{"id":"2715e389-7fc8-42c0-8d06-16b0a5fb4180","arxiv_id":"2505.16667","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":7.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":3,"one_line_summary":"ELABORATION provides a four-stage human-feedback taxonomy and an 8,320-problem dataset, with experiments showing human-LLM collaboration improves pass@1 by about 7 percent.","lead":"This paper introduces ELABORATION, a benchmark and dataset for testing how human feedback helps AI models solve competitive programming problems. It organizes feedback into four stages and shows that human-LLM collaboration improves performance, with coding-stage feedback being the most effective.","discovery_kind":"new_method","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Coding-stage 'human feedback' gain is confounded by teacher simulator's access to ground-truth solutions; the stage ranking is not yet established for real humans.","rationale":"The reader's weakest-assumption analysis already points at the O1-Mini teacher simulator's access to ELABORATIONSET annotations and ground-truth solutions. My stress test sharpens this into a concrete confound: the coding-stage teacher prompt is given the ground-truth solution explicitly, so the large-scale stage ranking in Figure 3 and the abstract's 'coding stage most beneficial' claim measure gold-solution informativeness at least as much as human feedback quality. This is a correctness risk rather than a consensus dispute. I considered whether the real-human debugging result (Table 6) rescues the central claim; it supports human-LLM collaboration for debugging, but it does not test the coding/planning/comprehension stages, so it cannot validate the stage ordering or the 7.0 pp average. The paper deserves credit for the taxonomy, the dataset construction, the contamination-free split, and the honest Appendix C.4 admission about ground-truth use; none of those are in question. The proposed ablation is feasible with the released dataset and would settle whether the stage-level conclusion survives without gold leakage. Since the reader already assigned CONDITIONAL for closely related reasons, my read does not move the verdict.","tokens_in":40670,"tokens_out":5562,"duration_ms":49002,"concrete_test":"Recompute the Section 4.3 stage ablation (Figure 3) with the teacher simulator's prompts stripped of all ELABORATIONSET-derived content - no GROUND_TRUTH_SOLUTION, no ANNOTATED_ALGORITHM, no Annotated_Statement - keeping only the problem statement, generated code, and compiler output. If coding-stage pass@1 improvement no longer exceeds planning-stage improvement, the 'coding stage is most beneficial' conclusion is an artifact of ground-truth leakage, and the claim should be rephrased as 'oracle-derived coding hints help most' rather than 'human coding-stage feedback helps most.'","verdict_should_be":"UNCHANGED","load_bearing_attack":"The headline claim that coding-stage feedback yields the largest pass@1 gain rests on the O1-Mini teacher simulator, whose coding-stage prompt explicitly includes the ground-truth solution from ELABORATIONSET (Appendix F, 'System prompt for teacher programmer ... coding stage': 'the ground_truth solutions {GROUND_TRUTH_SOLUTION} in ELABORATIONSET'). The planning and comprehension teacher prompts similarly inject annotated algorithms/pseudocode and annotated statements. Figure 3 therefore compares how much gold-derived information is leaked into the LLM at each stage, not how human feedback helps. The paper's own Appendix C.4 concedes that because the teacher references ground truth, adding real human feedback to coding provides limited additional benefit. The real-human study (Section 4.4, Table 6) is restricted to debugging, so it cannot validate the claimed stage ordering for comprehension/planning/coding. The average +7.0 pp and 'coding stage most beneficial' conclusion generalize to real humans only under the assumption that a gold-solution-prompted LLM is an adequate human proxy - exactly the load-bearing assumption that is untested. The taxonomy, dataset, contamination-free split, and real debugging experiment remain valuable independently; the concern is specifically about the quantitative stage-level and teacher-level claims.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper presents ELABORATION, a benchmark for human-LLM competitive programming built on a four-stage taxonomy of human feedback (problem comprehension, solution planning, code generation, debugging) and ELABORATIONSET, a dataset of 8,320 Codeforces/AtCoder problems with statement clarifications, algorithm summaries, and ground-truth solutions. The evaluation protocol runs 13 LLMs through the four stages with two O1-Mini-based simulators (student and teacher) and reports pass@1 improvement from feedback, plus a smaller real-human debugging study with five graduate students on 300 problems. The central claims are that human-LLM collaboration improves pass@1 by about 7 percentage points on average, with coding-stage feedback providing the largest benefit and teacher feedback being more effective but more