When Hiring Becomes Agent-Mediated: Evaluating Access and Recurrence in Two-Agent R\'esum\'e Screening

# When Hiring Becomes Agent-Mediated:Evaluating Access and Recurrence in Two-Agent Résumé Screening
Source: [https://arxiv.org/html/2609.19530](https://arxiv.org/html/2609.19530)
Hang JiangAffiliation:Northeastern UniversityAffiliation:Boston, MA, USA

###### Abstract

Hiring is bilateral: employers assess fit, while candidates present and defend evidence of their qualifications\. Yet résumé screening—the first gate—is commonly automated as a static, one\-call judgment over a résumé–job pair\. We study a two\-agent alternative in which employer\-side and candidate\-side agents represent these roles, exchange evidence, and update their judgments before deciding who advances\. We compare procedures on 600 constructed résumé–job pairs using GPT\-5\.5 and Claude Opus 4\.7\. Two\-agent screening advances more applications \(33\.3% to 39\.3% for GPT\-5\.5; 34\.0% to 35\.5% for Opus 4\.7\)\. Across three runs on the common 191\-pair borderline pool, pass\-instance rates rise from 4\.5% to 26\.2% and from 6\.5% to 16\.1%, respectively\. This is not a uniform relaxation: two\-agent screening rejects applications one\-call advances, changing decisions in both directions\. At similar pass volumes, the procedures advance different applications, and no one\-call threshold recovers applications consistently selected by two\-agent screening\. Among discovery\-selected cases re\-executed in fresh runs, two\-agent\-only selections recur less often than shared selections—clearly under GPT\-5\.5, less certainly under Opus 4\.7—while a separate one\-call follow\-up shows no comparable decline\. As hiring becomes agent\-mediated on both sides, the screening procedure—not only the model behind it—shapes who reaches human review and how reliably that access recurs\.

## 1Introduction

Résumé screening has traditionally been an employer\-controlled gate: employers review application materials and decide which candidates receive further consideration\. Current LLM\-based screening largely preserves this structure, replacing the initial review with a single model call over a fixed résumé–job pair\([An et al\., 2024](https://arxiv.org/html/2609.19530#bib.bib10);[Iso et al\., 2025](https://arxiv.org/html/2609.19530#bib.bib15);[Vaishampayan et al\., 2025](https://arxiv.org/html/2609.19530#bib.bib11)\)\. As agent systems mature, however, employers and candidates may both delegate parts of this process\. A candidate\-side agent can surface and explain evidence already contained in the résumé, while an employer\-side agent can evaluate that evidence against the job description and raise unresolved requirements\([Lo et al\., 2025](https://arxiv.org/html/2609.19530#bib.bib16);[Yuksel et al\., 2026](https://arxiv.org/html/2609.19530#bib.bib17)\)\.

This two\-agent design is motivated by a division of functions, not by an assumption that dialogue is inherently better\. The candidate\-side agent responds to employer\-side concerns using résumé\-grounded evidence, while the employer\-side agent updates its assessment as those concerns are addressed\. No new applicant information is introduced\. What changes is how evaluation and advocacy are assigned, how evidence is exchanged, and when judgments are revised before the screening decision\. Two\-agent screening is therefore a different decision procedure rather than merely a longer prompt\.

APassdoes not imply hiring, but it controls access to human review\. Pass rates alone are therefore insufficient: two procedures may advance similar numbers but different applications, and a procedure\-specific selection may disappear when the pipeline is executed again\. The relevant question is not only whether two\-agent screening changes the number of applications advanced, but how it reallocates access relative to one\-call screening and whether those changes recur\.

We compare fixed one\-call and two\-agent procedures on 600 constructed résumé–job pairs under GPT\-5\.5 and Opus 4\.7, treating re\-execution as a first\-class measurement\. First, two\-agent screening changes decisions in both directions rather than uniformly relaxing one\-call screening\. Second, even at similar pass volumes, the procedures advance different applications, and no threshold on the stored one\-call mean reconstructs the persistent two\-agent set\. Third, within discovery\-selected cohorts, fresh runs show that two\-agent\-only selections recur less often than selections shared across procedures, while a separate one\-call follow\-up shows no comparable decline\. We compare complete procedures: we neither rank their hiring quality nor attribute the observed differences to role separation, dialogue, compute, the decision rules, or any single component\.

