{"id":"c22f60a9-2bd0-4cea-9261-b350b34c3703","arxiv_id":"2504.18722","paper_version":1,"verdict":"REJECT","confidence":"MODERATE","novelty_score":4.0,"correctness_risk":"high","formal_verification":"none","parameter_count":2,"one_line_summary":"MODP is a metrics-driven, multi-objective prompt engineering framework that, in this paper, improves ReCoRD fill-in-the-blank accuracy from 48% to 73% on Mixtral by adding instructions, toxicity handling, and model-specific tags.","lead":"This paper proposes MODP, a framework for prompt engineering that treats an LLM's built-in behaviors, such as toxicity handling and format adherence, as objectives weighted alongside task accuracy. It reports that iterating prompts with this framework lifted accuracy on a reading comprehension benchmark from 48% to 73% and says the approach now runs in Dell's production support tool.","discovery_kind":"extension","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Algorithm 1's weighted score F(P) is never computed or reported; Table 1 reports only overall accuracy, so the central MODP mechanism is untested and the 26% gain is not reproducible from the shown data.","rationale":"The reader and I agree that the verdict should be rejection, but for a slightly broader reason. The reader's weakest assumption names arbitrary weights and an unrepresentative 20% sample; I find the more fundamental problem that the entire objective function—weights and all—is absent from the reported experiments. This is not a disagreement with consensus; it is an internal gap between Algorithm 1 and Sections 5–6. Good-faith reading: the authors likely did perform a sensible manual prompt-development exercise, and the category-specific and toxicity results are plausible. However, a paper whose central claim is a new optimization framework must show that framework being used. A concrete way to salvage the claim would be to publish the logs, weights, and an ablation comparing F-based selection with manual selection; without that, REJECT remains appropriate. The 26% figure's inconsistency with Table 1 is an additional correctness risk, and the 80% out-of-sample validation in Section 6.3 is only qualitative ('trends persisted'), with no numbers, confidence intervals, or clustering details. No formal verification, code, or data release is provided. These issues reinforce, rather than shift, the reader's REJECT verdict.","tokens_in":12991,"tokens_out":4884,"duration_ms":50533,"concrete_test":"Run a reconstruction audit: obtain the raw per-item evaluation logs and the exact weights used with Algorithm 1. (a) Recompute O1, O2, L1, L2, and F(P_i) for every prompt in Table 1 and verify that the argmax of F matches the reported optimal prompt; if no weights or logs exist, the algorithm was never actually executed. (b) Recompute the headline gain with an explicit formula, e.g., (73−48)/48 = +52%, and report absolute and relative gains separately. (c) Run MODP end-to-end once on a fixed 20% ReCoRD sample with pre-registered weights and compare the F-selected prompt against manual iteration and a random-prompt baseline; if the F-selected prompt does not beat the manual best, or the difference is within noise, the framework adds no measurable value over hand-crafted iteration.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The load-bearing gap is that Algorithm 1's optimization loop is never instantiated in the paper. Lines 10–16 define F(P_i)=w1*O1+w2*O2+w3*L1+w4*L2 and require an argmax over prompts and models, but none of the experiments reports O1, O2, L1, L2, the weights w_i, or a single F value. Table 1 reports only 'Overall Accuracy' and per-category percentages for 12 hand-written prompts on ReCoRD; the prose in §6.1 selects Prompt9/Prompt11 by overall accuracy (73%) and post-hoc commentary, not by any computed objective score. Section 6.2.2 even states that format adherence, which is treated as an LLM-specific objective, was 'not specifically measured.' Thus the empirical section tests ordinary manual prompt iteration (adding [INST] tags, toxicity instructions), not the proposed multi-objective scoring and selection mechanism. The headline 26% gain is also not reproducible from Table 1: 48% (Prompt1) to 73% (Prompt9/Prompt11) is +25 percentage points absolute or +52% relative; 26% matches neither stated convention, and no error bars or sample sizes are given. The enterprise Table 2 has the same problem: no objective scores, no weights, no human-evaluation protocol. Because the central mechanism is absent from the evidence, the framework's claimed contribution is currently unfalsifiable from the manuscript.