{"id":"6ca66c84-f537-415f-8e60-94227a83c9a4","arxiv_id":"2604.15134","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":0,"one_line_summary":"PIE-V is a framework that injects plausible mistakes and corrections into egocentric procedural videos via psychology-informed planning and LLM-assisted video synthesis, paired with a nine-metric human rubric for benchmarking.","lead":"The paper introduces PIE-V, a system that adds controlled human-like mistakes and recoveries to clean egocentric videos of step-by-step tasks using psychology rules, language models, and video editing. This addresses the lack of realistic error examples in existing procedural video datasets for training monitoring AI.","discovery_kind":"new_method","skeptic_critique":{"model":"grok-4.3","headline":"Core claim rests on untested assumption that LLM-cascade + text-to-video edits produce procedurally coherent, artifact-free mistakes at scale","rationale":"The reader's weakest assumption directly identifies the generation pipeline's reliability as the critical unverified link; the abstract-only review correctly flags the absence of evidence that the components produce coherent output. Full-text details on the planner's psychology operationalization or judge prompts would not remove the need for external validation against real errors, so the concern remains load-bearing. This moves the verdict from UNVERDICTED to CONDITIONAL pending the proposed human study.","tokens_in":1762,"tokens_out":460,"duration_ms":17352,"concrete_test":"Select 20 clean Ego-Exo4D segments; generate PIE-V mistakes using the exact planner+LLM+video pipeline described; have 5 independent annotators (blind to source) rate each on the paper's nine-metric rubric plus a binary 'contains visible artifact or logical inconsistency' flag; compare mean scores and artifact rate against the same segments with real human mistakes from the original recordings. If artifact rate >15% or plausibility drops >1.5 points on the rubric, the generation step fails to support the central claim.","verdict_should_be":"CONDITIONAL","load_bearing_attack":"The framework's central promise is that a psychology-informed planner (conditioned on phase and semantic load), followed by LLM cascade rewrites, correction planner, LLM judge, and text-guided video synthesis, will reliably yield human-plausible mistakes and recoveries without visual or logical artifacts. This is the load-bearing step: every downstream benchmarking claim (102 injected mistakes across 17 tasks, 27 recoveries, superiority over freeform LLM baseline, utility for mistake-aware training) presupposes that the generated segments pass the nine-metric human rubric for plausibility, state-change coherence, and grounding. No independent validation against real human error distributions or controlled ablation of the planner/LLM-judge loop is described; the LLM judge itself is part of the generation pipeline, creating potential circularity. If the planner or video edits systematically introduce subtle inconsistencies (e.g., impossible hand-object trajectories or state changes invisible in egocentric views), the entire constructed dataset and the rubric-based audit become unreliable.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The paper introduces PIE-V, a framework for constructing mistake-aware egocentric procedural videos by augmenting clean keystep procedures with controlled, human-plausible deviations. It combines a psychology-informed error planner (conditioned on procedure phase and semantic step load), a correction planner, LLM cascade rewrites, an LLM judge for coherence validation, and text-guided video synthesis for segment edits. Applied to 17 tasks across 50 Ego-Exo4D scenarios, it generates 102 mistakes and 27 recovery corrections. The work also proposes a unified taxonomy and a nine-metric human rubric covering plausibility, procedure logic, state-change coherence, and text-video grounding, which is used to audit existing resources and compare PIE-V to a freeform LLM baseline.","tokens_in":1985,"tokens_out":677,"duration_ms":27513,"significance":"If the generated videos prove to be artifact-free and human-plausible, the framework could provide a scalable method for creating synthetic training data for mistake detection and correction in procedural monitoring systems, addressing the scarcity of error traces in egocentric datasets. The introduction of a standardized nine-metric rubric offers a methodological contribution that could enable more consistent benchmarking across future work in this area.","major_comments":[{"comment":"The abstract and framework description report the injection of 102 mistakes and 27 corrections along with the use of the nine-metric human rubric for auditing and baseline comparison, but supply no quantitative rubric scores, inter-annotator agreement, or statistical results from these evaluations. This is load-bearing for the claims of superiority over the freeform LLM baseline and overall utility for mistake-aware training.","section":"Benchmarking and Evaluation"},{"comment":"The LLM judge is described as both validating procedural coherence during generation and repairing failures, yet no independent human validation or comparison against real human error distributions is reported. This risks circularity in the quality claims, as the same LLM-based mechanism certifies the outputs it helps produce.","section":"Generation Pipeline (§3)"},{"comment":"No ablation studies or component-wise analysis are described to test the contribution of the psychology-informed planner (conditioned on phase and semantic load) versus simpler freeform prompting, leaving the necessity of the full cascade unverified despite its central role in the framework.","section":"Framework Components"}],"minor_comments":[{"comment":"The abstract states application to '17 tasks and 50 Ego-Exo4D scenarios' without detailing task selection criteria or diversity coverage, which would help readers assess generalizability.","section":"Abstract"},{"comment":"Clarify early in the text how the text-guided video synthesis step ensures consistency with egocentric viewpoint and hand occlusions, as this is critical for visual plausibility but only briefly mentioned.","section":"Video Synthesis"}],"recommendation":"major_revision","confidential_remarks":"The manuscript is primarily a constructive framework paper whose central claims depend on empirical validation that appears absent from the provided abstract and high-level description; if the full text contains detailed rubric scores and ablations, this would substantially strengthen the submission. The work fits the scope of a methods-oriented CV venue but may require expansion of the evaluation section to meet typical standards for acceptance."