{"id":"a5e0b42d-25f9-4ed9-9f5d-c2452441b62d","arxiv_id":"2603.11044","paper_version":2,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":4.0,"correctness_risk":"high","formal_verification":"none","parameter_count":2,"one_line_summary":"A finance-specialized vision-language OCR system restores cross-page structure and cell-level table references, and FinDocBench benchmarks that capability on expert-annotated financial documents.","lead":"LingDT-VL-OCR is a finance-focused document parser that turns long PDFs into structured text with cross-page continuity, a global table of contents, and cell-level table localization. It also introduces FinDocBench to measure how well models handle real financial layouts and provenance needs.","discovery_kind":"new_application","skeptic_critique":{"model":"grok-4.5","headline":"Central claims remain uncheckable: wrong full manuscript supplied, and abstract gives no quantitative support for CellBBoxRegressor or auditing-grade provenance.","rationale":"The reader correctly treated the review as abstract-only after detecting the manuscript swap (LingDT-VL-OCR abstract vs NeFTY body) and correctly flagged the CellBBoxRegressor / auditing-grade provenance claim as the weakest assumption. That remains the single most load-bearing concern: the system’s value proposition for financial use hinges on reliable cell-level visual reference without detectors, yet neither the abstract nor the mis-attached full text supplies the quantitative evidence (C-IoU, provenance error, ablations) needed to support it. No stronger internal inconsistency can be diagnosed without the real paper; the honest posture is therefore to leave the verdict UNVERDICTED and confidence low. Agreement with the reader is full: same weakest assumption, same verdict, same reason (unverifiable central mechanism). The concrete test above would settle whether the concern lands once the correct manuscript is in hand.","tokens_in":4121,"tokens_out":563,"duration_ms":12966,"concrete_test":"Obtain the correct arXiv:2603.11044 PDF (not NeFTY). Check whether FinDocBench results report C-IoU for LingDT-VL-OCR with an ablation that removes structural anchor tokens and/or compares against an external detector baseline; if C-IoU is unreported, or drops sharply without anchors, or trails a standard detector by a large margin, the auditing-grade cell-provenance claim does not hold.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The strongest claim requires that (i) Cross-page Contents Consolidation + DHR yield a globally consistent TOC, and (ii) CellBBoxRegressor localizes table cells from decoder hidden states plus structural anchor tokens alone, without external detectors, at a quality sufficient for auditing-grade provenance on multi-page financial tables. The abstract asserts both but reports no C-IoU, no provenance error rates, and no ablation of the anchor-token mechanism for LingDT-VL-OCR itself—only that the model is strong on OmniDocBench Overall and that FinDocBench defines TocEDS / cross-page TEDS / C-IoU. The CACHEABLE full text is a different paper (NeFTY, arXiv:2603.11045), so methods, tables, and ablations for LingDT-VL-OCR cannot be inspected. Until the correct manuscript is available, the load-bearing sufficiency of decoder-state cell localization for auditing-grade use is an untested assumption, not an established result.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.5","summary":"Based solely on the abstract of arXiv:2603.11044, the paper proposes LingDT-VL-OCR, a document-parsing system for ultra-long financial PDFs that aims to produce semantically consistent structured outputs with cell-level, auditing-grade provenance. It combines a Cross-page Contents Consolidation algorithm and Document-level Heading Hierarchy Reconstruction (DHR) to build a globally consistent TOC tree, plus difficulty-adaptive curriculum learning for tables and a CellBBoxRegressor that localizes table cells from decoder hidden states using structural anchor tokens without external detectors. The authors report strong Overall performance on OmniDocBench and introduce FinDocBench (six financial document categories, expert annotations, TocEDS, cross-page TEDS, C-IoU), on which they evaluate SOTA models. The full manuscript text supplied in the review package is not this paper; it is instead NeFTY (Neural Field Thermal Tomography, arXiv:2603.11045), so methods, equations, tables, and ablations for LingDT-VL-OCR cannot be inspected.","tokens_in":4410,"tokens_out":925,"duration_ms":11451,"significance":"If the claims hold under proper evaluation, the work would be practically significant for financial document AI: structure-aware multi-page parsing, TOC-level retrieval, and detector-free cell provenance address real auditing and compliance needs. FinDocBench, if expert-verified and well-designed, would fill a clear vertical evaluation gap. Those contributions cannot be credited as established results from the materials available for this review, because the load-bearing technical content of LingDT-VL-OCR is not present in the supplied full text.","major_comments":[{"comment":"Manuscript identity mismatch: the review package labels paper_id 2603.11044 (LingDT-VL-OCR, cs.CV) but the FULL MANUSCRIPT TEXT is