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Top signals · Oct 29–31, 2026 · Updated Oct 31, 05:03 UTC
AgentDecept: Benchmarking Deceptive Behavior in Tool-Using LLM Agents under Pressure
LLM agents increasingly carry out multi-turn tasks across everyday applications, yet users often see only the agent's report rather than the observations, API calls, and state changes behind it. Existing deception ben... Context & related coverage →
Elucidating the Design Space of LM Data Attribution
Data attribution methods for Language Models (LMs) are typically evaluated using downstream tasks like fact retrieval. However, existing evaluations vary multiple dataset properties simultaneously, making it impossibl... Context & related coverage →
dbreunig/nesformer
dbreunig/nesformer Inspired by qwenputer by Divyansh Agrawal, Drew Breunig baked a working emulator for a Nintendo Entertainment System 6502 CPU into the weights of Qwen3.5-4B. You can now play NES ROMs such as Super... Context & related coverage →
ATLAS-Bench: An Agentic Tool-Use Benchmark for Local Area Search
Reliable local search is essential for LLM agents to support everyday decisions and longer plans by finding places that meet users' needs at given times and locations. Yet existing benchmarks provide limited coverage... Context & related coverage →
Innovate on your own business terms: Google Cloud spend caps now generally available
Today, we’re announcing that spend caps on Google Cloud Budgets are generally available. With this release, we’re delivering ways to protect your workloads from runaway costs without risking resource deletion. More im... Context & related coverage →
How to Make Artificial Intelligence More Environmentally Sustainable
IT is responsible for about 2 - 4% of all global emissions, and electricity use at Microsoft and Google has climbed because of AI, according to their own reports. Sustainability ought to be a top-level architectural c... Context & related coverage →
An updated Model Spec for more capable and beneficial AI
OpenAI’s updated Model Spec outlines five core values to guide more capable AI toward sound judgment, human agency, and faithfulness to human intent. Context & related coverage →
How Ropes & Gray is developing AI for transactional diligence
Ropes & Gray is developing AI for transactional diligence with OpenAI. Lawyers review grouped documents, summaries, and reports before advising clients. Context & related coverage →
Fair use benefits everyone
OpenAI shares excerpts from court briefs by creators, academics, accessibility advocates, and legal experts, among others, on how fair use supports AI training and innovation. Context & related coverage →