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arxiv: 2605.31365 · v1 · pith:GNIKLJTXnew · submitted 2026-05-29 · 💻 cs.AI

Learning to Adapt: Self-Improving Web Agent via Cognitive-Aware Exploration

Weile Chen , Bingchen Miao , Qifan Yu , Wendong Bu , Guoming Wang , Wenqiao Zhang , Shengyu Zhang , Juncheng Li
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Siliang Tang
This is my paper

Pith reviewed 2026-06-28 22:16 UTC · model grok-4.3

classification 💻 cs.AI
keywords web agentsself-improving agentsmultimodal large language modelsadversarial rolescognitive explorationgraph explorationautonomous agents
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The pith

SCALE lets web agents use three adversarial roles to discover their own limitations and expand capabilities through exploration.

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

The paper presents SCALE, a framework in which web agents employ three adversarial roles—Selector, Predictor, and Judger—to identify their own shortcomings and broaden their cognitive reach by interacting with the environment. It introduces SCALE-Hop, a graph exploration approach that supports global planning and prevents agents from getting stuck in local areas. The authors generate SCALE-20k, a dataset of structured demonstrations drawn from 19 real-world websites across varied task types. Experiments indicate that this setup yields clear gains in performance and generalization for several multimodal large language models operating in web settings. The method seeks to lessen dependence on manually designed pipelines or costly expert examples.

Core claim

By deploying Selector, Predictor, and Judger in an adversarial loop, agents can autonomously locate their limitations and enlarge their cognitive boundaries via direct environmental exploration; SCALE-Hop further aids global planning, and the resulting traces produce the SCALE-20k dataset that improves MLLM results across real websites without handcrafted pipelines or expert trajectories.

What carries the argument

The three adversarial roles (Selector, Predictor, Judger) that interact to surface the agent's limitations, together with the SCALE-Hop graph exploration strategy.

If this is right

  • Agents adapt to complex dynamic web environments without external expert demonstrations.
  • Multiple MLLMs achieve higher task success and better transfer across different websites.
  • Exploration traces become a source of training data that replaces handcrafted pipelines.
  • The approach scales to building more autonomous web agents from real-site interactions.

Where Pith is reading between the lines

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

  • The same role-based self-critique loop could be tested in non-web domains such as mobile app control or code execution agents.
  • Measuring how well SCALE-Hop avoids traps on sites with deeper navigation structures would test its planning benefit directly.
  • Releasing SCALE-20k allows other groups to benchmark new exploration methods against the same real-world task distribution.

Load-bearing premise

The three adversarial roles can autonomously discover the agent's limitations and expand its cognitive boundaries through environmental exploration without requiring handcrafted pipelines or expert trajectories.

What would settle it

An experiment that applies the same web tasks to MLLMs with and without the three adversarial roles and finds no measurable gain in success rate or generalization.

Figures

Figures reproduced from arXiv: 2605.31365 by Bingchen Miao, Guoming Wang, Juncheng Li, Qifan Yu, Shengyu Zhang, Siliang Tang, Weile Chen, Wendong Bu, Wenqiao Zhang.

Figure 1
Figure 1. Figure 1: A comparison between prior methods and our SCALE framework. SCALE enables autonomous exploration with diverse and scalable task generation, overcoming the limi￾tation in previous approaches. works usually depend on the design of manually crafted execution pipelines [7, 12, 32] or on the use of human￾annotated expert trajectories [3, 10, 29, 30] for fine-tuning web agents. However, these two types of paradi… view at source ↗
Figure 2
Figure 2. Figure 2: The overview of SCALE and SCALE-Hop. SCALE consists of Input Encoding, Self-Check, and Iterative Update. It enables agents to identify unfamiliar actions, verify predictions, and iteratively improve their reasoning. SCALE-Hop builds a graph to represent exploration history. It uses verification-guided backtracking to mark nodes as fully explored and guide the agent toward underexplored areas for global nav… view at source ↗
Figure 3
Figure 3. Figure 3: Overview of the SCALE-20k construction pipeline and composition. The dataset is constructed in three stages: 1) Single￾step tasks are reverse-generated from valid exploration steps; 2) Multi-step tasks are synthesized from coherent trajectories extracted via SCALE-Hop graphs; 3) Page QA pairs are created to test content comprehension. The dataset includes 19 real-world websites and supports three task type… view at source ↗
Figure 4
Figure 4. Figure 4: A case of cognitive boundary discovery by the [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Comparison of SCALE-20k, OS-Genesis, and Visual [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: (a) Impact of training data size on Success Rate in shop [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
read the original abstract

