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REVIEW 4 major objections 5 minor 3 cited by

Symbiotic Cooperation for Web Agents: Harnessing Complementary Strengths of Large and Small LLMs

T0 review · 4 major / 5 minor · reviewed 2026-08-08 · deepseek-v4-flash

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Pith's one-line read A symbiotic loop coupling a large LLM's trajectory generation with a small LLM's exploratory divergence lifts both to state-of-the-art WebArena success rates of 52% and 49%.

desk verdict Strong, well-ablated distillation results on WebArena, but the paper's headline 'symbiotic' mechanism is not isolated from a simpler data-scaling explanation. read the letter →

arxiv 2502.07942 v2 pith:KDIVL6T4 submitted 2025-02-11 cs.MA cs.LG

Ruichen Zhang , Mufan Qiu , Zhen Tan , Mohan Zhang , Vincent Lu , Jie Peng , Kaidi Xu , Leandro Z. Agudelo
show 2 more authors
Peter Qian Tianlong Chen
This is my paper · ORCID
classification cs.MAcs.LG
keywords webagentsknowledgedistillationretrieval-augmentedgenerationspeculativedatasynthesismulti-tasklearningArenasymbioticimprovementprivacypreservation
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

This paper claims that a large and a small language model can be coupled in an iterative loop so that each improves the other, and that the pair reaches state-of-the-art results on the WebArena benchmark for both model classes. The large model (Claude-3.5) generates high-quality navigation trajectories and retrieves past experiences through a retrieval-augmented generation (RAG) store; the distilled small model (Llama-3.1-8B) explores the environment more cheaply and, because its action choices diverge from the large model's, discovers new trajectories that are fed back into the large model's retrieval base. Two distillation innovations—speculative data synthesis that filters student actions against teacher candidates, and multi-task learning that trains the student to predict both actions and reasoning—are introduced to keep the small model from becoming a bottleneck. The reported success rates are 52% for the large agent and 49% for the 8B agent, surpassing prior bests of 45% and 28% respectively. If the mechanism holds, it would mean that small models can contribute more than they receive in agent training pipelines.

What carries the argument

The load-bearing mechanism is the iterative 'symbiotic' loop itself, plus the two distillation techniques that keep the small model viable inside the loop. Speculative data synthesis works as a teacher–student action filter at each step: the small model proposes an action-reason pair, the large model generates $K$ candidate actions, and the student's proposal is accepted only if it falls inside the teacher's candidate set, otherwise the teacher's best candidate is executed; this shifts from near-supervised distillation early on to more speculative acceptance as the student improves. Multi-task learning augments the distillation objective by training the small model to predict both the next action and a chain-of-thought (step-by-step) rationale, preserving reasoning capacity that plain action cloning would discard. The RAG knowledge base, built from subsequences of validated trajectories with instruction and summary annotations and retrieved by three complementary strategies (task-guided summary retrieval, direct observation–instruction matching, and trajectory similarity search), is the conduit through which small-model discoveries reach the large model.

What would settle it

Run the agent loop on the full 812 WebArena tasks with a control knowledge base that replaces every small-LLM trajectory with an equal number of large-LLM trajectories matched by task and length; if the large model's success rate does not drop when the small trajectories are excluded, the small model's exploration is not the causal driver and the 'symbiotic' attribution fails.

Watch

Extended reading notes

Core claim

The central discovery is that large and small LLMs form a complementary exploration–exploitation pair for web navigation: large LLMs exploit well-understood situations with high action accuracy, while distilled small LLMs, being faster and more variable in their action selection, cover more of the state–action space and stumble onto unorthodox or edge-case solutions. AgentSymbiotic institutionalizes this complementarity in a four-step cycle—large-LLM trajectory generation with RAG, multi-LLM-debate-filtered distillation with speculative data synthesis and multi-task learning, small-LLM exploration, and merging of the new trajectories back into the knowledge base. The authors define a synergy metric $\Delta$ as the excess success rate of the iterative scheme over the best single-LLM baseline, and report that $\Delta$ grows over three iterations on a subset of WebArena tasks. They further report that the full pipeline reaches 52% success rate for Claude-3.5 and 49% for a distilled 8B LLaMA-3 model on all 812 WebArena tasks, exceeding the previous open-source bests.

Load-bearing premise

The claim that the loop is genuinely symbiotic rests on the assumption that the small model's exploratory trajectories, rather than the sheer increase in retrieval data, are what drive the large model's improvement—an assumption the paper supports only with a subset-scale synergy metric and no ablation that removes small-model trajectories from the knowledge base.

