Act2See trains VLMs via supervised fine-tuning on verified reasoning traces to interleave active frame calls within text CoTs, yielding SOTA results on video reasoning benchmarks.
MINERVA: evaluating complex video reasoning.CoRR, abs/2505.00681, 2025
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A survey that taxonomizes efficiency methods for LVLMs across the full inference pipeline, decouples the problem into information density, long-context attention, and memory limits, and outlines four future research frontiers with pilot insights.
citing papers explorer
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Act2See: Emergent Active Visual Perception for Video Reasoning
Act2See trains VLMs via supervised fine-tuning on verified reasoning traces to interleave active frame calls within text CoTs, yielding SOTA results on video reasoning benchmarks.
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Seed1.8 Model Card: Towards Generalized Real-World Agency
Seed1.8 is a new foundation model that adds unified agentic capabilities for search, code execution, and GUI interaction to existing LLM and vision strengths.
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EasyVideoR1: Easier RL for Video Understanding
EasyVideoR1 delivers an optimized RL pipeline for video understanding in large vision-language models, achieving 1.47x throughput gains and aligned results on 22 benchmarks.
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Efficient Inference for Large Vision-Language Models: Bottlenecks, Techniques, and Prospects
A survey that taxonomizes efficiency methods for LVLMs across the full inference pipeline, decouples the problem into information density, long-context attention, and memory limits, and outlines four future research frontiers with pilot insights.