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ARXIV:2606.03290 · GRAPH FOUNDATION MODELS · SUBMITTED 03 JUN · 20:43 UTC · FRESHNESS FRESH
ARXIV:2606.03290GRAPH FOUNDATION MODELSSUBMITTED 03 JUN · 20:43 UTCFRESHNESS FRESHYancheng Chen · Dun Ma · Shuai Zhang · Yang Liu · Xixun Lin · Xiangyu Zhao · +3 at arXiv
Message Tuning for GFMs (MTG) is a novel adaptation method that outperforms graph prompt tuning by injecting learnable message prototypes, exceeding theoretical capacity bounds.
Opportunity summary
Pain Message Tuning for GFMs (MTG) is a novel adaptation method that outperforms graph prompt tuning by injecting learnable message prototypes, exceeding theoretical capacity bounds.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
Message Tuning for GFMs (MTG) is a novel adaptation method that outperforms graph prompt tuning by injecting learnable message prototypes, exceeding theoretical capacity bounds. For GNN-based GFMs, graph prompt tuning has become the prevailing…
Graph Foundation Models (GFMs), built upon the Pre-training and Adaptation paradigm, have emerged as a research hotspot in graph learning. For GNN-based GFMs, graph prompt tuning has become the prevailing adaptation method for downstream…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Extensive experiments demonstrate that MTG consistently outperforms graph prompt baselines across diverse benchmark datasets, providing strong empirical support for our theoretical findings. Code availability…
Graph Foundation Models moved forward this cycle; last verified June 2026. Public score 7.0/10. Production flags indicate code availability.
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Message Tuning for GFMs (MTG) is a novel adaptation method that outperforms graph prompt tuning by injecting learnable message prototypes, exceeding theoretical capacity bounds.
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10.48550/arXiv.2606.03290Message Tuning for GFMs (MTG) is a novel adaptation method that outperforms graph prompt tuning by injecting learnable message prototypes, exceeding theoretical capacity bounds.
Abstract
Graph Foundation Models (GFMs), built upon the Pre-training and Adaptation paradigm, have emerged as a research hotspot in graph learning. For GNN-based GFMs, graph prompt tuning has become the prevailing adaptation method for downstream tasks. Although recent methods explain why graph prompt tuning works, how to rigorously measure its adaptation capacity remains an open problem. Addressing this problem is critical for understanding the capability limits of graph prompt tuning and for developing more powerful adaptation methods. In this paper, we propose Prismatic Space Theory (PS-Theory), a novel mathematical framework to quantify the capacity of adaptation methods, while focusing on establishing the upper bound for the adaptation capacity of graph prompt tuning. Building upon the proposed PS-Theory, we further introduce Message Tuning for GFMs (MTG), a lightweight approach that injects a small set of learnable message prototypes into each layer of the GNN backbone to adaptively guide message fusion without updating pre-trained weights. Through our PS-Theory, we prove that the adaptation capacity of MTG can exceed the theoretical upper bound of graph prompt tuning. Extensive experiments demonstrate that MTG consistently outperforms graph prompt baselines across diverse benchmark datasets, providing strong empirical support for our theoretical findings.
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PROBLEM
Message Tuning for GFMs (MTG) is a novel adaptation method that outperforms graph prompt tuning by injecting learnable message prototypes, exceeding theoretical capacity bounds. For GNN-based GFMs, graph prompt tuning has become the prevailing adaptation method for downstream ta...
METHOD
Graph Foundation Models (GFMs), built upon the Pre-training and Adaptation paradigm, have emerged as a research hotspot in graph learning. For GNN-based GFMs, graph prompt tuning has become the prevailing adaptation method for downstream tasks.
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Extensive experiments demonstrate that MTG consistently outperforms graph prompt baselines across diverse benchmark datasets, providing strong empirical support for our theoretical findings. Code availabi...
WHY NOW
Graph Foundation Models moved forward this cycle; last verified June 2026. Public score 7.0/10. Production flags indicate code availability.
{"file name": "input.pdf", "number of pages": 42, "author": "Yancheng Chen; Dun Ma; Shuai Zhang; Yang Liu; Xixun Lin; Xiangyu Zhao; Wenguo Yang; Wei Chen; Chuan Zhou"
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Message Tuning for GFMs (MTG) is a novel adaptation method that outperforms graph prompt tuning by injecting learnable message prototypes, exceeding theoretical capacity bounds.
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