OpenAI

Latest AI news, models and releases from OpenAI. ['Atlas', 'autonomous agent built on OpenAI models', 'autonomous model', 'ChatGPT', 'ChatGPT Codex', 'ChatGPT Enterprise', 'ChatGPT-Live', 'ChatGPT-User', 'ChatGPT Work', 'Claude Fable 5', 'CLIP', 'Codex', 'Codex CLI', 'Codex Micro', 'Codex Security CLI', 'DALL-E', 'DALL-E 2', 'DALL-E 3', 'DALL·E 3', 'experimental prototype', 'GPT', 'GPT-2', 'GPT2', 'GPT-3', 'GPT 3.5', 'GPT-3.5', 'gpt-3.5-turbo', 'GPT-3.5-turbo', 'GPT-3.5 Turbo', 'GPT-3-XL', 'GPT-4', 'gpt-4.1', 'GPT-4.1', 'gpt-4.1-mini', 'GPT-4 (ChatGPT)', 'GPT4-o', 'gpt-4o', 'GPT-4o', 'GPT-4o-0806', 'GPT-4o mini']

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Agents 🇺🇸

Automatic Blame Attribution in LLM-Based Multi-Agent Systems: New Research

Researchers from Penn State University and Duke University, in collaboration with Google DeepMind, introduced the task of automatic fault attribution in LLM-based multi-agent systems. They developed the Who&When benchmark with 127 fault logs and three attribution methods (All-at-Once, Step-by-Step, Binary Search). Experiments showed that even the best models (GPT-4o, o1, DeepSeek R1) perform poorly: accuracy in identifying the responsible agent is about 53.5%, and the error step is only 14.2%.

Google/DeepMind

Google DeepMind

OpenAI

DeepSeek

Synced27.07 · 17:05

Research 🇺🇸

Beyond Standard LLMs: Linear Attention Hybrids, Text Diffusion, Code World Models, and Small Recursive Transformers

The article by Sebastian Raschka explores alternatives to standard autoregressive transformer LLMs, including linear attention hybrids (e.g., MiniMax-M1, Qwen3-Next, DeepSeek V3.2, Kimi Linear), text diffusion models, code world models, and small recursive transformers. It discusses the revival of linear attention mechanisms and challenges, such as MiniMax reverting to standard attention in its M2 model due to poor performance on reasoning tasks.

Moonshot AI

MiniMax

Alibaba/Qwen

DeepSeek

Google/DeepMind

Mistral