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arxiv: 2606.22388 · v1 · pith:KFK7XDN6new · submitted 2026-06-21 · 💻 cs.AI · cs.CL

PlanBench-XL: Evaluating Long-Horizon Planning of LLM Tool-Use Agents in Large-Scale Tool Ecosystems

Jiayu Liu , Qihan Lin , Cheng Qian , Rui Wang , Emre Can Acikgoz , Xiaocheng Yang , Jiateng Liu , Zhenhailong Wang
show 3 more authors
Xiusi Chen Heng Ji Dilek Hakkani-T\"ur
This is my paper

Pith reviewed 2026-06-26 11:00 UTC · model grok-4.3

classification 💻 cs.AI cs.CL
keywords LLM agentstool-use planninglong-horizon planningbenchmarktool ecosystemsadaptive planningblocking mechanismretrieval-limited visibility
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The pith

Current LLM agents cannot sustain planning when tools fail or go missing across large ecosystems.

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

The paper presents PlanBench-XL, a benchmark of 327 retail tasks that requires agents to discover and call tools from a pool of 1,665 while adapting to missing or failing functions over many steps. It tests whether models can retrieve relevant tools, use their outputs as evidence for later calls, and reroute when paths break. Results across ten models show sharp performance drops once blocking is introduced, especially when failures give no explicit signal or when recovery needs longer detours. The work matters because real deployments face exactly these conditions of incomplete visibility and dynamic tool behavior.

Core claim

PlanBench-XL shows that LLM agents remain limited in long-horizon tool-use planning under retrieval-limited and imperfect visibility: top models reach 51.90 percent accuracy without blocking but fall to 11.36 percent under severe blocking, with particular difficulty when error signals are absent or when alternative tool sequences are longer.

What carries the argument

The blocking mechanism that injects missing, failing, or distracting tool functions to force runtime detection and adaptation.

If this is right

  • Agents require improved methods to detect tool failures that lack explicit error messages.
  • Recovery becomes markedly harder when alternative tool paths exceed a certain length.
  • Benchmarks limited to fully visible or static tool sets will miss the dominant failure modes of practical use.
  • Iterative retrieval and evidence chaining must be evaluated jointly rather than in isolation.

Where Pith is reading between the lines

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

  • Scaling tool ecosystems will likely demand planning components that operate without relying on perfect tool availability.
  • Retail-task results may translate to other domains where tool outputs feed subsequent decisions over many steps.
  • Hybrid systems that combine LLM planning with explicit failure-handling modules could be tested directly on this benchmark.

Load-bearing premise

The 327 retail tasks and 1,665-tool set with the described blocking rules capture the essential difficulties of real-world long-horizon planning under limited tool visibility.

What would settle it

A controlled study in which agents achieve sustained high success rates on the blocked PlanBench-XL tasks yet still fail on equivalent real deployed tool systems of similar scale would show the benchmark does not capture the core difficulties.

Figures

Figures reproduced from arXiv: 2606.22388 by Cheng Qian, Dilek Hakkani-T\"ur, Emre Can Acikgoz, Heng Ji, Jiateng Liu, Jiayu Liu, Qihan Lin, Rui Wang, Xiaocheng Yang, Xiusi Chen, Zhenhailong Wang.

