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:2610.10989v1 Announce Type: new Abstract: Researchers are exploring effective one-step generative model continuously, and, Drifting Models (Deng et al., 2026), demonstrate great potential in one-step generation…
arXiv:2610.10829v1 Announce Type: cross Abstract: Conformal prediction provides prediction sets with finite-sample guarantees, but the label verification required for calibration can be expensive. We develop a partial…
arXiv:2610.11270v1 Announce Type: cross Abstract: Personalized conversational AI relies on long-term memory systems that extract facts from user utterances and store them in persistent vector stores. Despite progress in…
arXiv:2509.02302v2 Announce Type: replace Abstract: We study history-dependent online problems with a possibly inaccurate prediction of the future request sequence. Motivated by several real-world applications, we…
arXiv:2610.12265v1 Announce Type: new Abstract: Accurate workload forecasting is critical for elastic resource provisioning in web-scale cloud services, where distribution shifts driven by viral content, product…
arXiv:2603.28744v2 Announce Type: replace Abstract: The linear representation hypothesis states that neural network activations encode high-level concepts as linear mixtures. However, under superposition, this encoding…
arXiv:2610.11281v1 Announce Type: new Abstract: Large language models (LLMs) are commonly post-trained against LLM judges and other cheap surrogates because the true reward, such as human preference, is too expensive to…
arXiv:2610.11164v1 Announce Type: new Abstract: We release Ride-Hailing, a large-scale ride-hailing time series dataset synthesized from DiDi's marketplace data across 200 spatial areas. Ride-Hailing spans four…
arXiv:2610.10805v1 Announce Type: cross Abstract: As AI systems increasingly interact with people and make decisions about them, understanding human interpretations becomes an important part of developing human-centered…
arXiv:2610.12338v1 Announce Type: cross Abstract: While caching key-value (KV) states accelerates Large Language Model (LLM) decoding, this cache can dominate memory usage at long context lengths. One solution is to…
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…
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