scan-v4.json (29835B)
1 { 2 "scan_version": 4, 3 "paper_type": "empirical", 4 "paper": { 5 "title": "A2H-MAS: An Algorithm-to-HLS Multi-Agent System for Automated and Reliable FPGA Implementation", 6 "authors": [ 7 "Jie Lei", 8 "Ruofan Jia", 9 "J. Andrew Zhang", 10 "Hao Zhang" 11 ], 12 "year": 2025, 13 "venue": "Unknown", 14 "arxiv_id": "2508.10904", 15 "doi": "10.48550/arXiv.2508.10904" 16 }, 17 "checklist": { 18 "claims_and_evidence": { 19 "abstract_claims_supported": { 20 "applies": true, 21 "answer": true, 22 "justification": "Abstract claims of 'functionally correct, resource-efficient, and latency-optimized HLS designs' are supported by Tables I and II showing working implementations with specific resource and timing numbers.", 23 "source": "opus" 24 }, 25 "causal_claims_justified": { 26 "applies": true, 27 "answer": true, 28 "justification": "The ablation study (Direct → Adaptation → Refinement) uses controlled single-variable manipulation to show each stage's causal contribution to resource reduction.", 29 "source": "opus" 30 }, 31 "generalization_bounded": { 32 "applies": true, 33 "answer": false, 34 "justification": "The title claims general 'Algorithm-to-HLS' capability, but results are limited to two wireless communication tasks. The conclusion mentions extending to 'computer vision and signal processing' without bounding current claims.", 35 "source": "opus" 36 }, 37 "alternative_explanations_discussed": { 38 "applies": true, 39 "answer": false, 40 "justification": "No discussion of alternative explanations. Whether improvements stem from the multi-agent architecture vs. the knowledge library vs. specific algorithmic transformations is not disentangled beyond the 3-level ablation.", 41 "source": "opus" 42 }, 43 "proxy_outcome_distinction": { 44 "applies": true, 45 "answer": false, 46 "justification": "The paper measures functional correctness (C simulation pass), resource usage (LUTs, FFs, DSP, BRAM), and clock frequency, then frames the system as producing 'reliable and high-quality hardware implementations' and demonstrating 'effectiveness and robustness for complex hardware development workflows.' The gap between measured metrics (correctness + resources on 2 tasks) and the broader claims (reliable, robust, complex workflows) is not acknowledged.", 47 "source": "opus" 48 } 49 }, 50 "limitations_and_scope": { 51 "limitations_section_present": { 52 "applies": true, 53 "answer": false, 54 "justification": "No dedicated limitations or threats-to-validity section. The conclusion mentions future work but does not discuss limitations of the current system.", 55 "source": "opus" 56 }, 57 "threats_to_validity_specific": { 58 "applies": true, 59 "answer": false, 60 "justification": "No specific threats to validity are discussed anywhere in the paper.", 61 "source": "opus" 62 }, 63 "scope_boundaries_stated": { 64 "applies": true, 65 "answer": false, 66 "justification": "No explicit statements about what the results do NOT show. Future work mentions extending to other domains but does not state specific scope boundaries for current claims.", 67 "source": "opus" 68 } 69 }, 70 "conflicts_of_interest": { 71 "funding_disclosed": { 72 "applies": true, 73 "answer": false, 74 "justification": "No funding information or acknowledgments section is present in the paper.", 75 "source": "opus" 76 }, 77 "affiliations_disclosed": { 78 "applies": true, 79 "answer": true, 80 "justification": "Author affiliations are clearly listed: University of Technology Sydney and Xidian University.", 81 "source": "opus" 82 }, 83 "funder_independent_of_outcome": { 84 "applies": true, 85 "answer": false, 86 "justification": "No funding is disclosed, so independence cannot be assessed. The paper uses Anthropic's Claude Code but does not disclose any relationship with Anthropic.", 87 "source": "opus" 88 }, 89 "financial_interests_declared": { 90 "applies": true, 91 "answer": false, 92 "justification": "No competing interests or financial interests statement is present in the paper.", 93 "source": "opus" 94 } 95 }, 96 "scope_and_framing": { 97 "key_terms_defined": { 98 "applies": true, 99 "answer": false, 100 "justification": "Core terms used without definition: 'HLS', 'FPGA', 'LLM', 'agent', 'streaming'. These are standard in hardware but not universally known; paper assumes reader familiarity.", 101 "source": "haiku" 102 }, 103 "intended_contribution_clear": { 104 "applies": true, 105 "answer": true, 106 "justification": "Contributions explicitly stated in abstract: (1) A2H-MAS framework for MATLAB-to-HLS conversion, (2) algorithm-hardware co-design methodology, (3) experimental validation on wireless algorithms.", 107 "source": "haiku" 108 }, 109 "engagement_with_prior_work": { 110 "applies": true, 111 "answer": true, 112 "justification": "Section II reviews VerilogEval, MG-Verilog, VGen, VeriMind, HLSPilot, HDLAgent, AutoChip and shows how this work differs: uses SOTA LLMs without fine-tuning, multi-agent with standardized interfaces, focuses on performance metrics beyond functional correctness.", 113 "source": "haiku" 114 } 115 } 116 }, 117 "type_checklist": { 118 "empirical": { 119 "artifacts": { 120 "code_released": { 121 "applies": true, 122 "answer": false, 123 "justification": "No repository URL, code archive, or link to source code is provided anywhere in the paper.", 124 "source": "opus" 125 }, 126 "data_released": { 127 "applies": true, 128 "answer": false, 129 "justification": "No datasets, MATLAB source files, or HLS outputs are released. The test algorithms are described but not made available.", 130 "source": "opus" 131 }, 132 "environment_specified": { 133 "applies": true, 134 "answer": false, 135 "justification": "The paper mentions Xilinx Vitis HLS, MATLAB, and NI USRP X310 but provides no version numbers, dependency specifications, or environment setup details.", 136 "source": "opus" 137 }, 138 "reproduction_instructions": { 139 "applies": true, 140 "answer": false, 141 "justification": "No step-by-step reproduction instructions, README, or scripts are provided.", 142 "source": "opus" 143 } 144 }, 145 "statistical_methodology": { 146 "confidence_intervals_or_error_bars": { 147 "applies": true, 148 "answer": false, 149 "justification": "Results in Tables I and II report only point estimates for resource usage, clock frequency, and latency with no confidence intervals or error bars.", 150 "source": "opus" 151 }, 152 "significance_tests": { 153 "applies": true, 154 "answer": false, 155 "justification": "The paper claims A2H-MAS is effective compared to direct translation but provides no statistical significance tests.", 156 "source": "opus" 157 }, 158 "effect_sizes_reported": { 159 "applies": true, 160 "answer": true, 161 "justification": "Concrete resource reductions with baseline context are reported, e.g., 'LUT consumption is reduced from 36,500 to 685 for calcThreshold' (Section V-B), providing magnitude of effect.", 162 "source": "opus" 163 }, 164 "sample_size_justified": { 165 "applies": true, 166 "answer": false, 167 "justification": "Only two wireless communication systems tested with a handful of submodules. No justification for why this sample is sufficient.", 168 "source": "opus" 169 }, 170 "variance_reported": { 171 "applies": true, 172 "answer": false, 173 "justification": "All results appear to be from single runs. No variance, standard deviation, or multiple-run results are reported.", 174 "source": "opus" 175 } 176 }, 177 "evaluation_design": { 178 "baselines_included": { 179 "applies": true, 180 "answer": true, 181 "justification": "The ablation study in Table II compares Direct (naive LLM translation), Adaptation, and Refinement strategies.", 182 "source": "opus" 183 }, 184 "baselines_contemporary": { 185 "applies": true, 186 "answer": false, 187 "justification": "No comparison against other contemporary