## 2Related Work

LLM\-based hiring research has examined discrimination, matching quality, and human–LLM agreement, largely by treating screening as a one\-call assessment of a jointly presented résumé and job description\([An et al\., 2024](https://arxiv.org/html/2609.19530#bib.bib10);[Iso et al\., 2025](https://arxiv.org/html/2609.19530#bib.bib15);[Vaishampayan et al\., 2025](https://arxiv.org/html/2609.19530#bib.bib11)\)\. Recent systems demonstrate agentic candidate assessment and multi\-agent résumé screening\([Lo et al\., 2025](https://arxiv.org/html/2609.19530#bib.bib16);[Yuksel et al\., 2026](https://arxiv.org/html/2609.19530#bib.bib17)\)\. We study the transition between these settings: when employer evaluation and candidate advocacy are assigned to separate agents, does the resulting procedure preserve which applications advance?

Our evaluation connects three reliability traditions\. Predictive multiplicity and prediction churn show that similar aggregate behavior can mask different instance\-level outcomes\([Marx et al\., 2020](https://arxiv.org/html/2609.19530#bib.bib2);[Black et al\., 2022](https://arxiv.org/html/2609.19530#bib.bib3);[Milani Fard et al\., 2016](https://arxiv.org/html/2609.19530#bib.bib4)\)\. Work on LLM and agent reliability emphasizes repeated execution, dependable long\-horizon behavior, and reproducible reporting\([Atil et al\., 2025](https://arxiv.org/html/2609.19530#bib.bib12);[Song et al\., 2025](https://arxiv.org/html/2609.19530#bib.bib9);[Gupta, 2026](https://arxiv.org/html/2609.19530#bib.bib6);[Ma et al\., 2026](https://arxiv.org/html/2609.19530#bib.bib7);[Rabanser et al\., 2026](https://arxiv.org/html/2609.19530#bib.bib5);[Pattnayak and Bhatia, 2026](https://arxiv.org/html/2609.19530#bib.bib8)\)\. Relatedly,[Jiang et al\. \(2024\)](https://arxiv.org/html/2609.19530#bib.bib18)study whether persona\-conditioned agents consistently express assigned traits across elicitation tasks\. We share their focus on agent consistency, but examine instance\-level decisions produced by a complete two\-agent procedure\. We move the multiplicity question from model replacement to procedure replacement and validate distinguishing cases with fresh runs\.

## 3Experimental Setup

#### Controlled testbed\.

The testbed contains 200 job postings, each paired with one constructed résumé in three intended\-fit strata, for 600 application pairs\. Strong résumés cover most required skills at the requested depth; borderline résumés deliberately combine matched and missing requirements; weak résumés come from mismatched functional domains\. A fixed, LLM\-free scorer checked the intended strata, with 600/600 agreement after regeneration\. Regeneration was triggered only by scorer disagreement, never by a one\-call or two\-agent outcome\. The strata support controlled comparison and are not qualification ground truth \(Appendix[A](https://arxiv.org/html/2609.19530#A1)\)\.

#### Procedures\.

Both procedures assess required skills, experience, and basic qualifications, and both represent employer\- and candidate\-side perspectives in their decision scores, but they combine those perspectives differently\. Theone\-call proceduregives one model joint access to both documents\. In a single call, it identifies job requirements, finds supporting résumé evidence, emits an employer\-perspective score and a candidate\-perspective score, and returnsPasswhen their stored mean is at least \.75\. Its final readout is therefore compensatory: a higher score on one perspective can offset a lower score on the other\.

