arXiv:2604.26506v1 Announce Type: new
Abstract: As Large Language Models (LLMs) are increasingly integrated into academic peer review, their vulnerability to adversarial prompts — adversarial instructions embedded in submissions to manipulate outcome…
arXiv:2505.13963v3 Announce Type: replace
Abstract: Quantization methods are widely used to accelerate inference and streamline the deployment of large language models (LLMs). Although quantization’s effects on various LLM capabilities have been exten…
arXiv:2604.26256v1 Announce Type: new
Abstract: Reinforcement learning (RL) has become a critical paradigm for LLM post-training, yet the rollout phase — accounting for 50–80% of total step time — is bottlenecked by skewed generation: long-tailed t…
arXiv:2505.21190v2 Announce Type: replace
Abstract: Radiology reports convey detailed clinical observations and capture diagnostic reasoning that evolves over time. However, existing evaluation methods are limited to single-report settings and rely on…
EU member states and the European Parliament failed to agree on changes that would have softened the bloc’s AI Act and pushed back its toughest enforcement deadlines.
The talks ran for about 12 hours on Tuesday and ended witho…
arXiv:2604.24994v1 Announce Type: cross
Abstract: We introduce a differentiable 3D representation that unifies the ray tracing capabilities of foam-based ray tracing with the efficiency of modern rasterization pipelines. While prior foam representatio…
arXiv:2512.16918v3 Announce Type: replace
Abstract: Recent advances have shown that multimodal large language models (MLLMs) benefit from multimodal interleaved chain-of-thought (CoT) with vision tool interactions. However, existing open-source models…
arXiv:2604.00270v3 Announce Type: replace
Abstract: Recent large multimodal models (LMMs) have made rapid progress in visual grounding, document understanding, and diagram reasoning tasks. However, their ability to convert Printed Circuit Board (PCB) …
arXiv:2604.07802v3 Announce Type: replace
Abstract: Large-scale vision-language models (VLMs) exhibit remarkable zero-shot capabilities, yet the internal mechanisms driving their anomaly detection (AD) performance remain poorly understood. Current met…
arXiv:2604.17070v2 Announce Type: replace
Abstract: This report presents the NTIRE 2026 Rip Current Detection and Segmentation (RipDetSeg) Challenge, which targets automatic rip current understanding in images. Rip currents are hazardous nearshore flo…