scan-v5.json (25213B)
1 { 2 "scan_version": 5, 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": "All core abstract claims (performance degradation with grid size, ranking instability, partial objective inference, ablation findings) are directly supported by Table 1, Figures 3-5, and the experimental sections.", 21 "source": "haiku" 22 }, 23 "causal_claims_justified": { 24 "applies": true, 25 "answer": true, 26 "justification": "Section 4.3 explicitly runs single-stressor ablations that deactivate all other modifiers to isolate 'the causal effect of each deployment stressor', which is appropriate for causal inference in this controlled simulation.", 27 "source": "haiku" 28 }, 29 "generalization_bounded": { 30 "applies": true, 31 "answer": false, 32 "justification": "The paper repeatedly invokes 'real-world readiness' and 'deployment-like robustness' based solely on a synthetic grid game with four stressors, without bounding these conclusions to the specific game design or discussing the gap to actual deployment contexts.", 33 "source": "haiku" 34 }, 35 "alternative_explanations_discussed": { 36 "applies": true, 37 "answer": false, 38 "justification": "Ranking instability and non-monotonic noise effects could reflect specific game design choices rather than generalizable deployment properties, but no alternative interpretations are considered.", 39 "source": "haiku" 40 }, 41 "proxy_outcome_distinction": { 42 "applies": true, 43 "answer": false, 44 "justification": "Grid game accuracy, score, and step counts are used as proxies for 'real-world deployment readiness' without discussing whether these synthetic metrics predict actual deployment performance.", 45 "source": "haiku" 46 } 47 }, 48 "limitations_and_scope": { 49 "limitations_section_present": { 50 "applies": true, 51 "answer": false, 52 "justification": "There is no dedicated limitations or threats-to-validity section; the conclusion briefly mentions future work directions but does not enumerate limitations of the current study.", 53 "source": "haiku" 54 }, 55 "threats_to_validity_specific": { 56 "applies": true, 57 "answer": false, 58 "justification": "No specific threats to validity are discussed, such as whether the synthetic grid game generalizes to real LLM agent tasks, or whether 50 and 5 episodes per condition are sufficient sample sizes.", 59 "source": "haiku" 60 }, 61 "scope_boundaries_stated": { 62 "applies": true, 63 "answer": false, 64 "justification": "The paper does not explicitly state what its results do not show—for example, that grid game robustness findings do not necessarily transfer to code agents, web agents, or other real-world agent settings.", 65 "source": "haiku" 66 } 67 }, 68 "conflicts_of_interest": { 69 "funding_disclosed": { 70 "applies": true, 71 "answer": false, 72 "justification": "No funding acknowledgment or grant numbers appear anywhere in the paper.", 73 "source": "haiku" 74 }, 75 "affiliations_disclosed": { 76 "applies": true, 77 "answer": true, 78 "justification": "Both authors list 'Megagon Labs' as their affiliation with contact emails, clearly disclosed in the header.", 79 "source": "haiku" 80 }, 81 "funder_independent_of_outcome": { 82 "applies": false, 83 "answer": false, 84 "justification": "No external funder is disclosed; Megagon Labs does not produce any of the five evaluated models, so institutional conflict is not a concern here.", 85 "source": "haiku" 86 }, 87 "financial_interests_declared": { 88 "applies": true, 89 "answer": false, 90 "justification": "No competing interests statement or declaration of financial interests (patents, equity, consulting) appears in the paper.", 91 "source": "haiku" 92 } 93 }, 94 "scope_and_framing": { 95 "key_terms_defined": { 96 "applies": true, 97 "answer": true, 98 "justification": "Key terms are operationally defined: 'robustness' is defined via four stressors (partial observability, noisy signals, dynamic environments, dynamic agent state), and 'agent' is defined as an LLM player with a specified text interface.", 99 "source": "haiku" 100 }, 101 "intended_contribution_clear": { 102 "applies": true, 103 "answer": true, 104 "justification": "The paper clearly states it introduces WildGrid, a controllable grid benchmark for stress-testing LLM agents under deployment-like conditions, explicitly differentiating it from prior clean-interface evaluations.", 105 "source": "haiku" 106 }, 107 "engagement_with_prior_work": { 108 "applies": true, 109 "answer": true, 110 "justification": "Section 5 systematically positions WildGrid against τ-bench, ToolSandbox, MiniGrid, TextWorld, ToolEmu, and