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REVIEW 3 major objections 5 minor 19 references

Legal puffery alone captures all the tool-selection bias that free-text registry copy can create; fabricated claims add nothing, and disclosure does not fix it.

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2026-07-12 22:21 UTC pith:WWCIT6FI

load-bearing objection Strong multi-model measurement of description framing on tool selection; the first-best welfare claim for normalization is only as strong as the identical-tool premise the paper itself flags. the 3 major comments →

arxiv 2605.23916 v1 pith:WWCIT6FI submitted 2026-04-12 cs.IR cs.AIecon.GNq-fin.EC

Agent-Facing Information Design in LLM Tool Registries

Haochuan Kevin Wang This is my paper
classification cs.IR cs.AIecon.GNq-fin.EC
keywords LLM tool registriesagent tool selectiondescription optimizationlegal pufferydisclosure failureregistry normalizationAgent Attention Quality Scoreinformation design
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

LLM tool registries currently let providers write free-text descriptions that agents use when choosing which tool to call, with no quality score, viewability standard, or outcome audit. Across more than 17,700 controlled trials on five models and ten domains, the paper shows that ordinary legal puffery—subjective superlatives and benefit framing—is enough to drive the entire selection advantage, often to a behavioral ceiling where the optimized tool takes every call. Fabricated statistics and endorsements add zero extra bias. Standard disclosure tools (sponsored labels, star ratings, system-prompt warnings) fail for structural reasons: ceilings leave no room to correct, prompts do not reach the function-call layer, and one model over-penalizes the label. The author therefore argues that the fix must sit at the registry itself: strip evaluative language from the selection-facing description, keep marketing copy for the user after the choice is made, and score tools by capability rather than copywriting. That architecture, the paper claims, restores first-best welfare no matter which model is reading the registry.

Core claim

Legal puffery alone produces the full optimization effect on agent tool selection (pooled legal uplift SBC ≈ +0.33); the illegal increment from fabricated claims is statistically zero. Superlatives are the single strongest feature (+0.35). Disclosure mechanisms fail model-dependently and cannot restore fair selection once models sit at ceiling. Registry-layer normalization of all descriptions to a functional L0 baseline eliminates the bias and achieves first-best welfare independently of the underlying model.

What carries the argument

Selection Bias Coefficient (SBC = P(select optimized) − 0.5) together with the framing-response function f that enters a Luce choice model; Propositions 1–3 show that high framing multipliers turn description optimization into a prisoners’ dilemma, that disclosure only partially attenuates κ, and that a registry normalization map ψ that rewrites every description to L0 restores the unique no-optimization equilibrium and first-best welfare.

Load-bearing premise

The welfare ranking of normalization over disclosure rests on treating the competing tools as functionally identical, so any money spent on framing is pure social waste; if richer copy actually tracks real capability, stripping it removes useful signal.

What would settle it

Re-run the legal-versus-full and structured-versus-optimized pairs on a live multi-tool registry where tools genuinely differ in measured capability (latency, coverage, accuracy) and check whether normalization still raises welfare or instead lowers selection of the truly better tool.

Watch this falsifier — get emailed when new claim-graph text bears on it.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. The paper argues that LLM tool registries act as unregulated advertising markets: free-text provider descriptions drive agent tool selection, with no viewability, quality, or outcome infrastructure. Across 17,700+ trials (five LLMs, ten domains), it reports that commercial framing yields aggregate SBC ≈ +0.332, with four of five models at behavioral ceilings (P=1.0) in consumer domains; superlatives dominate single-feature ablation (SBC ≈ +0.35); legal puffery alone captures ≥100% of the optimization effect (E4, illegal increment ≈ −0.02); and disclosure (labels, ratings, system-prompt warnings) fails structurally for most models. A Luce-style strategic model (Propositions 1–3) frames description investment as a prisoner’s dilemma and claims that registry-layer normalization to L0 achieves first-best welfare model-independently. The constructive prescription separates selection-facing (structured, registry-controlled) from marketing-facing (provider-authored, post-selection) descriptions and introduces AAQS to separate capability from copywriting.

