The article dissects why many B2B GenAI projects fail to scale despite heavy investment, highlighting overlooked challenges in data preparation, model specialization, product integration, user experience, and organizational culture, and proposes concrete ways to bridge these gaps.
This article explains why prompt engineering is essential for effective LLM use, outlines core PE formulas and design languages, compares basic and advanced prompting techniques with Retrieval‑Augmented Generation and fine‑tuning, and discusses evaluation strategies and the future role of prompts.
The article provides a comprehensive analysis of DeepSeek’s rise in the large‑model market, examining its technical merits, security and customization hurdles, slowing innovation, fierce competition from OpenAI, Google and Alibaba’s Qwen3, as well as the fragility of its open‑source ecosystem and data preparation, ultimately questioning its long‑term viability.
This article provides a comprehensive overview of DeepSeek's model distillation technology, detailing its definition, key innovations, architecture, training methods, performance gains, and the remaining challenges such as the implicit performance ceiling and multimodal data distillation.
This guide explains how to use Baidu Baige's AI heterogeneous computing platform to deploy the eight‑model Qwen3 family—including dense and MoE variants—via a one‑click process, covering resource configuration, inference acceleration options, and post‑deployment service access.
This article demonstrates how large language models can augment business development engineers by providing data insight, automating algorithm training, and enabling low‑cost rapid product prototyping, thereby transforming traditional backend‑focused roles into full‑stack, AI‑enhanced innovators.
A new study introduces the lightweight “Canon” layer for large language models, showing how it improves information flow, inference depth, and scalability across Transformers, linear attention, and state‑space architectures, while offering a controlled synthetic pre‑training benchmark for deeper architectural analysis.
Amid the AI wave, large language models like DeepSeek R1 are poised to explode by 2025, driven by open-source, low-cost access and superior reasoning, with successful deployment requiring four key factors—domain expertise, knowledge bases, robust search, and engineered agent architectures—to unlock value beyond simple chat.
The article introduces Cursor, an AI‑powered coding assistant, outlines its supported large models, demonstrates practical front‑end use cases such as automatic layout creation, button logic, screenshot‑to‑code generation, error fixing and code cleanup, and reflects on prompt engineering and tool selection.
Agentic RAG‑R1, an open‑source project from Peking University, combines Retrieval‑Augmented Generation with an agentic AI loop, introduces the GRPO reinforcement‑learning optimizer, supports LoRA‑based fine‑tuning, quantization and multimodal tool calls, and demonstrates significant accuracy gains on the MedQA benchmark across both Chinese and English test sets.
The article reviews five years of AI model evolution, analyzes current scaling and reinforcement‑learning trends, and forecasts architectural, mathematical, and infrastructure directions for large language models through 2030, highlighting potential breakthroughs and the risks of over‑reliance on benchmarks.
This article clarifies three major misunderstandings about the Model Context Protocol (MCP), explaining that it does not require large‑model support, works even without function‑calling capabilities, and is not natively built into models, while outlining how MCP standardizes context augmentation through a black‑box server architecture.
The JD Supply Chain algorithm team introduces TimeHF, a billion‑parameter time‑series large model that leverages RLHF to boost demand‑forecast accuracy by over 10%, detailing dataset construction, the PCTLM architecture, a custom RLHF framework (TPO), and extensive SOTA experimental results.
The article critiques AI giants’ claims of nearing human-level intelligence, highlighting research that shows current models rely on massive rule memorization rather than genuine reasoning, leading to brittleness in navigation, mathematics, and adaptability, and emphasizing the need to understand these limitations for future progress.
This article chronicles the evolution of large language models from the 2017 Transformer breakthrough through BERT, GPT series, multimodal models, and recent cost‑efficient innovations like DeepSeek‑R1, highlighting key architectures, training methods, alignment techniques, and their transformative impact on AI applications.
