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.
This line is the mean of every item scored on each day. It deliberately ignores the reading filter, because a daily mean of only the mixed or uncertain items would move with how many were filtered out rather than with the discourse.
56 items read as mixed or uncertain
- Generalized Agent Iteration: One Formal Framework for Iterative Policy Improvement and Recursive Self-Improvement
- ClawLoop: Self-Improving Agents via Harness-Native Training Loop
- Stochastic Dynamics of Generative AI: From Autoregressive Models to Interacting LLM Networks and Neural SDEs
- KDASO: Knowledge-Data Dual-Driven Automated Skill Optimization for Liability Adjudication Tasks
- OpenAI says it cracked 90-year-old maths problem in 88 hours
- The World Won't Wait: Evaluating and Teaching Agents Under a Live Clock
- ZGCM-1: A Fully Open and Extremely Efficient Foundation Model for Math and Agentic Search
- Stability and Diversity of Networked Self-Consuming Generative Ecosystems
- OpenAI announces ‘dots’ agent after scrapping launch of new AI model over safety concerns
- AI’s transformation of mathematics is a turning point for science
- China’s extraordinary rise in science and how it will change the world
- DataPraxis: Scaling Analytics Tasks via Verified Workspace Evolution
- Navalia: Advancing Cowork Agents through Verifiable Task Synthesis and Long-Horizon Post-Training
- ‘Immature playground boasting’: Mathematicians uneasy at OpenAI’s latest scalp
- StoreBench: A Live-Commerce Environment for Evaluating and Training Autonomous Operator Agents
- Evaluating Model Capabilities After Benchmark Saturation
- Gemini 4 Argon: Built to solve your hardest problems
- Gemini 4 Carbon: Our next frontier model
- The Trinity:Capability-Driven Recursive Self-Improvement for Multimodal Agents
- Genie 4: A new generation of world simulation
- Google announces Gemini 4 Argon AI model, but you can't use it yet
- Research with AI Agents: How Agentic Systems Are Changing Scientific Work
- Reimagining research papers as interactive and reliable AI agents
- Partnering with Hacktron to find vulnerabilities in OpenAI’s systems
- A Feasibility Condition for Confidence-Gated Tiered LLM Inference
- Don't Pick a Winner: Knowledge Conflicts Need a Prior, Not a Rule
- AOS-Bench: Can LLM Agents Manage Dynamic Workloads?
- Large Language Models for Agent-Based Modelling: Current and possible uses across the modelling cycle
- When Planning Problems Become Hard for LLMs: Separating Structural Complexity from Semantic Interpretability
- Bringing GPT-Rosalind to academic research labs
- Amp puts AI agents to work beyond code with OpenAI models
- Large Language Models, Machine-Assisted Cultural Analytics, and the Agentic Turn
- Verification and Self-Improvement in Agentic AI: Foundations and Limits
- OpenAI’s release of mathematical findings draws concerns from experts
- Principles that Guide, Actions that Inform: Agent Evolution via Knowledge Abstraction
- Towards safety cases for frontier AI training
- Bringing AI to Autonomous Systems -- From Cognition to Collective Intelligence
- Anthropic Pursues IPO Despite Its A.I. Safety Warnings
- What Do Current Systematic Generalization Tasks Miss? A Reasoning-Centered Analysis
- AI and Human Approaches to Mathematical Problem Solving
- Is There Anything Google’s Fruit Fly Brain Can’t Do?
- OpenAI Says It Has Cracked One of Math’s ‘Millennium Problems’
- Progressive Skill Discovery as Access Control for Tool-Using LLM Agents: Structural Governance through Role-Scoped Capability Delivery
- Offloaded inference for real-world physical AI robotics
- Endogenous Exploration in Reinforcement Learning with Intrinsic Curiosity
- Algorithmic Information Dynamics of Learning: A Certified, Differentiable Complexity Controller for Grokking
- HACHIMI-II: From Role-Play to Learner Behavior Modeling for Faithful Student Simulation
- Decision language models as affordable and rapid alternatives to LLM judges of clinical generative AI
- Why Read a Research Paper When You Can Turn It Into an AI Agent?
- AegisFlow: A Multi-Agent Agentic AI Framework for Autonomous Remediation and Self-Healing in Fragile Data Ecosystems
- Synthesizing symbolic models of human and machine reasoning at scale
- OpenFrontiers: A Collection of Open Research Problems and a Benchmark for Scientific Rediscovery
- The Moral Check: Strategic AI Governance for the Pacing Problem
- Bounded Autonomy and Verifiable Safety for Agentic AI Enabled Automation
- Unsnarling the Red Tape: Computational Infrastructure for Regulatory Systems
- Objective-Conditioned Active Preference Learning for Multi-Objective Alignment
Across the whole window
The two tables below describe every item in the last 365 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
| Topic | Branch | Items | Mean | Position |
|---|---|---|---|---|
| Existential and catastrophic risk | risk | 568 | -42 | |
| Governance and regulation | structural | 459 | -31 | |
| Concentration of power | risk | 451 | -36 | |
| Loss of control and alignment | risk | 337 | -49 | |
| Human agency and epistemics | risk | 166 | -26 | |
| Cyber capability | risk | 165 | -44 | |
| Evaluation and measurement | structural | 159 | -19 | |
| Scaling and architecture | structural | 137 | -16 | |
| AGI and timelines | structural | 132 | -29 | |
| Synthetic media and manipulation | risk | 104 | -44 | |
| Employment and displacement | risk | 62 | -31 | |
| Medicine and health | benefit | 43 | -14 | |
| Scientific acceleration | benefit | 42 | -4 | |
| Education and access to expertise | benefit | 29 | -17 | |
| Biological and chemical uplift | risk | 26 | -25 | |
| Climate and energy | structural | 20 | -33 | |
| Compute and infrastructure | structural | 18 | -8 | |
| Productivity and growth | benefit | 6 | -3 | |
| Abundance and material wellbeing | benefit | 5 | -1 | |
| Hype and disillusionment | structural | 5 | -45 |
By source
A source's mean says how the items it published were read. It is not a rating of the source.
| Source | Kind | Tier | Items | Mean |
|---|---|---|---|---|
| The Guardian: Artificial Intelligence | news | 1 | 241 | -52 |
| arXiv cs.CY (Computers and Society) | academic | 1 | 217 | -24 |
| Financial Times: Artificial Intelligence | news | 1 | 112 | -40 |
| arXiv cs.AI | academic | 1 | 99 | -15 |
| The New York Times: Technology | news | 1 | 60 | -36 |
| Ars Technica: AI | news | 1 | 59 | -51 |
| MIT Technology Review: AI | news | 1 | 34 | -42 |
| Nature | academic | 1 | 31 | -25 |
| BBC News: Technology | news | 1 | 29 | -35 |
| OpenAI | lab | 1 | 24 | -8 |
| arXiv cs.LG (Machine Learning) | academic | 1 | 24 | -16 |
| IEEE Spectrum: AI | news | 1 | 19 | -19 |
| The Economist: Science and Technology | news | 1 | 10 | -37 |
| CSET, Georgetown | government | 1 | 9 | -37 |
| Microsoft Research | lab | 1 | 8 | 0 |
| Google DeepMind | lab | 1 | 6 | -4 |
| Google AI Blog | lab | 1 | 2 | -9 |