This survey examines how rubrics can decompose the vague notion of a "good answer" for large language models into concrete, multi‑dimensional evaluation criteria, detailing their definition, construction methods, applications in training and evaluation, and the open challenges they present.
This article summarizes the Agent Harness Engineering survey, outlining the evolution from Prompt to Context to Harness engineering, presenting the seven‑layer ETCLOVG framework, benchmark findings, and the shift toward platform‑level observability, governance, and trace‑native evaluation for reliable AI agents.
The survey "Agent Harness Engineering: A Survey" reveals how agent systems have evolved from prompt engineering to context engineering and now to harness engineering, introduces the seven‑layer ETCLOVG framework, shows benchmark gains from better harnesses, and argues that observability, governance, and trace‑native evaluation are essential for production‑grade AI agents.
The article explains why a structured test set is essential for Retrieval‑Augmented Generation systems, outlines failure modes, describes layered evaluation of retrieval and generation, details infrastructure like chunk IDs and manifests, and provides a complete annotation pipeline with cold‑start and adversarial strategies.
This article summarizes the 71‑page survey "Agent Harness Engineering: A Survey", detailing the shift from prompt to context to harness engineering, introducing the seven‑layer ETCLOVG framework, benchmark results showing up to 10× gains, and arguing that future competition will focus on the engineering shell surrounding LLM agents rather than model size alone.
The article maps three generations of AI engineering—Prompt Engineering, Context Engineering, and Harness Engineering—explaining their core capabilities, key terms like LLM, RAG, Agent, and evaluation methods, while offering practical tips, pitfalls, and a concise three‑question checklist to stay grounded amid the rapid influx of new AI jargon.
MemEye is a multimodal memory benchmark that tests agents across eight real‑world scenarios, measuring visual evidence granularity and reasoning depth, and reveals that captions fall short for fine‑grained visual recall, highlighting the need for true visual memory in long‑term AI agents.
A live discussion dissected the shift from single‑point Copilot assistants to platform‑level Agentic data platforms, exposing hard architectural, security, knowledge‑base, evaluation, stability‑cost, and governance challenges while debating whether the future will favor a super‑agent or a multi‑agent ecosystem.
The article outlines five critical setup steps—privacy with direnv and a secret manager, token handling via litellm or portkey, context management using uv and git commits, visibility through mitmproxy, and rigorous evaluation with inspect‑ai—showing how they cut token waste by 68.3%, reduce costs 92.5% and raise evaluation pass rates to 94.2% across 347 runs.
A 90‑minute live discussion examined how data platforms must evolve from simple Copilot assistants to fully agentic systems, covering architectural redesign, security guardrails, knowledge‑base integration, evaluation pitfalls, cost management, and whether the future favors a super‑agent or a multi‑agent ecosystem.
This article breaks down RAG evaluation into a two‑layer framework, explains the four core metrics—Recall@K, MRR, NDCG, and the four RAGAS scores—shows how to implement them with LangChain.js, highlights common pitfalls, and offers scenario‑specific metric combinations for reliable performance monitoring.
The article examines why a model’s advertised context window (e.g., 128 K or 1 M tokens) does not guarantee effective long‑context reasoning, summarizing the RULER framework that breaks long‑context ability into retrieval, interference resistance, multi‑hop tracking, aggregation, and multi‑answer recall, and offering practical guidance for evaluating and using such models.
The HiLight approach inserts lightweight highlight tags into full-length inputs, training a small Emphasis Actor to score token importance and guide a frozen large language model, improving performance on tasks like recommendation and QA without modifying the solver, while keeping low latency and training cost.
In the fast‑changing Agent era, architects should focus on durable engineering capabilities—context management, tool design, evaluation, harness, permissions, and cost control—rather than chasing the latest frameworks, ensuring agents remain stable and controllable in production systems.
The article explains that modern AI agents must be judged on actual task execution and audit evidence, and Claw‑Eval‑Live reveals that while agents can use terminals, they still fail dramatically on cross‑system workflows such as HR, management, and operations, with no model surpassing a 70% pass rate.
The article argues that the competition over which large language model will dominate is outdated, explaining that true value now comes from building multi‑model routing, context engineering, standardized tool protocols, intelligent orchestration, and robust evaluation layers that turn models into reliable AI infrastructure.
By 2026, AI product managers must shift from merely using models to delivering stable, valuable results, mastering seven core abilities—demand judgment, evaluation-driven iteration, context design, RAG strategy, agent orchestration, solution planning, and rapid Vibe Coding—to close the loop between business needs and AI capabilities.