This article introduces Vibe Coding, an AI‑driven programming approach proposed by Andrej Karpathy, explains its core concepts, workflow, advantages, tools, use cases, best practices, and future outlook, and provides a complete example of generating a simple weather app using natural‑language prompts.
In this interview, an automotive industry expert explains how large language models and advanced operations‑optimization techniques are reshaping vehicle design, production planning, logistics, and customer services, while also discussing implementation challenges, team requirements, and future AI‑driven opportunities.
This weekly technology digest highlights significant advancements in artificial intelligence, including OpenAI's GPT-4o upgrades, Tencent's Hunyuan 3D v2.5 release, and major funding rounds for xAI and Manus, alongside expert discussions on the future evolution of AI agent networks and the theoretical possibility of machine consciousness.
ByteDance’s ByteBrain team showcases how large‑model AI, operations research, and system‑level innovations have produced award‑winning papers and billions of yuan in cost savings while improving on‑call efficiency, database estimation, and cloud infrastructure reliability.
This study introduces DGDE, a document‑grounded dialogue framework that leverages large language model‑generated evidence, combining retrieval, reranking, fine‑tuning, and iterative question correction to markedly improve accuracy, comprehensiveness, coherence, and completeness on the Doc2dial benchmark.
Recent empirical research by Tsinghua’s LeapLab and Shanghai Jiao Tong University reveals that reinforcement‑learning‑based fine‑tuning (RLVR) improves sampling efficiency but does not extend the fundamental reasoning abilities of large language models beyond their base capabilities, as demonstrated across mathematics, code, and visual reasoning benchmarks.
The article surveys the Algorithmic Foundations of Engineering R&D series, tracing NLP’s evolution from rule‑based systems to today’s multimodal large‑model era, reviewing core machine‑learning and deep‑learning techniques, transformer breakthroughs, representation learning, optimization methods, and emerging research such as retrieval‑augmented generation and AI agents.
The campus AI Agent competition showcased how large‑language‑model‑powered agents can reason, deceive, and collaborate in a social deduction game, revealing model performance trends, participant insights, and future directions for multi‑agent AI research.
This article critically examines popular agent frameworks, compares OpenAI and Anthropic definitions, highlights the core difficulty of maintaining proper context for reliable agents, and presents LangGraph’s declarative and imperative features along with practical guidance for building production‑grade agent systems.
This article provides a comprehensive overview of AI agents, covering their definition, classification into language‑based, vision‑based, and multimodal types, core capabilities such as understanding, perception, planning, and action, and recent breakthroughs like OpenAI ComputerUse, SpiritSight, and MobileFlow.
This article introduces DistilQwen2.5-DS3-0324, a distilled language model series that balances rapid inference with strong reasoning by applying a fast‑thinking chain‑of‑thought strategy, details its two‑stage distillation framework, evaluation on diverse benchmarks, and provides code for downloading and using the models.
Microsoft Research introduced BitNet b1.58 2B4T, a native 1‑bit large language model with 2 billion parameters trained on 4 trillion tokens, achieving only 0.4 GB non‑embedding memory, 0.028 J decoding energy, and 29 ms CPU latency while matching full‑precision performance.
The author argues that since August 2023 AI large‑model improvements have stalled in practical applications, with benchmark scores diverging from user experience, citing security‑scanning experiments, possible benchmark gaming, and alignment bottlenecks that undermine confidence in claimed progress.
The China AI Industry Alliance unveiled its Q1 2025 Fangsheng benchmark, showing Baidu’s new multimodal models—Wenxin 4.5 leading basic abilities and Wenxin X1 excelling in reasoning—available for free on the Wenxin Yiyan platform, while Baidu pledges major 2025 investments in AI, data‑center and cloud infrastructure.
At the AIIA’s 14th plenary meeting in Nanjing, the FactTesting benchmark released its Q1 2025 results, evaluating over 200 large models and highlighting Baidu’s Wenxin 4.5 and Wenxin X1 as leaders in basic and reasoning capabilities, while outlining the expanded multimodal and agent testing roadmap for the year.
