334 citations · 1.4k across the 49 of their papers we have counts for
12 papers · 1 filter
Block Parallelism For Efficient Distributed Long-Context Diffusion Language Model Training
Tarun Suresh, Pranshu Chaturvedi, Hangoo Kang +4
Block diffusion language models (BDLMs) combine autoregressive dependencies across blocks with parallel denoising within blocks, but long-context training is constrained by distrib…
LLM-as-a-Verifier: A General-Purpose Verification Framework
Jacky Kwok, Shulu Li, Pranav Atreya +6
Scaling pre-training, post-training, and test-time compute have become the central paradigms for improving the capabilities of LLMs. In this work, we identify verification, the abi…
Distill to Detect: Exposing Stealth Biases in LLMs through Cartridge Distillation
Shayan Talaei, Abhinav Chinta, Devvrit Khatri +3
Language models deployed in high-stakes roles can potentially favor certain entities, brands, or viewpoints, steering user decisions at scale. Such preferential biases can be intro…
KernelBench-Verified: Do LLM-Generated Kernels Actually Beat PyTorch?
Yunxiang Zhang, Ping Yu, Jianyu Wang +5
Recent large language models (LLMs) can generate custom CUDA kernels that appear to outperform PyTorch on benchmarks such as KernelBench. Building upon this foundational framework,…
Decentralized Multi-Agent Systems with Shared Context
Yuzhen Mao, Azalia Mirhoseini
Multi-agent systems (MAS) can scale large language model reasoning at test time by decomposing complex problems into parallel subtasks. However, most existing MAS rely on centraliz…
PolyAlign: Conditional Human-Distribution Alignment
L. D. M. S. Sai Teja, Ufaq Khan, Sathira Silva +2
Post-training methods such as supervised fine-tuning (SFT) and preference optimization typically align language models toward a single global assistant behavior. While effective fo…