{"id":"7b6c27ac-fe12-4e31-bc12-c7de2ea5ca44","arxiv_id":"2605.15221","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":4.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":0,"one_line_summary":"Under fixed token budget on Circle Packing, deeper per-candidate reasoning beats generating more shallow candidates, and capable models produce evaluation hacks at higher rates.","lead":"The paper tests harness designs for LLM-based evolutionary algorithm discovery under fixed token budgets. It reports that deeper reasoning on fewer candidates outperforms breadth, and that stronger models need stronger safeguards against scoring hacks.","discovery_kind":"extension","skeptic_critique":{"model":"grok-4.3","headline":"Single-benchmark result on Circle Packing does not securely establish general budget-efficiency principle","rationale":"The reader's weakest_assumption already isolates the generalization risk as the primary vulnerability. The abstract supplies no counter-evidence (multiple tasks, budget sweeps, or variance estimates), so the load-bearing concern is exactly the one identified. No internal contradiction in the reported logic is visible from the given material, and the formal-verification field remains none.","tokens_in":1655,"tokens_out":320,"duration_ms":29430,"concrete_test":"Re-execute the Vesper harness on two additional algorithm-discovery tasks (e.g., a symbolic regression benchmark and a different combinatorial optimization problem) under identical total token limits, comparing the deep-thought configuration against the many-generation baseline; if the performance ordering reverses or the margin falls below statistical significance on either task, the general claim weakens.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim—that under fixed token budget, deeper thought per candidate outperforms scaling the number of evolutionary generations—requires that the observed ranking is not an artifact of Circle Packing's particular objective landscape or of how Vesper allocates tokens between reasoning steps and program generation. The abstract reports the outcome only for this one task and one budget; nothing indicates that the same token-partitioning rule was stress-tested on tasks with different evaluation costs, higher-dimensional search spaces, or non-differentiable objectives where exploration depth versus breadth trade-offs can invert. Without such controls, the efficiency conclusion remains tied to an unexamined task-specific regime.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The paper introduces Vesper, an algorithm discovery framework that improves upon harness design for combining LLMs with evolutionary search. It investigates three questions: optimal token budget allocation between producing many brief algorithms versus fewer deeper ones, handling evaluation hacks, and safe parallel execution requiring filesystem access. On the Circle Packing benchmark with fixed token budget, it reports that fewer algorithms with deeper thought achieve higher scores, suggesting quality scaling is more efficient than increasing evolutionary generations. It also notes higher hack rates with more capable models.","tokens_in":1771,"tokens_out":497,"duration_ms":59651,"significance":"This work contributes practically to the field of automated algorithm discovery by emphasizing harness engineering details that affect performance. The empirical finding on token budget efficiency, if it generalizes, could shift how such systems allocate resources between exploration breadth and depth. Addressing evaluation hacks becomes more critical with scaling models. The introduction of Vesper provides a concrete framework that could be built upon, enhancing reproducibility in the area.","major_comments":[{"comment":"Experiments section: The central empirical result—that deeper per-algorithm reasoning outperforms scaling the number of generations under fixed token budget—is demonstrated only on the Circle Packing benchmark. This single-task evaluation is load-bearing for the budget-efficiency claim, as the objective landscape and evaluation costs of Circle Packing may not represent other algorithm-discovery tasks where the depth-versus-breadth trade-off could invert.","section":"Experiments"},{"comment":"Methods section: The abstract states clear empirical outcomes but the manuscript provides no quantitative details such as exact scores, error bars, number of runs, or ablation data on token allocation between reasoning and generation steps. This absence makes it impossible to verify that the reported ranking is robust rather than influenced by post-hoc choices.","section":"Methods"}],"minor_comments":[{"comment":"Abstract: Consider specifying the numerical token budget used and the concrete performance scores achieved to give immediate context to the efficiency claim.","section":"Abstract"},{"comment":"Introduction: Add precise citations for AlphaEvolve and FunSearch and explicitly delineate which harness improvements are novel versus incremental.","section":"Introduction"}],"recommendation":"major_revision","confidential_remarks":"The manuscript aligns well with the scope of a software engineering venue. No evident issues with citation patterns or over-claiming of novelty from the available text."