POMDP literature, explaining how this work jointly combines stressors that prior work addresses separately.", 111 "source": "haiku" 112 } 113 } 114 }, 115 "type_checklist": { 116 "empirical": { 117 "artifacts": { 118 "code_released": { 119 "applies": true, 120 "answer": true, 121 "justification": "Footnote 1 links to https://github.com/megagonlabs/wildgrid, providing a concrete GitHub repository for the benchmark.", 122 "source": "haiku" 123 }, 124 "data_released": { 125 "applies": true, 126 "answer": true, 127 "justification": "Episodes are procedurally generated with specified random seeds and documented parameters; with the released code, evaluation instances can be regenerated.", 128 "source": "haiku" 129 }, 130 "environment_specified": { 131 "applies": true, 132 "answer": false, 133 "justification": "No requirements.txt, Dockerfile, or explicit dependency specification is mentioned in the paper; only a GitHub link is provided without setup documentation.", 134 "source": "haiku" 135 }, 136 "reproduction_instructions": { 137 "applies": true, 138 "answer": false, 139 "justification": "No step-by-step instructions for reproducing the experiments are provided in the paper beyond the GitHub link.", 140 "source": "haiku" 141 } 142 }, 143 "statistical_methodology": { 144 "confidence_intervals_or_error_bars": { 145 "applies": true, 146 "answer": false, 147 "justification": "Table 1 reports accuracy, score, and steps as point estimates with no confidence intervals or error bars; ablation figures also lack spread measures despite stochastic environments.", 148 "source": "haiku" 149 }, 150 "significance_tests": { 151 "applies": true, 152 "answer": false, 153 "justification": "No statistical significance tests are applied to any of the comparative performance results across models or conditions.", 154 "source": "haiku" 155 }, 156 "effect_sizes_reported": { 157 "applies": true, 158 "answer": true, 159 "justification": "Raw percentage accuracy differences and score differences between models are reported in Table 1 (e.g., Gemini-3 Pro 50% vs. GPT-5 mini 34% on 6x6), providing magnitude of effects without significance testing.", 160 "source": "haiku" 161 }, 162 "sample_size_justified": { 163 "applies": true, 164 "answer": false, 165 "justification": "50 episodes per grid size for main results and only 5 instances per data point for ablations are used with no power analysis or justification for these choices.", 166 "source": "haiku" 167 }, 168 "variance_reported": { 169 "applies": true, 170 "answer": false, 171 "justification": "No standard deviation or variance is reported for any metric in Table 1 or the ablation figures; only point estimates are shown.", 172 "source": "haiku" 173 } 174 }, 175 "evaluation_design": { 176 "baselines_included": { 177 "applies": true, 178 "answer": true, 179 "justification": "Five contemporary LLMs serve as mutual baselines for each other; ablations additionally compare single-stressor conditions against the full-modifier setting.", 180 "source": "haiku" 181 }, 182 "baselines_contemporary": { 183 "applies": true, 184 "answer": true, 185 "justification": "All five models (GPT-5.2, GPT-5 Mini, Gemini-3 Pro/Flash, Qwen3-235B-A22B) are 2025-2026 frontier models cited from recent system card papers.", 186 "source": "haiku" 187 }, 188 "ablation_study": { 189 "applies": true, 190 "answer": true, 191 "justification": "Section 4.3 runs controlled single-stressor ablations, deactivating all other modifiers and sweeping one factor at a time across noise, latent fraction, hazard spread, and teleport schedule.", 192 "source": "haiku" 193 }, 194 "multiple_metrics": { 195 "applies": true, 196 "answer": true, 197 "justification": "Results are reported across accuracy (completion rate), score (cumulative reward), step count, action-frequency profiles over time, and logistic regression feature attributions.", 198 "source": "haiku" 199 }, 200 "human_evaluation": { 201 "applies": false, 202 "answer": false, 203 "justification": "The evaluation is fully automated against a programmatic game environment; no human evaluation of agent outputs is needed or performed.", 204 "source": "haiku" 205 }, 206 "held_out_test_set": { 207 "applies": false, 208 "answer": false, 209 "justification": "Not applicable: evaluation uses procedurally generated game instances rather than a fixed prediction task requiring train/test splits.", 210 "source": "haiku" 211 }, 212 "per_category_breakdown": { 213 "applies": true, 214 "answer": true, 215 "justification": "Results are broken down by grid size (6x6, 8x8, 10x10), by individual stressor