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arXiv:2601.19938v1 Announce Type: new Abstract: Decentralized Federated Learning (DFL) is a serverless collaborative machine learning paradigm where devices collaborate directly with neighbouring devices to exchange...
arXiv:2601.19939v1 Announce Type: new Abstract: Oculomics - the concept of predicting systemic diseases, such as cardiovascular disease and dementia, through retinal imaging - has advanced...
arXiv:2601.19940v1 Announce Type: new Abstract: Among hardware accelerators for deep-learning inference, data flow implementations offer low latency and high throughput capabilities. In these architectures, each...
arXiv:2601.19941v1 Announce Type: new Abstract: In last two years, large language models (LLMs) have shown strong capabilities in code generation, including hardware design at register-transfer...
arXiv:2601.19942v1 Announce Type: new Abstract: We study the emergence of multi-step reasoning in deep Transformer language models through a geometric and statistical-physics lens. Treating the...
arXiv:2601.19943v1 Announce Type: new Abstract: How can populations of learners develop coordinated, diverse behaviors without explicit communication or diversity incentives? We demonstrate that competition alone...
arXiv:2601.19944v1 Announce Type: new Abstract: We study model-agnostic post-hoc calibration methods intended to improve probabilistic predictions in supervised binary classification on real i.i.d. tabular data,...
arXiv:2601.19945v1 Announce Type: new Abstract: Automatic Speech Recognition (ASR) offers significant potential to reduce the workload of medical personnel, for example, through the automation of...
arXiv:2601.19947v1 Announce Type: new Abstract: Learning from Noisy Labels (LNL) presents a fundamental challenge in deep learning, as real-world datasets often contain erroneous or corrupted...
arXiv:2601.19950v1 Announce Type: new Abstract: This paper introduces and analyzes \emph{defensive rebalancing}, a novel mechanism for protecting constant-function market makers (CFMMs) from value leakage due...
arXiv:2601.19951v1 Announce Type: new Abstract: Symbolic music representation is a fundamental challenge in computational musicology. While grid-based representations effectively preserve pitch-time spatial correspondence, their inherent...
arXiv:2601.19952v1 Announce Type: new Abstract: Real-time voice agents face a dilemma: end-to-end models often lack deep reasoning, while cascaded pipelines incur high latency by executing...
arXiv:2601.19953v1 Announce Type: new Abstract: Extending the intelligence of sensors to the data-acquisition process - deciding whether to sample or not - can result in...
arXiv:2601.19955v1 Announce Type: new Abstract: Neuroscience and Artificial Intelligence (AI) have made significant progress in the past few years but have only been loosely inter-connected....
arXiv:2601.19961v1 Announce Type: new Abstract: We present MeanCache, a training-free caching framework for efficient Flow Matching inference. Existing caching methods reduce redundant computation but typically...
arXiv:2601.19963v1 Announce Type: new Abstract: Cross-session nonstationarity in neural activity recorded by implanted electrodes is a major challenge for invasive Brain-computer interfaces (BCIs), as decoders...
arXiv:2601.19964v1 Announce Type: new Abstract: We discuss Google's journey in developing and refining two internal AI-based IDE features: code completion and natural-language-driven code transformation (Transform...
arXiv:2601.19965v1 Announce Type: new Abstract: In industrial recommender systems, conversion rate (CVR) is widely used for traffic allocation, but it fails to fully reflect recommendation...
arXiv:2601.19967v1 Announce Type: new Abstract: Collecting web data to train deep models has become increasingly common, raising concerns about unauthorized data usage. To mitigate this...
arXiv:2601.19968v1 Announce Type: new Abstract: What is the AGI in Offensive Security? One can break it down into two questions : (1) any offensive security...
arXiv:2601.19969v1 Announce Type: new Abstract: Human-in-the-loop guidance has emerged as an effective approach for enabling faster convergence in online reinforcement learning (RL) of complex real-world...
arXiv:2601.19970v1 Announce Type: new Abstract: As large language models (LLMs) move from research prototypes to enterprise systems, their security vulnerabilities pose serious risks to data...
arXiv:2601.19972v1 Announce Type: new Abstract: In high-dimensional robotic path planning, traditional sampling-based methods often struggle to efficiently identify both feasible and optimal paths in complex,...
arXiv:2601.19992v1 Announce Type: new Abstract: Industrial image anomaly detection is a challenging problem owing to extreme class imbalance and the scarcity of labeled defective samples,...