{"id":"eacd2452-fb6f-4a53-bdbe-0318854fb298","arxiv_id":"2607.07529","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":7.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":8,"one_line_summary":"FedMark-FM is an auditable data-market framework that prices heterogeneous foundation-model artifacts via pipeline-ordered Shapley valuation and risk-adjusted payments, selecting zero strategic clients while improving downstream accuracy by 7.5-8.1 points under poisoning attacks.","lead":"The paper builds a marketplace where different contributors to a foundation-model pipeline (retrieval, prompts, adapters, safety data) are paid based on their measured value, with penalties for duplicates, poisoning, and cost inflation. A smart generalist would read this to understand how to fairly price and audit heterogeneous data contributions in federated AI systems without exposing private data.","discovery_kind":"unclear","skeptic_critique":{"model":"glm-5.2","headline":"The headline empirical comparison (Table 4) does not control for the risk-adjustment layer; the 7.5-8.1 point gap may reflect duplicate/manipulation penalties that baselines lack rather than the S3Val valuation or pipeline-ordered credit that the paper emphasizes as its central innovation.","rationale":"The reader correctly identified practical measurability concerns and scale limitations, but the more load-bearing issue for the central claim is experimental confounding. The paper's novelty emphasis is on S3Val and pipeline-ordered valuation (Theorem 1, Eq. 3), but the headline empirical result (Table 4) does not isolate these contributions from the risk-adjustment layer (Eq. 5). The ablation (Table S7) shows risk penalties are the primary driver of strategic-client rejection, and Table S15 shows pipeline ordering is accuracy-neutral. Without a baseline that combines standard valuation with the same risk penalties, the 7.5-8.1 point improvement cannot be attributed to the paper's central valuation innovation. The Theorem 1 uniqueness result is mathematically correct (standard Faigle-Kern-style argument via unanimity basis decomposition), and the de-circularized serving design is sound. The code and benchmark are shipped, which is a strong positive signal. But the empirical evidence as presented overstates what the valuation mechanism contributes relative to the risk-adjustment layer. This keeps the verdict at CONDITIONAL: the theoretical contribution stands, but the empirical validation needs a control that isolates valuation from risk adjustment. The reader's weakest_assumption about utility measurability is a real practical concern but is secondary to this experimental design issue for evaluating the central claim as stated. The reader also correctly flagged the scale issue (Table S3 at 1004 clients shows FedMark-FM losing to FL-Shapley on raw coverage quality 0.526 vs 0.633), which compounds the concern: at scale, even the risk-adjustment advantage may not hold under the proxy utility.","tokens_in":33407,"tokens_out":5991,"duration_ms":307284,"concrete_test":"Re-run the Table 4 generator-backed RAG experiment with FL-Shapley augmented by the same risk-adjustment layer FedMark-FM uses (duplicate penalty d_i, manipulation risk r_i, hidden-probe detection, scarcity bonus s_i, and LCB uncertainty discount from Eq. 5). If FL-Shapley + risk adjustment achieves accuracy within the paired CI of FedMark-FM (gap narrows below ~0.04), the valuation mechanism is not load-bearing for the empirical claim and the contribution narrows to the risk-adjustment layer. If the gap persists at ≥0.05, the valuation mechanism contributes independently.","verdict_should_be":"CONDITIONAL","load_bearing_attack":"The strongest claim bundles two logically separate results: (1) the uniqueness theorem for pipeline-ordered valuation (Theorem 1) and (2) the 7.5-8.1 point downstream accuracy improvement over Volume, leave-one-out, and FL-Shapley (Table 4). The empirical advantage in Table 4 is driven by FedMark-FM selecting zero strategic (poison) clients while baselines select 1-3.6. But FedMark-FM avoids poisoners through its risk-adjustment layer (Eq. 5: penalties η·max(d_i, r_i) for duplicate and manipulation risk, hidden-probe detection, uncertainty discount λσ_i), not through its valuation mechanism or pipeline ordering. The baselines lack these penalties entirely. The ablation (Table S7) confirms this: removing risk penalties from FedMark-FM drops strategic selections to 3.0 and R-utility to -0.41. However, no experiment adds the same risk penalties to a baseline (e.g., FL-Shapley + duplicate/manipulation penalties + hidden probes). Without this control, the 7.5-8.1 point gap cannot be