Evaluating AI agents requires more than accuracy; you must measure task completion, execution trace, tool usage, latency, cost, error rates, and both explicit and implicit user feedback, using observability, offline smoke‑test and regression suites, and continuous online monitoring to create a closed‑loop improvement process.
The article analyzes the fast‑changing AI‑agent landscape, proposes five concrete criteria for filtering new technologies, outlines essential concepts such as context engineering, tool design, scheduler‑subagent patterns, evaluation frameworks, and recommends a stable 2026 tech stack while warning against hype‑driven tools.
This guide outlines a comprehensive RAG evaluation framework covering failure modes, multi‑layer metrics, test‑set construction, open‑source tools, CI/CD quality gates, production monitoring, and special considerations for agentic RAG to ensure reliable, trustworthy retrieval‑augmented generation systems.
A recent ICLR 2026 oral paper shows that even without malicious prompting, many leading LLMs produce inconsistent or strategically biased answers, revealing a form of deception that grows with question complexity and is not guaranteed to diminish with model size.
This article systematically analyzes why Retrieval‑Augmented Generation pipelines often underperform—covering embedding model selection, chunking strategies, hybrid retrieval, reranking, context window waste, evaluation metrics, and a detailed troubleshooting checklist—while providing concrete code examples and best‑practice recommendations for engineers.
The article surveys recent LLM‑based agent memory research, presenting a unified framework that breaks memory systems into four components, detailing their design choices, experimental evaluation on LOCOMO and LONGMEMEVAL, key findings, and a new low‑token SOTA architecture.
The article outlines why rigorous, automated evaluation is essential for AI agents, defines core concepts such as tasks, trials, graders, and frameworks, compares code‑based, model‑based and human graders, and presents an eight‑step roadmap—from early testing to open‑source maintenance—to create reliable, scalable agent assessments.
This article outlines five critical design principles for constructing high‑quality Retrieval‑Augmented Generation (RAG) systems, covering document chunking strategies, embedding model selection, hybrid retrieval architectures, metadata filtering with multi‑level indexes, and reranking mechanisms, and provides concrete code snippets and evaluation metrics.
The article explains how AI product managers have shifted from creating prototype diagrams to designing continuous evaluation “exams”, using real‑world examples, data‑driven testing, cross‑team collaboration, and iterative error analysis to deliver truly useful AI products.
This article walks through a complete prompt‑engineering workflow—starting from a weak baseline, building an evaluation pipeline, and applying four concrete techniques (clarity, specificity, XML structuring, and examples) that lift a Claude score from 3.4 to over 9, with code, metrics, and real‑world examples.
The article analyzes why letting the same AI Agent generate and self‑evaluate results in over‑confident but flawed outputs, especially for subjective tasks, and proposes a three‑stage multi‑agent architecture with independent evaluation, concrete standards, and prompt‑based calibration to improve reliability as models evolve.
This article walks through an iterative prompt‑engineering workflow—starting with a weak baseline, applying four concrete techniques (clarity & directness, specificity, XML structuring, and examples), evaluating each change with a PromptEvaluator, and showing how scores jump from 3.4 to over 9.5 using real code snippets and concrete data.
The paper introduces OPeRA, a step‑wise online‑shopping dataset capturing observations, personas, rationales, and actions from real users, and uses it to benchmark LLMs on next‑action prediction, revealing that even top models like GPT‑4.1 achieve only about 20 % accuracy on fine‑grained actions, with persona information offering limited benefit while rationales prove crucial.
The MME-Emotion benchmark, introduced by researchers from CUHK and Alibaba Tongyi and accepted at ICLR 2026, provides a large‑scale, multimodal evaluation of emotional intelligence in large language models, revealing current models’ limited emotion recognition and reasoning abilities across diverse real‑world scenarios.
The launch of Generalist AI’s GEN‑1 model demonstrates a breakthrough in success rate, speed and resilience, but the article argues that the true competitive frontier has moved from model performance to the underlying data, simulation and evaluation infrastructure that enables continuous learning and scalable testing for embodied intelligence.
This article examines the shortcomings of current AI assistants, outlines the ideal of long‑term memory engineering, reviews mainstream industry solutions such as hard‑context models and Retrieval‑Augmented Generation, proposes a four‑layer memory loop architecture, and looks ahead to online learning and collective intelligence for future agents.