Recent advances in Multimodal Large Language Models (MLLMs) have led to promising progress in web agents. However, existing web agents often rely on handcrafted execution pipelines or expensive expert trajectories, limiting their adaptability to complex, dynamic environments. To address these challenges, we propose SCALE (Self-Cognitive-Aware Learning and Exploration), which leverages three adversarial roles, Selector, Predictor, and Judger to autonomously discover the agent's limitations and expand its cognitive boundaries through environmental exploration. Moreover, we propose SCALE-Hop, a graph exploration strategy that facilitates global planning and helps agents avoid local exploration traps. To further support learning, we construct SCALE-20k, a large-scale dataset collected from 19 real-world websites, containing diverse task types and structured demonstrations generated from SCALE's exploration traces. Experimental results show that our approach significantly improves the performance and generalization of multiple MLLMs in various web environments. Our framework offers a scalable and generalizable solution for building truly autonomous and adaptive web agents.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 2 minor

Summary. The manuscript proposes SCALE (Self-Cognitive-Aware Learning and Exploration), a framework that uses three adversarial roles—Selector, Predictor, and Judger—together with the SCALE-Hop graph exploration strategy to enable web agents to autonomously discover their own limitations and generate exploration traces. These traces are used to construct the SCALE-20k dataset from 19 real-world websites; the authors claim that fine-tuning multiple MLLMs on this data yields significant gains in performance and generalization across web environments without relying on handcrafted pipelines or expert trajectories.

Significance. If the reported gains are reproducible and the autonomy claim holds, the work would provide a concrete route to scalable, self-generated training data for web agents and reduce dependence on expert demonstrations. The SCALE-Hop mechanism and the three-role adversarial setup could be of broader interest for exploration in partially observable environments.

major comments (2)
  1. [Abstract, §3] Abstract and §3 (Role Definitions): The central claim that the Selector/Predictor/Judger roles 'autonomously discover the agent's limitations ... without requiring handcrafted pipelines' is load-bearing for the self-improving loop and the purity of SCALE-20k. The manuscript must supply the exact system prompts, interaction protocol, termination criteria, and initial seeding procedure so that readers can verify whether domain-specific heuristics are encoded in the role definitions.
  2. [§4, Table X] §4 (Experiments) and Table X: The abstract asserts 'significantly improves the performance and generalization of multiple MLLMs' yet the provided abstract supplies no numerical results, baselines, error bars, or ablation statistics. The experimental section must report concrete metrics (success rate, generalization gap, etc.) with statistical controls; without them the performance claim cannot be evaluated.
minor comments (2)
  1. [§3.2] Notation for SCALE-Hop graph construction is introduced without a formal definition or pseudocode; a small algorithm box would improve clarity.
  2. [§4.1] The manuscript should state the exact number of websites, task categories, and total trajectories in SCALE-20k (currently only '19 real-world websites' and '20k' are given).

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive feedback. We address each major comment below and will revise the manuscript to incorporate the requested clarifications and enhancements.

read point-by-point responses

  1. Referee: [Abstract, §3] Abstract and §3 (Role Definitions): The central claim that the Selector/Predictor/Judger roles 'autonomously discover the agent's limitations ... without requiring handcrafted pipelines' is load-bearing for the self-improving loop and the purity of SCALE-20k. The manuscript must supply the exact system prompts, interaction protocol, termination criteria, and initial seeding procedure so that readers can verify whether domain-specific heuristics are encoded in the role definitions.

