Knows: Agent-Native Structured Research Representations

• URL: http://arxiv.org/abs/2604.17309v1
• Date: Sun, 19 Apr 2026 07:53:52 GMT
• Title: Knows: Agent-Native Structured Research Representations
• Abstract summary: We introduce Knows, a lightweight specification that binds structured claims, evidence, provenance, and verifiable relations to existing research artifacts.<n>Knows addresses the gap with a thin YAML sidecar that coexists with the original PDF, requiring no changes to the publication itself, and validated by a deterministic schema linter.<n>We evaluate Knows on 140 comprehension questions across 20 papers spanning 14 academic disciplines, comparing PDF-only, sidecar-only, and hybrid conditions.
• Score: 5.037922001284019
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Research artifacts are distributed primarily as reader-oriented documents like PDFs. This creates a bottleneck for increasingly agent-assisted and agent-native research workflows, in which LLM agents need to infer fine-grained, task-relevant information from lengthy full documents, a process that is expensive, repetitive, and unstable at scale. We introduce Knows, a lightweight companion specification that binds structured claims, evidence, provenance, and verifiable relations to existing research artifacts in a form LLM agents can consume directly. Knows addresses the gap with a thin YAML sidecar (KnowsRecord) that coexists with the original PDF, requiring no changes to the publication itself, and validated by a deterministic schema linter. We evaluate Knows on 140 comprehension questions across 20 papers spanning 14 academic disciplines, comparing PDF-only, sidecar-only, and hybrid conditions across six LLM agents of varying capacity. Weak models (0.8B--2B parameters) improve from 19--25\% to 47--67\% accuracy (+29 to +42 percentage points) when reading sidecar instead of PDF, while consuming 29--86\% fewer input tokens; an LLM-as-judge re-scoring confirms that weak-model sidecar accuracy (75--77\%) approaches stronger-model PDF accuracy (78--83\%). Beyond this controlled evaluation, a community sidecar hub at https://knows.academy/ has already indexed over ten thousand publications and continues to grow daily, providing independent evidence that the format is adoption-ready at scale.

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