Claim Plane: Reliability Gains and the Limits of Selective Concurrency for Parallel Coding Agents: A 30-Pair, Three-Seed Confirmatory Study of Deterministic Pre-Write Admission
On the map: LLM agents and tool use → Software engineering coding agents
Abstract (arXiv)
Parallel coding agents can produce locally valid changes that fail when combined. Claim Plane addresses this failure mode as deterministic pre-write admission over versioned change intents. This paper reports a confirmatory study on 30 frozen CooperBench feature pairs, balanced between 15 conflict and 15 clean labels, with three coder seeds, four coordination arms, and 360 completed executions. DeepSeek V4 Pro generated 60 feature-level planner declarations once; the declarations were frozen across all arms and coder seeds, while DeepSeek V4 Flash performed the coding work. Static Claim Plane raised pair pass from 23.3% under unconstrained parallel execution to 50.0%, a paired task-cluster difference of +26.7 percentage points (95% bootstrap CI 9.6 to 60.0), and raised integration success from 65.6% to 96.7%. On conflict-labeled pairs, pair pass rose from 6.7% to 60.0%. However, static admission serialized 96.7% of executions, including 93.3% of clean cases, and therefore recovered reliability largely by collapsing toward serial execution. Dynamic admission was more selective, serializing 66.7% of conflict cases and 13.3% of clean cases, but 46 of 90 executions failed closed on undeclared scope, reducing pair pass to 22.2%. Forty-five of those 46 blocks targeted files already present in the frozen declarations, indicating region undercoverage and insufficient amendment handling rather than wholly unknown files. The results support pre-write admission as a reliability mechanism, but they do not establish useful wall-clock parallel speedup: provider calls were physically sequential, and the conservative policy largely serialized the workload. The complete study artifacts, hashes, and clustered bootstrap analysis are publicly released for reproduction.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 11 of its 12 references that are in the map. First posted on arXiv 2 Aug 2026.
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11 of 12 references.
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Same direction, newest first
- 2026Claim Plane: Enforceable Change Intents and Dynamic Scope for Parallel Coding Agents24 Jul
- 2026ATM: CID-Brokered Pre-Write Admission for Multi-Agent Code Co-Synthesis29 Jun
- 2026A Deterministic Control Plane for LLM Coding Agents25 Jun
- 2026CoAgent: Concurrency Control for Multi-Agent Systems13 Jun
- 2026Code as Agent Harness18 May
- 2026Effective Strategies for Asynchronous Software Engineering Agents23 Mar
- 2025CodeCRDT: Observation-Driven Coordination for Multi-Agent LLM Code Generation18 Oct
- 2024SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering6 May
Other references · 3
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- 2026Verifying Coordination in Parallel Coding Agents: NP-Bench and a Scheduling Planner
- 2026EvoCode-Bench: Evaluating Coding Agents in Multi-Turn Iterative Interactions
- 2026Before the Pull Request: Mining Multi-Agent Coordination
- 2026When the Specification Emerges: Benchmarking Faithfulness Loss in Long-Horizon Coding Agents
- 2026LoopsBench: From Harness Engineering to Loop Engineering in Coding Agent Evaluation
- 2026TicTacBench: Benchmarking Timing Closure Capabilities of Coding Agents
- 2026Passes Alone, Fails Together: Benchmarking Semantic Coordination in Parallel LLM-Agent Development
- 2026Looping Is Not Reliability: State-Bound Evidence and Typed Revision Contracts for Agentic Code Repair