    Authors: We agree that full transparency on the role definitions is necessary to support the autonomy claim. In the revised manuscript we will add the complete system prompts for Selector, Predictor, and Judger as a new appendix. We will also expand Section 3 to include the precise interaction protocol, termination criteria, and initial seeding procedure, allowing readers to directly inspect whether any domain-specific heuristics are present. revision: yes

  2. Referee: [§4, Table X] §4 (Experiments) and Table X: The abstract asserts 'significantly improves the performance and generalization of multiple MLLMs' yet the provided abstract supplies no numerical results, baselines, error bars, or ablation statistics. The experimental section must report concrete metrics (success rate, generalization gap, etc.) with statistical controls; without them the performance claim cannot be evaluated.

    Authors: We acknowledge that the abstract currently states the performance improvement only qualitatively. In the revision we will update the abstract to report key quantitative results (e.g., success-rate gains and generalization gaps). We will also augment Section 4 and Table X with explicit baselines, error bars, ablation statistics, and statistical controls so that the performance claims can be fully evaluated. revision: yes

Circularity Check

0 steps flagged

No significant circularity; framework generates new traces rather than re-deriving inputs

full rationale

The paper proposes SCALE using three roles (Selector, Predictor, Judger) and SCALE-Hop to generate exploration traces, from which SCALE-20k is constructed, followed by experimental validation on MLLMs. No equations, fitted parameters, or self-referential derivations appear. The central claim rests on the generated dataset and empirical gains, which are independent of the input assumptions once the roles execute. No load-bearing self-citation chains or ansatz smuggling are quoted. This matches the default expectation of a non-circular empirical framework.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review; no free parameters, axioms, or invented entities are specified in the provided text.

pith-pipeline@v0.9.1-grok · 5726 in / 1214 out tokens · 17984 ms · 2026-06-28T22:16:01.899136+00:00 · methodology

discussion (0)

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Reference graph

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    The action command should start with action: followed by a concise command.(for example,action: click [<insert item number in picture>], type [<insert item number in picture>][<typing text>],action: hover [<insert item number in picture>],action: scroll [<down or up>])

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    When you usetypeorfillaction you must provide the specific element in the im- age and the fill content

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    Click [ob- ject]

    Your output must include both the action and reason parts, separated by a newline, exactly in the following format: action: <insert action> reason: <insert why you choose this action, and why this action will lead to unknown interactions> In any given instance, you should generate only one action and its corresponding reason. If it hasn’t been generated b...

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    N/A” in the bracket. Output Format: First, generate the reasoning process for the action. Then, generate the action in the correct format. Start with a

    that matches the set range. In summary, the next action I will perform is```click [16]``` STEP 4: User Input: Image Observation: Task Description: You are an intelligent agent completing web-based tasks. Based on the user’s objective (i.e. instruc- tion), current interface information (i.e. screenshot and its corresponding accessibility tree), and action ...

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    reason”: “<brief justification about reasoning quality>

    Screenshots for context. Your Objective: - Evaluate ONLY the REASONING (not the ac- tion’s optimality). Prioritize ACCURACY over length. - If the task is simple, concise reasoning is pre- ferred; if complex, more elaboration is accept- able. - The reasoning MUST be tightly aligned with the final action/answer: no off-topic chains, and no mismatch between ...

  43. [45]

    OBJECTIVE — the task goal

  44. [46]

    Agent’s reasoning and proposed next action (as- sistant content)

  45. [47]

    Your Objective: - Evaluate whether the proposed NEXT ACTION (or final answer) is the BEST choice for the current environment/state

    Screenshots for context. Your Objective: - Evaluate whether the proposed NEXT ACTION (or final answer) is the BEST choice for the current environment/state. - STRICTLY check the following RULES are obeyed:

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    The action must be V ALID given the current observation

  47. [49]

    Only ONE action at a time

  48. [50]

    Follow examples to reason step by step and then issue the next action

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    In summary, the next action I will perform is

    Correct output format: must start with the phrase: “In summary, the next action I will perform is” followed by the action inside triple backticks. - The action MUST be one of the ALLOWED AC- TIONS (whitelist): Page Operation Actions: - click [id] - type [id] [content] (optional enter suppres- sion: type [id] [content] [0]) - hover [id] - press [key comb] ...