AgentRoom introduces a real‑time collaborative editing protocol that enables concurrent coding by multiple large language model agents within a CRDT‑backed shared workspace. By providing file‑level claim, status, and broadcast tools, it allows agents to coordinate directly rather than relying on serial phase handoffs or independent sampling. Experiments with five frontier coding‑CLI models show that AgentRoom reduces task abandonment and run‑to‑run variation compared to solo or parallel‑merge approaches, highlighting the importance of coordination over mere parallelism.
arXiv:2606. 15376v1 Announce Type: cross Abstract: Multi-agent LLM systems -- coding agents, devops agents, document agents -- now routinely run several agents in parallel against the same git tree, Kubernetes cluster, or document.
arXiv:2607. 22917v1 Announce Type: new Abstract: Large Language Model (LLM) agents have significantly improved coding and programming workflows.
arXiv:2603. 21489v2 Announce Type: replace-cross Abstract: AI agents have become increasingly capable at isolated software engineering (SWE) tasks such as resolving issues on Github.
arXiv:2607. 22917v2 Announce Type: replace-cross Abstract: Large Language Model (LLM) agents have significantly improved coding and programming workflows.
arXiv:2608. 16801v1 Announce Type: new Abstract: We study how teams of AI coding agents coordinate while solving programming tasks.
arXiv:2606. 09751v1 Announce Type: new Abstract: Foundation models are moving from response generation into operational roles.
arXiv:2606. 00953v1 Announce Type: new Abstract: Multi-agent Large Language Model (LLM) systems offer a way to decompose complex tasks, such as coding, through parallelization and context isolation.
The paper investigates how coordination among AI agents serving different users degrades performance compared to a single coordinating agent. Across five advanced models and 77 scenarios in four shared-resource environments—API key budgets, clinic calendars, personal assistant bookings, and merge queues—the study finds that multi‑agent teams consistently underperform, sometimes collapsing entirely, and that even with communication channels coordination overhead remains significant. The authors identify specific failure modes such as stalling, action overriding, and claim fabrication, and propose environment‑specific mitigations like team leads and procedural instructions, while releasing the MAMUBench benchmark for future research.
arXiv:2608. 08654v1 Announce Type: new Abstract: How much an AI coding agent costs to run can depend more on the agent scaffolding that drives it than on the interface through which it reaches its tools.
arXiv:2606. 19616v1 Announce Type: cross Abstract: Autonomous coding agents now open millions of pull requests, yet large-scale studies find their PRs are produced faster but accepted less often - a coordination and trust gap that pull-request-level telemetry cannot explain.
Dr. Claw is an open‑source AI scientist workspace that integrates existing command‑line coding agents into a single, auditable, human‑in‑the‑loop workflow. It uses persistent state objects, a reusable skill library, and multi‑executor coordination to link human decisions with AI execution, creating a traceable and recoverable loop for planning, execution, and writing. The authors demonstrate the system with an interactive scenario and a failure‑recovery walkthrough, and show that, when the underlying executor is held constant, Dr. Claw achieves higher research completeness while preserving an auditable process trail.
OpenCollab is a multi‑agent coding framework that unifies organization design, enforces experimental control on a shared runtime, and tracks execution via fine‑grained event streams. It introduces the metric Adherence to measure whether the declared organization is actually realized, showing that small configuration changes can shift adherence from 47.2% to 97.2%. Experiments demonstrate that a two‑coder workflow built on OpenCollab achieves new state‑of‑the‑art performance against mainstream harnesses while using the fewest tokens, and that a well‑designed organization can outperform strong existing harnesses.