Linear Probes Deep Learning, For example, in im-ages Apr 4, 2025 ยท Developing effective world models is crucial for creating artificial agents that can reason about and navigate complex environments. We optimize a deep linear probe generator to create suitable probes for the model. In this paper, we investigate a deep supervision technique for encouraging the development of a world model in a network trained end-to-end to predict the next observation. The task of Ml consists of learning either linear i classifier probes [2], Concept Activation Vectors (CAV) [16] or Re-gression Concept Vectors (RCVs) [12,13]. They reveal how semantic content evolves across network depths, providing actionable insights for model interpretability and performance assessment. ProbeGen adds a shared generator module with a deep linear architecture, providing an inductive bias towards structured probes thus reducing We propose Deep Linear Probe Generators (ProbeGen) for learning better probes. We use linear classifiers, which we refer to as "probes", trained entirely independently of the model itself. Meaning, our generator includes no activations between its linear layers, yet the addition of linear layers reinforces a desired structure for the probes. ProbeGen factorizes its probes into two parts, a per-probe latent code and a global probe generator. For example, simple probes have shown language models to contain information about simple syntactical features like Part of Speech tags, and more complex probes have shown models to contain entire Parse trees of sentences. f1, 6fncee, lt, po, liqfdmz, tfhd11, cyqdz, poa, gqt3g, h8,