scan-v4.json (30024B)
1 { 2 "scan_version": 4, 3 "paper_type": "empirical", 4 "paper": { 5 "title": "From Task Solving to Robust Real-World Adaptation in LLM Agents", 6 "authors": [ 7 "Pouya Pezeshkpour", 8 "Estevam Hruschka" 9 ], 10 "year": 2026, 11 "venue": "arXiv", 12 "arxiv_id": "2602.02760", 13 "doi": null 14 }, 15 "checklist": { 16 "claims_and_evidence": { 17 "abstract_claims_supported": { 18 "applies": true, 19 "answer": true, 20 "justification": "Abstract claims are supported: 'large gaps between nominal task-solving and deployment-like robustness' (Table 1 shows degradation), 'rankings are unstable' (different leaders per grid size), 'agents trade off completion, efficiency, and penalty avoidance' (Score/Step variation in Table 1), 'model-specific sensitivities' (Figures 4-5).", 21 "source": "opus" 22 }, 23 "causal_claims_justified": { 24 "applies": true, 25 "answer": true, 26 "justification": "The paper explicitly makes causal claims ('to isolate the causal effect of each deployment stressor') and uses controlled single-factor ablations (Section 4.3) where all modifiers are disabled except one. This controlled manipulation design is adequate for causal inference about individual stressors.", 27 "source": "opus" 28 }, 29 "generalization_bounded": { 30 "applies": true, 31 "answer": false, 32 "justification": "The title claims 'Robust Real-World Adaptation' and the paper repeatedly frames findings as indicating 'deployment-like robustness' and 'real-world readiness,' but all evidence comes from a synthetic grid game. No explicit bounding of claims to the grid-game setting is provided.", 33 "source": "opus" 34 }, 35 "alternative_explanations_discussed": { 36 "applies": true, 37 "answer": false, 38 "justification": "The paper does not substantively discuss alternative explanations for observed differences between models. Differences could stem from prompt sensitivity, model size, training data composition, or thinking-mode budget differences rather than 'strategy' differences. None of these alternatives are discussed.", 39 "source": "opus" 40 }, 41 "proxy_outcome_distinction": { 42 "applies": true, 43 "answer": false, 44 "justification": "The paper measures grid game performance (accuracy, score, steps) but frames results as evidence of 'real-world readiness' and 'deployment-like robustness.' The gap between grid game metrics and actual deployment robustness is never acknowledged.", 45 "source": "opus" 46 } 47 }, 48 "limitations_and_scope": { 49 "limitations_section_present": { 50 "applies": true, 51 "answer": false, 52 "justification": "No dedicated Limitations or Threats to Validity section exists. The conclusion mentions future work directions but does not discuss limitations of the current study.", 53 "source": "opus" 54 }, 55 "threats_to_validity_specific": { 56 "applies": true, 57 "answer": false, 58 "justification": "No specific threats to validity are discussed anywhere in the paper.", 59 "source": "opus" 60 }, 61 "scope_boundaries_stated": { 62 "applies": true, 63 "answer": false, 64 "justification": "No explicit scope boundaries are stated. The paper does not state what the results do NOT show or which settings/populations are excluded from claims.", 65 "source": "opus" 66 } 67 }, 68 "conflicts_of_interest": { 69 "funding_disclosed": { 70 "applies": true, 71 "answer": false, 72 "justification": "No funding sources are disclosed anywhere in the paper. Both authors are from Megagon Labs (a corporate research lab) but no funding acknowledgment is present.", 73 "source": "opus" 74 }, 75 "affiliations_disclosed": { 76 "applies": true, 77 "answer": true, 78 "justification": "Both authors list Megagon Labs as their affiliation. They are not evaluating a Megagon product—they test third-party models (GPT, Gemini, Qwen).", 79 "source": "opus" 80 }, 81 "funder_independent_of_outcome": { 82 "applies": true, 83 "answer": false, 84 "justification": "No funding is disclosed, so independence cannot