type in ablation figures, and by model across all analyses.", 216 "source": "haiku" 217 }, 218 "failure_cases_discussed": { 219 "applies": true, 220 "answer": true, 221 "justification": "QWEN3's failure mode (myopic INTERACT-heavy behavior leading to energy depletion) is analyzed in detail, and model-specific failure drivers are identified through action profiles and logistic regression coefficients.", 222 "source": "haiku" 223 }, 224 "negative_results_reported": { 225 "applies": true, 226 "answer": true, 227 "justification": "QWEN3's near-zero accuracy (2% on 6x6, 0% on larger grids) is fully reported, and performance degradation including negative scores across conditions is shown throughout.", 228 "source": "haiku" 229 } 230 }, 231 "setup_transparency": { 232 "model_versions_specified": { 233 "applies": true, 234 "answer": true, 235 "justification": "Each model is cited with a specific arXiv system card (Singh et al. 2025 for GPT-5, Comanici et al. 2025 for Gemini-3, Yang et al. 2025 for Qwen3-235B-A22B), providing traceable version references.", 236 "source": "haiku" 237 }, 238 "prompts_provided": { 239 "applies": true, 240 "answer": true, 241 "justification": "Appendix A provides both the system prompt and user prompt verbatim with all template variables shown, allowing exact prompt reproduction.", 242 "source": "haiku" 243 }, 244 "hyperparameters_reported": { 245 "applies": true, 246 "answer": false, 247 "justification": "Temperature, top-p, and other API parameters are not reported; only 'default thinking budget (medium or high)' is mentioned for thinking-enabled models.", 248 "source": "haiku" 249 }, 250 "scaffolding_described": { 251 "applies": true, 252 "answer": true, 253 "justification": "Section 2.3 describes the text-only interface, structured state summary format, action history passing, and event logging mechanism in sufficient detail to understand the scaffolding.", 254 "source": "haiku" 255 }, 256 "data_preprocessing_documented": { 257 "applies": true, 258 "answer": true, 259 "justification": "Game generation parameters are documented: density hyperparameters, uniform sampling of noise/move-fail/latent fraction from specified ranges (e.g., noise~U(0,0.2)), and fixed counts for unique objects.", 260 "source": "haiku" 261 } 262 }, 263 "data_integrity": { 264 "raw_data_available": { 265 "applies": true, 266 "answer": false, 267 "justification": "Raw episode trajectories and outcomes are not explicitly released or archived; the code is available but the specific random seeds used in the paper are not documented.", 268 "source": "haiku" 269 }, 270 "data_collection_described": { 271 "applies": true, 272 "answer": true, 273 "justification": "Section 3 documents the collection procedure: 50 random game instances per grid size for main results, 5 per data point for ablations, with uniform sampling of key parameters from specified distributions.", 274 "source": "haiku" 275 }, 276 "recruitment_methods_described": { 277 "applies": false, 278 "answer": false, 279 "justification": "No human participants; programmatic game instance generation is used.", 280 "source": "haiku" 281 }, 282 "data_pipeline_documented": { 283 "applies": true, 284 "answer": true, 285 "justification": "The pipeline from game generation to LLM evaluation to metric computation (accuracy, score, steps) is described in sufficient detail across Sections 2-4.", 286 "source": "haiku" 287 } 288 }, 289 "contamination": { 290 "training_cutoff_stated": { 291 "applies": true, 292 "answer": false, 293 "justification": "No training data cutoffs are stated for any of the five evaluated models.", 294 "source": "haiku" 295 }, 296 "train_test_overlap_discussed": { 297 "applies": true, 298 "answer": false, 299 "justification": "The paper does not discuss whether LLMs may have been trained on similar grid-world tasks (MiniGrid, TextWorld) that could inflate performance estimates on the WildGrid benchmark.", 300 "source": "haiku" 301 }, 302 "benchmark_contamination_addressed": { 303 "applies": true, 304 "answer": false, 305 "justification": "WildGrid appears to be novel but similar grid environments exist in pretraining corpora; the paper does not address whether exposure to analogous environments during training affects results.", 306 "source": "haiku" 307 } 308 }, 309 "human_studies": { 310 "pre_registered": { 311 "applies": false, 312 "answer": false, 313 "justification": "No human participants.", 314 "source": "haiku" 315 }, 316 "irb_or_ethics_approval": { 317 "applies": false, 318 "answer": false, 319 "justification": "No human participants.", 320 "source": "haiku" 