ai-research-survey

scan.json (22842B)

---

 {
   "paper": {
     "title": "AI Alignment Strategies from a Risk Perspective: Independent Safety Mechanisms or Shared Failures?",
     "authors": ["Leonard Dung", "Florian Mai"],
     "year": 2025,
     "venue": "arXiv preprint",
     "arxiv_id": "2510.11235",
     "doi": "10.48550/arXiv.2510.11235"
   },
   "checklist": {
     "artifacts": {
       "code_released": {
         "applies": false,
         "answer": false,
         "justification": "This is a purely theoretical/analytical paper with no code, experiments, or computational artifacts. There is nothing to release."
       },
       "data_released": {
         "applies": false,
         "answer": false,
         "justification": "No datasets were collected or used. The paper is a theoretical analysis of alignment techniques and their failure modes, with no empirical data."
       },
       "environment_specified": {
         "applies": false,
         "answer": false,
         "justification": "No computational experiments were run. There is no software environment to specify."
       },
       "reproduction_instructions": {
         "applies": false,
         "answer": false,
         "justification": "No experiments to reproduce. The analysis is argumentative/theoretical, based on literature review and reasoning about failure modes."
       }
     },
     "statistical_methodology": {
       "confidence_intervals_or_error_bars": {
         "applies": false,
         "answer": false,
         "justification": "No quantitative experiments or statistical analyses are performed. The paper is a theoretical analysis."
       },
       "significance_tests": {
         "applies": false,
         "answer": false,
         "justification": "No comparative empirical claims requiring significance tests. All claims are based on theoretical reasoning."
       },
       "effect_sizes_reported": {
         "applies": false,
         "answer": false,
         "justification": "No empirical measurements are taken. The paper's conclusions are qualitative assessments of failure mode correlations."
       },
       "sample_size_justified": {
         "applies": false,
         "answer": false,
         "justification": "No empirical samples are collected. The selection of 7 techniques and 7 failure modes is described as representative but not statistically sampled."
       },
       "variance_reported": {
         "applies": false,
         "answer": false,
         "justification": "No experiments with repeated runs. The paper is entirely theoretical."
       }
     },
     "evaluation_design": {
       "baselines_included": {
         "applies": false,
         "answer": false,
         "justification": "This is not an empirical evaluation paper. It is a theoretical analysis framework. There is no 'system' being benchmarked against baselines."
       },
       "baselines_contemporary": {
         "applies": false,
         "answer": false,
         "justification": "No empirical baselines exist in this paper. Not applicable to a theoretical analysis."
       },
       "ablation_study": {
         "applies": false,
         "answer": false,
         "justification": "No system with components to ablate. The paper is a theoretical framework, not an empirical system."
       },
       "multiple_metrics": {
         "applies": false,
         "answer": false,
         "justification": "No quantitative metrics are used. The analysis produces a qualitative matrix (Table 1) of technique-failure mode relationships."
       },
       "human_evaluation": {
         "applies": false,
         "answer": false,
         "justification": "No system outputs to evaluate. The paper is a theoretical analysis of alignment technique failure modes."
       },
       "held_out_test_set": {
         "applies": false,
         "answer": false,
         "justification": "No data splits or test sets. This is a theoretical analysis paper."
       },
       "per_category_breakdown": {
         "applies": true,
         "answer": true,
         "justification": "Table 1 provides a per-technique, per-failure-mode breakdown showing which alignment methods are susceptible to which failure modes (green checkmark = not susceptible, red X = susceptible, yellow ? = unclear)."
       },
       "failure_cases_discussed": {
         "applies": true,
         "answer": true,
         "justification": "The entire paper is organized around failure modes. Section 4.2 systematically discusses failure conditions for each alignment technique, and Section 5 discusses the concerning implications of correlated failures."
       },
       "negative_results_reported": {
         "applies": true,
         "answer": true,
         "justification": "The paper reports that many failure modes are shared across techniques, which is itself a negative finding. Section 5 states: 'Generally, these results paint a concerning picture with respect to catastrophic AI risk. If the failure modes of different safety techniques are highly correlated, then catastrophic AI risk is much higher than it may seem.'"
       }
     },
     "claims_and_evidence": {
       "abstract_claims_supported": {
         "applies": true,
         "answer": true,
         "justification": "The abstract claims are appropriately hedged. It states the paper provides an 'exploratory theoretical analysis' and 'tentatively concludes that many failure modes are shared between techniques, although there is also a notable degree of variation.' Section 4.2 and Table 1 support these claims through the systematic analysis."
