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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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#reproducibility

5 curated events
papersTODAY 04:00 UTC

Study Analyzes 160,000 Training Runs to Improve Offline Policy Learning Baselines

A new arXiv paper examines how reporting choices, hyperparameter tuning, and dataset characteristics affect offline policy learning results. Drawing on roughly 160,000 training runs, the authors argue that reliable progress requires careful reporting, well-tuned baselines, and evaluation across varied conditions. The work offers practical guidance for making policy-learning benchmarks more reproducible and comparable.

reinforcement-learning/benchmarks/reproducibility/hyperparameters
papersTODAY 04:00 UTC

GzDRL: Single-process deep reinforcement learning framework for Gazebo

Researchers introduce GzDRL, a reinforcement learning framework that runs Gazebo robotics simulations in a single process rather than relying on conventional middleware-based bridges. The authors say this design targets long-standing obstacles to reproducible and scalable robotics experimentation. The work is posted as an arXiv preprint (2609.13243).

robotics/reinforcement-learning/simulation/reproducibility
papersTODAY 04:00 UTC

arXiv paper proposes OpenAI4S, a session-based framework for AI co-scientists

A new arXiv preprint introduces OpenAI4S, a system that frames computational research by AI co-scientists as sessions in which code serves as the action taken at each step. The authors argue that long-running studies require persistent computational state and provenance so that workflows remain inspectable, resumable and reproducible.

agents/research/reproducibility/arxiv
papersSEP 10 04:00 UTC

IdeaAMBIG Benchmark Targets Implementation-Critical Gaps in Research-Idea Specifications

Researchers introduce IdeaAMBIG, a benchmark that assesses how well written research-idea specifications support faithful implementation of the proposed methods. The work targets details that are critical for turning a method into working code but are often left implicit or ambiguous in idea descriptions. It highlights the gap between concepts that appear novel and plausible on paper and methods that can actually be reproduced as specified.

benchmark/reproducibility/research/nlp
papersSEP 10 04:00 UTC

Researchers Propose Discovery Certification Protocol for Auditing AI Research Agents

An arXiv paper argues that benchmark scores by themselves are not sufficient evidence that AI research agents have made genuine scientific discoveries. The authors introduce the Discovery Certification Protocol, which converts an agent's claimed results into executable recovery and feedback tests that can be independently run. The first of its gates checks that reported results can actually be reproduced from the recorded evidence.

agents/evaluation/reproducibility/research