{"id":"b2b2ccac-605c-4d8d-9f4a-e405bfc094ca","arxiv_id":"2606.28379","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":6.0,"correctness_risk":"high","formal_verification":"none","parameter_count":0,"one_line_summary":"LEDGER improves consistency in agentic document editing from 56% to 76% across six models by using a dependency graph for targeted context retrieval while cutting token usage.","lead":"LEDGER builds a lightweight dependency graph from document structure to retrieve only relevant context for localized edits in long documents. A smart generalist might read it to see how explicit modeling of references and semantics can let AI agents edit complex files more consistently with less computation.","discovery_kind":"new_method","skeptic_critique":{"model":"grok-4.3","headline":"Dependency graph construction risks missing implicit links or introducing errors","rationale":"The reader's weakest assumption is precisely the load-bearing point; the abstract-only review correctly flags the unverified graph construction as the central uncertainty. No other internal inconsistency is visible from the supplied text.","tokens_in":1653,"tokens_out":252,"duration_ms":21746,"concrete_test":"Sample 30 documents from the 1.9k benchmark; obtain expert-annotated gold dependency graphs; compute edge-level precision/recall/F1 of LEDGER's graphs against gold; if recall < 0.85 on implicit or semantic edges, re-run the consistency evaluation with oracle graphs to isolate the effect.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The headline consistency gain (56% → 76%) and token reduction rest on the claim that the automatically built graph captures every cross-reference and semantic relationship needed for safe localized edits. The abstract asserts the graph models hierarchical structure, explicit references, implicit dependencies, and semantic relationships, yet supplies no construction algorithm, validation procedure, or error analysis. If any critical link is omitted, graph-guided retrieval will omit necessary context and the reported improvement cannot be attributed to the method.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The paper introduces LEDGER, a method for agentic document editing that constructs a lightweight dependency graph modeling hierarchical structure, explicit references, implicit dependencies, and semantic relationships. Graph-guided retrieval then selects minimal context for localized edits. On a curated 1.9k-case benchmark spanning six models and various document types, LEDGER is reported to raise consistency from 56% to 76% while cutting token usage; low-reasoning-effort LEDGER is claimed to match high-reasoning baselines.","tokens_in":1739,"tokens_out":522,"duration_ms":23424,"significance":"If the reported gains are reproducible and the graph construction is shown to be reliable, the work would provide a concrete, graph-based alternative to full-context or high-reasoning approaches for maintaining consistency in long structured documents, with direct relevance to document-centric agent systems.","major_comments":[{"comment":"Abstract and Evaluation section: the headline consistency improvement (56% → 76%) and token-reduction claims rest on a 1.9k-case benchmark whose construction, consistency metric, baseline prompting protocol, and error analysis are not described, rendering the empirical link unverifiable.","section":"Abstract / Evaluation"},{"comment":"§3 (Dependency Graph Construction): the central attribution of gains to the graph requires an explicit construction algorithm, validation procedure against ground-truth cross-references, and error analysis; none are supplied, so it is impossible to assess whether omitted links or construction errors undermine the reported improvements.","section":"§3"},{"comment":"Table 1 / Results: without per-model, per-scenario breakdowns, statistical significance tests, or ablation of the graph components, the aggregate 56%→76% figure cannot be assessed for robustness across the six models.","section":"Table 1"}],"minor_comments":[{"comment":"Notation for the dependency graph (nodes, edge types, retrieval function) should be formalized with a small example in §3 to make the retrieval step reproducible.","section":"§3"},{"comment":"The abstract states 'various document types and lengths' but supplies no breakdown by length or type; a supplementary table would clarify coverage.","section":"Abstract"}],"recommendation":"major_revision","confidential_remarks":"The manuscript's empirical claims cannot be evaluated without the missing methodological details; this is a scope issue rather than a citation or novelty concern."