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REVIEW 3 major objections 2 minor 2 cited by

When Tools Fail: Benchmarking Dynamic Replanning and Anomaly Recovery in LLM Agents

T0 review · 3 major / 2 minor · reviewed 2026-06-28 · grok-4.3

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Pith's one-line read ToolMaze benchmark reveals that LLM agents recover from tool failures 3.66 times slower than they execute basic tasks as models scale.

desk verdict ToolMaze gives a structured way to test replanning under tool failures, but the scaling claim depends on how well the 2x2 taxonomy matches real failures. read the letter →

arxiv 2606.05806 v1 pith:CPRLJF3B submitted 2026-06-04 cs.AI

Dongsheng Zhu , Xuchen Ma , Yucheng Shen , Xiang Li , Yukun Zhao , Shuaiqiang Wang , Lingyong Yan , Dawei Yin This is my paper · ORCID
classification cs.AI
keywords LLMagentstool-integratedreasoningbenchmarkdynamicreplanningfaulttoleranceerrorrecoveryperturbationsDAGcomplexity
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper introduces ToolMaze to test how Tool-Integrated Reasoning agents handle real tool failures rather than perfect scenarios. It structures tests around graph complexity and four kinds of perturbations to separate deliberate replanning from random guessing. Results show performance drops sharply on implicit failures because agents trust bad outputs, and recovery ability grows far more slowly with model size than simple task success does. This gap persists despite larger models or standard prompting. The work positions dynamic replanning as a capability that current scaling trends leave behind.

What carries the argument

ToolMaze benchmark, built on DAG topological complexity paired with a 2x2 taxonomy of tool perturbations (explicit versus implicit, transient versus permanent) to measure systematic recovery in Tool-Integrated Reasoning agents.

What would settle it

Measuring that Perturbation Recovery Rate improves at the same rate or faster than basic task success when model size increases would show that replanning is not a separate bottleneck.

Watch

Extended reading notes

Core claim

ToolMaze shows that tool perturbations degrade agent performance across models, with the largest losses under implicit semantic failures that drive a 37 percent drop in Perturbation Recovery Rate, while complex task graphs cause agents to loop in trial-and-error; crucially, fault-tolerance scales 3.66 times slower with model size than basic execution, establishing replanning as a distinct bottleneck.

Load-bearing premise

The chosen taxonomy of four perturbation types together with DAG complexity is assumed to cover the main real-world tool failures agents will meet.

Editorial extensions

If this is right

  • Larger models will continue to underperform on recovery tasks relative to their gains on clean execution.
  • Implicit failures will remain especially damaging because agents over-trust corrupted outputs.
  • Complex task structures will keep agents trapped in unproductive loops without new recovery methods.
  • Prompting alone will not close the gap between execution and replanning performance.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Benchmarks limited to happy-path tool use will systematically overestimate how well agents will perform once deployed.
  • Training objectives that directly reward recovery sequences could be tested as one way to accelerate the slower scaling curve.
  • Extending the taxonomy to cover cascading or multi-step failures would check whether the current four categories miss important cases.
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Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

3 major / 2 minor

Summary. The paper introduces ToolMaze, a benchmark for Tool-Integrated Reasoning (TIR) agents that evaluates dynamic replanning and recovery under tool failures. It employs DAG-based topological complexity together with a 2×2 perturbation taxonomy (explicit/implicit, transient/permanent) to distinguish systematic replanning from trial-and-error. Key results include a ~37% drop in Perturbation Recovery Rate (PRR) under implicit semantic failures, agents becoming trapped in futile loops on complex topologies, and agentic fault-tolerance scaling 3.66× slower with model size than basic task execution, establishing replanning as a distinct unaddressed bottleneck.

Significance. If the benchmark results and scaling differential hold under broader conditions, the work identifies a concrete limitation in current LLM agents that model scaling and prompting alone do not resolve, which is relevant for deploying reliable tool-using agents in realistic settings where failures occur.

