DyGFM introduces decoupled pre-training and divergence-conditioned prompts to create the first multi-domain dynamic graph foundation model that outperforms baselines on node classification and link prediction.
The perceptron: a probabilistic model for information storage and organization in the brain
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WORC improves multi-agent LLM reasoning to 82.2% average accuracy by predicting and compensating for the weakest agent via targeted extra sampling rather than uniform reinforcement.
CSI-JEPA learns temporal-spectral representations from unlabeled CSI via masked prediction and achieves up to 10.64 percentage points accuracy gain and 98% label savings on seven real-world Wi-Fi sensing tasks.
citing papers explorer
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Decoupled and Divergence-Conditioned Prompt for Multi-domain Dynamic Graph Foundation Models
DyGFM introduces decoupled pre-training and divergence-conditioned prompts to create the first multi-domain dynamic graph foundation model that outperforms baselines on node classification and link prediction.
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Weak-Link Optimization for Multi-Agent Reasoning and Collaboration
WORC improves multi-agent LLM reasoning to 82.2% average accuracy by predicting and compensating for the weakest agent via targeted extra sampling rather than uniform reinforcement.
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CSI-JEPA: Towards Foundation Representations for Ubiquitous Sensing with Minimal Supervision
CSI-JEPA learns temporal-spectral representations from unlabeled CSI via masked prediction and achieves up to 10.64 percentage points accuracy gain and 98% label savings on seven real-world Wi-Fi sensing tasks.