OpenAI Releases GPT-5.5, a Fully Retrained Agentic Model That Scores 82.7% on Terminal-Bench 2.0 and 84.9% on GDPval ...

Retrieval is where most RAG systems quietly break. Traditional pipelines rely on vector similarity—embedding queries and document chunks into the same space and fetching the “closest” matches. But ...

For years, the computer vision community has operated on two separate tracks: generative models (which produce images) and discriminative models (which understand them). The assumption was ...

There is a quiet failure mode that lives at the center of every AI-assisted coding workflow. You ask Claude Code, Cursor, or Windsurf to modify a function. The agent does it confidently, cleanly, and ...

Xiaomi MiMo team publicly released two new models: MiMo-V2.5-Pro and MiMo-V2.5. The benchmarks, combined with some genuinely striking real-world task demos, make a compelling case that open agentic AI ...

Meta Reality Labs releases a new foundation model family for human-centric vision that pushes pose estimation, segmentation, and 3D geometry to new state-of-the-art levels — all from a single backbone ...

Most AI agents today have a fundamental amnesia problem. Deploy one to browse the web, resolve GitHub issues, or navigate a shopping platform, and it approaches every single task as if it has never ...

Moonshot AI, the Chinese AI lab behind the Kimi assistant, today open-sourced Kimi K2.6 — a native multimodal agentic model that pushes the boundaries of what an AI system can do when left to run ...

Anthropic has never published a technical paper on Claude Mythos. That has not stopped the research community from theorizing. A new open-source project called OpenMythos, released on GitHub by Kye ...

Tabular data—structured information stored in rows and columns—is at the heart of most real-world machine learning problems, from healthcare records to financial transactions. Over the years, models ...

The dominant recipe for building better language models has not changed much since the Chinchilla era: spend more FLOPs, add more parameters, train on more tokens. But as inference deployments consume ...