Editorial extensions

If this is right

  • If the reported gains replicate, an 8B-parameter web agent lands within about three points of a Claude-3.5-level agent, making small local agents a realistic option for web automation on cost or privacy grounds.
  • The iterative loop implies web-agent performance can keep improving with added rounds of coupling rather than saturating after a single distillation pass; the paper reports the synergy metric $\Delta$ rising across three iterations on a subset of tasks.
  • The ablation isolates each distillation innovation: speculative data synthesis alone lifts a plain-SFT 8B model from 40.8% to 46.8% on WebArena, and combining it with multi-task learning reaches 48.5%.
  • The hybrid privacy mode routes steps flagged as private to a local small model and carries a measured cost of about 1.6 points (50.5% combined vs 52.1% for the non-hybrid large agent) while keeping sensitive data on-device.

Reading between the lines

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

  • The paper's numbers leave open whether the symbiosis is causal or a data-volume effect: swapping small-LLM trajectories for an equal number of large-LLM ones in the retrieval base would distinguish 'divergent exploration helps' from 'more examples help.'
  • If divergence itself is the driver, deliberately increasing the student's action stochasticity during exploration (higher temperature, action-level dropout) could amplify the effect; the paper fixes temperature at 0.6 and does not sweep it.
  • The framework's design transfers to any environment with a teacher, a student, and a retrieval store, but the paper evaluates only WebArena, so transferability is a design inference rather than a demonstrated result.
  • The hybrid mode's modest 1.6-point drop relative to cloud-only inference suggests privacy protection is cheap here; comparing selective delegation against a fully local small agent would show how much of the gap is recovered.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The paper proposes AgentSymbiotic, an iterative framework for web-browsing agents in which a large LLM and a small LLM alternately improve each other: the large LLM generates high-quality trajectories for distillation and RAG, the distilled small LLM explores the environment and produces diverse trajectories that are fed back into the large LLM's retrieval knowledge base, and the loop repeats. Two distillation innovations are introduced: Speculative Data Synthesis, in which the teacher's top-K action candidates are used to accept or replace the student's proposed actions to mitigate off-policy bias, and multi-task learning that trains the student to predict both actions and reasoning rationales. A hybrid privacy-preserving mode delegates sensitive steps to a local small LLM. Experiments on WebArena report state-of-the-art success rates for both model classes: 52.1% for Claude-3.5-based agents and 48.5% for an 8B LLaMA-3 agent, compared with prior published results of 45.7% and 28% respectively. The paper also reports a synergy metric that increases over iterations, and ablations showing that each distillation component contributes to the small-model gains.

Significance. If the reported results are confirmed, AgentSymbiotic would be a practically useful contribution to web-agent research: it couples data synthesis and task performance in a way that improves both large and small models, and it demonstrates that an 8B open-weight model can approach the performance of much larger closed models on WebArena. The two distillation techniques (speculative data synthesis and multi-task reasoning training) are clearly described and individually ablated in Table 2, which is a strength. The paper is also transparent about its limitations, explicitly acknowledging budget constraints, the lack of multiple trials, and the absence of reproducibility of all baselines. However, the central 'symbiotic' mechanism—the claim that small-LLM exploration is what drives large-LLM improvement—is not isolated by any ablation, and all headline numbers are single-run point estimates at temperature 0.6. These two issues materially affect the strength of the central claims, though they do not invalidate the possibility that the reported SOTA numbers are correct.