major comments (3)
  1. [Abstract] Abstract: the central quantitative claims (37% PRR drop for implicit cases; 3.66× scaling differential) are stated without any reference to the models evaluated, number of runs, error bars, or statistical tests, which directly affects assessment of whether the scaling observation is load-bearing for the 'distinct bottleneck' conclusion.
  2. [Benchmark design section] Benchmark design (2×2 taxonomy + DAG complexity): the claim that this design isolates systematic replanning and demonstrates a general replanning bottleneck rests on the assumption that the chosen axes sufficiently cover real-world TIR failure modes; additional modes such as timing jitter, partial outputs, or cascading dependencies are not addressed, and if prevalent they would weaken the reported PRR drop and scaling ratio as evidence of a distinct, unaddressed limitation.
  3. [Results / scaling analysis section] Results on scaling: the 3.66× slower improvement in fault-tolerance versus basic execution is presented as a key finding, yet the manuscript provides no explicit definition or formula for how the scaling ratio is computed across models or perturbation types, making it impossible to verify whether the differential is robust or sensitive to the specific model list and perturbation instances used.
minor comments (2)
  1. [Abstract] Abstract and results sections should report run counts, variance, and confidence intervals for all quantitative metrics (PRR, scaling ratios) to support reproducibility.
  2. The GitHub repository link is provided but the manuscript should explicitly state which experimental artifacts (prompts, exact perturbation implementations, model versions) are released to allow independent verification of the 37% and 3.66× figures.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive comments. We address each major comment below and commit to revisions that improve clarity without altering the core claims.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central quantitative claims (37% PRR drop for implicit cases; 3.66× scaling differential) are stated without any reference to the models evaluated, number of runs, error bars, or statistical tests, which directly affects assessment of whether the scaling observation is load-bearing for the 'distinct bottleneck' conclusion.

    Authors: We agree the abstract would benefit from additional context. In the revision we will update the abstract to name the primary models evaluated and state that the 37% figure is an average across implicit cases with error bars and run counts (typically 5–10 per cell) reported in Section 4 and the figures; the scaling ratio is obtained from regression slopes detailed in the results. revision: yes

  2. Referee: [Benchmark design section] Benchmark design (2×2 taxonomy + DAG complexity): the claim that this design isolates systematic replanning and demonstrates a general replanning bottleneck rests on the assumption that the chosen axes sufficiently cover real-world TIR failure modes; additional modes such as timing jitter, partial outputs, or cascading dependencies are not addressed, and if prevalent they would weaken the reported PRR drop and scaling ratio as evidence of a distinct, unaddressed limitation.

    Authors: The 2×2 axes were chosen because detectability and persistence directly govern whether agents must replan rather than retry. We acknowledge that timing jitter, partial outputs, and cascading failures are omitted and could modulate the observed effects. The revision will add an explicit limitations paragraph in the benchmark section discussing these modes and their potential impact on generalizability. revision: partial

  3. Referee: [Results / scaling analysis section] Results on scaling: the 3.66× slower improvement in fault-tolerance versus basic execution is presented as a key finding, yet the manuscript provides no explicit definition or formula for how the scaling ratio is computed across models or perturbation types, making it impossible to verify whether the differential is robust or sensitive to the specific model list and perturbation instances used.

    Authors: We will insert the explicit definition and formula in the revised results section: the ratio equals the slope of PRR versus log(model size) divided by the slope of basic-execution accuracy versus log(model size), obtained via ordinary least-squares regression over the evaluated models and aggregated across perturbation types. The model list, fitted coefficients, and a sensitivity check will be added to the main text or an appendix. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

Empirical benchmark study with no derivations or self-referential reductions

full rationale

This paper is an empirical benchmark study that introduces ToolMaze, defines a 2x2 perturbation taxonomy and DAG topological complexity to structure test cases, then reports direct performance measurements (e.g., PRR drops of ~37% and 3.66x slower scaling of fault-tolerance). No equations, fitted parameters, predictions derived from inputs, or self-citations appear in the provided text that would reduce any reported metric or central claim to a prior quantity by construction. The findings consist of observed results from model evaluations on the new benchmark and are therefore self-contained.

Assumptions & free parameters 0 free parameters · 1 assumptions · 0 invented entities

No free parameters or invented entities are introduced; the work relies on standard benchmarking assumptions such as representative model sampling and perturbation realism.

assumptions (1)
  • domain assumption Standard assumptions in LLM agent benchmarking that model outputs can be meaningfully aggregated across runs and that the chosen perturbation types reflect realistic tool behavior.
    Invoked implicitly when reporting aggregate performance drops across models.

