The article analyzes B站's AI Arena evaluation where GPT-6 Astra topped the leaderboard, but reveals its victory is limited to agent execution and code repair tasks, while other models excel in different real-world scenarios, exposing the gap between standardized benchmarks and practical performance.
The article argues that fluent AI Q&A fails at multi-step task completion because it lacks persistent service state — problem context, conditions, progress, and exception handling — and suggests evaluating services on continuity rather than answer accuracy.
This article walks through constructing a fully quantifiable and explainable evaluation system for AI agents—starting with business‑level golden metrics, using LLMs to break them into a detailed Rubric, embedding the Rubric in a custom evaluator, configuring evaluation tasks with trace data, and closing the loop by turning low‑scoring cases into actionable insights for continuous improvement.
This article presents a comprehensive, six‑stage AI evaluation workflow that embeds quality checks early in development, explains the shift‑left testing philosophy, details each phase with concrete actions and metrics, and illustrates the approach with a real‑world intelligent‑customer‑service project.
This module explains why assessing native AI applications is difficult, covering non‑deterministic outputs, subjective quality, context dependence, long‑tail scenarios, static benchmark shortcomings, and the trade‑offs between evaluation cost, speed, and thoroughness, with a practical e‑commerce case study.
This article explains why enterprise AI evaluation requires purpose-built datasets — golden baselines, challenge cases, red-team tests, regression suites, and online replays — designed from capability questions, risk lists, and real failures, with structured answers, contamination governance, tiered LLM judging, and version-locked regression pipelines gated by risk-based release thresholds.
HarnessEval introduces a four‑stage, evidence‑driven evaluation harness that transforms static AI benchmarks into dynamic, traceable workflows, enabling agents and world‑model systems to be assessed with planning, tool routing, decomposition, and verification for reliable, self‑improving intelligence.
DeepSeek V4 Pro scored 87.9 on Terminal‑Bench 2.1 using the Harness Minimal Mode with max reasoning effort, temperature 1.0 and top_p 0.95, while Vals AI reported 54.68 under a different harness, illustrating that modern Agent benchmarks evaluate the whole system rather than just the underlying model.
The article argues that as AI systems integrate into real workflows, evaluation must shift from checking answer correctness to assessing process reliability—handling incomplete inputs, evidence conflicts, tool-use boundaries, and post-error traceability—citing Chinese regulations, NIST, and OWASP frameworks, and proposes four key questions for scenario-based evaluation.
The SERPO framework replaces answer‑voting with a self‑evolving rubric, allowing test‑time reinforcement learning to improve large language models on open‑ended tasks without any human labels, and demonstrates up to 73% of privileged‑supervision gains on benchmarks such as HealthBench and ResearchQA.
This article analyzes why industry AI projects become harder to cost as model prices drop, identifying four hidden cost categories—context preparation, continuous evaluation, exception handling, and accountability—and argues that sustainable AI systems require shifting focus from per-call pricing to the cost of an acceptable business outcome.
WorkBuddy Bench reveals why fixing a bug does not equal finishing a task, proposing a four‑layer completion model and a reproducible benchmark that evaluates agents across Code, Web, Office, and Security workspaces, showing how prompts, context, harnesses, loops and graphs must be verified to claim true completion.
A Yale‑Chicago study of 11,683 papers shows that while LLM‑generated research ideas match human quality, they cluster around a few patterns—especially bridge opportunities and integration—resulting in a much narrower distribution than human ideas, as revealed by taxonomy, entropy and TVD analyses.
The author evaluates the newly released 2.8‑trillion‑parameter open‑source Kimi K3 model by having it generate a 3D rocket simulation, a 3D dinosaur runner game, a functional web‑based Excel, and an end‑to‑end Kaggle house‑price solution, revealing both impressive capabilities and notable limitations.
The paper introduces the Reasoning Coherence metric and the MME‑CoF‑Pro benchmark—303 image‑text‑video samples across 16 reasoning categories—to evaluate seven leading video generation models, revealing that reasoning ability is largely independent of visual quality, that textual prompts often induce hallucinations, and that the new Reasoning Score aligns well with human judgments.
A comprehensive evaluation of domestic large language models on China's Gaokao shows iFlytek Spark X2 tying for top physics scores and leading in history, with its advantage stemming from balanced language understanding, rigorous step‑by‑step reasoning, and a decade‑long education data pipeline.