LLM-based hardware generation systems (VeriMind, HLSPilot, HDLAgent, AutoChip) despite discussing them in related work. The only baseline is the authors' own naive Direct translation.", 188 "source": "opus" 189 }, 190 "ablation_study": { 191 "applies": true, 192 "answer": true, 193 "justification": "Table II presents ablation results comparing Direct, Adaptation, and Refinement stages on calcThreshold and extractSSBsig modules.", 194 "source": "opus" 195 }, 196 "multiple_metrics": { 197 "applies": true, 198 "answer": true, 199 "justification": "Results report LUTs, FFs, DSP, BRAMs, clock frequency (MHz), and latency — multiple complementary hardware metrics.", 200 "source": "opus" 201 }, 202 "human_evaluation": { 203 "applies": false, 204 "answer": false, 205 "justification": "Human evaluation is not relevant here; correctness is verified through automated simulation (C simulation, synthesis, RTL co-simulation) and on-board hardware validation.", 206 "source": "opus" 207 }, 208 "held_out_test_set": { 209 "applies": false, 210 "answer": false, 211 "justification": "Not a ML model evaluated on train/test splits. The system is tested on engineering tasks with deterministic correctness criteria.", 212 "source": "opus" 213 }, 214 "per_category_breakdown": { 215 "applies": true, 216 "answer": true, 217 "justification": "Table I provides per-submodule breakdowns for all modules in both 5G NR (5 submodules + top) and WLAN (4 submodules + top) tasks.", 218 "source": "opus" 219 }, 220 "failure_cases_discussed": { 221 "applies": true, 222 "answer": true, 223 "justification": "The Direct strategy for calcThreshold 'Failed' to achieve post-route timing closure (Table II), explicitly reported and discussed.", 224 "source": "opus" 225 }, 226 "negative_results_reported": { 227 "applies": true, 228 "answer": true, 229 "justification": "The Direct baseline failing timing closure for calcThreshold is a negative result. Increased BRAM usage from integration overhead is also noted.", 230 "source": "opus" 231 } 232 }, 233 "setup_transparency": { 234 "model_versions_specified": { 235 "applies": true, 236 "answer": false, 237 "justification": "Section V states 'Claude Code was employed' but provides no model version, snapshot date, or API version. Reference [6] cites 'Claude sonnet 4' without a specific version identifier.", 238 "source": "opus" 239 }, 240 "prompts_provided": { 241 "applies": true, 242 "answer": true, 243 "justification": "Figures 2 and 3 show structured prompt templates with agent type, core mission, input/output parameters, workflow phases, and tool commands. Substantial detail on agent prompt structure is provided.", 244 "source": "opus" 245 }, 246 "hyperparameters_reported": { 247 "applies": true, 248 "answer": false, 249 "justification": "No LLM hyperparameters (temperature, top-p, max tokens) are reported for the Claude Code usage.", 250 "source": "opus" 251 }, 252 "scaffolding_described": { 253 "applies": true, 254 "answer": true, 255 "justification": "The multi-agent scaffolding is described in detail: Sections III and IV cover standardized interfaces (Fig. 2), rule-guided workflows (Fig. 3), deterministic tool usage, feedback mechanisms, and the 8-phase pipeline (Fig. 5).", 256 "source": "opus" 257 }, 258 "data_preprocessing_documented": { 259 "applies": true, 260 "answer": true, 261 "justification": "Preprocessing is documented: modularization (Phase I), test data generation from intermediate variables (Phase II), function flattening (Phase III), with standardized naming conventions.", 262 "source": "opus" 263 } 264 }, 265 "data_integrity": { 266 "raw_data_available": { 267 "applies": true, 268 "answer": false, 269 "justification": "No raw data (MATLAB source files, generated HLS code, synthesis reports) is made available for independent verification.", 270 "source": "opus" 271 }, 272 "data_collection_described": { 273 "applies": true, 274 "answer": true, 