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FilingLoop: Evaluating and Evolving LLM Agents for Corporate Disclosure Review and Revision

Wentao Zhang, Yilei Zhao, Guangyan Gan, Shuo Sun, Lin William Cong

arXiv:2610.37905v1cs.AI

TL;DR

Disclosure-review agents must discover unstated deficiencies, obtain missing company facts, and verify requested revisions, capabilities that existing financial QA evaluations provide limited evidence about. FilingLoop reconstructs historical SEC review tasks and couples iterative review and revision with role-specific procedural learning across cases. On the evaluated stream, requirement fulfillment rises from 24.28% to 27.96% and full comment resolution from 16.90% to 20.75%.

  • Problem

    Existing financial question-answering evaluations provide limited evidence about agents discovering unstated disclosure gaps, acquiring missing company facts, and verifying requested revisions.

    Source: §1 · p. 1

  • Method

    FilingLoop reconstructs historical SEC IPO review tasks and combines iterative Reviewer–Filer interactions with requirement-level feedback that updates role-specific guidance for later cases.

    Source: §1 · p. 2

  • Results

    Requirement Accuracy rises from 24.28% to 27.96%, and Comment Accuracy from 16.90% to 20.75% under procedural evolution.

    Source: §4.5 · p. 8

  • Takeaways & Limitations

    FilingLoop links discovered concerns, acquired company facts, and committed revisions to provide requirement-level feedback for procedures used in later review tasks.

    Source: §5 · p. 10

    The online adaptation is not evaluated on an independent held-out test set, and unseen-company generalization requires a separate protocol.

    Source: §3.2.3 · p. 6

Abstract

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Public disclosure repositories such as SEC EDGAR support informed investment through web access to corporate filings. Supporting their review with language model agents requires discovering unstated deficiencies across related documents, acquiring missing company facts, and verifying that requested revisions satisfy evidence-backed requirements. Existing financial question-answering evaluations provide limited evidence of these interactive capabilities. We introduce \textbf{FilingLoop}, an interactive benchmark and a self-evolving agent framework for corporate disclosure review and revision. Grounded in historical U.S.\ Securities and Exchange Commission correspondence, FilingLoop reconstructs 380 predominantly IPO review tasks from 96 companies under distinct information constraints. An evaluator separate from the acting agents scores final filings and responses against 6,099 eligible requirements and audits unsupported claims and harmful revisions. Our framework couples iterative review and revision within each case with procedural learning across cases, converting requirement-level evaluation feedback into role-specific guidance without updating model weights. Updates affect only subsequent cases, allowing procedural adaptation to be assessed without retrying scored tasks. On the evaluated stream, evolution increases requirement fulfillment from 24.28\% to 27.96\% and full comment resolution from 16.90\% to 20.75\%, with a 5.8\% increase in total cost over the fixed-policy variant. FilingLoop connects the concerns an agent discovers with the company facts it obtains and the revisions it commits, providing requirement-level feedback for procedures used in later review tasks.

1 Introduction

Corporate disclosure review requires reviewers and company counsel to identify gaps, obtain supporting facts, and verify revisions across documents. FilingLoop addresses the evaluation gap by testing these linked capabilities and using requirement-level feedback to improve later review procedures.

1 Introduction

  • Existing financial QA and agent benchmarks provide limited evidence about discovering unstated disclosure issues, requesting missing company facts, and checking each requirement in its deliverable.A promised revision or a response to only part of a compound comment does not establish fulfillment.Source: §1 · pp. 1–2
  • FilingLoop evaluates whether agents discover disclosure gaps, acquire company facts, and verify requested changes in final filings and responses.Its independent evaluator also audits unsupported claims and harmful actions.Source: §1 · p. 2
  • FilingLoop reconstructs historical SEC IPO review trajectories and adapts role-specific guidance from locked requirement-level feedback for later cases, without changing model weights.Within each case, agents iteratively review and revise disclosures under distinct information constraints.Source: §1 · p. 2

2 Related Work

Prior financial and agent benchmarks cover complementary capabilities, but regulatory disclosure review adds discovering omissions, acquiring company facts, and verifying document changes. FilingLoop applies role-specific procedural learning to that review workflow, comparing evolving agents with a matched fixed-policy control.

