$π^2$: Structure-Originated Reasoning Data Improves Long-Context Reasoning Ability of Large Language Models explores A pipeline for curating reasoning data from structured sources to enhance LLM long-context reasoning capabilities, with open-source code and data.. Commercial viability score: 7/10 in LLM Reasoning.
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This route is the stable paper-level surface for citations, viability, references, and downstream handoffs. Use it as the proof layer behind Signal Canvas, workspace creation, and launch-pack generation.
Page Freshness
Canonical route: /paper/2-structure-originated-reasoning-data-improves-long-context-reasoning-ability-of-large-language-models
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Agent Handoff
Canonical ID 2-structure-originated-reasoning-data-improves-long-context-reasoning-ability-of-large-language-models | Route /paper/2-structure-originated-reasoning-data-improves-long-context-reasoning-ability-of-large-language-models
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/paper/2-structure-originated-reasoning-data-improves-long-context-reasoning-ability-of-large-language-modelsMCP example
{
"tool": "get_paper",
"arguments": {
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}
}source_context
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}Constellation, claims, and market context stay visible on the paper proof page even when commercialization rails are held back for incomplete proof receipts.
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Dimensions overall score 7.0
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