The 2024‑2025 comparative review evaluates cloud‑based AI development platforms, AI‑native code editors, IDE plugins, and their underlying large language models—detailing features, user experience, pricing, open‑source status, strengths and weaknesses, offering recommendations for UI prototyping, full‑stack projects, and forecasting future multimodal, collaborative AI‑assisted development trends.
Professor Li Hongyi’s lecture provides a comprehensive, step‑by‑step exploration of AI agents, covering their definitions, reinforcement‑learning roots, LLM integration, memory mechanisms, tool usage, planning strategies, benchmarks, and practical examples, offering a valuable resource for anyone studying modern artificial intelligence.
PaddlePaddle Framework 3.0 delivers five breakthroughs—dynamic‑static unified automatic parallelism, integrated training‑inference pipelines, high‑order scientific differentiation, a neural‑network compiler with automatic operator fusion, and streamlined heterogeneous chip adaptation—drastically reducing development effort, boosting training speed, and expanding compatibility for large‑scale AI models.
The article explains GRPO, a reinforcement‑learning algorithm that extends PPO with group sampling, no value network, dual penalties and KL regularisation, showing how it improves efficiency and stability when fine‑tuning large language models such as DeepSeek‑Math and DeepSeek‑R1.
At the ICLR 2025 live session in Singapore, Ant Group showcased four cutting‑edge papers—CodePlan, Animate‑X, Group Position Embedding, and OmniKV—demonstrating advances in large‑language‑model reasoning, universal character animation, layout‑aware document understanding, and efficient long‑context inference.
JD’s supply‑chain algorithm team introduces TimeHF, a billion‑scale time‑series large model that leverages RLHF to improve demand forecasting accuracy by over 10%, detailing dataset creation, the PCTLM architecture, a custom RL framework (TPO), and superior benchmark results.
This article surveys classic prompt‑engineering methods such as Chain‑of‑Thought, Self‑Consistency, Least‑to‑Most, Boosting of Thoughts, Tree of Thoughts, and AutoGPT, summarizing their core ideas, advantages, limitations, and experimental results to help readers understand how to enhance large language model reasoning without model fine‑tuning.
This article introduces the concept of AI-powered Data Agents, outlines their key features and use cases across enterprise analytics, data governance, and intelligent customer service, and reviews recent implementations from Google, Alibaba Cloud, and ByteDance, highlighting their impact on modern data-driven workflows.
AgenticIR combines visual language models and large language models in a multi‑stage reasoning workflow—perception, planning, execution, reflection, and adjustment—to evaluate, plan, and iteratively apply specialized restoration tools, achieving superior results on complexly degraded images compared to baseline methods.
The article argues that despite the rapid rise of powerful large language models, mastering machine learning remains essential because it underpins these models, offers customized solutions for specialized tasks, and cultivates the mathematical, programming, and analytical skills needed to effectively use and extend AI technologies.
This guide introduces Prompt Optimizer, a client‑side AI prompt‑enhancement tool with over 2k GitHub stars, outlines its key features, provides step‑by‑step Docker installation commands, showcases a real‑world SpringBoot‑Vue e‑commerce project, and demonstrates how to generate and compare optimized prompts for better LLM responses.
This article introduces the fundamental paradigms of large language models, explaining how simple prompts, messages, and tools like RAG and ReAct enable powerful applications, while providing practical code examples, translation strategies, and insights on prompt engineering, tool usage, and model fine‑tuning.
This article examines how Retrieval‑Augmented Generation (RAG) and multi‑agent technologies narrow the gap between large language models and practical deployment, highlighting their roles in operations automation, financial risk control, intelligent data governance, database localization, edge inference, and future AI‑driven solutions.