275 "justification": "Section IV-B describes test data generation: executing the original algorithm, recording intermediate variables, and storing with standardized naming conventions.", 276 "source": "opus" 277 }, 278 "recruitment_methods_described": { 279 "applies": false, 280 "answer": false, 281 "justification": "No human participants. The study evaluates automated hardware generation on specific algorithms.", 282 "source": "opus" 283 }, 284 "data_pipeline_documented": { 285 "applies": true, 286 "answer": true, 287 "justification": "The full pipeline from MATLAB input through modularization, flattening, optimization, translation, refinement, and integration is documented in Section IV with figures.", 288 "source": "opus" 289 } 290 }, 291 "contamination": { 292 "training_cutoff_stated": { 293 "applies": false, 294 "answer": false, 295 "justification": "The paper evaluates a multi-agent system's engineering outputs, not a pre-trained model's knowledge on a standard benchmark.", 296 "source": "opus" 297 }, 298 "train_test_overlap_discussed": { 299 "applies": false, 300 "answer": false, 301 "justification": "Not evaluating a pre-trained model on a benchmark; evaluating a tool pipeline on custom engineering tasks.", 302 "source": "opus" 303 }, 304 "benchmark_contamination_addressed": { 305 "applies": false, 306 "answer": false, 307 "justification": "No standard benchmark evaluation of model knowledge is conducted.", 308 "source": "opus" 309 } 310 }, 311 "human_studies": { 312 "pre_registered": { 313 "applies": false, 314 "answer": false, 315 "justification": "No human participants.", 316 "source": "opus" 317 }, 318 "irb_or_ethics_approval": { 319 "applies": false, 320 "answer": false, 321 "justification": "No human participants.", 322 "source": "opus" 323 }, 324 "demographics_reported": { 325 "applies": false, 326 "answer": false, 327 "justification": "No human participants.", 328 "source": "opus" 329 }, 330 "inclusion_exclusion_criteria": { 331 "applies": false, 332 "answer": false, 333 "justification": "No human participants.", 334 "source": "opus" 335 }, 336 "randomization_described": { 337 "applies": false, 338 "answer": false, 339 "justification": "No human participants.", 340 "source": "opus" 341 }, 342 "blinding_described": { 343 "applies": false, 344 "answer": false, 345 "justification": "No human participants.", 346 "source": "opus" 347 }, 348 "attrition_reported": { 349 "applies": false, 350 "answer": false, 351 "justification": "No human participants.", 352 "source": "opus" 353 } 354 }, 355 "cost_and_practicality": { 356 "inference_cost_reported": { 357 "applies": true, 358 "answer": false, 359 "justification": "No API costs, token consumption, or wall-clock time for the LLM-based code generation process is reported despite using Claude Code extensively.", 360 "source": "opus" 361 }, 362 "compute_budget_stated": { 363 "applies": true, 364 "answer": false, 365 "justification": "No total computational budget, API spend, or hardware resources used for the generation process is stated.", 366 "source": "opus" 367 } 368 }, 369 "experimental_rigor": { 370 "seed_sensitivity_reported": { 371 "applies": true, 372 "answer": false, 373 "justification": "LLM outputs are non-deterministic, but no sensitivity analysis across multiple runs is reported. All results appear to be from single runs.", 374 "source": "opus" 375 }, 376 "number_of_runs_stated": { 377 "applies": true, 378 "answer": false, 379 "justification": "The number of experimental runs is not stated. Results appear to be single-run.", 380 "source": "opus" 381 }, 382 "hyperparameter_search_budget": { 383 "applies": true, 384 "answer": false, 385 "justification": "The Refinement phase includes design space exploration (DSE) but no budget (number of configurations tried, compute spent on search) is reported.", 386 "source": "opus" 387 }, 388 "best_config_selection_justified": { 389 "applies": true, 