{"id":"3864cfcc-0237-4a36-b13d-43e429090dd0","arxiv_id":"2607.19865","paper_version":1,"verdict":"ACCEPT","confidence":"MODERATE","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":4,"one_line_summary":"DocOps benchmarks 210 native-format document-editing tasks with deterministic final-artifact verifiers and finds best agents pass 67%, collapsing on long workflows due to state-tracking, verification, and destructive-editing failures.","lead":"This paper introduces DocOps, a benchmark of 210 document-editing tasks with deterministic verifiers that check not just whether the requested edit happened but whether hidden structure survived. It reports that the best current AI agent setup passes only 67% of tasks, with sharp drops on long multi-step workflows.","discovery_kind":"new_method","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Verifier-fidelity audit is under-powered to support the headline: 128 decisions, no pass-conditional false-pass rate, and one acknowledged false pass is a global-state omission — the very construct being measured.","rationale":"The reader's weakest assumption correctly identifies the verifier/task external-validity risk and notes the three false passes. My stress-test narrows this to a sharper, more load-bearing issue: the fidelity evidence is too weak to rule out systematic false passes on exactly the kind of global-state violations the central claim is about. Appendix F's own example of an undetected canvas-dimension change is a preservation-state omission, not a peripheral formatting nit, because the paper defines destructive editing and state-tracking failures in terms of global document state. With only 128 audited decisions and no pass-conditional breakdown, the 95.31% agreement cannot establish that the verifier's false-pass rate is negligible. The failure-mode proportions in Figure 5 are derived from verifier-triggered assertions, so if the verifier misses certain state classes, the proportions are shifted as well. This does not mean the paper is wrong; the aggregate finding that agents fail one-third of tasks is likely robust even under moderate false-pass corrections. But because the headline numerical claims are the paper's main contribution and the verifier is the instrument generating those numbers, the appropriate verdict is conditional acceptance pending an independent, statistically adequate verifier audit. I do not see an internal inconsistency or a reason to reject outright; the concern is about the strength of the evidence, not the integrity of the work.","tokens_in":26824,"tokens_out":8247,"duration_ms":96891,"concrete_test":"Run an independent blind audit: take all verifier-pass outputs from the best configuration (or a stratified sample of ~100 passes across L1–L4), have two document-ops experts judge global-state preservation (order, hidden sheets, formulas, bookmarks, canvas dimensions) without seeing verifier decisions, and compute the false-pass rate among verifier passes and per difficulty. If the upper confidence bound on the pass-conditional false-pass rate exceeds 5%, or if false passes are concentrated in L3/L4, re-report Table 2 and Figure 2 with corrected pass rates. Also report inter-annotator agreement to confirm the audit standard is reliable.","verdict_should_be":"CONDITIONAL","load_bearing_attack":"The central claim is that even the best configuration passes only 0.671 overall and collapses on L3/L4 because agents lose global document state. This depends on the verifier suite actually detecting global-state violations. Appendix F reports only 128 audited decisions and 3 false passes; one false pass altered global canvas dimensions, a state change the verifier did not cover. The paper does not report the false-pass rate conditional on verifier success, per-difficulty false-pass rates, confidence intervals, or a second independent rater. If the true false-pass rate among successful runs is materially higher than 2–3%, the headline 0.671 is inflated; if false passes concentrate in L3/L4, the 'rapid drop' is overstated. The mutation stress test (96.67%) only checks injected corruptions and cannot detect omitted state dimensions the verifier was never designed to inspect. This is not a peripheral limitation: the benchmark's core novelty is deterministic preservation-aware verification, so an unmeasured class of hidden-state omissions directly undermines the main empirical claim.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper introduces DocOps, a benchmark of 210 document-manipulation tasks (XLSX, DOCX, PPTX, PDF) organized along an operation taxonomy (content/format/structure) and a four-level difficulty gradient (L1 atomic, L2 composite, L3 workflow, L4 cross-document). Each task ships with a deterministic verifier that inspects the final native artifact via structural predicates, linguistic anchors, and preservation predicates. The authors evaluate 12 models across four harnesses (DocTools, Terminus-2, Codex, Claude Code) with and without document skills. Main findings: the best configuration (GPT-5.5 + Codex + skills) reaches only 0.671 overall pass rate; performance collapses on L3/L4, especially for Excel; three failure modes (long-term state tracking, semantic verification gap, destructive editing) account for a majority of failures; harness choice strongly modulates performance; and skills help mid-tier open-source models more than frontier models. Verifier fidelity is reported as 95.31% agreement with a human audit of 128 decisions and 96.67% detection on 180 injected mutations.","tokens_in":27102,"tokens_out":10261,"duration_ms":104099,"significance":"If the results hold, DocOps is a valuable and well-scoped contribution. Its