321 }, 322 "demographics_reported": { 323 "applies": false, 324 "answer": false, 325 "justification": "No human participants.", 326 "source": "haiku" 327 }, 328 "inclusion_exclusion_criteria": { 329 "applies": false, 330 "answer": false, 331 "justification": "No human participants.", 332 "source": "haiku" 333 }, 334 "randomization_described": { 335 "applies": false, 336 "answer": false, 337 "justification": "No human participants.", 338 "source": "haiku" 339 }, 340 "blinding_described": { 341 "applies": false, 342 "answer": false, 343 "justification": "No human participants.", 344 "source": "haiku" 345 }, 346 "attrition_reported": { 347 "applies": false, 348 "answer": false, 349 "justification": "No human participants.", 350 "source": "haiku" 351 } 352 }, 353 "cost_and_practicality": { 354 "inference_cost_reported": { 355 "applies": true, 356 "answer": false, 357 "justification": "No inference cost, latency, or API cost for running 50+ episodes per model per grid size across five models is reported.", 358 "source": "haiku" 359 }, 360 "compute_budget_stated": { 361 "applies": true, 362 "answer": false, 363 "justification": "Total computational budget (API calls, wall time, dollar cost) for the full evaluation is not stated anywhere in the paper.", 364 "source": "haiku" 365 } 366 } 367 } 368 }, 369 "claims": [ 370 { 371 "claim": "Performance degrades as grid size and horizon increase for all frontier models", 372 "evidence": "Table 1 shows accuracy declining from 6x6 to 10x10 for GPT-5.2 (48%→26%), Gemini-3 Pro (50%→38%), Gemini-3 Flash (48%→32%), GPT-5 mini (34%→30%)", 373 "supported": "strong" 374 }, 375 { 376 "claim": "Model rankings are unstable across grid sizes and stressor regimes", 377 "evidence": "Gemini-3 Pro leads 6x6 and 10x10 accuracy but Gemini-3 Flash leads 8x8; GPT-5 mini has low raw accuracy but best step efficiency and scores on 6x6 and 10x10", 378 "supported": "strong" 379 }, 380 { 381 "claim": "Agents exhibit partial implicit objective inference (efficiency/score trade-offs) without explicit instruction", 382 "evidence": "GPT-5 mini shows lowest step counts and competitive scores without being instructed to optimize them; action profiles in Figure 3 show model-specific sensing-then-act strategies", 383 "supported": "moderate" 384 }, 385 { 386 "claim": "Moderate noise can improve accuracy for some models (non-monotonic noise effect)", 387 "evidence": "Figure 4 shows GPT-5.2 and Gemini-3 Pro peak at mid-range noise levels in the single-stressor ablation, consistent with Findling & Wyart (2024) cited by the paper", 388 "supported": "moderate" 389 }, 390 { 391 "claim": "QWEN3 largely fails the task due to a myopic trial-and-error INTERACT strategy", 392 "evidence": "QWEN3 achieves 2% accuracy on 6x6 and 0% on larger grids; Figure 3e shows unusually high INTERACT probability with minimal SCAN/MEASURE, leading to early energy depletion", 393 "supported": "strong" 394 }, 395 { 396 "claim": "Logistic regression on 9 environment features identifies interpretable model-specific failure drivers", 397 "evidence": "Figure 5 heatmap shows per-model coefficients; however, models achieve only ~60% win/loss prediction accuracy on average with the linear predictor", 398 "supported": "weak" 399 } 400 ], 401 "methodology_tags": [ 402 "benchmark-eval", 403 "ablation" 404 ], 405 "key_findings": "Five frontier LLMs show substantial gaps between nominal task-solving and deployment-like robustness on WildGrid, a controllable grid benchmark with four stressors (partial observability, noisy sensing, non-stationarity, agent-state drift), with accuracy declining across grid sizes. Model rankings are unstable across regimes, with sensing strategy diversity (information-gathering vs. reactive) driving outcome differences independent of raw capability. Single-stressor ablations reveal non-monotonic sensitivities where moderate noise or teleportation can improve performance for some models. QWEN3 fails almost completely due to a myopic INTERACT-heavy strategy that depletes energy early, while GPT-5 mini's front-loaded sensing strategy yields better efficiency despite lower raw accuracy.", 406 "red_flags": [ 407 { 408 "flag": "Tiny ablation sample", 409 "detail": "Only 5 episodes per data point in ablations — far too small to draw reliable conclusions about non-monotonic stressor effects without any confidence intervals or significance testing." 410 }, 411 { 412 "flag": "No variance reporting", 413 "detail": "All metrics in Table 1 and ablation figures are point estimates with no standard deviation, error bars, or confidence intervals despite stochastic game environments and limited replications." 