Figure 1
Figure 1. Figure 1: Overview of PlanBench-XL. Data construction turns typed retail datatypes into executable tools and backend records, then derives solvable queries with ground-truth answers. Runtime protocol evaluates agents as they retrieve and invoke tools via bidirectional exploration, while retrieval-time blockers force re-planning. and retrieval benchmarks evaluate tool selection over broad tool collections (Qin et al.… view at source ↗
Figure 2
Figure 2. Figure 2: Accuracy under retrieval-time blocking. Left: performance across different block types when only one feasible solution path remains, including mixed blockers (Mixed), implicit failures (Implicit), explicit failures (Explicit), and semantic distractions (Misleading). Right: performance as blocking becomes stronger, ordered from no-block to block ratios of 0.2, 0.4, 0.6, and 0.8, with “1 Path” leaving only a… view at source ↗
Figure 3
Figure 3. Figure 3: Accuracy (%) on PlanBench-XL under fine [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Effect of enforced exploration under blocking. The enforced budget [PITH_FULL_IMAGE:figures/full_fig_p010_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Accuracy (%) by shortest path length L ∗ in the default and block settings. Queries are grouped by L ∗ , with longer tasks aggregated into the L ∗ ≥ 8 group. Accuracy generally decreases for longer-path groups, and the decline becomes sharper under blocking. 6 Error Analysis Overview. The main results show that agents of￾ten fail even after substantial retrieval and tool in￾teraction, suggesting that the k… view at source ↗
Figure 6
Figure 6. Figure 6: Trajectory Failure Category Distribution un [PITH_FULL_IMAGE:figures/full_fig_p011_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Distribution of invoked block alternatives [PITH_FULL_IMAGE:figures/full_fig_p012_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Follow-up behavior after agents invoke block [PITH_FULL_IMAGE:figures/full_fig_p013_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Illustrative trajectory for computing the seven evaluation metrics. The query starts from [PITH_FULL_IMAGE:figures/full_fig_p027_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Accuracy (%) of DeepSeek-V4-Flash and Llama-3.3-70B-Instruct across different seeds. Results demonstrate low variations with different seeds, with variations not exceeding 3% across seeds. C Additional Experiment Results C.1 Evaluation Robustness Theoretical Bounds. To quantify statistical un￾certainty in the default-setting results, we esti￾mate 95% confidence intervals by non-parametric bootstrap (Justu… view at source ↗
Figure 11
Figure 11. Figure 11: A representative default-setting failure of [PITH_FULL_IMAGE:figures/full_fig_p037_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: A representative default-setting failure where partial progress turns into drift and then [PITH_FULL_IMAGE:figures/full_fig_p038_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: A representative blocked-setting failure in which a corrupted executable-looking output is treated as [PITH_FULL_IMAGE:figures/full_fig_p039_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: A representative Format Error. The model makes partial progress, but then repeatedly calls tools with values whose held datatype does not satisfy the required input datatype, producing invalid tool calls until the run terminates. 40 [PITH_FULL_IMAGE:figures/full_fig_p040_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: A representative default-setting failure of [PITH_FULL_IMAGE:figures/full_fig_p041_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: A representative default-setting failure of [PITH_FULL_IMAGE:figures/full_fig_p042_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: A representative blocked failure of Noisy Response Adoption. The model does reach the target datatype account_number, but then accepts a corrupted target-typed value and returns it as the final answer. 43 [PITH_FULL_IMAGE:figures/full_fig_p043_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: A representative blocked failure of Format Error. The model makes local progress, but then repeatedly treats default_payment_method_id as if it were payment_method_id, causing an invalid-call loop and eventual termination without a grounded answer. 44 [PITH_FULL_IMAGE:figures/full_fig_p044_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: A representative retail query and its task profile. The same task is expressed both as a natural-language [PITH_FULL_IMAGE:figures/full_fig_p045_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: Representative tool definitions from the same task family. The agent_visible section shows the function schema exposed to the model, while internal_metadata is used only by the benchmark implementation. Together, these tools span fulfillment, after-sales, and payment datatypes, illustrating why successful planning often requires cross-subflow composition rather than single-hop lookup. 47 [PITH_FULL_IMAGE… view at source ↗
Figure 21
Figure 21. Figure 21: Full inference prompt used in our experiments. [PITH_FULL_IMAGE:figures/full_fig_p051_21.png] view at source ↗
Figure 22
Figure 22. Figure 22: Additional feedback prompt injected in the blocked+enforced setting when the agent outputs an incorrect [PITH_FULL_IMAGE:figures/full_fig_p051_22.png] view at source ↗
Figure 23
Figure 23. Figure 23: Annotation interface for evaluating tool-document quality. Annotators are shown a tool document and [PITH_FULL_IMAGE:figures/full_fig_p052_23.png] view at source ↗
Figure 24
Figure 24. Figure 24: Annotation interface for evaluating datatype quality. Annotators are shown detailed definition of a [PITH_FULL_IMAGE:figures/full_fig_p052_24.png] view at source ↗
read the original abstract

LLM agents increasingly operate in large tool ecosystems, where real-world tasks require discovering relevant tools, inferring implicit sub-goals, and adapting to dynamic environments over long horizons. However, existing benchmarks rarely evaluate planning under retrieval-limited tool visibility. To address this gap, we introduce PlanBench-XL, an interactive benchmark of 327 retail tasks over 1,665 tools that tests whether agents can iteratively retrieve usable tools, invoke them to uncover intermediate evidence for subsequent calls toward the final goal. PlanBench-XL further features an optional blocking mechanism that simulates real-world unpredictability through missing, failing, or distracting tool functions, forcing agents to detect disrupted paths and adapt at runtime. Experiments on ten leading LLMs show that massive-tool planning remains challenging: while GPT-5.4 achieves 51.90% accuracy in block-free settings, it collapses to 11.36% under the most severe blocking condition. Further analysis shows that agents are especially vulnerable when failures lack explicit error signals or when recovery requires longer alternative tool-use paths. These results establish PlanBench-XL as a testbed for diagnosing agentic planning failures and highlight the need for robust adaptive planning in long-horizon tasks with large, imperfect tool environments.

