How the monitored publications were assessed

Discourse valence across every scored item: what the sampled sources published and how independent models read it. It is not a measure of public opinion, not a measure of the state of the world, and article volume is not agreement.

10 items read as favourable

• Designing an AI serving harness built to last
  +20 MIT Technology Review: AI 2026-10-22 single model
• Who Should We Listen to More? Welfare-Aware Preference Acquisition for Pluralistic Alignment
  +20 arXiv cs.LG (Machine Learning) 2026-10-15 single model
• Meet the Anthropic Engineer Keeping Its AI Agent Trustworthy
  +20 IEEE Spectrum: AI 2026-10-12 single model
• Community safety report and new protections for teens
  +31 OpenAI 9d ago low agreement · ±60
• OpenHamoye: A Multi-Scale Open Geospatial Intelligence Framework for Decision Support Across Nigeria
  +50 arXiv cs.CY (Computers and Society) 12d ago single model
• ClinGuard: a governance framework for reliable, transparent, and auditable clinical coordination
  +50 arXiv cs.CY (Computers and Society) 2026-10-23 single model
• The Privacy Paradox Is a Misnomer: Data Under Structural Uncertainty
  +50 arXiv cs.CY (Computers and Society) 2026-10-22 single model
• ValiAssist: Scaling LLM-generated Programming Feedback Validation by Demonstration
  +50 arXiv cs.CY (Computers and Society) 2026-10-21 single model
• SPEM: Structure-Aware Procedural Memory for Efficient Skill Use in LLM Agents
  +50 arXiv cs.AI 2026-10-21 single model
• L2G-SR: Learning to Guide LLM-based Symbolic Regression
  +50 arXiv cs.AI 2026-10-16 single model

Across the whole window

The two tables below describe every item in the last 30 days under evaluation. They are deliberately not narrowed by the reading filter (currently favourable), because a mean taken only over items already selected for their score would just restate the filter.

By topic

By source

A source's mean says how the items it published were read. It is not a rating of the source.