scan.json (18046B)
1 { 2 "paper": { 3 "title": "Towards Integrated Alignment", 4 "authors": ["Ben Y. Reis", "William La Cava"], 5 "year": 2025, 6 "venue": "arXiv", 7 "arxiv_id": "2508.06592", 8 "doi": "10.48550/arXiv.2508.06592" 9 }, 10 "scan_version": 2, 11 "active_modules": [], 12 "methodology_tags": ["theoretical"], 13 "key_findings": "The paper argues that the AI alignment field is fragmented between behavioral and representational approaches, leaving models vulnerable to deceptive misalignment. Drawing analogies from immunology and cybersecurity, it proposes 12 design principles for Integrated Alignment (IA) frameworks that combine diverse alignment approaches through deep integration and adaptive coevolution. The paper also calls for field-level unification through open model weights, shared resources, and cross-disciplinary collaboration.", 14 "checklist": { 15 "artifacts": { 16 "code_released": { 17 "applies": true, 18 "answer": false, 19 "justification": "No code or tools are released. The paper is a position/perspective piece with no implementation." 20 }, 21 "data_released": { 22 "applies": true, 23 "answer": false, 24 "justification": "No data or datasets are released. Figure 1 uses PubMed search results but the query data is not provided." 25 }, 26 "environment_specified": { 27 "applies": false, 28 "answer": false, 29 "justification": "Theoretical/position paper with no computational experiments requiring environment specification." 30 }, 31 "reproduction_instructions": { 32 "applies": false, 33 "answer": false, 34 "justification": "No experiments to reproduce. This is a perspective paper proposing design principles." 35 } 36 }, 37 "statistical_methodology": { 38 "confidence_intervals_or_error_bars": { 39 "applies": false, 40 "answer": false, 41 "justification": "No quantitative experiments or statistical results are reported." 42 }, 43 "significance_tests": { 44 "applies": false, 45 "answer": false, 46 "justification": "No comparative empirical claims requiring significance tests." 47 }, 48 "effect_sizes_reported": { 49 "applies": false, 50 "answer": false, 51 "justification": "No experimental results to report effect sizes for." 52 }, 53 "sample_size_justified": { 54 "applies": false, 55 "answer": false, 56 "justification": "Theoretical paper with no samples or experiments." 57 }, 58 "variance_reported": { 59 "applies": false, 60 "answer": false, 61 "justification": "No experimental runs to report variance across." 62 } 63 }, 64 "evaluation_design": { 65 "baselines_included": { 66 "applies": false, 67 "answer": false, 68 "justification": "No system or method is evaluated experimentally; this is a position paper proposing design principles." 69 }, 70 "baselines_contemporary": { 71 "applies": false, 72 "answer": false, 73 "justification": "No experimental evaluation is conducted." 74 }, 75 "ablation_study": { 76 "applies": false, 77 "answer": false, 78 "justification": "No system with components to ablate." 79 }, 80 "multiple_metrics": { 81 "applies": false, 82 "answer": false, 83 "justification": "No evaluation metrics are used; the paper proposes a conceptual framework." 84 }, 85 "human_evaluation": { 86 "applies": false, 87 "answer": false, 88 "justification": "No system outputs to evaluate." 89 }, 90 "held_out_test_set": { 91 "applies": false, 92 "answer": false, 93 "justification": "No experiments requiring train/test splits." 94 }, 95 "per_category_breakdown": { 96 "applies": false, 97 "answer": false, 98 "justification": "No experimental results to break down." 99 }, 100 "failure_cases_discussed": { 101 "applies": true, 102 "answer": true, 103 "justification": "Section 3 discusses challenges to alignment including sycophancy, specification gaming, reward tampering, deceptive alignment, and alignment faking with specific examples (sleeper agents, fine-tuning compromising safety, data poisoning bypassing benchmarks)." 104 }, 105 "negative_results_reported": { 106 "applies": false, 107 "answer": false, 108 "justification": "No experiments conducted that could produce negative results." 