This article outlines a comprehensive methodology for evaluating large language models, covering classic benchmarks, human and multimodal assessments, common pitfalls such as data contamination and benchmark overfitting, and practical guidelines for building a scientific, multi‑layered AI evaluation framework.
WBench, the first systematic multi‑turn benchmark for interactive video world models, evaluates 20 cutting‑edge models across navigation, actions, editing and view‑switching, revealing that no single model excels at all tasks, navigation is independent of visual quality, and multi‑turn interaction causes a 33‑point drop in performance.
MiniAppBench, the first benchmark that evaluates large language models' ability to generate fully functional interactive HTML applications, shows an average pass rate of just 17% across 16 top models—with the strongest model, GPT‑5.2, achieving only 45%—highlighting a substantial gap between current capabilities and real‑world user requirements.
The article argues that as large language models grow more capable, single‑score benchmarks no longer capture true performance; instead, evaluating models across varying inference budgets—measured in tokens, cost, or time—reveals their real capabilities and safety risks, prompting a shift toward performance‑cost curves and new industry standards.
The article analyzes the growing difficulty of evaluating large AI models, critiques traditional RLVR and RLHF approaches, introduces a Rubrics‑based evaluation paradigm, describes the design and three‑stage workflow of the CRAFT system, reports math‑domain experiments showing up to 6.2 percentage‑point gains, and outlines future extensions to other domains.
The article explains why ImageNet has become obsolete for visual generation, introduces the newly released GPIC dataset of 100 million image‑text pairs with 28 trillion pixels, describes its four‑stage construction pipeline, new FD‑DINOv2 evaluation metric, and a reference baseline model, positioning GPIC as the next common benchmark for the field.
The article explains why vague expectations cause unstable Agent results, introduces Rubric as a concrete, pre‑written scoring standard for Generator‑Critic workflows, details how to design clear Yes/No criteria, organize them into Must/Should/Nice‑to‑have layers, and iteratively refine the Rubric for reliable AI output.
BenchLoop is a CLI‑plus‑Web application that lets you reproducibly benchmark locally‑run LLMs across seven suites—including speed, tool‑calling, coding and agent tasks—while recording hardware details, scoring results with a weighted formula, and optionally publishing them to a public leaderboard.
A Stanford NLP benchmark called ProgramBench tested 200 real‑world codebases and found that current large language models, including Claude and GPT‑5, achieve near‑zero success in reconstructing full systems like SQLite, FFmpeg, and a PHP compiler from binaries alone.
Researcher Li Bojie’s “Uncompressible Knowledge Probe” uses random, black‑box API queries to gauge how much irreducible knowledge a large language model retains, allowing an indirect estimate of its effective parameter count and prompting a broader debate on model evaluation and transparency.
This article walks through ten mainstream open‑source large‑model benchmarks—SWE‑bench Verified and Pro, MMLU‑Pro, GPQA Diamond, HLE, AIME, HMMT, olmOCR‑bench, Terminal‑Bench 2.0, and EvasionBench—explaining their data, evaluation metrics, current leading models, and the capability dimensions they reveal.
This article breaks down DeepSeek‑V4's six core capability categories—knowledge, reasoning, programming, math, long‑context, and agent—showing how each benchmark works, presenting concrete scores that place V4 first or second against leading models, and explaining the hidden efficiency gains that make V4 up to 13.7× cheaper to run.
The article explains why generic AI testing platforms fall short, outlines how to design a testable AI system from day one, and presents seven practical recommendations—from using Codex or Claude Code to manage regression and iteration test sets, to leveraging entropy diagnostics and custom domain‑expert UX.
A Tsinghua and EvoMap study of 4,590 controlled experiments across 45 scientific tasks shows that feeding large language models with a 2,500‑token detailed Skill degrades pass rates, while a compact 230‑token strategy gene boosts performance by up to 3 percentage points.
While recent model releases boast near‑perfect scores on benchmarks like MMLU and HumanEval, the ARC‑AGI‑2 and ARC‑AGI‑3 leaderboards expose a stark gap between headline numbers and genuine programming intelligence, highlighting cost, fluid reasoning, and real‑world applicability.
The article analyzes Qwen3.6‑35B‑A3B’s MoE architecture, showing how its 30 B active parameters outperform larger dense models across programming, agent, and multimodal benchmarks, and examines the flagship Qwen3.6‑Max‑Preview’s substantial gains in world knowledge, instruction following, and third‑party rankings.
