Speculative Actions: A Lossless Framework for Faster Agentic Systems

• URL: http://arxiv.org/abs/2510.04371v1
• Date: Sun, 05 Oct 2025 21:28:11 GMT
• Title: Speculative Actions: A Lossless Framework for Faster Agentic Systems
• Authors: Naimeng Ye, Arnav Ahuja, Georgios Liargkovas, Yunan Lu, Kostis Kaffes, Tianyi Peng,
• Abstract summary: Execution of AI agents is often slow, hampering training, evaluation, and deployment.<n>Inspired by speculative execution in microprocessors, we propose a framework that predicts likely actions using faster models.<n>We evaluate this framework across three agentic environments: gaming, e-commerce, web search, and a "lossy" extension for an operating systems environment.
• Score: 6.708126506152481
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite growing interest in AI agents across industry and academia, their execution in an environment is often slow, hampering training, evaluation, and deployment. For example, a game of chess between two state-of-the-art agents may take hours. A critical bottleneck is that agent behavior unfolds sequentially: each action requires an API call, and these calls can be time-consuming. Inspired by speculative execution in microprocessors and speculative decoding in LLM inference, we propose speculative actions, a lossless framework for general agentic systems that predicts likely actions using faster models, enabling multiple steps to be executed in parallel. We evaluate this framework across three agentic environments: gaming, e-commerce, web search, and a "lossy" extension for an operating systems environment. In all cases, speculative actions achieve substantial accuracy in next-action prediction (up to 55%), translating into significant reductions in end-to-end latency. Moreover, performance can be further improved through stronger guessing models, top-K action prediction, multi-step speculation, and uncertainty-aware optimization, opening a promising path toward deploying low-latency agentic systems in the real world.

