VITAL-RAG: Object-Level Context Allocation for Coding Agents

Repository-level coding agents depend on retrieved code, but relevant evidence may still fail to reach the coding model's input. Multiple views of one object can consume several context positions, while sequential packing can discard evidence from objects already selected. We study this retrieval-to-context gap and introduce VITAL-RAG, an object-level context-allocation system for coding agents. Controlled comparisons on 16,490 RepoBench tasks show that object grouping provides a substantial structural gain: full-source evidence containment increases from 24.99% to 39.16%. With candidates fixed task by task, the system's rendering configuration raises complete rendered-unit availability from 39.59% to 63.67% while using 35.63% fewer evidence tokens. Packing gains grow from 1.29 to 14.13 points as the candidate limit increases from five to twenty on a disjoint set of 14,442 tasks. Under a shared renderer and a twelve-object limit, cost-aware selection improves rendered-unit availability by 3.58 points over rank-only selection on 13,121 held-out tasks. A complete two-candidate RepoExec workflow using VITAL-RAG context construction reaches 65.63% Pass@1 with Claude versus 54.65% for the strongest local style control evaluated here; this complete-workflow result includes post-generation output selection. A 100-task controlled generation probe does not establish a statistically reliable code-accuracy gain from allocation alone. Together, these results separate evidence discovery, evidence delivery, and generated-code correctness in repository-level coding agents.