arXiv:2607. 25816v1 Announce Type: new Abstract: Large language model agents often spend substantial wall-clock time waiting for tool call results.
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arXiv:2606. 00135v1 Announce Type: cross Abstract: Tool-calling is a central component of modern large language model (LLM) agents, equipping them with skills beyond their parametric knowledge.
arXiv:2608. 14624v1 Announce Type: new Abstract: Multi-agent LLM systems have emerged as an important deployment paradigm for AI services, where each user request is decomposed into a sequence of specialized agents.
The paper introduces the Agent-Editing World Model (AEWM), a new approach that models how reasoning and actions influence future task progress instead of simulating tool responses. AEWM includes an Action Judge that classifies decisions as Critical, Exploratory, or Noisy, and a State Revision mechanism that edits noisy reasoning–action continuations from the same observed history. The integrated system, EditAct, directly updates the underlying state during real execution, leading to significant performance gains across multiple benchmarks and agent backbones.
arXiv:2607. 03333v1 Announce Type: cross Abstract: LLM agents are becoming a common interface for research, coding, and question answering, yet their Thought-Action-Observation loop is often serial: the model reasons, emits a tool call, then idles the GPU until the result returns.
Speculative Macro Commit (SMC) is a runtime technique for tool‑using language‑model agents that separates an authoritative actor model from a faster speculative drafter model. The drafter predicts and executes future action chains on a snapshot, storing recurring multi‑action patterns in a macro library. When the actor’s next tool call aligns with a drafted action, SMC commits the pre‑executed steps, reducing latency by up to 18.59% on certain benchmarks while maintaining accuracy.
Speculative Macro Commit (SMC) is a runtime technique that speeds up tool‑using language‑model agents by having a fast speculative drafter model predict and execute future action chains on a separate environment snapshot. The drafter’s predictions are matched against a macro library of recurring multi‑action skeletons; when the authoritative actor’s next tool call aligns with the first drafted action, SMC commits the remaining pre‑executed steps to the official trajectory. Experiments with Qwen3.5 models show that SMC maintains overall accuracy while cutting latency by up to 18.6% on telecom benchmarks and 44.9% on AppWorld compared to sequential execution.