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arxiv: 2511.02734 · v2 · submitted 2025-11-04 · 💻 cs.AI · cs.CL

CostBench: Evaluating Multi-Turn Cost-Optimal Planning and Adaptation in Dynamic Environments for LLM Tool-Use Agents

Jiayu Liu , Cheng Qian , Zhaochen Su , Qing Zong , Shijue Huang , Bingxiang He , Yi R. Fung This is my paper

Pith reviewed 2026-05-18 01:07 UTC · model grok-4.3

classification 💻 cs.AI cs.CL
keywords CostBenchLLM agentstool usecost-optimal planningdynamic adaptationbenchmarktravel planningeconomic reasoning
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0 comments X

The pith

LLM agents struggle to select cost-optimal tool plans and adapt when costs or tools change in dynamic settings.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents CostBench, a benchmark built around travel-planning tasks that can be solved by different sequences of atomic and composite tools, each carrying customizable costs. Tasks include both static versions where agents must pick the lowest-cost path upfront and dynamic versions that introduce blocking events such as tool failures or sudden cost increases, forcing real-time replanning. Evaluations across leading models show consistent shortfalls: even the strongest proprietary model reaches under 75 percent exact-match accuracy on the hardest static cases, with performance falling roughly 40 percent once dynamics are added. A sympathetic reader would see this as evidence that current agents lack reliable economic reasoning and robustness to change, two capabilities needed for practical deployment where resources are limited and conditions shift.

Core claim

CostBench evaluates multi-turn cost-optimal planning by providing tasks solvable through multiple tool sequences with diverse costs, plus four categories of dynamic blocking events that require agents to detect changes and revise plans on the fly. When tested, open-source and proprietary models frequently select non-optimal paths even without dynamics, and their success rates decline sharply once environmental changes are introduced.

What carries the argument

CostBench, a benchmark that supplies travel-planning tasks with multiple cost-bearing tool sequences and four types of dynamic blocking events to test both initial cost-optimal selection and real-time adaptation.

If this is right

  • Agents passing CostBench would exhibit both cost-minimizing planning and the ability to revise plans after tool or cost changes occur.
  • Persistent low performance on the benchmark indicates that current LLM agents do not reliably track or optimize cumulative resource use across multiple steps.
  • The benchmark supplies a concrete testbed for training or prompting methods aimed at improving economic rationality.
  • Results can guide development of agents intended for domains where wasted resources carry direct penalties.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the observed gaps persist across other domains, training pipelines may need explicit modules for cost tracking rather than relying solely on next-token prediction.
  • The benchmark could be extended by adding continuous cost variation or multi-agent competition to probe more complex economic behaviors.
  • High-performing agents on CostBench might transfer to resource-constrained settings such as edge-device orchestration or personal finance automation.

Load-bearing premise

The travel-planning domain, the chosen set of tools with adjustable costs, and the four specific dynamic events are representative of the economic reasoning and adaptation problems LLM agents face in wider real-world use.

What would settle it

A clear falsifier would be an agent that consistently achieves over 95 percent exact cost-optimal matches on the hardest static tasks and maintains at least 80 percent success under all dynamic events while generalizing to a second cost-sensitive domain such as logistics scheduling.

Figures

Figures reproduced from arXiv: 2511.02734 by Bingxiang He, Cheng Qian, Jiayu Liu, Qing Zong, Shijue Huang, Yi R. Fung, Zhaochen Su.