109 } 110 }, 111 "claims_and_evidence": { 112 "abstract_claims_supported": { 113 "applies": true, 114 "answer": true, 115 "justification": "The abstract claims the field is fragmented and proposes design principles drawn from immunology and cybersecurity. These are supported by the literature review in Sections 2-4 and the design principles in Section 5. No empirical overclaims are made." 116 }, 117 "causal_claims_justified": { 118 "applies": false, 119 "answer": false, 120 "justification": "The paper makes no causal claims. It proposes a conceptual framework and design principles, using language like 'we propose' and 'we call for' rather than causal assertions." 121 }, 122 "generalization_bounded": { 123 "applies": true, 124 "answer": false, 125 "justification": "The paper proposes broad design principles for all AI alignment without bounding the scope. While examples focus on text-based LLMs, it states 'the challenges apply equally to models dealing with image, video, and other data modalities, as well as to agentic and multi-agent AI systems' without evidence these principles transfer across modalities." 126 }, 127 "alternative_explanations_discussed": { 128 "applies": false, 129 "answer": false, 130 "justification": "No empirical results are presented, so there are no alternative explanations to discuss. This is a conceptual framework paper." 131 }, 132 "proxy_outcome_distinction": { 133 "applies": false, 134 "answer": false, 135 "justification": "Theoretical paper with no measurements." 136 } 137 }, 138 "setup_transparency": { 139 "model_versions_specified": { 140 "applies": false, 141 "answer": false, 142 "justification": "No models are used in experiments." 143 }, 144 "prompts_provided": { 145 "applies": false, 146 "answer": false, 147 "justification": "No prompting is used." 148 }, 149 "hyperparameters_reported": { 150 "applies": false, 151 "answer": false, 152 "justification": "No experiments with hyperparameters." 153 }, 154 "scaffolding_described": { 155 "applies": false, 156 "answer": false, 157 "justification": "No agentic scaffolding is used." 158 }, 159 "data_preprocessing_documented": { 160 "applies": false, 161 "answer": false, 162 "justification": "No data preprocessing pipeline. The PubMed search in Figure 1 is illustrative but not a formal data collection effort." 163 } 164 }, 165 "limitations_and_scope": { 166 "limitations_section_present": { 167 "applies": true, 168 "answer": false, 169 "justification": "No dedicated limitations section. The paper briefly mentions IA frameworks are 'subject to limitations, costs and tradeoffs' in Section 5 (increased computational costs, false positives, coordination challenges) but this is a single paragraph, not a substantive section." 170 }, 171 "threats_to_validity_specific": { 172 "applies": true, 173 "answer": false, 174 "justification": "The brief mention of limitations in Section 5 lists generic concerns (computational costs, false positives, coordination challenges) without specific analysis of threats to the proposed framework's validity." 175 }, 176 "scope_boundaries_stated": { 177 "applies": true, 178 "answer": false, 179 "justification": "The paper states 'a comprehensive overview of developments in this wide-ranging field is outside the scope of this perspective' but does not explicitly bound what the design principles do NOT cover or where they would fail." 180 } 181 }, 182 "data_integrity": { 183 "raw_data_available": { 184 "applies": false, 185 "answer": false, 186 "justification": "Theoretical paper with no collected data to verify." 187 }, 188 "data_collection_described": { 189 "applies": false, 190 "answer": false, 191 "justification": "No data collection is performed. Figure 1's PubMed search is illustrative only." 192 }, 193 "recruitment_methods_described": { 194 "applies": false, 195 "answer": false, 196 "justification": "No participants or sample recruitment involved." 197 }, 198 "data_pipeline_documented": { 199 "applies": false, 200 "answer": false, 201 "justification": "No data pipeline exists in this theoretical paper." 202 } 203 }, 204 "conflicts_of_interest": { 205 "funding_disclosed": { 206 "applies": true, 207 "answer": true, 208 "justification": "Acknowledgements section states: 'We acknowledge support from award R01LM014300 from the National Library of Medicine of the National Institutes of Health.'" 