ICLR2026 Rishi: Public Impact
Last edited: July 7, 2026AI Safety as a Public Engagement
To what extend should we be making AI Safety as a public problem.
One-Liner
Novelty
Motivation
Notable Methods
Key Figs
- Public generally more pessimistic about AI
- Rates of adverse impact by AI decisions is reasonably low
New Concepts
Data Partnerships
Try to find independent sources of data, with contracts for how the data must be used.
Impact Horizons
Some random Instagram influencer had more accesses than our usual methods of scientific communication.
ICLR2026 Workshop: Learning Dynamics
Last edited: July 7, 2026ICLR2026 Workshop: Weight-Space Symmatrices
Last edited: July 7, 2026One-Liner
Novelty
Motivation
Notable Methods
Key Figs
New Concepts
Notes
ICLR2026 Zhou: DynMuon
Last edited: July 7, 2026https://arxiv.org/pdf/2605.17109
Motivation
“How can we evolve the optimization algorithm itself during training?”
Notable Methods
For \(W_{t}\) weight matrix and \(M_{t}\) update matrix, we weight the singular directions:
\begin{equation} W_{t+1} = W_{t} - \eta U^{(t)} \Sigma^{\rho^{(t)}} V^{(t)} \end{equation}
and in particular we can dial up and down your choice of \(\rho\) and do stuff. some choices of \(p\):
- \(p=0\), muon OG, the idea of muon is to set all singular values to 1 so that we only prioritize direction
- \(p=1\), some aggressive weight-based updates, proposed idea,
- \(p < 0\), then we prioritize adjusting small directions
generally key insight: “in the beginning, we should prioritize large directions; then, we should prioritize small directions more.”