Papers with Rigorous

2 papers
HTCCN: Temporal Causal Convolutional Networks with Hawkes Process for Extrapolation Reasoning in Temporal Knowledge Graphs (2024.naacl-long)

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Challenge: Temporal knowledge graphs (TKGs) are powerful tools for storing and modeling dynamic facts.
Approach: They propose a Hawkes process-based temporal causal convolutional network for temporal reasoning under extrapolation settings.
Outcome: The proposed network is based on Hawkes process-based temporal causal convolutional network and captures the temporal evolution of facts.
LongInsightBench: A Comprehensive Benchmark for Evaluating Omni-Modal Models on Human-Centric Long-Video Understanding. (2026.findings-acl)

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Challenge: LongInsightBench is the first benchmark designed to assess models’ ability to understand long videos, with a focus on human language, viewpoints, actions, and other contextual elements.
Approach: They propose a benchmark to assess models’ ability to understand long videos with a focus on human language, viewpoints, actions, and other contextual elements.
Outcome: The proposed model excels in three key areas: a) long-duration, human-centric videos; b) diversifying and challenging task scenarios; c) quality assurance pipeline; and d) reliability.

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