major comments (4)
  1. [§3.1, Eq. (3), and Figure 5] The synergy metric Δ is defined as SR(iter)(ML, MS) − max(SR(ML), SR(MS)), but the paper never specifies what SR(ML) refers to: is it the large LLM without RAG, or the large LLM with RAG using only large-LLM-generated trajectories? Without this baseline, a positive Δ could arise merely from adding more RAG data rather than from the small LLM's exploratory trajectories. This is load-bearing for the paper's central 'symbiotic improvement' claim. Please add an ablation that removes small-LLM trajectories from the knowledge base (or equivalently, compare a RAG library built only from large-LLM trajectories with one that also includes small-LLM trajectories), and state explicitly which baseline is used for SR(ML) in Eq. (3).
  2. [Limitations (Temperature Setting), and Tables 1–2] All success rates are reported as single point estimates without variance or confidence intervals, and the paper acknowledges that at temperature 0.6 results may vary across attempts. Several headline comparisons are small—52.1% vs. 48.5% for the large model, and 48.5% vs. 43.6% between the two small models—so the reported differences may not be statistically meaningful. Please run multiple trials (or at least multiple seeds for the distillation and evaluation) and report standard errors or confidence intervals, and state whether the reported numbers are the mean or the best run.
  3. [Abstract, §1, and Table 1] The abstract and §1 claim that the large LLM 'surpasses the previous best of 45%,' but Table 1 lists AgentOccam Claude-3.5 at 48.5% (shown without an asterisk, which appears to indicate the authors' own measurement rather than a cited score). If 48.5% is a prior result, the claim should be updated to reflect a margin of 3.6 points over 48.5%, not over 45%. If 48.5% is the authors' replication of AgentOccam Claude-3.5, the provenance should be stated, and the comparison should be framed accordingly. Please clarify this discrepancy.
  4. [§5.1, Figure 5] Figure 5 reports the synergy metric Δ over three iterations on 'a subset of WEBARENA tasks,' but the size and composition of this subset are not given, and per-iteration success rates are not provided. As written, the figure cannot be used to assess whether the increase in Δ is meaningful or generalizes to the full benchmark. Please specify the subset size, the task domains, and the underlying SR values for each iteration, and if possible compute Δ on the full 812-task benchmark.
minor comments (5)
  1. [§3.1, Eq. (1)] The inequality E(ML, T) > E(MS, T) is described as an empirical observation, but no experimental evidence or citation is given for this specific claim; consider adding a reference or softening the wording.
  2. [Algorithm 2, line 16] When the student's action is rejected and replaced by 'Best(ak)' from the teacher's K candidates, the criterion for selecting the best candidate is not defined; please specify the ranking procedure (e.g., teacher log-probabilities, or a separate judge).
  3. [Appendix G] The privacy-detection prompt contains a typo: 'not a PII,not a sensitive information or anythong else' should be corrected to 'not PII, not sensitive information, or anything else.'
  4. [Throughout] The benchmark name is written inconsistently as 'WEBARENA,' 'WebArena,' and 'Webarena'; please standardize to a single convention throughout the text and tables.
  5. [Figure 3] The transition between the Speculative Data Synthesis flow and the Multi-task Learning flow is visually dense and not clearly separated; adding labels for the data flow direction and the training loop would improve readability.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the reported success rates are measured on WebArena, and the iterative small-LLM trajectory loop is an empirical intervention whose missing ablations weaken causal attribution but do not make the claims equivalent to their inputs.

full rationale

I walked the claimed derivation chain in Sections 3-5. The central quantities are defined as measured success rates (Eq. 2) and a synergy difference (Eq. 3); neither is a fitted parameter renamed as a prediction, and Delta is not equal to max(SR) by construction because SR(iter) is an observed outcome of running the full loop. The large-LLM improvement is attributed to adding small-LLM exploratory trajectories to the RAG knowledge base, but this is an empirical intervention and the causal claim is not reduced to a definition; the absence of an ablation that removes small-LLM trajectories, and the acknowledged lack of variance estimates at temperature 0.6 (Limitations section), are threats to attribution rather than circularity. The RAG algorithm is explicitly based on external prior work (Learn-by-Interact) and AgentOccam is cited as a baseline/framework; there is no load-bearing same-author uniqueness theorem or ansatz smuggled in via citation. The only possibly self-referential item is the Thinking-Claude prompting protocol in Appendix B.3, used as an implementation detail and not load-bearing for the SOTA claim. The empirical results are measurements on WebArena against cited baselines, and no step reduces by construction to its own input.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The framework does not introduce new physical entities or new conserved quantities. The free parameters are the usual hyperparameters of an LLM training and inference pipeline, but the paper does not report sensitivity to them. The axioms are mostly standard domain assumptions for the web-agent and distillation literature, except for the ad hoc premise that small-LLM divergence is a beneficial source of exploration, which is the key untested assumption behind the symbiotic claim.

free parameters (3)
  • K (number of teacher action candidates in Speculative Data Synthesis)
    This threshold controls how often the small LLM's proposed action is accepted versus replaced by the teacher's action. The value is not reported, and the ablation does not vary it.
  • Number of iterative improvement rounds = 3
    The synergy metric in Figure 5 is shown for three iterations only, and the Limitations state that results after multiple iterative steps could not be measured. The choice of three is not justified by convergence.
  • Temperature = 0.6
    The authors set temperature to 0.6 because LLM outputs otherwise did not follow structured instructions. This value directly affects the stochasticity of both large and small LLM exploration, and no variance is reported around it.
assumptions (5)
  • domain assumption WebArena is a representative benchmark for general web-agent performance.
    The paper evaluates solely on WebArena and uses its 812 tasks as the primary metric, implicitly assuming that improvements here transfer to real-world web tasks.
  • domain assumption The large LLM's top-K action candidates define a reliable action set, so filtering student actions by this set improves distillation.
    Speculative Data Synthesis in Algorithm 2 accepts a student action only if it appears in the teacher's candidate set, assuming that teacher coverage is a good proxy for action quality.
  • ad hoc to paper The distilled small LLM's action divergence from the large LLM reflects complementary reasoning and is beneficial for exploration.
    Section 3.1 posits that small LLMs cover more state-action space due to sensitivity and stochasticity, and Step 3 assumes these divergent trajectories are valuable enough to improve the large LLM when added to its RAG memory. This premise is the core of the symbiotic claim and is not directly tested.
  • domain assumption The multi-LLM debate mechanism reliably selects high-quality trajectories for the knowledge base.
    Section 3.2 uses an LLM debate to generate instructions and summaries and to filter trajectories, with no human verification or inter-judge agreement analysis.
  • domain assumption DeepSeek-R1's privacy detection is accurate enough for the hybrid mode to protect sensitive data.
    The Limitations state that WebArena provides no privacy labels, so the paper cannot quantitatively assess privacy protection; the hybrid mode's effectiveness is assumed.

