scan-v4.json (29663B)
1 { 2 "scan_version": 4, 3 "paper_type": "empirical", 4 "paper": { 5 "title": "Does It Tie Out? Towards Autonomous Legal Agents in Venture Capital", 6 "authors": [ 7 "Pierre Colombo", 8 "Malik Boudiaf", 9 "Allyn Sweet", 10 "Michael Desa", 11 "Hongxi Wang" 12 ], 13 "year": 2025, 14 "venue": "arXiv.org", 15 "arxiv_id": "2512.18658", 16 "doi": "10.48550/arXiv.2512.18658" 17 }, 18 "checklist": { 19 "claims_and_evidence": { 20 "abstract_claims_supported": { 21 "applies": true, 22 "answer": true, 23 "justification": "The abstract claims that existing agentic systems fail at tie-out and proposes a world model architecture. The results (29% F1 for agentic vs 85% for Equall) support these claims.", 24 "source": "opus" 25 }, 26 "causal_claims_justified": { 27 "applies": true, 28 "answer": false, 29 "justification": "The paper makes causal claims about why Equall outperforms ('pre-computed knowledge graph turns these complex reasoning chains into reliable graph queries') but the study design (4 datarooms, no controlled manipulation of individual components beyond one ablation) is insufficient for strong causal inference.", 30 "source": "opus" 31 }, 32 "generalization_bounded": { 33 "applies": true, 34 "answer": false, 35 "justification": "The title says 'Venture Capital' broadly but results are from only 4 US-focused datarooms (Seed to Series B). No discussion of whether results generalize to other jurisdictions, deal types, or company sizes.", 36 "source": "opus" 37 }, 38 "alternative_explanations_discussed": { 39 "applies": true, 40 "answer": false, 41 "justification": "No alternative explanations are discussed. The Equall system may benefit from domain-specific engineering advantages not shared with the generic agentic baseline, but this is not addressed.", 42 "source": "opus" 43 }, 44 "proxy_outcome_distinction": { 45 "applies": true, 46 "answer": false, 47 "justification": "The paper measures F1 on anomaly detection against expert annotations and frames this as 'tie-out automation,' but does not discuss whether matching expert flags is the same as producing legally reliable tie-out (experts may disagree, annotations may be incomplete).", 48 "source": "opus" 49 } 50 }, 51 "limitations_and_scope": { 52 "limitations_section_present": { 53 "applies": true, 54 "answer": false, 55 "justification": "There is no limitations or threats-to-validity section. The paper moves directly from results to conclusion.", 56 "source": "opus" 57 }, 58 "threats_to_validity_specific": { 59 "applies": true, 60 "answer": false, 61 "justification": "No threats to validity are discussed anywhere in the paper.", 62 "source": "opus" 63 }, 64 "scope_boundaries_stated": { 65 "applies": true, 66 "answer": false, 67 "justification": "No explicit scope boundaries. The paper does not state what the results do NOT show or what settings were excluded.", 68 "source": "opus" 69 } 70 }, 71 "conflicts_of_interest": { 72 "funding_disclosed": { 73 "applies": true, 74 "answer": false, 75 "justification": "No funding disclosure or acknowledgments section is present in the paper.", 76 "source": "opus" 77 }, 78 "affiliations_disclosed": { 79 "applies": true, 80 "answer": true, 81 "justification": "All authors list Equall email addresses ({firstname}@equall.com), making the company affiliation clear.", 82 "source": "opus" 83 }, 84 "funder_independent_of_outcome": { 85 "applies": true, 86 "answer": false, 87 "justification": "All authors are from Equall, the company whose product is being evaluated. Equall has a direct financial interest in the system appearing to outperform alternatives.", 88 "source": "opus" 89 }, 90 "financial_interests_declared": { 91 "applies": true, 92 "answer": false, 93 "justification": "No competing interests statement. The authors presumably hold equity or employment stakes in Equall but this is not declared.", 94 "source": "opus" 95 } 96 }, 97 "scope_and_framing": { 98 "key_terms_defined": { 99 "applies": true, 100 "answer": true, 101 "justification": "Core terms — tie-out, dataroom, cap table, anomaly types (Terms Discrepancy, Missing Documentation, Missing from Cap Table), and the world model stages — are all formally defined in Sections 2 and 4.", 102 "source": "haiku" 103 }, 104 "intended_contribution_clear": { 105 "applies": true, 106 "answer": true, 107 "justification": "The paper explicitly states it contributes a formal characterization of tie-out, an empirical complexity analysis, and a world-model architecture (Equall) evaluated against agentic RAG baselines.", 