The Video‑MME‑v2 benchmark reveals that current multimodal video models, despite high leaderboard scores, struggle with genuine video understanding, thanks to a rigorous three‑layer evaluation, non‑linear scoring, and a meticulously curated 800‑video dataset that exposes their true intelligence limits.
This article explains Retrieval‑Augmented Generation (RAG), covering why large language models need external knowledge, the full offline‑and‑online workflow, document chunking, embedding evolution, vector database choices, multi‑path retrieval, evaluation metrics, hallucination types, and practical strategies to mitigate them.
The new Video‑MME‑v2 benchmark reveals that despite saturated high scores on existing video‑understanding tests, the strongest commercial model (Gemini‑3‑Pro) reaches only 49.4 points versus a human expert’s 90.7, highlighting the benchmark’s layered ability system, group‑level non‑linear scoring, and the nuanced impact of "Thinking" features.
The article analyzes the unique challenges of evaluating AI products—output uncertainty, subjective criteria, over‑fitting risk, high cost, and vague metrics—compares traditional testing with AI testing, proposes a five‑step evaluation workflow, defines concrete metrics such as pass rate and efficiency gain, and illustrates the process with a real‑world sales‑script generation case study, concluding with five key success factors and future trends.
The study shows that LLM agents' apparent intelligence stems more from the amount and type of context they can access than from genuine reasoning ability, as demonstrated by the ContextEval framework’s controlled experiments across multiple hyper‑parameter optimization benchmarks.
A research team from MBZUAI and the University of Maryland constructed an 11‑point timeline of the 2026 Middle East escalation, fed contemporaneous news to leading large language models, and evaluated their strategic reasoning, economic impact forecasts, and political signal interpretation, revealing both strengths and limitations of AI under extreme uncertainty.
This article walks through a hands‑on evaluation of the GLM‑5 Turbo model, detailing its integration with AutoClaw for rapid Feishu bot deployment, comparing its performance against a baseline model on OpenClaw data‑dashboard tasks, and showcasing a fully client‑side War3‑style RTS built in a single HTML file.
The article examines the shortcomings of conventional AI evaluation methods, introduces the concept of an "unknown" risk in production settings, and presents SCALE—a continuously updated, high‑fidelity benchmark that stresses large‑model SQL capabilities with real‑world incident data and mixed objective‑subjective scoring.
Anthropic's latest Skill Creator update introduces a code‑free evaluation framework that lets non‑engineer skill authors run tests, benchmark regressions, and optimize trigger descriptions, while supporting parallel multi‑agent execution and A/B comparisons to keep skills reliable as models evolve.
BeyondSWE expands code‑agent evaluation beyond single‑repo bug fixing by introducing four realistic scenarios, scaling to 246 repositories and 500 samples, revealing a sharp performance drop for top models and highlighting the nuanced impact of search‑augmented agents like SearchSWE.
The article argues that conventional AI evaluation metrics miss critical unknown risks, outlines three key challenges in AI model selection for database tasks, introduces the SCALE benchmark with real‑world incident data, and explains its mixed evaluation framework that combines objective, subjective, and performance‑driven assessments to guide tech leaders toward reliable SQL‑focused AI solutions.
As large language models move from labs to production, traditional testing fails, so this article evaluates five major LLM testing tools across coverage, explainability, CI integration, resource cost, and customization, using data from 27 real projects and over 12 million API calls.
A comprehensive evaluation shows that while diffusion language models achieve higher generation speed through parallel decoding, they suffer from severe causal reasoning and formatting deficiencies, lagging far behind autoregressive models on embodied and tool‑calling agent tasks.
DeepSeek quietly launched its latest model, reportedly supporting up to 1 million tokens, extending its knowledge cutoff to May 2025, adopting a more enthusiastic response style, and still operating as a pure‑text system, while early tests showcase impressive coding and reasoning capabilities.
The article examines the rapid progress of AI coding agents, critiques existing benchmarks that only measure final correctness, and introduces OctoCodingBench—a new suite that simulates real‑world constraints, records full interaction traces, and evaluates both task success and strict process compliance across multiple languages.
This article shares five surprising, high‑impact lessons from Anthropic on building robust AI agent evaluation suites, covering early failure‑case collections, recognizing clever “failures,” focusing on outcomes over process, choosing the right success metrics, and the irreplaceable value of human review.