strengths are concrete: a released dataset and codebase, deterministic per-task verifiers over native file formats, an openly described construction pipeline, an honest limitations section, and a fidelity audit with both human judgments and mutation-based stress testing. The empirical pattern—frontier agents failing roughly one-third of tasks and degrading sharply on coupled, long-horizon workflows—is plausible and likely to be influential for the agent-evaluation community. The resource should be genuinely reusable. However, the strength of the quantitative claims currently exceeds the strength of the supporting verifier-fidelity evidence: several load-bearing, fixable gaps in the audit design and uncertainty reporting should be addressed before the benchmark's numbers are taken as definitive.","major_comments":[{"comment":"The verifier-fidelity audit is under-powered for the load it carries in §4.2.1. Agreement of 95.31% is computed over 128 decisions with no pass-conditional false-pass rate, no difficulty-stratified agreement, and no confidence interval; the 3/128 false-pass rate alone has a Wilson upper bound near 7%. The one representative false pass described in Appendix F altered the presentation's global canvas dimensions—a persistent, out-of-scope state property—yet the verifier was not designed to inspect it. Since deterministic preservation-aware verification is the benchmark's core novelty, an unmeasured class of omitted state dimensions directly affects the headline 0.671 and the L3/L4 drop magnitudes in Figure 2. False passes inflate reported pass rates; false fails (3/128) deflate them, so the net bias is unknown. The mutation stress test (96.67%) cannot detect state dimensions the verifier ne","section":"Appendix F / §3.3"},{"comment":"Table 2 reports the central results—the headline 0.671 and all cross-harness/cross-model comparisons—without uncertainty quantification. With 210 binary trials, the sampling standard error for a 0.67 rate is about 3.2 percentage points, so differences such as GPT-5.5 Codex w/skill (0.671) vs w/o skill (0.648) and vs GPT-5.4 (0.662) are within noise. The claim in §4.2.1 and the abstract that skills offer only 'marginal utility for frontier models' rests on a +0.024 delta (Table 5) with no confidence interval; the Figure 7 bootstrap procedure is applied only to open-source models. Please add bootstrap CIs (or equivalent) to Table 2 or the headline text, and run the paired-bootstrap analysis for the frontier-model skill comparisons as well.","section":"Table 2 / §4.2.1"},{"comment":"The three failure-mode definitions overlap. 'Semantic verification gap' includes violations of 'required formulas, value ranges, hierarchy, or structural conditions,' while 'destructive editing' includes 'formulas, validation rules, styles, heading hierarchy, native tables, bookmarks.' A single failed run can trigger assertions in multiple categories, yet Figure 5 reports a single distribution with no stated rule for assigning a run to one mode. In addition, the mode labels impute causes (the agent 'accepts' plausible outputs without verification) from verifier-assertion outcomes; the quantified distribution is based on 'verifier-grounded signals,' not on the trajectory evidence used in Figure 3. Please state the assignment rule (e.g., priority or primary-trigger) and either restrict causal wording to the trajectory case studies or add trajectory-based coding for the quantified distribut","section":"§4.2.3 / Figure 5"},{"comment":"Both the task-construction review (Stage 4) and the verifier-fidelity audit rely on a single human judge: one PhD reviewer for the 210-task pipeline and one PhD candidate for the 128-decision audit. No inter-rater reliability is reported for either. Since the benchmark's construct—'correct, globally consistent, non-destructive document editing'—is ultimately defined by human judgment, a second independent rater on a stratified sample is needed to bound systematic bias in task selection and in the gold labels used to measure the verifier. This matters because the audit's three false passes and three false fails are dismissed as 'peripheral' without a systematic argument that such cases cannot concentrate in L3/L4.","section":"§3.2 Stage 4 / Appendix F"}],"minor_comments":[{"comment":"Report the full 2×2 contingency table (manual pass/fail × verifier pass/fail) and the sampling stratification over formats and difficulty levels for the 128 decisions; currently only aggregate agreement is given.","section":"Appendix F"},{"comment":"The legend order and the percentages do not make the mapping explicit (e.g., 'Other failures' is 1.13% in panel (a) but 11.49% in panel (c)). Label categories directly on the bars or align the legend order with the segments.","section":"Figure 5"},{"comment":"Typos: 'ὑ7' appears before the project URL in the author block; 'T able/Sheet operations' in Figure 8(a); 'We report s/task' in Appendix C should be 'seconds/task'.","section":"Author block / Figure 8 / Appendix C"},{"comment":"Within-level heterogeneity is large: L3_v2_011 and L3_v2_016 list all 13 operation labels, while L3_v2_012-013 list two. The L3/L4 aggregates in Figure 2 thus mix tasks of very different width and composition; consider reporting the L3_v2 sub-family separately or controlling for operation count in the coupling analysis of §4.2.2.","section":"Table 8 / Figure 2"},{"comment":"The FormatCV bootstrap confidence intervals reflect sampling variability only, not verifier disagreement or benchmark-construction variability. State this caveat when interpreting the non-overlapping intervals between L1 and L3.","section":"§4.2.2"},{"comment":"The abstract carries