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O. Susladkar

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#artificial intelligence Preprint Oct 2026

CoDA: Co-Evolution of Data Engines and Action Policies

Generative simulation can automate robot environment construction, but physically valid environments are not necessarily useful for training. The challenge is to identify environments that address a policy's failures and supervision that helps it overcome them. We introduce CoDA, a framework in which a simulation data...

Tianjiao Yu, Zhi-Lin Ou, Yi-Fan Shen et al. · 0 citations
Jul 2026

GraphVid: Interactive Graph-Controllable Video Generation

GraphVid is introduced, a graph-conditioned image-to-video generation model that enables interactive control through structured interaction graphs that delivers strong controllability and video quality and highlights the potential of structured semantic interfaces as a powerful paradigm for controllable video generatio...

Vedant r Shah, O. Susladkar, Tushar Prakash et al. · 0 citations
#artificial intelligence Preprint Sep 2026

One Editor, Many Edits: A Unified Training-Free Framework for Diverse Video Editing

Video editing spans diverse editing paradigms, yet achieving high-quality instruction-guided and subject-guided editing within a single unified framework remains challenging. We introduce EditVid, a training-free framework combining sparse causal memory for local coherence, correspondence-based post-attention token inj...

A. Juvekar, O. Susladkar, K. A. Nguyen et al. · 0 citations
#large language models Preprint Sep 2026

ViTAL-X: Video-Text Alignment with Cross-Modal Temporal Edits

ViTAL-X, a lightweight model that equips frozen image-text backbones with temporal awareness while preserving their foundational spatial knowledge, achieves state-of-the-art performance and demonstrates that targeted, high-quality temporal alignment provides a highly efficient alternative to pure scaling.

V. SethuramanT., Savya Khosla, O. Susladkar et al. · 0 citations

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