},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for their constructive and detailed feedback. We address each major comment below with clarifications and commitments to revisions that will strengthen the empirical support and transparency of the work.","responses":[{"response":"We agree that the absence of quantitative results weakens the presentation. The manuscript describes the rubric application and baseline comparison but does not report aggregated scores, agreement metrics, or statistical tests in the main text. In the revision we will add these results, including per-metric average scores for PIE-V versus the freeform baseline, inter-annotator agreement (e.g., Krippendorff’s alpha), and significance tests, presented in a dedicated evaluation table to directly substantiate the superiority claims.","revision_made":"yes","referee_comment":"[Benchmarking and Evaluation] The abstract and framework description report the injection of 102 mistakes and 27 corrections along with the use of the nine-metric human rubric for auditing and baseline comparison, but supply no quantitative rubric scores, inter-annotator agreement, or statistical results from these evaluations. This is load-bearing for the claims of superiority over the freeform LLM baseline and overall utility for mistake-aware training."},{"response":"We clarify the separation of concerns: the LLM judge is an internal generation tool for coherence checking and repair, while all quality claims rest on a separate human evaluation protocol using the nine-metric rubric applied by independent annotators. This human rubric directly assesses plausibility, procedure logic, state-change coherence, and text-video grounding. We acknowledge that a direct quantitative comparison to real human error distributions is not reported; such annotated traces remain scarce in existing egocentric datasets, which motivates the framework. In revision we will explicitly distinguish the LLM judge from the human rubric and add a limitations paragraph on the lack of real-distribution benchmarks.","revision_made":"partial","referee_comment":"[Generation Pipeline (§3)] The LLM judge is described as both validating procedural coherence during generation and repairing failures, yet no independent human validation or comparison against real human error distributions is reported. This risks circularity in the quality claims, as the same LLM-based mechanism certifies the outputs it helps produce."},{"response":"The existing comparison to the freeform LLM baseline already functions as a test of the psychology-informed planner’s contribution, since the baseline uses simpler prompting without phase or semantic-load conditioning. In the revision we will reframe this comparison as an explicit ablation study, adding a subsection that reports rubric-score differences attributable to the planner and discusses the incremental value of the correction planner and cascade rewrites.","revision_made":"yes","referee_comment":"[Framework Components] No ablation studies or component-wise analysis are described to test the contribution of the psychology-informed planner (conditioned on phase and semantic load) versus simpler freeform prompting, leaving the necessity of the full cascade unverified despite its central role in the framework."}],"tokens_in":1532,"tokens_out":650,"duration_ms":46963,"standing_objections":["Direct quantitative comparison of generated mistakes to real human error distributions, as sufficiently annotated real error traces are not available in the source egocentric datasets."]},"desk_editor":{"model":"grok-4.3","letter":"The main takeaway is that this paper builds a concrete system for turning clean keystep videos into ones that contain controlled mistakes and recoveries. It starts with an error planner that draws on psychology to pick deviation types based on procedure phase and step complexity, adds a correction planner, runs LLM rewrites for consistency, uses an LLM judge to catch problems, and finishes with text-guided video synthesis to insert the edited segments. They applied it to 50 Ego-Exo4D scenarios across 17 tasks and produced 102 mistakes plus 27 recoveries, then compared the output against a freeform LLM baseline using their own rubric. That rubric, which scores step-level and procedure-level traits like plausibility, state-change coherence, and text-video grounding, is the clearest addition here and could be reused by others working on procedural monitoring. The taxonomy of mistakes also organizes the space in a way that matches how errors actually appear in egocentric views, where hands often hide the action. These pieces together address a practical shortage in existing datasets that mostly lack recovery traces. The soft spots sit in the validation. The abstract and description give application numbers but no tabulated human scores, no breakdown of how often the LLM judge had to intervene, and no ablation that isolates the psychology conditioning or the video stitching step. Without those numbers it is difficult to know whether the generated clips avoid the usual artifacts in hand trajectories or object states. The fact that the same LLM family is used for planning, rewriting, judging, and generation also leaves open the possibility that coherence is partly self-reinforcing rather than