HyLaR with DePO enables effective RL in hybrid discrete-continuous spaces for multimodal models, outperforming prior MLLMs on perception and understanding benchmarks.
In: Pro- ceedings of the IEEE/CVF conference on computer vision and pattern recognition
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DO-Bench is a controlled benchmark that attributes VLM object hallucination errors to textual prior pressure, perceptual limits, or their interaction via two diagnostic dimensions and metrics.
SMART uses marginal benefit-cost analysis to dynamically build efficient speculative trees, achieving 15-20% additional speedup in LLM and MLLM inference.
citing papers explorer
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Hybrid Latent Reasoning with Decoupled Policy Optimization
HyLaR with DePO enables effective RL in hybrid discrete-continuous spaces for multimodal models, outperforming prior MLLMs on perception and understanding benchmarks.
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DO-Bench: An Attributable Benchmark for Diagnosing Object Hallucination in Vision-Language Models
DO-Bench is a controlled benchmark that attributes VLM object hallucination errors to textual prior pressure, perceptual limits, or their interaction via two diagnostic dimensions and metrics.
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SMART: When is it Actually Worth Expanding a Speculative Tree?
SMART uses marginal benefit-cost analysis to dynamically build efficient speculative trees, achieving 15-20% additional speedup in LLM and MLLM inference.