arXiv:2507.04341v2 Announce Type: replace Abstract: While continuous diffusion models excel in modeling continuous distributions, their application to categorical data has been less effective. Recent work has shown that ratio-matching through score-entropy within a continuous-time discrete Markov chain (CTMC) framework serves as a competitive alternative to autoregressive models in language modeling. To enhance this framework, we first introduce three new theorems concerning the KL divergence between the data and learned distribution. Our results serve as the discrete counterpart to […]

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Consensus mechanisms are the core of any blockchain system. However, the majority of these mechanisms do not target federated learning directly nor do they aid in the aggregation step. This paper introduces Proof of Reasoning (PoR), a novel consensus mechanism specifically designed for federated learning using blockchain, aimed at preserving data privacy, defending against malicious attacks, and enhancing the validation of participating networks. Unlike generic blockchain consensus mechanisms commonly found in the literature, PoR integrates three distinct processes […]

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arXiv:2601.03259v1 Announce Type: new Abstract: Existing sequential recommendation models, even advanced diffusion-based approaches, often struggle to capture the rich semantic intent underlying user behavior, especially for new users or long-tail items. This limitation stems from their reliance on ID-based embeddings, which lack semantic grounding. We introduce LLMDiRec, a new approach that addresses this gap by integrating Large Language Models (LLMs) into an intent-aware diffusion model. Our approach combines collaborative signals from ID embeddings with rich semantic representations from […]

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arXiv:2602.00935v1 Announce Type: new Abstract: Ants are highly capable of grasping objects in clutter, and we have recently observed that this involves substantial use of their forelegs. The forelegs, more specifically the tarsi, have high friction microstructures (setal pads), are covered in hairs, and have a flexible under-actuated tip. Here we abstract these features to test their functional advantages for a novel low-cost gripper design, suitable for bin-picking applications. In our implementation, the gripper legs are long and […]

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Wilson dismantles the absurd claims of religious and political authorities. His core message is a call to intellectual rebellion: since the world is fundamentally chaotic and run by fools, you must trust your own conscience. Question all dogma, laugh at the absurdity, and become the pope of your own personal religion.

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arXiv:2602.10173v1 Announce Type: new Abstract: Representation in the family of 3D Gaussian Splats (3DGS) are growing into a viable alternative to traditional graphics for an expanding number of application, including recent techniques that facilitate physics simulation and animation. However, extracting usable objects from in-the-wild captures remains challenging and controllable editing techniques for this representation are limited. Unlike the bulk of emerging techniques, focused on automatic solutions or high-level editing, we introduce an interactive suite of tools centered around […]

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AI (VLM-based) radiology models can sound confident and still be wrong ; hallucinating diagnoses that their own findings don’t support. This is a silent, and dangerous failure mode. This new paper introduces a verification layer that checks every diagnostic claim an AI makes before it reaches a clinician. When our system says a diagnosis is supported, it’s been mathematically proven – not just guessed. Every model tested improved significantly after verification, with the best result hitting 99% soundness. […]

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While much of the current research in deep learning-based vulnerability detection relies on disassembled binaries, this paper explores the feasibility of extracting features directly from raw x86-64 machine code. Although assembly language is more interpretable for humans, it requires more complex models to capture token-level context. In contrast, machine code may enable more efficient, lightweight models and preserve all information that might be lost in disassembly. This paper approaches the task of vulnerability detection through an exploratory study […]

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Memory is increasingly central to Large Language Model (LLM) agents operating beyond a single context window, yet most existing systems rely on offline, query-agnostic memory construction that can be inefficient and may discard query-critical information. Although runtime memory utilization is a natural alternative, prior work often incurs substantial overhead and offers limited explicit control over the performance-cost trade-off. In this work, we present textbf{BudgetMem}, a runtime agent memory framework for explicit, query-aware performance-cost control. BudgetMem structures memory processing […]

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