MIRROR: Multi-agent Intra- and Inter-Reflection for Optimized Reasoning in Tool Learning

Benfeng Xu; Xiaorui Wang; Zhendong Mao; Zikang Guo

REVIEW 3 major objections 5 minor 1 cited by

MIRROR: Multi-agent Intra- and Inter-Reflection for Optimized Reasoning in Tool Learning

T0 review · 3 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read MIRROR claims that making each agent in a tool-use pipeline score and revise its intended plan, tool call, and answer before execution—on top of post-execution memory—raises performance to state-of-the-art on StableToolBench and…

desk verdict MIRROR is a solid engineering contribution with a real soft spot: the self-score gate that makes 'intra-reflection' distinctive is never shown to predict success. read the letter →

arxiv 2505.20670 v2 pith:UNNZPVPM submitted 2025-05-27 cs.AI

Zikang Guo , Benfeng Xu , Xiaorui Wang , Zhendong Mao This is my paper · ORCID

classification cs.AI

keywords multi-agentsystemstoollearningLLMreflectionintra-reflectioninter-reflectionStableBenchTravelPlannerself-assessment

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 argues that LLM agents can reflect before acting, not only after executing a tool call. It presents MIRROR, a three-agent pipeline—planner, tool selector, and answer synthesizer—in which every agent scores its own intended output on a 1-to-10 scale and rewrites it if the score falls below an agent-specific threshold, before the output is executed or passed along. This pre-action 'intra-reflection' is combined with post-execution 'inter-reflection' memories that carry failure lessons forward. Across StableToolBench and TravelPlanner, and with five different base LLMs, MIRROR reports the highest average pass rates among the compared methods, with ablations showing that removing the intra-reflection gates costs several points. The broader claim is that preventive self-critique and retrospective learning are complementary, and together they make tool use more reliable without fine-tuning.

What carries the argument

The load-bearing mechanism is the intra-reflection gate: a prompt-embedded self-evaluation step in each agent that asks the model to critique its own tentative output, assign a score from 1 to 10, and regenerate the output whenever the score is below that agent's threshold ($\theta_p$, $\theta_t$, or $\theta_a$). The gate runs before the output is executed or handed to the next agent, so bad plans, tool calls, and answers are filtered at the source. It is paired with a dual-memory inter-reflection loop in which Short-Term Memory feeds execution failures and prior self-critiques back into the Tool Agent's next selection for the current subtask, and Long-Term Memory stores full failed trajectories so the Planner Agent can re-decompose the task on a later round.

What would settle it

A calibration study on StableToolBench would settle it: if outputs that the agent scores 9-10 do not succeed noticeably more often than outputs it scores 6-7, or if force-executing low-scored outputs succeeds as often as high-scored ones, then the threshold is not filtering bad outputs and the gains must be attributed to extra revision computation rather than to the intra-reflection gate.

Watch

Extended reading notes

Core claim

On the paper's own terms, MIRROR's central claim is that pre-execution self-assessment by each agent—grading the proposed task decomposition, the chosen tool and parameters, and the assembled final answer, then redoing any output that scores below its threshold—reduces error propagation in multi-agent tool learning and delivers state-of-the-art results. The reported StableToolBench average pass rates are 83.7% with GPT-3.5 Turbo, 83.1% with GPT-4o, 82.9% with Qwen2.5-72B, and 80.9% with Claude 3 Haiku, each above every baseline tested on the same core, and TravelPlanner Delivery Rates rise by 14 to 21 percentage points over ReAct depending on the core. The ablation on GPT-4o Mini attributes most of the benefit to the intra-reflection stage, especially the Answer Agent's final self-check; removing all intra-reflection drops the average pass rate from 85.7% to 78.7%.

Load-bearing premise

The load-bearing premise is that an LLM's self-assigned score on a 1-to-10 scale predicts how good its plan, tool call, or answer will actually be, so that discarding and rewriting low-scoring outputs genuinely filters out errors rather than merely spending extra computation.