390 "answer": false, 391 "justification": "DSE is mentioned in Phase VII but no details on how many alternatives were explored or how the best configuration was selected.", 392 "source": "opus" 393 }, 394 "multiple_comparison_correction": { 395 "applies": false, 396 "answer": false, 397 "justification": "No statistical tests are performed, so multiple comparison correction is not applicable.", 398 "source": "opus" 399 }, 400 "self_comparison_bias_addressed": { 401 "applies": true, 402 "answer": false, 403 "justification": "The authors evaluate their own system against their own naive baseline (Direct translation). No acknowledgment of self-comparison bias or independent evaluation.", 404 "source": "opus" 405 }, 406 "compute_budget_vs_performance": { 407 "applies": true, 408 "answer": false, 409 "justification": "Adaptation and Refinement stages require additional LLM calls and synthesis runs compared to Direct translation, but compute costs are not compared across the three strategies.", 410 "source": "opus" 411 }, 412 "benchmark_construct_validity": { 413 "applies": true, 414 "answer": false, 415 "justification": "No discussion of whether the two wireless communication tasks are representative of the broader claim of 'automated and reliable FPGA implementation.'", 416 "source": "opus" 417 }, 418 "scaffold_confound_addressed": { 419 "applies": true, 420 "answer": false, 421 "justification": "The ablation compares Direct (single LLM call) vs Adaptation vs Refinement (multi-agent pipeline with knowledge library), but these differ in both algorithmic approach AND scaffolding complexity. The paper does not discuss whether improvements stem from the multi-agent scaffold vs. the algorithmic transformations vs. the knowledge library, attributing all gains to the system as a whole.", 422 "source": "opus" 423 } 424 } 425 } 426 }, 427 "claims": [ 428 { 429 "claim": "Algorithm-level transformation has greater impact on hardware efficiency than pragma-level tuning", 430 "evidence": "Table II ablation: Adaption stage reduces LUTs 98% (36,500→685 for calcThreshold), while Refinement stage reduces further by 75% (685→173). Algorithm-level changes dominate pragmas.", 431 "supported": "strong" 432 }, 433 { 434 "claim": "Order-of-magnitude improvements in resource efficiency are achievable through algorithm selection", 435 "evidence": "Table II shows LUT reduction from 36,500 to 275 (132x) for calcThreshold via streaming algorithm restructuring.", 436 "supported": "strong" 437 }, 438 { 439 "claim": "A2H-MAS produces functionally correct hardware implementations for wireless communication algorithms", 440 "evidence": "Table I reports successful implementations for 5G NR SSB detection (operating at 292.23 MHz) and WLAN synchronization (337.61 MHz); functional validation via C simulation and RTL co-simulation confirmed.", 441 "supported": "strong" 442 }, 443 { 444 "claim": "Multi-agent system with standardized interfaces improves reliability by reducing hallucinations and forgetting", 445 "evidence": "Design principle articulated in Figure 1 and Section III, but no empirical comparison to single-agent baseline or quantified reduction in errors.", 446 "supported": "weak" 447 }, 448 { 449 "claim": "Dataflow-oriented modular decomposition enables scalable system extensions and targeted optimization", 450 "evidence": "Demonstrated through 8-phase workflow and ability to optimize individual submodules independently, but no comparison to alternative decomposition strategies.", 451 "supported": "moderate" 452 }, 453 { 454 "claim": "Deterministic tool-driven validation ensures correctness and reproducibility of generated code", 455 "evidence": "Described in Section III-B: MATLAB batch execution for Phase IV validation, C simulation and co-simulation in Phase VI. But validation methodology not systematically benchmarked.", 456 "supported": "moderate" 457 }, 458 { 459 "claim": "A2H-MAS