arXiv:2511.10234v3 Announce Type: replace Abstract: While promising, graph reasoners based on Large Language Models (LLMs) lack built-in invariance to symmetries in graph representations. Operating on sequential graph serializations, LLMs can produce different outputs under node reindexing, edge reordering, or formatting changes, raising robustness concerns. We systematically analyze these effects, studying how fine-tuning impacts encoding sensitivity as well generalization on unseen tasks. We propose a principled decomposition of graph serializations into node labeling, edge encoding, and syn
The rapid advancement and deployment of Large Language Models (LLMs) into specialized reasoning tasks highlight current limitations, making robust and invariant graph reasoners a critical next step.
This research addresses fundamental robustness and generalization issues in LLM-based graph reasoning, which are crucial for reliable AI agents and complex system automation in enterprise and defense applications.
The understanding of LLM vulnerabilities to data representation changes will inform the development of more stable and trustworthy AI, moving beyond purely statistical correlations to more principled reasoning.
- · AI research labs
- · LLM developers
- · Enterprises adopting AI for complex workflows
- · AI ethics and safety organizations
- · Developers of brittle LLM graph reasoners
- · Applications relying on un-robust graph AI
- · Sectors with high-stakes AI deployment until issues are resolved
Increased focus on architectural innovations to embed invariance directly into LLM reasoning processes.
Development of new benchmarking standards and certification processes for AI systems operating on graph data.
Accelerated adoption of AI in critical infrastructure and defense, as issues of trust and reliability are systematically addressed.
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Read at arXiv cs.LG