be assessed. Megagon Labs (a Recruit Holdings subsidiary) does not appear to have a direct stake in the models evaluated, but without disclosure this cannot be verified.", 85 "source": "opus" 86 }, 87 "financial_interests_declared": { 88 "applies": true, 89 "answer": false, 90 "justification": "No competing interests or financial interests statement is present in the paper.", 91 "source": "opus" 92 } 93 }, 94 "scope_and_framing": { 95 "key_terms_defined": { 96 "applies": true, 97 "answer": false, 98 "justification": "'Agent', 'robustness', and 'real-world readiness' are central terms but never formally defined; only the four operational circumstances are operationalized.", 99 "source": "haiku" 100 }, 101 "intended_contribution_clear": { 102 "applies": true, 103 "answer": true, 104 "justification": "The paper clearly states its contribution: introducing WildGrid, a controllable grid-based benchmark with four deployment-relevant stressors, and evaluating five SOTA LLMs on it.", 105 "source": "haiku" 106 }, 107 "engagement_with_prior_work": { 108 "applies": true, 109 "answer": true, 110 "justification": "Section 5 systematically relates WildGrid to prior work on agentic evaluation (ToolEmu, τ-bench), partial observability (POMDPs, MiniGrid), goal inference, and synthetic benchmarks, explaining differentiation.", 111 "source": "haiku" 112 } 113 } 114 }, 115 "type_checklist": { 116 "empirical": { 117 "artifacts": { 118 "code_released": { 119 "applies": true, 120 "answer": true, 121 "justification": "A GitHub repository is provided: https://github.com/megagonlabs/wildgrid (footnote 1 in the abstract).", 122 "source": "opus" 123 }, 124 "data_released": { 125 "applies": true, 126 "answer": true, 127 "justification": "The benchmark is procedurally generated from random seeds with documented parameters. The released code repository enables regeneration of all game instances.", 128 "source": "opus" 129 }, 130 "environment_specified": { 131 "applies": true, 132 "answer": false, 133 "justification": "No requirements.txt, Dockerfile, or detailed environment setup is mentioned in the paper. No library versions are specified.", 134 "source": "opus" 135 }, 136 "reproduction_instructions": { 137 "applies": true, 138 "answer": false, 139 "justification": "No step-by-step reproduction instructions are provided in the paper. The experimental setup is described (Section 3) but no commands or scripts to reproduce results.", 140 "source": "opus" 141 } 142 }, 143 "statistical_methodology": { 144 "confidence_intervals_or_error_bars": { 145 "applies": true, 146 "answer": false, 147 "justification": "Table 1 reports only point estimates for Accuracy, Score, and Steps. No confidence intervals or error bars are reported anywhere. Ablation figures (Figure 4) also show curves without error bars.", 148 "source": "opus" 149 }, 150 "significance_tests": { 151 "applies": true, 152 "answer": false, 153 "justification": "Comparative claims (e.g., 'Gemini-3 Pro attains the highest accuracy on 6×6 and 10×10') are made by comparing raw numbers without any statistical significance tests.", 154 "source": "opus" 155 }, 156 "effect_sizes_reported": { 157 "applies": true, 158 "answer": false, 159 "justification": "Only raw accuracy, score, and step values are reported in Table 1. No formal effect sizes (Cohen's d, odds ratios, or contextualized percentage improvements) are provided.", 160 "source": "opus" 161 }, 162 "sample_size_justified": { 163 "applies": true, 164 "answer": false, 165 "justification": "50 instances per grid size for main evaluation and only 5 instances per data point for ablations are used, with no justification for these choices and no power analysis.", 166 "source": "opus" 167 }, 168 "variance_reported": { 169 "applies": true, 170 "answer": false, 171 "justification": "No standard deviations, interquartile ranges, or any spread measures are reported. Table 1 shows averages only. The 50-episode results have no variance