# Speculative Actions: A Lossless Framework for Faster Agentic Systems

arXiv: https://arxiv.org/abs/2510.04371 · HTML: https://mlsanity.com/paper/2510.04371/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 16 of its 28 references that are in the map.
- First posted on arXiv 5 Oct 2025.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 16 of 16)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://mlsanity.com/paper/1809.09600/) (2018)
- [Fast Inference from Transformers via Speculative Decoding](https://mlsanity.com/paper/2211.17192/) (2022)
- [τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains](https://mlsanity.com/paper/2406.12045/) (2024)
- [Accelerating Large Language Model Decoding with Speculative Sampling](https://mlsanity.com/paper/2302.01318/) (2023)
- [LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion](https://mlsanity.com/paper/2306.02561/) (2023)
- [GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning](https://mlsanity.com/paper/2507.19457/) (2025)
- [Harnessing Multiple Large Language Models: A Survey on LLM Ensemble](https://mlsanity.com/paper/2502.18036/) (2025)
- [OSWorld-Human: Benchmarking the Efficiency of Computer-Use Agents](https://mlsanity.com/paper/2506.16042/) (2025)
- [Efficient Reasoning for LLMs through Speculative Chain-of-Thought](https://mlsanity.com/paper/2504.19095/) (2025)
- [Interactive Speculative Planning: Enhance Agent Efficiency through Co-design of System and User Interface](https://mlsanity.com/paper/2410.00079/) (2024)
- [ELT-Bench: An End-to-End Benchmark for Evaluating AI Agents on ELT Pipelines](https://mlsanity.com/paper/2504.04808/) (2025)
- [Accelerating Large Language Model Reasoning via Speculative Search](https://mlsanity.com/paper/2505.02865/) (2025)
- [Scaling Speculative Decoding with Lookahead Reasoning](https://mlsanity.com/paper/2506.19830/) (2025)
- [Beyond the Speculative Game: A Survey of Speculative Execution in Large Language Models](https://mlsanity.com/paper/2404.14897/) (2024)
- [Speeding up Policy Simulation in Supply Chain RL](https://mlsanity.com/paper/2406.01939/) (2024)

## Built on by (most cited first, 22 of 22)

- [Atomix: Timely, Transactional Tool Use for Reliable Agentic Workflows](https://mlsanity.com/paper/2602.14849/) (2026)
- [Parallelizing Tool Execution and LLM Generation for Low-Latency Agent Serving](https://mlsanity.com/paper/2603.18897/) (2026)
- [Crab: A Semantics-Aware Checkpoint/Restore Runtime for Agent Sandboxes](https://mlsanity.com/paper/2604.28138/) (2026)
- [Sherlock: Reliable and Efficient Agentic Workflow Execution](https://mlsanity.com/paper/2511.00330/) (2025)
- [Reducing Latency of LLM Search Agent via Speculation-based Algorithm-System Co-Design](https://mlsanity.com/paper/2511.20048/) (2025)
- [DualSpec: Accelerating Deep Research Agents via Dual-Process Action Speculation](https://mlsanity.com/paper/2603.07416/) (2026)
- [SPORK: Self-Speculative Forking to Accelerate Agentic LLM Inference](https://mlsanity.com/paper/2607.03333/) (2026)
- [Fast LLM Post-training via Decoupled and Fastest-of-N Speculation](https://mlsanity.com/paper/2511.16193/) (2025)
- [Speculate with Memory: Lossless Acceleration for LLM Agents](https://mlsanity.com/paper/2607.12236/) (2026)
- [AOSpec: Action and Observation Co-Speculation for Low-Latency Agent Serving](https://mlsanity.com/paper/2608.00881/) (2026)
- [SpecHop: Continuous Speculation for Accelerating Multi-Hop Retrieval Agents](https://mlsanity.com/paper/2605.21965/) (2026)
- [Architectural Implications of Agentic AI Workflows](https://mlsanity.com/paper/2608.04458/) (2026)
- [Skim: Speculative Execution for Fast and Efficient Web Agents](https://mlsanity.com/paper/2605.16565/) (2026)
- [Certified Speculative Execution for Untrusted AI Agents](https://mlsanity.com/paper/2606.31023/) (2026)
- [AgentOpt v0.1 Technical Report: Client-Side Optimization for LLM-Based Agent](https://mlsanity.com/paper/2604.06296/) (2026)
- [Why Are GUI Agents Correct but Late? Decode on the Decision-Time Critical Path, Tested with Pre-Compiled Policy Trees](https://mlsanity.com/paper/2607.28399/) (2026)
- [Second Thought: Reasoning in Parallel as LLM Agents Act and Observe](https://mlsanity.com/paper/2608.13667/) (2026)
- [DynBranch: Speculative Subgraph Reuse for Dynamic Agentic LLM Serving](https://mlsanity.com/paper/2609.31047/) (2026)
- [TomasuLLM: Out-of-Order Speculative Execution for LLM Agents](https://mlsanity.com/paper/2609.38201/) (2026)
- [Speculative Safety Honeypot: Toward Proactive Defense Against Multi-turn Agent Attacks](https://mlsanity.com/paper/2609.39549/) (2026)
- [LEAP: Learning Efficient Action Proposals For LLM Agents](https://mlsanity.com/paper/2610.02670/) (2026)
- [ObsSpec: Accelerating Agentic Reinforcement Learning with Observation Speculation](https://mlsanity.com/paper/2610.15246/) (2026)

## Abstract (arXiv)

AI agents are increasingly deployed in complex, interactive environments, yet their runtime remains a major bottleneck for training, evaluation, and real-world use. Typical agent behavior unfolds sequentially, with each action requiring an API call that can incur substantial latency. For example, a game of chess between two state-of-the-art agents can take hours. We introduce Speculative Actions, a lossless acceleration framework for general agentic systems. Inspired by speculative execution in microprocessors and speculative decoding in LLM inference, our method uses faster models to predict likely future actions and execute them in parallel, committing only when predictions match. We evaluate speculative actions across gaming, e-commerce, and web search environments, and additionally study a lossy extension in an operating systems setting. Across domains, we achieve up to 55% next-action prediction accuracy, translating into up to 20% latency reductions. Finally, we present a cost-latency analysis that formalizes the tradeoff between speculative breadth and time savings. This analysis enables principled tuning and selective branch launching to ensure that multi-branch speculation delivers practical speedups without prohibitive cost growth.