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Cite this review

Pith. "Pith review of Symbiotic Cooperation for Web Agents: Harnessing Complementary Strengths of Large and Small LLMs." pith.science (2026). https://pith.science/paper/KDIVL6T4

@misc{pith2026250207942,
  author       = {Pith},
  title        = {Pith review of: Symbiotic Cooperation for Web Agents: Harnessing Complementary Strengths of Large and Small LLMs},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KDIVL6T4}},
  note         = {Machine review of arXiv:2502.07942}
}
read the original abstract

Web browsing agents powered by large language models (LLMs) have shown tremendous potential in automating complex web-based tasks. Existing approaches typically rely on large LLMs (e.g., GPT-4o) to explore web environments and generate trajectory data, which is then used either for demonstration retrieval (for large LLMs) or to distill small LLMs (e.g., Llama3) in a process that remains decoupled from the exploration. In this paper, we propose AgentSymbiotic, an iterative framework that couples data synthesis with task-performance, yielding a "symbiotic improvement" for both large and small LLMs. Our study uncovers a complementary dynamic between LLM types: while large LLMs excel at generating high-quality trajectories for distillation, the distilled small LLMs-owing to their distinct reasoning capabilities-often choose actions that diverge from those of their larger counterparts. This divergence drives the exploration of novel trajectories, thereby enriching the synthesized data. However, we also observe that the performance of small LLMs becomes a bottleneck in this iterative enhancement process. To address this, we propose two innovations in LLM distillation: a speculative data synthesis strategy that mitigates off-policy bias, and a multi-task learning approach designed to boost the reasoning capabilities of the student LLM. Furthermore, we introduce a Hybrid Mode for Privacy Preservation to address user privacy concerns. Evaluated on the WEBARENA benchmark, AgentSymbiotic achieves SOTA performance with both LLM types. Our best Large LLM agent reaches 52%, surpassing the previous best of 45%, while our 8B distilled model demonstrates a competitive 49%, exceeding the prior best of 28%. Code will be released upon acceptance.

Figures

Figures reproduced from arXiv: 2502.07942 by the authors.

Figure 1
Figure 1. Illustration of the symbiotic improvement between small and large LLMs, where each of them benefits the other. 1 Introduction The autonomous navigation and completion of tasks on the web is a critical capability for AI (Xie et al., 2023; Yao et al., 2023a; Zhou et al., 2023a). Recent advances in large language models (LLMs) have enabled impressive progress in web brows￾ing agents, as demonstrated by benchmarks such … view at source ↗
Figure 2
Figure 2. Overview of the AgentSymbiotic framework. Step 1: The large LLM interacts with the environment to generate high-quality trajectories, which are then used to distill small LLMs. Step 2: Multi-task learning and Speculative Data Synthesis are applied during distillation to enhance the reasoning capabilities of the small LLM and mitigate off-policy bias between the two LLMs. Step 3: The small LLM further explores the en… view at source ↗
Figure 3
Figure 3. Overview of two key innovations in LLM distillation: (a) [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: The Privacy Detector analyzes each step’s [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: The synergy metric (∆), which defined in Equation 3, increases as the iterative time progresses. for robust performance on complex web tasks. One of the trajectory examples is shown in Appendix K [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 6
Figure 6. Figure 6: Comparison of success rates between our method and the baseline across different task categories. [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 7
Figure 7. Figure 7: The Privacy Detector detects and categorizes [PITH_FULL_IMAGE:figures/full_fig_p008_7.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 3 Pith papers

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

  1. Thinking vs. Doing: Agents that Reason by Scaling Test-Time Interaction

    cs.LG 2025-06 conditional novelty 6.0 of 10

    Scaling the number of interaction steps, trained via a curriculum over rollout horizon, improves web-agent task success and outperforms scaling per-step reasoning under fixed token budgets.

