Even after a model and its API are working, real‑world AI agents often fail because of missing infrastructure such as tool definitions, sandbox boundaries, state persistence, memory handling, tracing, and evaluation, requiring a systematic approach to turn model outputs into controlled, repeatable actions.
The article proposes treating AI Skills as games by adding explicit goals, state tracking, referees, and failure costs, showing how this gamified design can clarify success criteria, improve prioritization, and enable measurable evaluation of multi‑step agent tasks.
The Agent Memory Leaderboard (AML) debuted on July 29, 2026, offering a unified, reproducible evaluation framework that combines multi‑source text and code memory datasets, standardized protocols, ability profiling, and low‑barrier integration to fairly compare memory systems while providing detailed performance diagnostics and incentives for participants.
PenguinHarness, the open‑source harness created by LlamaFactory’s author, enables anyone to automatically construct, evaluate, and continuously improve large‑model agents—including DeepSeek—at a fraction of the cost and time of Codex, using a four‑step self‑evolution loop, a custom GDPevo benchmark, and strict contract rules to ensure safe, reproducible upgrades.
This article dissects why Retrieval‑Augmented Generation pipelines often underperform, examines root causes such as embedding model choice, chunking strategy, hybrid retrieval, and reranking, and provides concrete code samples, evaluation metrics, and step‑by‑step troubleshooting to dramatically improve results.
MemoHarness expands the notion of self‑evolving agents by keeping the language model frozen and continuously improving the surrounding control system—context assembly, tool interaction, generation settings, workflow orchestration, memory management, and output handling—demonstrating measurable gains on terminal, code‑generation, and finance tasks while highlighting limited experimental scale and selective cross‑task transfer.
The article analyzes how combining formal verification with expert‑derived Rubrics provides fine‑grained process supervision for large language models, presents the CRAFT data‑production pipeline, and shows experimental gains on math and medical benchmarks using Rubric‑driven RL, SFT, and alternating RL‑SFT training.
The article explains why AI projects that use the same large model often fail to move beyond flashy demos, attributing the gap to a missing "Harness Engineering" layer, and outlines concrete data, fatal production pitfalls, six engineering pillars, a 427‑project GitHub survey, and a step‑by‑step adoption guide.
After running DeepEval on an AI agent, the article explains how to move from raw metric scores to actionable code changes by reading detailed transcripts, diagnosing root causes, applying quality‑gate thresholds, choosing appropriate pass@k or pass^k metrics, and iterating with CI/CD integration.
The article reviews recent AI agent research, exposing three structural paradoxes—memory, reasoning, and evolution—each illustrated with concrete systems, benchmarks, and safety studies, and argues that only coordinated progress across all three dimensions can yield truly mature, self‑improving agents.
MemoHarness introduces an Agent Harness that keeps the language model frozen while iteratively optimizing the surrounding control system across six dimensions, showing measurable gains on terminal automation, code generation, and financial analysis tasks, yet acknowledges limited experimental scale and selective transferability.
The TVIR benchmark and TVIR‑Agent framework introduce a multimodal, text‑visual interleaved approach to deep research report generation, providing a unified evaluation suite, a four‑stage hierarchical agent pipeline, and extensive experiments that show TVIR‑Agent variants outperform commercial systems in overall score, citation support, and structural reliability.
The article analyzes why 40% of Agentic AI projects will be cancelled by 2027, presents ROI data showing up to 540% returns, and offers a detailed framework of maturity models, governance, evaluation stacks, and phased rollout methods to ensure successful enterprise Agent deployment.
OmniaBench, a new benchmark built from 90 primary and 354 secondary real‑world domains, evaluates 22 leading AI agents on 644 high‑difficulty tasks, revealing that even top models achieve less than 60% overall success, with detailed analysis of capability dimensions, efficiency, failure modes, and the impact of user simulators.
The article explains why treating complex AI agents as a single long prompt leads to instability, and proposes a reusable closed‑loop architecture—scheduler, planner, executor, evaluator, repairer, and finalizer—that makes agents explainable, recoverable, and safely deliverable in production.
The article analyzes why AI agents that shine in demos often fail in production, identifies five common mistakes—including over‑reliance on prompts, manual evaluation, unchecked cost and latency, fragile tool integrations, and missing safety guards—and introduces a five‑layer Harness Engineering framework with a practical four‑week rollout plan to make agents reliable at scale.