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Cite this review

Pith. "Pith review of When Tools Fail: Benchmarking Dynamic Replanning and Anomaly Recovery in LLM Agents." pith.science (2026). https://pith.science/paper/CPRLJF3B

@misc{pith2026260605806,
  author       = {Pith},
  title        = {Pith review of: When Tools Fail: Benchmarking Dynamic Replanning and Anomaly Recovery in LLM Agents},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CPRLJF3B}},
  note         = {Machine review of arXiv:2606.05806}
}
abstract

Existing benchmarks evaluate Tool-Integrated Reasoning (TIR) in LLMs on idealized ''happy paths'', largely overlooking real-world tool failures. We introduce ToolMaze, a benchmark for dynamic path discovery and error recovery in TIR agents. To separate systematic replanning from blind trial-and-error, ToolMaze adopts a two-dimensional design: DAG-based topological complexity and a $2 \times 2$ taxonomy of tool perturbations (explicit/implicit, transient/permanent). Evaluations show that perturbations degrade performance across nearly all models, with the sharpest drops under implicit semantic failures. Driven by systemic over-trust in corrupted outputs, Perturbation Recovery Rate (PRR) plummets by around 37\% in these scenarios, while complex topologies trap agents in futile trial-and-error loops. Crucially, agentic fault-tolerance improves with model scale $3.66\times$ slower than basic task execution, highlighting dynamic replanning as a distinct bottleneck unaddressed by model scaling or prompting. Data and code are available at https://github.com/Zhudongsheng75/ToolMaze.

Figures

Figures reproduced from arXiv: 2606.05806 by the authors.

Figure 1
Figure 1. An illustrative example of agent behavior [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Overview of the TOOLMAZE framework, illustrating its main components: (1) task generation from a curated tool corpus, (2) four levels of topological task complexity (C1–C4), (3) 2 × 2 taxonomy of perturbation modes (P1–P4), and (4) the evaluation framework with metrics including TSR, PRR, and RC. ures, highlighting how early errors can cascade through downstream tool interactions (Ruan et al., 2023). Other methods i… view at source ↗
Figure 3
Figure 3. Mean metrics across complexity levels C1–C4, averaged over all evaluated models. Solid (dashed) lines correspond to the w/ (w/o) hint prompt. Execution. Each task runs in a sandboxed envi￾ronment with a maximum of 25 agent steps. All LLM sampling uses temperature 1 and max to￾kens 16,000. To isolate the impact of prompting, we evaluate agents under two prompt configura￾tions (Appendix C.1). The first applies a stand… view at source ↗
Figures from the paper (18 more)
Figure 4
Figure 4. Figure 4: Mean PRR and RC across perturbation modes [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 6
Figure 6. Figure 6: Average PRR performance (w/ hint) between explicit perturbation and implicit perturbation. planning and lead to many unnecessary tool calls during recovery. Fault-tolerance does not scale at the same rate as task completion [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 7
Figure 7. Figure 7: C1P1 — Explicit-Transient (C1_task_089). The victim tool convert_datetime returns an explicit error on the first call. The successful model retries and recovers; the failing model calls the tool only once and abandons it, leaving the downstream pipeline incomplete. Cas…
Figure 8
Figure 8. Figure 8: C1P2 — Explicit-Permanent (C1_task_089). The victim tool returns a permanent explicit error. The successful model detects the irrecoverable failure and stops gracefully; the failing model continues to invoke downstream tools after the failure, violating the stop-on-per…
Figure 9
Figure 9. Figure 9: C1P3 — Implicit-Transient (C1_task_089). The victim tool convert_datetime returns semantically corrupted data. The successful model detects the anomaly and retries, obtaining a clean response; the failing model accepts the corrupted output without re-querying and propa…
Figure 10
Figure 10. Figure 10: C1P4 — Implicit-Permanent (C1_task_089). The victim tool returns persistently corrupted data. The successful model identifies the permanent corruption and halts the pipeline; the failing model recognises the corruption yet still proceeds to invoke a downstream tool th…
Figure 11
Figure 11. Figure 11: C2P1 — Explicit-Transient (C2_task_084). In a task with an alternative IoT-control path, the victim tool adjust_temperature returns an explicit error. The successful model retries and completes all shared downstream steps; the failing model skips the victim entirely, …
Figure 12
Figure 12. Figure 12: C2P2 — Explicit-Permanent (C2_task_084). The victim tool set_light_state fails permanently. The successful model switches to the alternative path and completes shared downstream tools; the failing model neither retries nor reroutes, omitting the shared tool get_iot_de…
Figure 13
Figure 13. Figure 13: C2P3 — Implicit-Transient (C2_task_084). The victim tool returns corrupted sensor data. The successful model detects the semantic inconsistency, retries and recovers; the failing model accepts the corrupted reading and proceeds without re-querying, never invoking the …
Figure 14
Figure 14. Figure 14: C2P4 — Implicit-Permanent (C2_task_084). Persistent semantic corruption in the victim tool. The successful model recognises the unrecoverable state and halts after completing reachable shared steps; the failing model propagates the corrupted value silently into adjust…
Figure 15
Figure 15. Figure 15: C3P1 — Explicit-Transient (C3_task_040). In a multi-branch weather-alert task, the victim tool get_weatherapi_alert_card_native returns an explicit error. The successful model retries until success and completes a valid path; the failing model neither retries sufficie…
Figure 16
Figure 16. Figure 16: C3P2 — Explicit-Permanent (C3_task_040). The victim tool fails permanently with an explicit error. The successful model abandons the failed branch and completes the task via an alternative path; the failing model remains stuck on the failed tool and does not reroute t…
Figure 17
Figure 17. Figure 17: C3P3 — Implicit-Transient (C3_task_040). The victim tool returns semantically corrupted weather data. The successful model detects the inconsistency and retries to obtain clean data; the failing model exhibits sanity ignorance—it consumes the corrupted output without …
Figure 18
Figure 18. Figure 18: C3P4 — Implicit-Permanent (C3_task_040). The victim tool persistently returns corrupted data. The successful model identifies the irrecoverable corruption and reroutes to a clean alternative branch to complete the task; the failing model never completes any valid path…
Figure 19
Figure 19. Figure 19: C4P1 — Explicit-Transient (C4_task_090). In a complex finance-and-crypto task with multiple tool groups, the victim tool get_crypto_price returns an explicit error. The successful model retries and, upon recovery, completes all required downstream steps; the failing m…
Figure 20
Figure 20. Figure 20: C4P2 — Explicit-Permanent (C4_task_090). The victim tool get_quote_snapshot_native fails permanently. The successful model detects the dead end and completes the task through an alternative quote source; the failing model abandons execution without attempting any alte…
Figure 21
Figure 21. Figure 21: C4P3 — Implicit-Transient (C4_task_090). The victim tool get_crypto_price returns a corrupted price value. The successful model cross-checks the result, detects the anomaly and retries to obtain a valid price; the failing model exhibits sanity ignorance—it uses the co…
Figure 22
Figure 22. Figure 22: C4P4 — Implicit-Permanent (C4_task_090). The victim tool get_quote_snapshot_native returns persistently corrupted market data. The successful model recognises the unrecoverable state and routes around the failure via alternative tools; the failing model does not explo…