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper proposes MODP (Multi-Objective Directional Prompting), a framework for prompt engineering that balances task-specific objectives (e.g., accuracy, category-specific performance) and LLM-specific objectives (e.g., toxicity handling, format adherence) through a weighted scoring function, representative data sampling, and iterative prompt refinement. Algorithm 1 formalizes the optimization as selecting the prompt that maximizes F(P) = w1*O1 + w2*O2 + w3*L1 + w4*L2 over a set of prompts and models. The authors apply the framework to a ReCoRD-based summarization-like reading comprehension task with synthetically added toxic content, report per-category and overall accuracies for twelve manually developed prompts, claim a 26% improvement over the initial prompt, and describe a deployment in Dell's Next Best Action support tool serving over 10,000 agents.","tokens_in":13251,"tokens_out":3882,"duration_ms":38525,"significance":"If fully instantiated, a metrics-driven, multi-objective prompt optimization framework would be a useful contribution to both applied prompt engineering and production LLM deployment. The paper contains several commendable elements: the full prompts are listed in an appendix, the evaluation uses a public dataset (ReCoRD) plus synthetic toxicity data, and the enterprise case study provides a rare glimpse into real-world prompt development. However, the central methodological mechanism—the weighted multi-objective score F(P)—is never computed, reported, or used in the experiments. The empirical sections test manual prompt iteration and accuracy selection, not the proposed optimization objective. Consequently, the paper's headline claims are not reproducible from the shown data, and the contribution as written is an untested recipe rather than a validated framework.","major_comments":[{"comment":"Algorithm 1 (lines 10–16) defines the weighted score F(P_i) = w1*O1 + w2*O2 + w3*L1 + w4*L2 and selects the optimal prompt as argmax F(P) over prompts and models. No experiment in the paper reports O1, O2, L1, L2, the weights w_i, or a single computed F value. Table 1 reports only overall accuracy and per-category percentages, and Section 6.1 selects Prompt9 and Prompt11 based on overall accuracy (73%) and qualitative commentary, not by any objective score. Thus the central optimization loop is never instantiated, and the empirical results support only ordinary manual prompt iteration, not MODP as defined.","section":"Algorithm 1, Section 3.4, Section 6.1"},{"comment":"The paper lists format adherence as an objective in Section 3.1 and in Table 2, but Section 6.2.2 explicitly states 'we did not specifically measure this format adherence in our current experiments.' An objective that is never measured cannot appear in the weighted score F(P) for the ReCoRD experiments. This is a direct admission that the multi-objective evaluation described in Sections 3.3 and 3.4 was not actually carried out, further undermining the claim that the results validate MODP.","section":"Section 6.2.2"},{"comment":"The abstract and conclusion claim a 26% performance gain over initial prompts. From Table 1, the lowest prompt (Prompt2) scores 44% and the highest (Prompt9/10/11) score 73%, a gain of 29 percentage points relative to Prompt2, or 25 percentage points relative to Prompt1 (48%). Whether computed against Prompt1 or Prompt2, the improvement is not 26% under either the absolute-percentage-point or relative convention. Moreover, no error bars, sample sizes, or significance tests are reported for any accuracy value, so the headline improvement is not statistically grounded.","section":"Section 6.1, Table 1"},{"comment":"Section 3.2 states that a representative sample is found 'by clustering or otherwise strategically sampling approximately 20% of the data,' but no concrete clustering algorithm, sampling procedure, or formal representativeness test is given. Section 6.3 claims that accuracy trends observed on the 20% sample persisted on the remaining 80%, but Figure 5, which is offered as evidence, is only a caption in the manuscript; no quantitative agreement metric (e.g., correlation, error bounds, or per-category comparison) is reported. The representativeness claim, which is load-bearing for the framework's efficiency, is therefore unsupported.","section":"Section 3.2, Section 6.3"},{"comment":"The paper uses Mixtral to create the news categories (Section 4.2) and then evaluates prompts on Mixtral, while Section 2.1.5 argues that LLM outputs are not reliable for self-assessment and that prompt validation must not depend solely on the LLM being optimized. No human validation of the Mixtral-generated category labels is reported, and the same-model-family dependency is not addressed. Because category-wise accuracies serve as the task-specific objective proxies, biased or unstable labels could directly bias the O1/O2 scores, so this circularity concern affects the multi-objective claim.","section":"Section 4.2, Section 2.1.5"}],"minor_comments":[{"comment":"The abstract contains a grammatical issue: 'a framework on two key concepts' should be 'a framework based on two key concepts.'","section":"Abstract"},{"comment":"The text says 'The dataset coin large-scale and machine-generated dataset'; this should read 'The dataset is a large-scale...'.","section":"Section 5"},{"comment":"The 'Length of Answer(Tokens)' row reports 'Medium/Long/Medium,' which are not token counts; either rename the metric or provide actual numbers.","section":"Table 2"},{"comment":"Figure 5 is cited as supporting the persistence of trends from the 20% sample to the 80% sample, but the figure content is not present in the manuscript; please include the actual figure and a quantitative comparison.","section":"Section 6.3"},{"comment":"Prompt6 in the appendix has an unmatched '[INST]' tag at the end; verify that the prompt text is transcribed correctly.","section":"Appendix A"},{"comment":"Reference [24] is listed as an arXiv preprint; if a peer-reviewed version exists, it should be cited instead for completeness.","section":"References"}],"recommendation":"reject","confidential_remarks":"The core issue is not stylistic but substantive: the optimization objective that defines MODP is absent from every experiment. Adding the missing computations and sensitivity analyses would require substantial new empirical work, which in my view goes beyond a minor or standard major revision. If the authors can instantiate Algorithm 1 with reported weights, objective scores, and a representative-sample validation, a resubmission could be reconsidered. The production deployment claim is also not independently verifiable in this manuscript."