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LoGIC method optimizes graph in-context learning for tabular foundation models

By PulseAugur Editorial · [1 sources] ·

Researchers have developed LoGIC, a novel method for optimizing context construction in node-level graph in-context learning (ICL) using tabular foundation models. LoGIC addresses the quadratic scaling and memory bottlenecks associated with using complete training tables by intelligently selecting labeled and unlabeled nodes for prompts. The system retrieves labeled nodes through structural, feature-based, and coverage channels, and incorporates an unlabeled halo for adapter message passing. Experiments on the GraphLand dataset demonstrate that LoGIC maintains performance comparable to full-context methods while significantly reducing memory requirements, enabling ICL on million-node graphs without retraining. AI

IMPACT Optimizes context construction for graph ICL, potentially enabling larger-scale graph analysis with existing tabular foundation models.

RANK_REASON This is a research paper detailing a new method for graph in-context learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LoGIC method optimizes graph in-context learning for tabular foundation models

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This is a research paper detailing a new method for graph in-context learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Mingqi Yang, Zidong Guo, Jihui Yang, Wenming Zuo ·

    LoGIC: Budgeted Context Construction for Node-Level Graph In-Context Learning with Tabular Foundation Models

    arXiv:2609.05955v1 Announce Type: new Abstract: Tabular foundation models have become powerful graph learners. Systems such as G2T-FM and GraphPFN encode each node as a feature row and make predictions through in-context learning (ICL), with labeled rows serving as the prompt. Cu…

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