consistently produces resource-efficient and latency-optimized designs compared to naive LLM translation", 460 "evidence": "Table II Direct→Adaption→Refinement shows improvements, but only compares to Direct baseline; no comparison to HLSPilot, HDLAgent, or other state-of-the-art methods.", 461 "supported": "moderate" 462 } 463 ], 464 "methodology_tags": [ 465 "benchmark-eval", 466 "case-study" 467 ], 468 "key_findings": "A2H-MAS, a multi-agent framework with standardized interfaces, automates the translation of MATLAB algorithms to hardware-efficient HLS code through eight modular phases. The system prioritizes algorithm-level transformations (e.g., shifting from frame-based to streaming paradigms) over pragma tuning, yielding order-of-magnitude resource reductions (LUTs: 36,500→275). Successfully implemented on two wireless communication systems (5G NR SSB detection, WLAN synchronization) with functional correctness validated via RTL co-simulation.", 469 "red_flags": [ 470 { 471 "flag": "No comparison to prior art", 472 "detail": "Only compared to naive Direct baseline; no evaluation against HLSPilot, HDLAgent, or other recent multi-agent/agent-based hardware design systems mentioned in related work." 473 }, 474 { 475 "flag": "Extremely limited evaluation scope", 476 "detail": "Only 2 application domains (5G NR, WLAN), both wireless/dataflow-oriented. No evidence applicability to control-flow-heavy algorithms, computer vision, or other domains." 477 }, 478 { 479 "flag": "LLM version unspecified", 480 "detail": "Paper states 'Claude Code employed' with no version, snapshot date, or model ID; impossible to replicate or assess contamination." 481 }, 482 { 483 "flag": "Code and data not released", 484 "detail": "No source code, test datasets, or generated outputs (C++/Verilog) made available; reproducibility impossible." 485 }, 486 { 487 "flag": "No failure mode analysis", 488 "detail": "Claims 'reliability' but only shows one failure (Direct→calcThreshold timing closure); no systematic analysis of when/why method fails." 489 }, 490 { 491 "flag": "Single runs, no variance reporting", 492 "detail": "Each module synthesized once; no error bars, no multiple random seeds, no variance estimate for synthesis results." 493 }, 494 { 495 "flag": "Sample size not justified", 496 "detail": "Only 2 applications with ~5 submodules each; no justification for why 2 domains suffice or power analysis." 497 }, 498 { 499 "flag": "No discussion of generalization boundaries", 500 "detail": "Claims applicability to 'complex wireless communication workloads' but never explicitly states limitations (e.g., streaming algorithms only, no adaptive control)." 501 } 502 ], 503 "cited_papers": [ 504 { 505 "title": "VerilogEval: Evaluating Large Language Models for Verilog Code Generation", 506 "relevance": "Benchmark for HDL generation; establishes baseline for LLM performance on Verilog without fine-tuning." 507 }, 508 { 509 "title": "MG-Verilog: Multi-Grained Dataset Towards Enhanced LLM-Assisted Verilog Generation", 510 "relevance": "Fine-tuning approach for Verilog; contrasts with this paper's general-purpose LLM strategy." 511 }, 512 { 513 "title": "VeriMind: Agentic LLM for Automated Verilog Generation with a Novel Evaluation Metric", 514 "relevance": "Multi-agent framework for HDL; represents prior work in agent-based hardware design." 515 }, 516 { 517 "title": "HLSPilot: LLM-Based High-Level Synthesis", 518 "relevance": "Concurrent work on LLM-driven HLS; baseline for comparison if evaluated." 519 }, 520 { 521 "title": "HDLAgent: A Benchmark for LLM-Driven RTL Design Using HDLAgent", 522 "relevance": "Agent-based RTL generation; establishes evaluation protocols for hardware design automation." 523 }, 524 { 525 "title": "ChatDev: Communicative Agents for Software Development", 526 "relevance": "Multi-agent collaboration framework; architectural pattern for role-based task decomposition." 