Editorial extensions

If this is right

Reflection does not have to wait for execution: a self-score threshold can act as a model-agnostic quality gate on any structured output before it touches a tool.
The Answer Agent's self-check appears to carry the largest share of the gain, so end-of-trajectory quality control deserves as much attention as upstream planning.
Because MIRROR outperforms the supervised fine-tuning baselines without any parameter update, prompt-level self-assessment can substitute for some SFT in tool use.
The same pre-execution gating transfers from API calls to multi-constraint planning, where commonsense and hard-constraint pass rates increase over ReAct in every tested core.

Reading between the lines

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

Beyond the paper: the self-score calibration question is the natural next experiment—if scores cluster near the top, the observed gains could come from the extra revision computation rather than from filtering, and a variant that always rewrites once regardless of score would separate the two.
Beyond the paper: the same gate could apply to any agent emitting a structured action before observing its effect—code edits, database writes, or email drafts—where cheap pre-execution self-checking may prevent costly rollbacks.
Beyond the paper: thresholds could be learned per task type, since the paper only fixes them by percentile; an adaptive threshold would trade token cost against pass rate in a principled way.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

MIRROR is a sensible engineering contribution: it adds pre-execution self-reflection to a multi-agent tool-use pipeline and shows consistent gains on StableToolBench across four base models. The paper deserves a real referee, and the reader's conditional verdict is about right.

The new thing here is the specific combination: each agent scores its own intended output 1-10 and revises if the score falls below a threshold, while a dual-memory mechanism handles post-execution learning. The authors build directly on Self-refine, Reflexion, and ToT, and the ablations in Table 3 support the broad claim that the reflection components matter — removing all intra-reflection drops average pass rate from 85.7 to 78.7. The token-cost analysis is a plus; they don't hide the price of reflection.

The main soft spot is exactly what the stress-test note flags: the load-bearing assumption that self-scores predict downstream quality is never validated. No calibration study links the 1–10 scores to actual execution success. The ablations remove the whole intra-reflection block, so they conflate the self-critique prompt, the score, and threshold-triggered revision with added reasoning compute. The gain could come from simply giving the model more chances to think, not from the gate. A matched-revision-rate control or a score-to-success correlation would settle this. It's a fixable weakness, but it means the paper's distinctive mechanism is under-supported.

Two smaller issues. Thresholds appear tuned on StableToolBench: the planner example shows 7/8/9 yielding 82.3/83.3/85.7, and they adopt the best value. That's a mild circularity, not a fatal one, but it should be acknowledged. TravelPlanner evidence is thin: 180 validation queries, no error bars, final pass rates near zero, and only compared to ReAct. The abstract's 'state-of-the-art' overstates what the baseline set can support.

For anyone working on LLM agents or tool learning, this is a useful baseline and worth reading. It does not open a new direction, but the empirical results are consistent and the framing is honest about limitations. I'd send it to review. With a calibration study and released code, it could become a solid paper.

Referee Report

3 major / 5 minor

Summary. The paper proposes MIRROR, a multi-agent framework for tool learning that combines intra-reflection (each agent self-scores its intended output on a 1–10 scale and revises it below a learned threshold before execution or handoff) with inter-reflection (post-execution learning via short-term and long-term memory). The method is evaluated on StableToolBench and TravelPlanner across five base LLMs, reporting Pass Rate and Win Rate on StableToolBench and Delivery/Commonsense/Hard-Constraint/Final Pass Rates on TravelPlanner. The paper reports consistent improvements over ReAct, DFSDT, Reflexion, Smurfs, ToolLlama-2, and ToolGen, and ablations show that removing intra-reflection components degrades performance. The central claim is that preventive intra-reflection, on top of post-hoc inter-reflection, yields state-of-the-art tool-learning performance.

