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5.0 Anthropic CEO Amodei calls for slower AI development and shared safety rules — 11 src2.7 Agility Robotics unveils Digit 5 humanoid for warehouses and factories — 2 src2.5 Apple ships rebuilt Siri with Google Gemini, but not in the EU — 2 src2.2 Siri AI in macOS 27 Golden Gate: FAQ, Germany availability, privacy questions — 2 src1.8 Sam Altman says OpenAI will not go public in 2026, citing AI safety concerns — 5 src1.4 OpenAI contractors review real ChatGPT conversations to rate responses, report says — 2 src1.4 Anthropic data retention policy prompts firms to limit Claude use for sensitive work — 1 src1.4 VoiceCodeBench arXiv paper proposes benchmark for exact structured-token recovery in speech recognition — 1 src1.4 Arabic-Russian Parallel Corpus and LLM Benchmark for Scientific Text — 1 src1.4 Study Analyzes Self-Reported Limitations in NLP Research — 1 src5.0 Anthropic CEO Amodei calls for slower AI development and shared safety rules — 11 src2.7 Agility Robotics unveils Digit 5 humanoid for warehouses and factories — 2 src2.5 Apple ships rebuilt Siri with Google Gemini, but not in the EU — 2 src2.2 Siri AI in macOS 27 Golden Gate: FAQ, Germany availability, privacy questions — 2 src1.8 Sam Altman says OpenAI will not go public in 2026, citing AI safety concerns — 5 src1.4 OpenAI contractors review real ChatGPT conversations to rate responses, report says — 2 src1.4 Anthropic data retention policy prompts firms to limit Claude use for sensitive work — 1 src1.4 VoiceCodeBench arXiv paper proposes benchmark for exact structured-token recovery in speech recognition — 1 src1.4 Arabic-Russian Parallel Corpus and LLM Benchmark for Scientific Text — 1 src1.4 Study Analyzes Self-Reported Limitations in NLP Research — 1 src
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#survey

12 curated events
papersTODAY 04:00 UTC

arXiv Survey Reviews Self-Supervised Learning for Event Stream Data

A new arXiv paper surveys self-supervised approaches to modeling event stream data, the timestamped sequences generated by digital activity in areas such as healthcare, e-commerce, gaming, and finance. The authors argue for unified methods across these domains and outline remaining challenges and future directions. The work is positioned as a progress-and-prospects review rather than a new model or benchmark.

self-supervised/event-streams/survey/arxiv
papersTODAY 04:00 UTC

Survey Maps Cybersecurity Threats and Defenses for Agentic AI Systems

A new arXiv survey examines the security landscape around agentic AI, which combines reasoning loops, long-term memory, tool use, and multi-agent coordination. It catalogs attack surfaces and defense architectures specific to these autonomous systems, and outlines unresolved research gaps. The authors argue that conventional security models do not adequately cover goal-directed agents.

agents/safety/cybersecurity/survey
papersSEP 10 04:00 UTC

No Free Checker: A Survey of Verifiers for Robot Policies

A new survey paper catalogues methods that score robot behaviors, ranging from success detectors and reward models to runtime monitors. It examines how these verification approaches serve two roles: evaluating vision-language-action policies and providing training signals for them. The work appears on arXiv, cross-listed between the AI and machine learning categories.

robotics/survey/verification/evaluation
papersTODAY 04:00 UTC

Survey Reviews Deep Learning Architectures for Gravitational-Wave Denoising

A new arXiv survey examines deep learning methods for cleaning noise from gravitational-wave detector data, arguing that techniques must cope with the full range of spinning and precessing binary systems. The paper notes that matched filtering remains the established approach but comes with trade-offs that learned models aim to address. Reconstructed waveforms feed into parameter estimation, tests of general relativity and population studies.

gravitational-waves/deep-learning/signal-processing/survey
papersTODAY 04:00 UTC

Survey Maps Probabilistic Forecasting Methods for Time Series and Spatiotemporal Data

A new arXiv survey examines how probabilistic forecasting methods have developed across time series and spatiotemporal research. The authors argue the field has grown fragmented, with statistical modeling, machine learning, and deep generative approaches advancing largely in parallel. The paper aims to organize this landscape and connect the differing methodological traditions used for forecasting under uncertainty.

research/time-series/probabilistic-forecasting/survey
papersSEP 10 04:00 UTC

Survey paper reviews overparameterized machine learning and the bias-variance tradeoff

A new overview article on arXiv surveys the theory of overparameterized machine learning, in which models with far more parameters than training examples still achieve strong performance. The paper explains how such behavior conflicts with the classical bias-variance tradeoff and organizes recent theoretical work developed to explain it. It serves as a structured introduction for readers interested in the statistical foundations of modern deep learning.

deep-learning/theory/generalization/survey
papersSEP 10 04:00 UTC

Survey Reviews Inference-Efficiency Methods for Video and Audiovisual LLMs

A new survey on arXiv examines mechanisms for reducing inference costs in video large language models, which pair video representations with pretrained LLMs to generate responses from text prompts. The paper addresses why video understanding remains computationally expensive and organizes existing efficiency techniques across video and audiovisual tasks.

video/efficiency/inference/survey
papersSEP 12 04:00 UTC

Survey Examines Maritime Professionals' Attitudes Toward AI Decision Support

A new arXiv paper reports on a survey of maritime professionals regarding their perceptions of and trust in AI-supported decision-making tools and autonomous surface ships. The authors argue that how crews and operators view these systems will shape whether they can be integrated safely into real-world maritime operations. The work focuses on operator attitudes as a key factor in adoption rather than on technical performance alone.

maritime/trust/human-factors/survey