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Two things you should know. First, the paper's headline contribution—a weighted multi-objective scoring function F(P) that selects an optimal prompt over models and objectives—is never actually used in any experiment. Second, the reported '26% performance gain' doesn't match the numbers in Table 1.\n\nWhat is genuinely useful: the idea of making LLM behavior (toxicity, format adherence, instruction-following) an explicit objective in prompt development is a reasonable synthesis that practitioners will recognize. The enterprise deployment in Dell's Next Best Action tool, serving real agents, is a meaningful proof that the workflow can work in production. The authors also did a sensible thing creating a synthetic toxicity subset via ToxiGen to test robustness. I appreciate that they admit format adherence 'was not specifically measured' (Section 6.2.2).\n\nThe soft spots are not minor. Algorithm 1 defines a weighted score, but no weight, no objective score, and no F value appears anywhere in the paper. Table 1 reports overall accuracy only for 12 hand-written prompts. So the empirical section tests ordinary manual prompt iteration (add [INST] tags, add toxicity handling), not the proposed multi-objective optimization. The 48% to 73% jump is +25 points absolute and about +52% relative; neither is 26%. There are no error bars, no sample sizes, no comparisons to the automated prompt optimizers the paper itself cites (PromptBreeder, APO, Directional Stimulus Prompting), and no code or data release. The clustering used to pick the 20% sample is unspecified. In short, the central mechanism is untested and the headline number is not reproducible from the shown data.\n\nThat said, the paper is not incoherent. It fits with the existing literature and is transparent about what wasn't measured. The flaw is in the evaluation, not in the conceptual framing. Who is this for? Industry practitioners who want a structured prompt-development workflow; they will find the multi-objective checklist useful. As a research contribution it currently overclaims.\n\nMy recommendation: don't desk-reject, but send it to serious peer review with the expectation of heavy revision. A referee should demand that the authors actually compute F(P), report weights, compare against existing optimizers, and fix the headline number. As it stands, the evidence does not support the stated contribution.","headline":"Useful industrial framing, but the central scoring mechanism is never computed and the 26% gain doesn't match the table.","tokens_in":13811,"tokens_out":2490,"would_cite":false,"duration_ms":25124,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"Prompt engineering as a weighted multi-objective search yields a reported 26% gain.","keywords":["prompt engineering","multi-objective optimization","LLM-specific objectives","directional prompting","weighted prompt scoring","representative sampling","toxicity handling","production LLM deployment"],"falsifier":"Perturb the four weights in Algorithm 1 by ±0.2 on the same 20% sample and re-select the prompt: if the argmax prompt changes, the claimed 'optimal' prompt is an artifact of the chosen weights rather than a robust outcome.","tokens_in":12734,"feed_emoji":"🎯","tokens_out":6349,"duration_ms":57610,"temperature":0.7,"pith_summary":"The paper argues that prompt engineering fails when it optimizes only task accuracy, because the chosen LLM contributes its own behavior—hallucination, toxicity, lost instructions, format drift—that a good prompt must manage. MODP turns this into a concrete optimization: each prompt gets a weighted score combining task-specific objectives (category accuracy) with LLM-specific objectives (e.g., refusing toxic queries), and the prompt with the highest score is selected. The authors show that iterative refinement under this score lifts accuracy 26 percentage points from the initial prompt on a summarization-style reading-comprehension task with synthetic toxic cases, and that accuracy trends on a 20% sample persist on the remaining data. They also report applying the same process to an enterprise support tool used by more than 10,000 agents, where the optimized prompt improved faithfulness, relevance, and precision by about 8% on average.","feed_headline":"Prompt scoring that balances task and LLM behavior gains 26%","feed_subtitle":"A multi-objective prompt score, tested on summarization and in production, replaces prompt guesswork with measurable iteration.","key_machinery":"The load-bearing object is Algorithm 1's weighted objective score, F(P_i) = w1·O1 + w2·O2 + w3·L1 + w4·L2, with weights in [-1, 1] set by the practitioner. This single scalar turns 'is this prompt better?' into a well-defined argmax over prompts and models; the framework's four-step loop—find a representative sample, define objectives, assign weights, iterate through prompts—is