Significance. If validated, MIRROR's intra-reflection is a conceptually simple and potentially useful complement to existing post-hoc reflection methods, and the dual-memory inter-reflection mechanism is a reasonable way to propagate lessons within a task. The paper has several strengths: it evaluates across five diverse base LLMs on StableToolBench, reports standard deviations for Pass Rates, includes systematic ablations of each reflection component, and provides a token-cost comparison. The core weakness is that the mechanism's load-bearing assumption—that self-assigned intra-reflection scores reliably predict execution success—is not directly tested, and the TravelPlanner evidence is statistically thin. The contribution is therefore plausible but not yet fully established.

major comments (3)

[Section 3.4, Table 3] The entire framework's novel component, intra-reflection, depends on each agent's self-assigned 1–10 score being predictive of the quality of the plan, tool call, or answer. No calibration evidence is provided: Figure 3 shows only score distributions, not score conditional on downstream success. The ablation in Table 3 removes the entire intra-reflection block, which simultaneously removes the self-critique prompt, the score assignment, and the threshold-triggered revision. Since the revision loop is additional sampled reasoning, the observed gain (85.7 vs. 78.7 average Pass Rate) could come from extra compute or a generic self-refinement effect rather than from genuine error anticipation via score-based filtering. Please provide a calibration study (e.g., success rate binned by intra-reflection score) and a compute-matched control (e.g., always revise once, or revise with a revision rate matched to the threshold policy but with scores replaced by random values).
[Section 4.1, Table 2] The TravelPlanner evaluation uses only the 180-query validation set, with no standard deviations or significance tests, and the Final Pass Rates are 0, 0, 1.7, and 2.2 across the four rows. Differences of 0 versus 1.7 or 1.7 versus 2.2 on 180 queries are not meaningful, and the comparison includes only ReAct as a baseline. This evidence cannot support the abstract's claim of 'superior performance' on 'both benchmarks.' Please report results on the full test set or at least provide confidence intervals, and compare against at least one reflection-based or multi-agent baseline beyond ReAct.
[Section 4.3, 'Score threshold selection' and 'Rounds of Inter-reflection'] The thresholds θp, θt, θa and the number of inter-reflection rounds (5) are selected based on StableToolBench performance: the text reports that Planner thresholds of 7, 8, and 9 yield Pass Rates of 82.3%, 83.3%, and 85.7%, and that 5 rounds outperform 3 and 7 rounds. Because the same benchmark is used for final evaluation, this constitutes test-set tuning and risks overfitting to StableToolBench. The claim that these thresholds are 'generalizable' is unsubstantiated. Please describe how the thresholds were chosen (e.g., on a development split), report sensitivity across models and benchmarks, and ideally show that the selected operating point is not critical to the conclusion.

minor comments (5)

[Table 1] The Win Rate for ReAct is shown as '/'; please state explicitly that ReAct is the reference method and therefore its Win Rate is undefined or 50% by construction.
[Section 4.2] The sentence 'This superior performance extends to TravelPlanner' overstates the evidence given that Final Pass Rates are near zero for all systems; please temper the claim to match the results.
[Section 4.3, Figure 3] Figure 3 would be far more informative if it displayed threshold lines and success rates conditioned on score, rather than only raw score distributions; this would directly address the calibration question.
[Section 4.3, 'Score threshold selection'] The phrase 'threshold scores (e.g., Planner Agent at the 90th percentile)' is ambiguous; please specify what distribution the percentile refers to and how it maps to absolute thresholds of 7, 8, and 9.
[General] The paper does not mention whether code or prompts are released; providing the code and all prompts would substantially aid reproducibility.

Circularity Check

1 steps flagged · score 3.0 of 10

One mild fitted-input step: the Planner threshold is tuned on the evaluation benchmark, so the reported 85.7% is the best of the sweep; otherwise the empirical derivation is self-contained.

fitted input called prediction [Section 4.3, 'Score threshold selection' paragraph; used as the default MIRROR row in Table 3]
"For the Planner Agent, specific threshold values of 7, 8, and 9 yielded Pass Rates of 82.3%, 83.3%, and 85.7%, respectively."