2.1 Financial Disclosure Analysis

  • Financial benchmarks test numerical reasoning, evidence-supported answers, and related tasks, while disclosure review requires finding omissions before explicit questions and obtaining facts for revisions.FilingLoop evaluates discovery and requirement fulfillment in final filings and formal responses.Source: §2.1 · p. 2

2.2 Interactive Agent Benchmarks

  • Interactive and multi-agent benchmarks assess tool use, collaboration, task completion, and final states; FilingLoop adds requirement-level checks of disclosure fulfillment and harmful changes.Reviewer and issuer counsel have distinct responsibilities and information access.Source: §2.2 · p. 2

2.3 Self-Evolving Agents

  • FilingLoop compares evidence-linked procedural reflection for Reviewer and Filer against a matched fixed-policy control, with scores locked before guidance updates for subsequent cases.Both groups can review and revise within a case; only the evolving group carries learned guidance forward.Source: §2.3 · p. 3

3 FilingLoop

FilingLoop reconstructs SEC disclosure-review tasks and evaluates whether agents discover and fulfill evidence-backed requirements across filings and responses. It combines within-case review and revision with cross-case procedural learning from locked evaluation feedback.

3.1.1 Task Construction.

  • The benchmark reconstructs historical SEC review rounds and links filings, comments, responses, and revisions so recurring issues can be traced across rounds.Each task exposes permitted records while hiding current comments, replies, and later amendments; reference comments are decomposed into atomic requirements.Source: §3.1.1 · pp. 3–4, §3 · p. 3, Figure 3 · p. 4

3.1.2 Task Characteristics.

  • The 380 tasks cover 96 companies, 3,012 scorable comments, and 6,099 requirements; 62.7% of comments contain multiple requirements and 50.5% of requirements need company facts.The corpus is predominantly IPO reviews, with two annual-report tasks from one issuer.Source: §3.1.2 · p. 5

3.1.3 Outcome Evaluation.

  • Evaluation separately matches concerns, checks every requirement in the final deliverable, and audits company assertions and document changes against permitted evidence.Undiscovered concerns remain in the denominators, and semantic validity still depends on interpreting evidence and requested changes.Source: §3.1.3 · p. 5

3.2.1 Review and Revision.

  • Reviewer and Filer agents work from role-specific instructions and access, exchange formal responses and revisions, and can repeat review until no active work remains or the exchange budget is exhausted.Final scoring retains eligible requirements even when the reviewer did not raise or close them.Source: §3.2.1 · p. 5

3.2.2 Information and Artifact Control.

  • The fact service releases evidence-supported information to the Filer, while the Reviewer sees it only through formal responses or revised disclosures.Committed edits are distinguished from attempted changes, and an empty service answer indicates limited evidence coverage rather than no company information.Source: §3.2.2 · pp. 5–6, §3.2.1 · p. 5

3.2.3 Online Skill Evolution.

  • Both arms process the same ordered batches, while the evolving arm updates separate Reviewer and Filer guidance from completed-batch feedback after scores are locked.Validation checks proposed updates, and only later, unattempted tasks use new guidance; model weights and base instructions remain fixed.Source: §3.2.3 · p. 6

4 Empirical Studies

The empirical studies compare fixed and evolving FilingLoop policies with external agents on a seeded stream of historical disclosure-review tasks. They assess resolution, procedural evolution, resource use, and harmful revisions.

4 Empirical Studies

  • The study evaluates discovery, fulfillment, resource use, and harmful revisions across four research questions.The questions compare agents and backbones, assess cross-task evolution, examine resource trade-offs, and audit harmful filing changes.Source: §4 · p. 6

4.1 Experimental Setup

  • The evaluation uses 380 eligible tasks from 96 companies; each task is attempted once, and evolving skills update only after batch scores are locked.Both variants begin with empty learned skills and follow the same seeded company order; results are limited to the evaluated sample and order.Source: §4.1 · pp. 6–7

