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This research proposes a novel architecture for repository-level code reasoning that eliminates the need for pre-built relation graphs by materializing connections dynamically during inference. Evaluated on SWE-bench Verified, the two-layer index approach achieves 95.6% success rates while addressing critical limitations like context window constraints and knowledge staleness in large software repositories. This method offers a scalable path forward for integrating deep codebase understanding into AI agents without incurring heavy training costs or maintenance overheads.
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The Thousand-Graph Hypothesis: A Testable Hypothesis of Task-Conditioned Relation Materialization in Repository-Level Code Reasoning

arXiv1 min read

arXiv:2608.26602v1 Announce Type: cross Abstract: Large software repositories are often beyond model context limits. Training repository knowledge into models is costly and quickly stale, while local retrieval can miss scattered requirements, and explicit relation graphs add ongoing maintenance burden. We propose an entity-only external interface with task-conditioned relation materialization during inference. A two-layer index separates global routing from local entity focus and is evaluated on DeepSeek-V4-Flash and SWE-bench Verified. The base, one-layer, and two-layer conditions achieve 92.1%, 94.2%, and 95.6% success, respectively, under zero pre-built entity-relation edges.

Abstract:Large software repositories are often beyond model context limits. Training repository knowledge into models is costly and quickly stale, while local retrieval can miss scattered requirements, and explicit relation graphs add ongoing maintenance burden. We propose an entity-only external interface with task-conditioned relation materialization during inference. A two-layer index separates global routing from local entity focus and is evaluated on DeepSeek-V4-Flash and SWE-bench Verified. The base, one-layer, and two-layer conditions achieve 92.1%, 94.2%, and 95.6% success, respectively, under zero pre-built entity-relation edges.

Software Engineering (cs.SE); Computation and Language (cs.CL)

D.2.3; D.2.8; I.2.7

arXiv:2608.26602 [cs.SE]

(or arXiv:2608.26602v1 [cs.SE] for this version)

https://doi.org/10.48550/arXiv.2608.26602 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Full text under the publisher's open licence · 183 words

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