The article discusses how defining clear problem boundaries is essential for AI agents, explains the challenges of vague tasks for large language models, and proposes multi‑stage decomposition, self‑reflection, and human‑in‑the‑loop strategies to improve AI performance on complex, dynamic tasks.
A recent leaderboard analysis of top LLMs reveals that while closed‑source models like Gemini‑2.5‑Pro and ChatGPT‑4o still lead overall, open‑source models such as DeepSeek‑V3 and Llama are rapidly narrowing the performance gap, especially in specialized tasks like coding, driven by faster tech diffusion, public datasets, community collaboration, and reduced compute costs.
This article compares the inference performance of a high‑end Intel Core Ultra 5 AI workstation with an Apple M1 MacBook Air using the IPEX‑LLM library, detailing installation steps, minimal code changes, resource usage, and benchmark results for small and large language models.
The paper introduces One4All, a scalable multi‑task generative recommendation framework for CPS advertising that combines few‑shot intent prompting, a Rewards‑in‑Context multi‑objective optimization, and an online model‑selection strategy, delivering 2‑3× offline HitRate/NDCG gains and notable online CTR, CVR, and commission improvements.
This article reviews the latest updates from major LLM providers—DeepSeek V3’s parameter boost and longer context, Qwen2.5‑Omni’s open‑source multimodal 7B model, Google Gemini 2.5 Pro’s 1 M‑token window and multimodal prowess, and OpenAI GPT‑4o’s image generation and reduced pricing—highlighting technical specs, capabilities, and availability.
The article explains why most projects should start with prompt engineering or simple agent workflows, outlines the scenarios where model fine‑tuning adds real value, compares fine‑tuning with Retrieval‑Augmented Generation, and offers practical criteria for deciding which approach to adopt.
OpenAI’s GPT‑4o image generation, launched only for paid users, quickly hit performance bottlenecks and sparked a flood of viral content, prompting technical analysis of its multimodal capabilities, speed issues, copyright concerns, and the broader impact on the AI industry.
The article presents the abstracts, publication types, links, and research areas of seventeen Ant Group papers accepted at ICLR 2025, covering topics such as embodied robot co‑design, efficient distributed training for large language models, optimization via LLMs, character animation, interactive frame interpolation, KV‑cache management, and privacy‑preserving Transformers.
This article reviews the evolution of large language models from the 2017 Transformer breakthrough through BERT, GPT series, alignment techniques, multimodal extensions, open‑weight releases, and the cost‑efficient DeepSeek‑R1 in 2025, highlighting key technical advances, scaling trends, and their societal impact.
This comprehensive analysis by the Peking University AI Alignment team dissects the technical innovations behind DeepSeek‑R1, DeepSeek‑R1 Zero, and Kimi‑K1.5, covering reinforcement‑learning‑based post‑training, rule‑based rewards, GRPO optimization, scaling laws, multimodal extensions, safety challenges, and future research directions.
This article provides a comprehensive survey of Retrieval‑Augmented Generation (RAG), covering its basic principles, key components, seven technical variants, challenges, evaluation methods, and future research directions across multimodal, graph‑based, and agentic extensions.
Google researchers discovered a linear relationship between brain activity recorded during natural conversation and the internal embeddings of a speech‑to‑text large language model, revealing that acoustic and lexical representations from the model can accurately predict neural responses in both language comprehension and production.
Prompt engineering uses in‑context learning to turn large language models into precise, task‑aware assistants by providing well‑crafted prompts that guide the model’s probability distribution, reduce hallucinations, and unlock hidden knowledge without any parameter tuning.
The article introduces the DistilQwen2.5‑R1 series, which leverages a novel knowledge‑distillation pipeline—including CoT data evaluation, improvement, and validation—to transfer deep reasoning abilities from large models like DeepSeek‑R1 to compact models, achieving superior performance across math, code, and scientific benchmarks and providing open‑source checkpoints and deployment guides for practical use.