4.2 Evaluation Metrics

  • Requirement Accuracy weights annotated requirement occurrences, whereas Comment Accuracy requires every obligation in a comment to pass; reviewer and Filer diagnostics use different target scopes.The paired Filer comparison uses Fixed’s assigned reference-comment pool and therefore does not isolate the effect of updating the Filer alone.Source: §4.2 · p. 7

4.3 Baselines

  • Eight configurations compare three external frameworks and two FilingLoop variants, including matched gpt-6.1-sol comparisons and four backbones within mini-SWE-agent.FilingLoop–Fixed retains the review–revision workflow without skill updates; FilingLoop–Evolving learns role-specific guidance from locked historical feedback.Source: §4.3 · p. 7, Table 1 · p. 8

4.4 Main Results (RQ1)

  • FilingLoop–Evolving achieves the highest Requirement Accuracy (27.96%) and Comment Accuracy (20.75%), beating the strongest external baseline by 4.22 and 4.38 pp, respectively.It fulfills 1,705 requirements and fully resolves 625 comments across 380 tasks.Source: §4.4 · p. 8, Table 1 · p. 8

4.5 Effect of Procedural Evolution (RQ2)

  • Procedural evolution raises Requirement Accuracy from 24.28% to 27.96% (+3.68 pp) and Comment Accuracy from 16.90% to 20.75% (+3.85 pp).The online protocol applies updates only to subsequent tasks; Evolving fulfills 224 additional requirements and resolves 116 additional comments.Source: §4.5 · p. 8, Figure 5 · p. 9

4.6 Resource Efficiency (RQ3)

  • Evolution costs $2,519.23 versus Fixed’s $2,380.15 (+5.8%), while fulfilled requirements and fully resolved comments grow by 15.1% and 22.8%.Cost per fulfilled requirement falls from $1.61 to $1.48; throughput comparisons use recorded Agent time, not wall-clock speed.Source: §4.6 · pp. 9–10, Figure 4 · p. 9

4.7 Harmful Revisions (RQ4)

  • Among audited tasks, harmful-revision rates are 5.8% for Fixed and 4.5% for Evolving, with edit-audit coverage of 99.74% and 100%, respectively.Both policies still produce harmful changes; unaudited tasks are excluded from the harm-rate denominator.Source: §4.7 · p. 10

5 Conclusion

FilingLoop evaluates disclosure review and revision using archived SEC EDGAR reviews. Across 380 tasks from 96 companies, its joint evolving policy improves requirement fulfillment and full comment resolution while reducing cost per fulfilled requirement.

5 Conclusion

  • Across 380 tasks from 96 companies, the joint evolving policy improves requirement fulfillment and full comment resolution while reducing cost per fulfilled requirement by 8.1%.The evaluation uses the first attempt, without changing model weights or retrying scored tasks.Source: §5 · p. 10
  • Improved revision cannot compensate for deficiencies that agents fail to discover.The conclusion identifies uncovering omissions, retaining unresolved obligations across rounds, and verifying requested changes as areas for further progress.Source: §5 · p. 10
  • FilingLoop records investigations, company facts, and revisions alongside which requested actions were fulfilled or remained unresolved.That record provides requirement-level feedback for the skill editor.Source: §5 · p. 10

A Data Sources and Benchmark Construction

The benchmark reconstructs historical disclosure reviews into tasks with hidden current-round evidence and independently checkable requirements. It links source records, review rounds, and required deliverables while filtering tasks and requirements for evaluation eligibility.

A Data Sources and Benchmark Construction

  • Source documents, cross-round issue threads, and task-specific requirements are separated, with source evidence fixing eligibility before agents act.The task begins before the round’s review, preserving the need for reviewers to discover issues independently.Source: A Data Sources and Benchmark… · p. 13

A.1 Source Corpus and Document Provenance

  • Five source groups retain distinct identities, while shared provenance links records to review rounds and reference evidence.The corpus preserves source identity and uses permitted earlier records for agent workspaces while withholding current correspondence and later amendments as reference evidence.Source: Figure 6 · p. 13, §A.1 · p. 13