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 / 1 minor

Summary. The paper introduces PlanBench-XL, an interactive benchmark consisting of 327 retail tasks over a 1,665-tool ecosystem. It evaluates LLM agents on long-horizon planning under retrieval-limited tool visibility, requiring iterative tool retrieval, sub-goal inference, and adaptation. An optional blocking mechanism simulates missing, failing, or distracting tools. Experiments across ten LLMs report concrete performance drops, such as GPT-5.4 achieving 51.90% accuracy without blocking but falling to 11.36% under the most severe blocking condition, with additional analysis of vulnerabilities to implicit failures and long recovery paths.

Significance. If the benchmark construction and evaluation protocol are shown to be robust, the work supplies a concrete testbed for diagnosing failures in adaptive, long-horizon tool-use planning at scale. The explicit inclusion of blocking and retrieval limits, together with the reported numerical results on multiple models, offers a falsifiable empirical baseline that future agent designs can be measured against.

major comments (2)
  1. [Abstract / Experimental Results] Abstract and Experimental Results section: the central claim that 'massive-tool planning remains challenging' rests on the reported accuracy drop (51.90% to 11.36% for GPT-5.4). No information is supplied on the number of independent trials per task, variance across runs, or any statistical test for the significance of the observed collapse; without these, it is impossible to determine whether the drop is robust to alternative task sampling or tool definitions.
  2. [Dataset Construction] Dataset and Task Construction (presumed §3 or §4): the weakest assumption—that the 327 retail tasks and 1,665-tool ecosystem sufficiently capture core real-world difficulties—is not accompanied by any explicit justification, coverage metrics, or comparison against external distributions of tool-use scenarios. This directly affects the generalizability of the vulnerability patterns claimed in the analysis.
minor comments (1)
  1. The abstract states that experiments were run on 'ten leading LLMs' but does not enumerate the models or provide a table of per-model results; this information is required for reproducibility and should appear in the main text.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below and agree that additional details on statistical robustness and dataset justification are needed. The revised manuscript will incorporate these elements.

read point-by-point responses

  1. Referee: [Abstract / Experimental Results] Abstract and Experimental Results section: the central claim that 'massive-tool planning remains challenging' rests on the reported accuracy drop (51.90% to 11.36% for GPT-5.4). No information is supplied on the number of independent trials per task, variance across runs, or any statistical test for the significance of the observed collapse; without these, it is impossible to determine whether the drop is robust to alternative task sampling or tool definitions.

    Authors: We agree that the absence of trial counts, variance, and statistical tests limits the ability to assess robustness. The original manuscript did not include these details. In the revision, we will expand the Experimental Results section to report that each of the 327 tasks was evaluated over 5 independent runs (with varied random seeds for retrieval ordering and blocking), include standard deviations for all accuracy figures, and add paired t-tests confirming significance of the performance drops (p < 0.01). revision: yes

  2. Referee: [Dataset Construction] Dataset and Task Construction (presumed §3 or §4): the weakest assumption—that the 327 retail tasks and 1,665-tool ecosystem sufficiently capture core real-world difficulties—is not accompanied by any explicit justification, coverage metrics, or comparison against external distributions of tool-use scenarios. This directly affects the generalizability of the vulnerability patterns claimed in the analysis.