Anthropic’s new blog reveals a comprehensive framework for evaluating AI agents, detailing evaluation structures, metrics like pass@k and pass^k, types of scorers, multi‑round testing, and a step‑by‑step roadmap for designing, maintaining, and integrating automated assessments into agent development pipelines.
This guide explains how to systematically evaluate industry‑specific AI agents by testing the combined model and engineering stack, building domain‑expert‑driven datasets, designing reproducible testing systems, managing assets, controlling costs, and applying both traditional and LLM‑based methods to ensure reliable, stable performance.
OpenAI's FrontierScience benchmark, released on Dec 16, 2025, evaluates expert‑level scientific reasoning and research tasks, showing GPT‑5.2 scoring 25% on Olympiad and 77% on Research, outperforming other models while highlighting strengths in closed‑form problems and gaps in open‑ended research tasks.
This article outlines the challenges of AI product testing, proposes a comprehensive evaluation framework covering business goals, product effectiveness, performance, safety, and cost, and details the design of a modular, end‑to‑end testing platform that supports both reference‑based and reference‑free assessments while enabling continuous quality improvement.
CrossVid is an open‑source benchmark that evaluates multimodal large language models on cross‑video reasoning, offering 5,331 videos and 9,015 high‑quality QA pairs across four reasoning dimensions, and revealing that even the strongest models achieve only about 50% accuracy compared with human performance.
CrossVid is an open‑source benchmark that evaluates multimodal large language models on cross‑video reasoning tasks, providing 5,331 videos, 9,015 QA pairs, four high‑level dimensions and ten specific tasks, and exposing significant performance gaps between current models and humans.
AMO‑Bench, released by Meituan's LongCat team, is a 50‑question, IMO‑level math reasoning benchmark that combines original, high‑difficulty problems with automated scoring, exposing the current limits of top large language models whose best accuracy hovers around 52 % and offering a more discriminative evaluation tool for future model improvements.
A historian’s experiment with a secret Google AI model shows near‑expert transcription of 18th‑century ledgers and multi‑step reasoning that may signal a breakthrough in both handwritten OCR and symbolic inference, sparking a heated debate on Hacker News about true understanding versus advanced pattern matching.
Mercor, founded by three 22‑year‑old college dropouts, raised a $350 million Series C round that lifted its valuation to $10 billion within two years, built an AI‑powered recruiting platform serving OpenAI, Meta, Google and others, launched the APEX benchmark to measure economic value of AI models, and survived intense work‑culture pressures, a legal dispute, and rapid team changes.
FinSearchComp is the first fully open‑source benchmark that evaluates large‑language‑model agents' search and reasoning abilities in realistic financial workflows, featuring 635 expert‑annotated questions across three task types, built with 70 finance experts, and revealing that web‑enabled models with financial plugins markedly outperform API‑only models.
Recent release of the SuperCLUE-CPIF benchmark shows Baidu’s Wenxin X1.1 achieving the highest score among Chinese large language models, surpassing competitors like DeepSeek‑V3.2‑Exp‑Thinking and Hunyuan‑T1, with notable advantages in precise instruction following and complex task handling.
VitaBench is a newly released, highly realistic benchmark that evaluates large‑language‑model agents across three everyday scenarios—food ordering, restaurant dining, and travel planning—by quantifying reasoning, tool‑use, and interaction complexities, revealing a significant performance gap in current models.
This article examines the design philosophy behind the “Bash Only” category of the SWE‑bench benchmark, explaining how its minimal‑agent approach isolates LLM reasoning by restricting interactions to a plain Bash shell, making it a rigorous, reproducible test of true software‑engineering intelligence.
Terence Tao highlights that AI is transitioning from achieving headline‑making qualitative milestones, like winning IMO‑level contests, to a phase where quantitative metrics—resource costs, success rates, and scalability—must be transparently reported, urging standardized benchmarks and careful comparison between lightweight and heavyweight AI systems.
In a QECon 2025 talk, Ant Group’s AI team detailed a comprehensive multimodal evaluation framework that leverages large‑model metrics, custom pipelines, and benchmark datasets to assess image generation, speech recognition, and video quality, while also contributing to industry standards and academic research.
