scan-v5.json (28350B)
1 { 2 "scan_version": 5, 3 "paper_type": "empirical", 4 "paper": { 5 "title": "HAI-Eval: Measuring Human-AI Synergy in Collaborative Coding", 6 "authors": [ 7 "Hanjun Luo", 8 "Chiming Ni", 9 "Jiaheng Wen", 10 "Zhimu Huang", 11 "Yiran Wang", 12 "Bingduo Liao", 13 "Sylvia Chung", 14 "Yingbin Jin", 15 "Xinfeng Li", 16 "Wenyuan Xu", 17 "XiaoFeng Wang", 18 "Hanan Salam" 19 ], 20 "year": 2025, 21 "venue": "arXiv.org", 22 "arxiv_id": "2512.04111", 23 "doi": "10.48550/arXiv.2512.04111" 24 }, 25 "checklist": { 26 "claims_and_evidence": { 27 "abstract_claims_supported": { 28 "applies": true, 29 "answer": true, 30 "justification": "Core quantitative claims (0.67% LLM pass rate, 18.89% human-only, 31.11% human-AI) are directly reported in Table 2; benchmark design claims (45 templates, 450 instances, 45 participants, 5 LLMs) are substantiated throughout the paper.", 31 "source": "haiku" 32 }, 33 "causal_claims_justified": { 34 "applies": true, 35 "answer": false, 36 "justification": "The paper claims C2 'statistically significantly improves' over CH but provides no test statistics, p-values, or identification of which statistical test was used despite stating 'Statistical comparisons use appropriate tests with averaged results.'", 37 "source": "haiku" 38 }, 39 "generalization_bounded": { 40 "applies": true, 41 "answer": true, 42 "justification": "Appendix A explicitly bounds scope to Python, Copilot-supported models as of July 26 2025, and East Asian university students, noting 'generalizability may be limited'; the main text repeats this caveat.", 43 "source": "haiku" 44 }, 45 "alternative_explanations_discussed": { 46 "applies": true, 47 "answer": false, 48 "justification": "LLM failure is attributed exclusively to 'higher-order reasoning' limitations; alternatives such as Copilot interface constraints, prompt-engineering gaps, or task-design artifacts are not considered.", 49 "source": "haiku" 50 }, 51 "proxy_outcome_distinction": { 52 "applies": true, 53 "answer": false, 54 "justification": "Binary pass/fail rates on dynamically generated benchmark tasks are conflated with the broader construct of 'human-AI synergy' and 'developer competencies in the AI era' without discussing what these metrics do and do not capture.", 55 "source": "haiku" 56 } 57 }, 58 "limitations_and_scope": { 59 "limitations_section_present": { 60 "applies": true, 61 "answer": true, 62 "justification": "Appendix A is a dedicated 'Limitation & Future Work' section listing four specific limitations (Python-only, Copilot model constraint, demographic homogeneity, binary metric transparency loss).", 63 "source": "haiku" 64 }, 65 "threats_to_validity_specific": { 66 "applies": true, 67 "answer": true, 68 "justification": "Specific threats include Python-only scope, Copilot constraint excluding o3/GPT-5/Deepseek/Llama/Qwen by name, East Asian student demographic limiting generalizability, and loss of interpretability from binary metric conversion.", 69 "source": "haiku" 70 }, 71 "scope_boundaries_stated": { 72 "applies": true, 73 "answer": true, 74 "justification": "Explicit scope boundaries stated: Python only, models available in Copilot Agent mode as of July 26 2025, East Asian CS students or recent graduates aged 19-26.", 75 "source": "haiku" 76 } 77 }, 78 "conflicts_of_interest": { 79 "funding_disclosed": { 80 "applies": true, 81 "answer": false, 82 "justification": "No funding acknowledgment or grant disclosure appears anywhere in the paper.", 83 "source": "haiku" 84 }, 85 "affiliations_disclosed": { 86 "applies": true, 87 "answer": true, 88 "justification": "All 12 author affiliations are listed on the title page (NYU Abu Dhabi, NTU, UIUC, Harvard, Zhejiang University, UESTC, BJUT, HKPolyU).", 89 "source": "haiku" 90 }, 91 "funder_independent_of_outcome": { 92 "applies": true, 93 "answer": false, 94 "justification": "Funding source is not disclosed, so funder independence cannot be assessed; the benchmark evaluates commercial products (GitHub Copilot, Claude, GPT) but no industry funding or competing interests are declared.", 95 "source": "haiku" 96 }, 97 "financial_interests_declared": { 98 "applies": true, 99 "answer": false, 100 "justification": "No competing interests statement or financial interest declaration is present anywhere in the paper.", 101 "source": "haiku" 102 } 103 }, 104 "scope_and_framing": { 105 "key_terms_defined": { 106 "applies": true, 107 "answer": true, 108 "justification": "'Collaboration-Necessary' is formally defined with mathematical constraints (Equation 1); 'Ecological Validity' is operationalized across three dimensions; 'higher-order reasoning' is grounded in Relational Complexity Theory (Halford et al., 1998).", 109 "source": "haiku" 110 }, 111 "intended_contribution_clear": { 112 "applies": true, 113 "answer": true, 114 "justification": "Three explicit contributions are enumerated: (1) Unified Benchmark, (2) Dual Interfaces for human and LLM evaluation, (3) Empirical Validation quantifying human-AI synergy.", 115 "source": "haiku" 116 }, 117 "engagement_with_prior_work": { 118 "applies": true, 119 "answer": true, 120 "justification": "Section 2 covers three distinct bodies of work (developer assessment platforms, LLM coding benchmarks, user studies in AI-assisted coding), situating HAI-Eval's contributions against each and identifying gaps.", 121 "source": "haiku" 122 } 123 } 124 }, 125 "type_checklist": { 126 "empirical": { 127 "artifacts": { 128 "code_released": { 129 "applies": true, 130 "answer": false, 131 "justification": "The abstract uses future tense ('will be openly accessible') and no repository URL is provided; the paper is marked 'Work in progress.'", 132 "source": "haiku" 133 }, 134 "data_released": { 135 "applies": true, 136 "answer": false, 137 "justification": "Section 4.4 says the 450-instance dataset 'is released as a standalone resource' but no URL is provided, and the abstract's future-tense language and preprint status suggest public availability is not confirmed.", 138 "source": "haiku" 139 }, 140 "environment_specified": { 141 "applies": true, 142 "answer": false, 143 "justification": "GitHub Codespaces with devcontainer files and Docker are mentioned, but no specific requirements.txt, Dockerfile, or versioned dependency manifest is included in the paper.", 144 "source": "haiku" 145 }, 146 "reproduction_instructions": { 147 "applies": true, 148 "answer": false, 149 "justification": "The evaluation workflow is described conceptually (Section 4.4) but no step-by-step instructions for rerunning the full benchmark are provided in the paper itself.", 150 "source": "haiku" 151 } 152 }, 153 "statistical_methodology": { 154 "confidence_intervals_or_error_bars": { 155 "applies": true, 156 "answer": false, 157 "justification": "Tables 1, 2, 6, 7, 8 report only point estimates; no confidence intervals or error bars appear for any main performance result.", 158 "source": "haiku" 159 }, 160 "significance_tests": { 161 "applies": true, 162 "answer": false, 163 "justification": "The paper asserts 'statistically significant improvement' for C2 vs CH but provides no test statistics, p-values, or specification of which test was used.", 164 "source": "haiku" 165 }, 166 "effect_sizes_reported": { 167 "applies": true, 168 "answer": true, 169 "justification": "Absolute pass rate differences are reported with clear baselines (e.g., 18.89% CH → 31.11% C2, Δ=+12.22pp), constituting effect size reporting.", 170 "source": "haiku" 171 }, 172 "sample_size_justified": { 173 "applies": true, 174 "answer": false, 175 "justification": "The choice of 45 participants is motivated by Latin Square counterbalancing logistics, not a power analysis; no statistical justification for this sample size is provided.", 176 "source": "haiku" 177 }, 178 "variance_reported": { 179 "applies": true, 180 "answer": false, 181 "justification": "Standard deviations appear only for subjective Likert metrics (Table 9); all main performance tables (1, 2, 6-8) report no variance measures.", 182 "source": "haiku" 183 } 184 }, 185 "evaluation_design": { 186 "baselines_included": { 187 "applies": true, 188 "answer": true, 189 "justification": "CH (human-only) and C0 (fully autonomous AI) serve as explicit baselines for the C2 (human-AI collaboration) condition.", 190 "source": "haiku" 191 }, 192 "baselines_contemporary": { 193 "applies": true, 194 "answer": true, 195 "justification": "Models include Claude-Sonnet-4, GPT-4.1, GPT-4o, Claude-Sonnet-3.7, and Gemini-2.5-Pro — all SOTA Copilot-supported models as of the evaluation date (July 26, 2025).", 196 "source": "haiku" 197 }, 198 "ablation_study": { 199 "applies": true, 200 "answer": true, 201 "justification": "C1 (minimally-intervened AI) is an explicit ablation of C2 isolating procedural execution failures from reasoning failures; C1 vs C0 isolates the impact of environmental assistance.", 202 "source": "haiku" 203 }, 204 "multiple_metrics": { 205 "applies": true, 206 "answer": true, 207 "justification": "Results reported using Overall Pass@1, Overall Pass@10, Partial Pass@1, Partial Pass@10, Completion Time, and Token Usage.", 208 "source": "haiku" 209 }, 210 "human_evaluation": { 211 "applies": true, 212 "answer": true, 213 "justification": "A within-subject user study with 45 expert participants evaluates task performance under human-only (CH) and human-AI collaboration (C2) conditions.", 214 "source": "haiku" 215 }, 216 "held_out_test_set": { 217 "applies": true, 218 "answer": true, 219 "justification": "Final scoring uses 'a comprehensive suite of hidden test cases executed on the backend,' distinct from visible unit tests provided during development.", 220 "source": "haiku" 221 }, 222 "per_category_breakdown": { 223 "applies": true, 224 "answer": true, 225 "justification": "Results are broken down by difficulty level (Easy/Medium/Hard, Tables 2 and 6) and professional track (SDE/MLE/DS, Tables 7 and 8).", 226 "source": "haiku" 227 }, 228 "failure_cases_discussed": { 229 "applies": true, 230 "answer": true, 231 "justification": "Appendix L presents a case study of AI failure modes (distracted by legacy code, unable to extract column-merging logic from underspecified requirements).", 232 "source": "haiku" 233 }, 234 "negative_results_reported": { 235 "applies": true, 236 "answer": true, 237 "justification": "Near-zero LLM autonomous pass rates (0.67% best-case, 0% for GPT-4o) are the central finding, prominently reported across all tables.", 238 "source": "haiku" 239 } 240 }, 241 "setup_transparency": { 242 "model_versions_specified": { 243 "applies": true, 244 "answer": false, 245 "justification": "Models identified by marketing names (Claude-Sonnet-4, GPT-4.1, Gemini-2.5-Pro) with an evaluation date of July 26, 2025 but no specific API model IDs or snapshot version strings.", 246 "source": "haiku" 247 }, 248 "prompts_provided": { 249 "applies": true, 250 "answer": false, 251 "justification": "Appendix C shows task-generation agent prompts, and Appendix F shows example task READMEs described as 'short summaries of the original text' — the actual verbose task texts sent to LLMs are not provided.", 252 "source": "haiku" 253 }, 254 "hyperparameters_reported": { 255 "applies": true, 256 "answer": true, 257 "justification": "GPT-4.1 task generation agent: temperature=0.7, top_p=0.9, max_tokens=8192 (Appendix C.1); LLM evaluation uses Copilot defaults, which cannot be customized — explicitly stated.", 258 "source": "haiku" 259 }, 260 "scaffolding_described": { 261 "applies": true, 262 "answer": true, 263 "justification": "HAI-EC (VS Code extension) scaffolding pipeline is described in detail: environment build, Copilot invocation via VS Code API, README upload, iterative test-feedback loop, and environment cleanup.", 264 "source": "haiku" 265 }, 266 "data_preprocessing_documented": { 267 "applies": true, 268 "answer": true, 269 "justification": "The template-to-instance generation pipeline (TechnicalParameterTool → ImplementationConstraintTool → contextual tools) and the two-stage quality validation protocol are documented in Sections 4.2 and Appendix D.", 270 "source": "haiku" 271 } 272 }, 273 "data_integrity": { 274 "raw_data_available": { 275 "applies": true, 276 "answer": false, 277 "justification": "Individual participant scores, per-task results, or raw operational logs are not made available; only aggregated results are reported in the paper.", 278 "source": "haiku" 279 }, 280 "data_collection_described": { 281 "applies": true, 282 "answer": true, 283 "justification": "Data collection via submission shell script, FastAPI backend endpoint, hidden test case execution, and operational log recording are all described in Section 4.3.", 284 "source": "haiku" 285 }, 286 "recruitment_methods_described": { 287 "applies": true, 288 "answer": true, 289 "justification": "Participants recruited via 'personal contacts and advertisements posted on university forums'; selection criteria, credential verification process, and track assignment criteria are detailed in Appendix H.1.", 290 "source": "haiku" 291 }, 292 "data_pipeline_documented": { 293 "applies": true, 294 "answer": true, 295 "justification": "Pipeline from submission (shell script → HTTPS POST → FastAPI → evaluation scripts → JSON output) is documented in Section 4.3; metric aggregation from raw to derived metrics is described in Section 4.5.", 296 "source": "haiku" 297 } 298 }, 299 "contamination": { 300 "training_cutoff_stated": { 301 "applies": true, 302 "answer": false, 303 "justification": "The paper provides an evaluation date (July 26, 2025) but does not state the training data cutoffs for any of the five evaluated LLMs.", 304 "source": "haiku" 305 }, 306 "train_test_overlap_discussed": { 307 "applies": true, 308 "answer": false, 309 "justification": "Although dynamic instantiation from templates provides some protection, the paper never discusses whether underlying algorithmic problems or template structures could overlap with LLM training data.", 310 "source": "haiku" 311 }, 312 "benchmark_contamination_addressed": { 313 "applies": true, 314 "answer": false, 315 "justification": "The underlying algorithmic cores (palindromes, sliding windows, graph traversal, palindromic numbers) are standard CS problems well-represented in training corpora; this contamination risk is not addressed.", 316 "source": "haiku" 317 } 318 }, 319 "human_studies": { 320 "pre_registered": { 321 "applies": true, 322 "answer": false, 323 "justification": "No pre-registration is mentioned anywhere in the paper.", 324 "source": "haiku" 325 }, 326 "irb_or_ethics_approval": { 327 "applies": true, 328 "answer": true, 329 "justification": "The informed consent form in Appendix J.1 explicitly states 'this study has been reviewed and approved by the Institutional Review Board (IRB).'", 330 "source": "haiku" 331 }, 332 "demographics_reported": { 333 "applies": true, 334 "answer": true, 335 "justification": "Appendix H.2 reports age range (19-26, mean 21.4), gender (28M/17F), education levels (undergrad/master/PhD), internship count (mean 1.47), and daily AI usage rate (84.4%).", 336 "source": "haiku" 337 }, 338 "inclusion_exclusion_criteria": { 339 "applies": true, 340 "answer": true, 341 "justification": "Appendix H.1 lists eight specific eligibility criteria including age threshold, CS major, minimum two years programming experience, VS Code proficiency, and AI tool usage frequency.", 342 "source": "haiku" 343 }, 344 "randomization_described": { 345 "applies": true, 346 "answer": true, 347 "justification": "Task sequences are randomly selected from all balanced permutations; a Latin Square design ensures every problem appears equally across conditions and is completed by different participants.", 348 "source": "haiku" 349 }, 350 "blinding_described": { 351 "applies": true, 352 "answer": false, 353 "justification": "Participants necessarily know which condition they are in (Copilot enabled or not); no outcome assessor blinding or any blinding procedure is mentioned.", 354 "source": "haiku" 355 }, 356 "attrition_reported": { 357 "applies": true, 358 "answer": false, 359 "justification": "No mention of dropouts, withdrawals, or attrition; it is not stated whether all 45 enrolled participants completed all four tasks.", 360 "source": "haiku" 361 } 362 }, 363 "cost_and_practicality": { 364 "inference_cost_reported": { 365 "applies": true, 366 "answer": true, 367 "justification": "Token usage is reported in Tables 2 and 8 in millions (e.g., 2.04-2.31M tokens for C2 across tracks), providing a direct cost proxy for LLM inference.", 368 "source": "haiku" 369 }, 370 "compute_budget_stated": { 371 "applies": true, 372 "answer": false, 373 "justification": "The total computational budget for running 4,500+ LLM evaluation instances across 5 models and 2 conditions is not stated in terms of cost, GPU-hours, or API expenditure.", 374 "source": "haiku" 375 } 376 } 377 } 378 }, 379 "claims": [ 380 { 381 "claim": "Current SOTA LLMs achieve near-zero pass rates (best 0.67% overall pass@1 for Claude-Sonnet-4 in C0) on collaboration-necessary coding tasks.", 382 "evidence": "Table 1 shows all five models achieve <1% overall pass@1 in autonomous condition C0, with GPT-4o at 0% across all pass metrics.", 383 "supported": "strong" 384 }, 385 { 386 "claim": "Human-AI collaboration (31.11% pass rate) significantly outperforms both standalone LLMs (~0.67%) and unaided humans (18.89%).", 387 "evidence": "Table 2 averaged overall pass@1: CH=18.89%, C0=0.67%, C2=31.11%; paper claims statistical significance but provides no test statistics.", 388 "supported": "moderate" 389 }, 390 { 391 "claim": "Human-AI collaboration is especially beneficial on harder tasks, with collaborative performance remaining stable while unaided human performance degrades sharply.", 392 "evidence": "Table 2 shows C2 range 23.33-43.33% vs CH range 6.67-36.67% across difficulty levels, but no significance tests reported for the interaction.", 393 "supported": "moderate" 394 }, 395 { 396 "claim": "LLMs have evolved from execution tools to co-reasoning partners; 51% of all participants and 80% of top performers reported AI suggested a fundamentally different algorithmic approach.", 397 "evidence": "Table 10 reports self-reported usage patterns post-hoc; 23/45 total, 12/15 top performers selected 'Suggest a fundamentally different approach.'", 398 "supported": "weak" 399 }, 400 { 401 "claim": "HAI-Eval templates are robustly collaboration-necessary: 42 of 45 templates achieved 0% pass rate across all three validation models.", 402 "evidence": "Table 4 shows overall validation pass rates of 0.33%, 0%, and 0.22% for Claude-Sonnet-4, GPT-4.1, and Gemini-2.5-Pro respectively.", 403 "supported": "strong" 404 } 405 ], 406 "methodology_tags": [ 407 "benchmark-eval", 408 "observational", 409 "qualitative" 410 ], 411 "key_findings": "HAI-Eval introduces 45 'collaboration-necessary' problem templates where five SOTA LLMs achieve near-zero autonomous pass rates (best 0.67%) while unaided human experts achieve 18.89%, but human-AI collaboration reaches 31.11% — demonstrating a measurable synergy gap. Uniform LLM failure across all difficulty levels and professional tracks (SDE/MLE/DS) is interpreted as a fundamental higher-order reasoning limitation, distinct from algorithmic or domain-specific deficiency. Self-report data from 45 participants indicates that 80% of top performers leveraged AI for strategic brainstorming and algorithmic approach selection, supporting an 'emergent co-reasoning partnership' framing, though this rests on post-hoc self-report rather than objective behavioral measurement.", 412 "red_flags": [ 413 { 414 "flag": "Statistical significance claimed without test statistics", 415 "detail": "The paper asserts 'statistically significant improvement' for C2 vs CH but reports no p-values, test statistics, degrees of freedom, or identification of which statistical test was used." 416 }, 417 { 418 "flag": "No confidence intervals or error bars", 419 "detail": "All main performance tables (1, 2, 6-8) report only point estimates; variance across participants, runs, or task instances is not reported for any performance metric." 420 }, 421 { 422 "flag": "Co-reasoning conclusion via self-report only", 423 "detail": "The 'co-reasoning partnership' central claim relies on post-hoc questionnaire responses rather than objective behavioral metrics from operational logs, which the paper collected but does not analyze quantitatively." 424 }, 425 { 426 "flag": "Artifacts not yet released", 427 "detail": "Code and dataset are described with future-tense ('will be openly accessible') and no repository URL; paper is marked 'Work in progress,' making reproducibility unverifiable." 428 }, 429 { 430 "flag": "Demographic homogeneity limits generalizability", 431 "detail": "All 45 participants are East Asian CS students or recent graduates from elite institutions; no industry professionals, no non-East-Asian participants, severely limiting claims about developer populations broadly." 432 }, 433 { 434 "flag": "Contamination not addressed", 435 "detail": "Underlying algorithmic cores (palindromes, sliding window, DFS with backtracking) are standard CS problems widely present in LLM training data; neither training cutoffs nor benchmark contamination risk is discussed." 436 }, 437 { 438 "flag": "Post-hoc top-performer subgroup analysis", 439 "detail": "Comparison of top-15 performers vs all-45 (Table 10) is a post-hoc subgroup partition with no pre-specification or correction for multiple comparisons, making the 80% vs 51% contrast unreliable." 