A.2 Package, Round, and Issue Reconstruction

  • Packages link filings, substantive staff letters, responses, and revisions; each letter defines a review round whose comments are aligned with replies and revision evidence.Recurring concerns connect across rounds, but each task hides the current letter, replies, and subsequent revisions from agents.Source: §A.2 · p. 13

A.3 Atomic Requirements and Evidence Verification

  • Requirements decompose issue threads into checkable actions and expected deliverables, with evidence verified in the artifact the request requires.Mixed comments retain separate evidence targets; the benchmark maps requirement occurrences to eligible comments and rounds rather than deduplicating them across threads.Source: §A.3 · p. 14, Table 3 · p. 14

A.4 Scoring Eligibility and Corpus Coverage

  • Of 412 reconstructed rounds, 380 are eligible, yielding 3,012 scorable comments and 6,099 requirement occurrences.Requirements without sufficient current-round fact evidence are excluded; the retained set includes 3,080 fact-dependent requirements, or 50.5% of 6,099.Source: §A.4 · p. 14
  • The annual-report extension adds two tasks but is too small to characterize annual-report review performance separately.Primary filings have a median length of 111,804 whitespace-separated words and a 90th percentile of 155,266, excluding earlier history.Source: §A.4 · p. 14

B Task Environment and Interaction Protocol

This section describes FilingLoop’s trusted-host-mediated Reviewer–Filer interaction, including role workspaces, bounded company facts, and committed artifacts. It also explains how assessed feedback updates separate role procedures for subsequent tasks while keeping the specified model, role, tool, evidence-access, and evaluation settings fixed.

B.1 Information Boundaries and Role Workspaces

  • The host routes comments, responses, and revisions while keeping source evidence, proposed edits, and committed outcomes distinct.The reviewed filing and historically permitted records form the task record; private role conversations and current-task grading judgments remain outside the public exchange.Source: B Task Environment and Interaction… · p. 15, Table 4 · p. 15, §B.1 · p. 15

B.2 Tools, Submissions, and Committed Artifacts

  • Reviewer comments require distinct identifiers and source-grounded quotations, while Filer responses and uniquely anchored edits are submitted as separate, host-validated transactions.Edits apply in order to the evolving draft; missing or ambiguous targets are rejected, preserving reproducible committed outcomes.Source: §B.2 · p. 15

B.3 Bounded Company-Fact Acquisition

  • The fact service releases historically eligible facts matched to an active concern, while Figure 7 shows supported edits and non-request responses persisting through finalization.A blank service response indicates no releasable material for the request, not that the company lacks the information; released statements are not independent certification.Source: Figure 7 · p. 16, §B.3 · p. 16

B.4 Multi-Turn Control and Termination

  • The main experiments allow at most three review–revision exchanges; unresolved and deferred concerns may remain when execution ends, and independent evaluation scores frozen final artifacts.A fact request and its finalization count as one exchange; execution also stops when discovery yields no valid comments or re-review leaves no actionable concerns.Source: §B.4 · p. 16

C Agent Prompts

  • Both agent variants start with identical fixed instructions and empty learned guidance, while evolving adds role-specific procedures that cannot override evidence permissions or fixed instructions.Shared instructions require evidence for company claims and restrict work to authorized files; task descriptions identify the mode, visible record, active comments, and output file.Source: C Agent Prompts · p. 17

C.1 Reviewer Prompt

  • The Reviewer prompt directs filing-wide, materiality-checked discovery and re-review that distinguishes resolved, actionable, and fact-deferred obligations, including separate requirements in compound comments.Re-review checks the committed filing for new problems and surviving contradictions; a persuasive response does not prove that a requested filing change was made.Source: §C.1 · pp. 17–18

C.2 Filer Prompt

  • The Filer must answer each comment and make every supported change without inventing company facts, while preserving committed edits across information requests and checking consistency across the full draft.Its four response actions distinguish revision, explanation, information request, and decline; finalization handles only remaining identifiers and cannot request more facts.Source: §C.2 · pp. 19–20