Alibaba’s Security AGI lab introduced three Yuvion LLMs—8B, 32B, and a 32B Agent—trained on Qwen‑3, and demonstrated that the 8B model already surpasses most SOTA baselines while the 32B variants achieve top rankings in comprehensive safety, adversarial, and business‑level evaluations, outpacing GPT‑5.4 and Qwen‑3‑Max.
This article provides an in‑depth technical analysis of Agent Skills, detailing the official specification, three‑layer progressive loading mechanism, engineering workflow of Skill‑Creator, naming and description rules, evaluation agents, practical advantages, known limitations, and five reusable design patterns for building robust AI agent capabilities.
Today the WebRetriever Global Challenge, co‑organized by Mingluo Technology, Peking University, and leading AI institutes, opens for individuals and teams worldwide, offering a $15,000 prize pool and inviting participants to evaluate their web agents on an 800‑site, 1,550‑task benchmark that measures both navigation success and full‑task completion.
The SEAGym benchmark reframes LLM agent evaluation from static success rates to dynamic harness evolution, offering multi‑view metrics, detailed snapshot diagnostics, and extensive experiments that reveal validation gains, OOD generalization gaps, batch‑size trade‑offs, and cross‑model transfer effects.
Despite the rise of million‑token long‑context models, hybrid retrieval‑augmented generation (RAG) solutions saw a 200% quarterly procurement surge while naive single‑vector RAG was abandoned by over 70% of firms, highlighting a mature, multi‑generation RAG technology stack that remains indispensable for enterprise AI.
The article introduces SEAGym, a benchmark that treats self‑evolving LLM agents as reinforcement‑learning processes, evaluates their harness updates across multiple dimensions, and reveals how batch size, training source diversity, and backend model affect performance, stability, and cost.
The article reviews a Nanjing University position paper that argues world‑model evaluation for embodied decision‑making should prioritize prediction of action consequences, strategy assessment, and planning support, while treating visual realism and semantic alignment as secondary diagnostics.
The article outlines how future software will serve AI agents instead of humans, describes three essential cognitive shifts for AI product managers, poses four critical questions, presents a detailed capability map covering business understanding, technical principles, data handling, evaluation, prompt design, product design, and ethics, and concludes with actionable advice for thriving in the fast‑moving AI product landscape.
The article argues that true AI self‑improvement starts not with changing model weights but by engineering a robust outer system—called Harness—that orchestrates tasks, manages context, persists state, and enables agents to reliably modify and evaluate their own execution environment.
OpenAI launched GPT‑Live, a full‑duplex voice model that lets ChatGPT listen and speak simultaneously, delegates complex tasks to newer back‑end models, and delivers a more natural, responsive conversation experience backed by a new human‑centred evaluation showing clear advantages over previous voice modes.
The article explains why traditional benchmark scores are insufficient for production AI agents and proposes a four‑dimensional evaluation framework—task success, step efficiency, cost, and safety—combined with an observability stack of metrics, structured logs, and full‑trace decision snapshots to continuously measure, debug, and improve digital employees.
The article outlines a practical framework for assessing data‑centric AI agents, highlighting challenges such as nondeterminism, black‑box behavior, and error amplification, and proposes concrete dimensions—result correctness, semantic consistency, query quality, security, and explainability—to ensure zero‑tolerance accuracy and reproducibility.
This survey from Rutgers and UNC Charlotte systematically reviews 19 agent‑skill evolution methods and 10 evaluation benchmarks, revealing critical gaps such as the lack of longitudinal tracking, binary pass/fail metrics, and one‑time security checks, and highlighting how separating diagnosis from rewrite improves cross‑task performance.
The article introduces MemSyco-Bench, a systematic benchmark that reveals how long‑term memory in LLM agents can amplify sycophancy, cause accuracy drops, and expose the need for careful memory utilization rather than mere retrieval.
This review systematically surveys 3D Scene Graph research from 2019‑2026, defining their structure, construction pipelines, applications, evaluation protocols, and highlighting open challenges such as unified definitions, dynamic modeling, functional affordances, and fragmented benchmarks that hinder real‑world deployment.
This article explains the concept of Agent Skill as a modular, file‑system‑based knowledge asset for AI agents, outlines its three‑layer progressive disclosure architecture, details suitable and unsuitable scenarios, and provides concrete iterative development practices—including decision‑tree design, dual verification, and tool‑supported workflows—to turn expert expertise into reusable, zero‑dependency SOPs.
