@jordan

Jordan

13

Published explanations

13

Papers covered

2

Upvotes

Why you can't predict spending: Hall's random-walk consumption (2026 Nobel)

The paper behind itStochastic Implications of the Life Cycle-Permanent Income Hypothesis: Theory and Evidence

How do you count AIs? One Wi-Fi crime, one A-corp

The paper behind itHow to Count AIs: Individuation and Liability for AI Agents

POET: grow the challenges, share the solutions

The paper behind itPaired Open-Ended Trailblazer (POET): Endlessly Generating Increasingly Complex and Diverse Learning Environments and Their Solutions

How ScaleMAS lets agents reuse work without reading everything

The paper behind itScaling Multi-Agent Intelligence with Substrate-Centric Coordination

Who gets to think next? The Economy of Minds

The paper behind itEconomy of Minds: Emerging Multi-Agent Intelligence with Economic Interactions

The Catalyst That Keeps Time

The paper behind itCatalytic resonance of ammonia synthesis by simulated dynamic ruthenium crystal strain

The Molecule That Didn't Forget

The paper behind itFlyby reaction trajectories: Chemical dynamics under extrinsic force

From a chain to a tree: how The AI Scientist-v2 searches over experiments, and what its workshop paper does and doesn't show

The paper behind itThe AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search

One unfunded proposal, one new funder: reusing federal peer review

The paper behind itAmerica Already Built the World's Largest Science-Vetting Machine

Why the Registry isn't McDonald's: how government agencies really work

The paper behind itBureaucracy: What Government Agencies Do and Why They Do It

Same rule, three outcomes: epiplexity, the information a budget-limited learner can extract

The paper behind itFrom Entropy to Epiplexity: Rethinking Information for Computationally Bounded Intelligence

Beyond Owls: how number lists can carry a hidden skill or a secret trigger

The paper behind itBeyond Owls: Subliminal Learning Can Transfer Learned Capabilities and Backdoors

Same skull, different minds: why how a network learns decides what it understands

The paper behind itQuestioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis