scan-v5.json (28526B)
1 { 2 "scan_version": 5, 3 "paper_type": "empirical", 4 "paper": { 5 "title": "The Emperor's New Clothes in Benchmarking? A Rigorous Examination of Mitigation Strategies for LLM Benchmark Data Contamination", 6 "authors": [ 7 "Yifan Sun", 8 "Han Wang", 9 "Dongbai Li", 10 "Gang Wang", 11 "Huan Zhang" 12 ], 13 "year": 2025, 14 "venue": "International Conference on Machine Learning", 15 "arxiv_id": "2503.16402", 16 "doi": "10.48550/arXiv.2503.16402" 17 }, 18 "checklist": { 19 "claims_and_evidence": { 20 "abstract_claims_supported": { 21 "applies": true, 22 "answer": true, 23 "justification": "The abstract's core claims—no strategy significantly improves resistance over vanilla across all benchmarks, none balances fidelity and resistance—are directly supported by Tables 3-4 and the paired hypothesis testing results in Section 5.", 24 "source": "haiku" 25 }, 26 "causal_claims_justified": { 27 "applies": true, 28 "answer": true, 29 "justification": "The paper uses a controlled pipeline: models are verified uncontaminated via three independent detection methods, then manually contaminated via fine-tuning, enabling genuine causal comparison of pre- vs. post-contamination evaluation vectors.", 30 "source": "haiku" 31 }, 32 "generalization_bounded": { 33 "applies": true, 34 "answer": false, 35 "justification": "The conclusion 'no existing strategy significantly improves resistance' is stated broadly but tested on only 5 benchmarks (all multiple-choice or short open-ended) and 20 strategies; code-generation, long-form, or domain-specific benchmarks are excluded without acknowledgment.", 36 "source": "haiku" 37 }, 38 "alternative_explanations_discussed": { 39 "applies": true, 40 "answer": false, 41 "justification": "The paper does not discuss alternative explanations for why mitigation fails—e.g., that fine-tuning contamination may not represent real pre-training contamination, or that detection methods used to verify 'clean' status might themselves have false negatives.", 42 "source": "haiku" 43 }, 44 "proxy_outcome_distinction": { 45 "applies": true, 46 "answer": true, 47 "justification": "The paper explicitly argues that scalar accuracy is a proxy that misrepresents question-level evaluation alignment, and introduces fidelity/resistance metrics tied to normalized Hamming distance on binary evaluation vectors to address this.", 48 "source": "haiku" 49 } 50 }, 51 "limitations_and_scope": { 52 "limitations_section_present": { 53 "applies": true, 54 "answer": false, 55 "justification": "There is only a brief 'Impact Statement' paragraph with no dedicated limitations or threats-to-validity section discussing methodological constraints.", 56 "source": "haiku" 57 }, 58 "threats_to_validity_specific": { 59 "applies": true, 60 "answer": false, 61 "justification": "No specific threats are enumerated; the impact statement mentions 'methodological advancements' and 'societal implications' but identifies no threats such as limited benchmark diversity, fine-tuning vs. pre-training contamination gap, or detection method false negatives.", 62 "source": "haiku" 63 }, 64 "scope_boundaries_stated": { 65 "applies": true, 66 "answer": false, 67 "justification": "The paper states it focuses on strategies that update existing benchmarks rather than creating new ones, but does not explicitly bound what the results cannot show (e.g., inapplicability to code or generative benchmarks, or to contamination from pre-training rather than fine-tuning).", 68 "source": "haiku" 69 } 70 }, 71 "conflicts_of_interest": { 72 "funding_disclosed": { 73 "applies": true, 74 "answer": false, 75 "justification": "No funding source is disclosed anywhere in the paper.", 76 "source": "haiku" 77 }, 78 "affiliations_disclosed": { 79 "applies": true, 80 "answer": true, 81 "justification": "All five authors are affiliated with University of Illinois Urbana-Champaign, stated in the author footnote.", 82 "source": "haiku" 83 }, 84 "funder_independent_of_outcome": { 85 "applies": false, 86 "answer": false, 87 "justification": "No funding is disclosed, so this criterion is not applicable.", 88 "source": "haiku" 89 }, 90 "financial_interests_declared": { 91 "applies": true, 92 "answer": false, 93 "justification": "No competing interests statement or declaration of financial interests appears in the paper.", 94 "source": "haiku" 95 } 96 }, 97 "scope_and_framing": { 98 "key_terms_defined": { 99 "applies": true, 100 "answer": true, 101 "justification": "BDC is defined in the introduction; fidelity and contamination resistance are formally defined in Section 3 with mathematical notation; contamination scenarios (clean, contaminated, mitigated) are defined in Table 1.", 102 "source": "haiku" 103 }, 104 "intended_contribution_clear": { 105 "applies": true, 106 "answer": true, 107 "justification": "The paper explicitly states three contributions: two novel metrics (fidelity and contamination resistance), a controlled pipeline with triple contamination verification and two contamination recipes, and empirical findings across 10 LLMs, 5 benchmarks, and 20 strategies.", 108 "source": "haiku" 109 }, 110 "engagement_with_prior_work": { 111 "applies": true, 112 "answer": true, 113 "justification": "Section 2 situates the work relative to BDC detection and mitigation literature, Section 3 identifies specific limitations of prior accuracy-drop and accuracy-matching assessment approaches used by Zhu et al. and Ying et al., and the paper directly evaluates all 20 previously proposed strategies.", 114 "source": "haiku" 115 } 116 } 117 }, 118 "type_checklist": { 119 "empirical": { 120 "artifacts": { 121 "code_released": { 122 "applies": true, 123 "answer": true, 124 "justification": "The abstract explicitly states 'Our code repository is available at https://github.com/ASTRAL-Group/BDC_mitigation_assessment'.", 125 "source": "haiku" 126 }, 127 "data_released": { 128 "applies": true, 129 "answer": true, 130 "justification": "All five benchmarks used (Arc-C, MMLU, TruthfulQA, GSM8K, RepliQA) are publicly available standard datasets; no proprietary datasets were created.", 131 "source": "haiku" 132 }, 133 "environment_specified": { 134 "applies": true, 135 "answer": false, 136 "justification": "GPU hardware is mentioned (9× NVIDIA L40S) and optimizer details are given, but no requirements.txt, Dockerfile, or dependency list is provided in the paper.", 137 "source": "haiku" 138 }, 139 "reproduction_instructions": { 140 "applies": true, 141 "answer": false, 142 "justification": "The methodology is detailed but no step-by-step reproduction instructions are provided in the paper; the reader is pointed to the GitHub repository without any numbered workflow.", 143 "source": "haiku" 144 } 145 }, 146 "statistical_methodology": { 147 "confidence_intervals_or_error_bars": { 148 "applies": true, 149 "answer": false, 150 "justification": "Tables 3 and 4 report averages across 10 LLMs but provide no standard deviations, error bars, or confidence intervals alongside the point estimates.", 151 "source": "haiku" 152 }, 153 "significance_tests": { 154 "applies": true, 155 "answer": true, 156 "justification": "One-sided paired hypothesis testing at the 0.05 significance level is used throughout Section 5 to determine whether resistance scores significantly exceed the vanilla baseline; results are highlighted green in the tables.", 157 "source": "haiku" 158 }, 159 "effect_sizes_reported": { 160 "applies": true, 161 "answer": true, 162 "justification": "Fidelity and resistance scores are reported as absolute proportions (e.g., vanilla resistance 0.923 vs. MPA 0.921 under mild contamination), providing interpretable effect magnitudes beyond statistical significance.", 163 "source": "haiku" 164 }, 165 "sample_size_justified": { 166 "applies": true, 167 "answer": false, 168 "justification": "The 10 LLMs are selected based on contamination status from 14 candidates, but no power analysis or justification for why 10 models provides adequate statistical power for the paired tests is given.", 169 "source": "haiku" 170 }, 171 "variance_reported": { 172 "applies": true, 173 "answer": false, 174 "justification": "All main results in Tables 3 and 4 report means averaged over 10 LLMs with no standard deviations or variance measures.", 175 "source": "haiku" 176 } 177 }, 178 "evaluation_design": { 179 "baselines_included": { 180 "applies": true, 181 "answer": true, 182 "justification": "The 'Vanilla' condition (no benchmark update) is used as the baseline in all comparisons and is included in every results table.", 183 "source": "haiku" 184 }, 185 "baselines_contemporary": { 186 "applies": true, 187 "answer": true, 188 "justification": "The vanilla baseline is appropriate for this study type; the paper also includes all contemporary published mitigation strategies (20 total) as comparison points.", 189 "source": "haiku" 190 }, 191 "ablation_study": { 192 "applies": true, 193 "answer": true, 194 "justification": "Individual strategies are tested separately and in combination (e.g., MPA = S2+S3+S4+S9+S10+S11), and mild vs. intensive contamination recipes provide ablation of contamination severity; a 25-shot vs. zero-shot evaluation ablation is also conducted on Arc-C.", 195 "source": "haiku" 196 }, 197 "multiple_metrics": { 198 "applies": true, 199 "answer": true, 200 "justification": "Both fidelity and contamination resistance are reported, alongside accuracy inflation and proportion of retained correctness as validation metrics.", 201 "source": "haiku" 202 }, 203 "human_evaluation": { 204 "applies": false, 205 "answer": false, 206 "justification": "No systematic human evaluation of system outputs is conducted; Appendix C.4.2 shows one qualitative expert check but this is illustrative only.", 207 "source": "haiku" 208 }, 209 "held_out_test_set": { 210 "applies": true, 211 "answer": true, 212 "justification": "RepliQA, released December 2024, is used as a held-out benchmark guaranteed not to appear in any tested model's training data due to its recent release and non-factual fictional content.", 213 "source": "haiku" 214 }, 215 "per_category_breakdown": { 216 "applies": true, 217 "answer": true, 218 "justification": "Results are broken down per benchmark (5 benchmarks), per mitigation strategy (20 strategies), and per contamination severity (mild vs. intensive) across all tables.", 219 "source": "haiku" 220 }, 221 "failure_cases_discussed": { 222 "applies": true, 223 "answer": true, 224 "justification": "Tables 5 and 6 provide qualitative examples of low-fidelity failures where strategies alter problem complexity or introduce contradictions; Appendix C.4.2 shows an LLM generating incorrect answers.", 225 "source": "haiku" 226 }, 227 "negative_results_reported": { 228 "applies": true, 229 "answer": true, 230 "justification": "The central finding of the paper is a negative result: no existing mitigation strategy significantly outperforms the vanilla baseline across all benchmarks, and none achieves both high fidelity and high resistance.", 231 "source": "haiku" 232 } 233 }, 234 "setup_transparency": { 235 "model_versions_specified": { 236 "applies": true, 237 "answer": true, 238 "justification": "Table 7 lists exact model versions with parameter counts and developers for all 14 candidate models (e.g., Llama-3.2-3B-Instruct, Qwen2.5-14B-Instruct, Phi-3-medium-128k-instruct).", 239 "source": "haiku" 240 }, 241 "prompts_provided": { 242 "applies": true, 243 "answer": true, 244 "justification": "The evaluation template format is provided in Appendix C.5, and the complete 5-shot GSM8K prompt is reproduced verbatim; mitigation strategy prompts are implicit in GPT-4o generation but illustrated in Appendix C.4.1 with examples.", 245 "source": "haiku" 246 }, 247 "hyperparameters_reported": { 248 "applies": true, 249 "answer": true, 250 "justification": "Table 12 provides detailed fine-tuning hyperparameters including optimizer (AdamW), batch size, learning rates (1e-5/3e-5), LR schedule, weight decay, warmup ratio, and epochs.", 251 "source": "haiku" 252 }, 253 "scaffolding_described": { 254 "applies": false, 255 "answer": false, 256 "justification": "No agentic scaffolding is used; this is a benchmark evaluation study with standard LM inference.", 257 "source": "haiku" 258 }, 259 "data_preprocessing_documented": { 260 "applies": true, 261 "answer": true, 262 "justification": "Benchmark subsets and splits are specified in Table 8; contamination recipes detail how benchmark data is mixed with OpenOrca or used alone; evaluation follows LM Eval Harness with zero-shot or 5-shot prompting.", 263 "source": "haiku" 264 } 265 }, 266 "data_integrity": { 267 "raw_data_available": { 268 "applies": true, 269 "answer": false, 270 "justification": "The evaluation vectors (binary per-question correctness) that are the foundation of all metrics are not explicitly released; only the code repository is provided without confirmed data dumps.", 271 "source": "haiku" 272 }, 273 "data_collection_described": { 274 "applies": true, 275 "answer": true, 276 "justification": "All benchmarks are publicly available standard datasets; their subsets, splits, and sample counts are documented in Table 8; contamination procedure using OpenOrca mixing is fully described.", 277 "source": "haiku" 278 }, 279 "recruitment_methods_described": { 280 "applies": false, 281 "answer": false, 282 "justification": "No human participants are recruited; the study uses existing model checkpoints and benchmark datasets.", 283 "source": "haiku" 284 }, 285 "data_pipeline_documented": { 286 "applies": true, 287 "answer": true, 288 "justification": "The full pipeline from uncontaminated model selection (triple detection method filtering) through contamination validation (accuracy inflation, retained correctness, perplexity checks) to evaluation vector computation is documented in Section 4 and Appendices C.2-C.3.", 289 "source": "haiku" 290 } 291 }, 292 "contamination": { 293 "training_cutoff_stated": { 294 "applies": true, 295 "answer": false, 296 "justification": "The paper verifies contamination status empirically via three detection methods rather than stating training cutoffs for the 10 LLMs; exact training cutoff dates are not reported.", 297 "source": "haiku" 298 }, 299 "train_test_overlap_discussed": { 300 "applies": true, 301 "answer": true, 302 "justification": "This is the central topic of the paper; three independent BDC detection methods are applied to verify absence of overlap before manual contamination is introduced.", 303 "source": "haiku" 304 }, 305 "benchmark_contamination_addressed": { 306 "applies": true, 307 "answer": true, 308 "justification": "The entire pipeline is designed around contamination: only model-benchmark pairs passing all three detection methods are used, and RepliQA is selected specifically because its post-training-cutoff release guarantees no contamination.", 309 "source": "haiku" 310 } 311 }, 312 "human_studies": { 313 "pre_registered": { 314 "applies": false, 315 "answer": false, 316 "justification": "No human participants.", 317 "source": "haiku" 318 }, 319 "irb_or_ethics_approval": { 320 "applies": false, 321 "answer": false, 322 "justification": "No human participants.", 323 "source": "haiku" 324 }, 325 "demographics_reported": { 326 "applies": false, 327 "answer": false, 328 "justification": "No human participants.", 329 "source": "haiku" 330 }, 331 "inclusion_exclusion_criteria": { 332 "applies": false, 333 "answer": false, 334 "justification": "No human participants.", 335 "source": "haiku" 336 }, 337 "randomization_described": { 338 "applies": false, 339 "answer": false, 340 "justification": "No human participants.", 341 "source": "haiku" 342 }, 343 "blinding_described": { 344 "applies": false, 345 "answer": false, 346 "justification": "No human participants.", 347 "source": "haiku" 348 }, 349 "attrition_reported": { 350 "applies": false, 351 "answer": false, 352 "justification": "No human participants.", 353 "source": "haiku" 354 } 355 }, 356 "cost_and_practicality": { 357 "inference_cost_reported": { 358 "applies": true, 359 "answer": false, 360 "justification": "GPT-4o-2024-08-06 is used to apply all 20 mitigation strategies and GPT-4o-mini for RepliQA evaluation, but no API cost or inference latency figures are reported.", 361 "source": "haiku" 362 }, 363 "compute_budget_stated": { 364 "applies": true, 365 "answer": false, 366 "justification": "GPU hardware (9× NVIDIA L40S) is mentioned in Table 12 but total compute hours or budget for running 10 LLMs × 5 benchmarks × 20 strategies × 2 contamination recipes is not stated.", 367 "source": "haiku" 368 } 369 } 370 } 371 }, 372 "claims": [ 373 { 374 "claim": "No existing BDC mitigation strategy achieves statistically significantly higher contamination resistance than the vanilla (no update) baseline across all five benchmarks.", 375 "evidence": "One-sided paired hypothesis tests at p<0.05 across 10 LLMs show that while some strategies (MPA, ITD) achieve significant improvements on a subset of benchmarks, none does so across all five; results highlighted in Tables 3-4.", 376 "supported": "strong" 377 }, 378 { 379 "claim": "Accuracy drop and accuracy matching are insufficient and potentially misleading assessment methods for BDC mitigation.", 380 "evidence": "Figure 2 demonstrates that accuracy matching can succeed (scalar accuracy aligns) while question-level evaluation vectors diverge substantially, undermining the validity of the assessment.", 381 "supported": "strong" 382 }, 383 { 384 "claim": "There is a fundamental fidelity-resistance tradeoff: no strategy achieves high scores on both metrics simultaneously.", 385 "evidence": "Figure 4 shows all strategies clustering either in the high-fidelity/low-resistance or low-fidelity/high-resistance regions, with no strategy reaching the upper-right quadrant.", 386 "supported": "strong" 387 }, 388 { 389 "claim": "Semantic-altering strategies achieve significantly higher resistance (~0.97) than vanilla but at the cost of ~0.15 lower fidelity compared to semantic-preserving strategies.", 390 "evidence": "Table 4 shows Remember-Understand Extension achieving resistance of 0.979/0.976 (mild/intensive) but fidelity of only 0.766 on Arc-C, vs. vanilla fidelity of 1.000.", 391 "supported": "strong" 392 }, 393 { 394 "claim": "Minor semantic-preserving modifications (synonym replacement, syntactic changes, typos) do not improve contamination resistance beyond the vanilla case.", 395 "evidence": "Table 3 shows resistance scores for S3-S5 are not highlighted green (not significantly above vanilla) on most benchmarks; S4 synonym replacement shows 0.924/0.924 vs. vanilla 0.923/0.882 under mild contamination on Arc-C.", 396 "supported": "strong" 397 }, 398 { 399 "claim": "The paper's pipeline design—triple contamination verification before manual contamination—is an improvement over prior work that failed to confirm uncontaminated status.", 400 "evidence": "Section 4.1 notes that existing accuracy-matching frameworks (Zhu et al. 2023b, 2024b, Ying et al. 2024) do not confirm uncontaminated status before introducing manual contamination, introducing noise into their 'clean' baselines.", 401 "supported": "moderate" 402 } 403 ], 404 "methodology_tags": [ 405 "benchmark-eval", 406 "empirical" 407 ], 408 "key_findings": "No existing benchmark data contamination (BDC) mitigation strategy provides statistically significant improvement in contamination resistance over simply leaving benchmarks unchanged, across all five tested benchmarks. There is a fundamental fidelity-resistance tradeoff: strategies achieving high resistance (semantic-altering methods reaching ~0.97) do so by substantially altering benchmark semantics (fidelity ~0.66-0.77), making the updated benchmark measure different capabilities than the original. The paper's proposed question-level metrics (fidelity and contamination resistance using normalized Hamming distance) expose this tradeoff invisible to prior accuracy-based assessments, and reveal that existing approaches evaluated with accuracy matching or accuracy drop can produce misleading conclusions about mitigation effectiveness.", 409 "red_flags": [ 410 { 411 "flag": "No limitations section", 412 "detail": "The paper contains only a brief 'Impact Statement' paragraph with no dedicated limitations or threats-to-validity section; no discussion of whether fine-tuning contamination generalizes to pre-training contamination, or of detection method false-negative rates." 413 }, 414 { 415 "flag": "No variance reported for main results", 416 "detail": "Tables 3 and 4 report averages across 10 LLMs with no standard deviations or error bars, making it impossible to assess consistency of results across models." 417 }, 418 { 419 "flag": "Generalization overclaim", 420 "detail": "The conclusion 'no existing strategy significantly improves resistance across all benchmarks' is stated broadly but tested only on multiple-choice and short-answer benchmarks; code generation, instruction-following, and domain-specific benchmarks are absent." 421 }, 422 { 423 "flag": "Funding not disclosed", 424 "detail": "No funding source is mentioned anywhere in the paper, making it impossible to assess potential conflicts of interest." 425 }, 426 { 427 "flag": "No sample size justification", 428 "detail": "10 LLMs is used for paired hypothesis testing but no power analysis or justification is provided for why this sample size is adequate." 429 } 430 ], 431 "cited_papers": [ 432 { 433 "title": "Inference-time decontamination: Reusing leaked benchmarks for large language model evaluation", 434 "relevance": "Proposes ITD strategy (S13), one of the primary mitigation strategies evaluated in this paper." 435 }, 436 { 437 "title": "Dynamic evaluation of large language models by meta probing agents", 438 "relevance": "Proposes MPA strategy (S14), the most aggressive semantic-preserving mitigation evaluated." 439 }, 440 { 441 "title": "Clean-eval: Clean evaluation on contaminated large language models", 442 "relevance": "Proposes Clean-Eval strategy (S12) and accuracy-matching assessment approach critiqued in this paper." 