arXiv:2604.24977v1 Announce Type: new
Abstract: User simulation has long played a vital role in computer science due to its potential to support a wide range of applications. Language, as the primary medium of human communication, forms the foundation…
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:2604.24955v1 Announce Type: new
Abstract: As benchmarks grow in complexity, many apparent agent failures are not failures of the agent at all – they are failures of the benchmark itself: broken specifications, implicit assumptions, and rigid eva…
arXiv:2604.24978v1 Announce Type: new
Abstract: Enterprise deep research often fails to produce decision-ready reports due to uneven information coverage, context explosion, and premature stopping. We propose a scalable Enterprise Deep Research (EDR) …
arXiv:2511.16518v2 Announce Type: replace-cross
Abstract: We open-source MiMo-Embodied, the first cross-embodied foundation model to successfully integrate and achieve state-of-the-art performance in both Autonomous Driving and Embodied AI. MiMo-Embod…
arXiv:2604.22117v2 Announce Type: replace-cross
Abstract: Aligned large language models (LLMs) remain vulnerable to adversarial manipulation, and their reliance on web-scale pretraining creates a subtle but consequential attack surface. We study Steal…
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:2503.06778v3 Announce Type: replace
Abstract: Event annotation is important for identifying market changes, monitoring breaking news, and understanding sociological trends. Although expert annotators set the gold standards, human coding is expen…
arXiv:2508.16198v3 Announce Type: replace
Abstract: Multimodal Large Language Models (MLLMs) have increasingly supported omni-modal processing across text, vision, and speech. However, existing evaluation frameworks for such models suffer from critica…
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…