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Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Model or Harness? An Interaction-Centric Taxonomy for Localizing Agent Failures

    cs.AI 2026-07 conditional novelty 6.0 of 10

    The paper introduces a 41-mode taxonomy that assigns each agent failure to an interaction edge and a fault side, and shows LLM judges can reproduce the labels with Cohen's κ=0.76.

  2. AgentCheck: A Reproduce-Intervene-Mitigate Workbench for LLM Agents over MCP

    cs.SE 2026-07 unverdicted novelty 6.0 of 10

    A tool that injects faults into an LLM agent's MCP tool calls, replays them under mitigation, and verifies the fix—revealing that agents mostly fail silently on stale or bad data.

Reference graph

Works this paper leans on

83 extracted references · 3 canonical work pages · cited by 2 Pith papers

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    **MANDATORY COVERAGE**: The task de- scription MUST require ALL steps in the execution trace, in order. - You will receive N steps. Your description MUST mention all N actions. - DO NOT skip any step, even if it seems redundant or obvious. - Each step must be explicitly or implicitly requested in the description

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    **Step-by-Step Verification** (Internal checklist - verify before outputting): - Step 1: Does my description request this action? YES/NO - Step 2: Does my description request this action? YES/NO - Step 3: Does my description request this action? YES/NO - ... (for all N steps) - If ANY answer is NO, rewrite the description to include that step

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    **REQUIRED - Dependency Expression**: - If step B uses output from step A, use connecting words: "then", "after that", "and then" - Example: "Book a flight, then get the flight details, and send them to me" - NOT: "Send me the flight details" (missing book step)

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    **REMEMBER**: Your description must request ALL {num_steps} steps

    **Verification Before Output**: - Count the steps in the trace: N steps - Count the actions in your description: M actions - If M < N, you MUST rewrite to include all steps - If M = N, verify each action maps to a step domains: {domains} language_hint: {language_hint} You will receive the trace blocks next (one per mes- sage). **REMEMBER**: Your descripti...

