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.
By Seonglae Cho, Donghyun Lee
arXiv:2606. 17182v1 Announce Type: new Abstract: Multi-agent LLM systems share state through memory stores, vector indices, and tool registries.
By Sajjad Khan
arXiv:2606. 06240v1 Announce Type: cross Abstract: Persistent memory for an LLM agent is a write-heavy substrate: every belief update is a versioned write, and a new claim may contradict a stored one.
By Ziming Wang
arXiv:2606. 22504v1 Announce Type: cross Abstract: Coding agents often receive broad tool access for an entire task, even when a resource is needed only for one subgoal.
By Igor Santos-Grueiro
The paper investigates how tool‑using language‑model agents can safely commit changes to infrastructure when external state may change between read and commit. By distinguishing invalidating races from predicate‑preserving and irrelevant ones, the authors evaluate three commit‑time guard granularities—global epoch, read‑set version, and semantic commit predicate—using a deterministic simulator and three quantized model families. The study finds that only the complete predicate guard consistently eliminates unsafe commits, while freshness‑based guards block a large proportion of benign races and model‑side signals fail to replace precise semantic enforcement.
By Zihao Zheng, Jiayu Long, Baichuan Li, Junyi Yao
arXiv:2609.08015v1 Announce Type: new
Abstract: Long-running AI agents may read state, reason, wait for tools or human approval, and perform an external action much later. The state that justified th...
By Yongjian Lyu, Yang Ren, Ruofei Lai, Wenting Liu
The paper introduces “Revise”, a runtime system that performs validity-guided, fine-grained recovery for online revisions in structured agent workflows. When a revision arrives, Revise intersects the change with recorded data and control dependencies, propagates the impact through the partially executed DAG, stops invalid work, preserves unaffected progress, and recomputes only the affected region. Experiments on real coding‑agent traces and LangGraph/LLMCompiler applications show that Revise matches a latest‑version oracle, reduces model calls by up to 56%, and improves service‑level objective goodput under load.
By Ruoling Qi, Xuaner Wu, Penghang Liu, Jian Chen, Yirui Liu
The paper argues that many failures in large language model–based multi‑agent systems stem from concurrency control issues rather than coordination or communication problems. It highlights how concurrent reads and writes to shared state, coupled with long inference windows, lead to stale data, lost updates, and inconsistent outcomes. The authors propose that MAS frameworks incorporate explicit concurrency controls—such as conflict detection, isolation guarantees, and structured resource access—to make concurrency a primary design concern.
By Xin Yang, Letian Li, Zimo Ji, Terry Jingchen Zhang, Wenyuan Jiang
The paper investigates where exactly‑once semantics should be enforced for tool‑using agents—within the model, the agent harness, or the tool contract—by evaluating 25,930 episodes across nine models, three harnesses, two contract variants, and fifteen recovery conditions. Using the LIMBO sandbox, the study shows that when an immediate read‑back is available, frontier models rarely duplicate lost acknowledgements, whereas weaker models do; when read‑back is unavailable, the contract’s idempotency keys explain most duplicate behavior. The authors prove that verification‑only policies cannot guarantee exactly‑once under late commits without bounded in‑flight time, and that waiting only helps when delays are short and predictable.
whyItMatters":"The findings clarify that enforcing exactly‑once semantics largely depends on the tool contract and fault type, guiding designers on where to focus reliability mechanisms for LLM agents."
By Jiapeng Li
Large language model (LLM) agents are evolving from request-response assistants into long-running software actors: they maintain state across model calls, fork subtasks, wait for external events, request human authority, generate tools, and perform side effects that must be resumed and audited. This paper presents Agent libOS, a library-OS-inspired runtime substrate for LLM agents.
arXiv:2602. 14849v2 Announce Type: replace-cross Abstract: LLM agents execute multi-step workflows that mutate external state through tools.
By Bardia Mohammadi, Nearchos Potamitis, Lars Klein, Akhil Arora, Laurent Bindschaedler
arXiv:2609. 00546v1 Announce Type: cross Abstract: Agent systems are commonly described by the model and harness that currently produce their behavior.
By Zhenyu Zhao (Independent Researcher), Roy Zhao (Paul G. Allen School of Computer Science & Engineering, University of Washington)