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ARXIV:2604.12133 · TABLE REASONING · SUBMITTED 15 APR · 16:43 UTC · FRESHNESS STALE
ARXIV:2604.12133TABLE REASONINGSUBMITTED 15 APR · 16:43 UTCFRESHNESS STALEWilly Carlos Tchuitcheu · Tan Lu · Ann Dooms · arXiv
A hypothesis and framework for permutation-invariant table representation learning to build robust table retrieval systems.
Opportunity summary
Pain A hypothesis and framework for permutation-invariant table representation learning to build robust table retrieval systems.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A hypothesis and framework for permutation-invariant table representation learning to build robust table retrieval systems. We argue that this linearization of tables discards their essential geometric and relational structure, creating representations that are brittle…
Historical approaches to Table Representation Learning (TRL) have largely adopted the sequential paradigms of Natural Language Processing (NLP). We argue that this linearization of tables discards their essential geometric and relational structure, creating representations…
ScienceToStartup currently rates this 5.0/10 on the public viability pass. This model demonstrates superior geometric stability and moves towards the PI ideal. Code availability is flagged in the production record; the public repository link…
Table Reasoning moved forward this cycle; last verified April 2026. Public score 5.0/10. Production flags indicate code availability.
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A hypothesis and framework for permutation-invariant table representation learning to build robust table retrieval systems.
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10.48550/arXiv.2604.12133A hypothesis and framework for permutation-invariant table representation learning to build robust table retrieval systems.
Abstract
Historical approaches to Table Representation Learning (TRL) have largely adopted the sequential paradigms of Natural Language Processing (NLP). We argue that this linearization of tables discards their essential geometric and relational structure, creating representations that are brittle to layout permutations. This paper introduces the Platonic Representation Hypothesis (PRH) for tables, positing that a semantically robust latent space for table reasoning must be intrinsically Permutation Invariant (PI). To ground this hypothesis, we first conduct a retrospective analysis of table-reasoning tasks, highlighting the pervasive serialization bias that compromises structural integrity. We then propose a formal framework to diagnose this bias, introducing two principled metrics based on Centered Kernel Alignment (CKA): (i) PI, which measures embedding drift under complete structural derangement, and (ii) rho, a Spearman-based metric that tracks the convergence of latent structures toward a canonical form as structural information is incrementally restored. Our empirical analysis quantifies an expected flaw in modern Large Language Models (LLMs): even minor layout permutations induce significant, disproportionate semantic shifts in their table embeddings. This exposes a fundamental vulnerability in RAG systems, in which table retrieval becomes fragile to layout-dependent noise rather than to semantic content. In response, we present a novel, structure-aware TRL encoder architecture that explicitly enforces the cognitive principle of cell header alignment. This model demonstrates superior geometric stability and moves towards the PI ideal. Our work provides both a foundational critique of linearized table encoders and the theoretical scaffolding for semantically stable, permutation invariant retrieval, charting a new direction for table reasoning in information systems.
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PROBLEM
A hypothesis and framework for permutation-invariant table representation learning to build robust table retrieval systems. We argue that this linearization of tables discards their essential geometric and relational structure, creating representations that are brittle to layout...
METHOD
Historical approaches to Table Representation Learning (TRL) have largely adopted the sequential paradigms of Natural Language Processing (NLP). We argue that this linearization of tables discards their essential geometric and relational structure, creating representations that...
RESULT
ScienceToStartup currently rates this 5.0/10 on the public viability pass. This model demonstrates superior geometric stability and moves towards the PI ideal. Code availability is flagged in the production record; the public repository link still needs proof alignment.
WHY NOW
Table Reasoning moved forward this cycle; last verified April 2026. Public score 5.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A hypothesis and framework for permutation-invariant table representation learning to build robust table retrieval systems. We argue that this linearization of tables discards their essential geometric and relational structure, creating representations that are brittle to layout permutations.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Historical approaches to Table Representation Learning (TRL) have largely adopted the sequential paradigms of Natural Language Processing (NLP). We argue that this linearization of tables discards their essential geometric and relational structure, creating representations that are brittle to layout permutations.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 5.0/10 on the public viability pass. This model demonstrates superior geometric stability and moves towards the PI ideal. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Table Reasoning moved forward this cycle; last verified April 2026. Public score 5.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A hypothesis and framework for permutation-invariant table representation learning to build robust table retrieval systems.
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Table Reasoning
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