quantification.", 172 "source": "opus" 173 } 174 }, 175 "evaluation_design": { 176 "baselines_included": { 177 "applies": true, 178 "answer": true, 179 "justification": "Five state-of-the-art LLMs are compared against each other (GPT-5.2, GPT-5 mini, Gemini-3 Pro, Gemini-3 Flash, Qwen3-235B), providing mutual baselines.", 180 "source": "opus" 181 }, 182 "baselines_contemporary": { 183 "applies": true, 184 "answer": true, 185 "justification": "All five models are current state-of-the-art: GPT-5.2, GPT-5 mini (Singh et al., 2025), Gemini-3 Pro/Flash (Comanici et al., 2025), and Qwen3-235B (Yang et al., 2025).", 186 "source": "opus" 187 }, 188 "ablation_study": { 189 "applies": true, 190 "answer": true, 191 "justification": "Section 4.3 presents single-stressor ablations: 'we deactivate all perturbations and vary only the single factor under study, to isolate its causal impact on performance.' Four factors are swept: Noise, Latent, Hazard-Spread, Teleport-Step.", 192 "source": "opus" 193 }, 194 "multiple_metrics": { 195 "applies": true, 196 "answer": true, 197 "justification": "Three metrics are reported: success rate (Acc), average Score, and Steps per grid size (Table 1). The paper explicitly notes these capture different aspects of performance.", 198 "source": "opus" 199 }, 200 "human_evaluation": { 201 "applies": true, 202 "answer": false, 203 "justification": "No human evaluation is included. All evaluation is automated through the grid game's built-in metrics (success/failure, score, steps).", 204 "source": "opus" 205 }, 206 "held_out_test_set": { 207 "applies": true, 208 "answer": true, 209 "justification": "Game instances are randomly generated for each evaluation. No training or tuning is performed on these instances—all models are evaluated zero-shot, so every instance is effectively held-out.", 210 "source": "opus" 211 }, 212 "per_category_breakdown": { 213 "applies": true, 214 "answer": true, 215 "justification": "Results are broken down by grid size (6×6, 8×8, 10×10) in Table 1 and by individual stressor in the ablation studies (Figure 4). Per-model action profiles are also shown (Figure 3).", 216 "source": "opus" 217 }, 218 "failure_cases_discussed": { 219 "applies": true, 220 "answer": true, 221 "justification": "Section 4.2 discusses Qwen3's myopic trial-and-error behavior, GPT-5.2's score degradation from miscalibrated interaction, and specific failure drivers identified through behavioral traces and feature attribution (Section 4.4).", 222 "source": "opus" 223 }, 224 "negative_results_reported": { 225 "applies": true, 226 "answer": true, 227 "justification": "Qwen3 'largely fails across grid sizes' (Section 4.1). The ablations show counterintuitive results: moderate noise can improve performance (Section 4.3), and teleports can help or hurt depending on frequency. GPT-5.2's score degrades sharply with grid size.", 228 "source": "opus" 229 } 230 }, 231 "setup_transparency": { 232 "model_versions_specified": { 233 "applies": true, 234 "answer": false, 235 "justification": "Models are listed as 'GPT-5.2, GPT-5 MINI, GEMINI 3 PRO, GEMINI 3 FLASH, and QWEN3-235B-A22B.' These are marketing names without API versions or snapshot dates. No version identifiers like API dates are provided.", 236 "source": "opus" 237 }, 238 "prompts_provided": { 239 "applies": true, 240 "answer": true, 241 "justification": "Full system and user prompts are provided in Appendix A (Prompts A.1 and A.2). The system prompt describes game mechanics and output format, and the user prompt provides the observation template.", 242 "source": "opus" 243 }, 244 "hyperparameters_reported": { 245 "applies": true, 246 "answer": false, 247 "justification": "Only 'default thinking budget (medium or high)' is mentioned for models supporting thinking mode. Temperature, top-p, max tokens, and other sampling parameters are not reported.", 248 "source": "opus" 249 }, 250 "scaffolding_described": { 251 "applies": true, 252 "answer": true, 