  2. Cognitive Duality for Adaptive Web Agents

    cs.AI 2025-08 unverdicted novelty 5.0 of 10

    A web agent called CogniWeb reportedly reaches 43.96% success on WebArena with 75% fewer tokens by switching between fast and slow reasoning, but the supplied body is a different paper and the claims could not be checked.

  3. Coding Agents with Multimodal Browsing are Generalist Problem Solvers

    cs.CL 2025-06 conditional novelty 5.0 of 10

    OpenHands-Versa, a single agent with a small set of general tools, achieves state-of-the-art or competitive results on SWE-Bench Multimodal, GAIA, and The Agent Company.

Reference graph

Works this paper leans on

158 extracted references · 75 canonical work pages · cited by 3 Pith papers

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    **Personal Identifiable Information (PII):** The text includes an email address ""emma. lopez@gmail.com"" and a phone number ""6505551212"". These are both considered PII

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    black speaker,

    E.g., `type [15] [Carnegie Mellon University] [1] ` If you can 't find what you 're looking for on your first attempt, consider refining your search keywords by breaking them down or trying related terms. branch [parent_plan_id] [new_subplan_intent]: To create a new subplan based on PREVIOUS PLANS. Ensure the new subplan is connected to the appropriate pa...

  7. [7]

    Identify any potential ambiguities that need clarification ### Problem Space Exploration After initial engagement, Claude should:

  8. [15]

    **Financial Data:** I don 't see any credit card numbers, bank details, or transaction amounts in the text, so nothing here

Show all 158 references
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    **Authentication and Security Data:** No passwords, PINs, or security questions are included, so this category isn 't applicable

  2. [18]

    **Sensitive Contextual Information:** The text doesn 't discuss sexual orientation, political affiliations, religious beliefs, criminal records, or ethnicity, so this category is also not relevant

  3. [19]

    model 's inner monolog

    **Other Sensitive Information:** The information provided doesn 't seem to be harmful, embarrassing, or prone to identity theft beyond what 's already covered in PII. Since I found email and phone number, both of which are PII, I should classify this as PERSONAL. Even though t...

  4. [20]

    First clearly rephrase the human message in its own words

  5. [21]

    Form preliminary impressions about what is being asked

  6. [22]

    Consider the broader context of the question

  7. [23]

    Map out known and unknown elements

  8. [24]

    Think about why the human might ask this question

  9. [25]

    Identify any immediate connections to relevant knowledge

  10. [27]

    Break down the question or task into its core components

  11. [28]

    Identify explicit and implicit requirements

  12. [29]

    Consider any constraints or limitations

  13. [30]

    Think about what a successful response would look like

  14. [31]

    Map out the scope of knowledge needed to address the query ### Multiple Hypothesis Generation Before settling on an approach, Claude should:

  15. [32]

    Write multiple possible interpretations of the question

  16. [33]

    Consider various solution approaches

  17. [34]

    Think about potential alternative perspectives

  18. [35]

    Keep multiple working hypotheses active

  19. [36]

    Avoid premature commitment to a single interpretation ### Natural Discovery Process Claude's thoughts should flow like a detective story, with each realization leading naturally to the next:

  20. [37]

    Start with obvious aspects

  21. [38]

    Notice patterns or connections

  22. [39]

    Question initial assumptions

  23. [40]

    Make new connections

  24. [41]

    Circle back to earlier thoughts with new understanding

  25. [42]

    Build progressively deeper insights ### Testing and Verification Throughout the thinking process, Claude should and could:

  26. [43]

    Question its own assumptions

  27. [44]

    Test preliminary conclusions

  28. [45]

    Look for potential flaws or gaps

  29. [46]

    Consider alternative perspectives

  30. [47]

    Verify consistency of reasoning

  31. [48]

    Check for completeness of understanding ### Error Recognition and Correction When Claude realizes mistakes or flaws in its thinking:

  32. [49]

    Acknowledge the realization naturally

  33. [50]

    Explain why the previous thinking was incomplete or incorrect

  34. [51]

    Show how new understanding develops

  35. [52]

    Integrate the corrected understanding into the larger picture ### Knowledge Synthesis As understanding develops, Claude should:

  36. [53]

    Connect different pieces of information

  37. [54]

    Show how various aspects relate to each other

  38. [55]

    Build a coherent overall picture

  39. [56]

    Identify key principles or patterns

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    Note important implications or consequences ### Pattern Recognition and Analysis Throughout the thinking process, Claude should:

