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Interactive Speculative Planning: Enhance Agent Efficiency through Co-design of System and User Interface

Wenyue Hua, Mengting Wan, Shashank Vadrevu, Ryan Nadel, Yongfeng Zhang, Chi Wang

arXiv:2410.00079v1cs.MAcs.AIcs.CLcs.HCcs.LG

TL;DR

LLM-based agents can incur planning latency from model inefficiency and extensive intermediate reasoning, and long interaction delays matter to user experience. Interactive Speculative Planning combines speculative planning with an active interface and human intervention; experiments on OpenAGI and TravelPlanner demonstrate improvements in time and cost efficiency.

  • Problem

    LLM-agent planning can be slow because large, heavily requested models and extensive intermediate thoughts consume time, while interaction delays matter to user experience.

    Source: §1 · p. 2

  • Method

    Interactive Speculative Planning co-designs speculative agent planning with an active interface, integrating human interruptions as intermediate results.

    Source: §1 · pp. 2–3, §3.1 · p. 5

  • Results

    Experiments on OpenAGI and TravelPlanner demonstrate the approach's effectiveness in improving time efficiency and cost efficiency.

    Source: §7 · p. 23

  • Takeaways & Limitations

    The approximation agent, target agent, and human agent collaborate and interleave operations to accelerate overall agent planning.

    Source: §1 · p. 3

    Unverified or inconsistent approximation-agent steps introduce vulnerabilities, and the paper calls for more sophisticated methods to relax action-acceptance conditions.

    Source: §6 · pp. 20, 22

Abstract

from arXiv · show

Agents, as user-centric tools, are increasingly deployed for human task delegation, assisting with a broad spectrum of requests by generating thoughts, engaging with user proxies, and producing action plans. However, agents based on large language models (LLMs) often face substantial planning latency due to two primary factors: the efficiency limitations of the underlying LLMs due to their large size and high demand, and the structural complexity of the agents due to the extensive generation of intermediate thoughts to produce the final output. Given that inefficiency in service provision can undermine the value of automation for users, this paper presents a human-centered efficient agent planning method -- Interactive Speculative Planning -- aiming at enhancing the efficiency of agent planning through both system design and human-AI interaction. Our approach advocates for the co-design of the agent system and user interface, underscoring the importance of an agent system that can fluidly manage user interactions and interruptions. By integrating human interruptions as a fundamental component of the system, we not only make it more user-centric but also expedite the entire process by leveraging human-in-the-loop interactions to provide accurate intermediate steps. Code and data will be released.

1 INTRODUCTION

LLM-agent planning can be slow because of model and agent-structure costs, while latency and extended delays matter to user experience. Interactive Speculative Planning pairs speculative planning with a user interface that supports intervention, and positions this approach against prior efficiency methods.

1 INTRODUCTION

  • Planning latency is attributed to large, heavily requested models, prolonged intermediate thought, and sequential plan actions, although not every step needs intensive reasoning.The introduction also describes latency as critical to user experience and argues that fully automated agents with long delays are suboptimal for it.Source: §1 · p. 2
  • Interactive Speculative Planning co-designs the agent system and interface, treating human interruptions as intermediate results to accelerate planning and make it more user-centric.The framework interleaves an approximation agent, a target agent, and a human agent.Source: §1 · pp. 2–3
  • A fast, less capable approximation agent generates steps sequentially while a slower target agent asynchronously checks provisional trajectories; mismatches halt the approximation and substitute the target's output.The strategy potentially reduces the target agent's task-completion time to that of the approximation agent.Source: §1 · pp. 2–3
  • Because target-agent calls are asynchronous, UI rescheduling presents approximation and target outputs sequentially, helping users perceive delays and intervene when steps are slow or erroneous.Users can evaluate whether to accept or complement the algorithm's results.Source: §1 · p. 3, Figure 3 · p. 4

2 RELATED WORK

  • Interactive Speculative Planning supports any approximation/target pairing without training and guarantees performance at least equivalent to the target agent alone, potentially exceeding it with user interventions.Source: §2 · p. 5

3 INTERACTIVE SPECULATIVE PLANNING

Interactive Speculative Planning combines approximation, target, and human agents to accelerate planning. Its speculative generation is paired with a UI that orders outputs and supports user intervention.