253 "justification": "Section 2.3 describes the player interface in detail: text-only observation, local view with facing direction, state vector, action space, short action history, and event-based execution log. Full prompts are in the appendix.", 254 "source": "opus" 255 }, 256 "data_preprocessing_documented": { 257 "applies": true, 258 "answer": true, 259 "justification": "Section 3 documents the game instance generation: 50 random instances per grid size, with parameter ranges specified (noise ∼U(0,0.2), move fail ∼U(0,0.1), latent fraction ∼U(0,0.2)), fixed dynamics (5×5 window, shifts every 25 steps, teleports every 50 steps, drift every 100 steps).", 260 "source": "opus" 261 } 262 }, 263 "data_integrity": { 264 "raw_data_available": { 265 "applies": true, 266 "answer": false, 267 "justification": "No raw trajectory data, episode logs, or per-instance results are made available. Only aggregated results are reported in tables and figures.", 268 "source": "opus" 269 }, 270 "data_collection_described": { 271 "applies": true, 272 "answer": true, 273 "justification": "The game instance generation procedure is fully described in Sections 2.1 and 3, including tile placement, parameter sampling ranges, and fixed dynamics schedules.", 274 "source": "opus" 275 }, 276 "recruitment_methods_described": { 277 "applies": false, 278 "answer": false, 279 "justification": "No human participants. The paper evaluates LLM agents on a procedural benchmark.", 280 "source": "opus" 281 }, 282 "data_pipeline_documented": { 283 "applies": true, 284 "answer": true, 285 "justification": "The pipeline from game generation (random seed → map sampling → parameter assignment) through evaluation (LLM agent plays → metrics computed) is documented in Sections 2 and 3.", 286 "source": "opus" 287 } 288 }, 289 "contamination": { 290 "training_cutoff_stated": { 291 "applies": true, 292 "answer": false, 293 "justification": "No training data cutoff dates are stated for any of the five evaluated models.", 294 "source": "opus" 295 }, 296 "train_test_overlap_discussed": { 297 "applies": true, 298 "answer": false, 299 "justification": "No discussion of whether models could have seen similar grid games or the WildGrid code/description during training. While the benchmark is new, the game mechanics could resemble training data.", 300 "source": "opus" 301 }, 302 "benchmark_contamination_addressed": { 303 "applies": true, 304 "answer": false, 305 "justification": "Although the benchmark is procedurally generated (making direct contamination unlikely), the paper never explicitly discusses this advantage or the contamination question.", 306 "source": "opus" 307 } 308 }, 309 "human_studies": { 310 "pre_registered": { 311 "applies": false, 312 "answer": false, 313 "justification": "No human participants in this study. It evaluates LLM agents on a procedural benchmark.", 314 "source": "opus" 315 }, 316 "irb_or_ethics_approval": { 317 "applies": false, 318 "answer": false, 319 "justification": "No human participants in this study.", 320 "source": "opus" 321 }, 322 "demographics_reported": { 323 "applies": false, 324 "answer": false, 325 "justification": "No human participants in this study.", 326 "source": "opus" 327 }, 328 "inclusion_exclusion_criteria": { 329 "applies": false, 330 "answer": false, 331 "justification": "No human participants in this study.", 332 "source": "opus" 333 }, 334 "randomization_described": { 335 "applies": false, 336 "answer": false, 337 "justification": "No human participants in this study.", 338 "source": "opus" 339 }, 340 "blinding_described": { 341 "applies": false, 342 "answer": false, 343 "justification": "No human participants in this study.", 344 "source": "opus" 345 }, 346 "attrition_reported": { 347 "applies": false, 348 "answer": false, 349 "justification": "No human participants in this study.", 350 "source": "opus" 351 } 352 }, 353 "cost_and_practicality": { 354 "inference_cost_reported": { 355 "applies": true, 356 "answer": false, 357 "justification": "No API costs, token counts, or latency measurements are reported. The study runs 750+ episodes (50 instances × 3 grid sizes × 5 models, each up to 200 steps) but total cost is not quantified.", 358 "source": "opus" 359 }, 360 "compute_budget_stated": { 361 "applies": true, 362 "answer": false, 363 "justification": "No total computational budget, API spend, or hardware specifications are stated.", 364 "source": "opus" 365 } 366 }, 367 "experimental_rigor": { 368 "seed_sensitivity_reported": { 369 "applies": true, 370 "answer": false, 371 "justification": "Results are averaged over 50 random game instances but no seed sensitivity analysis is reported. No variance across instances is shown.", 372 "source": "opus" 373 }, 374 "number_of_runs_stated": { 375 "applies": true, 376 "answer": true, 377 "justification": "Section 3: 'we generate 50 random game instances for each grid size' for main evaluation and '5 instances for each data point per condition' for ablations.", 378 "source": "opus" 379 }, 380 "hyperparameter_search_budget": { 381 "applies": true, 382 "answer": false, 383 "justification": "No hyperparameter search budget is reported. The choice of parameter ranges (noise, latent fraction, etc.) and thinking budget settings are not justified.", 384 "source": "opus" 385 }, 386 "best_config_selection_justified": { 387 "applies": true, 388 "answer": true, 389 "justification": "A single fixed configuration is used for all models with the same parameters. All results for all models are reported—no selection of best configuration occurs.", 390 "source": "opus" 391 }, 392 "multiple_comparison_correction": { 393 "applies": false, 394 "answer": false, 395 "justification": "No statistical tests are performed, so multiple comparison correction is not applicable.", 396 "source": "opus" 397 }, 398 "self_comparison_bias_addressed": { 399 "applies": true, 400 "answer": false, 401 "justification": "The authors designed the benchmark and evaluate third-party models on it. They do not acknowledge the potential bias of benchmark designers selecting game mechanics that may favor certain model capabilities.", 402 "source": "opus" 403 }, 404 "compute_budget_vs_performance": { 405 "applies": true, 406 "answer": false, 407 "justification": "Models of vastly different sizes and compute costs are compared (e.g., Qwen3-235B vs GPT-5 mini) without any discussion of compute budget differences or performance normalized by cost.", 408 "source": "opus" 409 }, 410 "benchmark_construct_validity": { 411 "applies": true, 412 "answer": false, 413 "justification": "The paper assumes grid game performance reflects 'real-world deployment robustness' but provides no validation of this construct mapping. Whether performance on a grid puzzle predicts robustness in actual deployment scenarios is not examined.", 414 "source": "opus" 415 }, 416 "scaffold_confound_addressed": { 417 "applies": true, 418 "answer": true, 419 "justification": "All models use the identical prompt and interaction interface (Section 2.3, Appendix A). The scaffold is controlled across all comparisons.", 420 "source": "opus" 421 } 422 }, 423 "data_leakage": { 424 "temporal_leakage_addressed": { 425 "applies": true, 426 "answer": false, 427 "justification": "No discussion of whether models' training data could include similar grid game descriptions or solutions, despite the benchmark being novel.", 428 "source": "opus" 429 }, 430 "feature_leakage_addressed": { 431 "applies": true, 432 "answer": false, 433 "justification": "No discussion of whether the observation format or prompt structure leaks information that aids performance beyond what a real deployment would provide.", 434 "source": "opus" 435 }, 436 "non_independence_addressed": { 437 "applies": true, 438 "answer": false, 439 "justification": "No discussion of independence between game instances or whether shared parameter ranges across instances create