LIVE PULSE
3.9 Anthropic CEO Amodei calls for slower AI development and shared safety rules11 src2.1 Agility Robotics unveils Digit 5 humanoid for warehouses and factories2 src2.0 Apple ships rebuilt Siri with Google Gemini, but not in the EU2 src1.7 Siri AI in macOS 27 Golden Gate: FAQ, Germany availability, privacy questions2 src1.4 Sam Altman says OpenAI will not go public in 2026, citing AI safety concerns5 src1.1 OpenAI contractors review real ChatGPT conversations to rate responses, report says2 src1.1 Anthropic data retention policy prompts firms to limit Claude use for sensitive work1 src1.1 Study examines issue bias in LLMs used as writing assistants before Swedish 2026 election1 src1.1 Study Audits Misalignment in Multi-Modal World Models1 src1.1 Retrieval-Grounded Reasoning Approach Proposed for Universal Multimodal Embeddings1 src3.9 Anthropic CEO Amodei calls for slower AI development and shared safety rules11 src2.1 Agility Robotics unveils Digit 5 humanoid for warehouses and factories2 src2.0 Apple ships rebuilt Siri with Google Gemini, but not in the EU2 src1.7 Siri AI in macOS 27 Golden Gate: FAQ, Germany availability, privacy questions2 src1.4 Sam Altman says OpenAI will not go public in 2026, citing AI safety concerns5 src1.1 OpenAI contractors review real ChatGPT conversations to rate responses, report says2 src1.1 Anthropic data retention policy prompts firms to limit Claude use for sensitive work1 src1.1 Study examines issue bias in LLMs used as writing assistants before Swedish 2026 election1 src1.1 Study Audits Misalignment in Multi-Modal World Models1 src1.1 Retrieval-Grounded Reasoning Approach Proposed for Universal Multimodal Embeddings1 src
HEATPULSEAI MAGAZINES
FLIP · FOLLOW · SAVE

autoregressive-prediction

topic1 events
papersTODAY 04:00 UTC

Study Ties Data and Memory Scaling to a Single Predictive Spectrum

A new arXiv paper argues that the benefit a model gains from more data and the amount of learned memory it needs are both determined by one predictive-energy spectrum in a positive-entropy autoregressive retrieval source. In this framework, each coordinate's contribution is the product of its query probability and a per-coordinate term, linking the two resources analytically. The work offers a theoretical account of how data volume and memory capacity trade off in autoregressive prediction.