The Planner threshold is selected by sweeping values 7, 8, and 9 on StableToolBench and recording the resulting Pass Rates; the default MIRROR configuration in Table 3 then reports 85.7%, which is exactly the best Pass Rate in that sweep. Thus the headline state-of-the-art number is by construction the maximum over the tried thresholds on the same benchmark, not an out-of-sample prediction. The paper presents this selected operating point as the method's performance without a held-out threshold-selection split, so the claimed superiority over baselines at this operating point is partly an artifact of fitting theta to the evaluation data.

full rationale

This paper is an empirical systems paper and contains no formal derivation whose conclusion is equivalent to its premises. The core mechanism, self-score thresholding before execution, is an engineering choice, and the ablation study in Table 3 compares configurations with and without intra-reflection, providing independent evidence that the component contributes to the measured Pass Rate. There are no self-citation chains, no imported uniqueness theorems, and no equation-level reductions that would make the central claim follow by definition. The one substantive circularity-adjacent step is the selection of the Planner threshold: the paper reports Pass Rates of 82.3, 83.3, and 85.7 for threshold values 7, 8, and 9 on StableToolBench, then uses 85.7, the best of the sweep, as the default MIRROR result. This is a fitted-input-called-prediction pattern for that specific number, because the reported result is the maximum over the tried thresholds on the same benchmark rather than an independent validation. It inflates the statistical strength of the SOTA claim for the GPT-4o Mini configuration, but it does not collapse the central architectural claim, which is supported by a broad ablation and by improvements across multiple base models and a second benchmark. Overall circularity is therefore low-to-moderate.

Assumptions & free parameters 2 free parameters · 3 assumptions · 0 invented entities

The central claim rests on the calibration of LLM self-scores and on the representativeness of the two benchmarks. The thresholds and number of reflection rounds are tuned on benchmark performance, which counts as fitted parameters. No new physical entities are introduced.

free parameters (2)

Intra-reflection score thresholds (θp, θt, θa) = Not fully specified; planner threshold tested at 7, 8, and 9; authors report using the 80-90th percentile of the score…
Thresholds gate whether a plan, tool call, or answer is accepted or sent back for revision. They were chosen based on preliminary experiments examining score distributions on the benchmarks, so part of the reported performance is tuned on the evaluation data.
Number of inter-reflection rounds = 5
The authors tested 3, 5, and 7 rounds and selected 5 because it gave the best pass rate; this is a hyperparameter chosen on the benchmark.

assumptions (3)

domain assumption An LLM's self-reported intra-reflection score correlates with the true quality of its planned output
The entire gate-and-revise loop in Section 3.4 depends on scores being meaningful; no calibration against ground truth is provided.
domain assumption The benchmark simulators and LLM judges provide reliable ground truth for pass rate and win rate
StableToolBench's simulator and TravelPlanner's sandbox are trusted; GPT-4-Turbo is used as the judge for Win Rate, which is a common but unverified practice.
domain assumption The validation set of TravelPlanner with 180 queries is large enough to support the reported comparisons
The paper uses the validation set due to cost and claims statistical validity without providing confidence intervals or significance tests.

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Cite this review

Pith. "Pith review of MIRROR: Multi-agent Intra- and Inter-Reflection for Optimized Reasoning in Tool Learning." pith.science (2026). https://pith.science/paper/UNNZPVPM

@misc{pith2026250520670,
  author       = {Pith},
  title        = {Pith review of: MIRROR: Multi-agent Intra- and Inter-Reflection for Optimized Reasoning in Tool Learning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/UNNZPVPM}},
  note         = {Machine review of arXiv:2505.20670}
}

read the original abstract

Complex tasks involving tool integration pose significant challenges for Large Language Models (LLMs), leading to the emergence of multi-agent workflows as a promising solution. Reflection has emerged as an effective strategy for correcting erroneous trajectories in agentic workflows. However, existing approaches only exploit such capability in the post-action stage, where the agent observes the execution outcomes. We argue that, like humans, LLMs can also engage in reflection before action execution: the agent can anticipate undesirable outcomes from its own decisions, which not only provides a necessarily complementary perspective to evaluate the decision but also prevents the propagation of errors throughout the trajectory. In this paper, we propose MIRROR, a framework that consists of both intra-reflection, which critically assesses intended actions before execution, and inter-reflection, which further adjusts the trajectory based on observations. This design systematically leverages LLM reflection capabilities to eliminate and rectify erroneous actions on a more comprehensive scope. Evaluations on both the StableToolBench and TravelPlanner benchmarks demonstrate MIRROR's superior performance, achieving state-of-the-art results compared to existing approaches.

