AI agents are a risky business. Even when stuck inside the chatbox window, LLMs will make mistakes and behave badly. Once ...
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: Memory safety violations in low-level code, written in languages like C, continues to remain one of the major sources of software vulnerabilities. One method of removing such violations by ...
Abstract: Large language models (LLMs) face significant deployment challenges due to their substantial memory and computational demands. While low-precision quantization offers a promising solution, ...
OntoMem is built on the concept of Ontology Memory—structured, coherent knowledge representation for AI systems. Give your AI agent a "coherent" memory, not just "fragmented" retrieval. Traditional ...
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 ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results