“benchmark”的搜索结果

检查GAIA基准测试中失败题目的执行记录，将原因归为工具缺失、推理错误或答案提取故障等类型，并支持提出修复方案、重新运行验证和保存排查结论。

• 调查指定题目答错的原因
• 诊断题目超时问题
• 排查基准测试通过率下降

测量claude-flow v3的启动时间、搜索速度和内存用量，验证是否达到改进目标，并通过持续监测发现性能退化，为优化方案提供依据。

• 比较v3优化前后的性能
• 评估搜索速度与内存用量
• 持续检测性能退化

Benchmark how CodeGraph affects an agent investigating a real codebase. Choose a version, language, repository, and execution mode, then compare cost, tool calls, file reads, and answer correctness with and without CodeGraph.

中文简介正在准备中。当前显示英语简介。

• Evaluating a local development build
• Auditing a published CodeGraph version
• Comparing investigation cost and file reads

Review SGLang Cookbook pull requests for configuration errors, documentation mismatches, benchmark issues, and broken links. Get findings for each file and an overall recommendation on whether to approve, request changes, or block the contribution.

中文简介正在准备中。当前显示英语简介。

• Reviewing a new model's Cookbook contribution
• Checking documentation against generated launch commands
• Checking benchmark definitions and measurement consistency

Add a CUDA/C++ GPU operation to sgl-kernel using AOT, meaning compilation before use. Follow the implementation through PyTorch registration, Python API exposure, SGLang integration, correctness tests, and benchmarks that account for cache reuse.

中文简介正在准备中。当前显示英语简介。

• Adding a CUDA/C++ operation to sgl-kernel
• Registering an operation with PyTorch and exposing a Python API
• Testing numerical results and invalid inputs

Explain portfolio performance by separating the effects of sector allocation, stock selection, market factors, and timing. Compare results with a benchmark, assess risk-adjusted returns, and prepare a report showing where excess returns came from.

中文简介正在准备中。当前显示英语简介。

• Explaining returns above or below a benchmark
• Separating sector allocation and stock-selection effects
• Assessing market exposure and trading timing

Analyze social media post data to calculate engagement, click rates, and estimated returns on advertising spend. Compare results with supplied platform benchmarks, identify strong and weak posts, and generate recommendations for improving campaign performance.

中文简介正在准备中。当前显示英语简介。

• Review social campaign performance
• Identify posts with strong or weak engagement
• Estimate advertising returns and interaction costs

Create, update, run, and debug shell-based performance scenarios for the but CLI. It separates repository setup from the operation being measured, uses Hyperfine for timing, and guides focused validation and inspection of test output.

中文简介正在准备中。当前显示英语简介。

• Add a performance scenario for a but CLI operation
• Update repository fixtures for an existing benchmark
• Run a short benchmark for one scenario

Turn a broad robotics research direction into ranked ideas with concrete benchmarks and simulation plans. The workflow combines literature review, novelty checks, and external critique while accounting for hardware access, safety, evaluation metrics, and failure cases.

中文简介正在准备中。当前显示英语简介。

• Explore research topics in manipulation or navigation
• Plan simulation studies before using physical robots
• Check novelty and feasibility of robotics ideas

Build Go services and command-line tools with clear interfaces, explicit error handling, and concurrent tasks using goroutines and channels. Use pprof to investigate performance, then check behavior with benchmarks, structured test cases, fuzzing, and race detection.

中文简介正在准备中。当前显示英语简介。

• Implementing concurrent tasks with cancellation and error handling
• Building gRPC or REST services and command-line tools
• Investigating Go performance with pprof and benchmarks

Run concurrent Omnigent conversations with real hosts and runners while replacing the LLM with a zero-latency mock. Read the generated report to explain response-time distributions, throughput, and failures, helping assess Omnigent's own processing overhead.

中文简介正在准备中。当前显示英语简介。

• Measuring performance with concurrent hosts
• Studying latency as conversation history grows
• Comparing throughput and failures under load

Refresh evidence behind paid advertising guidance by checking platform changes, APIs, policies, regulations, and benchmarks. Record sources and contradictions, flag outdated or unsupported claims, and propose updates grounded in current primary sources.