108 "source": "haiku" 109 }, 110 "engagement_with_prior_work": { 111 "applies": true, 112 "answer": true, 113 "justification": "The paper engages substantively with LegalBench, SaulLM, and the multi-agent failure literature, explaining why existing approaches do not transfer to tie-out rather than merely listing references.", 114 "source": "haiku" 115 } 116 } 117 }, 118 "type_checklist": { 119 "empirical": { 120 "artifacts": { 121 "code_released": { 122 "applies": true, 123 "answer": false, 124 "justification": "No code repository, GitHub link, or archive is provided anywhere in the paper.", 125 "source": "opus" 126 }, 127 "data_released": { 128 "applies": true, 129 "answer": false, 130 "justification": "The four datarooms are anonymized and proprietary. No dataset download link or supplementary data is provided.", 131 "source": "opus" 132 }, 133 "environment_specified": { 134 "applies": true, 135 "answer": false, 136 "justification": "No environment specifications, dependency lists, or hardware details are mentioned.", 137 "source": "opus" 138 }, 139 "reproduction_instructions": { 140 "applies": true, 141 "answer": false, 142 "justification": "No reproduction instructions or scripts are provided. The system is proprietary.", 143 "source": "opus" 144 } 145 }, 146 "statistical_methodology": { 147 "confidence_intervals_or_error_bars": { 148 "applies": true, 149 "answer": false, 150 "justification": "Figure 11 shows 95% error bars for time comparisons, but the main F1 results in Figure 8 have no confidence intervals or error bars.", 151 "source": "opus" 152 }, 153 "significance_tests": { 154 "applies": true, 155 "answer": false, 156 "justification": "No statistical significance tests are used. Claims like 'significantly outperforming' (Section 5.1) are based on raw number comparisons without any statistical test.", 157 "source": "opus" 158 }, 159 "effect_sizes_reported": { 160 "applies": true, 161 "answer": true, 162 "justification": "Absolute F1 scores and differences are reported with baseline context: Equall 85.1% vs agentic+structured 42.1% vs pure agentic 29.0%. Speed comparison provides concrete ratios (22x per check).", 163 "source": "opus" 164 }, 165 "sample_size_justified": { 166 "applies": true, 167 "answer": false, 168 "justification": "Only 4 datarooms are used with no justification for this sample size and no acknowledgment that N=4 may be too small for generalizable conclusions.", 169 "source": "opus" 170 }, 171 "variance_reported": { 172 "applies": true, 173 "answer": false, 174 "justification": "No variance, standard deviation, or spread measures are reported for the F1 results. Each dataroom appears evaluated once.", 175 "source": "opus" 176 } 177 }, 178 "evaluation_design": { 179 "baselines_included": { 180 "applies": true, 181 "answer": true, 182 "justification": "Two baselines are included: a pure agentic RAG baseline (GPT-5.1 with iterative RAG) and an ablation (agentic + Equall's structured representation).", 183 "source": "opus" 184 }, 185 "baselines_contemporary": { 186 "applies": true, 187 "answer": true, 188 "justification": "The agentic baseline uses GPT-5.1, a contemporary model. However, no other commercial legal AI systems are compared.", 189 "source": "opus" 190 }, 191 "ablation_study": { 192 "applies": true, 193 "answer": true, 194 "justification": "The 'Agentic + Structured Repr.' baseline serves as an ablation, isolating the effect of Equall's Event Graph (Stage 2) from the full pipeline.", 195 "source": "opus" 196 }, 197 "multiple_metrics": { 198 "applies": true, 199 "answer": true, 200 "justification": "Precision, recall, and F1 are reported per flag category (Figure 8). Speed/latency comparisons are also provided (Figure 9).", 201 "source": "opus" 202 }, 203 "human_evaluation": { 204 "applies": true, 205 "answer": true, 206 "justification": "Ground-truth flags were annotated by experienced legal professionals (Section 5). The evaluation compares system output against expert human annotations.", 207 "source": "opus" 208 }, 209 "held_out_test_set": { 210 "applies": true, 211 "answer": false, 212 "justification": "With only 4 datarooms, there is no held-out test set. All datarooms appear to be used for both development and evaluation.", 213 "source": "opus" 214 }, 215 "per_category_breakdown": { 216 "applies": true, 217 "answer": true, 218 "justification": "Figure 8 breaks down precision, recall, and F1 across four flag categories: Data Discrepancy, Issuance Missing, Board Approval Missing, and Cap Table Missing.", 219 "source": "opus" 220 }, 221 "failure_cases_discussed": { 222 "applies": true, 223 "answer": true, 224 "justification": "Section 5.1 discusses where agentic baselines fail: 'Missing Documentation' and 'Missing from Cap Table' categories requiring global reasoning. Equall's limitations on these categories are also visible in results.", 225 "source": "opus" 226 }, 227 "negative_results_reported": { 228 "applies": true, 229 "answer": false, 230 "justification": "No negative results for Equall are reported. Every experiment shows Equall outperforming baselines. No failed approaches or configurations are mentioned.", 231 "source": "opus" 232 } 233 }, 234 "setup_transparency": { 235 "model_versions_specified": { 236 "applies": true, 237 "answer": false, 238 "justification": "The agentic baseline uses 'GPT-5.1' but no snapshot date or API version is given. The LLM used inside Equall's extraction pipeline is not specified at all.", 239 "source": "opus" 240 }, 241 "prompts_provided": { 242 "applies": true, 243 "answer": false, 244 "justification": "No prompts or system instructions are provided for any of the LLM components (agentic baseline, Equall's extractors, or classifiers).", 245 "source": "opus" 246 }, 247 "hyperparameters_reported": { 248 "applies": true, 249 "answer": false, 250 "justification": "No hyperparameters (temperature, top-p, max tokens, chunk size for RAG, etc.) are reported for any system.", 251 "source": "opus" 252 }, 253 "scaffolding_described": { 254 "applies": true, 255 "answer": true, 256 "justification": "Section 4 describes both paradigms in detail: the agentic RAG pipeline (query generation → retrieval → reasoning → verification) and Equall's three-stage pipeline (foundational extraction → inductive event modeling → neuro-symbolic verification).", 257 "source": "opus" 258 }, 259 "data_preprocessing_documented": { 260 "applies": true, 261 "answer": false, 262 "justification": "No description of how raw dataroom documents were preprocessed (OCR, text extraction, chunking for RAG, etc.). Section 3 mentions OCR quality issues but does not describe the actual preprocessing pipeline.", 263 "source": "opus" 264 } 265 }, 266 "data_integrity": { 267 "raw_data_available": { 268 "applies": true, 269 "answer": false, 270 "justification": "No raw data is available. The datarooms are proprietary and anonymized. No supplementary data files are provided.", 271 "source": "opus" 272 }, 273 "data_collection_described": { 274 "applies": true, 275 "answer": true, 276 "justification": "Section 3 describes the four datarooms with statistics (pages, documents, securities, shareholders) across financing stages. The dataroom composition is characterized by document type distribution (Figures 2, 5).", 277 "source": "opus" 278 }, 279 "recruitment_methods_described": { 280 "applies": true, 281 "answer": false, 282 "justification": "No description of how the four companies/datarooms were selected. Were they Equall customers? Were they chosen to be representative? Selection criteria are absent.", 283 "source": "opus" 284 }, 285 "data_pipeline_documented": { 286 "applies": true, 287 "answer": false, 288 "justification": "The pipeline from raw documents to ground-truth annotations is not documented. How legal experts annotated ground-truth flags, inter-annotator agreement, and annotation guidelines are all absent.", 289 "source": "opus" 290 } 291 }, 292 "contamination": { 293 "training_cutoff_stated": { 294 "applies": true, 295 "answer": false, 296 "justification": "No training data cutoff is stated for GPT-5.1 or any other model used.", 297 "source": "opus" 298 }, 299 "train_test_overlap_discussed": { 300 "applies": true, 301 "answer": false, 302 "justification": "No discussion of whether the models could have seen similar legal documents during training.", 303 "source": "opus" 304 }, 305 "benchmark_contamination_addressed": { 306 "applies": true, 307 "answer": false, 308 "justification": "No contamination analysis. Although the datarooms are proprietary (reducing contamination risk), this is not discussed.", 309 "source": "opus" 310 } 311 }, 312 "human_studies": { 313 "pre_registered": { 314 "applies": false, 315 "answer": false, 316 "justification": "No human participants. The study evaluates automated systems against expert-annotated ground truth.", 317 "source": "opus" 318 }, 319 "irb_or_ethics_approval": { 320 "applies": false, 321 "answer": false, 322 "justification": "No human participants in the study.", 323 "source": "opus" 324 }, 325 "demographics_reported": { 326 "applies": false, 327 "answer": false, 328 "justification": "No human participants in the study.", 329 "source": "opus" 330 }, 331 "inclusion_exclusion_criteria": { 332 "applies": false, 333 "answer": false, 334 "justification": "No human participants in the study.", 335 "source": "opus" 336 }, 337 "randomization_described": { 338 "applies": false, 339 "answer": false, 340 "justification": "No human participants in the study.", 341 "source": "opus" 342 }, 343 "blinding_described": { 344 "applies": false, 345 "answer": false, 346 "justification": "No human participants in the study.", 347 "source": "opus" 348 }, 349 "attrition_reported": { 350 "applies": false, 351 "answer": false, 352 "justification": "No human participants in the study.", 353 "source": "opus" 354 } 355 }, 356 "cost_and_practicality": { 357 "inference_cost_reported": { 358 "applies": true, 359 "answer": true, 360 "justification": "Figure 9 reports per-check inference time (45 sec agentic vs 2 sec Equall) and total times for 100 and 500 checks. Figure 11 reports end-to-end time for Equall-assisted workflows (64m to 300m).", 361 "source": "opus" 362 }, 363 "compute_budget_stated": { 364 "applies": true, 365 "answer": false, 366 "justification": "No total computational budget (GPU hours, API costs, total spend) is stated. Only relative timing comparisons are provided.", 367 "source": "opus" 368 } 369 }, 370 "experimental_rigor": { 371 "seed_sensitivity_reported": { 372 "applies": true, 373 "answer": false, 374 "justification": "No multiple-seed experiments are reported. Each system appears to be run once per dataroom.", 375 "source": "opus" 376 }, 377 "number_of_runs_stated": { 378 "applies": true, 379 "answer": false, 380 "justification": "The number of experimental runs is never stated. Results appear to be single-run.", 381 "source": "opus" 382 }, 383 "hyperparameter_search_budget": { 384 "applies": true, 385 "answer": false, 386 "justification": "No hyperparameter search budget is reported for any system.", 387 "source": "opus" 388 }, 389 "best_config_selection_justified": { 390 "applies": true, 391 "answer": false, 392 "justification": "No description of how configurations were selected for any of the three systems.", 393 "source": "opus" 394 }, 395 "multiple_comparison_correction": { 396 "applies": false, 397 "answer": false, 398 "justification": "No statistical tests are performed, so multiple comparison correction is not applicable.", 399 "source": "opus" 400 }, 401 "self_comparison_bias_addressed": { 402 "applies": true, 403 "answer": false, 404 "justification": "The authors evaluate their own product (Equall) against baselines they implemented. No acknowledgment of author-evaluation bias.", 405 "source": "opus" 406 }, 407 "compute_budget_vs_performance": { 408 "applies": true, 409 "answer": true, 410 "justification": "Figure 9 explicitly compares speed (a compute proxy) vs. paradigm, showing the trade-off between eager (15 min indexing, 2 sec/check) and lazy (2 min indexing, 45 sec/check) approaches.", 411 "source": "opus" 412 }, 413 "benchmark_construct_validity": { 414 "applies": true, 415 "answer": false, 416 "justification": "No discussion of whether matching expert-annotated flags constitutes valid measurement of tie-out quality. Inter-annotator agreement is not reported.", 417 "source": "opus" 418 }, 419 "scaffold_confound_addressed": { 420 "applies": true, 421 "answer": false, 422 "justification": "The agentic baseline uses a generic RAG scaffold while Equall uses a highly engineered domain-specific pipeline. The performance difference could be due to engineering quality rather than architectural paradigm, but this confound is not discussed.", 423 "source": "opus" 424 } 425 }, 426 "data_leakage": { 427 "temporal_leakage_addressed": { 428 "applies": true, 429 "answer": false, 430 "justification": "No discussion of whether GPT-5.1 may have seen similar legal documents or patterns in training data.", 431 "source": "opus" 432 }, 433 "feature_leakage_addressed": { 434 "applies": true, 435 "answer": false, 436 "justification": "No discussion of whether the evaluation setup provides information not available in real usage scenarios.", 437 "source": "opus" 438 }, 439 "non_independence_addressed": { 440 "applies": true, 441 "answer": false, 442 "justification": "With only 4 datarooms, independence between evaluation instances is not discussed.", 443 "source": "opus" 444 }, 445 "leakage_detection_method": { 446 "applies": true, 447 "answer": false, 448 "justification": "No leakage detection or