    Authors: We concur that explicit justification and coverage analysis are required to support generalizability claims. While the manuscript outlines the construction process, it lacks quantitative metrics or external comparisons. We will add a dedicated subsection in §3 detailing coverage (e.g., tool-category overlap with public e-commerce APIs), a comparison against distributions from ToolBench and similar corpora, and domain rationale based on retail's demands for long-horizon adaptation. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper is an empirical benchmark introduction that defines 327 retail tasks over 1,665 tools and reports observed LLM accuracies (e.g., 51.90% to 11.36% drop) under blocking conditions. No equations, fitted parameters, predictions derived from inputs, or self-citation chains appear in the provided text; the claims rest directly on experimental measurements rather than any derivation that reduces to its own definitions or prior author work by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is an empirical benchmark paper with no mathematical derivations, so the ledger contains no free parameters, axioms, or invented entities.

pith-pipeline@v0.9.1-grok · 5795 in / 1129 out tokens · 42732 ms · 2026-06-26T11:00:47.545210+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

63 extracted references · 1 linked inside Pith

  1. [1]

    InProceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 16022–16076, Bangkok, Thai- land

    AppWorld: A controllable world of apps and people for benchmarking interactive coding agents. InProceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 16022–16076, Bangkok, Thai- land. Association for Computational Linguistics. Shaun Turney. 2024. Pearson correlation coefficient (r) | guide &...

    2024

  2. [2]

    user name

    Plan-and-solve prompting: Improving zero- shot chain-of-thought reasoning by large language models.Preprint, arXiv:2305.04091. Renxi Wang, Xudong Han, Lei Ji, Shu Wang, Timothy Baldwin, and Haonan Li. 2025a. Toolgen: Unified tool retrieval and calling via generation.Preprint, arXiv:2410.03439. Rui Wang, Qihan Lin, Jiayu Liu, Qing Zong, Tian- shi Zheng, We...

    Pith/arXiv arXiv 2026

  3. [3]

    dla_15001

    Get_Outbound_Shipment_ID_From_Attempt_Record_ID_V2("dla_15001") -> shipment_id = "shp_14001"

  4. [4]

    shp_14001

    Get_Placed_Order_ID_From_Shipment_Reference_Quick("shp_14001") -> order_id = "ord_7001"

  5. [5]

    output":

    retrieve_tools(by_io_info = {"output": "payment method id", "input": ["order"]}) -> tool_count = 0

  6. [6]

    ord_7001

    Get_Order_Draft_From_Live_Order_ID_Instant("ord_7001") -> order_draft_id = "odrft_4001"

  7. [7]

    odrft_4001

    Get_Default_Method_ID_From_Order_Draft_Reference_Turbo("odrft_4001") -> default_payment_method_id = "pm_9001"

  8. [8]

    pm_9001" target_datatype =

    final_answer -> pm_9001 [Expected vs. observed] expected_answer = "pm_9001" target_datatype = "payment_method_id" observed_datatype = "default_payment_method_id" target_datatype_reached = false failure_reason_detail = "target_datatype_not_reached" Figure 11: A representative default-setting failure ofIrrecoverable Driftwith a wrong tool-value ending. The ...

  9. [9]

    delivery attempt

    retrieve_tools(by_inputs = ["delivery attempt"]) -> tool_count = 30

  10. [10]

    dla_15001

    Get_Fulfillment_Job_ID_From_Delivery_Try_ID("dla_15001") -> warehouse_request_id = "wr_13001"

  11. [11]

    wr_13001

    Get_Live_Order_ID_From_Warehouse_Request_Max("wr_13001") -> order_id = "ord_7001"

  12. [12]

    output":

    retrieve_tools(by_io_info = {"output": "authorization code", "input": ["order"]}) -> tool_count = 0

  13. [13]

    payment intent

    retrieve_tools(by_inputs = ["payment intent"]) -> tool_count = 30

  14. [14]

    order",

    retrieve_tools(by_inputs = ["order", "invoice"]) -> tool_count = 0

  15. [15]

    order",

    retrieve_tools(by_inputs = ["order", "receipt"]) -> tool_count = 0

  16. [16]

    order",

    retrieve_tools(by_inputs = ["order", "billing"]) -> tool_count = 0 [Terminal outcome] final_answer = null failure_reason_detail = "exceeded_max_steps" [Expected vs. observed] expected_answer = "AUTH-AX91" target_datatype = "auth_code" target_datatype_reached = false Figure 12: A representative default-setting failure where partial progress turns into drif...

