The authors developed ProAR for video-native reasoning, where solving a task means generating a valid sequence of visual states leading to a target outcome.
Video models reason better when trained to anticipate future states
ProAR guides autoregressive generation with predicted goal frames and representations of upcoming transitions.
Big Tech
Linghui Shen · Tinghui Zhu · Sheng Zhang · Muhao Chen
The Hong Kong Polytechnic University · University of California, Davis · Microsoft
Research Digest··3 min read
Shen and colleagues modify an autoregressive video model so that each generated segment is informed by both an anticipated final state and the likely next transition.
Why this paper
From Microsoft and 2 others
In one line
ProAR makes autoregressive video reasoning more goal-directed by predicting outcomes and aligning current representations with future transitions, improving accuracy and training efficiency.
What we could check
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- ✓Reports numbers on named benchmarks (2 benchmarks)
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