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 ...

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 ...

Abstract: Large language models (LLMs) are prominent for their superior ability in language understanding and generation. However, a notorious problem for LLM inference is low computational ...

ABSTRACT: The golden age of digital chips seems to be coming to an end. For decades, we have relied on making transistors smaller and increasing clock speeds to improve performance. However, when chip ...