3 INTERACTIVE SPECULATIVE PLANNING

  • The framework interleaves approximation, target, and human agents to improve planning efficiency and accuracy.The approximation agent proposes quick but potentially inaccurate steps, the target agent verifies and refines them, and the human can intervene.Source: §3 · p. 5

3.1 SPECULATIVE PLANNING

  • Fast A proposes sequential steps while T asynchronously generates successors; matches preserve speculative progress, whereas mismatches replace the step and invalidate later work.The cap k limits how many steps A can propose before corresponding target processes complete.Source: §3.1 · pp. 5–6

5 DoParallel

  • DoParallel executes and caches approximation steps with observations, then uses target steps to revise the prompt and cancel downstream work after a mismatch.Source: §5 · p. 6

3.2 UI INTERACTION ALGORITHM

  • The UI reschedules outputs to hide steps based on unverified prefixes, restore T’s sequential order, and expose A–T latency for bottleneck detection.Source: §3.2 · pp. 7–8
  • Users can replace a slow pending T step during displayed latency or override an unsatisfactory current step with their preferred step as an oracle.Source: §3.2 · p. 8

4 EFFICIENCY ANALYSIS

The section frames speculative planning’s efficiency through theoretical analyses and simulated experiments, including a baseline expression for whole-plan latency.

4 EFFICIENCY ANALYSIS

  • The analysis covers latency, total token generation, and concurrent API-call rate, with simulated experiments to support the analysis.Source: §4 · p. 8
  • Simulated experiment results are presented to support the analysis and demonstrate the proposed method’s effectiveness.Source: §4 · p. 8

4.1 LATENCY ANALYSIS

  • Without speculative planning, whole-plan generation and execution time is Σ_i≤n(time(T, s_i) + e(s_i)).Source: §4.1 · p. 8

4 End Function

The code fragment registers an interruption handler and routes user input into the target result for the selected task.

  • The user’s input is assigned to the selected target task as its action step.Source: §4 · p. 9
  • The cited line shows ts.append(ttarget task id), indicating that the target task ID is appended to ts; it does not identify the step’s source.Source: §4 · p. 9

8 End Function

This code fragment tracks approximation and target process results and presents completed approximation outputs through the UI.

  • The mechanism takes approximation and target process queues and result lists, along with presentation trackers, as inputs.Source: §8 · p. 9
  • When an approximation process completes, its result is presented in the UI.Source: §8 · p. 9
  • A signal handler is set up for the period when the user is waiting for a target result.Source: §8 · p. 9

17 else

The procedure presents a completed target step in the interface and permits the user to modify it.

17 else

  • When the target process finishes, the procedure presents its step result in the user interface.The displayed result may be modified by the user.Source: §17 · p. 9
  • The interface permits the presented target result ts[i] to be changed to ts[i]′.Source: §17 · p. 9
  • The implementation comment specifies that users can directly change the target agent’s computed result after presentation.Source: §17 · p. 9

23 end

Interactive speculative planning is analyzed through its time, token, and concurrency requirements, followed by simulations of how approximation accuracy, speed, k, and user interruptions affect planning. The analysis finds that worst-case latency is bounded by normal planning, while simulated interruptions reduce latency.

4.2 TOTAL TOKEN REQUIRED

  • Speculation uses more tokens than normal planning because both A and T traverse the plan and may generate unused tokens; matching proposals eliminate wasted tokens in the best case.Source: §4.2 · pp. 10–11

23 end

  • Worst-case speculative planning requires the same sequential generation and execution time as normal planning, so its latency cannot exceed the non-speculative approach.Source: §23 · p. 10, Figure 8 · p. 11

4.3 RATE REQUIRED

  • Speculative planning requires at least two concurrent calls, and at most k+1 between consecutive breaking points, including the approximation call.Source: §4.3 · p. 12

4.4 SIMULATION EXPERIMENT FOR SPECULATIVE PLANNING

  • Higher A accuracy shortens planning time; k=1, 2, or 3 slows planning, while higher k helps more as accuracy rises, and lower k generally reduces tokens except when k=1.Source: Figure 9 · p. 13, §4.4 · p. 13
  • For k=5, faster A and higher A accuracy each shorten planning time; token trends vary with A’s speed and accuracy.When A is very quick, higher accuracy monotonically reduces tokens; when A is around half as fast as T, accuracy has little impact until it is very high.Source: §4.4 · p. 13, Figure 10 · p. 14
  • More user interruptions reduce overall latency in the simulation, which uses k=n=10, A accuracy 0.5, and up to 10 interruptions.Source: §4.4 · p. 14

5 EXPERIMENT

Experiments on OpenAGI and TravelPlanner compare four approximation-target settings and report latency, resource use, accuracy-conditioned savings, and simulated user interruptions. The reported results show reduced average generation times across the settings evaluated.

