Daily deep-dives into the latest AI/ML papers from arXiv — explained with math, code, and real implementation insights. No fluff, just what matters.
Long-context LLM inference has a memory problem that compounds with scale. Every additional token grows the KV cache linearly — a single 32K-token forward pass through an 8B model can exhaust several
The Problem Nobody Had Solved Every production LLM system eventually confronts the same wall: you want to swap between models — route a cheap query to the 14B, an expensive one to the 32B — but the mo
The Problem Nobody Had Solved Test-time scaling is one of the most active frontiers in LLM research: spending more compute at inference to get better answers without touching model weights. Chain-of-T
You've spent 40 GPU-hours fine-tuning a model with GRPO. It works great — until your downstream pipeline changes, or the base model gets upgraded, and you're back to square one. Meanwhile, your engine
Your GPU runs out of memory at 128k tokens. Your RAG pipeline loses critical context in the middle. You've tried KV cache compression and watched accuracy drop. These aren't configuration problems — t
You've spent hours tuning your RLVR training run. GRPO converges too aggressively on hard problems. You switch to Dr. GRPO for stability, but then you read that DAPO gets better results by throwing aw
You switched to sparse attention. You followed the DeepSeek-V3.2 architecture. You even tuned your top-k carefully. Yet at 128K context, inference is still painfully slow. The culprit isn't where you
