Topic - Edition
How a decision-first model can catch risky tool calls before they turn into real-world actions The post Can TypeSafe's Jev Make AI Agents Safer Without Another LLM? appeared first on Towards Data Science.
The cost of control The post Where Does the Money Go Across Long-Running Coding Agents? appeared first on Towards Data Science.
AI Agents for Beginners, Part 1 The post Build Your First AI Agent with One Tool Call appeared first on Towards Data Science.
AI agents can now write more code than any of us can read. That changed how I think about AI. The post Stop Using AI. Start Hiring It. appeared first on Towards Data Science.
Microsoft Research engineer Alex Lavaee explains how to verify AI-written code using Atomic, independent reviewers, real-world tests, and reproducible evidence.
Anthropic’s new policy prohibits extreme, purposeless abuse toward Claude. The restriction brings AI welfare into product rules while researchers continue to investigate whether chatbots can suffer.
arXiv:2610.12132v1 Announce Type: cross Abstract: Predicting the structure and response of inhomogeneous polymer electrolytes requires a description of ion correlations that retains molecular-scale accuracy while…
arXiv:2606.09701v2 Announce Type: replace-cross Abstract: Language model safety must continually adapt to evolving attacks. Recent works have demonstrated that reinforcement learning can be used to train stronger…
arXiv:2610.11486v1 Announce Type: cross Abstract: Sparse identification of nonlinear dynamics (SINDy) is a data-driven framework for discovering governing dynamics from time-series data by identifying a sparse subset of…
arXiv:2603.08706v2 Announce Type: replace-cross Abstract: Imitation learning (IL) teaches language-model agents to reproduce expert actions but not to distinguish them from plausible mistakes. Self-reflection methods…
arXiv:2512.16545v3 Announce Type: replace-cross Abstract: Cu NPs have a broad applicability, yet their synthesis is sensitive to subtle changes in reaction parameters. This sensitivity, combined with the time- and…
arXiv:2603.13228v3 Announce Type: replace Abstract: Recent progress in text-conditioned human motion generation has been largely driven by diffusion models trained on large-scale human motion data. Building on this…
arXiv:2610.12039v1 Announce Type: new Abstract: Graph neural networks (GNNs) predict molecular properties from chemical graph data, but predictive accuracy does not explain how graph information supports an individual…
arXiv:2610.11869v1 Announce Type: cross Abstract: We consider the problem of learning structured linear dynamical systems over convex sets $\mathcal{K}$, where only a small subset of the observations are available at…
arXiv:2610.11257v1 Announce Type: new Abstract: Learned representations can lose latent degrees of freedom successively, suggesting a cascade of transitions whose underlying stability principle remains unclear. Here we…
arXiv:2604.04328v4 Announce Type: replace-cross Abstract: Soft Tournament Equilibrium (STE) is a differentiable layer for Top-Cycle (TC) and Uncovered-Set (UC) inference from reciprocal pair probabilities. Normalized…
arXiv:2610.11082v1 Announce Type: cross Abstract: The kernel bandit problem consists of sequentially optimizing an unknown function with noisy feedback, where the function has bounded norm in a given Reproducing Kernel…
arXiv:2610.11863v1 Announce Type: cross Abstract: Kernel Stein discrepancies (KSDs) provide a versatile tool for comparing distributions. One of their main applications is in quantifying the goodness-of-fit (GoF)…
arXiv:2610.10552v1 Announce Type: new Abstract: Comparing parameter-efficient fine-tuning recipes under a single, shared learning rate is a common but flawed practice: when the arms being compared have very different…
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