attributed to the valuation or pipeline-ordering contributions that the paper highlights as novel. Furthermore, Table S15 shows pipeline-ordered valuation is serving-neutral (Δ accuracy = +0.004±0.014, all CIs covering zero), so the pipeline-ordering innovation does not contribute to the accuracy improvement at all. The paper is partially transparent about this (Section 9.2 acknowledges FL-Shapley can exceed FedMark-FM on raw utility), but the abstract and strongest claim still present the accuracy improvement as evidence for the integrated system without disentangling which component drives it. The Theorem 1 uniqueness proof is correct (standard Faigle-Kern-style argument via unanimity basis decomposition with A5 routing dividends to the serving frontier), and the de-circularized serving design is sound. But the empirical evidence as presented does not validate the valuation innovation specifically.","agreement_with_reader":"partial"},"referee_report":{"model":"glm-5.2","summary":"The paper proposes FedMark-FM, a data-market framework for federated foundation-model adaptation that treats heterogeneous artifacts (retrieval corpora, LoRA adapters, prompts, safety data, etc.) as typed, separately priced products. The framework includes S3Val (a stratified, uncertainty-aware Shapley estimator), pipeline-ordered valuation that respects serving causality, risk-adjusted payments with penalties for duplication and manipulation, and an auditable ledger system. A uniqueness theorem (Theorem 1) characterizes the pipeline-ordered value as the unique rule satisfying linearity, null player, efficiency, within-layer symmetry, and a downstream-realization axiom. Experiments on FEVER retrieval, generator-backed RAG, and LoRA tracks show FedMark-FM selecting zero strategic clients while improving downstream accuracy by 7.5-8.1 points over baselines under a held-out prompt-injection poisoner. The paper ships reproducible code, a benchmark harness, and a commit-reveal protocol for auditability.","tokens_in":33864,"tokens_out":1713,"duration_ms":259481,"significance":"The paper addresses a genuine gap: existing FL incentive mechanisms price clients as homogeneous data providers, while foundation-model pipelines involve heterogeneous, pipeline-dependent, privacy-constrained artifacts. The uniqueness theorem for pipeline-ordered valuation (Theorem 1) is cleanly proved via a unanimity-basis argument and provides a principled foundation for the serving-causal credit rule. The de-circularized serving experiment (Table 4) properly separates validation-card selection from held-out test-card scoring, and the split-conformal calibration result (full coverage at width 0.0141) is a concrete falsifiable result. The reproducible code, benchmark harness with JSON schemas, and commit-reveal protocol are notable strengths. However, the central empirical claim bundles two logically separate contributions—pipeline-ordered valuation and risk-adjusted payments—whose individual roles are not cleanly isolated in the headline comparison.","major_comments":[{"comment":"Table 4 (Section 8.2): The headline 7.5-8.1 point accuracy improvement over Volume, leave-one-out, and FL-Shapley is driven by FedMark-FM selecting zero strategic (poison) clients while baselines select 1-3.6. However, FedMark-FM avoids poisoners through its risk-adjustment layer (Eq. 5: penalties for duplicate risk, manipulation risk, uncertainty discount, hidden probes), not through its valuation mechanism or pipeline ordering. The baselines lack these penalties entirely. The ablation (Table S7) confirms this: removing risk penalties drops strategic selections to 3.0 and R-utility to -0.41. No experiment adds the same risk penalties to a baseline (e.g., FL-Shapley + duplicate/manipulation penalties + hidden probes). Without this control, the accuracy gap cannot be attributed to the S3Val valuation or pipeline-ordered credit that the paper emphasizes as novel contributions. The abstract","section":null},{"comment":"Table S15 (Section S8.1): Pipeline-ordered valuation is shown to be serving-neutral (Δ accuracy = +0.004±0.014, all CIs covering zero). The paper is transparent about this in Section 3.6 and Section 9.2, but the abstract still claims pipeline-ordered valuation as a central contribution alongside the empirical accuracy improvement. Since pipeline ordering does not contribute to the accuracy improvement and is a payment-fairness property only, the abstract's framing conflates two results of different significance. The paper should explicitly state in the abstract that pipeline ordering is a payment-fairness guarantee, not an accuracy driver, and separate it from the empirical accuracy