aiqbench analysis
This paper introduces Predictive State Retrieval (PSR), a novel task combining future-state anticipation with cross-instance retrieval from video prefixes, distinct from standard action prediction or moment localization. The authors demonstrate that current predictors face significant bottlenecks in forecasting rather than perception and propose LFTR, a lightweight retriever using frozen encoders to bridge this gap efficiently via cross-space fusion. By releasing a new benchmark with human-validated ground truth across four datasets, the work establishes a clear frontier for models aiming to retrieve specific future states from multiple temporal horizons at lower inference costs.
Predict, Then Retrieve: Cross-Instance Future-State Retrieval from Video Prefixes
arXiv:2608.04426v1 Announce Type: cross Abstract: We introduce Predictive State Retrieval (PSR), a task in which a model observes a short video prefix and a temporal question about an object's future state, then retrieves instances from other videos or images that depict that state. Unlike action anticipation, which predicts a label, moment retrieval, which localizes an observed event within a video, or video generation, which synthesizes pixels, PSR combines anticipation with cross-instance retrieval across multiple temporal horizons. We construct a benchmark from four datasets with graded, human-validated ground truth, difficulty tiers, and an oracle ceiling. We also propose LFTR, a lightweight retriever with frozen encoders that predicts a question- and horizon-conditioned future latent and matches it in complementary semantic and visual spaces. A ceiling decomposition reveals a clear bottleneck: the true future state is highly retrievable once specified, whereas every predictor we evaluate, including a large multimodal language model with access to the prefix frames, remains far below the oracle. Thus, forecasting rather than perception is the central learnable challenge. LFTR narrows this gap at substantially lower inference cost, and ablations attribute its gains to cross-space fusion and hard-negative training rather than latent rollout. We release the benchmark, code, and evaluation scripts.
Abstract:We introduce Predictive State Retrieval (PSR), a task in which a model observes a short video prefix and a temporal question about an object's future state, then retrieves instances from other videos or images that depict that state. Unlike action anticipation, which predicts a label, moment retrieval, which localizes an observed event within a video, or video generation, which synthesizes pixels, PSR combines anticipation with cross-instance retrieval across multiple temporal horizons. We construct a benchmark from four datasets with graded, human-validated ground truth, difficulty tiers, and an oracle ceiling. We also propose LFTR, a lightweight retriever with frozen encoders that predicts a question- and horizon-conditioned future latent and matches it in complementary semantic and visual spaces. A ceiling decomposition reveals a clear bottleneck: the true future state is highly retrievable once specified, whereas every predictor we evaluate, including a large multimodal language model with access to the prefix frames, remains far below the oracle. Thus, forecasting rather than perception is the central learnable challenge. LFTR narrows this gap at substantially lower inference cost, and ablations attribute its gains to cross-space fusion and hard-negative training rather than latent rollout. We release the benchmark, code, and evaluation scripts.
Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
arXiv:2608.04426 [cs.CV]
(or arXiv:2608.04426v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2608.04426 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 297 words
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