arXiv:2601.08703v1 Announce Type: cross Abstract: Explainable artificial intelligence (XAI) is concerned with producing explanations indicating the inner workings of models. For a Rashomon set of similarly performing models, explanations provide a way of disambiguating the behavior of individual models, helping select models for deployment. However explanations themselves can vary depending on the explainer used, and need to be evaluated. In the paper “Evaluating Model Explanations without Ground Truth”, we proposed three principles of explanation evaluation and a new […]

Ver mais

Author(s): Shashwata Bhattacharjee Originally published on Towards AI. The narrative of solo founders building eight-figure SaaS businesses using AI tools has become increasingly prevalent in entrepreneurial discourse. While the surface-level story focuses on individual success, the underlying technical transformation represents something far more fundamental: a complete decomposition of the traditional software development stack, enabled by the convergence of large language models, code generation capabilities, and automation frameworks. This analysis examines the technical architecture, economic implications, and systemic changes […]

Ver mais

The failure wasn’t technical When you’re responsible for rolling out AI inside a company, it’s easy to focus on the visible parts. Models. Connectors. Retrieval. Permissions. Latency. That’s the work everyone sees. But that’s not why my rollout struggled. It didn’t fail because the system was broken. It failed because I was designing AI for teams instead of with them. At Nextdoor, our early Work AI rollout stalled even though the technology was solid. Accuracy was fine. Performance […]

Ver mais

Whether you are speaking to corporate managers, Silicon Valley script kiddies, or seasoned academics pitching commercial applications of their research, you’re likely to hear a lot of claims about what AI is going to do. Hysterical discussions about AI machine learning’s applicability begin with a breathless recap of breakthroughs in predictive modeling (9X.XX% accuracy on ImageNet!, 5.XX% word error rate on speech recognition!) and then abruptly leap to prophesies of miraculous technologies that AI will drive in the […]

Ver mais

arXiv:2602.10272v1 Announce Type: new Abstract: 5G presents numerous advantages compared to previous generations: improved throughput, lower latency, and improved privacy protection for subscribers. Attacks against 5G standalone (SA) commonly use fake base stations (FBS), which need to operate at a very high output power level to lure victim phones to connect to them and are thus highly detectable. In this paper, we introduce 5Gone, a powerful software-defined radio (SDR)-based uplink overshadowing attack method against 5G-SA. 5Gone exploits deficiencies […]

Ver mais

arXiv:2602.00923v1 Announce Type: new Abstract: The challenge of generating reliable local plans has long hindered practical applications in highly cluttered and dynamic environments. Key fundamental bottlenecks include acquiring large-scale expert demonstrations across diverse scenes and improving learning efficiency with limited data. This paper proposes SanD-Planner, a sample-efficient diffusion-based local planner that conducts depth image-based imitation learning within the clamped B-spline space. By operating within this compact space, the proposed algorithm inherently yields smooth outputs with bounded prediction errors […]

Ver mais

arXiv:2602.16903v1 Announce Type: new Abstract: In the context of asynchronous concurrent shared-memory systems, a snapshot algorithm allows failure-prone processes to concurrently and atomically write on the entries of a shared array MEM , and also atomically read the whole array. Recently, Read-Modify-Writable (RMWable) snapshot was proposed, a variant of snapshot that allows processes to perform operations more complex than just read and write, specifically, each entry MEM[k] is an arbitrary readable object. The known RMWable snapshot algorithms heavily […]

Ver mais

arXiv:2506.11214v2 Announce Type: replace-cross Abstract: In this paper, we propose practical normalized stochastic first-order methods with Polyak momentum, multi-extrapolated momentum, and recursive momentum for solving unconstrained optimization problems. These methods employ dynamically updated algorithmic parameters and do not require explicit knowledge of problem-dependent quantities such as the Lipschitz constant or noise bound. We establish first-order oracle complexity results for finding approximate stochastic stationary points under heavy-tailed noise and weakly average smoothness conditions — both of which are weaker […]

Ver mais