May 2026

arXiv:2605.12507v1 Announce Type: new Abstract: While Large Language Model (LLM) multi-agent systems (MAS) offer a transformative approach to simulating human behavior in complex systems, it remains largely unexplored whether these simulations can replicate realistic structural and temporal dynamics from a dynamic network perspective. Our evaluation indicates that existing frameworks excel at generating plausible micro-level interactions but fail to capture the emergent, macroscopic topologies necessary for domains that rely on realistic network dynamics, such as modeling information propagation and […]

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arXiv:2605.12506v1 Announce Type: new Abstract: Realizing on-device ML-based gesture detection under tight real-time performance, energy and memory constraints is challenging, especially when considering mobile devices with varying battery-power levels. Existing EdgeAI deployments typically rely on a single fixed detector, limiting optimization opportunities. We present Scale-Gest, a novel run-time adaptive gesture detection framework that expands the detector space into a dense family of tiny-YOLO architectures. We introduce multiple novel device-calibrated ACE (Accuracy-Complexity-Energy) profiles by analyzing different model-resolution-stride operating points. […]

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arXiv:2605.12505v1 Announce Type: new Abstract: Autonomous AI systems generate responsibility gaps: consequential actions that cannot be satisfactorily attributed to developers, operators, or users under existing legal frameworks. The prevailing subject-object dichotomy fails to accommodate entities that exhibit autonomous, goal-directed behavior without recognized consciousness. Given irreducible epistemic uncertainty regarding artificial consciousness and the prospect of high-impact harms, the precautionary principle supports institutional design rather than regulatory inaction. This article advances limited legal personhood as a functional governance instrument for […]

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arXiv:2605.12504v1 Announce Type: new Abstract: We develop conjectures and theorems expressing the idea that the prime sequence exhibits computational irreducibility in the transition from one prime to its successor. Informally, given a prime pp p, no general algorithm can compute the least prime greater than pp p substantially faster than sequentially testing candidates for primality, except possibly on sparse input sets. Our framework proceeds along complementary lines. First, we formalize Prime Successor Irreducibility in a Turing-machine complexity model […]

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Neural models get the hype. But that lightning-fast word suggestion as you type? That’s not a neural model. It’s a lookup table. And it’s beating transformers on the metric that matters. I’ve spent a lot of time thinking about prediction under pressure. When I was working on Indian-language typing for early Kindle devices, we had a problem that no amount of ML enthusiasm could solve: the hardware simply didn’t care about our ambitions. We had roughly 512 MB […]

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In this tutorial, we build a realistic Zero-Trust network simulation by modeling a micro-segmented environment as a directed graph and forcing every request to earn access through continuous verification. We implement a dynamic policy engine that blends ABAC-style permissions with device posture, MFA, path reachability, zone sensitivity, and live risk signals such as anomaly and data-volume indicators. We then operationalize the model through a Flask API and run mixed traffic, including insider-lateral movement and exfiltration attempts, to show […]

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Welcome to the Datasette blog We have a bunch of neat Datasette announcements in the pipeline so we decided it was time the project grew an official blog. I built this using OpenAI Codex desktop, which turns out to have the Markdown session transcript export feature I’ve always wanted. Here’s the session that built the blog. See also issue 179. Tags: ai, datasette, generative-ai, llms, ai-assisted-programming, codex

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The mouse pointer has sat at the center of personal computing for more than half a century. It tracks cursor position. It registers clicks. Beyond that, it does almost nothing. Google DeepMind researchers outlined a set of experimental principles and demos for an AI-enabled pointer that goes considerably further: one that understands not just where you are pointing, but what you are pointing at and why it matters. The system is powered by Gemini and is currently in […]

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Most AI systems today work in turns. You type or speak, the model waits, processes your input, and then responds. That’s the entire interaction loop. Thinking Machines Lab, an AI research lab, is arguing that this model of interaction is a fundamental bottleneck. Thinking Machines Lab team introduced a research preview of a new class of system they call interaction models to address it. The main idea for their research is interactivity should be native to the model […]

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