dependencies.", 440 "source": "opus" 441 }, 442 "leakage_detection_method": { 443 "applies": true, 444 "answer": false, 445 "justification": "No concrete leakage detection or prevention method is used or described.", 446 "source": "opus" 447 } 448 } 449 } 450 }, 451 "claims": [ 452 { 453 "claim": "LLM agents show large gaps between nominal task-solving and deployment-like robustness across all five evaluated models.", 454 "evidence": "Table 1: accuracy ranges 2–50% at 6×6 declining to 0–38% at 10×10 under full modifiers; Qwen3 fails almost entirely at larger grids.", 455 "supported": "strong" 456 }, 457 { 458 "claim": "Rankings are unstable across grid sizes: weaker models can outperform stronger ones when strategy matches the uncertainty regime.", 459 "evidence": "Gemini-3 Flash leads at 8×8 (42%) while Gemini-3 Pro leads at 6×6 and 10×10; GPT-5 mini outperforms GPT-5.2 on efficiency despite not leading in accuracy.", 460 "supported": "moderate" 461 }, 462 { 463 "claim": "GPT-5 mini adopts an efficiency-aware sensing strategy — front-loading SCAN/MEASURE actions — yielding lower step counts and better scores.", 464 "evidence": "Figure 3b shows highest early SCAN/MEASURE probability mass for GPT-5 mini; Table 1 shows lowest step count (23.2 at 6×6) and best score at 6×6 and 10×10.", 465 "supported": "strong" 466 }, 467 { 468 "claim": "Some deployment stressors exhibit non-monotonic effects — moderate noise or disruption can improve performance for some models.", 469 "evidence": "Figure 4 shows GPT-5.2 and Gemini-3 Pro accuracy peaks at mid-range noise; Teleport-Step at high frequency substantially helps GPT-5.2 (near-perfect accuracy).", 470 "supported": "weak" 471 }, 472 { 473 "claim": "Agents partially infer implicit efficiency and score objectives without explicit instruction.", 474 "evidence": "Models exhibit different completion vs. steps vs. score trade-offs (e.g., GPT-5 mini consistently minimizes steps) despite prompts that only specify task completion.", 475 "supported": "weak" 476 }, 477 { 478 "claim": "Agent-door distance and hazard spread are the strongest cross-model predictors of failure.", 479 "evidence": "Figure 5 logistic regression heatmap shows consistently negative coefficients for Agent-Door-Dist and Hazard-Spread across all four frontier models.", 480 "supported": "moderate" 481 } 482 ], 483 "methodology_tags": [ 484 "benchmark-eval" 485 ], 486 "key_findings": "WildGrid, a synthetic grid-based benchmark combining partial observability, dynamic environments, noisy signals, and agent state drift, reveals consistent gaps between LLM task-solving capability and deployment robustness across five SOTA models. Performance degrades with scale and rankings are unstable across conditions — strategy-environment fit matters as much as raw capability. GPT-5 mini's front-loaded sensing strategy yields superior efficiency despite not leading in accuracy, while Qwen3 fails almost entirely due to myopic interaction behavior that depletes energy. Single-stressor ablations reveal strongly non-monotonic, model-specific sensitivities, with some disruptive conditions improving performance by forcing exploration.", 487 "red_flags": [ 488 { 489 "flag": "Tiny ablation sample (n=5)", 490 "detail": "Single-stressor ablations use only 5 episodes per condition — far too small for reliable conclusions, especially the non-monotonic sensitivity claims that drive the paper's key interpretations." 491 }, 492 { 493 "flag": "No statistical tests or error bars", 494 "detail": "All comparative claims (model rankings, performance differences across grid sizes) lack confidence intervals, significance tests, or variance estimates across 50 episodes." 495 }, 496 { 497 "flag": "Synthetic-to-real generalization gap", 498 "detail": "Claims about 'real-world deployment readiness' and 'practical, in-the-wild use' are drawn from a single synthetic grid game; this leap is neither bounded nor empirically validated." 