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3.9 Anthropic CEO Amodei calls for slower AI development and shared safety rules — 11 src2.1 Agility Robotics unveils Digit 5 humanoid for warehouses and factories — 2 src2.0 Apple ships rebuilt Siri with Google Gemini, but not in the EU — 2 src1.7 Siri AI in macOS 27 Golden Gate: FAQ, Germany availability, privacy questions — 2 src1.4 Sam Altman says OpenAI will not go public in 2026, citing AI safety concerns — 5 src1.1 OpenAI contractors review real ChatGPT conversations to rate responses, report says — 2 src1.1 Anthropic data retention policy prompts firms to limit Claude use for sensitive work — 1 src1.1 Study examines issue bias in LLMs used as writing assistants before Swedish 2026 election — 1 src1.1 Study Audits Misalignment in Multi-Modal World Models — 1 src1.1 Retrieval-Grounded Reasoning Approach Proposed for Universal Multimodal Embeddings — 1 src3.9 Anthropic CEO Amodei calls for slower AI development and shared safety rules — 11 src2.1 Agility Robotics unveils Digit 5 humanoid for warehouses and factories — 2 src2.0 Apple ships rebuilt Siri with Google Gemini, but not in the EU — 2 src1.7 Siri AI in macOS 27 Golden Gate: FAQ, Germany availability, privacy questions — 2 src1.4 Sam Altman says OpenAI will not go public in 2026, citing AI safety concerns — 5 src1.1 OpenAI contractors review real ChatGPT conversations to rate responses, report says — 2 src1.1 Anthropic data retention policy prompts firms to limit Claude use for sensitive work — 1 src1.1 Study examines issue bias in LLMs used as writing assistants before Swedish 2026 election — 1 src1.1 Study Audits Misalignment in Multi-Modal World Models — 1 src1.1 Retrieval-Grounded Reasoning Approach Proposed for Universal Multimodal Embeddings — 1 src
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internet-of-things

topic4 events
papersTODAY 04:00 UTC

Sylvas: Learning-Value-Based Device Scheduling for Federated Continual Learning

A new arXiv paper introduces Sylvas, a scheduling method for federated continual learning that selects which devices contribute updates based on their estimated learning value. The approach targets distributed, non-stationary data streams in Internet of Things settings such as intelligent transportation and industrial monitoring. The work appears under both cs.AI (cross) and cs.LG (new) listings as arXiv:2609.15763v1.

federated-learning/continual-learning/device-scheduling/iot
papersTODAY 04:00 UTC

LLM-Assisted Multi-Agent RL Framework Coordinates EV Charging, Stations and Grid

A new arXiv paper proposes combining large language models with multi-agent reinforcement learning to jointly optimize electric vehicle charging scheduling in public charging systems. The approach targets three competing goals at once: driver charging satisfaction, charging station profitability, and stability of the smart grid. It is positioned as a unified optimization method for connected EV infrastructure in IoT settings.

agents/reinforcement-learning/energy/llm
papersSEP 11 04:00 UTC

BiHDTrans: Binary Hyperdimensional Transformer for Edge Time Series Classification

Researchers propose BiHDTrans, a transformer variant that uses binary hyperdimensional computing to classify multivariate time series from IoT sensors. The design targets resource-constrained edge devices, where large data volumes and limited compute make standard models impractical. It is presented as an arXiv preprint focused on balancing efficiency with classification accuracy.

research/edge-computing/time-series/efficiency
papersSEP 10 04:00 UTC

LLMs combine with deep reinforcement learning for IoT-edge-cloud resource management

A new arXiv paper surveys how large language models can support deep reinforcement learning in managing resources across IoT, edge, and cloud layers. The work focuses on continuous, context-aware decision-making in environments where constraints shift constantly. It positions LLMs as a complement to established DRL techniques for adaptive computing across the computing continuum.

llms/reinforcement-learning/edge-computing/iot