Alibaba’s Taobao Group unveiled its AIGX technology suite, including the RecGPT recommendation model, the AIGB generative bidding system, and a new AI‑generated video engine, detailing open‑source benchmarks, NeurIPS workshop participation, and measurable ROI improvements for e‑commerce advertising.
This article walks readers through the fundamentals of prompt engineering—covering role, context, instruction, examples, and output formatting—then demonstrates a step‑by‑step construction of a sophisticated SQL‑generation prompt, complete with concrete code snippets, best‑practice tips, and reference resources.
DeepSeek, a Chinese AI startup, offers open‑source large language models—DeepSeek‑V3 for general tasks and DeepSeek‑R1 for intensive reasoning—featuring MoE, MLA, low‑cost training, and competitive performance against OpenAI’s GPT‑4o, while providing detailed usage guidance and cost analysis.
The article analyzes the challenges of deploying large language models on cloud servers—such as latency, security, and constant connectivity—and explains how near‑memory computing architectures (PNM, PIM, CIM) can integrate storage and processing to enable efficient, high‑performance edge AI deployments, outlining the trade‑offs of each approach.
This guide explains how to leverage AI tools with deep web‑search capabilities, covering site‑specific queries, wildcard operators, date ranges, Boolean logic, and effective prompt engineering techniques—including Socratic questioning and CRISPE framework—to improve information retrieval accuracy and efficiency across various domains.
The article compiles insights from the AIR 2025 conference and related talks, covering the evolution of agents from reinforcement‑learning to LLM‑driven systems, novel agent architectures like AIDE, GUI agents, natural‑language reinforcement learning, and scaling advances in large language models such as Qwen, while highlighting key algorithms, benchmarks, and open research questions.
The 2025 DeepSeek Insight report analyzes DeepSeek’s new large‑model releases, compares US and Chinese AI ecosystems, outlines diverse application scenarios such as government, healthcare and aerospace, and provides practical guidance for safely leveraging these models despite their current limitations.
DeepSeek’s R1 inference mode replaces linear chain‑of‑thought with a transparent, multi‑path tree reasoning system, offering layered analysis, intent understanding, memory management, emotion detection, and hallucination mitigation, illustrated through a practical example of buying authentic cigarettes and detailed technical breakdowns.
This article presents an AI‑powered framework that automatically scans codebases, generates comprehensive unit tests using large language models, and includes self‑repair agents, detailing its workflow, core components, strategies for accuracy, practical benefits, and current limitations.
This article surveys the latest multimodal large language model research, dissecting the design, training strategies, and performance trade‑offs of models such as Llama 3.2, Molmo, NVLM, Qwen2‑VL, Pixtral, MM1.5, Emu3, and Janus, and highlights the challenges of fair cross‑model evaluation.
This article introduces the MV‑MATH dataset, a large‑scale multi‑image math benchmark, and evaluates 24 open‑source and closed‑source multimodal large language models, revealing significant performance gaps, especially on complex visual dependencies and higher difficulty levels.
MiniMax’s rapid 2025 releases—including a video model, open‑source LLM, and high‑fidelity voice model—showcase its multimodal linear‑attention architecture that handles up to 4 million tokens, earns a16z recognition, and signals China’s growing influence in open‑source AI innovation.
The article analyzes Mercury Coder, a diffusion‑based language model that generates text and code in parallel, compares its speed and quality against traditional autoregressive LLMs like GPT‑4o‑mini using a ball‑collision benchmark, and discusses the underlying score‑entropy training, current limitations, and future multimodal potential.
This weekly technology digest highlights significant advancements in artificial intelligence, including OpenAI's Python-enabled o1 model, Google's open-source Gemma 3, and Alibaba's AI-driven Quark application, alongside major industry partnerships, expert forecasts on AI agent proliferation, and emerging developments in robotics and wearable technology.
As large language models become increasingly adept at natural language, mastering prompt engineering remains essential; this article compiles 23 practical strategies—from concise commands and role‑playing to structured formatting and output guidance—that empower users to communicate clearly with AI and obtain high‑quality, targeted results.
