Papers
- Telasian Labs
On Governance at the Substrate Level
Governance interventions at the deployment layer (model cards, API gating, refusal training) are downstream of the actual decisions that shape model behavior. The real governance lever is upstream: what enters training data, what evaluations gate releases, what architectural commitments lock in path-dependent behaviors. This paper argues for an upstream governance framework and identifies three interventions that would move the field more than any current deployment-layer policy.
- Telasian Labs
Compute, Data, and the Shape of the Next Generation
Scaling laws met the data wall in 2024-2025. The next generation of frontier models is characterized by synthetic data composition, post-training compute reallocation, and reasoning at inference. This paper maps the shifting compute economics and argues the dominant lever for capability gain is no longer pre-training compute scaling but inference-time reasoning depth, with second-order implications for capital allocation across the field.
- Telasian Labs
Architectures of Frontier AI: A Map of the Choices That Compound
A survey of architectural decisions publicly made by frontier labs over the past 24 months, with analysis of which choices compound across capability dimensions and which are marginal. The argument: architectural commitments are path-dependent in ways the field underestimates, and the visible convergence on transformer-MoE-with-attention-variants is masking a much larger decision space than the major labs are actively exploring.