Author

Constantin Venhoff

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#machine learning Preprint Oct 2026

Weight Oracles: Reading Neural Network Weights with Language Models

Interpretability methods for neural networks are predominantly reactive: they analyse activations produced during specific forward passes, requiring known inputs to find hidden capabilities such as backdoors. We propose Weight Oracles, fine-tuned language models that diagnose properties of a target network by reading i...

Krish Kabra, Constantin Venhoff, Christian Schroeder de Witt · 0 citations

#machine learning Preprint Aug 2026

VBVR-Pro: A Scalable and Verifiable Suite for Native Visual Reasoning

This work introduces VBVR-Pro, a closed-loop testbed that makes native visual reasoning through generation trainable, verifiable, optimizable, and experimentally controllable, and identifies recurring failure modes of the prevalent VLM-as-a-judge paradigm.

Junhua Xu, Rui-Si Wang, Fanyi Pu et al. · 5 citations