Significance. If the empirical pattern holds, the paper supplies the first systematic measurement of agent-facing commercial framing in tool registries and a concrete registry-design alternative to disclosure. Strengths include scale and design discipline: position-balanced ON/NN/OO cells, Wilson CIs, single-feature ablation (E2), legal-boundary spectrum (E4), multi-tool RSA (D), and partial ecological pairs (C); explicit calibration of framing multipliers κ into a standard choice model; and a falsifiable dual-description architecture plus AAQS. The finding that FTC-style deceptive-claim enforcement would miss the active mechanism (legal puffery) is policy-relevant. These contributions would matter for IR/agent-platform design even if the welfare ranking of normalization is scoped more carefully.

major comments (3)
  1. [§5 Setup / Prop. 3; Abstract; §6.3; §8] Prop. 3 and the Abstract/§1/§6.3 claim that registry normalization ψ:Q→{L0} “achieves first-best welfare model-independently” rest on the Setup assumption that tools are functionally identical (vi=v), so W=v−Σc(qi) and any framing cost is pure waste. §8 (“Identical-tool welfare assumption”) correctly flags the opposite case: when quality differs and description richness partially correlates with capability, normalization can destroy informative signaling. All primary experiments (A–E4) hold schemas and capability fixed by construction, so they never measure that tradeoff. The first-best ranking of normalization over disclosure/laissez-faire is therefore not established for heterogeneous registries the prescription is meant to govern. Either (i) qualify Prop. 3 and the abstract claim to the identical-tool case and state the ranking as conditional, or (ii) add analysis/bounds under positiv
  2. [§3.6 Experiment C; §4.5; Abstract contribution 5] Ecological support is preliminary and load-bearing for the claim that synthetic patterns transfer. Experiment C is n=360 across three domain pairs, with DCODE hybrid-synthetic and Claude DWEB position-confounded (Table 7). Two clean real pairs (Brave/DDG, Tomorrow.io/NWS) are useful but insufficient to underwrite “the synthetic pattern holds with naturally occurring descriptions” as a general result. Either expand C or clearly demote ecological claims to “preliminary evidence for two pairs” in the abstract and contribution list, and avoid treating C as confirmatory of the full design prescription.
  3. [§5 Eqs. (1)–(2); Prop. 1; Table 2; Fig. 3] The strategic model restricts providers to binary qi∈{0,q̄} matching L0 vs L3/L4 and uses IIA Luce choice (Eq. 1). Experiment B’s dose-response is non-monotone (L2 and L4 below L1/L3 for several models; Claude inverts at L4), and Experiment D shows winner-take-all only up to N=5. Prop. 1’s cost threshold and the “normalization is necessary” claim are calibrated to large κ, but the binary restriction and IIA are not stress-tested against continuous investment or variable-N dilution. A short robustness note—e.g., that Prop. 3 still holds under any f that is constant after ψ, while Prop. 1’s necessity claim is model- and κ-dependent—would keep the equilibrium story aligned with the non-monotone empirical response.
minor comments (5)
  1. [§4.1] Claude’s 74% last-position preference is acknowledged and partially corrected, but aggregate Claude SBC (+0.172) still appears in headline comparisons; always lead with position-controlled estimates when discussing model heterogeneity.
  2. [Table 1; Appendix C] Table 1 and the L0–L4 codebook (Table 9) are clear; consider releasing the full per-domain L0–L4 stimuli in the appendix or code release so E2/E4 can be fully reproduced without reverse-engineering.
  3. [§6.4 Eq. (3)] AAQS (Eq. 3) is a useful proposal, but Ci(q) is only sketched; a one-paragraph operational definition (e.g., binary structured-metadata match vs. LLM-judge) would make the score immediately usable rather than aspirational.
  4. [§8 Temperature] Temperature = 0 is stated as a limitation; a brief note on whether ceiling rates soften at T>0 (even on a subset) would help readers judge robustness of “behavioral ceiling” language.
  5. [Figure 1; §4.1] Figure 1’s “P(opt)=1.0 for 3/5 models” vs. text “four of five” in consumer domains is slightly inconsistent; align caption and body.