independently verified. This work is aimed at groups building models for mistake detection in robotics or human-AI collaboration, or anyone who needs large volumes of labeled error data without filming new mistakes from scratch. A reader who wants a ready-to-adapt framework and an evaluation protocol will find usable material even if the current numbers are preliminary. It deserves peer review because the gap it targets is real and the components are described in enough detail to be tested by others, though any referee should ask for the missing quantitative results and failure cases.","headline":"PIE-V gives a workable pipeline for injecting psychology-based mistakes into egocentric procedure videos and supplies a nine-metric rubric to check them, but the reported results stay at the level of counts rather than measured quality.","tokens_in":2440,"tokens_out":501,"would_cite":false,"duration_ms":20563,"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":"PIE-V builds egocentric videos of procedural tasks by injecting controlled, human-plausible mistakes and recoveries into clean recordings.","keywords":["egocentric video","procedural tasks","mistake injection","error recovery","video synthesis","benchmarking rubric","psychology-informed errors","LLM video editing"],"falsifier":"A controlled human study in which raters compare PIE-V generated mistake segments against real human error recordings from the same tasks and find either lower plausibility scores or detectable editing artifacts and inconsistent object states.","tokens_in":2673,"feed_emoji":"📹","tokens_out":699,"duration_ms":29030,"temperature":0.7,"pith_summary":"The paper introduces PIE-V to address the scarcity of natural errors in existing egocentric procedural video datasets, which limits training for reliable mistake detection and recovery monitoring. It augments clean keystep videos with deviations planned according to psychological principles about task phases and cognitive load, then uses language models to maintain consistency and video synthesis to insert replacement segments seamlessly. A new taxonomy and nine-metric human evaluation rubric assess the results for plausibility, logical coherence, state changes, and text-video alignment. This matters for building systems that can observe and assist with real-world procedures where people make and correct mistakes.","feed_headline":"PIE-V injects plausible mistakes into egocentric task videos","feed_subtitle":"Psychology-guided planning and clip synthesis add controlled errors and recoveries to 50 scenarios for mistake detection benchmarks.","key_machinery":"The psychology-informed error planner conditioned on procedure phase and semantic step load, which generates human-plausible mistakes, paired with LLM cascade rewrites and text-guided video synthesis for seamless clip insertion and coherence validation.","core_discovery":"PIE-V augments clean keystep procedures from egocentric videos with controlled human-plausible deviations using a psychology-informed error planner conditioned on procedure phase and semantic step load, a correction planner that models recovery behavior, an LLM writer for cascade-consistent rewrites, an LLM judge that validates procedural coherence, and text-guided video generation to synthesize and stitch replacement clips, applied to 17 tasks and 50 Ego-Exo4D scenarios to inject 102 mistakes and generate 27 recovery corrections, while introducing a unified taxonomy and nine-metric rubric covering step-level and procedure-level quality.","pith_inferences":["The approach could generalize to generate synthetic errors in non-egocentric instructional videos for domains like cooking tutorials or assembly tasks.","Adoption of the nine-metric rubric might encourage standardized benchmarks across future work on video-based error simulation.","Training on these augmented videos could improve robustness of AI assistants that intervene during user mistakes in real time."],"forward_implications":["Enables creation of large-scale mistake and recovery traces in egocentric procedural videos for model training.","Provides a consistent protocol to audit and compare existing datasets on mistake awareness.","Supports development of models that detect both step-level errors and full procedure failures.","Establishes a baseline showing advantages of structured psychology-informed injection over unstructured LLM generation."],"fun_headline_variants":["PIE-V adds plausible mistakes to egocentric videos","Mistake-aware egocentric videos created with PIE-V","Benchmarking egocentric videos with injected mistakes","Human-plausible errors in procedural videos using PIE-V"],"cache_read_input_tokens":64,"weakest_assumption_plain":"That the psychology-informed error planner together with LLM rewrites and text-guided video generation will reliably produce mistakes that are both human-plausible and procedurally coherent without introducing visual or logical artifacts.","fun_headline_variants_meta":{"raw":{"variants":["PIE-V adds plausible mistakes to egocentric videos","Mistake-aware egocentric videos created with PIE-V","Benchmarking egocentric videos with injected mistakes","Human-plausible errors in procedural videos using PIE-V"]},"model":"grok-4.3","cost_usd":0.008579,"raw_usage":{"total_tokens":3916,"prompt_tokens":754,"num_sources_used":0,"completion_tokens":58,"cost_in_usd_ticks":85787000,"prompt_tokens_details":{"text_tokens":754,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":3104,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":754,"tokens_out":58,"duration_ms":32778,"temperature":1.0,"reasoning_tokens":3104,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-05-10T11:49:23.325071+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"A controlled human study in which raters compare PIE-V generated mistake segments against real human error recordings from the same tasks and find either lower plausibility scores or detectable editing artifacts and inconsistent object states.","supporting_citations":[],"review_version":1}