arXiv:2604.27272v2 Announce Type: replace-cross Abstract: In the LLM era, many symbolic and structured problems are presented to models through 1D text serialization. Yet some such problems are natively two-dimensional: their relevant relations, such as row--column correspondence or spatial adjacency, are defined by position in a 2D layout rather than by sequential order. This raises a representational question: does preserving the same symbolic entries in a 1D sequence also preserve the relational structure needed for computation? We study this issue through the lens of serialization friction
The proliferation of LLMs handling symbolic tasks through 1D serialization makes the challenges of preserving multi-dimensional relational structures increasingly salient.
Understanding 'serialization friction' is critical for advancing LLM capabilities beyond simple text processing to complex, structured problems where spatial and relational integrity are paramount.
This research highlights a fundamental representational limitation in current LLMs, implying that a naive application of 1D serialization for 2D tasks will yield suboptimal or incorrect results.
- · Researchers specializing in multi-modal AI
- · Developers of next-gen LLM architectures
- · Companies with complex structured data challenges
- · LLMs relying solely on 1D text serialization
- · Applications that convert multi-dimensional data into simple text without consid
Immediate awareness of a key limitation in current LLM approaches to structured data.
Increased research and development efforts into more sophisticated encoding and architectural solutions for handling multi-dimensional information without loss of relational integrity.
The development of new frameworks and benchmarks for evaluating LLMs on structured tasks that explicitly challenge their ability to infer or maintain 2D relationships from 1D inputs.
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Read at arXiv cs.LG