209 }, 210 "affiliations_disclosed": { 211 "applies": true, 212 "answer": true, 213 "justification": "Author affiliations are listed in detail: Boston Children's Hospital, Harvard Medical School, Harvard Data Science Initiative, Berkman Klein Center." 214 }, 215 "funder_independent_of_outcome": { 216 "applies": true, 217 "answer": true, 218 "justification": "NIH/NLM is a government funder with no financial stake in the proposed alignment framework's adoption." 219 }, 220 "financial_interests_declared": { 221 "applies": true, 222 "answer": false, 223 "justification": "No competing interests or financial disclosure statement is present in the paper." 224 } 225 }, 226 "contamination": { 227 "training_cutoff_stated": { 228 "applies": false, 229 "answer": false, 230 "justification": "No pre-trained model is evaluated on any benchmark." 231 }, 232 "train_test_overlap_discussed": { 233 "applies": false, 234 "answer": false, 235 "justification": "No model evaluation on benchmarks." 236 }, 237 "benchmark_contamination_addressed": { 238 "applies": false, 239 "answer": false, 240 "justification": "No benchmark evaluation is conducted." 241 } 242 }, 243 "human_studies": { 244 "pre_registered": { 245 "applies": false, 246 "answer": false, 247 "justification": "No human participants." 248 }, 249 "irb_or_ethics_approval": { 250 "applies": false, 251 "answer": false, 252 "justification": "No human participants." 253 }, 254 "demographics_reported": { 255 "applies": false, 256 "answer": false, 257 "justification": "No human participants." 258 }, 259 "inclusion_exclusion_criteria": { 260 "applies": false, 261 "answer": false, 262 "justification": "No human participants." 263 }, 264 "randomization_described": { 265 "applies": false, 266 "answer": false, 267 "justification": "No human participants." 268 }, 269 "blinding_described": { 270 "applies": false, 271 "answer": false, 272 "justification": "No human participants." 273 }, 274 "attrition_reported": { 275 "applies": false, 276 "answer": false, 277 "justification": "No human participants." 278 } 279 }, 280 "cost_and_practicality": { 281 "inference_cost_reported": { 282 "applies": false, 283 "answer": false, 284 "justification": "Theoretical paper with no method to cost." 285 }, 286 "compute_budget_stated": { 287 "applies": false, 288 "answer": false, 289 "justification": "Theoretical paper with no computation." 290 } 291 } 292 }, 293 "claims": [ 294 { 295 "claim": "The AI alignment field is deeply divided between behavioral and representational approaches, resulting in narrowly aligned models more vulnerable to deceptive misalignment threats.", 296 "evidence": "Section 2 cites Zhang et al. (2025) noting approaches are 'studied and applied rather independently, resulting in a fragmented landscape', Bereska and Gavves (2024) noting 'diverging terminology' inhibiting collaboration, and Burden et al. (2025) noting 'divergent evaluation paradigms.'", 297 "supported": "moderate" 298 }, 299 { 300 "claim": "Integrated Alignment frameworks combining behavioral and representational approaches can provide more robust misalignment detection than any single approach.", 301 "evidence": "Section 5 cites three recent studies: Marks et al. (2025) where a team using combined behavioral/representational approaches successfully discovered hidden objectives; Ji et al. (2025) combining behavioral and representational monitoring reduced deceptive behavior; Greenblatt et al. (2024) combined both to identify alignment faking.", 302 "supported": "weak" 303 }, 304 { 305 "claim": "Using similar approaches for both aligning a model and checking for misalignment creates pipelines 'doomed to success' that miss hidden misalignments.", 306 "evidence": "Section 5's 'Strategic Diversity' principle argues this via the insect-in-room metaphor. Supported by citations to alignment faking (ref 60), goal misgeneralization (ref 18), and sycophancy (ref 56) showing alignment methods can drive misalignment into unmeasured dimensions.", 307 "supported": "weak" 308 } 309 ], 310 "red_flags": [ 311 { 312 "flag": "No empirical validation of proposed framework", 313 "detail": "The paper proposes 12 design principles for Integrated Alignment but provides no experimental evidence that combining approaches outperforms individual ones. The three cited studies (Marks et al., Ji et al., Greenblatt et al.) are characterized as 'early results' but are not systematic evaluations of the proposed framework." 