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Zero-shot graph models (ZGMs), which learn transferable knowledge from source graphs and directly apply to unseen target graphs without any adaptation, have achieved promising performance and attracted considerable attention. Despite their proliferation, existing ZGMs are predominantly evaluated on clean graphs, while...
A unified benchmark that evaluates representative machine learning, reinforcement learning, LLM-based, and agent-based trading methods in cryptocurrency markets through three progressively more realistic stages through historical backtesting, prospective exchange-based paper trading, and real-money live trading is pres...
This work proposes UGE, a two-stage training strategy that progressively and stably aligns images, text, and spatial structures by combining instruction-guided contrastive learning with graph-based spatial encoding, and introduces \dataset, a spatially grounded dataset that anchors street-view images to structured spat...
This work introduces a Query-Key router that represents the expertise required by each token under the current market context as a low-dimensional query and matches it with learnable expert keys and proposes TradingMoE, a trading-oriented sparse MoE that augments a frozen dense LLM with lightweight residual experts.
This work compares country-continent questions with noun, adjective, and code answers while keeping several fitted measurements distinct across Qwen, Llama, and Gemma to separate early readability, natural strength, causal steering, and later content dependence.
CurvPrompt is proposed, a topology-routed geometry prompting framework for dynamic graphs that significantly advances few-shot link prediction while delivering strong, consistent performance on node classification tasks, validating the necessity of geometry-adaptive prompting.
This work proposes HyperClaim, a discriminative temporal hypergraph framework for sample-level authenticity classification, which preserves fine-grained cross-modal and temporal structure that global fusion tends to flatten.
Learning transferable multimodal embeddings for urban environments is challenging because urban understanding is inherently spatial, yet existing datasets and benchmarks lack explicit alignment between street-view images and urban structure. We introduce \dataset, a spatially grounded dataset that anchors street-view i...
Molecular graph representation learning is widely used in chemical and biomedical research, and reusing widely available and well-validated pre-trained 2D encoders, while incorporating molecular domain knowledge during downstream adaptation, offers a more practical alternative.
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