The article examines whether the surge of general‑purpose large language models such as Manus, Claude Sonet, and Qwen undermines the Bessemer Venture Partners claim that Vertical AI will grow tenfold, by analysing market size, use‑case demand, technical challenges, emerging business models, and competitive moats.
The article summarizes Zhihu’s technical talk on the ZhiLight large‑model inference framework, detailing model execution mechanisms, GPU load analysis, multi‑GPU parallel strategies, open‑source engine comparisons, compute‑communication overlap, quantization techniques, benchmark results, and future directions for scalable LLM deployment.
In 2024 Alibaba Mama outlines how large‑language models transform search advertising through a three‑line scaling roadmap—explicit inductive‑bias design, implicit compute growth, and auxiliary CV/NLP advances—implemented via a pre‑train/post‑train/CTR paradigm and the LUM user‑behavior model, promising gains in relevance, recall, and real‑time serving while highlighting inference efficiency challenges.
PaddlePaddle 3.0 introduces a full‑stack inference engine that supports FP8, INT8, and 4‑bit quantization for popular LLMs such as DeepSeek V3/R1, delivers up to 2× token throughput on a single H800 GPU, and provides detailed deployment scripts for single‑node and multi‑node setups, including MTP speculative decoding and SageAttention for long‑sequence acceleration.
This article traces the evolution of large‑model agents from a simple chain‑of‑thought design through tool and agent instantiation, structured PEER patterns, and self‑evolving architectures, highlighting practical challenges, middleware solutions, and open‑source resources for building robust AI agents.
An in‑depth analysis reveals how DeepSeek’s V3 and R1 large‑language models achieve superior inference performance and cost efficiency on Huawei’s Ascend AI platform, detailing architectural optimizations, KV‑Cache reductions, multimodal support, real‑world deployments across finance, government, manufacturing, and the projected impact on the AI industry.
This article provides a comprehensive overview of AI Agents, tracing their evolution from traditional chatbots to LLM‑driven agents, explaining core components such as perception, reasoning, action, knowledge bases, learning and communication interfaces, and discussing practical use cases, interaction cycles, and future prospects.
This article presents a comprehensive case study on how large language models are integrated into 58.com’s real‑estate recommendation platform, detailing challenges, data adaptation, prompt and parameter optimizations, embedding generation, conversational recommendation, and future directions for multimodal and generative recommendation systems.
LLMOps, the end-to-end methodology for managing large language models, encompasses a curated set of development, deployment, monitoring, and local management tools—such as LangChain, vLLM, LangSmith, and Ollama—enabling practitioners to efficiently build, scale, and maintain AI applications.
This article provides a comprehensive analysis of DeepSeek, covering its foundational AI research, technology stack, product offerings, and the broader upstream, midstream, and downstream AI industry landscape, including hardware, server, cloud, and market trends.
This tutorial explains how to enhance large language models' logical reasoning by using DeepSeek‑R1's deep‑thinking mode, few‑shot prompting, chain‑of‑thought, and zero‑shot chain‑of‑thought techniques, providing concrete examples, comparisons, and a step‑by‑step template for effective prompt design.
This article introduces the fundamentals of prompt engineering and presents fourteen practical techniques—ranging from role‑playing and step‑by‑step reasoning to chain‑of‑thought and ReAct—that help users craft precise, high‑quality prompts for any large language model, dramatically improving AI output.
This article traces the development of AI models—from early word embeddings like Word2Vec and ELMo, through transformer‑based encoders such as BERT and decoder‑only models like GPT‑1/2/3, to recent multimodal systems and scaling laws—explaining their architectures, training methods, and impact on modern AI applications.
This article explains how the Pai‑Megatron‑Patch toolkit enhances the usability and training performance of the Qwen2‑VL multimodal large model by introducing model‑parallel weight conversion, user‑friendly data loading, visual feature processing optimizations, optimizer offloading, and pipeline parallelism techniques, supported by extensive experimental analysis.