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    Chain must have exactly **5-7 steps**

  12. [20]

    **No alternative nodes** —`is_alternative`must be`false`for every step

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    **No duplicate tool names** in the chain

  14. [22]

    5.`user_input_schema`must cover every`user_input.xxx`field referenced in bindings

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    **Flexible Data References**: - Step N can reference output from any previous step (step 1 to N-1) - Arguments can come from`user_input`,`constant`,`prev`(previous step), or` step`(any earlier step) - For`constant`, always use`{"source": "constant", "value": ...}`(NOT`field`) ...

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    **Alternative Node Parameter Mapping**: - Use the parameter mapping defined in the alternative specification - Direct params:`{city: "city"}`- takes from input field named "city" - Constants:`{from_currency: "'USD'"}`- single quotes for string literals - References:`{a: "$stoc...

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    **CRITICAL - Type Compatibility**: - **Object parameters**: If a parameter expects`type: object`, you MUST bind the entire object from`prev`, NOT individual fields - WRONG:`weather_data: {"source": "prev", "field": "temperature_celsius"}`( scalar field) - CORRECT:`weather_data...

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    success": true,

    Properly connects data between steps ### Output Format (JSON Only) ```json { "success": true, "chain": [ { "step": 1, "tool_name": "source_tool_name", "is_alternative": false, "reasoning": "Starts the chain by fetching...", "arg_bindings": { "param": {"source": "user_input", "...

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    **5-7 steps** - flexible length

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    is_alternative

    **At least 1 alternative node** - mark with`"is_alternative": true`and provide `"alternative_id"`

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    Alternative nodes should NOT be the first or last step (prefer middle positions)

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    All required parameters must have an entry in`arg_bindings`

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    Field names MUST match exactly from Observed output keys

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    Prefer valid type-safe flows over aggressive chaining; do not force every step to consume previous output. 8.`alternative_id`MUST be selected exactly from the "VALID alternative_id values" list below the alternatives. 9.`user_input_schema`must cover every`user_input.xxx`field ...

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    **CRITICAL - No Duplicate Tools**: Each tool can only appear ONCE in the chain. Do NOT use the same tool_name multiple times

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    **CRITICAL - Alternative Output Compatibility**: - Check "Alternative Output Field Hints" section for each alternative - If an alternative shows "common keys across all paths: (none)", the NEXT step CANNOT use`source=prev` - If step N is alternative and step N+1 uses`source=pr...

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    source":

    **CRITICAL - Output Field Names**: - When using`{"source": "prev", "field": "xxx"}`, use exact field names from the observed output keys or the alternative output hints, UNLESS you are referencing the entire output object as defined in Critical Rules 13

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    source":

    **CRITICAL - Type Compatibility**: - **Object parameters**: If a parameter expects`type: object`, you MUST bind the entire object from`prev`, NOT individual fields - WRONG:`weather_data: {"source": "prev", "field": "temperature_celsius"}`( temperature_celsius is a number, not ...

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    source":

    **CRITICAL - Binding Object Format**: - EVERY`arg_bindings[param]`value must be an object like`{"source": " user_input", "field": "amount"}` - Do NOT output raw strings, raw numbers, or bare field names directly inside` arg_bindings`

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    source":

    **CRITICAL -`get_field_value`Usage**: - Prefer direct`prev`field references over`get_field_value`for simple access - If you use`get_field_value`,`target_field`,`field_name`, and`index`should normally be`constant`bindings - Example:`{"source": "constant", "value": "filled_avera...

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    **CRITICAL - Numeric Tools**: - For numeric params such as`amount`,`a`,`b`,`quantity`, only bind from numeric`prev`fields or numeric`user_input`fields - Do NOT bind a numeric param from a person name, email, room name, message text, or any generic string field ### Available To...

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    Uses at least ONE`many_to_many`alternative group from the provided alternatives

  42. [52]

    Creates a coherent, useful task flow

  43. [53]

    Properly connects data between steps

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    success": true,

    Includes`user_input_schema`for every referenced`user_input.xxx`field ### Output Format (JSON Only) ```json { "success": true, "chain": [ { "step": 1, "tool_name": "source_tool_name", "is_alternative": false, "reasoning": "Starts the chain by fetching...", "arg_bindings": { "pa...

