with partial observations. The core challenges lie in accurately imputing missing views without introducing bias, while maintaining semantic consistency across views and…
arXiv:2503.16771v2 Announce Type: replace-cross Abstract: As Large Language Models for Code (LM4Code) become integral to software engineering, establishing trust in their output becomes critical. However, standard accuracy metrics obscure the underlying reasoning of generative models, offering little insight into how…
arXiv:2601.09071v1 Announce Type: new Abstract: The existence of multiple, equally accurate models for a given predictive task leads to predictive multiplicity, where a “Rashomon set” of models achieve similar accuracy but diverges in their individual predictions. This inconsistency undermines trust…
arXiv:2508.10337v2 Announce Type: replace-cross Abstract: This paper describes the solutions of the Dianping-Trust-Safety team for the META CRAG-MM challenge. The challenge requires building a comprehensive retrieval-augmented generation system capable for multi-modal multi-turn question answering. The competition consists of three tasks:…
arXiv:2601.09076v1 Announce Type: new Abstract: Split Federated Learning (SFL) enables collaborative training between resource-constrained edge devices and a compute-rich server. Communication overhead is a central issue in SFL and can be mitigated with auxiliary networks. Yet, the fundamental client-side computation…
arXiv:2512.17340v2 Announce Type: replace-cross Abstract: Fair regression methods have the potential to mitigate societal bias concerns in health care, but there has been little work on penalized fair regression when multiple groups experience such bias. We propose a general regression…
arXiv:2601.09083v1 Announce Type: new Abstract: We present Speculative Rollout with Tree-Structured Cache (SRT), a simple, model-free approach to accelerate on-policy reinforcement learning (RL) for language models without sacrificing distributional correctness. SRT exploits the empirical similarity of rollouts for the same…
arXiv:2502.20580v4 Announce Type: replace Abstract: Training deep neural networks typically relies on backpropagating high dimensional error signals a computationally intensive process with little evidence supporting its implementation in the brain. However, since most tasks involve low-dimensional outputs, we propose that…