440 } 441 ], 442 "cited_papers": [ 443 { 444 "title": "SWE-bench: Can Language Models Solve Real-World Software Engineering Problems?", 445 "relevance": "Major real-world coding benchmark that HAI-Eval contrasts with as an example of well-defined task framing that excludes higher-order reasoning" 446 }, 447 { 448 "title": "Evaluating Large Language Models Trained on Code (HumanEval)", 449 "relevance": "Foundational function-level code generation benchmark used as a baseline example of what HAI-Eval moves beyond" 450 }, 451 { 452 "title": "LiveCodeBench: A Comprehensive Benchmark for General-Purpose Language Agents", 453 "relevance": "Cited as focusing on extreme algorithmic complexity without measuring higher-order skills like requirement engineering" 454 }, 455 { 456 "title": "Grounded Copilot: How Programmers Interact with Code-Generating Models", 457 "relevance": "Academic user study of Copilot interaction patterns directly related to HAI-Eval's human study design" 458 }, 459 { 460 "title": "Reading Between the Lines: Modeling User Behavior and Costs in AI-Assisted Programming", 461 "relevance": "Closest prior work on modeling human cognitive costs in AI-assisted programming, motivating HAI-Eval's 'necessary collaboration' design principle" 462 }, 463 { 464 "title": "Collaborative Gym: A Framework for Enabling and Evaluating Human-Agent Collaboration", 465 "relevance": "Most closely related framework for human-agent collaboration evaluation; HAI-Eval applies this paradigm specifically to coding" 466 }, 467 { 468 "title": "When Combinations of Humans and AI Are Useful: A Systematic Review and Meta-Analysis", 469 "relevance": "Meta-analysis providing theoretical grounding for HAI-Eval's complementarity hypothesis" 470 }, 471 { 472 "title": "How Much Does AI Impact Development Speed? An Enterprise-Based Randomized Controlled Trial", 473 "relevance": "RCT on AI coding productivity representing the enterprise evidence base HAI-Eval situates against as lacking standardized benchmark reproducibility" 474 }, 475 { 476 "title": "Sea Change in Software Development: Economic and Productivity Analysis of the AI-Powered Developer Lifecycle", 477 "relevance": "Enterprise AI productivity study reporting 15-30% gains; represents the class of productivity-focused studies HAI-Eval aims to methodologically complement" 478 } 479 ], 480 "engagement_factors": { 481 "practical_relevance": { 482 "score": 3, 483 "justification": "Directly addresses how to evaluate developer competence and AI tool effectiveness in the AI era, with clear implications for hiring, education, and tool benchmarking." 484 }, 485 "surprise_contrarian": { 486 "score": 2, 487 "justification": "Near-zero SOTA LLM performance on tasks solvable with human collaboration is striking; the co-reasoning framing challenges the simple tool-use model of AI-assisted coding." 488 }, 489 "fear_safety": { 490 "score": 0, 491 "justification": "No AI safety or risk concerns raised; focus is purely on capability evaluation and benchmark design." 492 }, 493 "drama_conflict": { 494 "score": 1, 495 "justification": "Implicitly challenges the LeetCode/HackerRank hiring paradigm and the 'AI replaces developers' narrative, but no explicit controversy or adversarial framing." 496 }, 497 "demo_ability": { 498 "score": 2, 499 "justification": "An interactive demo is promised and the VS Code/Copilot setup is familiar to practitioners; however availability is future-tense at paper submission time." 500 }, 501 "brand_recognition": { 502 "score": 1, 503 "justification": "Multi-institution academic paper from NYU Abu Dhabi, UIUC, Harvard, NTU — notable universities but not a major AI lab; no industry co-authors." 504 } 505 }, 506 "hn_data": { 507 "threads": [ 508 { 509 "hn_id": "25314295", 510 "title": "Neural Teleportation", 511 "points": 3, 512 "comments": 0, 513 "url": "https://news.ycombinator.com/item?id=25314295", 514 "created_at": "2020-12-05T13:14:38Z" 515 }, 516 { 517 "hn_id": "41124882", 518 "title": "Luck, skill, and depth of competition in games and social hierarchies", 519 "points": 2, 520 "comments": 0, 521 "url": "https://news.ycombinator.com/item?id=41124882", 522 "created_at": "2024-08-01T00:08:49Z" 523 }, 524 { 525 "hn_id": "46208288", 526 "title": "Autodeleveraging: Impossibilities and Optimization", 527 "points": 1, 528 "comments": 0, 529 "url": "https://news.ycombinator.com/item?id=46208288", 530 "created_at": "2025-12-09T18:07:46Z" 531 }, 532 { 533 "hn_id": "42348424", 534 "title": "Enhancing Mathematical Reasoning in LLMs with Background Operators", 535 "points": 1, 536 "comments": 0, 537 "url": "https://news.ycombinator.com/item?id=42348424", 538 "created_at": "2024-12-07T09:23:20Z" 539 } 540 ], 541 "top_points": 3, 542 "total_points": 7, 543 "total_comments": 0 544 } 545 }