499 }, 500 { 501 "flag": "No non-LLM baseline", 502 "detail": "No random agent or rule-based heuristic is included; it is impossible to determine whether LLM performance is meaningfully above chance on this benchmark." 503 }, 504 { 505 "flag": "No limitations section", 506 "detail": "The paper lacks a dedicated limitations or threats-to-validity section; the conclusion discusses only future work without acknowledging methodological weaknesses." 507 }, 508 { 509 "flag": "LLM hyperparameters missing", 510 "detail": "Temperature, top-p, and other generation parameters are not reported, preventing exact reproduction; only thinking budget mode (medium or high) is mentioned." 511 } 512 ], 513 "cited_papers": [ 514 { 515 "title": "Identifying the Risks of LM Agents with an LM-Emulated Sandbox (ToolEmu)", 516 "relevance": "Key comparison benchmark for LLM agent robustness evaluation; directly contrasted with WildGrid's approach." 517 }, 518 { 519 "title": "τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains", 520 "relevance": "Related agentic evaluation benchmark with persistent state and trajectory-level metrics exposing agent brittleness." 521 }, 522 { 523 "title": "MiniGrid & MiniWorld: Modular & Customizable Reinforcement Learning Environments", 524 "relevance": "Prior synthetic controllable environment for agent evaluation; WildGrid extends this paradigm to LLM agents with deployment stressors." 525 }, 526 { 527 "title": "TextWorld: A Learning Environment for Text-Based Games", 528 "relevance": "Precedent for text-based agent evaluation; directly compared as related work on synthetic testbeds." 529 }, 530 { 531 "title": "HAZARD Challenge: Embodied Decision Making in Dynamically Changing Environments", 532 "relevance": "Closest prior art on dynamic environment benchmarks for agents; WildGrid extends to non-stationarity and internal drift." 533 }, 534 { 535 "title": "Cooperative Inverse Reinforcement Learning", 536 "relevance": "Theoretical foundation for implicit objective inference under partial observability — core to WildGrid's multi-objective framing." 537 }, 538 { 539 "title": "Tools Fail: Detecting Silent Errors in Faulty Tools", 540 "relevance": "Related work on LLM agent robustness to unreliable tool signals." 541 }, 542 { 543 "title": "Hell or High Water: Evaluating Agentic Recovery from External Failures", 544 "relevance": "Related evaluation of LLM agents under external disruption and unclear instructions; directly contrasted in related work." 545 } 546 ], 547 "engagement_factors": { 548 "practical_relevance": { 549 "score": 2, 550 "justification": "Developers deploying LLM agents can use WildGrid to probe robustness, though the synthetic setting limits direct transfer to real applications." 551 }, 552 "surprise_contrarian": { 553 "score": 2, 554 "justification": "Finding that weaker models can outperform stronger ones, and that moderate noise or disruption can improve performance, challenges naive capability-scaling assumptions." 555 }, 556 "fear_safety": { 557 "score": 1, 558 "justification": "Raises concerns about LLM agent reliability under realistic deployment conditions, but in a contained research context without alarming safety implications." 559 }, 560 "drama_conflict": { 561 "score": 1, 562 "justification": "Ranking instability between prominent model families (GPT vs. Gemini vs. Qwen) is mildly interesting but lacks direct competitive conflict framing." 563 }, 564 "demo_ability": { 565 "score": 3, 566 "justification": "Code released at github.com/megagonlabs/wildgrid; readers can immediately run their own LLM agents through the benchmark." 567 }, 568 "brand_recognition": { 569 "score": 1, 570 "justification": "Megagon Labs is not a prominent AI lab; the evaluated models (GPT-5, Gemini 3, Qwen3) are well-known but confer no brand recognition to the paper itself." 571 } 572 }, 573 "hn_data": { 574 "threads": [], 575 "top_points": 0, 576 "total_points": 0, 577 "total_comments": 0 578 } 579 }