--========  Review Reports  ========--

The review report from reviewer #1:

*1: Is the paper relevant to Workshop?
  [_] No
  [X] Yes

*2: How innovative is the paper?
  [_] 6 (Very innovative)
  [X] 3 (Innovative)
  [_] -2 (Marginally)
  [_] -4 (Not very much)
  [_] -6 (Not at all)

*3: How would you rate the technical quality of the paper?
  [_] 6 (Very high)
  [_] 3 (High)
  [X] -2 (Marginal)
  [_] -4 (Low)
  [_] -6 (Very low)

*4: How is the presentation?
  [_] 6 (Excellent)
  [X] 3 (Good)
  [_] -2 (Marginal)
  [_] -4 (Below average)
  [_] -6 (Poor)

*5: Is the paper of interest to Workshop users and practitioners?
  [X] 3 (Yes)
  [_] 2 (May be)
  [_] 1 (No)
  [_] 0 (Not applicable)

*6: What is your confidence in your review of this paper?
  [X] 2 (High)
  [_] 1 (Medium)
  [_] 0 (Low)

*7: Overall recommendation
  [_] 5 (Strong Accept: top quality)
  [_] 4 (Accept: a regular paper)
  [X] 3 (Weak Accept: could be a poster)
  [_] 2 (Weak Reject: don't like it, but won't argue to reject it)
  [_] 1 (Reject: will argue to reject it)
  [_] 0 (Strong Reject: hopeless)

*8: Detailed comments for the authors
  Agent2UCB combines content rewriting, generative-visibility scoring, and a dual-UCB tool selector in an interactive GEO dashboard. The architecture and screenshots show a concrete workflow, while Algorithm 1 explains how agent advice and observed rewards influence selection. Figure 2 reports a GEO score of 35.1 versus 23.0 for the category baseline and 32.3 for the agentic comparison. Table I also exposes trade-offs: overall SEO and credibility scores improve while readability declines. Presenting these competing effects is useful for practitioners.

The main concerns are the underspecified experimental protocol and unsupported efficiency and convergence claims. Dataset size, model configurations, scoring rules, and call accounting are needed to interpret the results. Nine tools, approximately 15 iterations, and a tenfold efficiency gain require a clearly defined comparison budget. More directly, Figure 2's right panel is a method-level bar chart, although its caption and Section III describe an iteration-level convergence curve; please reconcile this mismatch and supply the corresponding evidence. Algorithm 1 also needs to explain how tool-selection probabilities become pseudo-rewards comparable to visibility gains. The paper explicitly uses a simulator, so conclusions should remain limited to it; independent evaluation can be future work. I recommend Weak Accept for the demonstrated workflow and interface value, without treating the reported efficiency or convergence advantages as established.

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