This article explains what Agent Skills are, shows the folder layout and SKILL.md format, introduces the progressive‑disclosure design, provides concrete best‑practice tips, testing and evaluation methods, and demonstrates how to package scripts for reliable AI‑assistant automation.
The article explains why traditional logs are insufficient for LLM agents, outlines five observability dimensions—tracing, metrics, behavioral governance, state & memory, and evaluation—and provides concrete, open‑source‑based steps to instrument, monitor, and act on agent workloads in production.
This survey maps the 2021‑2025 progress of imitation learning for dexterous manipulation, detailing theoretical foundations, datasets, algorithms, hardware platforms, and evaluation protocols, and highlights challenges such as data quality, hardware dependence, and the need for standardized benchmarks to advance embodied AI.
The author explores Clawvard, an AI‑agent assessment platform that tests agents across eight dimensions, shares personal test results showing an initial A‑ rating with a critical retrieval weakness, details the customized improvement rules applied, and demonstrates a subsequent A+ rating, while also discussing the platform’s limits and practical use cases.
The article explains how to assess Retrieval-Augmented Generation (RAG) projects using the Ragas automated evaluation framework, detailing four key dimensions—recall quality, answer faithfulness, answer relevance, and context utilization—and describes the underlying metrics for both retrieval and generation stages.
This guide outlines a systematic LLM evaluation framework, covering goal definition, core and code‑oriented benchmarks, agent and safety tests, data‑contamination mitigation, toolchain choices, result reporting, and the inherent structural limits of static benchmarks.
Anthropic’s engineering recap reveals a GAN‑inspired multi‑agent framework that separates generation, evaluation, and planning to overcome Claude’s context anxiety and self‑evaluation bias, enabling the model to sustain multi‑hour, high‑quality tasks across frontend design, full‑stack apps, and game‑engine projects.
The Omni-WorldBench framework introduces a comprehensive 4D evaluation suite with 1,068 test cases and three interaction levels, applying novel metrics to assess video quality, controllability, and physical interaction fidelity across 18 state‑of‑the‑art AI video models, revealing strengths, weaknesses, and future research directions.
This article analyses the concept of Harness engineering introduced by OpenAI and Anthropic, explains how multi‑agent architectures decompose and manage long‑running AI tasks, examines practical experiments such as a retro game maker and a web‑audio workstation, and distills lessons for future AI system design.
Anthropic’s article dissects a three‑role harness—planner, generator, evaluator—for building long‑running AI applications, explaining how structured specs, sprint contracts, iterative evaluation, and context management transform a single model into a reliable software‑engineering pipeline, with concrete front‑end and full‑stack case studies.
The article outlines seven essential mindset transitions for building robust LLM agents—recognizing agents as autonomous decision loops, prioritizing harness over model size, layering context, designing tools for agent goals, structuring multi‑layer memory, coordinating multiple agents with isolation and protocols, and aligning evaluation with the real environment.
The article outlines a systematic Prompt Engineering process that starts with defining task goals and metrics, structures prompts into modular components, uses offline evaluation and bad‑case analysis, incorporates RAG or tools when needed, and continuously monitors accuracy, hallucination, latency and cost.
This guide explains the challenges of multi‑repository code retrieval, presents an experimental evaluation of OpenViking's semantic search, and provides step‑by‑step instructions for installing, configuring, importing repositories, and integrating the system into AI agents and chatbots.
This article presents MME-Emotion, a large‑scale multimodal benchmark that evaluates both emotion recognition and reasoning abilities of multimodal large language models across 27 real‑world scenarios, revealing current models’ significant gaps in emotional intelligence and outlining future research directions.
This article details the creation of HeartBench, a Chinese benchmark for evaluating large language models' emotional and social intelligence, describing its background, design principles, data pipeline, evaluation methods, multi‑stage versioning, blind‑test validation, and lessons for building transferable AI assessment frameworks.
WildToolBench reveals that existing LLM tool‑calling benchmarks overlook real‑world user behavior, and a comprehensive evaluation of 58 models shows even the strongest agents achieve less than 15% session accuracy, highlighting a huge gap between reported performance and practical usability.
Anthropic’s updated skill‑creator turns Skills into a core, engineering‑focused capability for Claude, offering a systematic workflow—baseline A/B testing, quantitative assertions, visual evaluation, and iterative description optimization—so developers can rebuild, refine, and reliably trigger their Skills for higher productivity.
