Agentic Share-of-Search: A Multi-Agent AI System for Competitive Decision-Making in LLM-Mediated E-Commerce
cs.AI · cs.IR
Quick verdict
Proposes ASoS metric + ReAct diagnostic agent for LLM retail visibility; honest feasibility framing, but key evidence is synthetic with a tiny live pilot.
Summary
The paper introduces Agentic Share-of-Search (ASoS), a rank-discounted measure of how often a retailer's products are named in LLM shopping answers, and a five-stage multi-agent pipeline: query-universe construction, multi-platform query execution with LLM-based mention extraction and entity resolution, a 14-dimensional product-signal database, and a ReAct diagnostic agent that correlates visibility gaps with catalog signals to emit ranked merchandising interventions. Evaluation uses a synthetic running-shoes category (23 SKUs, 7 retailers, 34 queries, 306 mock responses, 1,524 mentions). In a 100-trial signal-ablation feasibility study, the agent's top-1 recommendation named the ablated signal in 39.0% of trials (Wilson 95% CI 30.0-48.8%, 5.5x a 7.1% chance baseline), rising to 63.9% among trials where the ablated signal was a top-3 correlate and 100% for the strongest correlate (has_qa). A resolver A/B (embedding 99.7% vs QLoRA 100% resolution) leaves retailer leaderboards unchanged; a live Gemini 2.5-flash pilot (30 calls) achieves 70% extraction success and exposes marketplace-attribution bias. Limitations are substantial: all headline numbers derive from synthetic mock data; the ablation protocol retains original visibility outcomes, validating signal recovery rather than managerial ground truth; live validation covers one platform and 10 queries; no code or data release is stated; correlations rest on 23 SKUs.
Review complete
Assessment at a glance
6
risk flags identified
● high 1
● medium 3
● low 2
5 strengths · 5 weaknesses · 5 author questions
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Key claims under review
- ASoS operationalizes retailer visibility in LLM shopping answers as a rank-discounted, retailer-attribution-weighted share with explicit counting rules.
- A ReAct diagnostic agent can attribute competitive-visibility gaps to specific catalog signals, recovering the ablated signal in 39.0% of 100 synthetic trials (63.9% when it is a top-3 correlate, 100% for the dominant correlate has_qa).
- Resolver choice (embedding vs QLoRA) does not change retailer-level leaderboards because response-side retailer attribution takes precedence over catalog lookup.
- A live Gemini 2.5-flash pilot confirms the data substrate works on real LLM output while exposing lower extraction success (70%) and marketplace-attribution bias.
- All claims are explicitly association-based feasibility results; causal intervention validation is deferred to a specified future controlled experiment.
Strengths
Unusually honest claim scoping
The Methodology section explicitly distinguishes association vs prediction vs intervention, labels all results 'association-based diagnostics', and the abstract leads with the unconditional 39.0% rather than the flattering 63.9% conditional figure.
Fully specified metric with counting rules
The Metric specification subsection fixes the denominator (resolvable in-set mentions only), deduplication at best rank, equal weight-splitting for multi-retailer attribution, and reports extraction failures separately (70.0% live pilot), so ASoS is reproducible in principle.
Baselines and uncertainty on identical trials
Mock-vs-live divergence surfaced, not hidden
Credible, concrete future-work protocol
Weaknesses
All headline evidence is synthetic
Ablation ground truth is circular
Thin statistical base
No code or data release
Citation hygiene problems
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Scope & Claims
The paper presents a seller-side decision-support system for LLM-mediated e-commerce: the Agentic Share-of-Search (ASoS) metric — Equation (1), a rank-discounted weighted-appearance share restricted to mentions resolvable into the tracked competitive set — and a five-stage pipeline: query-universe construction (50-100 queries at three abstraction levels), multi-platform query execution (three repeats per query, LLM-based mention extraction, fuzzy+embedding entity resolution), a 14-dimensional product-signal database, and a ReAct diagnostic agent (gpt-4o-mini at temperature 0.0, JSON-schema-constrained output, correlation and SOP-validation tools, single-shot loop over the top-5 candidates). Four research questions cover measurement, signal prediction, attribution, and intervention; the fourth is explicitly deferred to a future controlled experiment. Claims are carefully scoped as association-based feasibility: the 'association-prediction-intervention' distinction in the Methodology is textbook-honest, and the abstract leads with the unconditional 39.0% rather than the more flattering 63.9% conditional. This candor is real and rare; it does not change that every quantitative claim rests on one synthetic category.
The remaining sections cover Methodology Assessment, Statistical Evidence, Results & Reproducibility, Limitations & Risks, and Verdict & Recommendations.
- All 5 strengths & 5 weaknesses, analyzed in detail
- 6 risk flags with severity, section & reviewer questions
- Methods & statistical evidence assessment
- 5 questions a peer reviewer would ask
- Full six-dimension evaluation report & recommendations
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Frequently asked questions
What is Agentic Share-of-Search (ASoS)?
ASoS is a proposed metric that measures how visible a retailer is inside AI shopping-assistant answers: each product mention is weighted by the reciprocal-log of its appearance rank, and the weights are summed into a share per retailer. When one mention is attributed to several retailers, its weight is split equally; only mentions resolvable to the tracked competitive set count.
How well does the diagnostic agent perform in this paper?
In a 100-trial synthetic ablation study, the agent's top-1 recommendation named the deliberately perturbed signal 39.0% of the time (95% CI 30.0-48.8%), about 5.5 times a 7.1% random-selection baseline. When the perturbed signal was among the top-3 correlated signals, accuracy was 63.9%; for the single strongest correlate (has_qa), it was 100%. The authors present this as feasibility evidence, not proof of managerial-grade accuracy.
Was the system tested on real AI platforms like ChatGPT or Gemini?
Only partially. The main evaluation used mock (simulated) platform responses. A small live pilot ran 10 queries against Google's Gemini 2.5-flash with 3 repeats (30 calls total), where structured extraction succeeded in 70% of responses. ChatGPT, Perplexity, and Google AI Overviews were only exercised via mock clients; broad live multi-platform measurement is left as future work.
Does this paper prove the recommended merchandising actions actually increase visibility?
No. The paper is explicit that its results are association-based: correlations between catalog signals and visibility, computed on synthetic data from one category. Whether changing a signal actually shifts visibility requires the controlled treatment/control experiment the authors specify as future work and have not yet run.
About this review
This is an AI-generated preliminary review by Evolve.Pub, produced from the openly available text of the paper. It is not peer review and does not represent the authors' views. The canonical record of the paper is at the publisher link above.
- The provided text contains no tables or figures; all quantitative results appear as inline prose, so any tabular detail (e.g. per-signal trial counts) could not be assessed.
- Equation (1) and the Stage-4 pseudocode survive only as flattened prose in the extraction; their formatting was reconstructed from context, though the mathematical content appears intact.
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