414 }, 415 { 416 "flag": "Weak logistic predictor as evidence", 417 "detail": "The feature attribution analysis (Section 4.4) achieves only ~60% win/loss prediction accuracy on average with a 9-feature logistic model, yet is used to draw specific conclusions about failure drivers." 418 }, 419 { 420 "flag": "Synthetic-to-real gap unaddressed", 421 "detail": "Claims about 'real-world readiness' and 'deployment-like robustness' are based entirely on a synthetic 10x10 grid game; no validation against or comparison to actual deployment contexts is provided." 422 }, 423 { 424 "flag": "Inference hyperparameters omitted", 425 "detail": "Temperature, top-p, and other API parameters are not reported for any of the five models, and thinking-enabled models are run with vague 'default' settings, making exact reproduction impossible." 426 }, 427 { 428 "flag": "Contamination from similar environments unaddressed", 429 "detail": "Training cutoffs are not stated and potential contamination from similar grid-world environments (MiniGrid, TextWorld) in model pretraining data is not discussed." 430 } 431 ], 432 "cited_papers": [ 433 { 434 "title": "τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains", 435 "relevance": "Key prior benchmark for tool-using LLM agents with persistent state and trajectory-level metrics, directly compared in related work" 436 }, 437 { 438 "title": "ToolSandbox: A Stateful, Conversational, Interactive Evaluation Benchmark for LLM Tool Use Capabilities", 439 "relevance": "Prior stateful evaluation benchmark exposing agent brittleness, part of the benchmark landscape WildGrid positions against" 440 }, 441 { 442 "title": "Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Tasks", 443 "relevance": "Foundational grid-based testbed for agent evaluation that WildGrid builds on conceptually and extends with LLM-specific deployment stressors" 444 }, 445 { 446 "title": "TextWorld: A Learning Environment for Text-Based Games", 447 "relevance": "Procedural text-game environment with modular generation closely related to WildGrid's design philosophy" 448 }, 449 { 450 "title": "ALFWorld: Aligning Text and Embodied Environments for Interactive Learning", 451 "relevance": "Long-horizon embodied agent benchmark in the same evaluation tradition as this paper" 452 }, 453 { 454 "title": "Identifying the Risks of LM Agents with an LM-Emulated Sandbox (ToolEmu)", 455 "relevance": "Prior approach to scaling LLM agent safety testing via emulation, positioned as a complement to WildGrid's controlled perturbation approach" 456 }, 457 { 458 "title": "Hell or High Water: Evaluating Agentic Recovery from External Failures", 459 "relevance": "Related benchmark for agent robustness to external failures addressing similar deployment resilience questions" 460 }, 461 { 462 "title": "Cooperative Inverse Reinforcement Learning", 463 "relevance": "Foundational work on implicit objective inference that the paper's 'partial objective inference' finding relates to theoretically" 464 } 465 ], 466 "engagement_factors": { 467 "practical_relevance": { 468 "score": 2, 469 "justification": "Findings about deployment robustness gaps and strategy-vs-capability tradeoffs are actionable for practitioners choosing and tuning LLM agents for real deployments." 470 }, 471 "surprise_contrarian": { 472 "score": 2, 473 "justification": "The finding that weaker models can outperform stronger ones under specific regimes, and that moderate noise can improve performance, challenges conventional capability-centric evaluation thinking." 474 }, 475 "fear_safety": { 476 "score": 1, 477 "justification": "Touches on deployment readiness concerns and agent failure modes but stops short of catastrophic risk framing; no safety-critical real-world scenarios examined." 478 }, 479 "drama_conflict": { 480 "score": 1, 481 "justification": "No significant controversy or adversarial narrative; the paper's tone is constructive and diagnostic rather than confrontational." 482 }, 483 "demo_ability": { 484 "score": 2, 485 "justification": "Code is released at github.com/megagonlabs/wildgrid and the grid game interface is simple enough to run locally with standard LLM API access." 486 }, 487 "brand_recognition": { 488 "score": 1, 489 "justification": "Megagon Labs is not a widely recognized AI lab; the evaluated models (GPT-5, Gemini-3) carry brand recognition but this is not a paper from those labs." 490 } 491 }, 492 "hn_data": { 493 "threads": [], 494 "top_points": 0, 495 "total_points": 0, 496 "total_comments": 0 497 } 498 }