Our COLM 2026 paper shows that a safety-aligned LLM can supply every step of its own jailbreak. SLIP needs no attacker model and reached 94.7% average attack success across 11 LLMs in about 7.9 queries, while per-turn defenses left it at 57–83%.
Blogs
Thoughts on AI, machine learning, and scientific discovery
Most RAG systems are no better on their hundredth question about a document set than on their first. SourceLearn keeps a persistent, revisable source model that improves through self-study and task feedback, beating Hybrid RAG in 13 of 15 settings and by up to 22.6 points.
Official project page for Agent Primitives, a framework that decomposes LLM-based multi-agent systems into reusable latent building blocks: Review, Voting and Selection, and Planning and Execution primitives, composed by an Organizer and connected through KV-cache communication.
The Hidden Cost of Prompt Injection Defense
July 2026 — ICML 2026 Spotlight
Prompt injection defenses are graded on attack success rate, which cannot tell a model that faithfully processes injected text as data from one that simply deletes it. Our SecFid benchmark makes that difference measurable — and across 48 models and defenses, none achieves both security and fidelity. The most secure systems drop between a quarter and a half of the content they should keep.
A step-by-step guide to measuring whether product descriptions influence AI shopping recommendations — using our CORE method and the ProductBench benchmark, with key definitions, a results table, and an FAQ.
Official project page for our paper introducing CORE, a black-box method for improving visibility in LLM-based search and generative engines by optimizing retrieved content. We introduce the ProductBench benchmark and report strong promotion-success results across GPT-4o, Gemini-2.5, Claude-4, and Grok-3.
A deep-dive explainer of our paper CORE — how controlling the content retrieved by LLM-based search can steer output rankings, the three optimization content types, the ProductBench benchmark, and results across GPT-4o, Gemini-2.5, Claude-4, and Grok-3.
We compare traditional SEO, Generative Engine Optimization (GEO), and our method CORE for visibility in LLM-based search — how they differ, what each optimizes, and why we recommend CORE for controlling output rankings in generative product recommendations.
