MORPH reformulates ZKP MSM and NTT kernels into GEMM operations for TPUs using a new Big-T complexity model, achieving up to 10x NTT throughput over GZKP.
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8 Pith papers cite this work, alongside 19 external citations. Polarity classification is still indexing.
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Mass matrix assembly for implicit PIC methods can be exactly reformulated cell-by-cell as tensor-core matrix products, delivering up to 3x kernel speedup and 15% end-to-end runtime reduction in ECSIM simulations.
KernelBench shows that even the best current LLMs generate correct and faster-than-baseline GPU kernels in fewer than 20 percent of realistic ML workloads.
AoiZora adds topology-aware physical placement planning to auto-parallel compilation for diffusion transformer inference, cutting one-step denoising latency by up to 1.42x on TPU v5e sub-slices.
LIRA aligns latent instruction representations in LLMs to defend against jailbreaks, backdoors, and undesired knowledge, blocking over 99% of PEZ attacks and achieving optimal WMDP forgetting.
Scaling and instruction tuning increase sycophancy in LLMs on opinion and fact tasks, but a synthetic data fine-tuning intervention reduces it on held-out prompts.
PortBERT releases two RoBERTa models for Portuguese that match or beat prior monolingual and multilingual models on translated GLUE/SuperGLUE tasks while reporting training and inference times.
citing papers explorer
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Enabling AI ASICs for Zero Knowledge Proof
MORPH reformulates ZKP MSM and NTT kernels into GEMM operations for TPUs using a new Big-T complexity model, achieving up to 10x NTT throughput over GZKP.
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Mass Matrix Assembly on Tensor Cores for Implicit Particle-In-Cell Methods
Mass matrix assembly for implicit PIC methods can be exactly reformulated cell-by-cell as tensor-core matrix products, delivering up to 3x kernel speedup and 15% end-to-end runtime reduction in ECSIM simulations.
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KernelBench: Can LLMs Write Efficient GPU Kernels?
KernelBench shows that even the best current LLMs generate correct and faster-than-baseline GPU kernels in fewer than 20 percent of realistic ML workloads.
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AoiZora: Topology-Aware Auto-Parallel Optimization for Inference of Diffusion Transformers
AoiZora adds topology-aware physical placement planning to auto-parallel compilation for diffusion transformer inference, cutting one-step denoising latency by up to 1.42x on TPU v5e sub-slices.
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Latent Instruction Representation Alignment: defending against jailbreaks, backdoors and undesired knowledge in LLMs
LIRA aligns latent instruction representations in LLMs to defend against jailbreaks, backdoors, and undesired knowledge, blocking over 99% of PEZ attacks and achieving optimal WMDP forgetting.
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Simple synthetic data reduces sycophancy in large language models
Scaling and instruction tuning increase sycophancy in LLMs on opinion and fact tasks, but a synthetic data fine-tuning intervention reduces it on held-out prompts.
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PortBERT: Navigating the Depths of Portuguese Language Models
PortBERT releases two RoBERTa models for Portuguese that match or beat prior monolingual and multilingual models on translated GLUE/SuperGLUE tasks while reporting training and inference times.
- A Switch-Centric In-Network Architecture for Accelerating LLM Inference in Shared-Memory Network