527 }, 528 { 529 "title": "MetaGPT: Meta Programming for a Multi-Agent Collaborative Framework", 530 "relevance": "Structured multi-agent workflows; applicable to hierarchical hardware design decomposition." 531 } 532 ], 533 "engagement_factors": { 534 "practical_relevance": { 535 "score": 1, 536 "justification": "Relevant only to the narrow intersection of FPGA designers working with MATLAB-to-HLS flows, not broadly applicable to most developers." 537 }, 538 "surprise_contrarian": { 539 "score": 1, 540 "justification": "The finding that algorithm-level restructuring matters more than pragma tuning is known in the HLS community, though the magnitude (98% LUT reduction) is notable." 541 }, 542 "fear_safety": { 543 "score": 0, 544 "justification": "No safety, security, or risk angle whatsoever." 545 }, 546 "drama_conflict": { 547 "score": 0, 548 "justification": "No controversy, no challenges to specific companies or benchmarks, purely constructive contribution." 549 }, 550 "demo_ability": { 551 "score": 0, 552 "justification": "No code, no demo, no reproducibility artifacts released; requires proprietary FPGA toolchains even conceptually." 553 }, 554 "brand_recognition": { 555 "score": 1, 556 "justification": "From University of Technology Sydney, a recognized but not famous-in-tech institution; mentions Claude Code but is not from Anthropic." 557 } 558 }, 559 "hn_data": { 560 "threads": [ 561 { 562 "hn_id": "29279146", 563 "title": "Crypto Wash Trading", 564 "points": 572, 565 "comments": 299, 566 "url": "https://news.ycombinator.com/item?id=29279146", 567 "created_at": "2021-11-19T16:44:26Z" 568 }, 569 { 570 "hn_id": "44271284", 571 "title": "Self-Adapting Language Models", 572 "points": 246, 573 "comments": 73, 574 "url": "https://news.ycombinator.com/item?id=44271284", 575 "created_at": "2025-06-13T19:03:42Z" 576 }, 577 { 578 "hn_id": "41306555", 579 "title": "Exploring Impact of Code in Pre-Training", 580 "points": 5, 581 "comments": 2, 582 "url": "https://news.ycombinator.com/item?id=41306555", 583 "created_at": "2024-08-21T03:38:33Z" 584 }, 585 { 586 "hn_id": "44443760", 587 "title": "Your Language Model Can Handle Non-Canonical Tokenizations", 588 "points": 2, 589 "comments": 0, 590 "url": "https://news.ycombinator.com/item?id=44443760", 591 "created_at": "2025-07-02T13:53:44Z" 592 }, 593 { 594 "hn_id": "41745068", 595 "title": "Pre-training with code improves performance on NL reasoning", 596 "points": 2, 597 "comments": 0, 598 "url": "https://news.ycombinator.com/item?id=41745068", 599 "created_at": "2024-10-04T20:02:19Z" 600 }, 601 { 602 "hn_id": "44116793", 603 "title": "When Models Don't Collapse: On the Consistency of Iterative MLE", 604 "points": 1, 605 "comments": 0, 606 "url": "https://news.ycombinator.com/item?id=44116793", 607 "created_at": "2025-05-28T15:06:51Z" 608 }, 609 { 610 "hn_id": "43503479", 611 "title": "The Quantum Technology Job Market: A Quantitative Investigation", 612 "points": 1, 613 "comments": 0, 614 "url": "https://news.ycombinator.com/item?id=43503479", 615 "created_at": "2025-03-28T10:05:27Z" 616 }, 617 { 618 "hn_id": "42884637", 619 "title": "Player Performance and Skill Rating in Esports [pdf]", 620 "points": 1, 621 "comments": 0, 622 "url": "https://news.ycombinator.com/item?id=42884637", 623 "created_at": "2025-01-31T04:14:07Z" 624 }, 625 { 626 "hn_id": "41367147", 627 "title": "Kotlin's Type System Is (Also) Unsound", 628 "points": 1, 629 "comments": 0, 630 "url": "https://news.ycombinator.com/item?id=41367147", 631 "created_at": "2024-08-27T13:11:45Z" 632 }, 633 { 634 "hn_id": "41318909", 635 "title": "To Code, or Not to Code? Exploring Impact of Code in Pre-Training", 636 "points": 1, 637 "comments": 0, 638 "url": "https://news.ycombinator.com/item?id=41318909", 639 "created_at": "2024-08-22T11:09:37Z" 640 } 641 ], 642 "top_points": 572, 643 "total_points": 832, 644 "total_comments": 374 645 } 646 }