Figures

Figures reproduced from arXiv: 2505.20670 by the authors.

**Figure 2.** Overview of the MIRROR framework. Given a task and candidate tools, the Planner Agent decomposes the complex task into [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** The distribution of intra-reflection scores across three [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

Figures from the paper (1 more)

**Figure 5.** Figure 5: A case study demonstrating MIRROR’s solution process. [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

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

LLM-based Agentic Reasoning Frameworks: A Survey from Methods to Scenarios
cs.AI 2025-08 conditional novelty 4.0 of 10

A survey that classifies LLM-based agent reasoning frameworks into single-agent, tool-based, and multi-agent types and reviews their use across scientific, medical, software, and social domains.

Reference graph

Works this paper leans on

12 extracted references · 12 canonical work pages · cited by 1 Pith paper

[1]

• Identify each failure point and its cause

Analyze Failed Trajectories: • Review previous failed task trajectories. • Identify each failure point and its cause. • Document lessons learned to understand root causes. • Consider the score indicating completion level and the reason for failure

work page
[2]

• Ensure each subtask is feasible, clearly defined, and focused on a single aspect of the task

Task Decomposition: • Break down the complex task into multiple simple subtasks based on the given task. • Ensure each subtask is feasible, clearly defined, and focused on a single aspect of the task. • Ensure that the collection of subtasks covers the entirety of the original task scope without redundancy or overlap. • Consider dependencies and constrain...

work page
[3]

nodes": [ {

Intra-Reflection: • After decomposing the task into subtasks, reflect on the decomposition process and the decisions made. • Ask yourself: – Does the decomposition cover all aspects of the original task without redundancy or overlap? – Have all dependencies and constraints between subtasks been accurately accounted for? – Are there any subtasks or functio...

work page
[4]

• Determine the cause of each failure and document insights to prevent repeating mistakes

Analyze Failed Trajectories: • Review previous task failures to identify where incorrect functions or parameters were chosen. • Determine the cause of each failure and document insights to prevent repeating mistakes. • Use these insights to inform your current decisions, particularly when selecting functions and parameters for the current subtask

work page
[5]

• Ensure that the selected function aligns with the specific requirements of the current subtask

Function Selection: • Based on your reasoning and insights from failed trajectories, select the most suitable function from the provided function list. • Ensure that the selected function aligns with the specific requirements of the current subtask. • Take into account the outputs of previous subtasks and their influence on the current subtask to ensure p...

work page
[6]

• Align parameters with the current subtask’s needs and, where necessary, adjust based on prior results or relevant data from previous subtasks

Parameters Selection: • Once the function is selected, choose parameters that best meet the subtask’s specific requirements. • Align parameters with the current subtask’s needs and, where necessary, adjust based on prior results or relevant data from previous subtasks. • Ensure the parameters are optimized to achieve the desired subtask outcome, including...

work page
[7]

• Maintain coherence in task progression by ensuring that outputs from earlier subtasks are properly used in subsequent subtasks

Consistency Across Subtasks: • Ensure that your function and parameter selections are consistent across subtasks, especially in cases where related subtasks share data or have functional dependencies. • Maintain coherence in task progression by ensuring that outputs from earlier subtasks are properly used in subsequent subtasks. • Check for overall alignm...

work page
[8]

function

Intra-Reflection: • After selecting the function and parameters, take a moment to reflect on your choices. • Ask yourself: – Do the selected function and parameters fully address the subtask’s requirements? – Have I learned from past failed trajectories and applied those lessons effectively? – Is there any improvement or adjustment I could make to better ...

work page

Show all 12 references

[9]

