#optimus-v3

#optimus-v3

PolarQuant is doing most of the compression, but the second step cleans up the rough spots. Google proposes smoothing that out with a technique called Quantized Johnson-Lindenstrauss (QJL).

Legacy software, often described as 'big balls of mud,' has accumulated over decades, becoming difficult to maintain and understand. These systems rely on extensive teams to function, despite their outdated technology.

The model's other capabilities, including support for multimodal inputs, multiple reasoning modes, and parallel sub-agents for complex queries, could help enterprises build faster, task-focused AI for customer support, automation, and internal copilots without relying on heavier models.

Meta's Muse Spark is the company's first AI model since the social media giant's previous initiatives, and analysts are optimistic about its potential.

For every project that needs guardrails, there's another one where they just get in the way. Some projects demand an LLM that returns the complete, unvarnished truth. For these situations, developers are creating unfettered LLMs that can interact without reservation. Some of these solutions are based on entirely new models while others remove or reduce the guardrails built into popular open source LLMs.

OpenAI's GPT-5.2 Pro does better at solving sophisticated math problems than older versions of the company's top large language model, according to a new study by Epoch AI, a non-profit research institute.

A major difference between LLMs and LTMs is the type of data they're able to synthesize and use. LLMs use unstructured data-think text, social media posts, emails, etc. LTMs, on the other hand, can extract information or insights from structured data, which could be contained in tables, for instance. Since many enterprises rely on structured data, often contained in spreadsheets, to run their operations, LTMs could have an immediate use case for many organizations.