Daily | Yixun Hong

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD

Dongfang Li, Xiaodong Luo, Ruoyu Sun, Xuhui Chen 2026-07-26

LLM × Training GPU Distributed

The problem is that full-parameter post-training of trillion-parameter-scale MoE models on Ascend NPU SuperPODs faces severe memory pressure, non-overlapped communication overhead, and inefficient kernel execution. The method is a hierarchical optimization framework spanning model-level parallelism, computation-communication orchestration, and low-level kernel execution, integrated as SLAI T-Rex, with a CPT and SFT workflow for Operations Research tasks. The system achieves 34.22% MFU with a 2.93x improvement over the baseline, and the specialized DeepSeek-V4-Flash model reaches 71.81% zero-shot Pass@1, outperforming GPT-5.4-Mini by 3.98 percentage points. This matters because it demonstrates a full-stack pathway from efficient trillion-parameter post-training on Ascend infrastructure to domain-specialized models for solver-grounded mathematical modeling, advancing frontier-model systems for complex reasoning.

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WaveformQA: Benchmarking LLM Temporal Reasoning on Digital Waveforms

Yichuan Liu, Daniel Cummings, Nick Vadlamudi 2026-07-26

Language Model LLM × Hardware

The problem is that LLMs' ability to perform temporal reasoning over digital waveforms, a critical bottleneck in design verification, remains unexplored despite their strong code generation capabilities. The method introduces WaveformQA, an open-source benchmark with 360 programmatically grounded questions across eight difficulty categories, including multi-signal correlation and event ordering. Experimental evidence shows frontier LLMs achieve reasonable accuracy on simple queries but degrade on complex temporal and multi-step questions due to context window and reasoning limitations, with event-time JSON representation outperforming VCD format. This matters because WaveformQA provides a reproducible, extensible framework for evaluating and improving LLM temporal reasoning in real hardware verification tasks.

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PerfAgent: Profiler-Guided Iterative Refinement for Repository-Level Code Optimization

Ryan Deng, Yuanzhe Liu, Bastian Lipka, Yao Ma 2026-07-26

Language Model LLM × Runtime

PerfAgent addresses the problem that LLM agents fail at repository-level code optimization because they miss hidden bottlenecks, stop after shallow speedups, and insufficiently test patches. The method is a profiler-guided, verifier-in-the-loop workflow that provides an off-the-shelf coding agent with feedback to find real hotspots and iteratively improve beyond the first passing patch. On GSO and SWE-fficiency-Lite, PerfAgent more than doubles the rate of expert-matching patches over OpenHands with GPT-5.1, improving from 19.6% to 39.2% and from 26% to 74%, respectively. This matters because it demonstrates that profiler-driven feedback, rather than additional test-time sampling, yields substantial and cost-effective optimization gains at the repository level.

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