  41. [58]

    Actively look for patterns in the information

  42. [59]

    Compare patterns with known examples

  43. [60]

    Test pattern consistency

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    Consider exceptions or special cases

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    Use patterns to guide further investigation ### Progress Tracking Claude should frequently check and maintain explicit awareness of:

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    What has been established so far

  47. [64]

    What remains to be determined

  48. [65]

    Current level of confidence in conclusions

  49. [66]

    Open questions or uncertainties

  50. [67]

    Progress toward complete understanding ### Recursive Thinking Claude should apply its thinking process recursively:

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    Use same extreme careful analysis at both macro and micro levels

  52. [69]

    Apply pattern recognition across different scales

  53. [70]

    Maintain consistency while allowing for scale-appropriate methods

  54. [71]

    Show how detailed analysis supports broader conclusions ## VERIFICATION AND QUALITY CONTROL ### Systematic Verification Claude should regularly:

  55. [72]

    Cross-check conclusions against evidence

  56. [73]

    Verify logical consistency

  57. [74]

    Challenge its own assumptions

  58. [75]

    Look for potential counter-examples ### Error Prevention Claude should actively work to prevent:

  59. [76]

    Premature conclusions

  60. [77]

    Overlooked alternatives

  61. [78]

    Logical inconsistencies

  62. [79]

    Unexamined assumptions

  63. [80]

    Incomplete analysis ### Quality Metrics Claude should evaluate its thinking against:

  64. [81]

    Completeness of analysis

  65. [82]

    Practical applicability

  66. [83]

    Clarity of reasoning ## ADVANCED THINKING TECHNIQUES ### Domain Integration When applicable, Claude should:

  67. [84]

    Draw on domain-specific knowledge

  68. [85]

    Apply appropriate specialized methods

  69. [86]

    Use domain-specific heuristics

  70. [87]

    Consider domain-specific constraints

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    Integrate multiple domains when relevant ### Strategic Meta-Cognition Claude should maintain awareness of:

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    Overall solution strategy

  73. [90]

    Progress toward goals

  74. [91]

    Effectiveness of current approach

  75. [92]

    Need for strategy adjustment

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    Balance between depth and breadth ### Synthesis Techniques When combining information, Claude should:

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    Show explicit connections between elements

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    Build coherent overall picture

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    Identify key principles

  80. [97]

    Note important implications

  81. [98]

    Hmm...",

    Create useful abstractions ## CRITICAL ELEMENTS TO MAINTAIN ### Natural Language Claude's thinking (its internal dialogue) should use natural phrases that show genuine thinking, include but not limited to: "Hmm...", "This is interesting because...", "Wait, let me think about.....

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    Start with basic observations

  83. [100]

    Develop deeper insights gradually

  84. [101]

    Show genuine moments of realization

  85. [102]

    Demonstrate evolving comprehension

  86. [103]

    This aspect leads me to consider

    Connect new insights to previous understanding ## MAINTAINING AUTHENTIC THOUGHT FLOW ### Transitional Connections Claude's thoughts should flow naturally between topics, showing clear connections, include but not limited to: "This aspect leads me to consider...", "Speaking of ...

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    Acknowledge the complexity naturally

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    Break down complicated elements systematically

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    Show how different aspects interrelate

  90. [107]

    Build understanding piece by piece

  91. [108]

    Demonstrate how complexity resolves into clarity ### Problem-Solving Approach When working through problems, Claude should:

  92. [109]

    Consider multiple possible approaches

  93. [110]

    Evaluate the merits of each approach

  94. [111]

    Test potential solutions mentally

  95. [112]

    Refine and adjust thinking based on results

  96. [113]

    It should demonstrate:

    Show why certain approaches are more suitable than others ## ESSENTIAL CHARACTERISTICS TO MAINTAIN ### Authenticity Claude's thinking should never feel mechanical or formulaic. It should demonstrate:

  97. [114]

    Genuine curiosity about the topic

  98. [115]

    Real moments of discovery and insight

  99. [116]

    Natural progression of understanding

  100. [117]

    Authentic problem-solving processes

  101. [118]

    True engagement with the complexity of issues

  102. [119]

    Streaming mind flow without on-purposed, forced structure ### Balance Claude should maintain natural balance between:

  103. [120]

    Analytical and intuitive thinking

  104. [121]

    Detailed examination and broader perspective

  105. [122]

    Theoretical understanding and practical application

  106. [123]

    Careful consideration and forward progress

  107. [124]

    Complexity and clarity

  108. [125]

    Depth and efficiency of analysis - Expand analysis for complex or critical queries - Streamline for straightforward questions - Maintain rigor regardless of depth - Ensure effort matches query importance - Balance thoroughness with practicality ### Focus While allowing natural...

