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ReMoMask-2 improves text-to-motion generation by embedding retrieval directly into the generator's latent space, eliminating representation gaps and achieving state-of-the-art results on benchmarks like KIT-ML and SnapMoGen.
UniMate is a unified diffusion transformer model that generates articulated motion for diverse skeletons from text and rigged 3D assets without per-skeleton retraining, using topology-aware attention and a large curated dataset.
DART-SD proposes a topology-aware retrieval and tuning framework for self-distillation of LLM-based tool-calling agents, improving policy diversity by correcting only critical topological breakpoints while preserving valid reasoning.
This paper presents a topology-aware data movement orchestrator for disaggregated LLM inference, which dynamically selects optimal transport based on interconnect hierarchy and overlaps KV cache transfer with computation, achieving 3-18x transfer latency reduction over uniform RDMA.
Proposes FedGAMMA, a federated multimodal graph foundation learning framework that aligns multimodal attributes and graph topology via two-stage pre-training and prompt-based fine-tuning, achieving significant gains on multiple datasets.
SkillCAT is a training-free framework for LLM agent skill self-evolution that addresses limitations of single-trace bias, unverified merging, and full corpus loading via three stages: Contrastive Causal Extraction, Assessment-Augmented Evolution, and Topology-Aware Task Execution, achieving up to 40.40% improvement on benchmarks.
This paper proposes TopoMamSurv, a Graph Mamba framework for whole-slide image survival analysis that uses topology-aware ordering to address Mamba's sensitivity to input order, and incorporates bidirectional Mamba and GCN for spatial context modeling.
TopoEvo is a topology-aware self-evolving multi-agent framework for root cause analysis in microservices that couples graph representation learning with structured, topology-constrained reasoning. It achieves absolute improvements of up to 3.44% in root cause localization accuracy and boosts fault-type classification performance by 4.39% to 16.81% across diverse datasets.