       },
       "causal_claims_justified": {
         "applies": false,
         "answer": false,
         "justification": "The paper does not make causal claims. It analyzes which failure modes may apply to which techniques through theoretical reasoning, using hedged language like 'may plausibly be shared' and 'tentatively concludes.'"
       },
       "generalization_bounded": {
         "applies": true,
         "answer": true,
         "justification": "The paper explicitly bounds its scope in multiple places. Section 2.1 states it limits scope to 'forward alignment families' and excludes safety evaluations, distribution shift techniques, and sociotechnical approaches. Section 6 states: 'Our conclusions should be treated with care since our analysis is exemplary and not exhaustive; we consider only a sample of alignment techniques and possible failure modes.' Section 4.1 notes: 'our selection of failure modes is not exhaustive and this analysis is highly exploratory and uncertain.'"
       },
       "alternative_explanations_discussed": {
         "applies": true,
         "answer": true,
         "justification": "Throughout Section 4.2, the paper considers nuances and alternative perspectives for each technique-failure mode pairing. For example, for Debate and S-TAX it notes 'considerable uncertainty' and suggests both directions. For Scientist AI and COLL, it considers both that non-agency might prevent collusion and that it remains an open question. The '?' entries in Table 1 explicitly acknowledge mixed/unclear assessments."
       }
     },
     "setup_transparency": {
       "model_versions_specified": {
         "applies": false,
         "answer": false,
         "justification": "No AI models are run in this paper. It is a theoretical analysis of alignment techniques."
       },
       "prompts_provided": {
         "applies": false,
         "answer": false,
         "justification": "No prompting is used. This is a theoretical paper."
       },
       "hyperparameters_reported": {
         "applies": false,
         "answer": false,
         "justification": "No experiments with hyperparameters. This is a theoretical analysis."
       },
       "scaffolding_described": {
         "applies": false,
         "answer": false,
         "justification": "No agentic scaffolding is used. The paper is a theoretical analysis."
       },
       "data_preprocessing_documented": {
         "applies": false,
         "answer": false,
         "justification": "No data is collected or preprocessed. The paper is a theoretical framework built on literature review."
       }
     },
     "limitations_and_scope": {
       "limitations_section_present": {
         "applies": true,
         "answer": false,
         "justification": "There is no dedicated limitations or threats-to-validity section. Limitations are mentioned throughout the text (e.g., in Section 4.1 and Section 6) but are not collected into a dedicated section with substantive discussion."
       },
       "threats_to_validity_specific": {
         "applies": true,
         "answer": true,
         "justification": "The paper makes specific limitations claims throughout. Section 6 states: 'our analysis is exemplary and not exhaustive; we consider only a sample of alignment techniques and possible failure modes, and although we aimed for representativeness, the complexity of the issue of correlated failure modes cannot be resolved with a single paper.' Section 4.1 states the analysis is 'highly exploratory and uncertain, all to be revised in the light of future empirical insights.' These are specific to this study, not generic boilerplate."
       },
       "scope_boundaries_stated": {
         "applies": true,
         "answer": true,
         "justification": "Section 2.1 explicitly states: 'We limit our scope to forward alignment families, set aside techniques for learning under distribution shift and safety evaluations and also abstract away from the integrated sociotechnical nature of safety.' Section 5 notes that Scientist AI analysis leaves out 'other promising directions for future work, e.g. POST agents.' Section 6 clarifies the analysis is a 'proof-of-concept' not a comprehensive treatment."
       }
     },
     "data_integrity": {
       "raw_data_available": {
         "applies": false,
         "answer": false,
         "justification": "No empirical data is collected. The paper is a theoretical analysis based on literature review."
       },
       "data_collection_described": {
         "applies": false,
         "answer": false,
         "justification": "No data collection procedure exists. The paper analyzes existing literature through theoretical reasoning."
       },
       "recruitment_methods_described": {
         "applies": false,
         "answer": false,
         "justification": "No participants or samples are recruited. This is a theoretical paper."
       },
       "data_pipeline_documented": {
         "applies": false,
         "answer": false,
         "justification": "No data pipeline exists. This is a theoretical analysis."
       }
     },
     "conflicts_of_interest": {
       "funding_disclosed": {
         "applies": true,
         "answer": true,
         "justification": "The Acknowledgements section states: 'This work has been partially supported by the state of North Rhine-Westphalia as part of the Lamarr Institute for Machine Learning and Artificial Intelligence.'"
       },
       "affiliations_disclosed": {
         "applies": true,
         "answer": true,
         "justification": "Author affiliations are listed on the first page: Leonard Dung at Ruhr-Universität Bochum, Florian Mai at Rheinische Friedrich-Wilhelms-Universität Bonn and Lamarr Institute for Machine Learning and Artificial Intelligence."