prevention method is described.", 449 "source": "opus" 450 } 451 } 452 } 453 }, 454 "claims": [ 455 { 456 "claim": "Equall achieves 85% average F1 on anomaly detection, vs 42% for Agentic + Structured Repr. and 29% for pure Agentic baseline.", 457 "evidence": "Figure 8 reports precision, recall, and F1 across four flag categories for all three conditions on the combined dataset.", 458 "supported": "moderate" 459 }, 460 { 461 "claim": "Evidentiary burden scales super-linearly: dataroom size roughly doubles from Seed to Series B while tracked securities increase 7× (184 → 1,292).", 462 "evidence": "Figure 4 shows aggregate statistics across four companies; the ratio shift is arithmetically derived from those figures.", 463 "supported": "moderate" 464 }, 465 { 466 "claim": "Human tie-out effort scales super-linearly, growing more than 5× from ~5 hours at Seed to ~27 hours at Series B.", 467 "evidence": "Figure 11 error bars are 'obtained by comparing time reported by customers/partners on real-world tieouts' — based on informal self-reported data rather than controlled measurement.", 468 "supported": "weak" 469 }, 470 { 471 "claim": "Agentic F1 degrades sharply with dataroom size (55% → 28%) while Equall remains robust (95% → 72%) across Seed to Series B.", 472 "evidence": "Figure 10 shows F1 vs. dataroom size for three systems across three of the four companies.", 473 "supported": "moderate" 474 }, 475 { 476 "claim": "Equall offers a 22× per-check speed advantage over agentic baseline after indexing (2 sec vs 45 sec per check).", 477 "evidence": "Figure 9 timing table on a 300-document dataroom; methodology for timing measurement is not described.", 478 "supported": "weak" 479 } 480 ], 481 "methodology_tags": [ 482 "benchmark-eval", 483 "case-study" 484 ], 485 "key_findings": "The paper formalizes cap table tie-out as an anomaly detection task requiring multi-document global reasoning and introduces Equall, an 'eager' world-model architecture that pre-builds a symbolic Event Graph before verification. On four real-world VC datarooms (Seed to Series B), Equall achieves 85% average F1 versus 42% and 29% for progressively weaker agentic RAG baselines, with the largest gains on 'global' anomaly types (proving absence of documentation, establishing complete ownership lineages) where RAG-based approaches are structurally weakest. Human verification effort is shown to scale super-linearly from roughly 5 to 27 hours across financing stages, while Equall-assisted workflows remain nearly linear. The results suggest that upfront world-model construction provides a durable quality and speed advantage for complex, combinatorial legal verification tasks.", 486 "red_flags": [ 487 { 488 "flag": "Self-evaluation bias", 489 "detail": "All authors are Equall employees evaluating their own commercial product; no independent replication or blind evaluation is performed, creating an uncorrected conflict of interest." 490 }, 491 { 492 "flag": "N=4 evaluation set", 493 "detail": "The entire empirical evaluation rests on four anonymized client datarooms; no statistical tests are applied, and the results are reported as point estimates with no variance or confidence intervals on the main F1 scores." 494 }, 495 { 496 "flag": "Zero reproducibility", 497 "detail": "No code, data, prompts, model versions for internal components, or environment specs are released; the system is proprietary and the evaluation data is confidential, making all results entirely unreproducible." 498 }, 499 { 500 "flag": "Human time data sourced from informal reports", 501 "detail": "The human effort figures in Figure 11 (used to claim 79–81.5% efficiency gains) are 'reported by customers/partners' rather than measured in a controlled study, introducing unknown self-reporting bias." 502 }, 503 { 504 "flag": "No failure analysis for proposed system", 505 "detail": "The paper discusses why baselines fail but provides no error analysis, qualitative examples, or discussion of conditions under which Equall itself makes errors." 506 }, 507 { 508 "flag": "No limitations section", 509 "detail": "The paper contains no dedicated limitations, threats to validity, or scope boundaries; the conclusion pivots directly to future directions with broader generalization claims." 