IdleSpec: Exploiting Idle Time via Speculative Planning for LLM Agents
Daewon Choi, Kyunghyun Park, Woomin Song, Saket Dingliwal, Sai Muralidhar Jayanthi, Jinwoo Shin, Aram Galstyan
Abstract
Large language model (LLM)-based agents solve complex tasks by leveraging multi-step reasoning with iterative tool calls and environment interactions, which incur idle time while waiting for observations. Despite the prevalence of idle time in most agentic scenarios, existing works treat it as an unavoidable overhead or propose restricted solutions that overlook varying computational budgets across different tool calls and future observation uncertainty, thereby leading to suboptimal utilization of idle time. In this paper, we introduce IdleSpec, a scalable and generic inference approach that leverages idle-time computation to improve agent performance while minimizing latency overhead. Specifically, IdleSpec iteratively generates plan candidates during idle periods and, once observations become available, aggregates them to guide the next reasoning step. For effective plan generation under observation uncertainty, IdleSpec samples between complementary drafting strategies (i.e., progressive and recovery) from a learned distribution that is updated via posterior feedback. Our experiments demonstrate that IdleSpec significantly improves agent performance in various agentic scenarios by effectively utilizing idle time. In particular, on the GAIA and FRAMES, IdleSpec achieves 55.6% average accuracy with Gemini-2.5-Flash, surpassing the vanilla baseline without idle-time usage by 5.1%. Furthermore, for MLE-Bench, which involves substantial delay from code executions, IdleSpec achieves performance gains of up to 9.1% on the Any Medal rate, highlighting its generalizability to long-horizon tasks.
AI Impact Assessments
(1 model)Scientific Impact Assessment: IdleSpec
1. Core Contribution
IdleSpec addresses a genuine and previously underexploited phenomenon in LLM agent workflows: the idle time agents spend waiting for tool execution, environment responses, and sub-agent completions. The key insight is that this waiting period—which dominates total execution time (81–93% across benchmarks)—can be productively used for speculative planning rather than being wasted.
The framework introduces a two-phase approach: (a) iteratively drafting plan candidates during idle periods using two complementary strategies (progressive and recovery), and (b) aggregating these candidates once observations arrive to guide subsequent reasoning. The strategy selection mechanism uses Thompson sampling with Beta-distributed posterior updates based on a binary forecast signal, providing an adaptive balance between exploitation (progressive planning) and exploration (recovery planning).
The distinction from prior work, particularly Sleep-Time Compute (Lin et al., 2025), is meaningful: IdleSpec accounts for heterogeneous idle-time durations across tool calls, handles observation uncertainty through dual-mode speculation, and treats generated plans as soft references rather than hard constraints.
2. Methodological Rigor
Strengths in experimental design:
Weaknesses:
3. Potential Impact
Practical impact: The framework is immediately applicable to any ReAct-style agent workflow, as demonstrated across two different agent frameworks (OAgents and SmolAgents). The gains are particularly notable for harder tasks (GAIA Level 3: +6.5% with Gemini-2.5-Flash) and execution-heavy environments (MLE-Bench: +9.1% medal rate), suggesting high value in production agentic systems.
Conceptual impact: The paper reframes idle time from a systems optimization problem (reducing latency) to a performance optimization problem (improving accuracy), which is a useful conceptual shift. The idea that speculative computation during tool execution can improve reasoning quality—not just speed—opens a new dimension for test-time compute scaling.
Limitations in impact scope: The approach assumes sequential tool calling; highly parallelized multi-tool workflows would have less idle time to exploit. The additional token cost during idle windows could be significant at scale.
4. Timeliness & Relevance
This work is highly timely. LLM agents are rapidly being deployed in production (coding assistants, research agents, customer support), and tool-call latency is a recognized bottleneck. The emergence of "test-time compute" as a scaling paradigm (including Sleep-Time Compute, which this paper directly extends and improves upon) makes this a natural and well-positioned contribution. The observation that idle time constitutes 81-93% of total execution time across diverse benchmarks establishes clear practical motivation.
5. Strengths & Limitations
Key Strengths:
Notable Limitations:
6. Overall Assessment
IdleSpec makes a solid, well-motivated contribution to the emerging area of compute-efficient LLM agent execution. The insight about exploiting idle time for quality improvement (rather than just latency reduction) is valuable, and the dual-mode speculative planning with adaptive selection is a clean technical contribution. The experimental validation is broad, though statistical rigor could be strengthened with more seeds and larger evaluation sets. The framework's simplicity and compatibility with existing methods enhance its practical adoptability.