Thetwo\-agent procedureassigns the roles and source documents separately\. The candidate\-side agent sees the résumé and responds only with evidence grounded in it; the employer\-side agent sees the posting, evaluates fit, and probes unresolved requirements\. After a fixed employer opening, turns alternate candidate\-then\-employer\. Both agents see the message history but not the other side’s source document, so job criteria and résumé evidence must cross through the exchange\. An engine\-level rule requires pending questions to be answered before a new topic is introduced\.

The evolving perspective scores feed a deterministic, non\-compensatory rule layer rather than a mean: it uses a \.75 principal commitment condition with \.65 sustained\-floor variants, and terminates on commitment, rejection, divergence, stall, or the round limit\.Passis unavailable before round 3\. Eight rounds form the soft cap, extendable to at most 12\. On the repeated borderline pool, one\-call uses exactly one model call per decision; two\-agent sessions use a median of eight and a mean of 9\.31 calls\. Prompts and decision rules were fixed before runs A–C\.

Before any model call, the two\-agent procedure also applies a deterministic*pre\-exchange skill\-overlap gate*: if fewer than 10% of normalized required\-skill names occur in the résumé’s declared skill list, the pair is recorded asFailwithout an exchange\. Across the testbed, this gate stops 203/600 pairs: 0 strong, 9 borderline, and 194 weak\. One\-call screening has no corresponding pre\-exchange gate and scores every pair it is run on\. For paired borderline analyses, we remove the same nine gated IDs from one\-call results to define a common 191\-pair pool\.

#### Runs and measures\.

Runs A–C execute each procedure three times on the 191 borderline pairs under GPT\-5\.5 and Opus 4\.7; Run A additionally covers all strong and weak pairs\. A pair is*persistent*if it passes all three runs and*mixed*if it passes one or two\. Three\-run borderline pass rates count pass instances over the common pool \(573 decisions per procedure and model\); Table[1](https://arxiv.org/html/2609.19530#S4.T1)instead reports the single Run\-A readout on all 200 borderline pairs\. Because the native one\-call rule passes far fewer borderline applications, we descriptively re\-read its stored scores at the cutoffs closest to the two\-agent pass volumes \(\.590 GPT\-5\.5; \.575 Opus 4\.7\)\. The model is not rerun, and two\-agent outcomes are not re\-thresholded\.

Fresh runs use discovery\-set membership at these readouts\. Runs D–F execute both procedures on 36 shared and 20 two\-agent\-only application–model cases\. Runs G–I separately execute only one\-call screening on the same 36 shared and 35 one\-call\-only cases\. “Only” means persistent under one procedure but below 3/3 under the other, not necessarily 3/3 versus 0/3\. The endpoint is whether the originating procedure again returns 3/3Pass\. Cohort labels are never shown to the models\.

## 4Findings

### 4\.1Two\-agent screening changes decisions in both directions

Run A is the only discovery run covering all 600 pairs\. As Table[1](https://arxiv.org/html/2609.19530#S4.T1)shows, two\-agent screening removes some one\-call passes and adds some one\-call failures, so net differences understate changed membership\.

Table 1:Run\-A outcomes for each 200\-pair stratum\. “Two rejects” counts applications with one\-callPassand two\-agentFail; “Two adds” counts the reverse\. These are single\-run disagreements, not the persistent\-set membership used in Sections[4\.2](https://arxiv.org/html/2609.19530#S4.SS2)and[4\.3](https://arxiv.org/html/2609.19530#S4.SS3)\. Weak rows are descriptive because the two\-agent pre\-exchange skill\-overlap gate stops 194/200 pairs before exchange\.Under GPT\-5\.5, two\-agent screening removes six strong applications and adds 42 borderline applications\. Under Opus 4\.7, disagreement runs both ways within both strata: 10 strong applications leave and five enter, while five borderline applications leave and 19 enter\. The GPT\-5\.5 strong decrease is nominally significant \(p=\.031p=\.031\), the Opus 4\.7 decrease is not \(p=\.302p=\.302\), and both borderline increases are supported by exact paired tests \(p<5×10−13p<5\\times 10^\{\-13\};p=\.0066p=\.0066\)\. A uniformly lower one\-call cutoff can add applications but cannot remove existing passes, so these bidirectional changes rule out simple uniform relaxation\. Because the procedures differ in role separation,aggregation, interaction, compute, and gating, this result is an end\-to\-end procedural contrast rather than an attribution to dialogue alone\.

