Abstract: The design space for edge AI hardware supporting large language model (LLM) inference and continual learning is underexplored. We present 3D-CIMlet, a thermal-aware modeling and co-design ...

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 ...

For this week’s Ask An SEO, a reader asked: “Is there any difference between how AI systems handle JavaScript-rendered or interactively hidden content compared to traditional Google indexing? What ...

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 ...

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 ...