5.1 BENCHMARKS

  • OpenAGI covers 117 multi-step tasks, while TravelPlanner supplies a travel-planning sandbox with nearly four million data records and constrained itinerary tasks.Source: §5.1 · pp. 14–15

5.2 SPECULATIVE PLANNING SETTINGS

  • The four settings pair direct generation or CoT approximation agents with ReAct, CoT, multiagent debate, or a stronger direct-generation target; Setting 4 lacks TravelPlanner results.Source: §5.2 · p. 15
  • OpenAGI action acceptance uses exact match, while TravelPlanner requires an exact function-name match and a Levenshtein distance below 0.3 for parameters.Source: §5.2 · pp. 15–16

5.3 EVALUATION METRICS

  • Evaluation reports average and minimum total and stepwise latency, token counts, maximum concurrent API calls, and average total cost.Source: §5.3 · p. 16

5.4 MAIN EXPERIMENT RESULT

  • 42.30% total time and 38.29% stepwise time reductions on OpenAGI occur in Setting 3; Settings 1, 2, and 4 also reduce both measures.Setting 1 reduces total and stepwise time by 22.27% and about 31.87%; Setting 2 by 28.32% and about 30.83%; Setting 4 by about 20.37% and about 23.50%.Source: Table 2 · p. 16, §5.4 · p. 16
  • 21.43% total and 29.52% stepwise latency reductions occur in TravelPlanner Setting 1; Settings 2 and 3 also reduce both measures.Setting 2 reduces average total and stepwise time by 19.18% and 32.53%; Setting 3 by 25.46% and 31.69%.Source: §5.4 · p. 16, Table 3 · p. 17

5.5 ANALYSIS OF LATENCY IMPROVEMENT BREAKDOWN

  • On OpenAGI, perfect-accuracy A datapoints often have the largest savings, but lower-accuracy datapoints also show savings; on TravelPlanner, savings exceed 40% above roughly 0.4 accuracy.OpenAGI Setting 3 reaches almost 60% stepwise time savings above 80% accuracy.Source: §5.5 · p. 18
  • Negative latency improvements occur in almost all settings, with variable agent speeds and concurrent target queries identified as theoretical-analysis assumptions that may explain them.Source: §5.5 · p. 19

6 LIMITATIONS AND FUTURE DIRECTIONS

The paper identifies limitations in action acceptance, user-interface control, security, and cost, and outlines possible directions for addressing them. It frames these as open issues for improving speculative planning.

Security

  • Unverified or inconsistent A actions may introduce security vulnerabilities, so the current approach should be constrained to non-high-stakes areas.Source: §6 · p. 20
  • Future security measures include human verification before execution and isolated execution environments such as Docker containers.Source: §6 · pp. 21–22

Effectiveness of step-by-step comparison

  • Step-by-step exact matching can reject different surface strings for the same step or alternative acceptable plans, decreasing A’s effective accuracy.Source: §6 · p. 22
  • The paper proposes relaxed step-by-step matching or postponed judgment of a sequence of n proposed steps as alternatives to exact per-step checks.Source: §6 · p. 22

Balancing time and cost efficiency in speculative planning

  • The current speculative-planning version may incur high additional cost, making the balance between time and cost efficiency an open issue.Source: §6 · p. 22
  • Proposed cost reductions include a cheaper A trained through distillation from T and a more sophisticated judgment method to improve perceived accuracy and reduce costly re-evaluations.Source: §6 · p. 22

Limitations of the Current User Interface

  • Users must intervene before results are fully presented; without backtracing, missed opportunities cannot be revisited or modified.Source: §6 · p. 22

7 CONCLUSION

Interactive Speculative Planning co-designs an efficient agent system with an active user interface and treats interruptions as part of planning. Experiments on OpenAGI and TravelPlanner report improved time and cost efficiency, while the paper notes unresolved interface, action-acceptance, and security limitations.

7 CONCLUSION

  • Interactive Speculative Planning co-designs an efficient agent system with an active interface, using human interruptions and intermediate steps to accelerate planning; two benchmarks show improved time and cost efficiency.Source: §7 · p. 23
  • The current system lacks backtracing, uses overly aggressive exact-match acceptance, and needs further security measures.Source: §7 · p. 23

APPENDIX

The appendix reports travel-planning results for normal and speculative planning using Delivery Rate and Commonsense Constraint Micro Pass Rate, and defines the evaluation measures.

IMPLEMENTATION DETAILS ON TRAVELPLANNER

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