#algorithm-selection

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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

2 curated events

papersTODAY 04:00 UTC

Paper Identifies Deployment Gaps in Oracle Partition Scores for Algorithm Selection

A new arXiv paper argues that oracle-style benchmarks such as virtual best solvers and best-in-family summaries are often treated as achievable ceilings, even though they ignore the constraints a real selector faces. The author examines decomposed algorithm selection and shows how partition-level scores diverge from what an actual deployed system can deliver. The work calls for evaluation practices that reflect deployment conditions rather than oracle upper bounds.

research/benchmarks/algorithm-selection/arxiv

papersSEP 10 04:00 UTC

Dual-channel graph neural network picks the best solver for maximum clique instances

Researchers present a dual-channel graph neural architecture that predicts which exact solver will perform best on a given maximum clique problem instance. Since no single solver dominates across all types of graphs, the approach learns from graph characteristics to make per-instance algorithm choices. The paper appears on arXiv in both the AI and machine learning categories.

graph-neural-networks/algorithm-selection/combinatorial-optimization