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.

56 items read as adverse

• Why this month's Microsoft patch release is a doozy
  -64 Ars Technica: AI 2026-09-08 low agreement · ±60
• MoFlow: Multi-Objective Agentic Workflow Generation
  -63 arXiv cs.AI 2026-10-01 low agreement · ±110
• Representation Alignment from a Few Paired Data via Distilled Optimal Transport
  -60 arXiv cs.AI 2026-10-12 low agreement · ±120
• Anthropic boss Dario Amodei calls for AI development to slow down
  -60 BBC News: Technology 2026-09-12 low agreement · ±60
• Will AI scoop your science? Some researchers see a gloomy future
  -57 Nature 2026-10-08 low agreement · ±85
• AI’s mathematics blitz pushes science into strange new territory
  -57 The Economist: Science and Technology 2026-10-12 low agreement · ±100
• OpenAI claims to have solved hundreds of long-standing maths problems
  -57 The Economist: Science and Technology 2026-10-07 low agreement · ±100
• Autonomous AI Agents Systematically Diverge From Human Preferences in Proof Formalization
  -55 arXiv cs.AI 2d ago low agreement · ±110
• They fought against datacenters. Now they are running for local offices
  -53 The Guardian: Artificial Intelligence 2026-09-15 low agreement · ±100
• The Helpful Agent Problem: When Emergent Cooperation Among LLM Agents Bypasses Intended Workflows
  -51 arXiv cs.AI 13d ago moderate agreement · ±30
• Can AI’s leaders really put aside rivalry for the common good?
  -51 Financial Times: Artificial Intelligence 2026-09-14 low agreement · ±100
• Democratic governors elected on promises to stop data centre building
  -50 Financial Times: Artificial Intelligence 4d ago single model
• Seq@K: Evaluating Language Models Under Sequential Feedback
  -50 arXiv cs.AI 8d ago low agreement · ±100
• Lessons from Luck: Do LLM-Written Lessons Carry Chance into an Agent's Next Decision?
  -50 arXiv cs.LG (Machine Learning) 11d ago single model
• SimHack: Predicting the onset of detectable reward hacking before RL training
  -50 arXiv cs.LG (Machine Learning) 2026-10-22 single model
• Inside the long, bitter fight over one man’s quest to build the biggest AI datacenter in California
  -50 The Guardian: Artificial Intelligence 2026-10-17 single model
• Proof-based general-purpose computing with Magnetar
  -50 Microsoft Research 2026-10-15 low agreement · ±150
• Harness^2: Recursive Agent Harnessing for an Open World
  -50 arXiv cs.AI 2026-10-15 low agreement · ±150
• SpireBench: Evaluating Long-Horizon Strategic Play of Agentic Models with Slay the Spire
  -50 arXiv cs.AI 2026-10-12 single model
• Connecting AI agents to enterprise knowledge
  -50 MIT Technology Review: AI 2026-10-05 single model
• Verifiable, Articulable, and Tacit Components of Preference
  -50 arXiv cs.CY (Computers and Society) 2026-10-05 single model
• OpenAI-HuggingFace: A Reproduction & Lessons for Alignment Testing
  -50 arXiv cs.AI 2026-09-30 single model
• Inside Beijing’s Chipmaking Offensive, One Year On
  -50 CSET, Georgetown 2026-09-15 single model
• What’s at stake in AI’s trillion-dollar gamble
  -50 MIT Technology Review: AI 2026-09-15 single model
• OpenAI chief scientist warns no-one is prepared for consequences of AI
  -50 BBC News: Technology 2026-09-07 single model
• Open AI and the million-dollar maths problem - video
  -49 The Guardian: Artificial Intelligence 2026-09-17 low agreement · ±90
• Stronger artificial intelligence breakthroughs hasten their own exhaustion
  -48 arXiv cs.CY (Computers and Society) 2026-10-21 low agreement · ±100
• Governing at Machine Speed: An Adaptive Intelligence Architecture for Real-Time AI Policy Enforcement
  -41 arXiv cs.AI 2026-09-15 low agreement · ±120
• When Reality Outpaces Cognition: A New Scaling Principle for Reliable Intelligence
  -40 arXiv cs.AI 2026-10-16 low agreement · ±150
