arXiv:2602.04335v1 Announce Type: new Abstract: Solving large scale Optimal Transport (OT) in machine learning typically relies on sampling measures to obtain a tractable discrete problem. While the discrete solver’s accuracy is controllable, the rate of convergence of the discretization error is governed by the intrinsic dimension of our data. Therefore, the true bottleneck is the knowledge and control of the sampling error. In this work, we tackle this issue by introducing novel estimators for both sampling error and […]

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These past months I realized that my blog, which is made using WordPress, is very slow and that hurts the traffic. So I went looking for the problems and their solutions but nothing really worked, so I decided to switch everything. Today I’ll talk about that decision, its advantages and disadvantages.

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arXiv:2602.16861v1 Announce Type: new Abstract: As the presence of autonomous robots in public spaces increases-whether navigating campus walkways or neighborhood sidewalks-understanding how to carefully study these robots becomes critical. While HRI research has conducted field studies in public spaces, these are often limited to controlled experiments with prototype robots or structured observational methods, such as the Wizard of Oz technique. However, the autonomous mobile robots we encounter today, particularly delivery robots, operate beyond the control of researchers, navigating […]

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arXiv:2508.13131v2 Announce Type: replace-cross Abstract: Watermarking has recently emerged as an effective strategy for detecting the generations of large language models (LLMs). The strength of a watermark typically depends strongly on the entropy afforded by the language model and the set of input prompts. However, entropy can be quite limited in practice, especially for models that are post-trained, for example via instruction tuning or reinforcement learning from human feedback (RLHF), which makes detection based on watermarking alone challenging. […]

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This work introduces a regime-aware in-context learning framework that leverages large language models (LLMs) for financial volatility forecasting under nonstationary market conditions. The proposed approach deploys pretrained LLMs to reason over historical volatility patterns and adjust their predictions without parameter fine-tuning. We develop an oracle-guided refinement procedure that constructs regime-aware demonstrations from training data. An LLM is then deployed as an in-context learner that predicts the next-step volatility from the input sequence using demonstrations sampled conditional to the […]

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arXiv:2602.21212v1 Announce Type: new Abstract: Natural disasters such as earthquakes, torrential rainfall, floods, and volcanic eruptions occur with extremely low frequency and affect limited geographic areas. When individuals face disaster situations, they often experience confusion and lack the domain-specific knowledge and experience necessary to determine appropriate responses and actions. While disaster information is continuously updated, even when utilizing RAG search and large language models for inquiries, obtaining relevant domain knowledge about natural disasters and experiences similar to one’s […]

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For nearly a century, Motorola has been a notable pioneer of communications (producing the first-ever handheld cell phone). As with any tech giant, it’s easy to rest on laurels. However, as of late, Motorola has been allocating resources to smart home security. This innovation and adaptation reflect a broader shift toward device interconnectivity. As homes become smarter, the need for safe and reliable IoT (Internet of Things) devices grows stronger. This expansion isn’t just a money grab for […]

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arXiv:2603.17055v1 Announce Type: new Abstract: Image Restoration (IR) agents, leveraging multimodal large language models to perceive degradation and invoke restoration tools, have shown promise in automating IR tasks. However, existing IR agents typically lack an insight summarization mechanism for past interactions, which results in an exhaustive search for the optimal IR tool. To address this limitation, we propose a portrait-aware IR agent, dubbed PaAgent, which incorporates a self-evolving portrait bank for IR tools and Retrieval-Augmented Generation (RAG) to […]

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arXiv:2602.23513v1 Announce Type: new Abstract: Wi-Fi deauthentication attacks remain a practical denial-of-service (DoS) threat by exploiting unprotected management frames to disrupt client connectivity. In this work, we introduce a software-defined testbed to measure Wi-Fi resilience to deauthentication attacks. We experimentally evaluate five wireless security configurations: open networks, WPA1, WPA2 without Protected Management Frames (PMF), WPA2 with PMF, and WPA3. Using controlled experiments, we measure client disconnection rates, packet injection volume, and time-to-disruption under each configuration. Packet-level behavior is […]

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