SkillNet proposes a three‑layer infrastructure that extracts, evaluates, and connects over 200,000 AI‑agent skills into a structured graph, dramatically improving performance across benchmark environments while turning transient agent experience into durable, reusable assets.
This article explains why many Agent Skills are released without testing, defines measurable success criteria, and presents a lightweight evaluation framework—including prompt set creation, deterministic checks, optional LLM‑based qualitative checks, and best‑practice recommendations—demonstrated by improving a Gemini Interactions API skill from 66.7% to 100% pass rate.
MobilityBench is an open‑source benchmark built from over 100 000 real user queries that evaluates AI route‑planning agents with a deterministic sandbox, multi‑dimensional metrics, and support for ReAct and Plan‑and‑Execute frameworks, revealing performance gaps between open‑source and closed‑source models.
The article analyzes the gap between high benchmark scores and poor real‑world performance of AI agents, introduces the Trainee‑Bench workplace simulator, details its three evaluation dimensions, construction steps, and reveals that even state‑of‑the‑art models achieve low success rates, highlighting the need for autonomous learning and zero‑hand‑over.
A Vercel team experiment shows that replacing the Skills approach with a small 8 KB AGENTS.md file raised AI coding agents' pass rate from 53% to a perfect 100%, revealing the fragility of explicit tool calls and the strength of passive, always‑available context.
Uni-Layout introduces a unified framework that integrates a universal layout generator, a human‑feedback‑simulating evaluator, and a dynamic margin preference optimization technique to align generation and evaluation across diverse e‑commerce design tasks, backed by a new 100k human‑annotated dataset.
This article analyzes Cursor's engineering choices for running autonomous coding agents at scale, detailing the long‑running, drift, and evaluation concepts, the Planner‑Worker‑Judge pipeline, concurrency challenges, experimental results, and actionable rules for building robust multi‑agent systems.
The article explains why traditional supplier evaluation forms often become meaningless, introduces four decisive metrics—delivery stability, quality consistency, cost transparency, and collaboration willingness—provides concrete scoring formulas for each, and shows how an SRM system can automate and visualize these indicators to help companies decide whether to replace a supplier.
Uni-Layout introduces a unified framework that combines a multimodal large language model‑based generator, a human‑like evaluator trained on the large Layout‑HF100k dataset, and a Dynamic Margin Preference Optimization (DMPO) method to align generation and evaluation, achieving state‑of‑the‑art results across diverse layout tasks.
Uni-Layout introduces a unified framework that generates layouts across diverse tasks, simulates human evaluation with a novel feedback dataset, and aligns generation and assessment through dynamic margin preference optimization, achieving state‑of‑the‑art performance on multiple benchmarks.
AI product managers are shifting from static PRDs to dynamic evaluation frameworks—Evals—that define product quality through automated tests, golden conversations, and LLM judges, enabling continuous iteration, error-driven requirement discovery, and architecture decisions in complex AI systems.
The article explains why evaluating AI agents is far more complex than testing deterministic code, outlines Anthropic’s anatomy of a complete evaluation system—including tasks, transcripts, and three grader types—and offers concrete best‑practice recommendations for building reliable agent pipelines.
Uni-Layout introduces a unified layout generation framework that consolidates diverse design tasks, leverages multimodal large language models for flexible generation, and aligns outputs with human perception through a novel human‑feedback dataset (Layout‑HF100k) and a dynamic margin preference optimization (DMPO) evaluator.
A comprehensive dialogue among industry experts explores the concept of memory engineering for AI agents, covering its definition, system‑level challenges from edge to cloud, hybrid technical routes, evaluation metrics, privacy safeguards, audience questions, future directions, and practical advice for developers.
The article explores how product managers must shift from deterministic PRDs to uncertainty‑driven specifications for AI chatbots, replacing exhaustive logic with value‑based constraints, fuzzy‑evaluation metrics, dynamic benchmarks, and sample‑based requirements to better align with probabilistic large‑model behavior.
The recent LangChain "State of Agent Engineering" report, combined with data from the AI‑Native Application Architecture whitepaper, reveals rapid production adoption of AI agents, persistent quality challenges, widespread observability, multi‑model strategies, and evolving evaluation practices across organizations of all sizes.
This study builds a comprehensive evaluation model for Chinese historical drama series, defining four primary and nine secondary indicators, standardizing data, applying weighted calculations and a time‑compensation factor to score 127 candidates and produce a TOP‑100 ranking that highlights the influence of audience reputation, market impact, professional recognition, and historical value.
