🔬 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 (13)
Intelligence + Continual Learning = Expertise — Yu Su, NeoCognition
Agents need continual learning about their specific operating environments to turn general reasoning ability into efficient expertise.
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.
Reinforcement Learning without Verifiable Rewards — Will Brown, Prime Intellect
Extending RL to real work requires better feedback and reward design beyond deterministic correctness checks.
How Autoresearch is changing ML research — Zhengyao Jiang, Weco
Autoresearch amplifies disciplined execution of existing ideas, making evaluation and search-space design especially valuable.
First Steps Toward Automated AI Research — Richard Socher, CEO Recursive AI
Automated scientific discovery should combine exploration with empirical selection rather than rely on idea generation alone.
Einstein Arena: Harnessing Collective Agent Intelligence for Open Science — James Zou, Together AI
Well-designed environments with verifiable rewards and shared discoveries can amplify collective scientific problem solving.
From RL to IRL — Gaurav Mishra, Amazon AGI Lab
Computer-use agents need training environments that teach recovery and judgment under real-world failure conditions.
Task Fidelity Scaling Laws — Kobie Crawdord, Snorkel
Agent training tasks should be difficult for meaningful reasons, with reliable environments that produce useful learning signals.
Adaption Labs: Gradient-Free Continual Learning — Sara Hooker, Adaption
Automating the full data-and-model adaptation loop can make specialized AI development more accessible and compute-efficient.
Scaling Compute on Context — Jack Morris, Engram
Scaling computation over private context is a proposed route from broad pretrained knowledge to personalized depth.
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.