This article dissects the essential components of a production‑ready Multi‑Agent Harness—its orchestration architecture, tool governance via a unified registry, layered state and memory management, comprehensive evaluation pipelines, token‑budget cost controls, MCP‑based tool integration, observability practices, and a phased roadmap for scaling, offering concrete guidelines and best‑practice recommendations for building reliable AI agent systems.
The article analyzes AI agents' technical layers, compares monolithic, multi‑agent, and workflow‑mixed architectures, outlines six evaluation criteria—including task complexity, tool compatibility, observability, cost, security, and team stack—and offers scenario‑specific recommendations and practical pitfalls for enterprise adoption.
The article introduces the MME‑CoF‑Pro benchmark, which uses 303 carefully crafted video‑reasoning samples across 16 categories to evaluate seven leading video generation models, revealing that current models lack true reasoning ability, that prompting can both help and hurt coherence, and that the new Reasoning Score aligns well with human judgments.
This 103‑page survey of over 300 recent papers organizes large language model reasoning into a periodic‑table framework, explains where reasoning emerges, categorizes 36 method families across six dimensions, critiques accuracy‑only evaluation, and outlines key open challenges such as fidelity, robustness, calibration, generalization, efficiency, and safety.
The article examines why large language models hallucinate, categorizes factual, faithfulness, and reasoning hallucinations, critiques existing benchmarks, and proposes a layered governance framework—including training‑time RLHF/DPO, retrieval‑augmented generation, post‑generation verification, uncertainty quantification, and compliance considerations—to mitigate risks in production systems.
The article outlines a comprehensive AI operations framework that covers observability, evaluation, governance, safety, and cost management, providing concrete metrics, tool comparisons, regulatory insights, and step‑by‑step practices to turn production AI systems into reliable, compliant, and cost‑effective services.
BrowserBC records a human’s complete web task, rewrites the noisy trace into a natural‑language skill card, and lets a smaller model repeatedly execute the same class of tasks, achieving large success‑rate gains on WebArena‑Hard and ClawBench benchmarks.
Over the past half‑year the author built dozens of AI Skills, discovering twelve common traps—from over‑relying on prompts and bloated skill sets to vague descriptions, hidden token costs, knowledge placement, security gaps, and the need for proper evaluation—offering concrete guidance to avoid them.
This survey examines how rubrics—structured, multi‑dimensional evaluation criteria—are defined, constructed, and applied to train and evaluate large language models, especially for open‑ended, high‑risk and agentic tasks, while highlighting current challenges such as reward hacking and bias.
Analyzing 25 micro‑tests from Superpowers 6.0, the author shows that adding “don’t” clauses often backfires, explains a low‑cost $0.15 per‑sample evaluation loop, presents five empirical laws and two hard rules for prompt wording, and offers a reusable framework for validating your own AI agent prompts.
The whitepaper explains how the SKILL.md‑based agent‑skill framework solves four major LLM pain points—prompt bloat, missing procedural memory, costly multi‑agent ops, and cross‑vendor migration—by introducing a three‑stage progressive loading mechanism, rigorous evaluation standards, and meta‑skill automation for scalable, low‑token AI agents.
This article walks through a real‑world contract‑review RAG project, diagnosing low recall, redesigning the system with multiple indexes, a RouterQueryEngine, re‑ranking, knowledge‑graph integration, multimodal support, incremental updates, and a rigorous evaluation framework that boosted recall from 60 % to 92 %.
TerminalWorld leverages over 80,000 developer‑recorded terminal sessions to automatically generate 1,530 verified CLI tasks across 18 workflow categories, and its evaluation of leading LLMs and agent frameworks reveals modest success rates, capability gaps, and the shortcomings of expert‑crafted benchmarks.
The article walks through a real‑world legal‑contract RAG project that stalled at 60% recall, diagnoses five root causes, and demonstrates how combining multiple LlamaIndex indexes, a Router, fusion retrieval, re‑ranking, knowledge‑graph and multimodal support raises recall to 92% while outlining evaluation metrics, latency trade‑offs, and practical deployment checklists.
The article explains how post‑training shifts AI investment from massive pre‑training compute to targeted, low‑cost fine‑tuning that lets enterprises embed proprietary business logic, outlines the ROI formula based on data exclusivity, eval formalizability, and task frequency, and presents real‑world case studies.
