The daily ledger

Your Model’s MSE Is Lying to You

First in a series on probabilistic forecasting for physical signals. Next: what happens when you roll the forecast forward more than one step. The post Your Model’s MSE Is Lying to You appeared first on Towards Data…

When to Use One Model and When to Use a Team of Agents

When Codex is the right shape for the problem, when Claude Code is, and how I split 5 specialist agents between them on dense AI capacity work. The post When to Use One Model and When to Use a Team of Agents appeared…

U.S. AI Leaders Want to Slow Down. China Offers No Such Promise.

Beijing has offered no promise to match an American AI slowdown. Its response exposes the challenge facing safety advocates: reducing risk while competing with a country that rejects the proposed constraints.

AI Is Turning Intelligence Into Infrastructure. Who Owns the Pipes?

AI models are becoming cheaper and more widely available. The bigger economic question is what remains scarce—and who captures the value when model access itself becomes less exclusive.

Graph Engineering for AI Agents: From Prompts and Loops to Workflows

A viral debate over loops versus graphs points to a bigger shift in how we build AI systems. Here’s what graph engineering actually means, how it differs from prompt, context, and loop engineering, and why it matters.…

From Static to Dynamic Skills: A Different Model for Agent Knowledge

Why the skill-inflation panic is aimed at the wrong thing, and what it costs to make agent knowledge a build artifact instead of a file. The post From Static to Dynamic Skills: A Different Model for Agent Knowledge…

AI Safety’s New Paradox: America May Need a Bigger Lead to Slow Down

Trump and Anthropic’s Dario Amodei both want America to maintain its AI lead over China. What they disagree on is what that advantage is for. One wants to keep pressing it; the other sees a large enough lead as the…

AI's biggest rivals agree: slow down

OpenAI’s IPO Can Wait. Can Its Investors?

OpenAI wants the freedom to slow AI development when safety demands it. Its IPO delay raises a question for future shareholders: how much waiting will they accept?

Can AI Companies Afford to Slow Their Own Progress?

Dario Amodei wants outside evaluators inside AI labs and shared rules to slow frontier development. The test is whether companies will accept delays when rivals could gain ground.

PLSP (Pre-hoc Liminal Space Profiling): OOD Prediction over Detection -- An Anticipatory Approach for Machine Learning Model Reliability

arXiv:2609.12225v1 Announce Type: new Abstract: Out-of-Distribution (OOD) data poses a significant threat to machine learning models, often leading to model failure during deployment. All existing OOD detection methods…

MInTRL: Off-policy Intervention can boost On-policy RL

arXiv:2609.12419v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards is typically performed on-policy, keeping training data close to the current policy but limiting learning to trajectories…

Distilled Continuous Diffusion Language Models Can Write Code in Few Steps---or One

arXiv:2609.04531v2 Announce Type: replace Abstract: Language generation is almost universally treated as a sequential process: autoregressive models emit one token at a time, while diffusion language models replace…

Prism-SQA: An Interpretable and Adaptable Neural Framework for Surface Electromyography Quality Assessment

arXiv:2609.12724v1 Announce Type: cross Abstract: sEMG is vulnerable to various contaminants that distort signal morphology and spectral content. Accurate signal quality assessment (SQA) is essential for identifying…

Diffusion learning reveals viable parameter manifolds and compensation geometry in biological dynamical systems

arXiv:2607.03671v2 Announce Type: replace-cross Abstract: Models of complex systems often have many parameters, yet are constrained by far fewer experimentally accessible observables; consequently, similar activity can…

ETHOS: Towards a Modular Ethics Framework for Clinical Multi-Agent Systems

arXiv:2608.15424v2 Announce Type: replace-cross Abstract: The rapid adoption of large language models has enabled the development of clinical multi-agent systems (MAS) capable of integrating multimodal patient data and…

Certified Safety Curation: Distribution-Free Guarantees for Safe Offline Reinforcement Learning

arXiv:2609.12014v1 Announce Type: new Abstract: Safe offline reinforcement learning assumes a cost function on every transition. We ask what remains possible when safety can be judged only by comparing short clips and…

Poisson-Corrector Complexity Bounds for Moreau--Yosida Unadjusted Langevin Sampling

arXiv:2609.12594v1 Announce Type: new Abstract: We study the classical Moreau--Yosida unadjusted Langevin algorithm (MYULA) for $\pi(\,\mathrm{d} x)\propto e^{-f(x)-g(x)}\,\mathrm{d} x$, where $f\in C^2(\mathbb{R}^d)$…

Receiver-Surface Hit Patterns via Legendre Approximation for Molecular Signal Detection

arXiv:2609.12089v1 Announce Type: cross Abstract: Detecting whether a transmitter is actively communicating with a receiver is a fundamental problem in molecular communications. A spherical receiver may measure not only…

Scalable Discrete-to-Continuous Channel Simulation for Compression and Privacy

arXiv:2609.12067v1 Announce Type: new Abstract: Channel simulation has recently emerged as a useful component in machine learning systems where samples from a prescribed probability distribution are to be compressed.…