C.3 Skill Editor Prompt

  • Between completed batches, a separate Skill Editor uses locked, role-specific feedback to improve reusable procedures only for later, not-yet-started tasks.Feedback includes discovered concerns, applicable requirements, fulfillment judgments, supporting quotations, and reasons; scored past tasks cannot be retried or changed.Source: §C.3 · p. 20
  • The editor returns either no_change with a rationale or a proposed replacement skill with a rationale, using reusable guidance rather than company-specific facts or executable code.The proposed skill includes the expected name, description, and role metadata; model weights, fixed role instructions, evidence access, and scoring rules remain unchanged.Source: §C.3 · p. 20

D Procedural Evolution

  • FilingLoop’s learned state comprises separate Reviewer and Filer instruction documents, updated only by Evolving between completed batches while Fixed guidance remains unchanged.Skills stay frozen during execution, and validated updates informed by locked outcomes are available only to subsequent tasks.Source: D Procedural Evolution · p. 21, Figure 8 · p. 21

D.1 Role-Specific Guidance

  • Role-specific skills guide evidence checks and task procedures, while batch feedback distinguishes missed concerns from discovered concerns whose requested amendments were not committed.Prior feedback may guide investigation procedures, but company assertions require support from the new case’s permitted record or released answers.Source: §D.1 · p. 21

D.2 Update Validation

  • Validation checks skill structure and content for leakage; invalid proposals receive up to three repair attempts before the current skill is retained, and accepted updates are evaluated on later first-pass tasks.The length target is advisory, and proposals do not undergo held-out performance selection or replay of completed cases.Source: §D.2 · pp. 21–22

D.3 Online Update Schedule

  • Seeded, interleaved batches of at most 16 tasks use frozen skills until all paired outcomes are assessed and locked, after which only future tasks can receive updates.Earlier rounds from an issuer may inform its later rounds, but their scores do not establish transfer to unseen issuers; saved batch records prevent duplicate updates.Source: §D.3 · p. 22

E Evaluation Metrics and Accounting

This section defines accuracy at requirement, comment, task, and company levels, distinguishes completed execution from resolved disclosure obligations, and specifies harm and resource accounting.

E.1 Accuracy Definitions and Reporting Scopes

  • System accuracy requires both discovery and fulfillment, while Reviewer and Filer scores measure their respective stages on scoped targets.Both variants use the same Fixed target pool for Filer comparisons, while additional Evolving discoveries remain in the system score.Source: §E.1 · p. 22, Table 5 · p. 22
  • Requirement accuracy weights each eligible requirement occurrence equally, including undiscovered concerns, while comment accuracy credits a comment only when all obligations pass.Partial resolution earns credit for satisfied requirements, but a comment with even one unsatisfied obligation receives no comment-level credit.Source: §E.1 · pp. 22–23, E Evaluation Metrics and Accounting · p. 22

E.2 Task Completion and Harm Audits

  • Completion tracks execution and assessment across scheduled tasks, not whether disclosure obligations were resolved.A completed interaction may still contain unresolved concerns or deferred requests.Source: §E.2 · p. 23
  • Harm audits flag unsupported claims, contradictions, or damaging filing edits, while unaudited tasks are not counted as harm-free.An unresolved original request alone is not classified as harm; audit counts supplement, rather than reduce, accuracy measures.Source: §E.2 · p. 23

E.3 Resource Usage and Efficiency

  • Resource accounting totals token usage and logged API costs across agent execution and shared services.Costs use recorded usage and saved prices, not reconciled invoice totals.Source: §E.3 · p. 23
  • Agent-time totals sum invocation durations across scheduled tasks, and efficiency measures normalize fulfilled requirements by cost or Agent hours.Concurrent tasks contribute cumulative work rather than elapsed experiment time; the accounting also defines cost per task and requirements fulfilled per $100.Source: §E.3 · p. 24

F Experimental Configuration and Reproducibility

The section fixes the shared configurations and execution conditions for comparison, then documents the records and reproduction steps needed to preserve the evaluated stream.

