DeepSeek-V4-Pro (AI model): news timeline & CVEs

KernelZero: Co-Evolving Proposer and Coder for Continuously Improved GPU Kernel Generation

KernelZero-7B beats Claude-4.5-Sonnet and DeepSeek-V4-Pro on CUDA and Triton kernel generation.

KernelZero co-evolves a Proposer that generates Torch modules and a Coder that translates them into CUDA or Triton kernels. Frontier-driven module generation supplies training data aligned with the Coder's weaknesses, and Correctness-Aware Group Relative Policy Optimization improves performance only after correctness is reliable. KernelZero-7B surpasses Claude-4.5-Sonnet on CUDA and DeepSeek-V4-Pro on Triton. On KernelBench Levels 1 and 2, CUDA pass@1 is 75.8% and 69.6%, with pass@10 of 100% and 97%; Triton pass@1 is 77.2% and 72.5%.

Hugging Face daily papersSep 26, 20:00 UTC · 5d agoAI research

PARSER: Read in Parallel, Reason in Depth for Long-Context LLM Agents

PARSER uses parallel reader subagents and an RL-trained lead agent for long-context QA, beating baselines and cutting latency up to 11x.

The PARSER paper decouples reading from reasoning: frozen subagents each read one document chunk in parallel while an RL-optimized lead agent iteratively broadcasts queries and aggregates evidence in scatter-gather rounds. On multi-hop QA with 7K to 896K token contexts, a 4B-backbone PARSER beats the strongest sequential memory baseline by 5.7 points on average and 12.0 points at 896K tokens, and a 9B version surpasses DeepSeek-V4-Pro by 6.3 points. Controlled experiments show robustness to evidence position, order, and distance perturbations, with inference latency reduced by up to 11x.

Hugging Face daily papersSep 5, 20:00 UTC · 26d agoAI research1

DeepSeek-V4-Pro

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KernelZero: Co-Evolving Proposer and Coder for Continuously Improved GPU Kernel Generation

PARSER: Read in Parallel, Reason in Depth for Long-Context LLM Agents