🔬 Research
Frontier research talks — new architectures, training techniques, theoretical insights, paper deep-dives.
The workflow
flowchart LR
A[Open problem] --> B[Hypothesis<br/>+ experiment design]
B --> C[Run + ablations]
C --> D[Compare to<br/>strong baselines]
D --> E{Holds up?}
E -->|No| B
E -->|Yes| F[Write-up +<br/>code release]
The cutting edge — usually 6-18 months ahead of production.
Key takeaways
Videos (4)
Code World Model: Building World Models for Computation – Jacob Kahn, FAIR Meta
Meta's Code World Model predicts program execution traces as an autoregressive sequence so agents can imagine outcomes before running code.
RL Environments at Scale – Will Brown, Prime Intellect
Scaling RL is now a talent and tooling problem; opening up RL environments and infra is how Prime Intellect plans to widen the researcher pool.
Top Ten Challenges to Reach AGI — Stephen Chin, Andreas Kollegger
AGI's hardest problems (memory, alignment, deception, idioms, hive-mind) map nicely onto sci-fi memes — and graph-based grounding is one tool worth taking seriously.
Measuring AGI: Interactive Reasoning Benchmarks for ARC-AGI-3 — Greg Kamradt, ARC Prize Foundation
AGI measurement needs interactive game-based benchmarks with hidden test sets so model intelligence can't be confused with memorized training data or developer-injected priors.