F Experimental Configuration and Reproducibility

F.1 Models, Inference, and Execution Settings

  • The same backbone serves Reviewer and Filer within each configuration, while inference settings and internal computation differ across backbones.Measured token use, cost, and execution time characterize each configuration; a common effort label does not imply equal computation.Source: §F.1 · p. 24

F.2 Baseline Adapters and Common Contracts

  • Framework adapters preserve separate role workspaces, authorized sources, and each framework’s native interaction tools and execution behavior.The reference budget is 100 steps per role invocation and at most three review–revision exchanges, but native frameworks count execution differently.Source: §F.2 · pp. 24–25

F.3 Execution Records and Continuation

  • Per-task records retain submissions, accepted or rejected edits, released facts, artifacts, evaluator judgments, and usage alongside locked outcomes and policy updates.Continuation reuses responses only when request and source identity match, and does not replace scored attempts with replays under improved guidance.Source: §F.3 · p. 25

F.4 Reproducing the Reported Analyses

  • Reproduction requires the frozen task plan, source records, prompts, inference settings, and batch policies, with company order preserved for Evolving.Analysis scripts regenerate reported summaries and figures from saved numbers without model inference or changing judgments.Source: §F.4 · p. 25

G Extended Results and Analyses

Near-complete task execution did not translate into complete disclosure resolution; requirement and company-level measures reveal distinctions obscured by task completion alone.

G.1 Completion and Resolution Granularity

  • 379 of 380 scheduled tasks completed in each FilingLoop variant, yet 364 Fixed and 360 Evolving completed tasks failed at least one requirement.Across all eight configurations, 360–364 completed tasks did not pass every requirement.Source: §G.1 · p. 25
  • The gap between finished interactions and fully resolved disclosures means completion alone is insufficient as a stopping condition for task or company review histories.The passage argues for tracking all outstanding obligations, including those remaining after individually successful edits.Source: §G.1 · p. 25
  • Evolving fulfilled 1,705 requirements and resolved 625 comments, the comparison’s highest counts, while mini-SWE-agent/Opus had 20 fully successful tasks versus Evolving’s 19.Evolving passed two companies, Fixed one, and the external configurations none.Source: §G.1 · p. 25

G.2 Discovery and Fulfillment Bottlenecks

Discovery and fulfillment accounting separates missed concerns from discovered but unresolved ones, showing that most remaining errors under Evolving were never brought into revision.

G.2 Discovery and Fulfillment Bottlenecks

  • Evolving leaves 3,851 requirements undiscovered and 543 discovered but unfulfilled, versus 3,937 and 681 for Fixed; undiscovered items are 87.6% of its unfulfilled requirements.At comment level, Fixed has 2,029 missed and 474 open comments, compared with 1,990 and 397 for Evolving; 83.4% of Evolving’s unresolved comments are missed. Figure 9 partitions missed, open, and fulfilled items.Source: §G.2 · pp. 25–26, Figure 9 · p. 26
  • Evolving’s net gain is 86 discovered requirements plus 138 fewer discovered-but-unfulfilled requirements, totaling 224 additional fulfilled requirements; the comment accounting is 39 + 77 = 116 additional resolved comments.These are net count identities, not evidence of individual state changes or causal contributions.Source: §G.2 · p. 26

G.3 Outcome Gains and Incremental Expenditure

  • Evolving adds $139.08 and 14.32 recorded Agent hours over Fixed, equivalent to $0.62 per extra fulfilled requirement and $1.20 per extra fully resolved comment.The time increment is 3.84 Agent minutes per additional fulfilled requirement; these stream-level ratios include role execution and shared services.Source: §G.3 · p. 26
  • Requirements fulfilled per $100 rise from 62.2 to 67.7, and requirements per recorded Agent hour rise from 3.25 to 3.63, while mean minutes per scheduled task rise from 71.85 to 74.11.The passage frames optimization in terms of task cost and obligations resolved in final documents and responses.Source: §G.3 · p. 26

G.4 Role and Shared-Service Resources

Resource accounting separates Reviewer and Filer usage from shared services, and shows that Evolving’s additional expenditure spans all three components.