We re\-executed all 21 Run\-A strong disagreements at least twice under both procedures\. Six remain oppositely classified across every execution, including cases in both directions under Opus 4\.7\. This selected follow\-up measures durability rather than prevalence, but confirms recurring counterexamples to a uniformly relaxed one\-call rule \(Appendix[B](https://arxiv.org/html/2609.19530#A2)\)\.

### 4\.2Similar pass volumes do not mean the same applications

The native procedures operate at very different pass volumes, which also inflates their apparent difference in mixed\-case rate\. Re\-reading the stored one\-call scores near the two\-agent volumes reduces the mixed\-case gap from 13\.6 to 6\.8 percentage points for GPT\-5\.5 and from 8\.4 to 3\.7 for Opus 4\.7 \(Table[2](https://arxiv.org/html/2609.19530#S4.T2)\)\. The native mixed\-case contrast should therefore not be interpreted as an architectural property\.

Table 2:Native and similar\-volume readouts on the common 191\-pair pool\. Pass instances are counted over 573 decisions and mixed cases over 191 three\-run histories\. Matched rows change only the stored one\-call cutoff and are descriptive\.Rate matching still does not make the procedures interchangeable\. At the similar\-volume readouts, run\-levelPass\-set Jaccard is \.741–\.865 across GPT\-5\.5 one\-call reruns and \.656–\.707 across GPT\-5\.5 two\-agent reruns, but only \.347–\.429 across procedures; the Opus 4\.7 ranges are \.725–\.811, \.568–\.750, and \.348–\.408\. Every within\-procedure rerun pair therefore overlaps more than every cross\-procedure pair\. For persistent selections, GPT\-5\.5 has 43 one\-call and 36 two\-agent passes, sharing 24; Opus 4\.7 has 28 and 20, sharing 12 \(Figure[1](https://arxiv.org/html/2609.19530#S4.F1)\)\. The resulting Jaccards are \.436 and \.333, leaving 31 GPT\-5\.5 and 24 Opus 4\.7 persistent selections specific to one procedure\.

GPT\-5\.5192412J=\.436J=\.436Opus 4\.716128J=\.333J=\.333one\-call onlysharedtwo\-agent onlyFigure 1:Membership among applications that pass all three discovery runs at the similar\-volume one\-call cutoffs\.Scanning every distinct stored one\-call mean\-score cutoff still cannot reconstruct the two\-agent persistent set: the best Jaccard is \.436 for GPT\-5\.5 and \.361 for Opus 4\.7 \(Appendix[C](https://arxiv.org/html/2609.19530#A3)\)\. Alternative readouts of the two component scores were not swept\. Thus two\-agent screening is not another operating point on the stored one\-call mean ranking\.

### 4\.3Recurrence differs within discovery\-selected cohorts

Overall fresh recurrence is similar: 45/56 \(80\.4%\) of the complete two\-agent persistent set and 60/71 \(84\.5%\) of the complete one\-call set again pass all three fresh runs\. The results therefore do not support a general claim that two\-agent screening is less repeatable\. The difference lies in how each total is composed \(Table[3](https://arxiv.org/html/2609.19530#S4.T3)\)\.

Table 3:Fresh 3/3 recurrence within discovery\-selected cohorts, by originating procedure and model\. “Origin only” denotes persistence under the originating procedure but below 3/3 under the other\.In D–F, two\-agent recurrence falls from 34/36 shared to 11/20 two\-agent\-only cases\. The contrast is clear for GPT\-5\.5 \(24/24 versus 7/12; Fisherp=\.0021p=\.0021\) but uncertain for Opus 4\.7 \(10/12 versus 4/8;p=\.161p=\.161\)\. In the separate G–I block, one\-call recurrence is 29/36 for shared and 31/35 for one\-call\-only cases, so the corresponding cohort does not show the same descriptive decline\. This comparison argues against a simple symmetric summary but does not establish an architectural asymmetry: the selected cohorts may differ in latent difficulty, and their discovery selection conditions are not equally diagnostic\. We therefore interpret the contrast as a property of these discovery cohorts, not as evidence that two\-agent screening is generally less reliable\.