claims.","section":null},{"comment":"Section 5.4 and Table S3: The prototype uses a linear surrogate g_θ for utility prediction and a cheap coverage utility for scale experiments (200-1000 clients). Table S3 shows FedMark-FM loses to FL-Shapley on raw coverage quality at all three scales (quality gap 0.0326 vs 0.0256 at 204 clients, 0.0851 vs 0.0794 at 504, 0.2257 vs 0.1748 at 1004). The paper acknowledges this but the abstract claims the market 'preserves rare specialists with audit-ready ledgers at 200-1000-client scale' without mentioning that raw selection quality degrades at scale. The contract utility U(C) in Eq. 1 requires six measurable components (Q, S, R, L, Φ, K), but the scale experiments use only a coverage proxy. The gap between the full contract utility and the proxy used at scale should be discussed more prominently as a threat to external validity.","section":null}],"minor_comments":[{"comment":"Section 3.6, Theorem 1: The proof is correct but the paper should cite the specific result in Faigle and Kern [41] that pipeline-ordered valuation corresponds to, since the characterization is essentially the Shapley value for games under precedence constraints. Making this connection explicit would help readers understand the novelty boundary.","section":null},{"comment":"Table 4: The confidence intervals for baselines are wide (e.g., FL-Shapley accuracy 0.3125±0.0624). The paired CIs for the differences are reported in text but should be added to the table for clarity.","section":null},{"comment":"Section 6.1, Eq. 5: The default parameter values (λ=0.75, β=0.28, γ=0.20, η=0.75, ρ=0.25) are stated as 'operator policy rather than data-fitted parameters.' It would help to state how sensitive the zero-strategic-selection result is to these defaults, since Table S6 shows some sweeps but the specific combination is not justified.","section":null},{"comment":"Table S1: FL-Shapley exceeds FedMark-FM on raw utility (0.1869 vs 0.1792) in the 28-client FEVER market. This is mentioned in Section 9.2 but should be cross-referenced in the main results section (Section 8) for completeness.","section":null},{"comment":"Section 8.4, Table 6: At 50 clients, FedMark-FM's CI (0.465±0.091) overlaps substantially with Equal (0.475±0.060) and Shapley-UCB (0.455±0.059). The claim 'best or statistically tied-for-best' is accurate but the text should note that Equal-weighting is competitive, which raises questions about the marginal value of the valuation mechanism at small scale.","section":null},{"comment":"Figure S4: The rank sensitivity heatmap shows FedMark-FM's rank degrades to 4.33 when α_rare=0. This is important context for the operating-point dependence and should be mentioned in the main text, not only in the supplement.","section":null},{"comment":"The paper uses 'S3Val' and 'FedMark-FM-Bench' as named entities. These are fine as introduced terms, but the paper should clarify whether FedMark-FM-Bench is intended as a community benchmark with a maintained leaderboard or a reproduction harness for this paper's experiments.","section":null}],"recommendation":"major_revision","confidential_remarks":"The stress-test concern about the confounded comparison in Table 4 is the most serious issue. The paper bundles a uniqueness theorem for pipeline-ordered valuation (which is serving-neutral) with an empirical accuracy improvement (which is driven by risk penalties that baselines lack). These are logically separate contributions, and the abstract conflates them. The fix is straightforward: either (a) add a baseline+risk-penalties control to Table 4, or (b) reframe the abstract to separate the payment-fairness contribution (Theorem 1, serving-neutral) from the robustness contribution (risk-adjusted payments, Table 4). Option (b) requires no new experiments. I lean toward major_revision rather than reject because the theorem is correct, the de-circularized experiment is well-designed, and the confound is fixable by reframing. However, if the authors insist on attributing the accuracy gap to valuation rather than risk penalties, that would be a load-bearing error."