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ARXIV:2603.21719 · LLM TRAINING · SUBMITTED 31 MAR · 20:30 UTC · FRESHNESS STALE
ARXIV:2603.21719LLM TRAININGSUBMITTED 31 MAR · 20:30 UTCFRESHNESS STALEHuaibing Xie · Guoliang Zhao · Yang Liu · Shihan Dou · Siming Huang · Yanling Xiao · +5 at arXiv
Synthesize structured table data to significantly boost LLM long-context reasoning capabilities, improving performance on both in-domain and out-of-domain tasks.
Opportunity summary
Pain Synthesize structured table data to significantly boost LLM long-context reasoning capabilities, improving performance on both in-domain and out-of-domain tasks.
Evidence 0 refs | 0 sources | 33% coverage
Blocker Evidence unverified
Synthesize structured table data to significantly boost LLM long-context reasoning capabilities, improving performance on both in-domain and out-of-domain tasks. However, few studies explore which data types are effective for long-context reasoning and why.
As real-world tasks grow increasingly complex, long-context reasoning has become a core capability for Large Language Models (LLMs). However, few studies explore which data types are effective for long-context reasoning and why.
ScienceToStartup currently rates this 7.0/10 on the public viability pass. We find that structured table data with periodic structures shows strong potential for long-context reasoning. Code availability is flagged in the production record; the…
LLM Training moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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Synthesize structured table data to significantly boost LLM long-context reasoning capabilities, improving performance on both in-domain and out-of-domain tasks.
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10.48550/arXiv.2603.21719Synthesize structured table data to significantly boost LLM long-context reasoning capabilities, improving performance on both in-domain and out-of-domain tasks.
Abstract
As real-world tasks grow increasingly complex, long-context reasoning has become a core capability for Large Language Models (LLMs). However, few studies explore which data types are effective for long-context reasoning and why. We find that structured table data with periodic structures shows strong potential for long-context reasoning. Motivated by this observation, we mathematically analyze tabular dependency structures using mutual information, revealing periodic non-vanishing dependencies in table data. Furthermore, we systematically analyze the capabilities of structured table data, conduct relevant scaling experiments, and validate its underlying mechanisms for enhancing long-context reasoning, yielding several meaningful insights. Leveraging these insights, we propose a simple yet scalable pipeline(TableLong) for synthesizing high-quality, diverse, and verifiable structured table data to boost long-context reasoning via RL. Extensive experimental results demonstrate that table data significantly enhances the long-context reasoning capability of LLMs across multiple long-context benchmarks (+8.24\% on average), and even improves performance on out-of-domain benchmarks (+8.06\% on average). We hope that our insights provide practical guidance for effective post-training data to enhance long-context reasoning in LLMs.
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Proof status
unverified0 refs; 0 sources; 33% coverage.
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Dimensions overall score 7.0
PROBLEM
Synthesize structured table data to significantly boost LLM long-context reasoning capabilities, improving performance on both in-domain and out-of-domain tasks. However, few studies explore which data types are effective for long-context reasoning and why.
METHOD
As real-world tasks grow increasingly complex, long-context reasoning has become a core capability for Large Language Models (LLMs). However, few studies explore which data types are effective for long-context reasoning and why.
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. We find that structured table data with periodic structures shows strong potential for long-context reasoning. Code availability is flagged in the production record; the public repository link still needs...
WHY NOW
LLM Training moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
Synthesize structured table data to significantly boost LLM long-context reasoning capabilities, improving performance on both in-domain and out-of-domain tasks. However, few studies explore which data types are effective for long-context reasoning and why.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
As real-world tasks grow increasingly complex, long-context reasoning has become a core capability for Large Language Models (LLMs). However, few studies explore which data types are effective for long-context reasoning and why.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. We find that structured table data with periodic structures shows strong potential for long-context reasoning. 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
LLM Training moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Synthesize structured table data to significantly boost LLM long-context reasoning capabilities, improving performance on both in-domain and out-of-domain tasks.
Segment
LLM Training
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Commercial read
7.0/10 public viability
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partial
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