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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)
Feb 10, 2026 · Artificial Intelligence

How Google NotebookLM Boosts Research Efficiency with Precise Source Tracing

Google NotebookLM, built on Gemini 1.5 Pro, lets researchers upload PDFs, PPTs, text or audio and receive AI‑generated summaries, slide decks, mind maps and audio overviews that are tightly anchored to the original documents through clickable citation markers, dramatically improving workflow and credibility.

AI research assistantAudio OverviewGemini 1.5 Pro
0 likes · 6 min read
How Google NotebookLM Boosts Research Efficiency with Precise Source Tracing
Network Intelligence Research Center (NIRC)
Network Intelligence Research Center (NIRC)
Feb 3, 2026 · Artificial Intelligence

INCS: A DRL‑Based Intent‑Driven Network‑Wide Configuration Synthesis Framework

The article presents INCS, a novel framework that combines graph neural networks and deep reinforcement learning to achieve protocol‑agnostic, millisecond‑level, globally optimized network configuration synthesis, addressing scalability, protocol dependence, and lack of optimization in traditional SMT‑based methods, and demonstrates its superior performance on large‑scale topologies.

DDPGGraph Neural NetworkNetwork Synthesis
0 likes · 8 min read
INCS: A DRL‑Based Intent‑Driven Network‑Wide Configuration Synthesis Framework
Network Intelligence Research Center (NIRC)
Network Intelligence Research Center (NIRC)
Jan 31, 2026 · Artificial Intelligence

How Engram Lets Large Models Swap GPU Memory for Cheap RAM to ‘Look Up’ Knowledge

The article dissects DeepSeek’s new Engram architecture, which separates computation from memory by using a large, cheap‑RAM‑based lookup table to store factual knowledge, allowing the transformer’s compute layers to focus on reasoning, dramatically reducing GPU memory demand while improving code, math, and long‑context performance.

EngramGPU memoryMemory-Compute Architecture
0 likes · 7 min read
How Engram Lets Large Models Swap GPU Memory for Cheap RAM to ‘Look Up’ Knowledge
Network Intelligence Research Center (NIRC)
Network Intelligence Research Center (NIRC)
Jan 25, 2026 · Artificial Intelligence

RecFlow Breaks DLRM Inference Bottleneck with Fine-Grained GPU Parallelism

RecFlow, a new inference engine from Beijing University of Posts and Telecommunications and Meituan, tackles the resource mismatch of DLRM models by coordinating embedding and DNN operators at the intra‑SM level and introducing interference‑aware adaptive scheduling and incremental batching, achieving up to 9.34× higher throughput on RTX 3090.

DLRMFine-grained parallelismGPU Acceleration
0 likes · 7 min read
RecFlow Breaks DLRM Inference Bottleneck with Fine-Grained GPU Parallelism
Network Intelligence Research Center (NIRC)
Network Intelligence Research Center (NIRC)
Jan 17, 2026 · Artificial Intelligence

DiffNBR: A Spatiotemporal Diffusion and Information‑Bottleneck Approach for Next‑Basket Recommendation

DiffNBR introduces a dual‑path diffusion framework combined with an information‑bottleneck mechanism to jointly model spatial co‑occurrence and temporal evolution in next‑basket recommendation, achieving state‑of‑the‑art performance and effectively disentangling repetitive and exploratory purchase patterns.

DiffNBRDiffusion ModelSpatiotemporal Modeling
0 likes · 8 min read
DiffNBR: A Spatiotemporal Diffusion and Information‑Bottleneck Approach for Next‑Basket Recommendation
Network Intelligence Research Center (NIRC)
Network Intelligence Research Center (NIRC)
Jan 14, 2026 · Artificial Intelligence

From Black‑Box Guessing to Quantitative Deconstruction: Unveiling the Mystery Inside Large Language Models

At EMNLP 2025, the BUPT NIRC team presented a paper that introduces the ARR metric to quantitatively separate latent reasoning from factual shortcuts in LLMs, using Logit Lens and Attention Knockout to reveal distinct internal pathways and shares their conference experience.

ARR metricAttention KnockoutEMNLP2025
0 likes · 6 min read
From Black‑Box Guessing to Quantitative Deconstruction: Unveiling the Mystery Inside Large Language Models
Network Intelligence Research Center (NIRC)
Network Intelligence Research Center (NIRC)
Jan 11, 2026 · Artificial Intelligence

Insights from NeurIPS 2025: Modeling Distributions and Venturing Beyond Them

The report summarizes NeurIPS 2025 in San Diego, highlighting four NIRC papers on noise‑robust 3D human pose estimation, LVLM video‑anomaly understanding, and hand‑object reconstruction, and discusses broader industry trends such as feed‑forward generation and large‑scale pre‑training showcased by leading AI companies.

3D human pose estimationAI researchLVLM
0 likes · 5 min read
Insights from NeurIPS 2025: Modeling Distributions and Venturing Beyond Them
Network Intelligence Research Center (NIRC)
Network Intelligence Research Center (NIRC)
Jan 7, 2026 · Operations

From Minutes to Milliseconds: Atlas Architecture Solves Verification Bottlenecks

The paper presents Atlas, a native three‑layer distributed verification system that replaces centralized tools with switch, region, and center adapters, achieving sub‑20 ms validation for thousands of nodes and up to 1500× speedup over EPVerifier, while supporting incremental updates and preserving scalability.

ATLASDistributed ArchitecturePerformance
0 likes · 7 min read
From Minutes to Milliseconds: Atlas Architecture Solves Verification Bottlenecks
Network Intelligence Research Center (NIRC)
Network Intelligence Research Center (NIRC)
Jan 4, 2026 · Artificial Intelligence

How UniCodebook’s Unified 2D‑3D Discrete Priors Boost Noise‑Robust, Calibration‑Free 3D Human Pose Estimation

UniCodebook introduces a unified 2D‑3D discrete prior that combines continuous and discrete representations, enabling calibration‑free multiview 3D human pose estimation with superior noise robustness and higher accuracy, as demonstrated by state‑of‑the‑art results on Human3.6M and MPI‑INF‑3DHP.

3D pose estimationNeurIPS 2025Transformer
0 likes · 8 min read
How UniCodebook’s Unified 2D‑3D Discrete Priors Boost Noise‑Robust, Calibration‑Free 3D Human Pose Estimation