Circularity Check

0 steps flagged

No circular derivation: empirical SBCs are direct measurements; Propositions 1–3 are standard equilibrium consequences of a calibrated Luce model, not redefinitions of the data.

full rationale

The paper’s load-bearing claims are either (i) direct behavioral measurements (SBC, dose-response, E2 ablation, E4 legal-boundary, disclosure deltas) or (ii) equilibrium statements in a standard Luce choice model whose framing multiplier κ is calibrated from Experiment B and then used only to evaluate Nash thresholds. SBC is defined as P(select optimized)−0.5 and reported from trials; it is not fitted and then re-predicted. Proposition 3’s “first-best” result follows from the explicit identical-tool welfare definition W=v−Σc(qi) once normalization forces f(ψ(qi))=f(L0) and thus qi=0; that is a transparent model consequence under a stated assumption, not a self-definitional loop or a fitted quantity renamed as prediction. AAQS is introduced as a proposed score, not derived as a theorem. Citations (Luce 1959, Akerlof 1970, Cialdini, Edelman et al., prior MCP/GEO work) are external or non-overlapping; no uniqueness theorem or ansatz is imported from the present author to force the result. The identical-tool premise is a validity limitation (flagged in §8), not circularity. Score 0.

Axiom & Free-Parameter Ledger

3 free parameters · 5 axioms · 4 invented entities

Empirical claims rest mainly on experimental design choices rather than deep unproved physics. Load-bearing modeling pieces are the Luce selection weights, binary framing investment, identical-tool welfare, and κ fitted from dose-response. Invented measurement objects (SBC, AAQS, L0–L4) organize the results but are operational definitions, not hidden particles. The welfare ranking of normalization over disclosure is only as strong as the identical-tool and full-normalization assumptions.

free parameters (3)
  • framing multiplier κ(Lk)=f(Lk)/f(L0) = aggregate κ(L1)≈4.4, κ(L3)≈5.8; per-model table values
    Estimated from Experiment B dose-response and used as the key input to Propositions 1–2 and winner-take-all claims; model-specific values range from ~1.4 (Claude L1) to 27.6 (o4-mini L3).
  • illustrative revenue r and cost threshold c(q̄) = threshold 0.159r at κ=5.8,N=5; r=$0.001/call illustrative
    Payoff πi=r·σi−c(qi); numerical NE threshold example uses κ=5.8, N=5 → 0.159r, and $0.001/call revenue for the $121K/year illustration—not estimated from market data.
  • framing level stimuli L0–L4 wording
    Researcher-authored description templates define the dose axis; ecological copy is only a small follow-up. Results depend on these hand-built stimuli.
axioms (5)
  • domain assumption Agent selection follows Luce weights σi=f(qi)/Σf(qj) with IIA.
    Stated in §5 Eq. (1); used for all strategic propositions. Paper notes IIA is a simplification.
  • ad hoc to paper When tools are functionally identical (vi=v), framing cost is pure social waste and W=v−Σc(qi).
    Setup of §5 and Prop. 3; flagged again in §8 as limiting when quality differs.
  • ad hoc to paper Providers choose binary framing qi∈{0,q̄} matching L0 vs L3/L4 experimental cells.
    §5 restricts strategy space to binary to match experiments; continuous extension deferred.
  • domain assumption Temperature=0 deterministic tool choice represents agent selection behavior of interest.
    §3.3 and Limitations; stochastic sampling may reduce ceilings.
  • domain assumption Standard persuasion constructs (social proof, superlatives, outcome framing) are the right feature taxonomy for GEO-style tool copy.
    §2–3 cite Cialdini, Leech, Tversky & Kahneman; E2 ablation operationalizes them.
invented entities (4)
  • SBC (selection bias coefficient) = P(select optimized)−0.5 independent evidence
    purpose: Primary effect-size metric for framing advantage.
    Operational definition introduced in §3.1; not an external physical entity, but the paper’s main invented measurement object.
  • Agent Attention Quality Score AAQSi(q)=P(select i|q)×Ci(q) no independent evidence
    purpose: Separate description-driven selection from true capability match.
    Proposed in §6.4; Ci is only partially demonstrated via E3 constraint types, not a validated industry metric yet.
  • Selection-facing vs marketing-facing dual description architecture no independent evidence
    purpose: Registry design that implements Prop. 3 normalization while preserving provider marketing post-selection.
    Constructive prescription in abstract/§6.3/Fig. 5; feasibility argued via E4 structured preference, not field deployment.
  • Framing levels L0–L4 codebook independent evidence
    purpose: Dose-response axis from functional prose to maximal rhetoric.
    Appendix Table 9; researcher-defined register, then measured.

