Before It Fades: Reinforcing Temporal Representations at Inference Time in VideoLLMs

arXiv:2610.01595 · cs.CV, cs.AI · Submitted 2026-10-01 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-10-01

Updated: 2026-10-01

Code: https://github.com/Youngwoo-git/Before-It-Fades

Terminology

• Temporal Reasoning — 33× in this paper · explained in 2 episodes
• Benchmark — 29× in this paper · explained in 2 episodes
• Steering — 12× in this paper · explained in 2 episodes
• Accuracy — 9× in this paper · explained in 1 episode
• Robustness — 6× in this paper · explained in 5 episodes
• Temporal Activation Injection — 5× in this paper
• Computational overhead — 3× in this paper · explained in 2 episodes
• Hallucination — 3× in this paper · explained in 3 episodes
• Representation Engineering — 3× in this paper · explained in 2 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Adaptation — 2× in this paper · explained in 1 episode
• Foundation Model — 2× in this paper · explained in 3 episodes
• Foundation Models — 2× in this paper · explained in 7 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Reinforcement Learning — 2× in this paper · explained in 4 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Activation Addition — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Aggregation — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Homogenization — 1× in this paper · explained in 2 episodes
• Instruction Tuning — 1× in this paper · explained in 2 episodes
• Korea Advanced Institute of Science and Technology — 1× in this paper
• Linear probe — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• Qwen3-VL Technical Report
• Lost in Time: A New Temporal Benchmark for VideoLLMs
• LLaVA-OneVision: Easy Visual Task Transfer
• Enhancing Temporal Understanding in Video-LLMs through Stacked Temporal Attention in Vision Encoders
• EventHallusion: Diagnosing Event Hallucinations in Video LLMs
• LLaVA-Video: Video Instruction Tuning With Synthetic Data
• Representation Engineering: A Top-Down Approach to AI Transparency

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