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ARXIV:2604.27272 · LLM INPUT REPRESENTATION · SUBMITTED 01 MAY · 20:34 UTC · FRESHNESS STALE
ARXIV:2604.27272LLM INPUT REPRESENTATIONSUBMITTED 01 MAY · 20:34 UTCFRESHNESS STALEChung-Hsiang Lo · Lu Li · Diji Yang · Tianyu Zhang · Yunkai Zhang · Yoshua Bengio · +1 at arXiv
This paper investigates 'serialization friction' in LLMs, showing that representing 2D structured tasks as 1D sequences hinders performance compared to vision-augmented pathways that preserve spatial layout.
Opportunity summary
Pain This paper investigates 'serialization friction' in LLMs, showing that representing 2D structured tasks as 1D sequences hinders performance compared to vision-augmented pathways that preserve spatial layout.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
This paper investigates 'serialization friction' in LLMs, showing that representing 2D structured tasks as 1D sequences hinders performance compared to vision-augmented pathways that preserve spatial layout. While natural for prose, such linearization may introduce…
Large language models (LLMs) conventionally process structured inputs as 1D token sequences. While natural for prose, such linearization may introduce additional representational burden for tasks whose computation depends directly on explicit 2D structure, because…
ScienceToStartup currently rates this 3.0/10 on the public viability pass. These findings indicate that the relationship between input representation and model performance on such tasks warrants further investigation, and suggest that preserving task-relevant 2D…
LLM Input Representation moved forward this cycle; last verified May 2026. Public score 3.0/10.
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mobile layout uses overflow-hidden min-w-0 break-wordsOpportunity summary
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This paper investigates 'serialization friction' in LLMs, showing that representing 2D structured tasks as 1D sequences hinders performance compared to vision-augmented pathways that preserve spatial layout.
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10.48550/arXiv.2604.27272This paper investigates 'serialization friction' in LLMs, showing that representing 2D structured tasks as 1D sequences hinders performance compared to vision-augmented pathways that preserve spatial layout.
Abstract
Large language models (LLMs) conventionally process structured inputs as 1D token sequences. While natural for prose, such linearization may introduce additional representational burden for tasks whose computation depends directly on explicit 2D structure, because row--column alignment and local neighborhoods are no longer directly expressed in the input. We study this setting, which we refer to as serialization friction, on a small diagnostic testbed of synthetic tasks with explicit 2D structure: matrix transpose, Conway's Game of Life, and LU decomposition. To examine this question, we compare a text-only language pathway over serialized inputs with a vision-augmented pathway, built on the same language backbone, that receives the same underlying content rendered in task-faithful 2D layout, yielding a system-level comparison between two end-to-end input pathways. Across the tasks and settings we study, the visual pathway consistently outperforms the textual pathway; the gap often widens at larger dimensions, and error patterns under serialization become increasingly spatially structured. These findings indicate that the relationship between input representation and model performance on such tasks warrants further investigation, and suggest that preserving task-relevant 2D layout is a promising direction for structured 2D tasks.
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PROBLEM
This paper investigates 'serialization friction' in LLMs, showing that representing 2D structured tasks as 1D sequences hinders performance compared to vision-augmented pathways that preserve spatial layout. While natural for prose, such linearization may introduce additional re...
METHOD
Large language models (LLMs) conventionally process structured inputs as 1D token sequences. While natural for prose, such linearization may introduce additional representational burden for tasks whose computation depends directly on explicit 2D structure, because row--column al...
RESULT
ScienceToStartup currently rates this 3.0/10 on the public viability pass. These findings indicate that the relationship between input representation and model performance on such tasks warrants further investigation, and suggest that preserving task-relevant 2D layout is a prom...
WHY NOW
LLM Input Representation moved forward this cycle; last verified May 2026. Public score 3.0/10.
{"file name": "input.pdf", "number of pages": 18, "author": "Chung-Hsiang Lo; Lu Li; Diji Yang; Tianyu Zhang; Yunkai Zhang; Yoshua Bengio; Yi Zhang"
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This paper investigates 'serialization friction' in LLMs, showing that representing 2D structured tasks as 1D sequences hinders performance compared to vision-augmented pathways that preserve spatial layout.
Segment
LLM Input Representation
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