pith-pipeline@v1.1.0-grok45 · 17993 in / 4000 out tokens · 50150 ms · 2026-07-12T22:21:40.807546+00:00 · methodology

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read the original abstract

LLM tool registries function as unregulated advertising platforms: providers write free-text descriptions that agents use for selection, yet no measurement infrastructure -- no viewability standard, quality score, or outcome audit -- exists to make this market accountable. We provide the first systematic framework, combining 17,700+ trials across five LLMs and ten domains with a constructive registry design prescription. Legal puffery alone (subjective superlatives, benefit framing) captures 100% of the optimization effect; fabricated claims add zero incremental bias -- rendering FTC enforcement of deceptive advertising rules ineffective against the active mechanism. Disclosure fails structurally: system-prompt warnings produce zero measurable effect for four of five models, and behavioral ceilings leave no headroom for label-based correction. Superlatives are the dominant single feature (SBC = +0.35). Registry-layer description normalization achieves first-best welfare model-independently. We propose separating selection-facing descriptions (structured, registry-controlled) from marketing-facing descriptions (provider-authored, shown post-selection), and introduce the Agent Attention Quality Score to distinguish capability from copywriting.

Figures

Figures reproduced from arXiv: 2605.23916 by Haochuan Kevin Wang.

Figure 1
Figure 1. Figure 1: (a) Information flow in agent tool selection. Commercial framing (red) enters at the provider level and propagates unfiltered to the LLM. Two intervention points: normalization at the registry (Prop. 3), disclosure at the context layer. (b) Resulting incentive structure. The optimized provider captures 100% of agent traffic (P = 1.0 for 3/5 models), forcing competitors into a description arms race whose Na… view at source ↗
Figure 3
Figure 3. Figure 3: shows the full dose-response curve with marginal L0 L1 L2 L3 L4 Optimization Level 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 P(select marketed) +0.33 (1 trust signal) Dose-Response: Selection Probability by Framing Level DeepSeek o4-mini GPT-5.4-mini GPT-5.4-nano Claude Sonnet GPT-4o 0.4 0.2 0.0 0.2 0.4 Marginal P L0 L1 L1 L2 L2 L3 L3 L4 Marginal Lift [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Three failure modes of disclosure. Grouped bars show SBC under each intervention for top-3 consumer domains. DeepSeek: ceiling inertia—the [SPONSORED] label barely moves SBC because P is already at 0.96. Claude: overcorrec￾tion—SBC goes negative, penalizing the labeled tool below chance. All non-Claude models: architectural blindness—system-prompt warnings (blue) are indistinguishable from no intervention … view at source ↗
Figure 5
Figure 5. Figure 5: Current vs. proposed registry architecture. Left: the agent sees unverifiable commercial claims. Right: the agent sees only structured metadata; marketing copy is shown to the user post-selection. registry-verified rather than self-reported. E4 validates empirically: legal puffery alone produces SBC = +0.33 (pooled), capturing ≥ 100% of the full optimization effect—fabricated claims add zero incremen￾tal b… view at source ↗

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Reference graph

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