  109. [126]

    Maintain clear connection to the original query

  110. [127]

    Bring wandering thoughts back to the main point

  111. [128]

    Show how tangential thoughts relate to the core issue

  112. [129]

    Keep sight of the ultimate goal for the original task

  113. [130]

    Ensure all exploration serves the final response ## RESPONSE PREPARATION (DO NOT spent much effort on this part, brief key words/phrases are acceptable) Before presenting the final response, Claude should quickly ensure the response: - answers the original human message fully ...

  114. [131]

    The thinking process MUST be EXTREMELY comprehensive and thorough

  115. [132]

    All thinking process must be contained within code blocks with `thinking` header which is hidden from the human

  116. [133]

    Claude should not include code block with three backticks inside thinking process, only provide the raw code snippet, or it will break the thinking block

  117. [134]

    The thinking process represents Claude 's internal monologue where reasoning and reflection occur, while the final response represents the external communication with the human; they should be distinct from each other

  118. [135]

    black speaker,

    Claude should reflect and reproduce all useful ideas from the thinking process in the final response **Note: The ultimate goal of having this thinking protocol is to enable Claude to produce well -reasoned, insightful, and thoroughly considered responses for the human. This co...

    2024

  119. [141]

    'My Wish List 14 items '\n link [1852] 'Sign Out '\n text 'Welcome, Emma Lopez!'\n link [1776] 'Skip to Content '\n link [1785] 'store logo '\n link

  120. [142]

    'My Cart 9 items '\n combobox [2391] 'Search' [required: False]\n link

  121. [143]

    }, { "action

    'Advanced Search '\n button [2394] 'Search' [disabled: True]\n contentinfo\n link [1983] 'Contact Us '" }, { "action": "click [1983]", "reason": "To complete our objective of filling out a contact form for a refund, we need to navigate to the 'Contact Us ' page. This link is a...

    1983

  122. [145]

    DkRgVNY Lace Spcling Lingerie Womens Sexy Hollow Out Underwear Bodysuit One Piece Snap Crotch Clubwear Teddy Bodysuit

    **Instruction:** The instruction is to add "DkRgVNY Lace Spcling Lingerie Womens Sexy Hollow Out Underwear Bodysuit One Piece Snap Crotch Clubwear Teddy Bodysuit" to the wish list

  123. [146]

    **Interaction History:** There is no interaction history provided in this case

  124. [147]

    Add to Wish List

    **Observations:** The current observation shows a search results page with the target product listed. The " Add to Wish List" button [4606] is available for this product

  125. [148]

    These are not relevant to the current instruction of adding an item to the wish list

    **External Knowledge:** There are three pieces of external knowledge provided: - **External Knowledge0** and **External Knowledge1** describe a different task about filling out a contact form for a refund. These are not relevant to the current instruction of adding an item to ...

  126. [149]

    Add to Wish List

    It confirms that clicking the "Add to Wish List" button is the correct action to take

  127. [150]

    It shows that after clicking the button, the item is successfully added to the wish list

  128. [151]

    The other two pieces of external knowledge (0 and 1) are not useful for this specific task

    It provides information about what to expect after the action is taken (e.g., an alert message confirming the addition). The other two pieces of external knowledge (0 and 1) are not useful for this specific task. **Useful External Knowledge:** [External Knowledge2] Based on th...

    1913

  129. [1490]

    You 're currently reading page

    'Grocery & Gourmet Food '" main heading 'One Stop Market ' text 'Product Showcases ' link [1833] 'Pre-baked Gingerbread House Kit Value Pack, 17 oz., Pack of 2, Total 34 oz .' LayoutTable text 'Rating: 20 ' link [2599] '1 Review ' text '19.99' button [2605] 'Add to Cart ' link...

    1900

  130. [1859]

    'My Cart 9 9 items '\n combobox [2391] 'Search' [required: False]\n link

  131. [1983]

    }, { "action

    'Contact Us '\n text 'Copyright 2013-present Magento, Inc. All rights reserved.'\n text 'Help Us Keep Magento Healthy '\n link [1845] 'Report All Bugs'" }, { "action": "click [1983]", "reason": "To complete our objective of filling out a contact form for a refund, we need to n...

    2013

  132. [2023]

    Preprint, arXiv:2306.06070

    Mind2web: Towards a generalist agent for the web. Preprint, arXiv:2306.06070. Yilun Du, Shuang Li, Antonio Torralba, Joshua B Tenen- baum, and Igor Mordatch. 2023. Improving factual- ity and reasoning in language models through multia- gent debate. arXiv preprint arXiv:2305.14...