G.4 Role and Shared-Service Resources

  • Shared cost is $495.34 for Fixed and $539.92 for Evolving, or 20.8% and 21.4% of their system totals, respectively.Table 7 separates role-attributed resources from residual shared cost; shared services include assessment, fact matching, and skill editing where applicable.Source: §G.4 · p. 26, Table 7 · p. 26
  • Of Evolving’s additional $139.08, $41.20 is Reviewer-attributed, $53.30 Filer-attributed, and $44.58 shared usage.The resource difference is distributed across the workflow rather than confined to the editor call.Source: §G.4 · p. 26
  • Budgeting for reusable review procedures must include applying them and assessing resulting artifacts, while cumulative Agent hours exclude independent assessment time.Agent hours sum recorded role execution.Source: §G.4 · p. 27

H Case Studies and Failure Analysis

The case studies trace selected disclosure concerns through generated comments, interactions, committed artifacts, and independent requirement judgments.

H Case Studies and Failure Analysis

  • Each case follows a selected reference concern through the comment, interaction, committed documents, and independent requirement checks.A requirement may call for a filing amendment, a Staff explanation, or a formal-reply commitment.Source: H Case Studies and Failure Analysis · p. 27
  • Concern-level outcomes remain distinct from whether the broader agent comment or complete review task succeeds.The full task may include further company-information requests and amendments.Source: H Case Studies and Failure Analysis · p. 27
  • The figures condense interaction wording while preserving numerical evidence and document targets.Source: H Case Studies and Failure Analysis · p. 27

H.1 Evidence-Grounded Disclosure Resolution

Three cases show how evidence reaches required disclosure artifacts, how a compound request can remain partly unresolved, and how a verification procedure transfers to a later task.

H.1 Evidence-Grounded Disclosure Resolution

  • Both selected acquisition-contribution requirements pass when Pacific Union’s contribution enters the filing and Paragon’s relative contribution appears in the formal reply.The case preserves the distinction between Pacific Union’s full-year revenue contribution and its incremental contribution to growth, while other R3 requests remain deferred.Source: Figure 10 · p. 27, §H.1 · p. 27

H.2 Compound Requirements and Failure Localization

  • Three of five eligible requirements pass; omitted royalty-rate justification leaves the disclosure alternative incomplete despite corrected amounts and note terms.The shared failure is evidence not reaching its required artifact; conditional obligations need checking along the selected response path.Source: Figure 11 · p. 28, §H.2 · p. 28

H.3 Procedural Transfer Across Cases

  • Contradiction feedback yields a filing-wide verification rule that supports revisions satisfying two requirements in a later accounting-election case.The transferable procedure reconciles competing statements across required locations without transferring the earlier issuer’s facts.Source: §H.3 · pp. 28–29, Figure 12 · p. 29

I Limitations and Responsible Data Use

The paper limits its conclusions to the historical records, eligibility rules, agents, and online protocol evaluated, and identifies boundaries on coverage, evaluation, data use, and practical application.

I Limitations and Responsible Data Use

I.1 Historical Reconstruction and Coverage

  • The corpus centers on IPO registration review, and two annual-report tasks do not establish broad annual-report generalization.Historical correspondence does not enumerate every defensible concern or resolution; eligibility favors requirements with attributable action and fact support, while pretraining exposure and related within-company observations remain possible.Source: §I.1 · pp. 29–30

I.2 Evaluator Validity and Online Adaptation

  • Model-assisted evaluation may misinterpret accounting context, and human–grader agreement and cross-grader sensitivity are unquantified.Results use one seeded task stream; the comparison treats both roles’ updates as a joint evolving policy, with role-level contributions requiring their own denominators.Source: §I.2 · p. 30
  • The bounded fact service reflects recorded evidence coverage rather than unrestricted company access, and online adaptation does not establish company-disjoint transfer or robustness to other task orders.Text filters cannot guarantee exclusion of every paraphrased case detail.Source: §I.2 · p. 30

I.3 Source Attribution and Data Handling

  • Role-private answers derive from public records, and reuse requires attention to source attribution and personal information in distributed examples.The role boundary does not imply access to confidential issuer records or direct company communications.Source: §I.3 · p. 30

I.4 Intended Use and Human Oversight

  • A committed edit verifies a document operation, not an external issuer action; practical use requires qualified review of evidence, interpretation, and deliverables.Unsupported assertions or inconsistent revisions may remain consequential despite improved aggregate accuracy.Source: §I.4 · p. 30
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