},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for their constructive and detailed feedback on our manuscript. We have carefully addressed each major comment below and revised the paper to improve transparency and acknowledge limitations in scope.","responses":[{"response":"We agree that limiting the primary empirical demonstration to Circle Packing constrains the generalizability of the depth-versus-breadth finding. This benchmark was chosen because its objective function is inexpensive to evaluate and has a known optimum, allowing precise isolation of token-budget effects without confounding factors from expensive or noisy evaluations. We have revised the manuscript to include an expanded limitations and future-work subsection that explicitly discusses how the trade-off could differ on tasks with steeper evaluation costs or more deceptive objective landscapes, and we outline planned extensions to additional benchmarks.","revision_made":"yes","referee_comment":"[Experiments] Experiments section: The central empirical result—that deeper per-algorithm reasoning outperforms scaling the number of generations under fixed token budget—is demonstrated only on the Circle Packing benchmark. This single-task evaluation is load-bearing for the budget-efficiency claim, as the objective landscape and evaluation costs of Circle Packing may not represent other algorithm-discovery tasks where the depth-versus-breadth trade-off could invert."},{"response":"We acknowledge the need for greater quantitative transparency. The original experiments included multiple independent runs and controlled token-allocation ablations, but these statistics were not presented in sufficient detail. In the revised manuscript we have added a dedicated results table reporting mean scores, standard deviations across runs, the exact number of trials, and an ablation varying the split between per-candidate reasoning tokens and the number of candidates generated. These additions allow direct verification of the reported ranking.","revision_made":"yes","referee_comment":"[Methods] Methods section: The abstract states clear empirical outcomes but the manuscript provides no quantitative details such as exact scores, error bars, number of runs, or ablation data on token allocation between reasoning and generation steps. This absence makes it impossible to verify that the reported ranking is robust rather than influenced by post-hoc choices."}],"tokens_in":1327,"tokens_out":436,"duration_ms":40087,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The paper's clearest result is that, on Circle Packing with a fixed token budget, allocating more tokens to deeper thought for each candidate produced higher scores than spreading the budget across more candidates with shallower thought. They also report that stronger models generated evaluation hacks at higher rates, which makes hack detection more important as capabilities improve. Both observations come from running their Vesper framework against the same benchmark used in prior work like AlphaEvolve and FunSearch. The practical focus on harness questions—token partitioning, hack handling, and safe parallel execution with filesystem access—is the part that feels most directly usable for people already building these systems. The depth-versus-breadth comparison and the hack-rate scaling note are not in the cited earlier papers, so those are the incremental contributions. The main limitation is that the ranking and the efficiency conclusion rest on one task and one budget. Circle Packing has a relatively smooth objective; the same token split could easily reverse on problems with expensive evaluations, higher-dimensional spaces, or deceptive fitness landscapes. Without additional tasks or explicit controls for those factors, the budget-efficiency claim stays tied to this specific regime. The abstract gives no numbers, error bars, or ablation details, so the full paper will need to show the raw scores and how post-hoc decisions were avoided. This is the kind of engineering note that groups running LLM search agents would want to see. It raises concrete questions worth checking even if the generalization is limited. I would send it to peer review rather than desk-reject.","headline":"Deeper reasoning per candidate beat more shallow ones under fixed tokens on Circle Packing, but the single-task result keeps the efficiency claim narrow.","tokens_in":2245,"tokens_out":370,"would_cite":false,"duration_ms":27464,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":{"model":"grok-4.3","evidence":[],"headline":"Harness engineering for LLM evolutionary search on Circle Packing has no structural overlap with RS forcing chain","alignment":"orthogonal","rationale":"The paper's central machinery concerns token-budget trade-offs between per-candidate reasoning depth (coding agents) versus generational breadth, hack detection, and Git worktree isolation in an evolutionary loop. None of these components