## 5Implications for Agent Evaluation

When résumé screening moves from a single call to an exchange between candidate\-side and employer\-side agents, the object of evaluation becomes the complete decision procedure, not merely the quality of the dialogue it produces\. Our results motivate three levels of measurement, none of which is sufficient on its own\.

#### Volume\.

How many applications advance? Because procedures may operate at different effective thresholds, pass\-rate differences alone cannot show whether a new procedure reallocates access rather than merely operating at a different threshold; rate alignment is needed to distinguish these descriptions\.

#### Membership\.

Which applications advance at similar pass volumes? The two procedures selected substantially different sets even when their pass volumes were similar, and no threshold on the one\-call score recovered the set consistently selected by two\-agent screening\. The evaluated two\-agent procedure therefore cannot be treated simply as a new operating point on the one\-call ranking\.

#### Recurrence\.

Do the selections that distinguish the procedures appear again when the pipeline is re\-executed? Within the discovery\-selected cohorts evaluated in fresh runs, two\-agent\-only selections recurred less often than selections shared by both procedures\. This selected\-cohort result shows why direct re\-execution is informative, but it does not establish that two\-agent screening is generally less reliable\.

Reporting all three levels matters most when selection controls access\. A procedure may advance the expected number of applications while changing who receives review, and some of those procedure\-specific changes may not recur; aggregate pass rates reveal neither\.

## 6Conclusion

We study what happens when the first gate in hiring moves from a static, one\-call judgment to an exchange between candidate\-side and employer\-side agents\. In our testbed, two\-agent screening reallocates access rather than merely changing pass volume\. Similar totals do not recover the same applications, and some procedure\-specific selections do not recur in fresh executions\. These results do not establish which procedure makes better hiring decisions; they show that aggregate agreement is insufficient evidence of procedural equivalence\. Evaluating agent\-mediated screening therefore requires application\-level comparison and direct rerun validation, not pass rates alone\.

## Limitations

#### Agent design\.

The candidate\-side agent sees only the résumé, so it can surface evidence already present but cannot draw on work samples, portfolios, preferences, or explanations that a real applicant might provide\. The employer\-side agent sees only the public job posting, without access to team needs, internal evaluation rubrics, or compensation constraints\. Role prompts, communication rules, stopping conditions, and decision thresholds are also fixed\. Richer information on either side and alternative interaction designs remain unexplored; our findings characterize the evaluated procedures rather than agent\-mediated screening in general\.

#### Procedure\-level comparison\.

One\-call and two\-agent screening differ simultaneously in information access, role separation, interaction structure, inference budget, and decision rules\. The one\-call readout averages two perspective\-specific scores, whereas the two\-agent procedure uses a non\-compensatory rule layer and a two\-agent\-only pre\-exchange skill\-overlap gate\. We evaluate these as complete procedures and do not isolate the contribution of dialogue, role separation, additional inference, or any individual rule\. A compute\-matched iterative single\-agent baseline—matched on calls or tokens and evaluated under a pre\-specified decision rule—together with component\-level ablations is needed for such attribution\. Because pairs stopped by the pre\-exchange skill\-overlap gate terminate before any model call, the existing logs contain no trajectories from which a gate\-off counterfactual can be reconstructed; evaluating that ablation requires new executions\.

#### Rate matching\.

Rate matching changes only the one\-call readout: we reapply alternative cutoffs to stored one\-call scores, choosing cutoffs to match the observed two\-agent pass volumes\. The two\-agent procedure remains at its native configuration, because changing its commitment and stopping rules could alter the preceding dialogue and would require new executions\. The one\-call procedure can therefore be examined across operating points, whereas the two\-agent procedure is evaluated at only one operating point\. The comparison is descriptive rather than symmetrically tuned\.

