arXiv:2605.11242v1 Announce Type: new
Abstract: In this paper, we present the RETUYT-INCO participation at the BEA 2026 shared task “Rubric-based Short Answer Scoring for German”. Our team participated in track 1 (Unseen answers three-way), track 3 (U…
arXiv:2605.11255v1 Announce Type: new
Abstract: We present Hebatron, a Hebrew-specialized open-weight large language model built on the NVIDIA Nemotron-3 sparse Mixture-of-Experts architecture. Training employs a three-phase easy-to-hard curriculum wi…
arXiv:2602.11126v2 Announce Type: replace
Abstract: Offline multi-objective optimization (MOO) aims to recover Pareto-optimal designs given a finite, static dataset. Recent generative approaches, including diffusion models, show strong performance und…
arXiv:2605.12456v1 Announce Type: cross
Abstract: We introduce TextSeal, a state-of-the-art watermark for large language models. Building on Gumbel-max sampling, TextSeal introduces dual-key generation to restore output diversity, along with entropy-w…
arXiv:2605.10983v1 Announce Type: new
Abstract: Reinforcement learning (RL) has shown extraordinary potential in aligning diffusion models to downstream tasks, yet most of them still suffer from significant reward hacking, which degrades generative di…
arXiv:2605.10989v1 Announce Type: new
Abstract: The training of Binary Neural Networks (BNNs) is fundamentally based on gradient approximation for non-differentiable binarization operations (e.g., sign function). However, prevailing methods including …
arXiv:2605.10999v1 Announce Type: new
Abstract: Skills are a promising way to improve LLM agent capabilities without retraining, while keeping the added procedure reusable and controllable. However, high-quality skills are still largely written by han…
arXiv:2605.11005v1 Announce Type: new
Abstract: Mixture-of-experts (MoE) architectures enable trillion-parameter LLMs with sparsely activated experts. Expert parallelism (EP) is a widely adopted MoE training strategy, but it suffers from severe all-to…
arXiv:2605.11887v1 Announce Type: cross
Abstract: Large language models have achieved remarkable capabilities across diverse tasks, yet their internal decision-making processes remain largely opaque, limiting our ability to inspect, control, and syste…
arXiv:2605.11010v1 Announce Type: new
Abstract: Federated Learning has emerged as a transformative paradigm for collaborative machine learning across distributed environments. However, its performance is strongly influenced by the aggregation strategy…