… The larger contribution is the repeatable loop: profile-driven benchmarks, pronunciation-aware TTS, explicit review gates, and entity-level evaluation. …
… The agent reads the control plane, reviews the literature, proposes a candidate, mutates only the permitted surface, runs the experiment, extracts a score, records the result, and decides whether to keep, narrow, or discard the candidate. …
… Weights, training frameworks, data, benchmarks, and recipes are provided to enable others to modify, deploy, train, and fine-tune their own models and variants for their specific QPUs. …
… MSE calibration reduced per-tensor weight error by 27.1% over four-over-six scaling in our Nemotron 3 Ultra experiments, yet produced no consistent improvement on downstream benchmarks. …
… Verified skills undergo a publishing flow including daily catalog updates, automated and human reviews, risk scanning with SkillSpector, signing, and documentation via machine-readable skill cards detailing ownership, dependencies, limitations, and verification status. …
… Your job as the developer is to define your objective function: for instance, a Python callable that takes a candidate compiler configuration, compiles your kernel with it, benchmarks the result, and returns a score. …
…VSS event reviewer performance VSS event reviewer has been benchmarked on the latest platforms for maximum number of streams, latency and throughput. The measurements were taken with the reference Grounding Dino based…
… While most papers still focus on popular benchmarks like LIBERO and other simulation benchmarks, RoboArena is one of the few public real-world, open-ended evaluations, which is what makes the snapshot below worth pausing on. …
… TensorRT for RTX 1.2 Available Versions TensorRT for RTX 1.2 CUDA 12.9 Linux TensorRT for RTX 1.2 CUDA 13.0 Linux TensorRT for RTX 1.2 CUDA 12.9 Windows TensorRT for RTX 1.2 CUDA 13.0 Windows Notable changes in this TensorRT-RTX release: Out-of-the-box support for CUDA graphs, providing significant… …
… In the following example, the backtester is initiated and compared to the performance of the optimized portfolio against an equal-weight portfolio allocate equal weights in every available asset : from src import backtest Create backtester and run backtest backtester = backtest.portfolio backtester… …