agentic evaluation

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4.9 Anthropic CEO Amodei calls for slower AI development and shared safety rules — 11 src2.6 Agility Robotics unveils Digit 5 humanoid for warehouses and factories — 2 src2.4 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 src1.7 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 src1.3 Anthropic data retention policy prompts firms to limit Claude use for sensitive work — 1 src1.3 CoMem Paper Proposes Shared and Individual Memory Design for LLM Multi-Agent Systems — 1 src1.3 Paper proposes evolving context parameterization for large language models — 1 src1.3 Fine-Tuning Vision-Language Models with Listener Gaze for Referring Expressions — 1 src4.9 Anthropic CEO Amodei calls for slower AI development and shared safety rules — 11 src2.6 Agility Robotics unveils Digit 5 humanoid for warehouses and factories — 2 src2.4 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 src1.7 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 src1.3 Anthropic data retention policy prompts firms to limit Claude use for sensitive work — 1 src1.3 CoMem Paper Proposes Shared and Individual Memory Design for LLM Multi-Agent Systems — 1 src1.3 Paper proposes evolving context parameterization for large language models — 1 src1.3 Fine-Tuning Vision-Language Models with Listener Gaze for Referring Expressions — 1 src

topic2 events

papersSEP 12 04:00 UTC

Agentic Framework Proposed to Evaluate AI-Generated Scientific Code in PETSc

A new arXiv paper argues that existing benchmarks for LLM-generated scientific code rely too heavily on functional correctness or task completion, which is inadequate for software built on production HPC libraries. The authors introduce an agentic evaluation framework that assesses AI-generated code in the PETSc numerical library across a broader set of criteria. The approach aims to give researchers a more thorough way to judge whether generated scientific software is fit for real-world use.

agents/benchmarks/hpc/code-generation

papersSEP 10 04:00 UTC

Harbor Adapters and Harbor-Index: New Infrastructure for Large-Scale Agentic Evaluation

A new paper introduces Harbor Adapters, an evaluation infrastructure designed to simplify running AI agents across agentic benchmarks that normally demand complex environments and custom integrations. Alongside it, the authors release Harbor-Index, a curated meta-dataset intended to support large-scale, standardized agent evaluation.

agents/evaluation/benchmarks