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Network Intelligence Research Center (NIRC)
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Network Intelligence Research Center (NIRC)

NIRC is based on the National Key Laboratory of Network and Switching Technology at Beijing University of Posts and Telecommunications. It has built a technology matrix across four AI domains—intelligent cloud networking, natural language processing, computer vision, and machine learning systems—dedicated to solving real‑world problems, creating top‑tier systems, publishing high‑impact papers, and contributing significantly to the rapid advancement of China's network technology.

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Recent Articles

Latest from Network Intelligence Research Center (NIRC)

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Network Intelligence Research Center (NIRC)
Network Intelligence Research Center (NIRC)
Dec 23, 2025 · Artificial Intelligence

ClusterAttn: Compressing KV Cache with Intrinsic Attention Clustering

ClusterAttn tackles the KV‑cache bottleneck of large language models by exploiting the natural clustering of attention scores, achieving up to 92% compression without accuracy loss, boosting throughput 2.6–4.8×, handling 128K‑token sequences on a single GPU, and outperforming existing training‑free compression methods.

KV cache compressionattention clusteringdensity clustering
0 likes · 8 min read
ClusterAttn: Compressing KV Cache with Intrinsic Attention Clustering
Network Intelligence Research Center (NIRC)
Network Intelligence Research Center (NIRC)
Dec 19, 2025 · Databases

DIVER: A Robust Text-to-SQL System Unveiled at SIGMOD 2026, Powering ChatBI

The paper introduces DIVER, an automated expert system that gives large language models human‑like exploration, reasoning, and verification abilities for Text‑to‑SQL, addressing the severe performance drop without expert evidence by innovating dynamic interactive value linking, multi‑agent automation, and adaptive evidence generation, and demonstrates up to 10.82% accuracy gains and strong robustness on real‑world benchmarks.

Automated Expert AgentChatBIDIVER
0 likes · 11 min read
DIVER: A Robust Text-to-SQL System Unveiled at SIGMOD 2026, Powering ChatBI
Network Intelligence Research Center (NIRC)
Network Intelligence Research Center (NIRC)
Dec 15, 2025 · Artificial Intelligence

Turning LLM-Generated Network Configurations into Verified, Safe Updates with Artanis

The paper introduces Artanis, an intent‑based network configuration update framework that combines large‑language‑model generation with a verification‑feedback loop and reinforcement‑learning optimization, addressing hallucination‑induced errors and ensuring safe, policy‑compliant deployments across diverse network scales.

Intent-based NetworkingLLMReinforcement Learning
0 likes · 9 min read
Turning LLM-Generated Network Configurations into Verified, Safe Updates with Artanis
Network Intelligence Research Center (NIRC)
Network Intelligence Research Center (NIRC)
Dec 5, 2025 · R&D Management

Linking Zotero and Obsidian: From Paper Collection to Visual Knowledge Graph

This guide walks graduate researchers through a step‑by‑step workflow—collecting papers with Zotero, translating them via an LLM plugin, generating structured markdown notes, and then using Obsidian’s bidirectional links and Canvas to build a local, visual knowledge graph that ties individual citations into a coherent research map.

LLM translationObsidianZotero
0 likes · 4 min read
Linking Zotero and Obsidian: From Paper Collection to Visual Knowledge Graph
Network Intelligence Research Center (NIRC)
Network Intelligence Research Center (NIRC)
Nov 24, 2025 · Artificial Intelligence

Simplifying AI Operator Development with TileLang DSL

TileLang is a Python‑style DSL built on TVM that separates algorithm logic from hardware scheduling, offers beginner to expert interfaces, supports multiple GPU and CPU backends, and delivers performance on par with or better than existing AI kernels, as demonstrated with GEMM, FlashAttention and other benchmarks.

AI operatorsDSLGEMM
0 likes · 10 min read
Simplifying AI Operator Development with TileLang DSL
Network Intelligence Research Center (NIRC)
Network Intelligence Research Center (NIRC)
Nov 11, 2025 · Artificial Intelligence

What Is Mechanistic Interpretability and Why It Matters for Large Language Models

The article defines mechanistic interpretability as reverse‑engineering LLMs to reveal how they represent knowledge and make decisions, explains its importance for transparency, risk mitigation, and model improvement, and surveys key techniques such as causal tracing, zero‑making, noise‑making, and logit‑lens methods with illustrative examples.

causal tracinglarge language modelslogit lens
0 likes · 8 min read
What Is Mechanistic Interpretability and Why It Matters for Large Language Models
Network Intelligence Research Center (NIRC)
Network Intelligence Research Center (NIRC)
Nov 7, 2025 · Artificial Intelligence

Introducing LangGraph: A Low‑Level Framework for Building Stateful AI Agents

This article explains why modern LLM‑based applications need agent capabilities, introduces LangGraph’s core features such as stateful execution, graph‑based orchestration, tool integration, human‑in‑the‑loop and multi‑agent support, and provides a step‑by‑step Python example that builds a simple chat‑bot agent.

Human-in-the-LoopLLM agentsLangGraph
0 likes · 11 min read
Introducing LangGraph: A Low‑Level Framework for Building Stateful AI Agents
Network Intelligence Research Center (NIRC)
Network Intelligence Research Center (NIRC)
Nov 4, 2025 · Artificial Intelligence

SEAgent: A Self‑Evolving Computer Agent that Learns Software Use Autonomously

SEAgent introduces a self‑evolving framework that enables a GUI agent to master unfamiliar software through autonomous exploration and experience learning, leveraging a curriculum generator, a world‑state model, and GRPO‑based reinforcement with adversarial imitation, achieving state‑of‑the‑art performance on OSWorld.

GUI automationReinforcement LearningSEAgent
0 likes · 6 min read
SEAgent: A Self‑Evolving Computer Agent that Learns Software Use Autonomously