Research signals.

What we are reading across frontier AI: papers, lab notes, talks and briefs, weighted towards agents and the harnesses that run them.

Updated 14 sources

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If you only have a few minutes, start with these.

Papers

6

New preprints on agents and tool use from arXiv, and the day’s papers on Hugging Face.

arXiv 2609.30250 Lior Biton and Oren Tsur

Agentic Detection of Online Conspiracies

Conspiratorial discourse on social media is not always expressed through explicit claims or stable lexical markers. The same surface content may express endorsement, legitimate concerns, criticism,…

arXiv 2609.30249 Yuyao Liu et al.

RAPID: Robot Agentic Programming from Demonstrations

Coding agents have demonstrated enormous success in solving complex programming problems. To leverage their potential for robot systems, this work introduces Robot Agentic Programming from…

Hugging Face Papers 2609.29028 Shaohua Dong et al.

RGBD20K: A Large-Scale Benchmark for RGB-D Semantic Segmentation

In this paper, we propose RGBD20K, a novel dataset for facilitating the development of more robust and general RGB-D semantic segmentation by encompassing abundant categories and high-quality…

Labs

4

Announcements and research notes from the frontier labs.

Harnesses

4

Engineering notes on building with models: tools, evals and agent loops.

Safety

4

Alignment and security research.

Alignment Forum

Why I'm scared of RL

Summary: First, I give several different angles on how I feel about reinforcement learning: Theoretical case: RL is a black-box source of agency — this should give us classic misalignment worries,…

Trail of Bits

Auditing in the age of (good enough) AI

Security firms have published numerous blog posts describing how they pointed their agent harness at a codebase and found dozens of bugs ( we’re one of them ). However, these posts tend to focus on…

Trail of Bits

1Password's AI patching benchmark is misleading

1Password’s FLAWED report , published on August 6, 2026, gives defenders a misleading picture of AI patching. Its headline says models produced clean fixes only 26% of the time. That figure includes…

Field

6

Newsletters, talks and reporting from the wider field.

Sources

14

Where each section is drawn from, and how often each source publishes.

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