Related papers

• Benchmark Test-Time Scaling of General LLM Agents [27.756239376314294]
  General AgentBench is a benchmark for evaluating general LLM agents across search, coding, reasoning, and tool-use domains.<n>We study performance degradation when moving from domain-specific evaluations to this general-agent setting.<n>We find that neither scaling yields effective performance improvements in practice, due to two fundamental limitations.
  arXiv  Detail & Related papers  (2026-02-22T01:08:02Z)
• DLLM Agent: See Farther, Run Faster [94.74432470237817]
  Diffusion large language models (DLLMs) have emerged as an alternative to autoregressive (AR) decoding with appealing efficiency and modeling properties.<n>We study this in a controlled setting by instantiatingDLLM and AR backbones within the same agent workflow.<n>We find thatDLLM Agents are on average over 30% faster end to end than AR agents, with some cases exceeding 8x speedup.
  arXiv  Detail & Related papers  (2026-02-07T09:01:18Z)
• AgentArk: Distilling Multi-Agent Intelligence into a Single LLM Agent [57.10083973844841]
  AgentArk is a novel framework to distill multi-agent dynamics into the weights of a single model.<n>We investigate three hierarchical distillation strategies across various models, tasks, scaling, and scenarios.<n>By shifting the burden of computation from inference to training, the distilled models preserve the efficiency of one agent while exhibiting strong reasoning and self-correction performance of multiple agents.
  arXiv  Detail & Related papers  (2026-02-03T19:18:28Z)
• Towards Efficient Agents: A Co-Design of Inference Architecture and System [66.59916327634639]
  This paper presents AgentInfer, a unified framework for end-to-end agent acceleration.<n>We decompose the problem into four synergistic components: AgentCollab, AgentSched, AgentSAM, and AgentCompress.<n>Experiments on the BrowseComp-zh and DeepDiver benchmarks demonstrate that through the synergistic collaboration of these methods, AgentInfer reduces ineffective token consumption by over 50%.
  arXiv  Detail & Related papers  (2025-12-20T12:06:13Z)
• SCOPE: Prompt Evolution for Enhancing Agent Effectiveness [53.75986399936395]
  Large Language Model (LLM) agents are increasingly deployed in environments that generate massive, dynamic contexts.<n>While agents have access to this context, their static prompts lack the mechanisms to manage it effectively.<n>We introduce textbfSCOPE (Self-evolving Context Optimization via Prompt Evolution)<n>We propose a Dual-Stream mechanism that balances tactical specificity (resolving immediate errors) with strategic generality (evolving long-term principles)
  arXiv  Detail & Related papers  (2025-12-17T12:25:05Z)
• Chameleon: Adaptive Adversarial Agents for Scaling-Based Visual Prompt Injection in Multimodal AI Systems [0.0]
  We propose a novel, adaptive adversarial framework designed to expose and exploit scaling vulnerabilities in production Vision-Language Models (VLMs)<n>Our experiments demonstrate that Chameleon achieves an Attack Success Rate (ASR) of 84.5% across varying scaling factors.<n>We show that these attacks effectively compromise agentic pipelines, reducing decision-making accuracy by over 45% in multi-step tasks.
  arXiv  Detail & Related papers  (2025-12-04T15:22:28Z)
• Reducing Latency of LLM Search Agent via Speculation-based Algorithm-System Co-Design [35.95362310928356]
  LLM-based search agents achieve strong performance but suffer from severe latency.<n>We revisit this bottleneck through the lens of speculation.<n>We present SPAgent, an algorithm-system co-design framework that expands the role of speculation in search agents to reduce latency.
  arXiv  Detail & Related papers  (2025-11-25T08:15:17Z)
• VeriOS: Query-Driven Proactive Human-Agent-GUI Interaction for Trustworthy OS Agents [39.3943822850841]
  We introduce VeriOS-Agent, a trustworthy OS agent trained with a two-stage learning paradigm.<n>We show that VeriOS-Agent improves the average step-wise success rate by 20.64% in untrustworthy scenarios over the state-of-the-art.
  arXiv  Detail & Related papers  (2025-09-09T09:46:01Z)
• Adaptive Reinforcement Learning for Unobservable Random Delays [46.04329493317009]
  We introduce a general framework that enables agents to adaptively handle unobservable and time-varying delays.<n>Specifically, the agent generates a matrix of possible future actions to handle both unpredictable delays and lost action packets sent over networks.<n>Our method significantly outperforms state-of-the-art approaches across a wide range of benchmark environments.
  arXiv  Detail & Related papers  (2025-06-17T11:11:37Z)
• Agentic Predictor: Performance Prediction for Agentic Workflows via Multi-View Encoding [56.565200973244146]
  Agentic Predictor is a lightweight predictor for efficient agentic workflow evaluation.<n>By learning to approximate task success rates, Agentic Predictor enables fast and accurate selection of optimal agentic workflow configurations.
  arXiv  Detail & Related papers  (2025-05-26T09:46:50Z)
• DARS: Dynamic Action Re-Sampling to Enhance Coding Agent Performance by Adaptive Tree Traversal [55.13854171147104]
  Large Language Models (LLMs) have revolutionized various domains, including natural language processing, data analysis, and software development.<n>We present Dynamic Action Re-Sampling (DARS), a novel inference time compute scaling approach for coding agents.<n>We evaluate our approach on SWE-Bench Lite benchmark, demonstrating that this scaling strategy achieves a pass@k score of 55% with Claude 3.5 Sonnet V2.
  arXiv  Detail & Related papers  (2025-03-18T14:02:59Z)
• DynaSaur: Large Language Agents Beyond Predefined Actions [126.98162266986554]
  Existing LLM agent systems typically select actions from a fixed and predefined set at every step.<n>We propose an LLM agent framework that can dynamically create and compose actions as needed.<n>In this framework, the agent interacts with its environment by generating and executing programs written in a general-purpose programming language.
  arXiv  Detail & Related papers  (2024-11-04T02:08:59Z)
• Instance-Aware Predictive Navigation in Multi-Agent Environments [93.15055834395304]
  We propose an Instance-Aware Predictive Control (IPC) approach, which forecasts interactions between agents as well as future scene structures. We adopt a novel multi-instance event prediction module to estimate the possible interaction among agents in the ego-centric view. We design a sequential action sampling strategy to better leverage predicted states on both scene-level and instance-level.
  arXiv  Detail & Related papers  (2021-01-14T22:21:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.