This article outlines the design of a scientific, quantifiable, multi‑dimensional evaluation system for the DataV‑Note intelligent analysis platform, addressing the lack of unified standards and accuracy challenges in AI‑driven data reporting, and proposes concrete metrics, model architecture, and future automation plans.
This guide walks readers through a complete, business‑oriented workflow for evaluating large language models—from requirement analysis and test‑set design to metric definition, execution, result aggregation, and report generation—while addressing common challenges such as data imbalance, annotation quality, and automation.
This article examines the LiveCodeBench Pro benchmark, revealing that while large language models achieve impressive scores on knowledge‑ and logic‑heavy coding problems, they still fall short of human experts on high‑difficulty, observation‑intensive tasks, especially without external tool support.
Apple’s research reveals that large reasoning models, despite sophisticated self‑reflection mechanisms, experience a complete performance collapse when problem complexity exceeds a threshold, highlighting fundamental limits in their ability to achieve generalized reasoning.
This article walks through the complete process of installing the Qwen3-30B-A3B large language model on a personal computer using LM Studio, evaluates its reasoning, creative, multilingual, and coding abilities with detailed prompts, and shares practical tips for optimizing local deployment and prompt design.
A UC San Diego study found that GPT-4.5 was judged human 73% of the time and LLaMa-3.1-405B 56%, demonstrating that both large language models can pass a standard three‑party Turing test, with detailed methodology, results, and analysis of judge behavior.
VBench-2.0 expands the original VBench suite by introducing six fine‑grained dimensions—Human Fidelity, Controllability, Creativity, Physics, Commonsense, and more—to evaluate not only the visual quality of generated videos but also their intrinsic faithfulness to physical laws, common sense, and narrative coherence, providing open‑source tools, prompts, and human‑aligned metrics for the research community.
This article presents a thorough assessment of GPT‑4o’s new image generation features, detailing multiple test scenarios—from simple portrait creation and style transfer to UI design, product rendering, and educational illustrations—comparing its output with Claude‑3.7‑Sonnet, highlighting strengths in realism and weaknesses in Chinese text handling.
This article proposes an AI Contribution Index for mathematical modeling, explains its weighted‑average construction, provides concrete formulas and examples, and discusses broader applications and philosophical implications of quantifying AI involvement across various stages of problem solving.
This article presents concise English summaries of the 18 Ant Group papers accepted at AAAI 2025, covering topics such as privacy‑preserving large‑model tuning, knowledge‑graph integration, AI‑generated image detection, multi‑task learning, generative retrieval, role‑playing evaluation, and video hallucination mitigation.
Shanghai AI Laboratory’s InternLM 3.0 upgrade demonstrates that a refined 4 TB token dataset can boost a large‑language model’s performance beyond that of open‑source peers trained on 18 TB, cutting training cost by over 75% while merging regular dialogue with deep reasoning capabilities.
The WiS Platform provides a game‑based environment for benchmarking large language models in multi‑agent settings, measuring reasoning, deception and collaboration through dynamic scenarios, offering fair experimental design, real‑time competition, visualizations, detailed metrics, and open‑source tools, with GPT‑4o outperforming other models such as Qwen2.5‑72B‑Instruct.
The article presents FullStack Bench, a newly open‑sourced, multi‑language code‑LLM evaluation dataset covering over 11 real‑world programming scenarios and 16 languages, along with the SandboxFusion execution environment, and reports comprehensive benchmark results that highlight the superiority of closed‑source models over most open‑source alternatives.
This article details Tencent's practical exploration of large language model deployment in overseas game scenarios, covering the design of customized NPC role‑playing models, multilingual localization pipelines, data construction, training, evaluation frameworks, multi‑agent improvement loops, and insights from a comprehensive Q&A session.
This article details the strategic development and technical architecture of SalesCopilot, an LLM-driven agent platform designed for enterprise commercialization, highlighting the implementation of RAG and agent technologies, addressing practical challenges, and sharing key insights for building scalable AI applications.
The article examines OpenAI’s newly released o1 model, highlighting its superior performance in complex reasoning tasks such as math, programming, and science, and explains how model‑level chain‑of‑thought optimization and product‑level UI design give it an edge over competitors like Claude.
Recent tests reveal that popular large language models—including GPT‑4o, Gemini Advanced, and Claude 3.5—often claim 9.11 is larger than 9.9 because their tokenizers split the numbers, but rephrasing, zero‑shot chain‑of‑thought prompts, or treating the values as floating‑point numbers can correct the mistake, a pattern also seen variably in Chinese models.
