TAI Fixes VideoLLM Temporal Reasoning Training-Free by Reinjecting Fading Signals
Youngwoo Shin · hf · 2026-10-02
New research reveals a structural cause of weak temporal reasoning in VideoLLMs and offers a training-free fix:
- Finding: reversing frame order—a transform that should invert temporal answers—often leaves predictions unchanged. Tracking a layer-wise temporal divergence vector τl shows divergence peaks at intermediate layers and fades toward the output: temporal information is acquired mid-network but not maintained.
- Method: Temporal Activation Injection (TAI) extracts τl at the peak and reinjects it into subsequent layers following the measured decay, requiring no training.
- Results: consistent temporal-reasoning gains across three VideoLLMs and four benchmarks, with negligible impact on non-temporal tasks. Code is open-sourced.
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