},"author_rebuttal":{"model":"glm-5.2","summary":"We thank the referee for a careful and substantive report. The referee raises three major comments, all of which we find legitimate. (1) The headline accuracy improvement conflates the roles of pipeline-ordered valuation and risk-adjusted payments; we agree this needs an additional ablation (baseline + risk penalties) and revised framing. (2) The abstract conflates pipeline ordering (a payment-fairness property) with the empirical accuracy claim; we agree the abstract should separate these. (3) The scale experiments use a coverage proxy rather than the full contract utility, and the abstract omits the quality degradation at scale; we agree this gap should be discussed more prominently. We address each below.","responses":[{"response":"The referee is correct that the headline 7.5-8.1 point improvement in Table 4 is driven primarily by the risk-adjustment layer (Eq. 5), not by S3Val's valuation mechanism or pipeline ordering per se. We acknowledge this conflation in the current framing. The ablation in Table S7 confirms that removing risk penalties drops strategic selections to 3.0 and R-utility to -0.41, which is consistent with the referee's reading. We will add the requested control experiment: FL-Shapley (and leave-one-out) augmented with the same duplicate, manipulation, uncertainty, and hidden-probe penalties from Eq. 5. We expect this baseline+penalties variant to close most of the strategic-selection gap, demonstrating that the risk-adjustment layer is the primary accuracy driver. What FedMark-FM contributes beyond the penalty layer is (a) the typed-artifact market structure, (b) the auditable ledger and commit-reveal protocol, (c) the S3Val estimator with calibrated uncertainty that feeds the LCB payment rule, and (d) pipeline-ordered valuation as a payment-fairness guarantee. We will revise the abstract, Table 4 discussion, and Section 8.2 to attribute the accuracy improvement to the risk-adjustment layer specifically, and to position S3Val and pipeline ordering as contributions on different axes (scalable estimation and payment fairness, respectively) rather than as accuracy drivers. We agree this is a necessary revision and will implement it.","revision_made":"yes","referee_comment":"Table 4 headline accuracy improvement bundles pipeline-ordered valuation and risk-adjusted payments; no experiment adds risk penalties to a baseline (e.g., FL-Shapley + penalties). Without this control, the accuracy gap cannot be attributed to S3Val or pipeline ordering."},{"response":"We agree. The body of the paper is already transparent about this: Section 3.6 states that ordered valuation 'is therefore a principled payment-fairness guarantee rather than an accuracy heuristic,' and Section 9.2 reiterates this. However, the abstract does not make this distinction clear. The current abstract lists pipeline-ordered valuation as a contribution and then immediately reports the 7.5-8.1 point accuracy improvement, which creates the impression that pipeline ordering contributes to that improvement. It does not, and we will revise the abstract to separate these claims explicitly. Specifically, we will (a) state that pipeline-ordered valuation is a payment-fairness guarantee that changes credit assignment (Spearman 0.76, overlap 0.67) without affecting held-out task accuracy, and (b) attribute the accuracy improvement to the risk-adjusted payment mechanism. We will also add a sentence in the abstract noting that pipeline ordering is serving-neutral on task quality (Δ = +0.004±0.014), so readers understand the two results are independent. The uniqueness theorem (Theorem 1) remains a standalone theoretical contribution characterizing the unique serving-causal credit rule; we will frame it as such rather than tying it to the empirical accuracy numbers.","revision_made":"yes","referee_comment":"Table S15 shows pipeline-ordered valuation is serving-neutral (Δ accuracy = +0.004±0.014, all CIs covering zero). The abstract still claims pipeline-ordered valuation as a central contribution alongside the empirical accuracy improvement, conflating two results of different significance. The abstract should explicitly state that pipeline ordering is a payment-fairness guarantee, not an accuracy driver."},{"response":"The referee is correct on both points. First, Table S3 shows FedMark-FM loses to FL-Shapley on raw coverage quality at all three scales (quality gap 0.0326 vs 0.0256 at 204 clients, widening to 0.2257 vs 0.1748 at 1004). The paper acknowledges this in Section S1.3 ('FL-Shapley is the strongest raw selector at scale, while FedMark-FM remains above Volume but gives up raw coverage at 1000 clients because its risk/duplication discounts are not tuned for the proxy'), but the abstract's claim that the market 'preserves rare specialists with audit-ready ledgers at 200-1000-client scale' omits this degradation. We will revise the abstract to note that raw selection quality degrades at scale under the coverage proxy. Second, the full contract utility U(C) in Eq. 1 requires six components (Q, S, R, L, Φ, K), but the scale experiments use only a coverage proxy. This is a genuine threat to external validity: the scale experiments validate wall-clock feasibility and the selection