Figure 1
Figure 1. Figure 1: Overview of the CostBench pipeline. Starting from high-quality queries generated from combinations of user preferences, the agent constructs its plan, then interacts with an environment set up with atomic and composite tools under flexible cost assignments (atomic tool costs are randomized between 15 and 25 in our experiments), and executes actions along an customizable dynamic blocking module to achieve i… view at source ↗
Figure 2
Figure 2. Figure 2: Models’ average normalized edit distance and exact match ratio for task sequences of length five to eight. [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: LLMs’ performance on CostBench under different standard deviations of composite tool cost noise. [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Coverage rates of different LLMs across task [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: LLMs’ performance in CostBench’s dynamic blocking setting. All models show consistent EMR drops [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Performance of Gemini-2.5-Pro and Qwen3-14B under increasing numbers of blocking events (task sequence length = 7). Each curve represents a different blocking type. Both models degrade with more blockings, especially under frequent cost changes or tool bans, with Qwen3-14B showing near-total failure. the EMR of GPT-5, which originally achieved nearly perfect EMR, drops by around 20 percentage points, while… view at source ↗
Figure 7
Figure 7. Figure 7: Example of sequential tool execution flow [PITH_FULL_IMAGE:figures/full_fig_p014_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Performance of Qwen3-8B and Qwen3-14B on ANED and EMR (%) across different seeds. Results demonstrate low variations with different seeds, with variations in ANED and EMR not exceeding 5% across seeds. commonsense filter retained a different proportion of combinations for each task. The “Location” task had a lower filter pass rate due to its unique dimen￾sional features, resulting in the final distribution… view at source ↗
Figure 9
Figure 9. Figure 9: Annotation Screenshot [PITH_FULL_IMAGE:figures/full_fig_p024_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: The prompts used in our query construction stage. All the word surrounded with “[ ]” would be replaced [PITH_FULL_IMAGE:figures/full_fig_p025_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: The prompts used in our path extraction stage. All the word surrounded with “[ ]” would be replaced [PITH_FULL_IMAGE:figures/full_fig_p026_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: An example user query in CostBench. Dimension Values Category flight, train, bus, car rental Tier luxury class, business class, standard class, budget class Style speed priority, comfort priority, scenic route, schedule flexibility priority Feature Package onboard connectivity and power, full meal and beverage service, special luggage allowance, lie flat or sleeper facility [PITH_FULL_IMAGE:figures/full_… view at source ↗
Figure 13
Figure 13. Figure 13: An example tool schema for the tools we used in the CostBench. [PITH_FULL_IMAGE:figures/full_fig_p027_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: The first part of the prompt used to benchmark agents during runtime. The example shown corresponds [PITH_FULL_IMAGE:figures/full_fig_p028_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: The second part for agent runtime prompt. [PITH_FULL_IMAGE:figures/full_fig_p029_15.png] view at source ↗
read the original abstract

Current evaluations of Large Language Model (LLM) agents primarily emphasize task completion, often overlooking resource efficiency and adaptability. This neglects a crucial capability: agents' ability to devise and adjust cost-optimal plans in response to changing environments. To bridge this gap, we introduce CostBench, a scalable, cost-centric benchmark designed to evaluate agents' economic reasoning and replanning abilities. Situated in the travel-planning domain, CostBench comprises tasks solvable via multiple sequences of atomic and composite tools with diverse, customizable costs. It also supports four types of dynamic blocking events, such as tool failures and cost changes, to simulate real-world unpredictability and necessitate agents to adapt in real time. Evaluating leading open-sourced and proprietary models on CostBench reveals a substantial gap in cost-aware planning: agents frequently fail to identify cost-optimal solutions in static settings, with even GPT-5 achieving less than 75% exact match rate on the hardest tasks, and performance further dropping by around 40% under dynamic conditions. By diagnosing these weaknesses, CostBench lays the groundwork for developing future agents that are both economically rational and robust.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces CostBench, a scalable benchmark situated in the travel-planning domain for evaluating LLM agents on cost-optimal multi-turn planning and real-time adaptation. Tasks are solvable via sequences of atomic and composite tools with customizable costs; the benchmark also incorporates four types of dynamic blocking events (tool failures, cost changes, etc.). Experiments on leading open-source and proprietary models, including GPT-5, report that agents frequently fail to identify cost-optimal solutions in static settings (GPT-5 <75% exact match on hardest tasks) and suffer an additional ~40% performance drop under dynamic conditions.

Significance. If the experimental design and metrics are shown to be robust and reproducible, CostBench would provide a useful, cost-centric complement to existing task-completion benchmarks. It directly targets economic reasoning and replanning, two capabilities that are increasingly relevant for deployed agents but currently under-evaluated.

major comments (2)
  1. [§4 and §5] §4 (Benchmark Construction) and §5 (Experimental Setup): the abstract and main text state clear performance gaps but supply no details on task generation procedure, exact definition of the 'exact match rate' metric, baseline implementations, number of trials per condition, or statistical significance testing. These omissions are load-bearing for the central claim of a 'substantial gap' and the reported 40% dynamic drop.
  2. [§5.3] §5.3 (Dynamic Conditions): the four blocking-event types are described, yet the paper does not report how events are sampled, their frequency distribution across tasks, or whether agents are given explicit feedback about which event occurred. Without this information the adaptation results cannot be interpreted or replicated.
minor comments (2)
  1. [Table 1] Table 1: column headers for tool costs and composite-tool definitions are not fully aligned with the textual description in §3.2; a small clarifying footnote would help.
  2. [Figure 2] Figure 2: axis labels and legend for the static vs. dynamic performance comparison could be enlarged for readability.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We have revised the manuscript to address the concerns about missing methodological details in Sections 4 and 5, which will improve reproducibility and allow readers to better interpret the reported performance gaps.

