May 12, 2026

Efficient Model Scaling: The Correlation Between Diffusion Training and Knowledge Distillation

The current trend in AI modeling is shifting from simply increasing parameter counts to focusing on how to maintain high performance with less data while operating models efficiently. Particularly, as Large Language Mode

AIDeep LearningKnowledge DistillationDiffusion Model+1

May 5, 2026

The Next-Gen Engine for LLMs, Ring Attention: A Technical Breakthrough in Conquering Long Contexts

Recent advancements in LLMs have seen context windows explode—from GPT-4 Turbo (128K) to Gemini 1.5 Pro (1M+)—making long-context processing a core competitive advantage [S2439]. However, amidst this technological surge,

LLMRing AttentionKV CacheLong Context+1

May 5, 2026

The Paradox of Knowledge Distillation: Why We Refine Models to Perfect Intelligence

Today, while AI technology has achieved phenomenal progress through the emergence of Large Language Models (LLMs) like GPT and Claude, it simultaneously faces practical challenges regarding immense computational resource

Knowledge DistillationDeep LearningModel LightweightingArtificial Intelligence+1

May 5, 2026

Speculative Decoding in Practice: Why Models Propose 'Hypotheses' and Verify Themselves

As LLM capabilities advance, models are becoming increasingly intelligent; however, when applying them to real-world services, we constantly encounter the physical limitation of "speed." Since LLMs generate text sequenti

Speculative DecodingLLMDeep LearningInference Optimization+1

May 4, 2026

SwiftKV: Understanding the Principles of Next-Generation KV Cache Compression for Maximizing LLM Inference Efficiency

At their core, Large Language Models (LLMs) are auto-regressive models that predict the next token based on preceding ones. This inherent characteristic leads to a structural problem: every time a new token is generated,

LLMKV CacheSwiftKVMemory Optimization+2