arXiv:2602.02602v1 Announce Type: new Abstract: 3D content acquisition and creation are expanding rapidly in the new era of machine learning and AI. 3D Gaussian Splatting (3DGS) has become a promising high-fidelity and real-time representation for 3D content. Similar to the initial wave of digital audio-visual content at the turn of the millennium, the demand for intellectual property protection is also increasing, since explicit and editable 3D parameterization makes unauthorized use and dissemination easier. In this position paper, we […]

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Hey hackers!  If you’ve ever wondered why some of your submissions take a while before they get published, it’s probably because you’re submitting incomplete articles.  While most contributors focus on the content and the headline, we often receive submissions that are missing their meta, TL;dr, and keyword sections. Worse yet, sometimes contributors will add some keywords but not maximize the space for some of that SEO goodness.  As we start the new year, the editorial team thinks it’s […]

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Parallel reasoning enhances Large Reasoning Models (LRMs) but incurs prohibitive costs due to futile paths caused by early errors. To mitigate this, path pruning at the prefix level is essential, yet existing research remains fragmented without a standardized framework. In this work, we propose the first systematic taxonomy of path pruning, categorizing methods by their signal source (internal vs. external) and learnability (learnable vs. non-learnable). This classification reveals the unexplored potential of learnable internal methods, motivating our proposal […]

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Agriculture is pivotal for the economy of a country as it is a major source of food, em-ployment and raw materials. However, challenges such as diseases, soil degradation, and water scarcity persist. Technology adoption can address these issues, improving production and quality. Machine learning enables prediction in agriculture. It opti-mizes irrigation, fertilization, and crop selection, aiding decision-making for food se-curity and crop management. This study proposes multi-model machine learning models for eleven staple (Bananas, Maize, Wheat, Cassava, Rice […]

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Cross-site scripting (XSS) remains a persistent web security vulnerability, especially because obfuscation can change the surface form of a malicious payload while preserving its behavior. These transformations make it difficult for traditional and machine learning-based detection systems to reliably identify attacks. Existing approaches for generating obfuscated payloads often emphasize syntactic diversity, but they do not always ensure that the generated samples remain behaviorally valid. This paper presents a structured pipeline for generating and evaluating obfuscated XSS payloads using […]

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arXiv:2602.23592v1 Announce Type: new Abstract: Memory-augmented Large Language Models (LLMs) have demonstrated remarkable capability for complex and long-horizon embodied planning. By keeping track of past experiences and environmental states, memory enables LLMs to maintain a global view, thereby avoiding repetitive exploration. However, existing approaches often store the memory as raw text, leading to excessively long prompts and high prefill latency. While it is possible to store and reuse the KV caches, the efficiency benefits are greatly undermined due […]

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arXiv:2603.08676v1 Announce Type: new Abstract: Maximum marginal likelihood estimation (MMLE) can be formulated as the optimization of a free energy functional. From this viewpoint, the Expectation-Maximisation (EM) algorithm admits a natural interpretation as a coordinate descent method over the joint space of model parameters and probability measures. Recently, a significant body of work has adopted this perspective, leading to interacting particle algorithms for MMLE. In this paper, we propose an accelerated version of one such procedure, based on […]

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