read point-by-point responses

  1. Referee: [§4 and §5] §4 (Benchmark Construction) and §5 (Experimental Setup): the abstract and main text state clear performance gaps but supply no details on task generation procedure, exact definition of the 'exact match rate' metric, baseline implementations, number of trials per condition, or statistical significance testing. These omissions are load-bearing for the central claim of a 'substantial gap' and the reported 40% dynamic drop.

    Authors: We agree that these details were insufficient in the original submission and are essential for supporting the central claims. In the revised manuscript, Section 4 now includes a full description of the task generation procedure, including the algorithm used to create tasks with multiple tool sequences of varying total costs and the criteria for designating a sequence as cost-optimal. We have added a precise definition of the exact match rate metric (whether the agent's final tool sequence exactly matches one of the precomputed minimum-cost sequences). Baseline agent implementations are described with prompting templates and decoding parameters. Each experimental condition was run for 100 trials, and we now report statistical significance using paired t-tests with p-values and confidence intervals in the updated results tables. revision: yes

  2. Referee: [§5.3] §5.3 (Dynamic Conditions): the four blocking-event types are described, yet the paper does not report how events are sampled, their frequency distribution across tasks, or whether agents are given explicit feedback about which event occurred. Without this information the adaptation results cannot be interpreted or replicated.

    Authors: We acknowledge this omission and have added the requested information to the revised Section 5.3. Dynamic events are sampled independently for each task with a fixed probability of 0.35; when an event occurs, each of the four types (tool failure, cost increase, cost decrease, and tool unavailability) is chosen uniformly at random. Agents receive explicit natural-language feedback messages immediately after the event (e.g., “Tool flight_search has failed with error code 503” or “The cost of hotel_booking has increased by 25%”). These details, along with pseudocode for the event injection process, have been added to the main text and an appendix for full replicability. revision: yes

Circularity Check

0 steps flagged

No significant circularity in empirical benchmark

full rationale

The paper introduces CostBench as an empirical benchmark for LLM agents in travel-planning tasks with customizable tool costs and dynamic blocking events. Central claims consist of direct performance measurements (e.g., exact match rates below 75% for GPT-5 on hardest static tasks and ~40% drop under dynamic conditions) obtained by running models on the defined task set. No mathematical derivations, predictions, fitted parameters, or self-citation chains are present that reduce any result to quantities defined by the paper's own inputs. The evaluation is self-contained against external model runs on the benchmark tasks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The paper's central contribution is the introduction of a new evaluation benchmark rather than new fitted parameters or externally validated entities; the main unstated premises are domain assumptions about how travel tasks decompose into costed tools.

axioms (1)
  • domain assumption Travel-planning tasks can be meaningfully decomposed into sequences of atomic and composite tools that carry customizable costs.
    This premise underpins the entire task construction and cost-optimality measurement.
invented entities (1)
  • CostBench benchmark and its dynamic blocking events no independent evidence
    purpose: To evaluate cost-optimal planning and real-time adaptation in LLM agents
    The benchmark and event types are defined in this paper; no independent external evidence or falsifiable prediction outside the benchmark itself is provided.

pith-pipeline@v0.9.0 · 5748 in / 1355 out tokens · 31040 ms · 2026-05-18T01:07:20.735568+00:00 · methodology

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Forward citations

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Reference graph

Works this paper leans on

17 extracted references · 17 canonical work pages · cited by 3 Pith papers

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    Negar Maleki, Balaji Padmanabhan, and Kaushik Dutta

    Revisiting epistemic markers in confidence estimation: Can markers accurately reflect large lan- guage models’ uncertainty? InProceedings of the 63rd Annual Meeting of the Association for Compu- tational Linguistics (Volume 2: Short Papers), pages 206–221, Vienna, Austria. Association for Computa- tional Linguistics. Jiarui Lu, Thomas Holleis, Yizhe Zhang...

    work page arXiv 2025

  2. [2]

    I want the Location tier to be ‘secluded_nature’

    work page

  3. [3]