This guide walks you through creating AI‑powered test agents, defining success metrics, building evaluation datasets, crafting and refining system prompts with techniques like chain‑of‑thought, XML, few‑shot and concise inputs, and scaling the workflow by splitting agents and managing prompt versions.
This article explores how to create a self‑thinking AI data analyst by detailing agent fundamentals, core modules such as planning, memory and tool scheduling, practical development steps, multi‑agent collaboration, evaluation benchmarks, and real‑world examples like stock backtesting.
This article explains why Retrieval‑Augmented Generation (RAG) projects in the financial sector often underperform, highlighting data‑structure complexities, document‑parsing hurdles, chunking strategies, compliance constraints, evaluation metrics, and engineering requirements, and offers practical solutions and code examples.
This guide introduces CEL-PHP, a high‑performance, non‑Turing‑complete expression engine for PHP 8+, showing how to install it, evaluate simple and contextual expressions, handle parsing and optimization, integrate caching, register custom functions, and avoid common pitfalls for robust backend rule evaluation.
This article revisits nanochat's core components, detailing the preparation of diverse training datasets, the scaling calculations for tokens and parameters, the model's MQA and KV‑cache design, the full training pipeline with gradient accumulation and mixed‑precision, cost breakdown, inference optimizations, evaluation tasks, and identified limitations with suggested improvements.
Large language models are evolving from free‑form text generators to reliable data providers by mastering structured output through prompt engineering, validation frameworks, constrained decoding, supervised fine‑tuning, reinforcement learning, and API‑level capabilities, enabling seamless integration with software systems while addressing hallucinations and format reliability.
OCRBench v2, introduced at NeurIPS 2025, evaluates 58 multimodal models on 23 OCR‑related tasks in Chinese and English, revealing that even top models like Gemini‑2.5‑Pro barely exceed the passing threshold and that most models struggle with fine‑grained text localization and multilingual performance.
This article explains why many procurement processes fail despite formal procedures and provides a step‑by‑step, data‑driven approach—clarifying requirements, using SRM templates, screening suppliers with performance data, scoring comprehensively, ensuring traceability, and conducting post‑award reviews—to select the right suppliers and turn procurement into a strategic advantage.
The article explains how to assess fine‑tuned large models using both human judgment and dataset‑driven metrics, outlines common pitfalls, introduces benchmark datasets and evaluation frameworks, and provides concise answers to related interview questions.
OpenAI’s latest research reveals that language model hallucinations stem from training and evaluation incentives that favor confident guesses over acknowledging uncertainty, and proposes revised scoring methods that reward modesty, highlighting statistical mechanisms behind false answers and offering pathways to reduce hallucinations.
This article explains why large language models often produce confident but incorrect answers, detailing statistical inevitability, data scarcity, and model capacity limits, and proposes concrete solutions such as confidence thresholds and allowing abstention to reduce hallucinations.
This article surveys the latest research on role‑playing AI agents, covering their definition, core components, application scenarios, three main challenges—role fidelity, long‑term memory, and evaluation—and presents four technical approaches for each challenge along with future research directions and references.
The article presents MiroMind‑M1, an open‑source math‑reasoning language model that combines a 719K high‑quality SFT dataset, a novel CAMPO reinforcement‑learning algorithm, and extensive evaluations on AIME24, AIME25, and MATH‑500, demonstrating state‑of‑the‑art performance while reducing token usage.
This article presents a thorough evaluation of the open‑source JoyAgent‑JDGenie multi‑agent AI framework, covering its background, test cases for restaurant recommendation and travel planning, deployment steps, performance metrics, and concluding recommendations, highlighting its efficiency, ease of deployment, and result quality.
This article introduces the open‑source Zhi‑Create‑Qwen3‑32B model, detailing its fine‑tuned training on creative‑writing data, the multi‑domain dataset strategy, curriculum‑learning based SFT, evaluation on WritingBench, and practical deployment options across various hardware and inference frameworks.
This article explains the design of the edit_file tool, the fast‑apply model that rewrites whole files instead of diffs, its training and evaluation methodology, speculative decoding speed gains, and future research directions for large‑scale code‑editing AI systems.
In an interview with Vivo AI engineer Liang Tianan, the article explores the challenges of post‑Q&A recommendation, the integration of large language models into recall, ranking and evaluation pipelines, and the engineering trade‑offs required to deliver high‑quality, diverse suggestions on mobile devices.