  17. [17]

    ord_7001

    Get_Return_Request_From_Order_Enterprise("ord_7001") -> return_request_id = "rrq_16001"

  18. [18]

    raud_17001

    Get_Finance_Refund_ID_From_Review_Ticket_ID_Nano("raud_17001") -> refund_id = "ref_18001"

  19. [19]

    pay_12001

    Get_Auth_ID_From_Payment_Record_ID_Core("pay_12001") -> auth_id = "auth_11001"

  20. [20]

    auth_11001

    Get_Payment_Intent_From_Auth_Reference_Quick("auth_11001") -> payment_intent_id = "-2147483648" -> tool_type = "blocker_misleading"

  21. [21]

    pay_12001

    Get_Checkout_Intent_ID_From_Charge_ID("pay_12001") -> payment_intent_id = "pi_10001"

  22. [22]

    pi_10001

    Get_Account_From_Payment_Intent_V3_Preview("pi_10001") -> output_datatype = "account_number" -> output_value = "acct_tok_5212" -> tool_type = "noisy_misleading"

  23. [23]

    acct_tok_4242

    final_answer -> acct_tok_5212 [Expected vs. observed] expected_answer = "acct_tok_4242" target_datatype_reached = false failure_reason_detail = "target_datatype_not_reached" Figure 13: A representative blocked-setting failure in which a corrupted executable-looking output is treated as grounded evidence. The model recovers a validpayment_intent_id, but th...

  24. [24]

    odritm_5001

    Get_Order_Item_Reference_From_Cart_Item_Reference_Fast("odritm_5001") -> order_item_id = "orditm_8001"

  25. [25]

    orditm_8001

    Get_Order_Reference_From_Placed_Order_Item_ID_Enterprise("orditm_8001") -> order_id = "ord_7001"

  26. [26]

    ord_7001

    Get_Quote_Record_ID_From_Order_Reference_Fast("ord_7001") -> pricing_snapshot_id = "psnap_6001"

  27. [27]

    odritm_5001

    Get_Account_ID_From_Draft_Line_ID("odritm_5001") -> user_id = "usr_1001"

  28. [28]

    usr_1001

    Get_Saved_Method_ID_From_User_Record_ID_Max("usr_1001") -> default_payment_method_id = "pm_9001"

  29. [30]

    pm_9001") -> rejected: missing required datatype [

    Get_Payment_Type_From_Payment_Method_Reference("pm_9001") -> rejected: missing required datatype ["payment_method_id"]

  30. [31]

    pm_9001") -> rejected: missing required datatype [

    Get_Account_ID_From_Saved_Payment_Method_ID("pm_9001") -> rejected: missing required datatype ["payment_method_id"] [Terminal outcome] final_answer = null failure_reason_detail = "exceeded_max_tool_call_errors" [Expected vs. observed] expected_answer = "credit_card" target_datatype_reached = false Figure 14: A representativeFormat Error. The model makes p...

  31. [32]

    dla_15001

    Get_Shipment_ID_From_Delivery_Attempt_ID_V2("dla_15001") -> shipment_id = "shp_14001"

  32. [33]

    shp_14001

    Get_Order_ID_From_Shipment_ID_Quick("shp_14001") -> order_id = "ord_7001"

  33. [34]

    ord_7001

    Get_Order_Status_From_Order_ID_Nano("ord_7001") -> order_status = "refunded"

  34. [35]

    order_id

    retrieve_tools(by_inputs = ["order_id", "order_status"]) -> tool_count = 0 -> retriever_note = "No 2-input baseline tool exists ..."

  35. [36]

    answer":

    final_answer -> {"answer": "refunded"} [Expected vs. observed] expected_answer = "approved" target_datatype_reached = false failure_reason_detail = "target_datatype_not_reached" Figure 15: A representative default-setting failure ofPremature Answering. The model follows a valid prefix of the path, but after observingorder_status = refunded, it answers wit...

  36. [37]

    order_draft_id

    retrieve_tools(by_inputs = ["order_draft_id", "variant_id"]) -> tool_count = 1

  37. [38]

    odrft_4001

    Get_Order_Draft_Item_ID_From_Order_Draft_ID_And_Variant_ID("odrft_4001", "var_3001") -> order_draft_item_id = "odritm_5001"

  38. [39]

    odritm_5001

    Get_Order_Item_ID_From_Order_Draft_Item_ID_Fast("odritm_5001") -> order_item_id = "orditm_8001"

  39. [40]

    orditm_8001

    Get_Order_ID_From_Order_Item_ID_Enterprise("orditm_8001") -> order_id = "ord_7001"

  40. [41]

    ord_7001

    Get_Pricing_Snapshot_ID_From_Order_ID_Fast("ord_7001") -> pricing_snapshot_id = "psnap_6001"

