BranchMind: Verified Evidence Sharing Across Parallel Coding Agents

Parallel coding agents explore independent branches, yet repeatedly inspect the same files, symbols, and tests. Their transcripts are isolated because an observation made on one branch can become invalid after another branch edits its code. We introduce BranchMind, an execution memory that makes repository evidence reusable without turning state validity into a language-model judgment. A publisher packages pre-edit tool observations with content-addressed receipts over the exact code ranges and dependency manifests that support them. A consumer receives only observations whose receipts still match its branch. We evaluate this mechanism on 20 executable maintenance tasks from 10 public repositories through 579 new agent trajectories and two model families. Independent agents revisit the same file family in 39.2% of the shorter pre-edit trajectory. Across 145 real commit transitions, range receipts recover 37 byte-stable reads that whole-file invalidation discards. A contemporaneous 120-trajectory attribution study shows that paths reduce calls by 2.88 per run (task-and-repeat 95% CI: 0.70–5.03 fewer), while the observed success count is three higher after restoring concrete observations at 0.68 additional calls. We then freeze all 18 eligible natural partial branches across seven tasks and cross four sharing policies with GPT-6 Luna, DeepSeek V4 Pro, and a runtime-enforced DeepSeek replay. Range validation retains all 32 stable observations and exposes none of 31 changed observations; whole-file hashing retains only 7. Across 54 paired receiver–branch cells, descriptive means are 15.74 versus 17.93 calls; the task-weighted delta is 1.95 fewer (33 lower, 7 ties; task-and-branch 95% CI: 0.18–3.80 fewer), with 3 versus 4 observed successes. Against no sharing the task-weighted delta is 3.51 fewer calls with the same 3/54 successes. BranchMind turns execution already paid for by one agent into compact, branch-valid progress for another, without training or model-based routing.