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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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#active-learning

8 curated events
papersTODAY 04:00 UTC

Active Learning with Bayesian Multi-Fidelity Laplace Neural Operators for Parametric PDEs

This arXiv paper proposes a surrogate modeling approach that combines Laplace neural operators with multi-fidelity Bayesian active learning for oscillatory parametric partial differential equations. High-fidelity simulation data is costly to generate, so the method uses Bayesian uncertainty estimates to decide which fidelity levels and parameter points to sample next. The authors frame the work around engineering uses such as design optimization and digital twins, where fast, repeated predictions are needed.

research/active-learning/pde/surrogate-models
papersTODAY 04:00 UTC

TypiCore: Hybrid Active Query Strategy for Class-Incremental Time Series Learning

A new arXiv paper introduces TypiCore, a method that combines active learning queries with class-incremental learning for time series data. The approach targets settings such as healthcare and manufacturing, where models must adapt to distribution shifts as new classes arrive over time. The work is posted as a cross-listing replacement on arXiv and has not yet undergone peer review.

time-series/continual-learning/active-learning/arxiv
papersSEP 10 04:00 UTC

Paper links active learning with lottery ticket sparsity for efficient network training

A newly posted arXiv paper explores combining active learning with the lottery ticket hypothesis, asking whether sparse subnetworks that match dense-model accuracy can be found while also reducing labeled data needs. The authors frame this as achieving both sparsity and sample efficiency within a single training process rather than running separate search stages. The work appears in both the machine learning and AI categories of arXiv.

sparsity/active-learning/efficiency/training
papersSEP 10 04:00 UTC

Paper introduces decision-focused active learning for scale-aware critical-materials recovery

A new study presents an active learning framework designed to connect laboratory-scale results with real decisions about which critical-materials recovery processes to scale up. The method accounts for product requirements, process costs, and scale effects, drawing on records from Pacific Northwest National Laboratory's critical-element recovery database.

active-learning/critical-materials/materials-recovery/scale-up
papersSEP 12 04:00 UTC

arXiv Paper Proposes Amortized Adaptive Design for CRISPR Screen Hit Discovery

A new arXiv preprint describes a "biology-in-the-loop" framework that chooses which perturbations to test next when experimental budgets are limited. The approach amortizes the cost of adaptive decision-making so that sequential selection can be applied efficiently to CRISPR screens. The authors frame the problem as sequential experimental design for biological discovery under constrained resources.

research/crispr/active-learning/biotech
papersSEP 12 04:00 UTC

arXiv Paper Proposes Active Test Selection for Timely Clinical Diagnosis

A new arXiv preprint argues that most machine learning approaches to clinical diagnosis assume fully observed, static datasets, which does not match how clinicians reason sequentially while weighing resource constraints. The authors propose a method that actively chooses which diagnostic test to order next, aiming to reach a diagnosis sooner with fewer tests. The work is presented as a replacement version of the paper (v5).

clinical-ai/active-learning/healthcare/research
papersSEP 11 04:00 UTC

Activation-Based Active Learning Tested for In-Context Example Selection

A new arXiv paper examines whether internal transformer activations can guide the selection of in-context examples for large language models. The authors note that earlier work applied active learning to in-context sample selection, but without drawing on recent findings about how activations encode information. The study reports mixed results and lessons from testing this activation-based approach.

active-learning/in-context-learning/llm/research