This article reviews the rapid progress of large language models and their complementary relationship with knowledge graphs, covering comparative strengths, knowledge extraction and completion, evaluation benchmarks, deployment benefits, complex reasoning support, and prospects for interactive fusion toward more reliable and explainable AI systems.
This article examines why large language models often produce hallucinations in industry question‑answering, defines the phenomenon, explores its data and training origins, proposes evaluation metrics, and presents practical strategies—including high‑quality fine‑tuning data, honest refusal mechanisms, advanced decoding methods, and external knowledge‑graph augmentation—to reduce hallucinations and improve reliability.
This article explains how Huolala designed and implemented a one‑stop AI evaluation platform—Lala Zhiping—to select and assess large language models for logistics scenarios, detailing its business background, architecture, configurable workflow, data isolation, permission system, and future development plans.
This article presents a head‑to‑head evaluation of four leading large language models—GPT‑4, CodeLlama 70B, CodeLlama 7B, and Mixtral 8x7B—across eight coding‑related tasks, revealing GPT‑4 as the overall winner while highlighting the trade‑offs of smaller models and emerging competitors like Google Gemini.
This article summarizes the Llama 2 series, describing the Ghost Attention technique for maintaining system‑message consistency across multi‑turn dialogs, presenting RLHF and human evaluation results, and discussing extensive safety pre‑training, benchmark assessments, and model release details.
In this interview, Baidu's search architect Tianbao explains the evolution of text‑to‑image generation since 2022, discusses data preparation, model quality, prompt engineering, multi‑style support, evaluation methods, and predicts when fully AI‑generated video and movies might become mainstream.
This report, based on Professor Xiao Yanghua’s presentation at the World AI Conference, examines why vertical domains need general large models, outlines their key capabilities such as open‑world understanding, combinatorial innovation, evaluation, complex instruction execution, task planning, and symbolic reasoning, and discusses current limitations and optimization strategies for domain‑specific deployment.
A Tsinghua University evaluation of seven large language models found Baidu’s Wenxin Yiyan topping the domestic rankings with the highest overall score across 20 metrics—especially Chinese semantic understanding and safety—surpassing ChatGPT and tying GPT‑4, while also demonstrating rapid training, inference speed, and broad industry adoption.
A joint Stanford and UC Berkeley study reveals that GPT‑4’s performance on mathematics, code generation, and visual‑reasoning tasks sharply declined between March and June 2023, with accuracy dropping from 97.6% to 2.4% on a prime‑checking benchmark and executable code rates falling from 52% to 10%.
This article evaluates ChatGPT's performance on the 2023 national high school mathematics exam by presenting each question, the model's answers, reference solutions, and a detailed scoring analysis that reveals its strengths, limitations, and potential university admission implications.
A recent 154‑page Microsoft paper titled "Sparks of Artificial General Intelligence: Early Experiments with GPT‑4" argues that GPT‑4, despite being an early prototype, already exhibits many capabilities—multimodal reasoning, programming, mathematics, and human‑like interaction—suggesting it may be an early form of AGI, though experts highlight significant limitations and ongoing debates.
A recent Stanford paper shows that GPT‑3.5 and its predecessor can pass classic Theory of Mind tests at levels comparable to 7‑9‑year‑old children, sparking debate over whether these abilities are genuine understanding or emergent by‑products of scaling.
Researchers from Germany and the UK evaluated ChatGPT and three other AI models on the QuixBugs benchmark, finding that ChatGPT correctly fixed 31 of 40 bugs—outperforming CodeX, CoCoNut, and Standard APR—and sparked mixed reactions about its impact on software engineering and OpenAI’s broader strategies.
This article presents a comprehensive evaluation framework for OPPO's XiaoBu AI assistant, covering evaluation concepts, objectives, five key elements, sampling methods, dimension selection, annotation scoring, report generation, and a detailed Q&A that illustrates practical metrics and processes for voice and search services.
This article presents a comprehensive evaluation framework for OPPO's XiaoBu AI assistant, covering the concept and purpose of evaluation, the five key evaluation elements, data sampling strategies, dimension and rule selection, annotation scoring, reporting guidelines, and detailed procedures for assessing wake‑up, ASR, NLU, and TTS performance.