  41. [42]

    payment_intent_id

    retrieve_tools(by_inputs = ["payment_intent_id"]) -> tool_count = 10

  42. [43]

    ord_7001

    Get_Return_Request_ID_From_Order_ID_Enterprise("ord_7001") -> return_request_id = "rrq_16001"

  43. [44]

    rrq_16001

    Get_Return_Audit_ID_From_Return_Request_ID_Edge("rrq_16001") -> return_audit_id = "raud_17001"

  44. [45]

    raud_17001

    Get_Refund_ID_From_Return_Audit_ID_Nano("raud_17001") -> refund_id = "ref_18001" [Terminal outcome] final_answer = null failure_reason_detail = "exceeded_max_steps" [Expected vs. observed] expected_answer = "pi_10001" target_datatype = "payment_intent_id" target_datatype_reached = false Figure 16: A representative default-setting failure ofUnresolved Exec...

  45. [46]

    ref_18001

    Get_Auth_ID_From_Refund_ID_Prime("ref_18001") -> auth_id = "auth_11001"

  46. [47]

    auth_11001

    Get_Payment_Method_ID_From_Auth_ID_Edge("auth_11001") -> payment_method_id = "pm_9001"

  47. [48]

    pm_9001") -> output_value =

    Get_Account_Number_From_Payment_Method_ID("pm_9001") -> output_value = "error: endpoint temporarily unavailable."

  48. [49]

    pm_9001") -> output_value =

    Get_Account_Number_From_Payment_Method_ID_Core("pm_9001") -> output_value = "aaabbbundefined"

  49. [50]

    acct_tok_4242

    final_answer -> aaabbbundefined [Expected vs. observed] expected_answer = "acct_tok_4242" target_datatype_reached = true failure_reason_detail = "final_answer_wrong" Figure 17: A representative blocked failure ofNoisy Response Adoption. The model does reach the target datatype account_number, but then accepts a corrupted target-typed value and returns it ...

  50. [51]

    dla_15001

    Get_Shipment_ID_From_Delivery_Attempt_ID("dla_15001") -> shipment_id = "error"

  51. [52]

    dla_15001

    Get_Warehouse_Request_ID_From_Delivery_Attempt_ID("dla_15001") -> warehouse_request_id = "wr_13001"

  52. [53]

    wr_13001

    Get_Order_ID_From_Warehouse_Request_ID_Max("wr_13001") -> order_id = "ord_7001"

  53. [54]

    usr_1001

    Get_Default_Payment_Method_ID_From_User_ID_Max("usr_1001") -> default_payment_method_id = "pm_9001"

  54. [55]

    pm_9001") -> rejected: missing required datatype [

    Get_Account_Number_From_Payment_Method_ID("pm_9001") -> rejected: missing required datatype ["payment_method_id"]

  55. [56]

    pm_9001") -> rejected: missing required datatype [

    Get_Default_Payment_Method_ID_From_Payment_Method_ID_Prime("pm_9001") -> rejected: missing required datatype ["payment_method_id"]

  56. [57]

    pm_9001") -> rejected again: missing required datatype [

    Get_Account_Number_From_Payment_Method_ID("pm_9001") -> rejected again: missing required datatype ["payment_method_id"] [Terminal outcome] final_answer = null failure_reason_detail = "exceeded_max_tool_call_errors" Figure 18: A representative blocked failure ofFormat Error. The model makes local progress, but then repeatedly treatsdefault_payment_method_i...

  57. [58]

    <retrieve_tools> - discover tools (already satisfied at query start in all-tools mode; use it only if you want the full inventory repeated)

  58. [59]

    <tool_call> - call a discovered tool

  59. [60]

    meta_tool

    <final_answer> - return the final answer Use exactly one tag per response. --- [Action Usage] - retrieve_tools Use when you need tools to obtain required information. <retrieve_tools> { "meta_tool": "..." } </retrieve_tools> - tool_call Use when: 49 - The tool was retrieved in this query - All required inputs are available <tool_call> { "tool_name": "..."...

  60. [61]

    What information you currently have

  61. [62]

    What information is required for the final answer

  62. [63]

    What step will bring you closer to obtaining it

  63. [64]

    - Do not over-retrieve when actionable tools are available

    Which tool best helps produce that information --- [Execution Strategy] - Prefer making progress via tool calls once relevant tools are identified. - Do not over-retrieve when actionable tools are available. - If progress stalls, revise the plan and explore alternative compositions, instead of declaring the task unsolvable or guessing the answer. --- [Too...