},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for the constructive feedback highlighting the need for greater transparency in our empirical evaluation and graph construction details. We address each major comment below and commit to revisions that will strengthen the manuscript's verifiability without altering the core claims.","responses":[{"response":"We agree that additional detail is required for full verifiability. The Evaluation section will be expanded in revision to describe the benchmark curation process (including document sampling, edit scenario generation, and ground-truth labeling), the precise definition and computation of the consistency metric, the exact baseline prompting protocols used across models, and a categorized error analysis of failure cases. These additions will directly support the reported 56% to 76% improvement and token reductions.","revision_made":"yes","referee_comment":"[Abstract / Evaluation] Abstract and Evaluation section: the headline consistency improvement (56% → 76%) and token-reduction claims rest on a 1.9k-case benchmark whose construction, consistency metric, baseline prompting protocol, and error analysis are not described, rendering the empirical link unverifiable."},{"response":"We acknowledge that §3 provides a high-level description but lacks the requested explicit elements. In the revised manuscript we will insert a formal algorithm (pseudocode and step-by-step procedure) for constructing the dependency graph from hierarchical structure, explicit references, implicit dependencies, and semantic relationships. We will also add a validation subsection reporting agreement with manually annotated ground-truth cross-references on a held-out subset and an error analysis quantifying the impact of any missed links.","revision_made":"yes","referee_comment":"[§3] §3 (Dependency Graph Construction): the central attribution of gains to the graph requires an explicit construction algorithm, validation procedure against ground-truth cross-references, and error analysis; none are supplied, so it is impossible to assess whether omitted links or construction errors undermine the reported improvements."},{"response":"We agree that aggregate results alone limit assessment of robustness. The revised Table 1 (or supplementary tables) will report per-model and per-scenario consistency and token-usage numbers. We will add statistical significance tests (e.g., paired t-tests or McNemar's test with p-values) comparing LEDGER against baselines. Finally, we will include an ablation study isolating the contribution of each graph component (hierarchical, explicit, implicit, semantic) to the observed gains.","revision_made":"yes","referee_comment":"[Table 1] Table 1 / Results: without per-model, per-scenario breakdowns, statistical significance tests, or ablation of the graph components, the aggregate 56%→76% figure cannot be assessed for robustness across the six models."}],"tokens_in":1316,"tokens_out":580,"duration_ms":24023,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"LEDGER builds a dependency graph over long documents to guide retrieval during agentic edits, so the model only sees the parts that matter for consistency instead of the whole text. The main result is that this lifts consistency from 56% to 76% across six models on 1.9k test cases while cutting tokens, and low-reasoning LEDGER matches high-reasoning baselines.\n\nThe approach is straightforward and targets a practical bottleneck: edits that break cross-references or semantic links. Framing the task as context engineering and showing that explicit structure can substitute for extra reasoning effort is useful engineering.\n\nThe soft spots sit in the parts the abstract does not show. Graph construction is described only at a high level—no algorithm, no validation against human links, no error rates. If the automatic graph misses implicit dependencies, the consistency numbers cannot be credited to the method. The benchmark construction and consistency metric are also undescribed, so it is impossible to tell whether the test cases are representative or whether baselines got equivalent prompting. These gaps make the data-to-claim link provisional.\n\nThe stress-test concern about missing links is therefore on target based on what is visible. If the full paper supplies the construction procedure and some validation, that would change the picture; otherwise the central assumption stays untested.\n\nThis is for people building or evaluating LLM agents that edit structured documents such as reports, manuals, or codebases. A reader who needs concrete retrieval tricks for long-context editing would get value from the numbers and the graph idea.