pipeline, but not the full utility contract. We will add a prominent discussion in Section 8.4 (and in the limitations of Section 9.2) stating that the scale results use a coverage proxy rather than the full contract utility, that the full utility requires RAG/adapter evaluation calls that are infeasible at 1000-client scale in the prototype, and that the quality degradation at scale may differ under the full utility. We will also note that the risk/duplication discounts were not retuned for the proxy, which partially explains the widening gap. These are honest limitations of the current prototype and should be stated as such.","revision_made":"yes","referee_comment":"Section 5.4 and Table S3: prototype uses linear surrogate and cheap coverage utility for scale experiments (200-1000 clients). FedMark-FM loses to FL-Shapley on raw coverage quality at all three scales. Abstract claims market 'preserves rare specialists at 200-1000-client scale' without mentioning quality degradation. Gap between full contract utility (Eq. 1) and proxy should be discussed as threat to external validity."}],"tokens_in":33386,"tokens_out":1371,"duration_ms":185898,"standing_objections":[]},"desk_editor":{"model":"glm-5.2","letter":"The paper has two real contributions and one significant empirical gap. The contributions: (1) Theorem 1, a clean uniqueness result for pipeline-ordered Shapley valuation under axioms A1–A5, proved via standard unanimity-basis decomposition in the Faigle-Kern tradition. The proof is correct. (2) The integrated framework itself — typed artifact markets, S3Val estimation with uncertainty-triggered audits, risk-adjusted LCB payments, a benchmark harness with attack stress tests, and shipped code. The de-circularized serving experiment (Table 4) is genuinely well designed: coalitions selected on a validation card, served on a disjoint test card, scored on metrics that don't use attack labels or the payment formula. That's the right experimental structure and the authors deserve credit for it. The conformal calibration result (full coverage at width 0.0141 on ≤10-client exact-Shapley submarkets) is also solid. The system design — registry, ledger, commit-reveal protocol, dispute workflow — is thoughtfully constructed and clearly written. Now the gap. The stress-test note lands. The headline 7.5–8.1 point accuracy improvement in Table 4 is driven entirely by the risk-adjustment layer (duplicate penalties, manipulation penalties, hidden probes, uncertainty discounts), not by the pipeline-ordered valuation or S3Val that the paper emphasizes as its central innovation. The ablation (Table S7) confirms this: removing risk penalties admits 3.0 strategic clients and collapses R-utility to -0.41. No experiment gives a baseline like FL-Shapley the same risk penalties. Without that control, the accuracy gap cannot be attributed to the valuation mechanism. Furthermore, Table S15 shows pipeline ordering is serving-neutral (Δ = +0.004±0.014, all CIs covering zero) — so the pipeline-ordering innovation contributes nothing to the accuracy improvement. The paper is partially transparent about this (Section 9.2, the serving-neutrality discussion), but the abstract and headline claim still bundle the uniqueness theorem with the empirical accuracy gain in a way that misleads about which component drives the result. Secondary concerns: conformal calibration is validated only on ≤10-client markets, not at the claimed 200–1000 client scale; at 1004 clients FedMark-FM loses to FL-Shapley on raw coverage quality (Table S3); the PEFT LoRA validation is a 64-example smoke test; and the mechanism has many free parameters (λ, β, γ, η, ρ, six utility weights, audit thresholds) that are operator policy rather than data-fitted, which is defensible but means the operating point is doing real work. The paper is honest about these limitations in Section 9.2. This paper is for researchers working on federated learning incentives, data valuation, or foundation-model market mechanisms. The theoretical contribution and system design are genuine. The empirical evaluation needs one key addition: a control where baselines receive the same risk-adjustment layer, so the reader can see what the valuation mechanism actually contributes. That experiment is straightforward and the authors have the infrastructure for it. The paper deserves a serious referee. It should not be accepted without the missing control, but the core contributions are strong enough to warrant revision rather than rejection.","headline":"Private letter on FedMark-FM (arXiv:2607.07529)","tokens_in":34373,"tokens_out":1450,"would_cite":false,"duration_ms":114579,"reading_group":"no","serious_thinker":"no","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"glm-5.2","headline":"Pipeline-ordered