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DataFunSummit

Official account of the DataFun community, dedicated to sharing big data and AI industry summit news and speaker talks, with regular downloadable resource packs.

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Recent Articles

Latest from DataFunSummit

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DataFunSummit
DataFunSummit
Jul 23, 2026 · Artificial Intelligence

How Agentic Architectures Power Next‑Gen Recommendation and Search Systems

The article reviews cutting‑edge AI search and recommendation techniques—including Alibaba Cloud's Agentic RAG, Huawei Noah's LLM‑enhanced recommendation evolution, and Baidu's generative ranking model GRAB—detailing their architectures, multi‑modal retrieval strategies, performance gains, and real‑world deployment insights.

AI SearchAgentic RAGAlibaba Cloud
0 likes · 6 min read
How Agentic Architectures Power Next‑Gen Recommendation and Search Systems
DataFunSummit
DataFunSummit
Jul 22, 2026 · Artificial Intelligence

Why Knowledge Bases Alone Can’t Empower AI Agents: The Need for Actionable Experience

Large language models may know a great deal, yet they still stumble on concrete tasks because knowledge must be transformed into actionable, context‑aware skills; this article analyses how skill representation, model‑specific cognition, and continuous practice reshape knowledge engineering for self‑evolving AI agents.

AI Agentsexperience learningknowledge engineering
0 likes · 16 min read
Why Knowledge Bases Alone Can’t Empower AI Agents: The Need for Actionable Experience
DataFunSummit
DataFunSummit
Jul 22, 2026 · Artificial Intelligence

Designing Next‑Generation Recommendation and Search Systems with Agentic Architectures

The article analyzes how agentic architectures, large language models, and generative ranking techniques are applied to overcome high‑concurrency, multimodal, and multi‑hop challenges in modern recommendation and search systems, showcasing concrete designs, performance gains, and real‑world deployments from Alibaba Cloud, Huawei Noah, and Baidu.

AI SearchAgentic RAGGenerative Ranking
0 likes · 5 min read
Designing Next‑Generation Recommendation and Search Systems with Agentic Architectures
DataFunSummit
DataFunSummit
Jul 22, 2026 · Big Data

How Tencent Redefines Data Architecture for the Agent Era

With agents moving from Q&A to execution, traditional architectures expose three critical flaws—data stored in lakes, models in the cloud, and split scheduling—forcing petabyte‑scale data movement; Tencent Cloud’s big data AI DLC resolves this by running Spark and Ray side‑by‑side on the same lake, enabling closed‑loop processing and automatic trajectory capture.

AIAgentBig Data
0 likes · 2 min read
How Tencent Redefines Data Architecture for the Agent Era
DataFunSummit
DataFunSummit
Jul 21, 2026 · Industry Insights

When Models Get Cheaper, Who’s Making Money?

The article argues that as large‑language‑model costs plunge, profit shifts from model providers to companies that prepare, govern, and route data for AI, citing Databricks’ $3 billion raise, Anthropic’s data‑engineered accuracy jump, and the emerging European compliance market.

AIAI governanceBusiness Models
0 likes · 12 min read
When Models Get Cheaper, Who’s Making Money?
DataFunSummit
DataFunSummit
Jul 21, 2026 · Industry Insights

Why Enterprises Still Hesitate to Deploy Production‑Ready AI Agents Despite Palantir’s New Stack

The article analyzes Palantir’s Agent Stack—Orchestrator, observability, optimization, and Ontology—explaining how moving AI agents from chat interfaces to long‑running production tasks raises challenges of state management, fault handling, permission control, and trust, shifting the focus from model capability to enterprise‑grade infrastructure.

AI AgentsObservabilityOntology
0 likes · 14 min read
Why Enterprises Still Hesitate to Deploy Production‑Ready AI Agents Despite Palantir’s New Stack
DataFunSummit
DataFunSummit
Jul 20, 2026 · Artificial Intelligence

How Ontology‑Driven Agents Enable Controllable Execution in Harness Engineering

The article analyzes Harness Engineering’s semantic foundation, showing how an ontology‑driven approach restructures agent constraints, context handling, and feedback loops to achieve safe, auditable, and business‑level controllable execution, illustrated with a Knora implementation case study.

AI AgentKnoraOntology
0 likes · 20 min read
How Ontology‑Driven Agents Enable Controllable Execution in Harness Engineering
DataFunSummit
DataFunSummit
Jul 20, 2026 · Artificial Intelligence

Why More Context, Tools, and Memory Make Agents Unstable—and How to Fix It

The article explains that in long‑running autonomous agents, larger context windows, excessive tool sets, and unstructured memory cause slower, costlier, and error‑prone behavior, and it proposes six design principles—dense context, minimal toolkits, task‑driven skill growth, hierarchical memory, action‑validated experience, and efficiency‑focused evaluation—to achieve stable, self‑evolving agents.

Autonomous AgentsContext Managementagentic AI
0 likes · 17 min read
Why More Context, Tools, and Memory Make Agents Unstable—and How to Fix It
DataFunSummit
DataFunSummit
Jul 19, 2026 · Artificial Intelligence

Asymmetric KV Decoupling and Head-Level Competition: Three Strategies for Efficient Long-Text Reasoning

The article analyzes the fundamental trade‑off between global competition in attention and efficiency when processing million‑token contexts, and presents three complementary techniques—AsymKV, Compression‑Aware Tuning (CAT), and Softmax Linear Attention (SLA)—that preserve or rebuild global competition while dramatically reducing memory and compute costs, achieving state‑of‑the‑art results on LongBench and various retrieval benchmarks.

Asymmetric KVAttention optimizationCompression-aware training
0 likes · 14 min read
Asymmetric KV Decoupling and Head-Level Competition: Three Strategies for Efficient Long-Text Reasoning