token-expensive than student feedback. A key complication is that the teacher simulator is prompted with the dataset's own ground-truth solutions and annotated algorithm summaries, so the simulated-feedback gains partly reflect answer-key leakage rather than human behavior.","tokens_in":40866,"tokens_out":7165,"duration_ms":59570,"significance":"The benchmark infrastructure is genuinely valuable. ELABORATIONSET's scale, the contamination-free temporal split, the public code/data release plan, and the real-human debugging experiment with a detailed syntactic/semantic bug taxonomy are concrete assets that can support future work. The four-stage taxonomy is a useful organizing device for comparing human-in-the-loop methods. However, the headline result about human feedback is not yet established for real humans: the teacher simulator's feedback is generated with access to the dataset's annotated solutions, so the large-scale quantitative comparisons in Table 3 and Figure 3 are more accurately described as oracle-information-injection studies than as measurements of human feedback. With reframing, additional non-gold simulator experiments, or real-human studies at all stages, the resource could support reliable conclusions about human-LLM collaboration; in its current form, the central claim overreaches the evidence.","major_comments":[{"comment":"The teacher-programmer simulator is not a valid proxy for human feedback in the paper's central quantitative claims. The Appendix F system prompt for the teacher at the coding stage explicitly provides 'the ground_truth solutions {GROUND_TRUTH_SOLUTION} in ELABORATIONSET', and the planning and comprehension prompts provide ANNOTATED_ALGORITHM/ANNOTATED_PSEDOCODE and Annotated_Statement, respectively. Consequently, the Teacher Programmer rows of Table 3 and the stage decomposition in Figure 3 measure how much gold-derived information is injected into the target LLM, not how much human feedback helps. The manuscript's own Appendix C.4 concedes this: 'the teacher-programmer simulator, which, by already referencing the ground truth solution, offers the correct approach. Consequently, adding human feedback to an already accurate solution provides limited additional benefit.' Because the abstract's improvement claim and RQ2's 'coding-stage feedback most beneficial' conclusion depend on these teacher-simulator numbers, the results must be reframed as an oracle/upper-bound study, or the teacher prompt must be rebuilt without gold annotations.","section":"§4.1, Appendix F"},{"comment":"The real-human experiments cannot validate the stage-level ordering of feedback effectiveness. Section 4.4 restricts human participants to the debugging stage, and the coding-stage real-human experiment in Appendix C.4 uses only 60 problems and produces an improvement that the authors themselves describe as 'relatively modest.' Thus the RQ2 conclusion that coding-stage feedback is the most beneficial stage is supported only by the gold-prompted teacher simulator, not by observed human behavior. The authors should either run real-human studies at all four feedback stages or restrict the stage-ranking claim to the simulated setting.","section":"§4.4, Table 6, Appendix C.4"},{"comment":"The abstract's '+7.0%, on average' is not reproducible from the reported data. In Table 3, the 'Average over All LLMs' contamination-free overall pass@1 gain is +9.3 percentage points for teacher feedback and +3.1 percentage points for student feedback; the contamination-evaluation averages are +11.5 and +4.0, respectively. No single aggregate listed equals 7.0%. The paper should state the exact aggregation used for the abstract number (for example, an unweighted average over stages, models, or feedback types), or correct the number to match Table 3.","section":"Abstract, Table 3"},{"comment":"The validity of the simulated-feedback results for real humans is asserted rather than tested. Both simulators are O1-Mini instances, and no calibration of simulator behavior against the real-human debugging data is reported. Since the real-human study covers only the debugging stage, it cannot tell us whether the student-versus-teacher ordering or the comprehension/planning/coding ordering transfers to human users. The authors should either provide such a calibration or explicitly label the large-scale results as simulator-only findings.","section":"§4.1, §4.4"}],"minor_comments":[{"comment":"There are multiple typos and grammatical errors: 'strengthes' in the abstract, 'Conclustion' as the Section 5 heading, 'specical focus' in Section 4.3, 'This include' in Section 4.1, 'we allows' in Section 4.4, and 'choosen'/'psedocode' in Appendix F prompts. A careful proofreading pass is needed.","section":"Throughout"},{"comment":"The caption states 'Since O1-Mini is expensive and recently released, experiments with it have been deferred,' but Table 3 includes O1-Mini base rows. The caption should clarify that only the O1-Mini plus-feedback rows were deferred, and ideally explain why.","section":"Table 3 caption"},{"comment":"The text says ELABORATIONSET incorporates 'fully accurate, static annotations,' but the validation process in Appendix A.2.2 involves manual review only of items where three LLM annotators disagreed, and only two annotators reviewing a subset of items. 