This comprehensive guide explains what prompts are, outlines essential prompt components and multiple engineering frameworks, presents practical strategies for crafting clear and structured prompts, addresses model limitations such as hallucinations, and showcases a wide range of advanced prompting techniques with code examples.
Amid a global digital surge, 45% of China’s central state‑owned enterprises have deployed the DeepSeek large‑model platform, rapidly integrating AI across energy, power, telecom, construction and other sectors to boost intelligent transformation and operational efficiency.
This article explores how open‑source large language models like DeepSeek empower SRE teams to shift from reactive firefighting to proactive, predictive operations, detailing technical principles, real‑world case studies, essential skill sets, and future trends that reshape the operations landscape.
Jia Xing’s research introduces Dynamic Margin Selection, a technique that repeatedly refreshes a core set of boundary‑close samples to train large language models efficiently on limited resources, achieving comparable loss to full‑data training, enabling six‑fold model compression, faster inference, and a proposed exponential scaling law for data‑efficient AI.
As open‑source large language model tools like Ollama, OpenWebUI and ComfyUI gain popularity, numerous security flaws such as unauthenticated APIs, CVE‑exploits, model theft and remote code execution emerge, prompting the development of AI‑Infra‑Guard—a lightweight, cross‑platform scanner that identifies over 30 component vulnerabilities and offers both web UI and CLI modes for rapid risk assessment.
The article analyzes DeepSeek's recent releases—V3 dialogue model and R1 inference model—detailing their launch dates, rapid popularity surge, R1's reinforcement‑learning‑based design for code and math tasks, and provides links to related Beijing University technical reports while stripping promotional sales content.
This article introduces GraphCLIP, a self‑supervised graph‑summary pre‑training framework that boosts zero‑ and few‑shot transferability of graph foundation models for text‑attributed graphs, and 2D‑TPE, a two‑dimensional positional encoding method that preserves table structure to markedly improve large language model performance on table‑understanding tasks, while also announcing a live paper session at WWW 2025 featuring the authors.
The paper presents a generative recommendation framework for JD Alliance advertising that combines semantic‑ID modeling, large‑model pre‑training and fine‑tuning, and Direct Preference Optimization (including Softmax‑DPO and β‑DPO) to jointly boost click‑through and conversion rates, achieving +0.6% UCTR and +8% UCVR in online tests while outlining future multi‑objective extensions.
This article explains how inference‑oriented large language models such as DeepSeek‑R1 and OpenAI o1‑mini shift AI research from training‑time scaling to test‑time computation, detailing the underlying principles, new scaling laws, verification techniques, reinforcement‑learning pipelines, and practical methods for distilling reasoning capabilities into smaller models.
The article examines whether the latest AI models that claim to perform true reasoning—by breaking problems into steps and using chain‑of‑thought—actually reason like humans, presenting skeptical and supportive expert viewpoints, and offering practical guidance on how to use such models responsibly.
This article details how NetEase Cloud Music leverages multimodal large language models to improve music recommendation across daily, personalized, and playlist scenarios by extracting rich audio, text, and visual features, addressing data skew, cold‑start challenges, and achieving measurable gains in user engagement and distribution efficiency.
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.
Hybrid reasoning lets Claude 3.7 Sonnet dynamically switch between fast, intuition‑like answers and step‑by‑step, deep analysis, improving both speed and accuracy for tasks ranging from simple code snippets to complex algorithm design, and signals a broader shift in large language model capabilities.
Chinese tech giants Huawei and Alibaba respond to President Xi’s call for stronger innovation, with Huawei showcasing its HarmonyOS and server‑grade Arm processor while Alibaba unveils the Qwen 2.5‑Max large language model that outperforms leading Western AI systems on multiple benchmarks, highlighting China’s accelerating AI ambitions.