#### Fresh validation\.

Fresh runs cover discovery\-selected persistent cases rather than the full borderline set\. Runs D–F and G–I evaluate different targeted cohorts, so we do not treat their contrast as a formal between\-procedure interaction test\. Because cases that were not persistent during discovery were not re\-executed, we cannot estimate full\-pool discovery\-to\-fresh overlap\.

#### Testbed and model scope\.

The résumés are constructed from one job\-posting collection, and the construction strata are design categories rather than validated labor\-market labels\. Because the testbed contains no real candidates or downstream interview, offer, or job\-performance outcomes, we cannot determine whether either procedure is more accurate, fair, or beneficial\. GPT\-5\.5 generated the résumés and also serves as one evaluator, creating potential generator–evaluator dependence; evaluation with Opus 4\.7 does not fully remove this concern\. Provider\-side sampling could not be fully controlled, and the magnitude and direction of some results differ across models\. Results may therefore not generalize beyond the evaluated job collection, models, prompts, thresholds, and executions\.

## Ethical Considerations

Hiring is high stakes\. This work evaluates screening procedures using constructed résumés and public job postings; it involves no real applicants or hiring decisions\. It does not recommend deployment, establish that any application is qualified, or claim that exchanges between agents can substitute for communication with actual candidates\. Responsible deployment would require job\-related validation, human oversight, a meaningful appeal process, privacy and accessibility protections, and demographic and disability evaluation\. Depending on the deployment context, relevant regulatory frameworks may include New York City Local Law 144 and the EU AI Act\([New York City Council, 2021](https://arxiv.org/html/2609.19530#bib.bib13);[European Union, 2024](https://arxiv.org/html/2609.19530#bib.bib14)\)\.

The constructed résumés use names from a fixed placeholder list and contain no explicit demographic attributes\. We perform no demographic analysis\. Indirect signals such as institution prestige, geography, and employment gaps were not controlled, so the testbed is unsuitable for fairness or disparate\-impact claims\.

## Acknowledgments

We thank the anonymous reviewers for their comments and suggestions\. The generative AI tool \(ChatGPT\) was used for language polishing and improving the readability of the paper\.

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- Vaishampayanet al\.\(2025\)S\. Vaishampayan, H\. Leary, Y\. B\. Alebachew, L\. Hickman, B\. Stevenor, W\. Beck, and C\. BrownHuman and LLM\-based resume matching: an observational study\.InFindings of the Association for Computational Linguistics: NAACL 2025,Albuquerque, New Mexico,pp\. 4823–4838\.External Links:[Link](https://aclanthology.org/2025.findings-naacl.270/),[Document](https://dx.doi.org/10.18653/v1/2025.findings-naacl.270)Cited by:[§1](https://arxiv.org/html/2609.19530#S1.p1.1),[§2](https://arxiv.org/html/2609.19530#S2.p1.1)\.
- Yukselet al\.\(2026\)K\. A\. Yuksel, A\. B\. Anees, A\. H\. Elneima, S\. Hewavitharana, M\. Al\-Badrashiny, and H\. SawafAgentic AI for human resources: LLM\-driven candidate assessment\.InProceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics \(Volume 3: System Demonstrations\),Rabat, Morocco,pp\. 341–348\.External Links:[Link](https://aclanthology.org/2026.eacl-demo.24/),[Document](https://dx.doi.org/10.18653/v1/2026.eacl-demo.24),ISBN 979\-8\-89176\-382\-1Cited by:[§1](https://arxiv.org/html/2609.19530#S1.p1.1),[§2](https://arxiv.org/html/2609.19530#S2.p1.1)\.

## Appendix ATestbed Construction and Protocol

The testbed uses 200 unique posting identifiers from theLinkedIn Job Postings \(2023–2024\)dataset\([Koneru, 2024](https://arxiv.org/html/2609.19530#bib.bib1)\), downloaded on March 14, 2026 under CC BY\-SA 4\.0\. Each posting is paired with one constructed résumé in each intended\-fit stratum\. Strong résumés cover at least 80% of required skills at the stated minimum depth or one level above, with experience and salary inside the posting’s range\. Borderline résumés cover about 60% at the minimum depth or one level below; omitted requirements are excluded from all fields, and postings rotate a skill, seniority, or salary mismatch\. Weak résumés have under 30% required/preferred skill\-name overlap and a clear domain or seniority mismatch\.

