Sai Chereddy
Independent Researcher
Navi Mumbai, India
saivivaswanthreddy@alumni.usc.edu

ABSTRACT

This paper investigates how video models represent nuanced information that does not alter the final classification. Using a minimal pair of videos—a bowling strike and a gutter ball—we analyze the Vanilla Video Vision Transformer (google/vivit-b-16x2-kinetics400) model, which robustly classifies both as "bowling." We reverse-engineer the internal circuit responsible for representing the action’s outcome, revealing that the "Success vs. Failure" signal is computed in a distinct amplification cascade. While low-level differences exist from Layer 0, the abstract, semantic representation of the outcome is progressively amplified from Layer 5 to 11. Through causal analysis using activation patching, we discover a clear division of labor: Attention Heads act as "evidence gatherers," providing necessary low-level information for a partial signal recovery, while MLP Blocks function as robust "concept composers," each sufficient to generate the entire "success" signal. This distributed and redundant circuit explains the model’s resilience to simple ablations and demonstrates a core computational pattern for processing action outcomes.

Keywords: Video Vision Transformers · Mechanistic Interpretability · Causal Analysis · Hidden Knowledge Elicitation

1.Introduction

A pre-trained VideoViT model correctly and robustly classifies both a successful bowling "strike" and a failed "gutter" ball under the single label "bowling". While this classification is accurate based on training, the two videos represent a semantically opposite, contrastive pair of positive-negative outcomes. This presents a fascinating puzzle: does the model simply discard this outcome-specific information, or does it compute and represent this nuance internally, hidden beneath the surface of its final classification? This work aims to answer that question by reverse-engineering the internal circuits of the model to search for this hidden knowledge. My investigation reveals critical insights not from initial successes, but from the failure of simple methods, which exposed the sophisticated, robust nature of the model's algorithm and compelled a more rigorous causal approach.

2. From Failed Experiments to a Clear Signal

Initial explorations using Direct Logit Attribution (DLA) and CLS Token Visualization successful...

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