中文简介正在准备中。当前显示英语简介。

• Refreshing overdue advertising research
• Rechecking platform and API claims
• Reviewing evidence behind advertising benchmarks

Evaluate a local Codex skill for metadata, structure, reference links, size, and helper code quality. Get prioritized fixes and explanations, with benchmark setup for deeper analysis and measurement recommendations when observed usage is available.

中文简介正在准备中。当前显示英语简介。

• Audit a local Codex skill
• Understand evaluation scores and findings
• Prioritize skill improvements

Evaluate a local Codex plugin to understand its scores and identify what to fix first. Review stronger and weaker skills, prepare benchmark runs, compare saved results across versions, and produce a readable report.

中文简介正在准备中。当前显示英语简介。

• Review an entire plugin before comparing versions
• Understand scores and prioritize fixes
• Compare strengths and weaknesses across included skills

Investigate observed slowdowns or increased CPU, memory allocation, and IO costs in a process you control. Use controlled timing and runtime-specific profiling tools to test a cause, then check performance and behavior after a fix.

中文简介正在准备中。当前显示英语简介。

• Investigate a command that has become slower
• Locate CPU or memory allocation hotspots
• Separate transport waits from backend processing costs

Compare baseline and current Pinot benchmark results from JMH, a tool for measuring code performance. The skill uses a separate baseline worktree, a working directory kept apart from the current code.

中文简介正在准备中。当前显示英语简介。

• Compare Pinot benchmarks before and after a code change
• Check performance differences against a baseline
• Keep baseline code in a separate working directory

Optimize .NET loops with SIMD, which processes multiple values in one operation, or review existing SIMD code. Check behavioral equivalence, memory safety, platform fallbacks, and benchmarks to establish whether the optimization is correct and worthwhile.

中文简介正在准备中。当前显示英语简介。

• Optimize numeric loops with SIMD
• Review existing vectorized code for correctness
• Test boundary lengths and overlapping buffers

Runs HTTP load tests with gradually increasing concurrent connections. It measures response-time percentiles, throughput, and errors, identifies where performance starts to deteriorate, and recommends a concurrency level based on the results.

中文简介正在准备中。当前显示英语简介。

• Benchmarking an API under increasing load
• Finding concurrency levels where p99 latency rises
• Checking throughput saturation and error growth

Measure and optimize claude-flow v3 startup, search, attention processing, and memory use. Compare benchmarks against performance targets, track changes over time, and identify regressions to guide further optimization.

中文简介正在准备中。当前显示英语简介。

• Compare performance before and after v3 optimization
• Evaluate vector search speed and memory use
• Track performance regressions over time

Improve vllm-mlx performance through research, small experiments, and benchmarks on real models. The skill records results, keeps changes that improve measured performance, and carries successful work through implementation, testing, review, and pull request creation.

中文简介正在准备中。当前显示英语简介。

• Reducing the time before a model starts responding
• Improving token generation speed
• Exploring memory and cache optimizations

Evaluate answer quality in NVIDIA RAG Blueprint using local documents and question data. The skill guides dataset preparation, RAGAS scoring, configuration comparisons, and error diagnosis for RAG systems that retrieve documents to support generated answers.

中文简介正在准备中。当前显示英语简介。

• Prepare documents and questions for evaluation
• Compare quality across retrieval and generation settings
• Interpret RAGAS result files

Benchmark a deployed NVIDIA RAG Blueprint server using a YAML configuration. Measure time spent in each processing stage, run optional aiperf load tests, and compare response latency, throughput, and bottlenecks across concurrency and retrieval settings.

中文简介正在准备中。当前显示英语简介。

• Find slow stages in a running RAG server
• Compare performance across concurrency levels
• Measure the impact of retrieval and reranking settings

Measure throughput, latency, or maximum bandwidth for DOCA libraries using doca_bench on a host or BlueField Arm. Capture commands and environment details for repeatable comparisons, check run stability, and investigate configuration or measurement problems.

中文简介正在准备中。当前显示英语简介。

• Measuring BlueField compression throughput
• Checking RDMA throughput or cryptographic latency
• Recording a baseline before driver or firmware upgrades

Measure how quickly a host or DPU CPU installs and deletes DOCA Flow rules. Choose a JSON workload and backend, run repeated benchmarks, and record hardware, software, and execution settings so results can be assessed.

中文简介正在准备中。当前显示英语简介。

• Measure BlueField rule installation rates
• Compare DPDK and DOCA backends
• Investigate differences from published benchmark results