A fixed, LLM\-free arithmetic scorer combines skill coverage, seniority, salary, and location fit and assigns strong, borderline, or weak construction labels\. A résumé was regenerated only when this scorer disagreed with the intended stratum; weights and cutoffs were unchanged, and no regeneration depended on a one\-call or two\-agent outcome\. The final testbed has 200 pairs per stratum and 600/600 agreement between intended and scored labels\. These labels describe designed posting fit rather than candidate quality\.

The one\-call procedure makes exactly one model call with joint access to both documents and applies the stored mean\-score rule\. The two\-agent procedure begins with a posting\-specific fixed employer message, then alternates candidate and employer model turns\. The employer\-side agent uses the posting to state criteria and probe unresolved requirements; the candidate\-side agent responds using only résumé\-grounded evidence\. Each agent sees the full message history and its own source information, but not the other side’s document\. Engine\-level instructions require direct answers to pending questions\. The state begins at \.40\. The fixed rule layer uses a \.75 principal commitment condition, sustained\-floor variants at \.65, and independent failure conditions\. Commitment is suppressed before round 3, but rejection may terminate earlier\. Eight rounds form the soft cap; sessions may extend to at most 12 rounds, and the four\-round stall condition is evaluated only after the soft cap\.

A two\-agent\-only*pre\-exchange skill\-overlap gate*recordsFailwithout a model call when fewer than 10% of normalized required\-skill names occur in the résumé’s declared skill list\. It gates 0 strong, 9 borderline, and 194 weak pairs \(203/600 total\), with identical IDs across models and runs\. One\-call screening has no such pre\-exchange gate; the nine borderline IDs are excluded from its results only to define the common 191\-pair analysis pool\. Records stopped by this gate contain the engine’s \.40/\.40 initialization values rather than measured model scores and are not treated as scored failures\. A separate*evidence\-confirmation rule*converts an employerPassinto another probe until 80% of required skills are evidence\-confirmed; it never directly producesFailand is distinct from the pre\-exchange gate\. Prompts, thresholds, routes, and stopping rules were fixed before runs A–C\.

## Appendix BStrong\-Stratum Re\-execution

The target manifest and persistence endpoint were frozen before collection\. Every Run\-A strong\-stratum disagreement was re\-executed under both procedures with no selective reruns: six application–model cases under GPT\-5\.5 and fifteen under Opus 4\.7, the latter comprising ten one\-call advances that two\-agent rejected and five one\-call rejections that two\-agent advanced\. Each case received three executions per procedure including Run A, except the GPT\-5\.5 one\-call arm, which received four; persistence outcomes are identical under either count\.

A disagreement persists when one\-call returns the same decision in every execution and two\-agent the opposite in every execution\. Three of six persist under GPT\-5\.5; under Opus 4\.7, two of ten removals and one of five advances persist, so both directions retain recurring cases\. Of the fifteen that dissolve, thirteen do so because the two\-agent decision changes\. The other two involve one\-call scores adjacent to \.75\. Because the cohort is selected on disagreement in Run A, these proportions describe the durability of observed disagreements, not the prevalence of disagreement in an independently repeated strong stratum\.

## Appendix CCutoff Sweep and Run Frequencies

The one\-call mean\-score cutoff is scanned over all distinct stored decision points after rounding scores to six decimals\. GPT\-5\.5 matches the two\-agent pass volume exactly at \.590\. For Opus 4\.7, \.575 gives 102 pass instances and the next distinct point gives 80; neither matches the two\-agent total of 92 because 22 instances are tied at \.575\.

