LLM

Multi-Turn Memory Collapse: Why LLM Agents Forget Mid-Conversation

LLM agents often perform well during the first few interactions but begin forgetting important details as conversations grow longer. This phenomenon—often called multi-turn memory collapse—is usually caused by context limitations, summarization strategies, retrieval failures, or prompt design

Jul 20, 2026 5m read 👁 1

Why Your Transformer Fine-Tune Degrades on the Original Task After Updating

Your transformer model performs well after fine-tuning on a new dataset, but suddenly becomes much worse at the task it originally mastered. This phenomenon, known as catastrophic forgetting, is one of the biggest challenges in continual learning. Learn why it happens and practical strategies

Jul 19, 2026 5m read 👁 1

Semantic Cache Misses: Why Identical Questions Bypass Your LLM Cache

Semantic caching can dramatically reduce LLM costs and response times, but many teams discover that seemingly identical questions still bypass the cache and trigger expensive model calls. Learn why semantic cache misses occur, how embedding similarity works, and how to build production-ready caching

Jul 14, 2026 5m read 👁 3

Attention Sink Tokens: Why the First Few Tokens Skew LLM Outputs

Large Language Models don't distribute attention evenly across a prompt. Researchers have discovered that certain early tokens can act as "attention sinks," attracting disproportionate attention and influencing outputs in unexpected ways. Learn how attention sink tokens work, why they affect model

Jun 30, 2026 5m read 👁 4