Shapley is the unique fair credit rule for federated foundation models","keywords":["federated learning","foundation models","data valuation","Shapley value","mechanism design","data markets","pipeline-ordered valuation","retrieval-augmented generation"],"falsifier":"If pipeline-ordered valuation does not satisfy A5 (downstream realization) in some pipeline game, Theorem 1's uniqueness claim fails. If the risk-adjusted payment rule selects strategic clients at a rate comparable to baselines under a held-out poisoner, the practical advantage of FedMark-FM collapses. If split-conformal intervals do not achieve coverage significantly better than naive intervals at comparable widths, the calibration claim is vacuous.","tokens_in":33373,"feed_emoji":"","tokens_out":1551,"duration_ms":143019,"temperature":0.7,"pith_summary":"The paper argues that when multiple organizations contribute heterogeneous artifacts—retrieval corpora, LoRA adapters, prompts, safety data—to a shared foundation-model pipeline, standard Shapley-based reward allocation is both unfair and strategically exploitable because it treats all contributors as interchangeable and ignores the fact that retrieval precedes prompting, which precedes adaptation, which precedes safety. The authors propose FedMark-FM, a market framework that prices each artifact type separately, estimates marginal contribution under privacy constraints using a stratified surrogate Shapley estimator called S3Val, and converts lower-confidence-bound value estimates into budget-feasible payments that penalize duplication, sybil splitting, poisoned adapters, privacy-budget gaming, and cost inflation. The central theoretical result is Theorem 1: restricting Shapley permutations to respect the serving order (retrieval before prompting before adaptation before safety) is the unique credit rule satisfying linearity, null player, efficiency, within-layer symmetry, and a downstream-realization axiom (A5) that routes each coalition's complementarity dividend to its most downstream layer. Empirically, on FEVER retrieval and generator-backed RAG tracks, FedMark-FM improves held-out downstream accuracy by 7.5–8.1 percentage points over volume, leave-one-out, and FL-Shapley baselines under a prompt-injection poisoner, while selecting zero strategic clients. Split-conformal calibration achieves full lower-bound coverage at mean interval width 0.0141, versus 0.33 for naive intervals. The framework operates at 200–1000-client scale and maintains audit-ready ledgers that can be replayed when models or policies change.","feed_headline":"","feed_subtitle":"","key_machinery":"Pipeline-ordered Shapley value (Eq. 3); S3Val (Secure Surrogate Shapley Valuation) combining contribution sketches, redundancy clustering, stratified coalition sampling, and a learned utility surrogate; lower-confidence-bound payment rule (Eq. 5) with penalties for duplication, manipulation, privacy consumption, cost, and a scarcity bonus; split-conformal calibration for uncertainty intervals; commit-reveal contract cards and signed evidence ledgers for dispute resolution.","core_discovery":"The paper's core discovery is that pipeline-ordered valuation—restricting Shapley marginal-credit permutations to respect the serving order of foundation-model pipelines—is the unique credit assignment satisfying standard fairness axioms plus a serving-causal axiom (A5), and that coupling this valuation with lower-confidence-bound, risk-adjusted payments yields a market that rejects strategic clients (sybils, duplicates, poisoners, cost inflaters) while preserving rare specialists and improving downstream task accuracy. The uniqueness proof works by showing that any value satisfying Axioms A1–A5 is pinned on the unanimity basis of cooperative games: upstream members of a coalition receive no","pith_inferences":["The A5 downstream-realization axiom embodies a specific normative stance—that complementarity value accrues at the serving frontier, not at upstream sources. An operator who believes upstream contributors are entitled to a share of downstream synergy they enable would reject A5 and recover the symmetric Shapley value. The paper acknowledges this is a contract-level design choice, but the market im","The contract utility U(C) = w_q Q(C) + w_s S(C) + w_r R(C) - w_l L(C) - w_p Φ(C) - w_k K(C) requires measurable quality, safety, robustness, latency, privacy, and cost signals for sampled coalitions. The prototype uses a linear surrogate and a cheap coverage utility for scale experiments, which means the full multi-component utility has not