510 }, 511 { 512 "flag": "Overstated generalizability", 513 "detail": "Claims that the Event Graph is 'a robust foundational substrate suitable for a wider array of downstream legal applications' and 'inherently generalizable' across legal domains are not supported by any evidence beyond a single narrow tie-out task." 514 } 515 ], 516 "cited_papers": [ 517 { 518 "title": "LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models", 519 "relevance": "Primary legal AI benchmark used to contextualize why existing LLM capabilities on benchmarks do not transfer to specialized legal workflows like tie-out." 520 }, 521 { 522 "title": "Why Do Multi-Agent LLM Systems Fail?", 523 "relevance": "Directly cited to support the claim that agentic LLM systems have systematic failure modes, motivating the world-model approach." 524 }, 525 { 526 "title": "SaulLM-54B & SaulLM-141B: Scaling Up Domain Adaptation for the Legal Domain", 527 "relevance": "Self-citation by paper authors; represents prior work on legal LLMs and contextualize the gap between general legal capability and specialized workflow automation." 528 }, 529 { 530 "title": "GPT-4 Passes the Bar Exam", 531 "relevance": "Representative legal AI benchmark result cited to establish that LLMs excel on legal reasoning tests while still failing at operational legal tasks." 532 }, 533 { 534 "title": "Evaluating AI for Law: Bridging the Gap with Open-Source Solutions", 535 "relevance": "Related work on evaluating LLMs for legal tasks, providing comparison context for the tie-out task framing." 536 }, 537 { 538 "title": "LEDGAR: A Large-Scale Multi-Label Corpus for Text Classification of Legal Provisions in Contracts", 539 "relevance": "Legal clause extraction work cited as an example of document-level legal AI that does not address the multi-document global reasoning required by tie-out." 540 }, 541 { 542 "title": "DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models", 543 "relevance": "Cited in the context of RL-driven training with dense reward signals as a template for future tie-out agent training." 544 } 545 ], 546 "engagement_factors": { 547 "practical_relevance": { 548 "score": 3, 549 "justification": "Directly addresses a high-stakes, time-intensive bottleneck in VC deal-making that costs lawyers tens of hours per transaction." 550 }, 551 "surprise_contrarian": { 552 "score": 2, 553 "justification": "The 56-point F1 gap between eager world-model construction and agentic RAG challenges the prevailing assumption that sophisticated RAG pipelines are sufficient for complex document reasoning." 554 }, 555 "fear_safety": { 556 "score": 1, 557 "justification": "Autonomous agents making legal determinations in multi-million dollar financing transactions carries implicit risk, though the paper frames this positively and does not engage with failure modes." 558 }, 559 "drama_conflict": { 560 "score": 1, 561 "justification": "A company publishing a paper showing its own product dramatically outperforms alternatives is a recognizable commercial evaluation pattern that may draw skeptical commentary." 562 }, 563 "demo_ability": { 564 "score": 1, 565 "justification": "Equall exists as a commercial product but is not publicly accessible for independent testing; the demo-ability is limited to customers." 566 }, 567 "brand_recognition": { 568 "score": 0, 569 "justification": "Equall is not a widely known AI lab or major commercial brand in the broader AI community." 570 } 571 }, 572 "hn_data": { 573 "threads": [ 574 { 575 "hn_id": "42550783", 576 "title": "Gamma-ray bursts: what do we know today that we did not know 10 years ago?", 577 "points": 16, 578 "comments": 0, 579 "url": "https://news.ycombinator.com/item?id=42550783", 580 "created_at": "2024-12-30T16:30:18Z" 581 }, 582 { 583 "hn_id": "43777601", 584 "title": "Assistance or Disruption? Evaluating the Design of Proactive AI Programming", 585 "points": 2, 586 "comments": 0, 587 "url": "https://news.ycombinator.com/item?id=43777601", 588 "created_at": "2025-04-23T23:02:02Z" 589 }, 590 { 591 "hn_id": "42566642", 592 "title": "1.58-Bit Flux", 593 "points": 2, 594 "comments": 1, 595 "url": "https://news.ycombinator.com/item?id=42566642", 596 "created_at": "2025-01-01T15:38:39Z" 597 }, 598 { 599 "hn_id": "43265832", 600 "title": "Evaluating Intelligence via Trial and Error", 601 "points": 2, 602 "comments": 0, 603 "url": "https://news.ycombinator.com/item?id=43265832", 604 "created_at": "2025-03-05T12:51:05Z" 605 }, 606 { 607 "hn_id": "43280105", 608 "title": "Evaluating Intelligence via Trial and Error", 609 "points": 1, 610 "comments": 0, 611 "url": "https://news.ycombinator.com/item?id=43280105", 612 "created_at": "2025-03-06T13:45:25Z" 613 } 614 ], 615 "top_points": 16, 616 "total_points": 23, 617 "total_comments": 1 618 } 619 }