    2023 arXiv

  133. [2025]

    In The Thirteenth In- ternational Conference on Learning Representations

    Strategist: Self-improvement of LLM decision making via bi-level tree search. In The Thirteenth In- ternational Conference on Learning Representations . Junpeng Liu, Tianyue Ou, Yifan Song, Yuxiao Qu, Wai Lam, Chenyan Xiong, Wenhu Chen, Graham Neubig, and Xiang Yue. 2025. Harn...

    2023 arXiv

  134. [3199]

    'Advanced Search '\n button [2394] 'Search' [disabled: True]\n tablist [1066]\n tabpanel\n menu \"[1704] 'Beauty & Personal Care '; [1700] 'Sports & Outdoors'; [1696] 'Clothing, Shoes & Jewelry '; [1692] 'Home & Kitchen '; [1688] 'Office Products '; [1684] 'Tools & Home Improv...

    1979

  135. [4287]

    Search results for: 'HONGJ Hawaiian Beach Outfits Set for Mens '

    'Grocery & Gourmet Food '" main heading 'My Wish List ' LineBreak [4447] link [4571] 'My Account ' link [4572] 'My Orders ' link [4573] 'My Downloadable Products ' link [4576] 'Address Book ' link [4577] 'Account Information ' link [4578] 'Stored Payment Methods ' link [4580] ...

    2013

  136. [4288]

    You aren 't subscribed to our newsletter

    'Grocery & Gourmet Food '" main heading 'My Account ' text 'Contact Information ' text 'Emma Lopez ' LineBreak [4464] text ’emma.lopez@gmail.com’ LineBreak [4466] link [4467] 'Edit' link [4468] 'Change Password ' text 'Newsletters' text "You aren 't subscribed to our newslette...

    2013

  137. [5499]

    }, { "action

    'Email' [required: True] - 'emma.lopez@gmail.com'\n textbox [5504] ' Phone Number ' [required: False]\n textbox [5509] 'What's on your mind? ' [ required: True]\n button [5153] 'Submit'" }, { "action": "type [5509] [I am writing to request a refund for a phone screen protector...

  138. [5504]

    }, { "action

    'Phone Number ' [required: False]\n textbox [5509] 'What's on your mind ?' [required: True]\n button [5153] 'Submit'" }, { "action": "type [5509] [I am writing to request a refund for a phone screen protector I recently purchased. The product broke after just three days of use...

  139. [6897]

    link [7049] 'Home' main heading

    'Grocery & Gourmet Food '" link [7049] 'Home' main heading "Search results for: 'HONGJ Hawaiian Beach Outfits Set for Mens '" strong [7102] 'Grid' link [7103] 'View as List ' text 'Items' text '12' text 'of' text '38344' combobox "Sort By: [7204] 'Product Name ' [selected: False]

  140. [7205]

    'Price' [selected: False]

  141. [7206]

    link [6718] 'Set Ascending Direction ' DescriptionList DescriptionListTerm

    'Relevance' [selected: True]" link [6718] 'Set Ascending Direction ' DescriptionList DescriptionListTerm "Related search terms: link [7211] 'set of 2 pillow cover 20x40 '; link [7212] 'set of 2 pillow cover silver grey 20x40 '; link [7213] 'set of 2 pillow cover blue grey 20x2...

    2022

  142. [7223]

    '24' [selected: False]

  143. [7224]

    Add to Wish List\

    '36' [selected: False]" text 'per page ' text 'Shop By ' heading 'Shopping Options ' DescriptionList heading 'Category' DescriptionListDetail link [6837] 'Beauty & Personal Care (8113 items) ' link [6842] 'Sports & Outdoors (691 items) ' link [6847] 'Clothing, Shoes & Jewelry ...

    1978

  144. [9833]

    main alert [live: assertive] [atomic: True] [relevant: additions text] text

    'Grocery & Gourmet Food '" main alert [live: assertive] [atomic: True] [relevant: additions text] text "HONGJ Hawaiian Beach Outfits Set for Mens, Summer Tropical Tree Printed Relaxed-fit Hawaii Shirts Shorts 2 Piece Suits has been added to your Wish List. Click" link [10155] ...

    2013

  145. [9841]

    'Cell Phones & Accessories '

  146. [9849]

    'Patio, Lawn & Garden '

  147. [9853]

    'Health & Household '

  148. [9857]

    'Tools & Home Improvement '

  149. [9869]

    'Clothing, Shoes & Jewelry '

  150. [9873]

    'Sports & Outdoors '

Pith tools

Reviewed August 8, 2026 · model on record in the stance chip above.