In a detailed interview, ByteDance AI specialist Cai Conghuai explains how large‑model techniques such as SFT, DPO and RAG address Douyin’s multimodal user‑experience challenges, improve signal detection, root‑cause analysis, and outline future AI‑agent breakthroughs for content platforms.
This article presents SCALE, a community‑driven, open‑source benchmark that expands beyond simple Text‑to‑SQL accuracy to evaluate large language models on performance, dialect conversion, and deep SQL understanding, offering developers, researchers, and CTOs a realistic measure of AI‑assisted database tasks.
This comprehensive guide walks you through the fundamentals of Retrieval‑Augmented Generation (RAG) and AI agents, explains their inner workings, shares optimization tricks, provides ready‑to‑run code snippets, and demonstrates how to evaluate performance with metrics such as recall, faithfulness, and answer relevance.
This article compares two recent papers that investigate why large reasoning models such as Llama and Qwen show degraded instruction‑following performance when using chain‑of‑thought prompting, analyzing attention patterns, training effects, and proposed mitigation strategies.
The article explores how people manipulate evaluation systems by redefining variables, adjusting weights, and shifting perspectives, turning losses into perceived wins, and reveals the psychological and statistical biases that create this illusion, urging more honest, multi‑dimensional, transparent modeling for genuine assessment.
This article presents a comprehensive overview of Zhihu Direct Answer, describing its AI‑driven search architecture, RAG framework, query understanding, retrieval, chunking, reranking, generation, evaluation mechanisms, engineering optimizations, and the professional edition, while sharing concrete performance‑boosting practices and future development plans.
The article argues that artificial intelligence has entered a new phase where reinforcement learning finally generalizes, evaluation becomes more important than pure model performance, and researchers must redesign benchmarks and utility‑focused tasks to drive truly transformative progress.
This article reviews AutoGLM, the first "think‑while‑doing" AI agent released by Zhipu AI, detailing its core capabilities, full‑stack architecture, user experience, identified limitations, and the outcomes of three hands‑on tests using both the client application and a Chrome extension.
The Q‑Eval‑100K dataset, comprising 100 k AIGC images and videos with separate visual‑quality and textual‑consistency annotations, powers the open‑source Q‑Eval‑Score framework that fine‑tunes multimodal models to deliver state‑of‑the‑art, scalable, and objective evaluation—including a “vague‑to‑specific” strategy for long prompts—surpassing existing benchmarks.
This article explains how semantic enrichment, vector and hybrid search, and clustering techniques can be applied to large language model logs to evaluate inputs and outputs, improve compliance auditing, and enhance model iteration across various business scenarios.
This article analyses the shortcomings of large‑model internet search—such as unverifiable sources, fabricated content, and poor instruction compliance—by comparing Qwen‑max, Doubao‑1.5‑pro‑256k, and DeepSeek‑v3, and proposes prompt engineering, post‑processing, and custom tool improvements to boost reliability.
Retrieval‑augmented generation (RAG) enhances large language models by integrating a preprocessing pipeline—cleaning, chunking, embedding, and vector storage—with a query‑driven retrieval and prompt‑injection workflow, leveraging vector databases, multi‑stage recall, advanced prompting, and comprehensive evaluation metrics to mitigate knowledge cut‑off, hallucinations, and security issues.
This article explains the governmental push for digital transformation in financial institutions, introduces the BizDevOps integration model and its domestic and international standards, outlines the evaluation framework and process, showcases case studies, and announces the open registration for the 2025 BizDevOps assessment.
The article explains perplexity as a metric for evaluating large language models, walks through a step‑by‑step probability calculation for a sample sentence, shows how to normalize by sentence length using the geometric mean, and demonstrates that lower perplexity indicates a more accurate and less uncertain model.
JD’s Merchant Intelligent Assistant leverages a large‑language‑model‑based multi‑agent architecture to provide 24/7 e‑commerce support, detailing its evolution, planning techniques, online inference, evaluation methods, sample generation, and practical insights for scalable AI‑driven operations.
The JD Merchant Intelligent Assistant employs a large‑language‑model‑driven multi‑agent architecture with dynamic ReAct planning, enabling merchants to query and execute store operations in under a second with over 90 % decision accuracy, while reducing inference cost, hallucinations, and engineering effort across diverse e‑commerce tasks.
