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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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Latest from Network Intelligence Research Center (NIRC)

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Network Intelligence Research Center (NIRC)

Apr 16, 2025 · Industry Insights

Our EuroSys'25 Experience: Presenting Atlas and Exploring Cutting‑Edge System Research

The article recounts the authors' participation in EuroSys'25 in Rotterdam, detailing the conference schedule, their presentation of the Atlas network verification paper, technical insights into distributed verification, interactions with peers, and memorable social and cultural experiences during the five‑day event.

ATLASEuroSysconference report

0 likes · 7 min read

Our EuroSys'25 Experience: Presenting Atlas and Exploring Cutting‑Edge System Research

Network Intelligence Research Center (NIRC)

Apr 14, 2025 · Artificial Intelligence

MCP Explained: Current Landscape and Future Prospects

The article analyzes the Model Context Protocol (MCP) as an emerging open standard that unifies how applications provide context to large language models, reviews its rapid ecosystem growth, highlights security and performance challenges, and discusses future directions such as vertical small‑model opportunities and broader protocol integrations.

AI interoperabilityMCPModel Context Protocol

0 likes · 9 min read

MCP Explained: Current Landscape and Future Prospects

Network Intelligence Research Center (NIRC)

Apr 9, 2025 · Artificial Intelligence

Why Scaling Laws Fail for Video MLLMs: Uncovering the Temporal Hacking Problem

The article analyzes the anti‑scaling phenomenon in video large‑language models, identifies a “temporal hacking” shortcut where models focus on a few key frames, formalizes it via reward‑hacking theory, introduces the Temporal Perplexity (TPL) metric, and proposes an Unhackable Temporal Rewarding (UTR) framework to mitigate the issue.

Scaling LawTemporal PerplexityUTR

0 likes · 14 min read

Why Scaling Laws Fail for Video MLLMs: Uncovering the Temporal Hacking Problem

Network Intelligence Research Center (NIRC)

Apr 7, 2025 · Artificial Intelligence

Getting Started with Hugging Face TRL: Fine‑tune LLaVA using DPO

This guide introduces Hugging Face's TRL library, explains how to install it alongside Transformers, and walks through modifying LLaVA's trainer, dataset, and data collator to apply the DPO reinforcement‑learning algorithm for multimodal model fine‑tuning.

DPOHugging FaceLLaVA

0 likes · 4 min read

Getting Started with Hugging Face TRL: Fine‑tune LLaVA using DPO

Network Intelligence Research Center (NIRC)

Mar 26, 2025 · Artificial Intelligence

Enable Traditional LLMs to Use DeepSeek’s Multi‑Head Latent Attention Without Retraining

The paper introduces MHA2MLA, a data‑efficient fine‑tuning framework that converts pre‑trained multi‑head attention LLMs to DeepSeek’s Multi‑Head Latent Attention architecture, achieving up to 92% KV‑cache compression with less than 0.5% performance loss on long‑context tasks.

LLMLow-Rank ApproximationMulti-Head Attention

0 likes · 8 min read

Enable Traditional LLMs to Use DeepSeek’s Multi‑Head Latent Attention Without Retraining

Network Intelligence Research Center (NIRC)

Mar 19, 2025 · Game Development

Quickly Build a MetaXR Interaction Lab in Unity

This guide walks through setting up Meta XR SDK in Unity, using Building Blocks to add camera rigs, hand tracking and passthrough, binding interaction events, accessing hand‑tracking data via OVRSkeleton/OVRHand, and integrating ONNX machine‑learning models for XR experiments.

BuildingBlocksHandTrackingMetaXR

0 likes · 7 min read

Quickly Build a MetaXR Interaction Lab in Unity

Network Intelligence Research Center (NIRC)

Mar 12, 2025 · Artificial Intelligence

How Sparse Autoencoders Uncover Monosemantic Features in Large Language Models

The article reviews the paper ‘Towards Monosemanticity: Decomposing Language Models With Dictionary Learning’, showing how Anthropic’s sparse autoencoders extract interpretable, monosemantic concepts from transformer layers, enable controlled generation, and reveal trade‑offs such as data‑intensive training and potential performance impacts.

Dictionary LearningFeature ControlLLM Interpretability

0 likes · 9 min read

How Sparse Autoencoders Uncover Monosemantic Features in Large Language Models

Network Intelligence Research Center (NIRC)

Mar 10, 2025 · Artificial Intelligence

Revisiting Knowledge Distillation for Autoregressive Language Models

The article analyzes why larger teacher models can hurt student performance in autoregressive language model distillation, reveals that different tokens require distinct teaching modes, proposes an Adaptive Token‑wise Knowledge Distillation (ATKD) method, and shows through extensive experiments that ATKD consistently improves accuracy by about 3 % and enhances generalization across model sizes.

Knowledge Distillationadaptive teachingautoregressive language models

0 likes · 9 min read

Revisiting Knowledge Distillation for Autoregressive Language Models

Network Intelligence Research Center (NIRC)

Jan 9, 2025 · User Experience Design

Introducing a Bare-Hand VR Whiteboard for Natural Handwriting

The article presents a VR bare‑hand whiteboard system that replaces controllers with natural hand gestures, detailing its evolution, technical challenges, error‑filtering and smoothing algorithms, multi‑board synchronization, required Unity SDKs, and its validation through a CSCW research paper and user studies.

UnityVRbare-hand interaction

0 likes · 14 min read

Introducing a Bare-Hand VR Whiteboard for Natural Handwriting

Network Intelligence Research Center (NIRC)

Oct 8, 2024 · Artificial Intelligence

Two NIRC Papers Accepted at NeurIPS 2024: FM-Delta Compression and GLAFF Forecasting

The Beijing University of Posts and Telecommunications' Network Intelligent Research Center (NIRC) had two papers accepted to NeurIPS 2024, presenting FM-Delta, a lossless compression technique that halves storage and cuts cloud costs by over 40%, and GLAFF, a global‑local fusion framework that markedly improves the robustness of time‑series forecasting across multiple domains.

AI ResearchFM-DeltaGLAFF

0 likes · 8 min read

Two NIRC Papers Accepted at NeurIPS 2024: FM-Delta Compression and GLAFF Forecasting