'Fully accurate' is too strong; a graded confidence statement would be more appropriate.","section":"§3.2"},{"comment":"The real-human debugging comparison is reported without any uncertainty quantification. With five participants and 300 problems, the 24-percentage-point improvement over automatic debugging could be driven by a subset of participants; reporting bootstrap confidence intervals or per-participant pass@1 variability would substantially strengthen the claim.","section":"§4.4, Table 6"},{"comment":"The token-cost analysis is under-specified. The reader cannot tell whether 'Avg. #Token of Human Feedback' counts only the feedback message tokens or also the prompt prefix, and the Pass@1/#token cost-benefit comparison does not account for the ground-truth tokens included in the teacher prompt. Please define the metric precisely.","section":"§4.3, Figure 3"},{"comment":"The nuanced manual evaluations use only 20 problems per stage and two annotators, but no inter-annotator agreement is reported. Given that these manual scores are used to support claims about small open-source models' weaknesses, reporting agreement would help.","section":"Appendix C.3, Tables 13 and 14"}],"recommendation":"major_revision","confidential_remarks":"The paper's own Appendix C.4 already concedes the central confound in the teacher-simulator design, so the revision should reframe the teacher results as oracle-information-injection results and soften the abstract and RQ2 claims accordingly. I think the benchmark artifacts are worth publishing after this reframing, but the current headline claim is not supported by the experiments as designed. The editors may also want to ask the authors to clarify the exact provenance of the '+7.0%' number in the abstract."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Bottom line: this is a useful benchmark and dataset, and the four-stage taxonomy is a reasonable way to organize human feedback in programming. But the paper's headline quantitative claims—specifically that coding-stage feedback gives the biggest pass@1 gain—are built on a teacher simulator that is prompted with the ground-truth solutions, so the stage ranking is not yet established for real humans. The paper itself concedes this in Appendix C.4, but the abstract and main text do not.\n\nWhat's actually new: the taxonomy (comprehension, planning, coding, debugging), ELABORATIONSET with 8,320 Codeforces/AtCoder problems and a clean date-based contamination split, and the evaluation protocol across 13 models. The dataset annotations—statement clarifications, algorithm summaries, ground-truth solutions—look carefully done, with two-stage human verification. The real debugging experiment is the strongest empirical piece: five grads on 300 unseen problems, human bug identification at 81% precision/recall versus 23%/40% automatic, and human debug improved GPT-4-Turbo pass@1 by 24 points versus 5. That is a real result, even if the n is small and it is one model.\n\nThe soft spot is exactly where the stress test points. The teacher prompts in Appendix F include the ground-truth solution at the coding stage, the annotated algorithm/pseudocode at planning, and the annotated statement at comprehension. So teacher gains measure how much gold-derived information helps the student LLM, not how human experts help. Figure 3's stage comparison is distorted by how much gold is injected per stage. The paper acknowledges in Appendix C.4 that adding real human feedback to coding provides limited additional benefit because the teacher already references the ground truth. That is an honest admission, but it means the central 'coding stage most beneficial' claim should be re-framed as conditional on gold access.\n\nOther soft spots: the real-human study is limited to debugging, so stage ordering for comprehension/planning/coding rests entirely on the simulator. The code and dataset are not yet available at a stable URL, so independent verification is currently not possible. The difficulty thresholds are arbitrary, but that is standard.\n\nWho this is for: people building human-in-the-loop coding agents and benchmark designers. The taxonomy and dataset give them a shared vocabulary and a reusable testbed. This deserves a serious referee, but the authors should either ablate the teacher simulator's gold access or make the conditional framing explicit in the abstract.","headline":"Useful benchmark and dataset, but the headline stage-level claims are inflated by a teacher simulator that is handed the answer key.","tokens_in":41417,"tokens_out":3052,"would_cite":true,"duration_ms":26058,"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":"The paper claims that four-stage human-LLM collaboration raises average pass@1 by about 7 points, that coding-stage feedback helps most, and that real humans find bugs at 81% precision versus 23% for automatic debug.","keywords":["human-LLM