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.

29 items read as mixed or uncertain

• ClawLoop: Self-Improving Agents via Harness-Native Training Loop
  -17 arXiv cs.AI 2026-10-15 low agreement · ±160
• Stochastic Dynamics of Generative AI: From Autoregressive Models to Interacting LLM Networks and Neural SDEs
  -16 arXiv cs.LG (Machine Learning) 2d ago low agreement · ±110
• KDASO: Knowledge-Data Dual-Driven Automated Skill Optimization for Liability Adjudication Tasks
  -15 arXiv cs.CY (Computers and Society) 2d ago low agreement · ±150
• The World Won't Wait: Evaluating and Teaching Agents Under a Live Clock
  -14 arXiv cs.AI 2026-10-16 low agreement · ±150
• AI’s transformation of mathematics is a turning point for science
  -12 The Economist: Science and Technology 2026-10-12 low agreement · ±150
• China’s extraordinary rise in science and how it will change the world
  -11 Nature 2026-10-14 low agreement · ±150
• DataPraxis: Scaling Analytics Tasks via Verified Workspace Evolution
  -10 arXiv cs.AI 2026-10-21 low agreement · ±150
• Navalia: Advancing Cowork Agents through Verifiable Task Synthesis and Long-Horizon Post-Training
  -9 arXiv cs.AI 2026-10-21 low agreement · ±150
• StoreBench: A Live-Commerce Environment for Evaluating and Training Autonomous Operator Agents
  -7 arXiv cs.AI 2026-10-09 low agreement · ±175
• Evaluating Model Capabilities After Benchmark Saturation
  -7 arXiv cs.AI 2026-10-16 low agreement · ±165
• Gemini 4 Argon: Built to solve your hardest problems
  -6 Google AI Blog 2026-10-13 low agreement · ±170
• Gemini 4 Carbon: Our next frontier model
  -5 Google DeepMind 11d ago low agreement · ±175
• The Trinity:Capability-Driven Recursive Self-Improvement for Multimodal Agents
  -4 arXiv cs.AI 11d ago low agreement · ±150
• Genie 4: A new generation of world simulation
  -3 Google DeepMind 12d ago low agreement · ±165
• Partnering with Hacktron to find vulnerabilities in OpenAI’s systems
  0 OpenAI 2d ago single model
• A Feasibility Condition for Confidence-Gated Tiered LLM Inference
  0 arXiv cs.AI 3d ago single model
• Don't Pick a Winner: Knowledge Conflicts Need a Prior, Not a Rule
  0 arXiv cs.AI 11d ago single model
• AOS-Bench: Can LLM Agents Manage Dynamic Workloads?
  0 arXiv cs.AI 12d ago single model
• Large Language Models for Agent-Based Modelling: Current and possible uses across the modelling cycle
  0 arXiv cs.CY (Computers and Society) 2026-10-21 single model
• When Planning Problems Become Hard for LLMs: Separating Structural Complexity from Semantic Interpretability
  0 arXiv cs.AI 2026-10-21 single model
• Bringing GPT-Rosalind to academic research labs
  0 OpenAI 2026-10-15 single model
• Amp puts AI agents to work beyond code with OpenAI models
  0 OpenAI 2026-10-14 single model
• Large Language Models, Machine-Assisted Cultural Analytics, and the Agentic Turn
  0 arXiv cs.CY (Computers and Society) 2026-10-12 single model
• Verification and Self-Improvement in Agentic AI: Foundations and Limits
  0 arXiv cs.AI 2026-10-09 single model
• HACHIMI-II: From Role-Play to Learner Behavior Modeling for Faithful Student Simulation
  +6 arXiv cs.CY (Computers and Society) 2026-10-15 low agreement · ±165
• Decision language models as affordable and rapid alternatives to LLM judges of clinical generative AI
  +6 arXiv cs.AI 2026-10-23 low agreement · ±160
• Synthesizing symbolic models of human and machine reasoning at scale
  +10 arXiv cs.AI 2d ago single model
• OpenFrontiers: A Collection of Open Research Problems and a Benchmark for Scientific Rediscovery
  +10 arXiv cs.AI 8d ago single model
• Objective-Conditioned Active Preference Learning for Multi-Objective Alignment
  +18 arXiv cs.LG (Machine Learning) 3d ago moderate agreement · ±45

Across the whole window

The two tables below describe every item in the last 30 days under scaling. 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.