\n\nIt deserves peer review. The empirical claim is quantified and the method is simple enough to reproduce or refute, so referees can check the missing pieces.","headline":"LEDGER gives a graph retrieval method for document editing agents that reports clear consistency and token gains, but the abstract leaves graph construction and benchmark details too thin to judge if the gains are real.","tokens_in":2240,"tokens_out":426,"would_cite":false,"duration_ms":30021,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"LEDGER builds a lightweight dependency graph to retrieve only relevant context for edits, lifting consistency from 56% to 76% across models while cutting tokens.","keywords":["agentic document editing","dependency graph","graph retrieval","consistency preservation","token efficiency","long-context editing","structured documents"],"falsifier":"A set of edits on documents where the graph misses at least one cross-reference, resulting in a measurable drop in consistency relative to full-document baselines.","tokens_in":2555,"feed_emoji":"📄","tokens_out":656,"duration_ms":21435,"temperature":0.7,"pith_summary":"The paper introduces LEDGER to solve the problem of making localized changes to long structured documents without breaking cross-references or overall meaning. It does this by first turning the document into an explicit graph that records hierarchy, direct references, implicit links, and semantic ties. For any proposed edit the system then pulls only the nodes and edges needed for that change instead of feeding the entire document to the model. Experiments on 1.9k test cases show the approach raises consistency scores for six different language models and lowers the number of tokens required. The same method also lets a low-reasoning-effort run match the quality of a high-reasoning-effort baseline that processes more context.","feed_headline":"Dependency graph raises editing consistency to 76% with fewer tokens","feed_subtitle":"Explicit structure lets models edit long documents without full context or high reasoning effort.","key_machinery":"The dependency graph that models hierarchical organization, explicit references, implicit dependencies, and semantic relationships, then supplies graph-guided retrieval for each edit.","core_discovery":"LEDGER constructs a dependency graph that explicitly encodes document hierarchy, explicit references, implicit dependencies, and semantic relationships; graph-guided retrieval then supplies only the minimal context required for each edit, which improves consistency from 56% to 76% across six models while reducing token consumption, and enables low-reasoning-effort runs to equal high-reasoning-effort baselines.","pith_inferences":["The same graph construction could be applied to other agentic tasks that require maintaining consistency across long structured outputs, such as code refactoring or legal contract revision.","If the graph can be updated incrementally after each edit, the method might scale to interactive, multi-turn document workflows without rebuilding the structure each time.","The approach suggests that lightweight symbolic structures can reduce reliance on expensive model reasoning steps in agent systems more broadly."],"forward_implications":["Consistency improves across all tested models and document types when the graph is used for retrieval.","Token usage drops because only graph-selected context is sent to the model.","Low-reasoning-effort runs achieve the same quality as high-reasoning-effort baselines.","Explicit dependency representations can substitute for some internal model reasoning in editing tasks."],"fun_headline_variants":["Dependency graph achieves 76% consistency in edits with fewer tokens","Graph retrieval enables 76% consistency using minimal document context","Dependency graph cuts token use to reach 76% edit consistency","LEDGER graph allows 76% consistency with low reasoning effort"],"cache_read_input_tokens":64,"weakest_assumption_plain":"The automatically built dependency graph correctly identifies every cross-reference and semantic relationship that matters for consistency and does not introduce construction errors or omit critical links.","fun_headline_variants_meta":{"raw":{"variants":["Dependency graph achieves 76% consistency in edits with fewer tokens","Graph retrieval enables 76% consistency using minimal document context","Dependency graph cuts token use to reach 76% edit consistency","LEDGER graph allows 76% consistency with low reasoning effort"]},"model":"grok-4.3","cost_usd":0.007924,"raw_usage":{"total_tokens":3574,"prompt_tokens":594,"num_sources_used":0,"completion_tokens":68,"cost_in_usd_ticks":79237000,"prompt_tokens_details":{"text_tokens":594,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":2912,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":594,"tokens_out":68,"duration_ms":32676,"temperature":1.0,"reasoning_tokens":2912,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-06-30T10:29:20.943715+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"A set of edits on documents where the graph misses at least one cross-reference, resulting in a measurable drop in consistency relative to full-document baselines.","supporting_citations":[],"review_version":1}