Discourse monitor
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.
Mean discourse valence per day · scaling · last 30 days
Discourse valence, window mean
-11
Mixed or uncertain
65 items · range -74 to +50
-100
-50
0
+50
+100
-11
Adverse readingFavourable reading
65 items scored in this window
- AI godfather Geoffrey Hinton: ‘We’re creating alien beings. I’m willing to be selfish and choose humans’
- Water-Energy-Data nexus reveals sustainability gaps and priorities for AI infrastructure
- Representation Alignment from a Few Paired Data via Distilled Optimal Transport
- AI’s mathematics blitz pushes science into strange new territory
- Autonomous AI Agents Systematically Diverge From Human Preferences in Proof Formalization
- The Helpful Agent Problem: When Emergent Cooperation Among LLM Agents Bypasses Intended Workflows
- Democratic governors elected on promises to stop data centre building
- Seq@K: Evaluating Language Models Under Sequential Feedback
- Lessons from Luck: Do LLM-Written Lessons Carry Chance into an Agent's Next Decision?
- SimHack: Predicting the onset of detectable reward hacking before RL training
- Inside the long, bitter fight over one man’s quest to build the biggest AI datacenter in California
- Proof-based general-purpose computing with Magnetar
- Harness^2: Recursive Agent Harnessing for an Open World
- SpireBench: Evaluating Long-Horizon Strategic Play of Agentic Models with Slay the Spire
- Stronger artificial intelligence breakthroughs hasten their own exhaustion
- When Reality Outpaces Cognition: A New Scaling Principle for Reliable Intelligence
- Hidden Messages Between Diffusion Language Model Agents: Causal Evidence for Latent Communication
- A thousand fewer paths to root
- Feature Elicitation for SAE Steering of Deceptive Behaviour in Small LLMs
- Open-world AI: a Tale of Three Requirements
- Artificiality's First Exam: An LLM-Referenced C-test for Supraintelligence
- AI companies want to become frontier scientific researchers
- Can a System-One LLM Perform Knowledge Tracing When Few or No Learners Are Logged?
- Zuckerberg says Meta has reached AI ‘frontier’ and rejects calls for slowdown
- The Harness as the Only Mutable Surface: Compliance-Bounded Self-Evolution of LLM Agents in Credit Pipelines, with a Measured Admission Gate
- 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
- The World Won't Wait: Evaluating and Teaching Agents Under a Live Clock
- 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
- 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
- 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
- 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
- Synthesizing symbolic models of human and machine reasoning at scale
- OpenFrontiers: A Collection of Open Research Problems and a Benchmark for Scientific Rediscovery
- Objective-Conditioned Active Preference Learning for Multi-Objective Alignment
- Designing an AI serving harness built to last
- EvoBelief: Falsifiable World Models for Self-Improving LLM Agents
- Formally verifying agent-written full-stack web apps with PDD Kit
- Agent Harness Awareness: Turning Verified Telemetry into Operational Control for Tool-Using Agents
- DeepMind unveils Genie 4 world model promising more realistic physics and humans
- OpenHamoye: A Multi-Scale Open Geospatial Intelligence Framework for Decision Support Across Nigeria
- Solving a 31-year-old grand challenge in mathematics – the KLS conjecture
- SPEM: Structure-Aware Procedural Memory for Efficient Skill Use in LLM Agents
- EvolvingBrain: An Adaptive Skill System for Multi-modal Agents Self-Evolution in Open Worlds
- L2G-SR: Learning to Guide LLM-based Symbolic Regression
- Transformer-Based Masked Diffusion Models Provably Learn Distributions with Latent Structure
3560
discovered, all time
984
passed triage
2574
rejected at triage
3464
model assessments
710
multi-model panels
1780
distinct claims tracked
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, 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 |