1 citations · 1 across the 5 of their papers we have counts for
8 papers
Only What You've Signed up For: Secure Workflows with Constrained Diffusion Agents
Michael Cardei, Anjun Hu, Chenrui Ma +4
As agentic AI systems take on increasingly complex and consequential tasks, securing them against prompt injection becomes critical. Existing defenses often rely on detecting or re…
Constrained Tabular Diffusion for Finance
Michael Cardei, Jose M Munoz, Oscar Barrera +2
Generative models in finance face the dual challenge of producing realistic data while satisfying strict regulatory and economic objectives, a requirement that standard tabular dif…
Constrained Code Generation with Discrete Diffusion
Lize Shao, Michael Cardei, Zichen Xie +2
Discrete diffusion models are a powerful, emerging paradigm for code generation. They construct programs through iterative refinement of partially corrupted token sequences and ena…
Simple Self-Conditioning Adaptation for Masked Diffusion Models
Michael Cardei, Huu Binh Ta, Ferdinando Fioretto
Masked diffusion models (MDMs) generate discrete sequences by iterative denoising under an absorbing masking process. In standard masked diffusion, if a token remains masked after…
Search-Augmented Masked Diffusion Models for Constrained Generation
Huu Binh Ta, Michael Cardei, Alvaro Velasquez +1
Discrete diffusion models generate sequences by iteratively denoising samples corrupted by categorical noise, offering an appealing alternative to autoregressive decoding for struc…
Neuro-Symbolic Generative Diffusion Models for Physically Grounded, Robust, and Safe Generation
Jacob K. Christopher, Michael Cardei, Jinhao Liang +1
Despite the remarkable generative capabilities of diffusion models, their integration into safety-critical or scientifically rigorous applications remains hindered by the need to e…
Constrained Discrete Diffusion
Michael Cardei, Jacob K Christopher, Thomas Hartvigsen +2
Discrete diffusion models are a class of generative models that construct sequences by progressively denoising samples from a categorical noise distribution. Beyond their rapidly g…
ROADWork: A Dataset and Benchmark for Learning to Recognize, Observe, Analyze and Drive Through Work Zones
Anurag Ghosh, Shen Zheng, Robert Tamburo +7
Perceiving and autonomously navigating through work zones is a challenging and underexplored problem. Open datasets for this long-tailed scenario are scarce. We propose the ROADWor…