TomasuLLM: Out-of-Order Speculative Execution for LLM Agents
On the map: LLM agents and tool use → Software engineering coding agents
Abstract (arXiv)
Long-running tools can dominate coding-agent latency: compilers, test suites, and repository commands take seconds to minutes while the agent idles. This observation stall presents the same tension that drove out-of-order processors -- asequential interface hides work that can be predicted and started early, but a speculative result may become visible only after it and every earlier step have been validated. We present TomasuLLM, a runtime that executes agent tool calls out of trajectory order while preserving task-execution correctness. It drafts future actions, runs them in isolated copy-on-write sandboxes, traces their dependencies and effects, and commits results in trajectory order only after validation against committed state. Across three benchmarks spanning sub-second to minutes-long tool calls, TomasuLLM improves the reported benchmark means and scales with tool latency: 1.31x on 100 SWE-bench Verified tasks, 1.35x on 28 Terminal-Bench 2.0 tasks, and 1.27x matched progress on 18 SWE-Marathon sessions. Across 4,010 audited commit-validation records, it produces zero false accepts.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 16 of its 37 references that are in the map. First posted on arXiv 22 Sep 2026.
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16 of 37 references.
Closest ancestors · 4
Same direction, newest first
- 2026AOSpec: Action and Observation Co-Speculation for Low-Latency Agent Serving1 Aug
- 2026SPORK: Self-Speculative Forking to Accelerate Agentic LLM Inference3 Jul
- 2026Parallelizing Tool Execution and LLM Generation for Low-Latency Agent Serving19 Mar
- 2025Speculative Actions: A Lossless Framework for Faster Agentic Systems5 Oct
Other references · 12
- 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
- 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,193
- 2022Fast Inference from Transformers via Speculative Decodingcited by 1,615
- 2023GAIA: a benchmark for General AI Assistantscited by 1,084
- 2023Accelerating Large Language Model Decoding with Speculative Samplingcited by 975
- 2023An LLM Compiler for Parallel Function Callingcited by 170
- 2025SagaLLM: Context Management, Validation, and Transaction Guarantees for Multi-Agent LLM Planningcited by 58
- 2026Atomix: Timely, Transactional Tool Use for Reliable Agentic Workflowscited by 27
- 2026Qwen-AgentWorld: Language World Models for General Agentscited by 22
- 2026SWE-Marathon: Can Agents Autonomously Complete Ultra-Long-Horizon Software Work?cited by 19
- 2026DualSpec: Accelerating Deep Research Agents via Dual-Process Action Speculationcited by 6
- 2026Speculate While You Reason: Teaching Agents to Predict Their Next Tool Call via Joint Agent-Speculator RLcited by 1
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- 2026Tool-Making and Self-Evolving LLM Agents in Low-Latency Systems
- 2026ProcCtrlBench: Evaluating Process-Level Defects and Control Preservation in LLM Coding Agents
- 2026Speculative Macro Commit for Faster Tool-Using Agents
- 2026AgentRewind: Recoverable Execution for Long-Horizon LLM Agents
- 2026Speculate with Memory: Lossless Acceleration for LLM Agents
- 2026TraceLab: Characterizing Coding Agent Workloads for LLM Serving