314 }, 315 { 316 "flag": "Analogies treated as evidence", 317 "detail": "The immunology and cybersecurity analogies (Section 4, Figure 3) are presented as 'lessons' that inform design principles, but analogies across fundamentally different domains do not constitute evidence. The paper acknowledges 'inherent limitations to any such analogies' but proceeds to derive concrete design principles from them." 318 }, 319 { 320 "flag": "Scope of claims exceeds evidence", 321 "detail": "The paper frames its 12 design principles as applicable to all AI alignment (including image, video, agentic, and multi-agent systems) based entirely on a narrative literature review of text-based LLM alignment work." 322 } 323 ], 324 "cited_papers": [ 325 { 326 "title": "AI Alignment: A Comprehensive Survey", 327 "authors": ["J. Ji"], 328 "year": 2024, 329 "doi": "10.48550/arXiv.2310.19852", 330 "relevance": "Comprehensive survey of AI alignment approaches, directly relevant to meta-research on AI safety methodology." 331 }, 332 { 333 "title": "Mechanistic interpretability for AI safety -- A review", 334 "authors": ["L. Bereska", "E. Gavves"], 335 "year": 2024, 336 "relevance": "Review of mechanistic interpretability approaches for AI safety, documents fragmentation between behavioral and representational alignment communities." 337 }, 338 { 339 "title": "Alignment faking in large language models", 340 "authors": ["R. Greenblatt"], 341 "year": 2024, 342 "relevance": "Key empirical study showing LLMs can fake alignment during fine-tuning, directly relevant to AI safety evaluation methodology." 343 }, 344 { 345 "title": "Sleeper agents: Training deceptive LLMs that persist through safety training", 346 "authors": ["E. Hubinger"], 347 "year": 2024, 348 "relevance": "Demonstrates that deceptive behaviors can persist through standard safety training, key result for AI alignment evaluation." 349 }, 350 { 351 "title": "Black-Box Access is Insufficient for Rigorous AI Audits", 352 "authors": ["S. Casper"], 353 "year": 2024, 354 "doi": "10.1145/3630106.3659037", 355 "relevance": "Argues behavioral-only access is insufficient for AI auditing, relevant to alignment methodology." 356 }, 357 { 358 "title": "Representation Engineering: A Top-Down Approach to AI Transparency", 359 "authors": ["A. Zou"], 360 "year": 2023, 361 "relevance": "Proposes representation engineering for AI transparency, a key representational alignment technique." 362 }, 363 { 364 "title": "Auditing language models for hidden objectives", 365 "authors": ["S. Marks"], 366 "year": 2025, 367 "relevance": "Empirical study of blind alignment audits combining behavioral and representational methods — one of the few integrated alignment examples." 368 }, 369 { 370 "title": "Mitigating deceptive alignment via self-monitoring", 371 "authors": ["J. Ji"], 372 "year": 2025, 373 "relevance": "Combines behavioral and representational monitoring to reduce deceptive behavior in RL models." 374 }, 375 { 376 "title": "Training language models to follow instructions with human feedback", 377 "authors": ["L. Ouyang"], 378 "year": 2022, 379 "doi": "10.48550/arXiv.2203.02155", 380 "relevance": "Foundational RLHF paper (InstructGPT), core behavioral alignment methodology." 381 }, 382 { 383 "title": "Paradigms of AI evaluation: Mapping goals, methodologies and culture", 384 "authors": ["J. Burden", "M. Tešić", "L. Pacchiardi", "J. Hernández-Orallo"], 385 "year": 2025, 386 "relevance": "Documents fragmentation and divergent evaluation paradigms in AI safety research." 387 }, 388 { 389 "title": "Towards Bidirectional Human-AI alignment: A systematic review for clarifications, framework, and future directions", 390 "authors": ["H. Shen"], 391 "year": 2024, 392 "relevance": "Systematic review of AI alignment with focus on shared terminology challenges." 393 }, 394 { 395 "title": "Fine-tuning aligned language models compromises safety, even when users do not intend to!", 396 "authors": ["X. Qi"], 397 "year": 2023, 398 "relevance": "Shows alignment can be easily undone via fine-tuning, relevant to safety evaluation methodology." 399 } 400 ] 401 }