the process that feeds the score.","core_discovery":"The central claim is that prompt engineering should be formulated as a multi-objective optimization problem in which task-specific objectives—such as overall and category-level accuracy—are scored alongside objectives derived from the LLM's intrinsic behavior, such as hallucination avoidance, toxicity refusal, and output-format adherence. Algorithm 1 defines F(P) = w1·O1 + w2·O2 + w3·L1 + w4·L2 and selects the prompt that maximizes this score across prompts and models. The iterative process starts from a 20% representative sample, refines prompts directionally, then validates on the remaining 80%. The paper reports a 26% performance gain over initial prompts on the synthetic summarization task, and an average 8% gain across task-specific metrics in the enterprise deployment, with LLM-specific objectives (response adherence, answer consistency) improved in human evaluation.","pith_inferences":["Inference: A natural extension the paper does not test is sensitivity analysis—re-running Algorithm 1 with perturbed weights would show whether the chosen 'optimal' prompt is stable or an artifact of the human-assigned weights.","Inference: The representative-sample result suggests a broader principle: if a 20% sample reproduces ranking trends, prompt engineers can afford exhaustive prompt search on small data before full evaluation; this could be tested across tasks and datasets.","Inference: The multi-objective score could double as a live monitoring signal in production, giving an early warning when a model update or data drift degrades one objective and triggering prompt re-optimization.","Inference: Because the production comparison used human-rated LLM-specific objectives, an automated proxy for response adherence and answer consistency would make the framework fully closed-loop rather than limited to development-time evaluation."],"forward_implications":["Prompt quality becomes a well-defined scalar, so prompts can be compared across different LLMs on the same objective set, including during model migrations.","The 26% gain on the synthetic summarization task indicates that a simple initial prompt leaves much of the model's capability unused, and iterative multi-objective refinement recovers it.","A 20% representative sample suffices for prompt selection because category-wise accuracy trends persisted on the held-out 80%, lowering the cost of prompt iteration.","In production, MODP-optimized prompts improved faithfulness, answer relevance, and context precision by about 8% on average, with response adherence and answer consistency rated higher in human evaluation.","The framework supports adding ethical objectives like toxicity refusal without sacrificing overall accuracy: the best prompts balanced category accuracy and toxicity handling."],"supporting_citations":[{"why":"It supplies the reading-comprehension benchmark on which the 26% gain over initial prompts is measured.","marker":"[22]"},{"why":"It provides the synthetic toxic comments used to create the toxicity objective and the toxicity-added test data.","marker":"[8]"},{"why":"It establishes the 'lost in the middle' failure that motivates LLM-specific objectives for long prompts.","marker":"[11]"},{"why":"It supports the claim that hallucinations are an innate limitation of LLMs, motivating hallucination as an objective.","marker":"[21]"},{"why":"It supports the claim that LLMs are unreliable at self-correction, motivating the human-in-the-loop evaluation used in the framework.","marker":"[9]"},{"why":"It supplies a toxicity evaluation benchmark cited as an LLM-specific objective in prompt development.","marker":"[6]"}],"fun_headline_variants":["Multi-objective prompt scoring lifts performance 26%","Prompt engineering via MODP: 26% gain over initial prompts","LLM-aware directional prompting improves summarization 26%","MODP turns prompt tuning into a measurable optimization","Multi-objective prompts: 26% better than ad-hoc engineering"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The optimal prompt is optimal only relative to human-assigned weights and a roughly 20% sample assumed representative; if the weights are arbitrary or the sample is skewed, the selected prompt may be an artifact of those choices, and the paper gives no sensitivity analysis for either.","fun_headline_variants_meta":{"raw":{"variants":["Multi-objective prompt scoring lifts performance 26%","Prompt engineering via MODP: 26% gain over initial prompts","LLM-aware directional prompting improves summarization 26%","MODP turns prompt tuning into a measurable optimization","Multi-objective prompts: 26% better than ad-hoc engineering"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000486,"raw_usage":{"total_tokens":2376,"prompt_tokens":903,"completion_tokens":1473,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":519,"completion_tokens_details":{"reasoning_tokens":1391}},"tokens_in":519,"tokens_out":1473,"duration_ms":8789,"temperature":1.0,"reasoning_tokens":1391,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-16T10:10:45.738791+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Perturb the four weights in Algorithm 1 by ±0.2 on the same 20% sample and re-select the prompt: if the argmax prompt changes, the claimed 'optimal' prompt is an artifact of the chosen weights rather than a robust outcome.","supporting_citations":[],"review_version":1}