a footnote marker ('ecosystems.1') but the footnote text appears at the start of the Introduction; move the footnote to the title/authors or the abstract itself.","section":"Footnotes"}],"recommendation":"major_revision","confidential_remarks":"The reader's ACCEPT verdict is understandable: the resource is genuinely useful, the code/data release is a real strength, and the qualitative conclusion (agents struggle on workflow-level document manipulation) is likely robust to the verifier-fidelity gap, since false passes would only make agents look better than they are. My recommendation of major_revision is driven by proportionality: deterministic verification is the paper's central novelty, yet the fidelity audit lacks the conditional and stratified statistics needed to bound the headline numbers, Table 2 has no uncertainty quantification, and the failure-mode taxonomy has an unstated assignment rule. All of these are fixable within the manuscript's scope without new experiments of prohibitive scale. I see no circularity or fabrication concerns."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Short version: this is a genuine contribution—a deterministic, preservation-aware document-editing benchmark across four native formats with cross-document workflows, plus a failure-mode taxonomy that product teams can actually use. The headline number (best config 0.671 overall, dropping to roughly 0.24 on L4) is plausible and backed by the protocol. But the verifier audit that the central claim leans on is thinner than it looks, and the related-work framing oversells the novelty in one spot.\n\nWhat's new: the combination of native-file editing (docx, xlsx, pptx, pdf) with explicit preservation predicates and deterministic artifact-level verification is not in any single prior benchmark. The harness and skill comparison is genuinely informative—Terminus-2 lifts open models, skills help mid-tier models but not frontier ones. The three failure modes (state-tracking collapse, shallow semantic verification, destructive editing) align with the trajectory examples. They ship the code and data, and the verifier snippets in Appendix G show real checks, not just string matching.\n\nWhere it wobbles: the verifier fidelity audit is 128 decisions, no confidence intervals, and no false-pass rate conditional on verifier success. One acknowledged false pass is a global canvas-dimension change—exactly the kind of global-state property the paper claims to measure. If the true false-pass rate among successful L3/L4 runs is a few points higher, the 0.671 headline and the rapid-drop narrative get softer. That doesn't invalidate the benchmark, but it should be reported honestly. Also, Table 2 has no CIs; differences of 3–5 points between harnesses or models may be noise. Task construction used a single PhD reviewer; fine as a starting point, but inter-rater reliability would help. The paper's own Table 1 credits OfficeBench and OdysseyBench with deterministic target verification, so the claim that prior work relies on coarse task-level success is overbroad—the actual delta is preservation predicates and cross-document native-file scope.\n\nWho this is for: anyone evaluating or building document-agent harnesses, plus the benchmark community. It is a solid measurement contribution, not a theoretical one. It deserves a serious referee, but I would send it back for a verifier-audit supplement and confidence intervals on the main results before accepting.","headline":"A useful, well-engineered benchmark for document-agent reliability, with a verifier-fidelity audit that is thinner than the central claim wants—still worth publishing after a round of revisions.","tokens_in":27582,"tokens_out":3308,"would_cite":true,"duration_ms":36675,"reading_group":"yes","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"DocOps claims that even the strongest AI agent configurations fail about a third of realistic document-editing tasks, with near-collapse on long, cross-file workflows.","keywords":["document manipulation","LLM agents","benchmark","deterministic verification","native file formats","state tracking","workflow difficulty","office documents"],"falsifier":"Run the full 210-task suite with expert human audit of every verifier decision, watching specifically for uncaught state corruption such as altered page-setup dimensions, changed document metadata, or broken cross-references that the current predicates do not inspect; if the false-pass rate exceeds the reported 3/128 (≈2.3%) or includes such hidden-state corruptions, the headline pass rates overstate agent reliability.","tokens_in":26728,"feed_emoji":"📄","tokens_out":6696,"duration_ms":65954,"temperature":0.7,"pith_summary":"This paper argues that reliable document manipulation—editing real Word, Excel, PowerPoint, and PDF files while keeping hidden state intact—is a distinct capability that existing benchmarks do not measure, and that current agents are far from mastering it. To make the point, it introduces DocOps, a benchmark of 210 tasks organized along two axes: atomic operations (content, format, structure) and workflow depth (from a single edit to cross-document projects), each paired with a deterministic verifier that inspects the final native file. The empirical centerpiece is that the strongest tested configuration—a frontier model in an open-ended coding harness with document skills—passes only 67.1% of tasks, and that workflow-level tasks (L3 and L4) see pass rates fall to roughly 24% or lower. The paper attributes most failures to three modes: losing track of global document state, accepting surface-plausible output without checking underlying semantics, and destructively