In a five\-point neighborhood around the reported cutoffs, GPT\-5\.5 mixed\-rate gaps range from 4\.7–6\.8 percentage points and Opus 4\.7 gaps from 3\.1–7\.3\. Across the full sweep, the maximum persistent\-set Jaccards with the two\-agent set are \.436 and \.361\. Thus the similar\-volume point is not selected to disadvantage reconstruction: it is the GPT\-5\.5 maximum, while the Opus 4\.7 matched Jaccard \(\.333\) lies below its maximum\. At the original \.75 cutoff the one\-call mixed rates are 2/191 \(1\.0%\) for GPT\-5\.5 and 5/191 \(2\.6%\) for Opus 4\.7, which give the 13\.6 and 8\.4 point native gaps reported in Section[4\.2](https://arxiv.org/html/2609.19530#S4.SS2); across the full sweep the highest one\-call mixed rates are 36/191 \(18\.8%\) and 32/191 \(16\.8%\)\. Table[4](https://arxiv.org/html/2609.19530#A3.T4)gives the full three\-run pass frequency distributions at the reported cutoffs\.

Table 4:Pass frequencies in the commonN=191N=191pool\.
## Appendix DFresh\-Run Details

The target manifests and endpoints were frozen before fresh collection\. Runs D–F cover 36 shared and 20 two\-agent\-only application–model cases under both procedures, producing 336 procedure\-level records with no errors or selective reruns\. Runs G–I separately cover the same 36 shared and 35 one\-call\-only cases under one\-call screening, producing 213 successful records\. The main\-text fresh table uses the within\-block shared comparator for each originating procedure: 34/36 versus 11/20 for two\-agent in D–F, and 29/36 versus 31/35 for one\-call in G–I\. D–F also reran one\-call on the shared cohort and obtained 31/36 at the aligned cutoff; this is a separate execution from the G–I 29/36 result and is not used as the G–I within\-block comparator\. Table[5](https://arxiv.org/html/2609.19530#A4.T5)gives the persistent\-set transition mapping for the two D–F cohorts\.

Table 5:Pooled transitions from discovery A–C to fresh runs D–F\. TA and OC denote two\-agent and one\-call persistent membership\.Of the 20 two\-agent\-only cases, 10 remain two\-agent\-only and one becomes shared, yielding the 11 two\-agent 3/3 recurrences in Table[3](https://arxiv.org/html/2609.19530#S4.T3); seven leave both persistent sets\. Of the 36 shared cases, 29 remain shared and none leaves both sets\. In a stricter post\-hoc subset requiring discovery two\-agent 3/3 and one\-call 0/3, fresh two\-agent persistence is 7/10 for GPT\-5\.5 and 3/5 for Opus 4\.7\.

For G–I, fresh one\-call persistence is 19/24 for GPT\-5\.5 shared versus 17/19 for GPT\-5\.5 one\-call\-only cases, and 10/12 versus 14/16 for Opus 4\.7\. Pooled, these are 29/36 and 31/35\. Twenty\-six pair IDs occur under both model configurations, so pooled percentages are descriptive rather than independent observations\.

#### Item\-level variation\.

Among borderline applications that pass at least once at the similar\-volume readouts, 43\.8% are mixed under two\-agent screening versus 25\.9% under one\-call for GPT\-5\.5, and 51\.2% versus 33\.3% for Opus 4\.7\. Conditioning on one\-call below 3/3 therefore selects more strongly for mixed two\-agent histories than the reverse condition\. Shared latent difficulty together with unequal item\-level mixed rates could produce both fresh\-block contrasts; the data do not isolate an architectural asymmetry\.

#### Model identifiers\.

All runs requestedgpt\-5\.5andclaude\-opus\-4\-7\. Returned\-model logging was added after runs A–C, so those runs carry no recorded identifier\. In D–F thegpt\-5\.5alias resolved togpt\-5\.5\-2026\-04\-23, the only published snapshot of that model, which was released before runs A–C; G–I requested it directly, and separate probes on 2026\-05\-27 and 2026\-06\-15 returned the same value\. The Anthropic endpoint returns the alias string rather than a dated identifier, so no dated Opus 4\.7 version is available for any run\.