Generated May 22, 2026
Comparison History (21)
IdleSpec addresses a broadly applicable problem in LLM agent inference—idle time during tool calls—with a novel speculative planning approach that is generalizable across diverse agentic scenarios. It demonstrates significant performance gains (5-9%) on established benchmarks. Paper 2 (LLMSurvival) presents an interesting but incremental contribution with modest improvements (0.5-3.1%) on a narrower domain (survival analysis), and serves more as a proof-of-concept. IdleSpec's broader applicability to the rapidly growing LLM agent ecosystem, combined with stronger empirical gains and methodological novelty, gives it higher potential impact.
Paper 2 addresses a fundamental limitation of current LLM deployments (context limits) by proposing a paradigm shift to weight-based consolidation for persistent memory. Its rigorous statistical analysis and valuable methodological insights on evaluation metrics provide a broader, more transformative impact for personalized AI compared to Paper 1's performance optimization technique for agent idle time.
Paper 1 addresses a highly novel and practical bottleneck in LLM agents—idle time during tool execution—by introducing speculative planning. This system-level and algorithmic co-design has broader applicability across all agentic workflows compared to Paper 2, which presents a more domain-specific, incremental application of multi-agent reinforcement learning for code generation.
Paper 1 addresses a highly prevalent and critical bottleneck in modern LLM agents—idle time during tool usage and environment interaction. By introducing speculative planning to utilize this idle time, it offers a universally applicable architectural improvement that enhances performance without latency overhead. Paper 2, while methodologically rigorous, focuses on a more specialized problem within on-policy distillation and identity injection, giving Paper 1 a broader potential impact across the rapidly expanding field of autonomous AI agents.
Paper 2 likely has higher scientific impact: it introduces a concrete, broadly applicable inference-time technique (IdleSpec) that improves LLM-agent performance under realistic tool/interaction latency, with measurable gains on multiple established benchmarks (GAIA, FRAMES, MLE-Bench). This is timely for deployed agent systems and can transfer across domains that involve asynchronous tools and long-horizon tasks. Paper 1 provides important conceptual guidance for benchmarking knowledge work, but it is primarily methodological/position-oriented and may diffuse impact more slowly without an immediately adoptable, quantitative standard or artifact.
Paper 2 (IdleSpec) likely has higher impact due to a more novel, general algorithmic contribution: exploiting idle time with speculative planning under observation uncertainty, applicable across many LLM-agent settings. It shows measurable gains on multiple established benchmarks (GAIA, FRAMES, MLE-Bench), suggesting methodological rigor and broad, timely relevance to agent latency/performance tradeoffs in real deployments. Paper 1 provides an important domain-specific benchmark and taxonomy for finance spreadsheets, with clear real-world relevance, but its impact is narrower (evaluation-focused, finance-centric) and less broadly transferable than IdleSpec’s inference strategy.
Paper 2 likely has higher impact: it introduces a general, system-level inference strategy (idle-time speculative planning) applicable across many LLM-agent settings with tool latency (web, code, robotics, assistants), offering immediate practical deployment value and broad cross-field relevance. The method is timely given agent tool-use latency and shows measurable gains on widely used benchmarks (GAIA, FRAMES, MLE-Bench). Paper 1 is novel and useful, but its impact is narrower (text-to-image prompting evaluation) and depends on adoption of a specific benchmark/judge setup, with more domain-specific applicability.
Paper 2 is more novel and broadly impactful: it introduces an adaptive “embedding by elicitation” representation for Bayesian optimization of variable-length text under aggregate-only feedback, a common real-world constraint in deployed AI systems. This creates a general framework that can transfer to many optimization problems over natural-language artifacts (system prompts, policies, rubrics), bridging LLMs, BO, and representation learning. Paper 1 is useful and timely but is a more incremental systems-level inference improvement (idle-time speculative planning) with narrower conceptual reach. Both seem empirically validated, but Paper 2’s framing and applicability suggest higher impact.
Paper 1 introduces a highly novel conceptual framework by bridging Bayesian optimization with LLMs, utilizing them as dynamic semantic representation builders rather than just text generators. This methodological innovation for optimizing discrete, variable-length text under sample-constrained aggregate feedback addresses a major challenge in AI alignment and deployment. While Paper 2 offers a practical systems-level optimization (speculative planning) for agents, Paper 1's core algorithmic contribution has broader implications for combining traditional probabilistic ML with modern LLMs.
IdleSpec introduces a novel, broadly applicable inference-time optimization that exploits idle time during LLM agent tool calls—a pervasive but underexplored inefficiency. Its generic, scalable approach with learned drafting strategies applies across diverse agentic scenarios (web browsing, coding, QA), offering broader impact potential. Spreadsheet-RL, while practically useful, addresses a narrower domain (spreadsheet automation) with a more incremental contribution (applying RL fine-tuning to a specific task type). IdleSpec's methodological innovation in speculative planning under uncertainty has wider implications for the growing field of LLM agents.