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The results suggest that dense to MoE adaptation with dynamic expert deactivation is a practical direction for reducing active VLA model size without severe performance loss.
Multimodal Large Language Models face significant efficiency challenges that stem from two distinct yet coupled sources: data redundancy and computational redundancy. While most methods focus on data redundancy by pruning visual tokens from the output of the visual encoder or computing redundancy in LLM decoders using...
STEP-OPD is proposed, an on-policy distillation framework for image generation that extends the student's learning target beyond the teacher and introduces explicit constraints on its internal representation evolution, showing that output extrapolation enables beyond-teacher learning.
This work develops HarnessBandit, an online scheduler that selects one harness per optimizer step, and improves over mixed-batch multi-harness training on both benchmarks, while training diagnostics indicate that learnability and transferability provide distinct, evolving signals.
BQ-LoRA is proposed, a low-rank adaptation framework that organizes trajectory updates through a local behavior quotient manifold and contains two modules, i.e., behavior quotient balancing (BQB) and decision preserving compression (DPC).
TAC-Merge is proposed for tracing and coordinating cross-layer influence in multimodal model merging and demonstrated the effectiveness in consolidating complementary expert capabilities and supporting generalization to unseen tasks.
This work argues that closing this gap requires alignment at every level of the pipeline, including execution, supervision, optimization, and evaluation, rather than scale alone, and presents Wuying-Browser-Agent, a unified framework that addresses each of these levels.
AViTS models spatial importance via latent-text attention and temporal importance via token-level feature variation across diffusion timesteps, and fuses them to enable spatiotemporal importance-aware selective upsampling: it prioritizes resolution refinement for critical tokens while deferring less important ones, the...
A statistical observation that the residuals between the features of full computation steps using caching methods and reference full-compute trajectory locally exhibit a zero-mean Gaussian distribution is presented, and a plug-and-play GP-Refiner correction framework is proposed.
This work proposes a general memory compression framework adaptable to various agent memory systems, MemForest, and introduces an anchor-guided propagation retrieval mechanism that retrieves relevant memory nodes from the temporal neighborhoods of key nodes, improving retrieval accuracy.
LinCa decomposes cached features into sub-components with distinct continuity properties via a lightweight invertible network and applies differentiated prediction orders matched to each component, forming a unified Decompose-Predict-Reconstruct pipeline.
Method, an egocentric world-action simulator that synthesizes controllable, high-quality manipulation videos to expand scarce real-world training data, is presented, demonstrating that the synthesized data substantially improve downstream WAM generalization.
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