443 }, 444 { 445 "title": "Automating dataset updates towards reliable and timely evaluation of large language models", 446 "relevance": "Proposes the four semantic-altering strategies (S17-S20) evaluated in Table 4." 447 }, 448 { 449 "title": "Detecting pretraining data from large language models (Min-K% Prob)", 450 "relevance": "One of three BDC detection methods used to verify uncontaminated model-benchmark pairs in the pipeline." 451 }, 452 { 453 "title": "Proving test set contamination in black box language models (Sharded Rank Comparison Test)", 454 "relevance": "Second detection method used for triple-verification of uncontaminated status." 455 }, 456 { 457 "title": "Investigating data contamination in modern benchmarks for large language models (TS-Guessing)", 458 "relevance": "Third detection method used for triple-verification of uncontaminated status." 459 }, 460 { 461 "title": "Benchmark data contamination of large language models: A survey", 462 "relevance": "Survey providing broader context for BDC detection and mitigation landscape that this paper builds on." 463 }, 464 { 465 "title": "RepliQA: A question-answering dataset for benchmarking LLMs on unseen reference content", 466 "relevance": "Recently released benchmark used as the guaranteed-uncontaminated test case in the controlled pipeline." 467 }, 468 { 469 "title": "Measuring massive multitask language understanding (MMLU)", 470 "relevance": "One of five benchmarks used in experiments; the benchmark with most extensive mitigation strategy results." 471 } 472 ], 473 "engagement_factors": { 474 "practical_relevance": { 475 "score": 3, 476 "justification": "Directly actionable for anyone building or evaluating LLMs: the finding that no mitigation strategy reliably works undermines a common practice in benchmark maintenance." 477 }, 478 "surprise_contrarian": { 479 "score": 3, 480 "justification": "Challenges the implicit assumption underlying much BDC mitigation work—that paraphrasing or modifying benchmarks actually reduces contamination effects—with rigorous evidence that it mostly does not." 481 }, 482 "fear_safety": { 483 "score": 1, 484 "justification": "Benchmark contamination inflating performance metrics is a reliability concern but not a direct AI safety or misuse risk." 485 }, 486 "drama_conflict": { 487 "score": 2, 488 "justification": "The 'Emperor's New Clothes' framing explicitly positions the paper as debunking prior mitigation work; the finding that published strategies don't work has a controversy angle." 489 }, 490 "demo_ability": { 491 "score": 2, 492 "justification": "Code is released on GitHub and benchmarks are public; a practitioner could reproduce the core analysis, though the compute requirements (10 LLMs, fine-tuning) are substantial." 493 }, 494 "brand_recognition": { 495 "score": 1, 496 "justification": "UIUC affiliation and ICML venue are respectable but no famous lab, product, or highly recognized name is associated." 497 } 498 }, 499 "hn_data": { 500 "threads": [ 501 { 502 "hn_id": "45489599", 503 "title": "Tutorials for Sandia's Lammps Simulation Package", 504 "points": 8, 505 "comments": 1, 506 "url": "https://news.ycombinator.com/item?id=45489599" 507 }, 508 { 509 "hn_id": "43454946", 510 "title": "Exploring Hidden Reasoning Process of Large Language Models by Misleading Them", 511 "points": 8, 512 "comments": 0, 513 "url": "https://news.ycombinator.com/item?id=43454946" 514 }, 515 { 516 "hn_id": "47533914", 517 "title": "An Efficient Heterogeneous Co-Design for Fine-Tuning on a Single GPU", 518 "points": 3, 519 "comments": 0, 520 "url": "https://news.ycombinator.com/item?id=47533914" 521 }, 522 { 523 "hn_id": "45015577", 524 "title": "AetherCode: Evaluating LLMs' Ability to Win in Premier Programming Competitions", 525 "points": 2, 526 "comments": 1, 527 "url": "https://news.ycombinator.com/item?id=45015577" 528 }, 529 { 530 "hn_id": "26657061", 531 "title": "Intel HEXL: Accelerating Homomorphic Encryption with Intel AVX512-IFMA52", 532 "points": 2, 533 "comments": 0, 534 "url": "https://news.ycombinator.com/item?id=26657061" 535 }, 536 { 537 "hn_id": "45010576", 538 "title": "AetherCode: Evaluating LLMs' Ability to Win in Premier Programming Competitions", 539 "points": 1, 540 "comments": 0, 541 "url": "https://news.ycombinator.com/item?id=45010576" 542 } 543 ], 544 "top_points": 8, 545 "total_points": 24, 546 "total_comments": 2 547 } 548 }