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4 Pith papers cite this work. Polarity classification is still indexing.

4 Pith papers citing it

years

2026 4

verdicts

UNVERDICTED 4

representative citing papers

MotiMotion: Motion-Controlled Video Generation with Visual Reasoning

cs.CV · 2026-05-21 · unverdicted · novelty 7.0

MotiMotion adds visual reasoning via a training-free VLM to refine primary trajectories and hallucinate secondary motions, plus a confidence-aware guidance scheme, yielding more plausible interactions on the new MotiBench benchmark.

Progressive Photorealistic Simplification

cs.CV · 2026-05-11 · unverdicted · novelty 7.0

Progressive semantic image simplification uses VLMs and a verifier to iteratively remove and inpaint scene elements while preserving photorealism, distilled into an image-to-video model for direct sequence prediction.

Measuring AI Reasoning: A Guide for Researchers

cs.AI · 2026-05-04 · unverdicted · novelty 4.0

Reasoning in language models should be measured by the faithfulness and validity of their multi-step search processes and intermediate traces, not final-answer accuracy.

citing papers explorer

Showing 4 of 4 citing papers.

  • MotiMotion: Motion-Controlled Video Generation with Visual Reasoning cs.CV · 2026-05-21 · unverdicted · none · ref 23

    MotiMotion adds visual reasoning via a training-free VLM to refine primary trajectories and hallucinate secondary motions, plus a confidence-aware guidance scheme, yielding more plausible interactions on the new MotiBench benchmark.

  • Progressive Photorealistic Simplification cs.CV · 2026-05-11 · unverdicted · none · ref 34

    Progressive semantic image simplification uses VLMs and a verifier to iteratively remove and inpaint scene elements while preserving photorealism, distilled into an image-to-video model for direct sequence prediction.

  • From Documents to Segments: A Contextual Reformulation for Topic Assignment cs.CL · 2026-05-18 · unverdicted · none · ref 36

    SBTA reformulates topic modeling to assign topics at the segment level rather than document level, yielding cleaner topics on a new SemEval-STM dataset created via LLM decomposition and human refinement.

  • Measuring AI Reasoning: A Guide for Researchers cs.AI · 2026-05-04 · unverdicted · none · ref 60

    Reasoning in language models should be measured by the faithfulness and validity of their multi-step search processes and intermediate traces, not final-answer accuracy.