flattening native structure. If right, this redirects agent research toward state-aware, non-destructive editing rather than better tool invocation.","feed_headline":"AI agents pass just 67% of document-editing tasks","feed_subtitle":"A verifier-grade benchmark reveals how agents lose state, fake formulas, and break native structure on real Office files.","key_machinery":"The deterministic verifier is the load-bearing mechanism: a per-task program that inspects the final artifact through document-native libraries, using structural predicates (e.g., a cell must contain an executable formula that recomputes the expected total from the workbook's own code map), linguistic anchors (diagnostic keywords that allow paraphrasing), and preservation predicates (out-of-scope sheets, styles, bookmarks, or page order must be unchanged). This artifact-level check is what separates a genuinely correct edit from a visually plausible one, and it grounds the taxonomy's categories in measurable failure modes.","core_discovery":"DocOps's central claim is that agent performance on document tasks collapses when tasks require maintaining document state across coupled steps, and that this is a systematic weakness of current systems, not an artifact of any single model or harness. The paper demonstrates this with verifiers that read submitted files through native libraries and check three types of predicates: structural predicates for hidden native state (executable formulas, heading hierarchies, real table objects), linguistic anchors for requested content, and preservation predicates for out-of-scope elements that must remain untouched. On this standard, the best configuration achieves an overall pass rate of 0.671, dr","pith_inferences":["The verifier-fidelity audit itself reports three false passes (e.g., a slide-reorder task that also changed canvas dimensions went undetected), so extending the predicates to cover peripheral document properties would likely push the reported pass rates down slightly.","If state-tracking collapse is the general bottleneck, the same two-axis taxonomy could transfer to other structured artifacts—codebases, CAD files, or HTML/CSS layouts—where a correct edit also means preserving hidden dependent state.","The near-zero Excel L3 results single out formula-reference and validation-boundary preservation as a concrete stress test for any agent deployed in finance or accounting; an agent that cannot pass that subset should not be trusted with real spreadsheets.","Because DocOps tasks are compact and hand-built, real-world files are likely messier and harder; the benchmark's numbers are plausibly an upper bound on current agent reliability in production document workflows."],"forward_implications":["Agents running in open-ended programming harnesses with file-system feedback substantially outperform those restricted to fixed document-tool interfaces, so harness design is a first-order determinant of document-operation reliability.","Pass rates degrade as task coupling increases, not merely as operation count increases: Excel workflows collapse while PDF workflows remain comparatively stable, suggesting that state-coupling is the key difficulty axis.","Explicit document skills improve some mid-tier open models but can hurt or have no effect on frontier models, so skill injection is an empirical design choice, not a guaranteed improvement.","Any evaluation that checks only task-level success or rendered output will miss the dominant failure modes; artifact-level verification of structural validity and preservation is necessary to measure true reliability."],"fun_headline_variants":["Agents fail 33% of document tasks due to state loss","DocOps benchmark: AI agents score 67% on complex doc edits","AI agents lose document state in 1 in 3 complex edits","Why AI agents break native document structure: DocOps findings","State collapse: Why AI agents fail 33% of doc tasks"],"cache_read_input_tokens":2304,"weakest_assumption_plain":"The load-bearing premise is that the 210 tasks, constructed by one main reviewer from filtered community and workflow seeds, and their hand-written verifier predicates capture what 'correct, globally consistent, non-destructive document editing' means outside the benchmark—if the predicates miss a hidden class of state, the reported pass rates and failure-mode proportions are shifted.","fun_headline_variants_meta":{"raw":{"variants":["Agents fail 33% of document tasks due to state loss","DocOps benchmark: AI agents score 67% on complex doc edits","AI agents lose document state in 1 in 3 complex edits","Why AI agents break native document structure: DocOps findings","State collapse: Why AI agents fail 33% of doc tasks"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000183,"raw_usage":{"total_tokens":1111,"prompt_tokens":666,"completion_tokens":445,"prompt_tokens_details":{"cached_tokens":256},"prompt_cache_hit_tokens":256,"prompt_cache_miss_tokens":410,"completion_tokens_details":{"reasoning_tokens":369}},"tokens_in":410,"tokens_out":445,"duration_ms":5101,"temperature":1.0,"reasoning_tokens":369,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-01T11:28:01.242290+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Run the full 210-task suite with expert human audit of every verifier decision, watching specifically for uncaught state corruption such as altered page-setup dimensions, changed document metadata, or broken cross-references that the current predicates do not inspect; if the false-pass rate exceeds the reported 3/128 (≈2.3%) or includes such hidden-state corruptions, the headline pass rates overstate agent reliability.","supporting_citations":[],"review_version":1}