NeFTY (arXiv:2603.11045, inverse heat conduction / differentiable physics). No methods section, equations, architecture diagrams, training details, ablations, or result tables for LingDT-VL-OCR are available. Central claims (OmniDocBench Overall performance; auditing-grade provenance; FinDocBench superiority) are therefore unverifiable. This blocks a sound accept/reject decision on the submitted work.","section":null},{"comment":"Abstract claim that CellBBoxRegressor localizes table cells from decoder hidden states plus structural anchor tokens alone, without external detectors, at quality sufficient for auditing-grade provenance: no C-IoU numbers, no provenance error rates, no ablation of the anchor-token mechanism, and no multi-page financial-table failure analysis appear in the only available text (the abstract). This is a load-bearing sufficiency assumption for the paper’s strongest claim and remains untested in the materials under review.","section":null},{"comment":"Abstract asserts Cross-page Contents Consolidation + DHR yield a globally consistent TOC tree enabling structure-aware retrieval and semantic consistency, and that FinDocBench metrics (TocEDS, cross-page concatenated TEDS, C-IoU) support realistic finance evaluation. Without the correct manuscript’s algorithm definitions, annotation protocol, baselines, and quantitative tables, neither the TOC reconstruction quality nor the benchmark’s ability to discriminate models can be assessed.","section":null}],"minor_comments":[{"comment":"Abstract-only presentation: invented module names (DHR, CellBBoxRegressor, FinDocBench metrics) are introduced without definitions, complexity, or failure modes; even a correct full paper would need clear formalization of these components.","section":null},{"comment":"The phrase “auditing-grade provenance” is strong and should be operationalized (e.g., required C-IoU / cell-link error thresholds, human audit protocol) rather than left as marketing language in the abstract.","section":null}],"recommendation":"uncertain","confidential_remarks":"The cacheable full-text prefix is the wrong arXiv paper (2603.11045 NeFTY vs 2603.11044 LingDT-VL-OCR). This is a process/data error, not an author fault visible from the abstract. I cannot produce a content-based accept/major_revision/reject on LingDT-VL-OCR until the correct PDF is supplied. Recommendation is uncertain for that reason alone. Please re-queue with the matching manuscript."},"author_rebuttal":null,"desk_editor":{"model":"grok-4.5","letter":"The one thing you need to know: the cache for 2603.11044 is the wrong paper (NeFTY thermal tomography, 2603.11045). Everything below is abstract-only. We cannot inspect methods, ablations, tables, or error rates for LingDT-VL-OCR.\n\nWhat is actually on offer, if the abstract is accurate, is a finance-specialized document parser plus a vertical benchmark. The engineering stack is clear: cross-page content consolidation, document-level heading hierarchy reconstruction for a global TOC, curriculum training for tables, and CellBBoxRegressor that maps decoder hidden states plus structural anchor tokens to cell boxes without an external detector. FinDocBench adds six expert-annotated financial categories and metrics that match the claimed pain points (TocEDS, cross-page TEDS, C-IoU). That combination is useful applied work for FinTech / compliance tooling, not a new scientific framework. OmniDocBench Overall is mentioned as strong; no numbers appear in the abstract.\n\nSoft spots are mostly verification gaps, not invented flaws. The load-bearing claim is that decoder-state cell localization plus anchors is good enough for auditing-grade provenance on multi-page financial tables. The abstract asserts it and defines C-IoU, but does not report C-IoU, provenance error rates, or an ablation of the anchor mechanism. Curriculum schedule and the regressor mapping are free parameters we cannot see. Citation pattern and math cannot be judged from the abstract alone; circularity is not the issue—missing evidence is.\n\nWho this is for: people building long-document OCR for finance who need structure and cell-level references. A serious referee should see the correct PDF, code, and FinDocBench release. Until then I would not cite it or bring it to reading group. Desk-accept for peer review only after the right manuscript is attached; as currently supplied, it is not reviewable.","headline":"Wrong full manuscript is cached for this arXiv ID, so the finance OCR claims stay abstract-only and uncheckable; treat as a provisional systems note, not a verified result.","tokens_in":5005,"tokens_out":479,"would_cite":false,"duration_ms":4761,"reading_group":"no","serious_thinker":"unclear","would_accept_peer_review":false},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.5","headline":"LingDT-VL-OCR turns ultra-long financial PDFs into structured, auditable outputs by restoring cross-page continuity and localizing table cells from the model itself.","keywords":["document parsing","financial OCR","vision-language models","table structure recognition","cross-page consolidation","cell localization","FinDocBench","heading hierarchy"],"falsifier":"On FinDocBench multi-page tables, compare CellBBoxRegressor boxes to expert cell