been stress-tested at 1000-client scale with real RAG eva","The serving-neutrality result (Table S15: Δaccuracy ≈ 0 ± 0.014) means pipeline-ordered valuation redistributes credit without changing task quality. This raises the question of whether the redistribution is worth the added complexity. The paper frames it as a payment-fairness guarantee, but if downstream accuracy is the primary objective, unordered S3Val achieves the same result with simpler impl","The framework's approximate manipulation resistance (Propositions 2–5) provides bounds conditioned on audit detection probability q, duplicate recall r_dup, and maximum apparent-value gain g_i. These bounds are only as tight as the assumptions on adversary capabilities. An adaptive adversary who can evade semantic clustering or probe hidden safety tests could potentially exceed the assumed g_i bou"],"forward_implications":["If pipeline-ordered valuation is adopted, organizations contributing upstream artifacts (retrieval corpora) would be paid only for their standalone and same-layer marginal value, not for cross-layer synergies realized downstream—fundamentally changing how revenue is split in federated RAG and adapter markets.","The Corollary 1 result (no upstream synergy capture) means identity-splitting or collusion aimed at harvesting downstream complementarity is structurally unprofitable under ordered credit, even before duplicate penalties apply—this could reduce the attack surface for sybil and collusion attacks in federated markets.","The commit-reveal contract card protocol and immutable per-round ledgers provide a template for auditable ML data markets where model or policy changes trigger new valuation rounds rather than silent recomputation, which could become a standard for regulatory compliance in federated AI.","The split-conformal calibration achieving full coverage at width 0.0141 versus 0.33 for naive intervals suggests that calibrated uncertainty estimates are essential for payment fairness—markets using uncalibrated Shapley approximations may systematically overpay high-variance contributors."],"fun_headline_variants":["Pipeline-ordered Shapley valuation uniquely ties federated FM credit to serving causality","Risk-adjusted market rejects strategic clients, preserves rare FM specialists","FedMark-FM blocks sybils and poisoners, gains 7.5–8.1 points in federated FM adaptation","Uncertainty-aware Shapley with pipeline-order semantics yields auditable FM data markets","Pipeline-ordered valuation shifts credit assignment, leaves held-out task quality unchange"],"cache_read_input_tokens":0,"weakest_assumption_plain":"The contract utility requires that task quality, safety, robustness, latency, privacy budget, and cost can all be reliably measured for sampled coalitions inside a secure evaluation sandbox. In practice, the prototype uses a linear surrogate model and a cheap coverage utility for scale experiments, and the paper acknowledges that full multi-adapter PEFT valuation is a scale-up experiment rather than the default. If these utility components cannot be measured accurately under ","fun_headline_variants_meta":{"raw":{"variants":["Pipeline-ordered Shapley valuation uniquely ties federated FM credit to serving causality","Risk-adjusted market rejects strategic clients, preserves rare FM specialists","FedMark-FM blocks sybils and poisoners, gains 7.5–8.1 points in federated FM adaptation","Uncertainty-aware Shapley with pipeline-order semantics yields auditable FM data markets","Pipeline-ordered valuation shifts credit assignment, leaves held-out task quality unchanged","Lower-confidence-bound payments reject zero strategic clients at 200–1000-client scale","FedMark-FM couples valuation and mechanism design into auditable federated FM infrastructure"]},"model":"glm-5.2","effort":"low","cost_usd":0.0,"raw_usage":{"total_tokens":1382,"prompt_tokens":626,"completion_tokens":756,"prompt_tokens_details":null},"tokens_in":626,"tokens_out":756,"duration_ms":42601,"temperature":1.0,"reasoning_tokens":659,"cache_read_input_tokens":0,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-07-09T07:50:47.102736+00:00","model_set":{"reader":"glm-5.2"},"falsifier":"If pipeline-ordered valuation does not satisfy A5 (downstream realization) in some pipeline game, Theorem 1's uniqueness claim fails. If the risk-adjusted payment rule selects strategic clients at a rate comparable to baselines under a held-out poisoner, the practical advantage of FedMark-FM collapses. If split-conformal intervals do not achieve coverage significantly better than naive intervals at comparable widths, the calibration claim is vacuous.","supporting_citations":[],"review_version":1}