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    **5-7 logical steps**

  46. [56]

    **At least 1 alternative node** using a provided`many_to_many`group

  47. [57]

    Alternative nodes should NOT be the first or last step

  48. [58]

    success": false,

    If you cannot construct a valid chain, return`{"success": false, "reason": " explanation"}`

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    All required parameters must have an entry in`arg_bindings` 6.`alternative_id`MUST be selected exactly from the valid allowlist below the alternatives

  50. [60]

    Include a non-empty`user_input_schema`covering every referenced`user_input.xxx `field

  51. [61]

    Prefer semantically clean, type-safe flows over aggressive chaining

  52. [62]

    Treat many_to_many as true path-level alternatives: if two paths would share the same tail tool(s), that is not a good C3-style N-to-N substitute

  53. [63]

    ALL paths within the alternative must accomplish the SAME goal

    **CRITICAL - Alternative Must Be a True Branch**: The alternative node replaces a single logical step with multiple implementation paths. ALL paths within the alternative must accomplish the SAME goal. Do NOT design an alternative where one path repeats work already done by a ...

  54. [64]

    **CRITICAL - Domain Consistency**: - All tools MUST belong to semantically related domains - FORBIDDEN combinations: Weather+Shopping, Weather+Flight, Weather+Finance, IoT+ Shopping, IoT+Finance, IoT+Flight, Shopping+Office, Finance+IoT - Adjacent steps MUST have compatible domains

  55. [65]

    Alternative Output Field Hints

    **CRITICAL - Alternative Output Compatibility**: - Check "Alternative Output Field Hints" for each alternative - If an alternative shows "common keys across all paths: (none)", the NEXT step CANNOT use`source=prev` - When in doubt, use`user_input`instead of forcing`prev`

  56. [66]

    source":

    **CRITICAL - Binding Entire Output (Object Parameters)**: - If a parameter expects`type: object`, you MUST use`{"source": "prev", "field ": "$output_name"}`to pass the entire result. - **FORBIDDEN**: Never guess field names like "hotel_info", "flight_result", or "data_obj". - ...

  57. [67]

    Look at the terminal tool's Output Fields for **each path** in the alternativegroup

  58. [68]

    If the field exists **in every path's terminal output**→safe to use

  59. [69]

    source":

    If the field is missing from **any path**→do NOT use that field from`prev`. - Use`{"source": "prev"}`if the parameter expects`object`. - Use`{"source": "user_input", ...}`if the parameter expects a scalar. ### Available Tools {tool_specifications} {alternative_specifications} ...

  60. [70]

    Uses at least **TWO** alternative groups from the provided alternatives

  61. [71]

    Creates one coherent, semantically consistent workflow

  62. [72]

    success": true,

    Every required parameter has a valid`arg_bindings`entry 4.`user_input_schema`covers every`user_input.xxx`field referenced in bindings ### Output Format (JSON only) ```json { "success": true, "chain": [ { "step": 1, "tool_name": "some_tool", "is_alternative": false, "reasoning"...

  63. [73]

    Chain must have exactly **8-12 logical steps**

  64. [74]

    At least **2 steps** must be alternative nodes

  65. [75]

    4.`alternative_id`must be selected **exactly** from the provided allowlist

    Alternative nodes must **not** be the first or last step. 4.`alternative_id`must be selected **exactly** from the provided allowlist

  66. [76]

    **No duplicate tool names** anywhere in the chain

  67. [77]

    **No duplicate`alternative_id`** values

  68. [78]

    Prefer **2-3 alternative groups** maximum

  69. [79]

    Alternative groups must represent true branching choices, not repeated work

  70. [80]

    success": false,

    If a valid chain cannot be constructed, return`{"success": false, "reason": "..."}`. Respond ONLY with the JSON object, no additional text. Table 17: Prompt ofC4template construction (part 2). Guidelines: "physics_violation": """Generate values that violate physics/common sens...

    2024

  71. [81]

    Return a valid JSON object (NO "error" field)

  72. [82]

    Apply the {corruption_type} corruption type

  73. [83]

    The corruption should be OBVIOUS when read by a human or AI

  74. [84]

    Keep the SAME keys as the original output — do NOT add new keys

  75. [85]

    Table 19: Prompt for implicit perturbationP3,P4generation (part 2)

    Keep errors realistic (not absurdly extreme) Return ONLY valid JSON, no explanations. Table 19: Prompt for implicit perturbationP3,P4generation (part 2). Model TSR(%)↑ PRR(%)↑ RC(%)↓ A vg.↑ C1C2C3C4Avg C1C2C3C4Avg C1C2C3C4Avg MiniMax-M2.7 (w/o hint) 23.00 42.40 26.20 14.40 26....

Pith tools

Reviewed June 28, 2026 · model on record in the stance chip above.