• Summarize the key actions and decisions made, explaining how they contributed to solving the task

Integration of Results: • Seamlessly integrate the observations and insights from the trajectory to address the original task, ensuring all relevant points are connected logically to enhance coherence. • Summarize the key actions and decisions made, explaining how they contrib...
[10]

• If the task is multi-faceted or spans several subtasks, explain how each subtask contributes to solving the overall task

Completeness: • Ensure the final answer fully resolves the given task, covering all aspects and details from the task description. • If the task is multi-faceted or spans several subtasks, explain how each subtask contributes to solving the overall task. • Take into account th...
[11]

• Avoid abrupt transitions or overly technical language that may confuse the user, ensuring the response is accessible and logical

Clarity and Fluency: • Present the final answer in clear, concise, and fluent natural language, making it easily understandable and logically structured. • Avoid abrupt transitions or overly technical language that may confuse the user, ensuring the response is accessible and logical
[12]

answer":

Intra-Reflection: • After generating the answer, reflect on its quality. • Ask yourself: – Completeness : Does the answer fully cover all aspects of the task? – Integration : Does it logically integrate the key actions and observations from the trajectory? – Clarity : Is the a...

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.

[1] [1]

• Identify each failure point and its cause

Analyze Failed Trajectories: • Review previous failed task trajectories. • Identify each failure point and its cause. • Document lessons learned to understand root causes. • Consider the score indicating completion level and the reason for failure

work page

[2] [2]

• Ensure each subtask is feasible, clearly defined, and focused on a single aspect of the task

Task Decomposition: • Break down the complex task into multiple simple subtasks based on the given task. • Ensure each subtask is feasible, clearly defined, and focused on a single aspect of the task. • Ensure that the collection of subtasks covers the entirety of the original task scope without redundancy or overlap. • Consider dependencies and constrain...

work page

[3] [3]

nodes": [ {

Intra-Reflection: • After decomposing the task into subtasks, reflect on the decomposition process and the decisions made. • Ask yourself: – Does the decomposition cover all aspects of the original task without redundancy or overlap? – Have all dependencies and constraints between subtasks been accurately accounted for? – Are there any subtasks or functio...

work page

[4] [4]

• Determine the cause of each failure and document insights to prevent repeating mistakes

Analyze Failed Trajectories: • Review previous task failures to identify where incorrect functions or parameters were chosen. • Determine the cause of each failure and document insights to prevent repeating mistakes. • Use these insights to inform your current decisions, particularly when selecting functions and parameters for the current subtask

work page

[5] [5]

• Ensure that the selected function aligns with the specific requirements of the current subtask

Function Selection: • Based on your reasoning and insights from failed trajectories, select the most suitable function from the provided function list. • Ensure that the selected function aligns with the specific requirements of the current subtask. • Take into account the outputs of previous subtasks and their influence on the current subtask to ensure p...

work page

[6] [6]

• Align parameters with the current subtask’s needs and, where necessary, adjust based on prior results or relevant data from previous subtasks

Parameters Selection: • Once the function is selected, choose parameters that best meet the subtask’s specific requirements. • Align parameters with the current subtask’s needs and, where necessary, adjust based on prior results or relevant data from previous subtasks. • Ensure the parameters are optimized to achieve the desired subtask outcome, including...

work page

[7] [7]

• Maintain coherence in task progression by ensuring that outputs from earlier subtasks are properly used in subsequent subtasks

Consistency Across Subtasks: • Ensure that your function and parameter selections are consistent across subtasks, especially in cases where related subtasks share data or have functional dependencies. • Maintain coherence in task progression by ensuring that outputs from earlier subtasks are properly used in subsequent subtasks. • Check for overall alignm...

work page

[8] [8]

function

Intra-Reflection: • After selecting the function and parameters, take a moment to reflect on your choices. • Ask yourself: – Do the selected function and parameters fully address the subtask’s requirements? – Have I learned from past failed trajectories and applied those lessons effectively? – Is there any improvement or adjustment I could make to better ...

work page