The article analyzes the challenges of building a self‑evolving enterprise agent—data collection, dataset construction, multi‑level evaluation, and asset consolidation—and explains how Alibaba Cloud’s AgentLoop addresses each step with full‑stack observation, ontology‑driven pipelines, standardized judges, and memory/experience libraries to close the evolution loop.
The article critically examines the assumption that longer chain‑of‑thought reasoning always improves large language model performance, presents the E‑GRM framework that dynamically decides when to invoke full CoT based on model‑internal uncertainty, and validates its efficiency and accuracy gains through extensive experiments and ablations.
The article outlines why a unified ASR evaluation pipeline—combining a TestSet Zoo, Model Zoo, and standardized Benchmark Pipeline—is essential for fair cross‑model comparison, describes 2025‑2026 trends such as multi‑track metrics and robustness, and provides a step‑by‑step implementation guide with best‑practice warnings.
The article analyzes why the bottleneck in AI‑assisted coding has shifted from model capability to workflow stability, introduces a three‑layer "harness" framework that externalizes discipline, details its evolution through four development phases, and presents a deterministic evaluation platform that quantifies the framework’s effectiveness.
The article explains how autonomous coding agents are evolving from prompt engineering to comprehensive control systems by defining contract‑style goals, integrating evaluators, implementing loop mechanisms, and visualizing work products, enabling agents to operate reliably over extended engineering cycles without continuous human input.
The article introduces the Frame‑Do‑Evaluate (FDE) capability framework, explains why engineers should shift from pure execution to problem definition, process integration, and result closure, and provides concrete steps, self‑assessment questions, and strategies to overcome organizational and personal obstacles for career advancement.
This guide explains how to use the Skill Creator tool to generate automated trigger tests, compare skill‑enabled versus baseline performance, continuously evaluate results, apply checklists, debug with a six‑step process, avoid six common anti‑patterns, and manage skill versioning and reuse so that AI skills become progressively smarter.
The article explains how AI product managers have shifted from creating prototype mock‑ups to designing continuous evaluation "exams", building test suites, analyzing data and model behavior, and coordinating cross‑functional teams to turn "usable" AI into truly "good" AI experiences.
The article explains how code serves as the executable, inspectable, and stateful medium that links reasoning, action, feedback, verification, and collaboration in long‑term AI agents, detailing the harness interface, planning‑execute‑verify loop, multi‑agent coordination, and open research challenges.
The survey from RUC‑Gaoling AI Institute reviews Rubrics for large language models, explaining why they are needed for open‑ended, high‑risk tasks, how they are constructed, and how they can be applied to policy and reward model training as well as multi‑dimensional evaluation across general and domain‑specific scenarios.
The article explains how intent recognition has evolved from simple keyword matching to a central decision hub in Agentic AI, covering basic concepts, LLM and small‑model solutions, hybrid architectures, clarification and out‑of‑scope handling, multi‑turn challenges, routing, evaluation methods, and best‑practice recommendations.
An extensive review of the Agent Harness Engineering survey shows that beyond model improvements, real‑world agent reliability hinges on a seven‑layer ETCLOVG framework—covering execution, tooling, context, lifecycle, observability, verification, and governance—highlighting the shift from prompt engineering to full harness engineering.
This article systematically reviews the state of agent long‑term memory by covering three core dimensions—benchmark datasets such as MUSE and LOCOMO, evaluation frameworks like MemoryAgentBench, LONGMEMEVAL and MemBench, and representative memory system implementations (THEANINE, RMM, M3‑Agent, Mem0)—while highlighting key capabilities, performance gaps, and future research directions.
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.
An end‑to‑end multi‑agent Retrieval‑Augmented Generation platform is presented, featuring compositional reasoning, systematic multi‑dimensional evaluation, and a controlled prompt‑improvement loop that automatically identifies weak prompt dimensions, proposes diffs, and requires human approval before deployment, with full observability via SSE and persisted logs.
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.
The article walks through a JD interview scenario where a candidate explains how a tool‑calling accuracy metric rose from 71% to 94% by detailing the full SFT data‑engineering pipeline, teacher‑model trajectory generation, quality validation, evaluation methodology, and interview‑ready talking points.
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.
The article explains why RAG systems require stage‑wise evaluation, detailing retrieval metrics such as Precision, Recall, F1, MRR, NDCG and Context Relevance, and generation metrics like Faithfulness, Answer Relevance and Completeness, while discussing LLM‑as‑Judge automation and a three‑layer assessment framework.
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.