    I want the Location style to be ‘adventure’

    work page

  4. [4]

    Generated response: **conflict** User prompt: Prompt construction You are a helpful assistant for generating queries

    I want the Location features to include ‘nightlife_central’. Generated response: **conflict** User prompt: Prompt construction You are a helpful assistant for generating queries. Please generate a search query for a [task] task based on detailed user requirements. The user requirements will be comprised of four dimensions (Category requirement, Tier requi...

    work page

  5. [5]

    For each requirement dimension, you should clearly distinguish the user required one from any other possible candidates in the generated query

    The query clearly discribe the user requirements without any possibilities of misunder- standing. For each requirement dimension, you should clearly distinguish the user required one from any other possible candidates in the generated query. Possible candidates are listed below: Category requirement: [category_candidates] Tier requirement: [tier_candidate...

    work page

  6. [6]

    That is to say, you shouldn’t use the exact word to describe the user requirements

    You should use human-like language to express the user requirements. That is to say, you shouldn’t use the exact word to describe the user requirements. Instead, you should paraphrase and rephrase the requirements to imply the user needs in a natural way. For example: For if the user has a ’luxury’ requirement, then you could say something like ’money is ...

    work page

  7. [7]

    The query should be concise and to the point, avoiding unnecessary details or overly complex sentences

    work page

  8. [8]

    The location and time information are just meaningless placeholders

    All the information you could use is from the user preferences. The location and time information are just meaningless placeholders. Please **DO NOT GENERATE ANYTHING OTHER THAN THE QUERY**. Figure 10: The prompts used in our query construction stage. All the word surrounded with “[ ]” would be replaced with real parameters in construction time. Prompts u...

    work page

  9. [9]

    Each tool call has a predefined cost listed in the tool description

    **Tool Cost**. Each tool call has a predefined cost listed in the tool description

    work page

  10. [10]

    Each tool defines its input types through its parameters (the parameter name indicates the data type) and its output type in its description

    **Tool Input and Output Types**. Each tool defines its input types through its parameters (the parameter name indicates the data type) and its output type in its description

    work page

  11. [11]

    Some tools depend on others through their input/output types

    **Tool Dependencies**. Some tools depend on others through their input/output types. Carefully read each tool’s input/output fields and description before calling the tool

    work page

  12. [12]

    Each Tool has a list of input data types and a output data type

    **Data types**. Each Tool has a list of input data types and a output data type. You should infer LocationCategory, LocationTier, LocationStyle, LocationFeaturePackage, TimeInfo from the user query. For other data types, you only obtain them when a certain tool explicitly returns them. The data types are specially designed, and using them incorrectly will...

    work page

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    The tools available could categorized into atomic tools and composite tools, which is specified in the tool description

    **Atomic vs Composite Tools**. The tools available could categorized into atomic tools and composite tools, which is specified in the tool description. An atomic tool performs a single and unseparable operation. A composite tool chains multiple atomic tools in sequence and lists its component atomic tools in its description. The cost of a composite tool i...

    work page

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    For this task, the basic atomic tool calling sequence is: Decide_Location_Preference, Search_Location_Candidates, Location_Refinement_Step1, Select_Final_Location

    **Sample Atomic Tool Sequence**. For this task, the basic atomic tool calling sequence is: Decide_Location_Preference, Search_Location_Candidates, Location_Refinement_Step1, Select_Final_Location. You should replace some atomic tools with composite tools if that reduces cost. You must then compare all possible equivalent tool-calling paths and pick the on...

    work page

  15. [15]

    To ensure the optimality of your plan, you should list out all possible tool-calling paths, sum up the cost of each path, and then select the path with the lowest cost

    **Explain your reasoning.** Write out your plan clearly, showing how you’ll minimize cost. To ensure the optimality of your plan, you should list out all possible tool-calling paths, sum up the cost of each path, and then select the path with the lowest cost

    work page

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    Do not describe or print the tool call in text, just make the call directly

    **Execute your plan.** Right after the explanation, invoke the required tool. Do not describe or print the tool call in text, just make the call directly

    work page

  17. [17]

    <TimeInfo00000>

    **Adapt and continue.** You should always keep an eye on the environment. On every step of execution, you should always check if anything about the tool changes (e.g. cost, availability, etc.). If something goes wrong or changes, adapt and continue along the most cost-optimal path. </Expected workflow> <Important rules> - **Cost is the most important.** Y...

    work page