boxes via C-IoU and check whether cell-level provenance passes audit-style checks; if C-IoU is low or provenance fails while text OCR looks fine, the no-external-detector claim fails.","tokens_in":5009,"feed_emoji":"📄","tokens_out":875,"duration_ms":18914,"temperature":0.7,"pith_summary":"Financial PDFs are long, layout-heavy, and full of tables that break across pages, so ordinary page-by-page OCR loses structure and provenance. This paper proposes LingDT-VL-OCR, a vision-language parsing system built for that setting: it consolidates content across page breaks, rebuilds a global heading hierarchy as a TOC tree for structure-aware retrieval, and trains table parsing with a difficulty-adaptive curriculum. A CellBBoxRegressor module uses structural anchor tokens and decoder hidden states to place cell boxes without a separate detector, aiming at auditing-grade cell-level reference. The authors also introduce FinDocBench—six expert-annotated financial document categories with metrics for TOC fidelity, cross-page table structure, and cell IoU—and report strong overall results on OmniDocBench plus a broad SOTA comparison on the new benchmark. The claim is that the system plus benchmark give a practical base for reliable finance-document applications.","feed_headline":"Finance PDFs become audit-ready structure without cell detectors","feed_subtitle":"Cross-page stitching, TOC trees, and decoder-based cell boxes define LingDT-VL-OCR and FinDocBench.","key_machinery":"CellBBoxRegressor—localizing table cells from decoder hidden states via structural anchor tokens without external detectors—together with Cross-page Contents Consolidation and Document-level Heading Hierarchy Reconstruction (DHR) that build a globally consistent TOC tree.","core_discovery":"LingDT-VL-OCR can transform ultra-long financial PDFs into semantically consistent, highly accurate structured outputs with auditing-grade provenance by combining cross-page contents consolidation, document-level heading hierarchy reconstruction, curriculum-trained table parsing, and CellBBoxRegressor cell localization from decoder states without external detectors; FinDocBench then exposes where existing models still fail on finance-specific structure.","pith_inferences":["If decoder-state cell localization holds up, multi-stage detect-then-OCR table pipelines may be unnecessary in many finance settings.","The same cross-page consolidation and hierarchy rebuild could transfer to legal or regulatory multi-page filings with similar layout breaks.","Treating C-IoU as a first-class metric may push future VL OCR models to train geometric fidelity instead of bolting on detectors later.","Claims of auditing-grade provenance will need public quantitative localization and provenance error rates under hard multi-page tables."],"forward_implications":["Cross-page financial tables can be recovered as continuous structured objects rather than page-isolated fragments.","A rebuilt TOC tree supports structure-aware retrieval over entire multi-page filings.","Cell-level boxes from the decoder can supply auditing provenance without a separate detection stack.","TocEDS, cross-page concatenated TEDS, and C-IoU become concrete targets for finance document parsers.","Downstream finance pipelines gain a practical foundation for reliable structured extraction."],"fun_headline_variants":["LingDT-VL-OCR: finance PDFs to audit-grade structure sans cell detectors","Cross-page TOC trees and decoder cell boxes parse ultra-long finance PDFs","CellBBoxRegressor localizes tables from decoder states in LingDT-VL-OCR","FinDocBench exposes finance structure gaps; LingDT-VL-OCR closes many","Curriculum table training plus DHR yields consistent finance document TOC"],"cache_read_input_tokens":128,"weakest_assumption_plain":"Decoder hidden states plus structural anchor tokens alone are enough for reliable cell-level boxes and auditing-grade provenance on real multi-page financial tables, without an external detector.","fun_headline_variants_meta":{"raw":{"variants":["LingDT-VL-OCR: finance PDFs to audit-grade structure sans cell detectors","Cross-page TOC trees and decoder cell boxes parse ultra-long finance PDFs","CellBBoxRegressor localizes tables from decoder states in LingDT-VL-OCR","FinDocBench exposes finance structure gaps; LingDT-VL-OCR closes many","Curriculum table training plus DHR yields consistent finance document TOC"]},"model":"grok-4.5","effort":"low","cost_usd":0.004794,"raw_usage":{"total_tokens":1421,"prompt_tokens":837,"num_sources_used":0,"completion_tokens":106,"cost_in_usd_ticks":47940000,"prompt_tokens_details":{"text_tokens":837,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":478,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":837,"tokens_out":106,"duration_ms":4744,"temperature":1.0,"reasoning_tokens":478,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-07-14T23:08:00.486339+00:00","model_set":{"reader":"grok-4.5"},"falsifier":"On FinDocBench multi-page tables, compare CellBBoxRegressor boxes to expert cell boxes via C-IoU and check whether cell-level provenance passes audit-style checks; if C-IoU is low or provenance fails while text OCR looks fine, the no-external-detector claim fails.","supporting_citations":[],"review_version":1}