Papers with TEMPO

2 papers
Localizing Moments in Video with Temporal Language (D18-1)

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Challenge: a novel model for localizing moments in a longer video using natural language queries is challenging . moment localization is similar to other language and vision tasks, but it offers an interesting opportunity to model temporal dependencies and reasoning in text.
Approach: They propose a model that explicitly reasons about different temporal segments in a video . their dataset includes a dataset with real videos and template sentences .
Outcome: The proposed model explicitly reasons about different temporal segments in a video . it shows that temporal context is important for localizing phrases which include temporal language .
Exploiting Tree Structure for Credit Assignment in Reinforcement Learning with Large Language Models (2026.findings-acl)

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Challenge: Reinforcement learning has shown strong promise for strengthening reasoning ability of large language models, but sparse, delayed rewards make token-level credit assignment a central challenge.
Approach: They propose a critic-free algorithm that rewards tokens that change the solution.
Outcome: The proposed algorithm improves on in-distribution benchmarks and out-of-disttribution settings.

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