• Hidden Messages Between Diffusion Language Model Agents: Causal Evidence for Latent Communication
  -37 arXiv cs.AI 2026-10-23 low agreement · ±150
• Closed-World Resolution Against Tool Hallucination in LLM Agents
  -37 arXiv cs.AI 2026-09-18 low agreement · ±110
• Anthropic’s CEO calls for AI slowdown as Nvidia’s urges acceleration
  -35 The Guardian: Artificial Intelligence 2026-09-15 low agreement · ±110
• US and China agree to dialogue on AI ahead of Trump-Xi meeting
  -34 Financial Times: Artificial Intelligence 2026-09-21 low agreement · ±100
• A thousand fewer paths to root
  -34 OpenAI 4d ago low agreement · ±110
• Feature Elicitation for SAE Steering of Deceptive Behaviour in Small LLMs
  -33 arXiv cs.LG (Machine Learning) 2026-10-16 low agreement · ±80
• Why A.I. Agents Are Going Rogue
  -32 The New York Times: Technology 2026-10-07 low agreement · ±100
• The week that changed maths for ever – podcast
  -32 The Guardian: Artificial Intelligence 2026-09-17 low agreement · ±100
• Open-world AI: a Tale of Three Requirements
  -32 arXiv cs.AI 2026-10-16 low agreement · ±105
• Artificiality's First Exam: An LLM-Referenced C-test for Supraintelligence
  -31 arXiv cs.AI 2026-10-15 low agreement · ±175
• Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses
  -31 Microsoft Research 2026-10-07 low agreement · ±165
• Anthropic withheld latest AI model from UK testing agency
  -31 Financial Times: Artificial Intelligence 2026-09-09 high agreement · ±5
• AI companies want to become frontier scientific researchers
  -30 The Economist: Science and Technology 2026-10-14 low agreement · ±110
• When AI Reviews Train AI Reviewers: Scientific-Judgment Collapse and Mitigation
  -30 arXiv cs.LG (Machine Learning) 2026-09-21 single model
• Space as an Interventional Invariant: Cross-Modal Predictive Geometry for Stratified Cities and Em-Spaced Intelligence
  -29 arXiv cs.LG (Machine Learning) 2026-09-14 low agreement · ±120
• EPOCH: Reliable Discovery through Evidence-Governed Search
  -29 arXiv cs.AI 2026-10-07 low agreement · ±175
• Can a System-One LLM Perform Knowledge Tracing When Few or No Learners Are Logged?
  -26 arXiv cs.CY (Computers and Society) 2026-10-09 low agreement · ±175
• Australian backlash to datacentres is faux import from US, officials say: ‘We are not the United States’
  -26 The Guardian: Artificial Intelligence 2026-09-26 low agreement · ±165
• Zuckerberg says Meta has reached AI ‘frontier’ and rejects calls for slowdown
  -26 Financial Times: Artificial Intelligence 2026-10-12 low agreement · ±105
• Self-discovering RL in the Era of Experience: Is Learning History an Asset or a Burden?
  -25 arXiv cs.AI 2026-09-30 low agreement · ±165
• A latent dimension of Condorcet's jury theorem for multiple AI advisers
  -24 arXiv cs.CY (Computers and Society) 2026-09-15 low agreement · ±175
• When Scientific Cognition Is No Longer Scarce
  -23 arXiv cs.CY (Computers and Society) 2026-10-08 low agreement · ±108
• Artificial intelligence now beats some of the best human forecasters
  -23 The Economist: Science and Technology 2026-09-16 low agreement · ±115
• The Harness as the Only Mutable Surface: Compliance-Bounded Self-Evolution of LLM Agents in Credit Pipelines, with a Measured Admission Gate
  -23 arXiv cs.AI 2026-10-09 low agreement · ±120
• Keep It CALM: Analyzing the Limits of Global Unsafety in Text-to-Image Generation
  -22 arXiv cs.AI 2026-10-05 low agreement · ±120
• How to spend $25 billion on science: OpenAI’s staggeringly rich research charity gets under way
  -21 Nature 2026-09-30 low agreement · ±115
• AI leaders want to hit the brakes after years of reckless speed
  -21 Ars Technica: AI 2026-09-14 low agreement · ±140

Across the whole window

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