7 papers
Simplifying Flow Matching Transformations with Low-Rank Mixture Models
Liam A. Kruse, Houjun Liu, Alexandros E. Tzikas +2
Normalizing flows are powerful generative models that learn an invertible mapping between complex data distributions and simple latent distributions, typically a standard normal de…
SecureForge: Finding and Preventing Vulnerabilities in LLM-Generated Code via Prompt Optimization
Houjun Liu, Lisa Einstein, John Yang +5
LLM coding agents now generate code at an unprecedented scale, yet LLM-generated code introduces cybersecurity vulnerabilities into codebases without human involvement. Even when f…
Foundational World Models Accurately Detect Bimanual Manipulator Failures
Isaac R. Ward, Michelle Ho, Houjun Liu +7
Deploying visuomotor robots at scale is challenging due to the potential for anomalous failures to degrade performance, cause damage, or endanger human life. Bimanual manipulators…
Thoughtbubbles: an Unsupervised Method for Parallel Thinking in Latent Space
Houjun Liu, Shikhar Murty, Christopher D. Manning +1
Current approaches for scaling inference-time compute in transformers train them to emit explicit chain-of-thought tokens before producing an answer. While these methods are powerf…
ASTPrompter: Preference-Aligned Automated Language Model Red-Teaming to Generate Low-Perplexity Unsafe Prompts
Amelia F. Hardy, Houjun Liu, Allie Griffith +3
Existing LLM red-teaming approaches prioritize high attack success rate, often resulting in high-perplexity prompts. This focus overlooks low-perplexity attacks that are more diffi…
Alto: Orchestrating Distributed Compound AI Systems with Nested Ancestry
Deepti Raghavan, Keshav Santhanam, Muhammad Shahir Rahman +7
Compound AI applications chain together subcomponents such as generative language models, document retrievers, and embedding models. Applying traditional systems optimizations such…