The saying “round pegs do not fit square holes” persists because it captures a deep engineering reality: inefficiency most often arises not from flawed components, but from misalignment between a ...

Abstract: On-device Large Language Model (LLM) inference enables private, personalized AI but faces memory constraints. Despite memory optimization efforts, scaling laws continue to increase model ...

At the start of 2025, I predicted the commoditization of large language models. As token prices collapsed and enterprises moved from experimentation to production, that prediction quickly became ...

When an enterprise LLM retrieves a product name, technical specification, or standard contract clause, it's using expensive GPU computation designed for complex reasoning — just to access static ...

According to @godofprompt on Twitter, Anthropic engineers have implemented a 'memory injection' technique that significantly enhances large language models (LLMs) used as coding assistants. By ...

NVIDIA introduces a novel approach to LLM memory using Test-Time Training (TTT-E2E), offering efficient long-context processing with reduced latency and loss, paving the way for future AI advancements ...

We introduce LEGOMem, a modular procedural memory framework for multi-agent large language model (LLM) systems in workflow automation. LEGOMem decomposes past task trajectories into reusable memory ...

We are working on models of memory to make factual knowledge in large language models both transparent and controllable. The goal is to enable high precision knowledge infusion at scale – with full ...

If we want to avoid making AI agents a huge new attack surface, we’ve got to treat agent memory the way we treat databases: with firewalls, audits, and access privileges. The pace at which large ...

In long conversations, chatbots generate large “conversation memories” (KV). KVzip selectively retains only the information useful for any future question, autonomously verifying and compressing its ...