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.

9 items read as mixed or uncertain

• DataPraxis: Scaling Analytics Tasks via Verified Workspace Evolution
  -10 arXiv cs.AI 2026-10-21 low agreement · ±150
• A Feasibility Condition for Confidence-Gated Tiered LLM Inference
  0 arXiv cs.AI 3d ago single model
• AOS-Bench: Can LLM Agents Manage Dynamic Workloads?
  0 arXiv cs.AI 12d ago single model
• When Planning Problems Become Hard for LLMs: Separating Structural Complexity from Semantic Interpretability
  0 arXiv cs.AI 2026-10-21 single model
• From Efficiency to Sufficiency: The Cost-Aware Measurement Design Framework for Automated Essay Scoring
  0 arXiv cs.CY (Computers and Society) 2026-10-15 single model
• Forecasting space weather risks on power grids
  0 Microsoft Research 2026-09-30 low agreement · ±175
• Is There Anything Google’s Fruit Fly Brain Can’t Do?
  0 The New York Times: Technology 2026-09-15 single model
• Offloaded inference for real-world physical AI robotics
  +2 Microsoft Research 2026-09-23 low agreement · ±150
• Algorithmic Information Dynamics of Learning: A Certified, Differentiable Complexity Controller for Grokking
  +4 arXiv cs.LG (Machine Learning) 2026-09-15 high agreement · ±10

Across the whole window

The two tables below describe every item in the last 180 days under compute. They are deliberately not narrowed by the reading filter (currently mixed or uncertain), 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.