Introduces OMTG benchmark with C-Acc and EtF1 metrics, a 56k dataset, and caption/temporal rewards, reaching 43.65% EtF1 SOTA on the new bench.
Cosmo: Contrastive streamlined multi- modal model with interleaved pre-training
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DRIFT adapts pretrained VLMs to continuous decoding via a base predictor plus residual flow matching, outperforming regression and generative baselines on grounding and robotic control tasks.
Cambrian-S introduces VSI-SUPER benchmarks for long-horizon spatial recall and counting, shows data scaling yields 30% gains on existing tests, and demonstrates a self-supervised next-latent predictor using surprise outperforms baselines on the new spatial supersensing tasks.
VideoChat-Flash applies hierarchical video token compression to achieve ~50x reduction in context length for long videos while maintaining near-original performance on long-context benchmarks.
MLLM-enabled video translation is usefully framed as three roles—Semantic Reasoner, Expressive Performer, and Visual Synthesizer—rather than a cascade of ASR, MT, TTS, and lip-sync.
InternVideo3 introduces Multimodal Contextual Reasoning and M^2LA attention to enable closed-loop evidence accumulation in long-video understanding and agentic tool use, reporting strong benchmark results.
citing papers explorer
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Towards One-to-Many Temporal Grounding
Introduces OMTG benchmark with C-Acc and EtF1 metrics, a 56k dataset, and caption/temporal rewards, reaching 43.65% EtF1 SOTA on the new bench.
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DRIFT: A Residual Flow Adapter for Decoding Continuous Outputs in Vision-Language Models
DRIFT adapts pretrained VLMs to continuous decoding via a base predictor plus residual flow matching, outperforming regression and generative baselines on grounding and robotic control tasks.
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Cambrian-S: Towards Spatial Supersensing in Video
Cambrian-S introduces VSI-SUPER benchmarks for long-horizon spatial recall and counting, shows data scaling yields 30% gains on existing tests, and demonstrates a self-supervised next-latent predictor using surprise outperforms baselines on the new spatial supersensing tasks.
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VideoChat-Flash: Hierarchical Compression for Long-Context Video Modeling
VideoChat-Flash applies hierarchical video token compression to achieve ~50x reduction in context length for long videos while maintaining near-original performance on long-context benchmarks.
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Multimodal Large Language Model-Enabled Video Translation: A Role-Oriented Survey
MLLM-enabled video translation is usefully framed as three roles—Semantic Reasoner, Expressive Performer, and Visual Synthesizer—rather than a cascade of ASR, MT, TTS, and lip-sync.
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InternVideo3: Agentify Foundation Models with Multimodal Contextual Reasoning
InternVideo3 introduces Multimodal Contextual Reasoning and M^2LA attention to enable closed-loop evidence accumulation in long-video understanding and agentic tool use, reporting strong benchmark results.