Inference-Time Steering Across Scaffolds and Epitopes for Sample-Efficient De Novo Nanobody Design
Generative molecular design typically relies on over-generation and post-hoc filtering.
ProofPaper ↗
Key points
- Inference-time steering methods like Feynman-Kac (FK) steering improve efficiency by biasing trajectories toward high-reward candidates, yet they optimize within a single fixed configuration.
- In de novo nanobody design, upfront commitment to conditioning choices like scaffold identity or target epitope creates a bottleneck.
- We recast generative condition selection as an inference-time resource-allocation problem.
- We propose two dynamic budget allocators: a joint Global Sequential Monte Carlo (SMC) formulation that steers and allocates compute across configurations concurrently, and a decoupled approach adapting Batched Successive Elimination (BaSE).
Sources (1)
- [1]Inference-Time Steering Across Scaffolds and Epitopes for Sample-Efficient De Novo Nanobody DesignarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 9, 04:54 PM
Generative molecular design typically relies on over-generation and post-hoc filtering.
Inference-time steering methods like Feynman-Kac (FK) steering improve efficiency by biasing trajectories toward high-reward candidates, yet they optimize within a single fixed configuration.
Extractive summary: sentences quoted from the sources.
Before this
- Oct 9, 2026KVeil: First-Place Solution to ECCV 2026 LifeGenIP Challenge on Unlearnable Videos
- Oct 9, 2026A Critical Look at Power Sampling when Test Time Sharpening LLMs
- Oct 9, 2026What We Talk About When We Talk About Emotion Editing
- Oct 9, 2026Latent World Action Model for GUI Agents
- Oct 9, 2026Rethinking Relative Policy Differences Transfer of On-Policy Distillation
- Oct 9, 2026InstantFusion: A Shared Latent Interface for Heterogeneous Image Generators