collaboration","competitive programming","benchmark","human feedback taxonomy","LLM code generation","pass@1","contamination-free evaluation","simulated human feedback"],"falsifier":"Run the same four-stage protocol with the five human participants on a shared subset of 300 unseen problems, having them give feedback at the planning and coding stages rather than only debugging; if real-human coding-stage feedback does not produce the largest pass@1 gain, or if total gains fall well below the simulator-based +7 points, the benchmark's central quantitative conclusions fail. A cheaper check: strip the teacher simulator's ground-truth solutions and annotated pseudocode and measure how much of the +9.3-point contamination-free gain survives.","tokens_in":40453,"feed_emoji":"🧑💻","tokens_out":14989,"duration_ms":108231,"temperature":0.7,"pith_summary":"This paper tries to establish that human-LLM collaboration, structured to cover the entire programming process, reliably improves competitive programming performance, and that the stage at which feedback is given determines how much it helps. To that end it introduces a four-stage taxonomy of human feedback (problem comprehension, solution planning, code generation, debugging), a dataset of 8,320 annotated Codeforces and AtCoder problems, and a benchmark that runs 13 LLMs with two simulated human skill levels and, in a smaller study, real human participants. The central empirical result is an average pass@1 gain of about 7 percentage points from human feedback: teacher-level feedback is worth roughly +9 points on unseen problems and +11.5 on previously seen ones, student-level feedback about +3 to +4, and coding-stage feedback delivers the largest gain. A companion real-human study finds humans identify bugs with 81% precision against 23% for automatic debugging, lifting pass@1 by 24 points. A reader should care because prior work used scattered, application-specific feedback, leaving no unified answer to where human input actually pays off.","feed_headline":"Human feedback lifts LLM coding pass rates by 7 points","feed_subtitle":"A four-stage benchmark finds expert guidance helps most during code generation, not debugging.","key_machinery":"The machinery that carries the argument is the four-stage human feedback taxonomy paired with an annotated dataset that makes stage-specific feedback cheap to produce at scale. ELABORATIONSET supplies, per problem, statement clarifications, algorithm knowledge summaries with pseudocode for 33 algorithms, and ground-truth solutions; the 'teacher' user simulator is an O1-Mini prompted with these annotations, while the 'student' simulator answers from its own internal knowledge, so the benchmark can generate expert-level or intermediate-level feedback for 8,320 problems without hiring programmers. The contamination-free protocol, which splits problems by publication date relative to each model's cutoff date, is the second load-bearing mechanism, separating genuine problem-solving from training-data memorization.","core_discovery":"The paper claims that human feedback applied across all four stages of competitive programming significantly improves LLM performance regardless of model size or whether a problem appeared in the training data, and that the value of feedback is concentrated in the code generation stage. Averaged over 13 models, teacher-programmer feedback raises pass@1, the share of problems solved on the first generation, by 9.3 percentage points on contamination-free problems and 11.5 points on previously seen problems, while student-programmer feedback raises it by 3.1 and 4.0 points; the abstract headline across expertise levels and contamination conditions is +7.0 points. Coding-stage feedback yields the largest improvement and comprehension feedback the least, because LLMs already summarize problem statements accurately (0.90 to 0.96) but fail to turn plans into correct code, and debugging-stage feedback alone gives only minimal gains. In the real-human study, five graduate students providing textual bug identification achieved 81% precision and 71% recall versus 23% and 40% for an automatic debugger, lifting GPT-4-Turbo's pass@1 by 24 points, and the LLM corrected 87% of bugs when human feedback was accurate. The paper also claims LLMs alone remain far from competitive at the hard end: average pass@1 is 3.4% on unseen hard problems, and contamination-free scores drop by about 9 points, implying that a meaningful share of reported performance is memorization.","pith_inferences":["A natural division of labor follows: automate comprehension checking, invest human effort in code review, and reserve debugging for error classes that automatic tools demonstrably miss.","The paper never ablated the teacher simulator; stripping its ground-truth solutions and annotated pseudocode would reveal how much of the +9 to +10 point gain is genuinely expert guidance versus the simulator being told the answer.","Because the real-human study ran only the debugging stage (plus a 60-problem coding pilot), the simulator-based ordering coding > planning > comprehension/debugging remains unverified for actual humans; a human replication at planning and coding stages would settle whether the ordering reflects the tasks or the simulator.","The complementary error profiles, with humans catching reference, calculation, and incomplete errors while automatic tools clear syntax, suggest hybrid pipelines that let compiler feedback handle syntactic bugs and spend human effort only on semantic ones."],"forward_implications":["Solo-LLM leaderboards understate what human-LLM teams can do: pairing any tested model with expert textual feedback raises pass@1 substantially, and on hard unseen problems teacher feedback lifts the average from 3.4% to 10.2%.","Systems should route scarce human attention to the code-generation stage: comprehension feedback is nearly wasted because LLMs already summarize statements at 0.9+ accuracy, and debugging-only feedback yields small gains.","Date-split evaluation is necessary for trustworthy coding benchmarks, since the average 9-point drop on unseen problems indicates a non-trivial memorization component in reported scores.","Bug finding in LLM code is currently a human-strength task: human identification at 81% precision converts into a 24-point pass@1 gain, and LLMs fix 87% of accurately reported bugs, so the bottleneck is identification rather than correction.","The token-cost analysis suggests planning-stage feedback may be the best value per unit of human effort, since the highest-gain coding stage is also the most token-hungry."],"supporting_citations":[{"why":"Prior human-LLM competitive programming work whose LLM-simulator approach and debugging-focused feedback ELABORATION extends, and the protocol basis for the real-human debugging study.","marker":"Shi et al. (2024)"},{"why":"OpenCodeInterpreter, cited as representative of debugging-only conversational feedback and as the practice of using an LLM as a simulated student programmer.","marker":"Zheng et al. (2024)"},{"why":"LiveCodeBench, source of the contamination-free cutoff-date evaluation design and of the practice of generating missing test cases.","marker":"Jain et al. (2024)"},{"why":"Defines the Pass@k metric in which all headline results are reported.","marker":"Chen et al. (2021)"},{"why":"TACO dataset; supplies the LLM-generated test-case approach used for problems without official tests.","marker":"Li et al. (2023b)"},{"why":"Earlier simulation of iterative human-AI programming restricted to solution-strategy feedback, the fragmentation the four-stage taxonomy responds to.","marker":"Mozannar et al. (2023)"},{"why":"DebugBench; the real-human protocol of textual error identification that the human participants follow.","marker":"Tian et al. (2024)"},{"why":"APPS benchmark, the standard reference point for the claim that LLMs alone underperform on competition-level problems.","marker":"Hendrycks et al. (2021b)"}],"fun_headline_variants":["Human feedback adds 7 points to LLM coding pass rate","LLM coding improves most with human feedback during code generation","Real human bug reports raise GPT-4-Turbo pass@1 by 24 points","New benchmark ELABORATION quantifies human-LLM coding gains","On unseen problems, human feedback adds 9.3 points to LLM pass@1"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The large-scale numbers assume that the O1-Mini-based user simulators, especially the teacher simulator which is prompted with ground-truth solutions and algorithm summaries, produce feedback that faithfully resembles what real human programmers would give, and that the carefully engineered prompts keep performing across problem sets and phrasings, a sensitivity the authors themselves flag.","fun_headline_variants_meta":{"raw":{"variants":["Human feedback adds 7 points to LLM coding pass rate","LLM coding improves most with human feedback during code generation","Real human bug reports raise GPT-4-Turbo pass@1 by 24 points","New benchmark ELABORATION quantifies human-LLM coding gains","On unseen problems, human feedback adds 9.3 points to LLM pass@1"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.001397,"raw_usage":{"total_tokens":5678,"prompt_tokens":1001,"completion_tokens":4677,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":617,"completion_tokens_details":{"reasoning_tokens":4578}},"tokens_in":617,"tokens_out":4677,"duration_ms":26641,"temperature":1.0,"reasoning_tokens":4578,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-07T14:56:31.301312+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Run the same four-stage protocol with the five human participants on a shared subset of 300 unseen problems, having them give feedback at the planning and coding stages rather than only debugging; if real-human coding-stage feedback does not produce the largest pass@1 gain, or if total gains fall well below the simulator-based +7 points, the benchmark's central quantitative conclusions fail. A cheaper check: strip the teacher simulator's ground-truth solutions and annotated pseudocode and measure how much of the +9.3-point contamination-free gain survives.","supporting_citations":[],"review_version":1}