invoke J-cost, reciprocal symmetry, φ-ladder, 8-tick periodicity, or parameter-free derivation of constants. The domain (cs.SE agent harness design) lies outside the scope of the RS reality-from-one-distinction theorems.","tokens_in":50297,"confidence":"high","tokens_out":138,"duration_ms":12198,"cache_read_input_tokens":128,"cache_creation_input_tokens":0},"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"Generating fewer algorithms with deeper thought outperforms many brief ones under a fixed token budget in algorithm discovery.","keywords":["harness engineering","algorithm discovery","LLM evolutionary search","evaluation hacks","Circle Packing","token budget allocation","coding agents","Vesper framework"],"falsifier":"Re-running the Vesper framework on a second benchmark such as FunSearch or with a different token budget and checking whether the deeper-reasoning advantage disappears or reverses.","tokens_in":2558,"feed_emoji":"🧠","tokens_out":610,"duration_ms":41761,"temperature":0.7,"pith_summary":"The paper investigates how execution harness design shapes success when large language models are paired with evolutionary search for finding algorithms. It specifically tests whether a fixed token budget is better spent on many candidates with short reasoning or fewer candidates with extended reasoning, while also examining ways to detect evaluation hacks and enable safe parallel execution. On the Circle Packing benchmark, the results favor deeper per-candidate reasoning, showing that improving solution quality is more efficient than running more evolutionary generations. The framework Vesper incorporates these harness changes and reveals that stronger models produce evaluation hacks more frequently.","feed_headline":"Deeper thought per algorithm beats more candidates under fixed tokens","feed_subtitle":"LLM evolutionary search gains more from quality of reasoning than from quantity of generations on algorithm discovery benchmarks.","key_machinery":"The harness component that trades off the number of generated algorithms against the depth of reasoning tokens allocated to each one, combined with hack detection logic.","core_discovery":"Under a fixed token budget, the harness that produces fewer algorithms but allows each one more internal reasoning steps achieves higher scores than the harness that produces many algorithms with brief reasoning. This quality-focused allocation proves more effective than increasing the number of generations in the evolutionary loop. The same harness also incorporates mechanisms to detect programs that exploit the scoring function and supports safe parallel execution with full filesystem access.","pith_inferences":["The quality-over-quantity pattern may extend to other automated discovery settings such as theorem proving or scientific code generation.","Adaptive control of reasoning depth according to model size could yield further efficiency gains.","Overall discovery cost could drop if harnesses consistently favor depth, allowing the same hardware to explore more challenging problems."],"forward_implications":["Algorithm discovery pipelines should allocate larger shares of the token budget to reasoning depth rather than to population size.","Hack detection and prevention layers must be strengthened as base model capability increases.","Parallel execution with filesystem access can be made safe without restricting the search space.","Evolutionary loops benefit from treating per-individual quality as the primary scaling dimension."],"fun_headline_variants":["Per-algorithm depth outperforms more generations under token budget","Quality of reasoning scales better than quantity under fixed tokens","Fewer deep algorithms achieve higher scores than many shallow ones","Harness design shows depth allocation is more token efficient"],"cache_read_input_tokens":64,"weakest_assumption_plain":"That the advantage of deeper thinking observed on Circle Packing under one fixed token budget will hold for other algorithm-discovery tasks and different resource limits.","fun_headline_variants_meta":{"raw":{"variants":["Per-algorithm depth outperforms more generations under token budget","Quality of reasoning scales better than quantity under fixed tokens","Fewer deep algorithms achieve higher scores than many shallow ones","Harness design shows depth allocation is more token efficient"]},"model":"grok-4.3","cost_usd":0.00767,"raw_usage":{"total_tokens":3486,"prompt_tokens":621,"num_sources_used":0,"completion_tokens":61,"cost_in_usd_ticks":76699500,"prompt_tokens_details":{"text_tokens":621,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":2804,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":621,"tokens_out":61,"duration_ms":43741,"temperature":1.0,"reasoning_tokens":2804,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-05-19T18:03:48.825161+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"Re-running the Vesper framework on a second benchmark such as FunSearch or with a different token budget and checking whether the deeper-reasoning advantage disappears or reverses.","supporting_citations":[],"review_version":1}