Latest News
Mark Vero was interviewed by WIRED about using LLM-powered honeypots to keep attackers occupied. The article features our Honeyval research on evaluating these systems against AI hacking agents.
SRI Lab presented two papers at COLM 2026 in San Francisco, held on October 6–9: Coding Agents Don’t Know When to Act and Delay, Plateau, or Collapse: Evaluating the Impact of Systematic Verification Error on RLVR.
Martin Vechev was selected by OpenAI to test and evaluate its new watermarking technique for AI-generated text. He joins a group of researchers studying the method’s capabilities and limitations. Read the ETH announcement.
Most Recent Publications
Latest Blog Posts
Probing the SynthID-text deployment on Gemini APIs
Google DeepMind's SynthID-Text is the first large-scale LLM watermark deployment, yet little is known about how it is actually deployed. Using our work, we, for the first time, identify several key watermark hyperparameters. We find that: (1) all Gemini models are watermarked and they all share the same key except...
Searching for errors in mathematical papers with GPT-6.1 Sol
We use GPT-6.1 Sol to search for counterexamples to main theorems in recent arXiv papers and flag major errors in 6% of papers published before August 2026. The reported rate drops to 1.5% in August and September, though the cause of this decline remains unclear.
AutoMark: Enabling Autoresearch to Discover Better LLM Watermarks
Can research agents discover better LLM watermarks? We build a harness that verifies and evaluates agent-designed watermarks, find schemes that outperform prior work, and break them down into components you can explore interactively.