arXiv:2601.22519v1 Announce Type: new Abstract: Discrete flow models (DFMs) have been proposed to learn the data distribution on a finite state space, offering a flexible framework as an alternative to discrete diffusion models. A line of recent work has studied samplers for discrete diffusion models, such as tau-leaping and Euler solver. However, these samplers require a large number of iterations to control discretization error, since the transition rates are frozen in time and evaluated at the initial state […]

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arXiv:2601.10848v1 Announce Type: new Abstract: Machine Learning (ML) has emerged as a pivotal technology in the operation of large and complex systems, driving advancements in fields such as autonomous vehicles, healthcare diagnostics, and financial fraud detection. Despite its benefits, the deployment of ML models brings significant security challenges, such as adversarial attacks, which can compromise the integrity and reliability of these systems. To address these challenges, this paper builds upon the concept of Secure Machine Learning Operations (SecMLOps), […]

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A conceptual companion to Working Code, Wrong Engineering AI can generate code, but engineered software still requires structure: intent, boundaries, scoped generation, contracts, evaluation harnesses, provenance, and accountable architecture. AI’s promise is to remove the complexity of development. When AI starts delivering code, that promise is often understood as a way to skip engineering. The same naive pattern appears elsewhere: asking AI to build the next application from a few vague lines, or expecting an agent with a […]

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Hey everyone! My company is releasing ARES (Agentic Research and Evaluation Suite) today: https://github.com/withmartian/ares We’re hoping ARES can be a new Gym style environment for long horizon coding tasks, with a couple opinionated design decisions: – async, so it can parallelize easily and to large workloads – treats LLMRequests as environment observations and LLMResponses as actions, so we can treat the underlying LLM as the policy instead of a full agent orchestrator – integrates with Harbor (harborframework.com) on […]

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arXiv:2604.18814v1 Announce Type: new Abstract: An algorithm for simulation of switching converters is proposed in the paper. The algorithm is based on simulation of averaged circuit model applying “switching cell” concept, and construction of instantaneous values of the waveforms using quasi steady state and linear ripple approximation. Simulation covers converters operating both in the continuous and the discontinuous conduction mode. Application of the algorithm is demonstrated by simulation results of all three of the basic converters: buck, boost […]

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arXiv:2604.16872v1 Announce Type: new Abstract: Large Language Models (LLMs) can be helpful for literature search and summarisation, but retracted articles can confuse them. This article asks three open weights (offline) LLMs whether 161 high profile retracted articles had been retracted, performing a similar check for a benchmark multidisciplinary set of 34,070 non-retracted articles. Based on titles and abstracts, in over 80% of cases the LLMs claimed that a retracted article had not been retracted (GPT OSS 120B: 82%; […]

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Geometric Representation Learning (GRL) aims to approximate the non-Euclidean topology of high-dimensional data through discrete graph structures, grounded in the manifold hypothesis. However, traditional static graph construction methods based on Euclidean distance often fail to capture the intrinsic curvature characteristics of the data manifold. Although Ollivier-Ricci Curvature Flow (OCF) has proven to be a powerful tool for dynamic topological optimization, its core reliance on Optimal Transport (Wasserstein distance) leads to prohibitive computational complexity, severely limiting its application in […]

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arXiv:2604.14267v1 Announce Type: new Abstract: Search agents extend Large Language Models (LLMs) beyond static parametric knowledge by enabling access to up-to-date and long-tail information unavailable during pretraining. While reinforcement learning has been widely adopted for training such agents, existing approaches face key limitations: process supervision often suffers from unstable value estimation, whereas outcome supervision struggles with credit assignment due to sparse, trajectory-level rewards. To bridge this gap, we propose Contribution-Weighted GRPO (CW-GRPO), a framework that integrates process supervision […]

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