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:2506.15971v2 Announce Type: replace-cross Abstract: Unsupervised domain adaptation (UDA) effectively bridges the domain gap between a labeled source domain and an unlabeled target domain, but assumes that the two…
arXiv:2610.12022v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in sociotechnical systems where social attribution, the reasoning process attributing external events to the…
arXiv:2610.11744v1 Announce Type: new Abstract: Neural representations can encode more features than they have dimensions, a phenomenon known as superposition. We study the dimension needed to compute Boolean gates from…
arXiv:2609.38561v2 Announce Type: replace-cross Abstract: Zeroth-order optimization methods are essential for solving black-box problems where gradient information is unavailable or expensive to compute. This paper…
arXiv:2610.12115v1 Announce Type: new Abstract: In many machine learning applications, it is necessary to guard against worst-case scenarios and predictions that could result in substantial losses. In principle, this…
arXiv:2606.26294v3 Announce Type: replace Abstract: Self-improving agents are state-of-the-art on agentic coding benchmarks, yet their search methods assume a stationary evaluation criterion. This ignores a central…
arXiv:2610.10942v1 Announce Type: cross Abstract: Reinforcement learning environments are now a primary lever for improving large language model (LLM) capabilities in post-training, yet most agentic benchmarks remain…
arXiv:2604.10166v2 Announce Type: replace Abstract: Intelligent operation of thermal energy networks aims to improve energy efficiency, reliability, and operational flexibility through data-driven control, predictive…
arXiv:2610.10934v1 Announce Type: cross Abstract: Actuator degradation turns quadruped locomotion into a coordination problem requiring joints to compensate for lost actuation. Prior work suggests that morphology-aware…
arXiv:2610.11628v1 Announce Type: cross Abstract: Machine unlearning updates a trained model after records are deleted, aiming to match exact retraining without repeating the full training procedure. We develop Gaussian…
arXiv:2610.12016v1 Announce Type: new Abstract: World models for control must capture which aspects of the environment respond to the agent's actions and which are relevant to reward. Generative world models such as…
arXiv:2409.01656v4 Announce Type: replace-cross Abstract: We consider the problem of estimating graph limits, known as graphons, from observations of sequences of sparse finite graphs. In this paper we show a simple…
arXiv:2610.12133v1 Announce Type: cross Abstract: Many key-value (KV) caches are compressed before anyone knows what will be asked of them: a document cached for retrieval, a prompt prefix shared across requests, the…
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