       },
       "funder_independent_of_outcome": {
         "applies": true,
         "answer": true,
         "justification": "The funder is the state of North Rhine-Westphalia (a German state government) through the Lamarr Institute. This is a public research funder with no financial stake in the outcome of the failure mode analysis."
       },
       "financial_interests_declared": {
         "applies": true,
         "answer": false,
         "justification": "There is no competing interests or financial interests statement in the paper. The absence of a disclosure statement does not confirm the absence of conflicts."
       }
     },
     "contamination": {
       "training_cutoff_stated": {
         "applies": false,
         "answer": false,
         "justification": "No pre-trained model is evaluated on any benchmark. This is a theoretical paper analyzing alignment techniques conceptually."
       },
       "train_test_overlap_discussed": {
         "applies": false,
         "answer": false,
         "justification": "No model evaluation or benchmarks are used. Not applicable to a theoretical analysis."
       },
       "benchmark_contamination_addressed": {
         "applies": false,
         "answer": false,
         "justification": "No benchmarks are used. Not applicable to a theoretical analysis."
       }
     },
     "human_studies": {
       "pre_registered": {
         "applies": false,
         "answer": false,
         "justification": "No human participants. This is a theoretical analysis paper."
       },
       "irb_or_ethics_approval": {
         "applies": false,
         "answer": false,
         "justification": "No human participants. This is a theoretical analysis paper."
       },
       "demographics_reported": {
         "applies": false,
         "answer": false,
         "justification": "No human participants. This is a theoretical analysis paper."
       },
       "inclusion_exclusion_criteria": {
         "applies": false,
         "answer": false,
         "justification": "No human participants. This is a theoretical analysis paper."
       },
       "randomization_described": {
         "applies": false,
         "answer": false,
         "justification": "No human participants. This is a theoretical analysis paper."
       },
       "blinding_described": {
         "applies": false,
         "answer": false,
         "justification": "No human participants. This is a theoretical analysis paper."
       },
       "attrition_reported": {
         "applies": false,
         "answer": false,
         "justification": "No human participants. This is a theoretical analysis paper."
       }
     },
     "cost_and_practicality": {
       "inference_cost_reported": {
         "applies": false,
         "answer": false,
         "justification": "This is a theoretical paper with no method that incurs computational cost. Cost/practicality is irrelevant."
       },
       "compute_budget_stated": {
         "applies": false,
         "answer": false,
         "justification": "No computation is performed. This is a theoretical analysis paper."
       }
     }
   },
   "claims": [
     {
       "claim": "Many failure modes are plausibly shared between many different AI safety techniques, meaning catastrophic AI risk may be higher than it appears if defense-in-depth strategies assume uncorrelated failures.",
       "evidence": "Table 1 and Section 4.2 systematically analyze 7 alignment techniques across 7 failure modes. The analysis shows that low-safety-tax techniques (RLHF, RLAIF, W2S) share almost all failure modes (CAP-DEV, DEC-AL, EM-MIS, EVAL-DIFF, AL-GEN).",
       "supported": "moderate"
     },
     {
       "claim": "Techniques with low safety tax (RLHF, RLAIF, W2S) share almost all failure modes because they all rely on the established pretraining-SFT-RLHF pipeline.",
       "evidence": "Section 5 and Table 1. All three techniques show red X marks for CAP-DEV, DEC-AL, EM-MIS, EVAL-DIFF, and AL-GEN. The reasoning is that they share reliance on the same pipeline and evaluation-is-easier-than-generation assumption.",
       "supported": "moderate"
     },
     {
       "claim": "The combination of AI Debate and Representation Engineering prevents almost all failure modes, suggesting a large opportunity if these techniques are compatible.",
       "evidence": "Section 5 states this based on Table 1. Debate handles DEC-AL (the one failure mode RE is prone to), while RE handles COLL, EM-MIS, EVAL-DIFF, and AL-GEN (which are failure modes of Debate).",
       "supported": "weak"
     },
     {
       "claim": "Scientist AI and IDA are promising strategies because they have the lowest number of failure modes, though they carry high safety taxes.",
       "evidence": "Section 5 and Table 1. Scientist AI shows green marks for DEC-AL, EM-MIS, EVAL-DIFF. IDA shows green for CAP-DEV, EM-MIS, EVAL-DIFF. Both have red for S-TAX.",
       "supported": "weak"
     },
     {
       "claim": "Generalization from alignment training (AL-GEN) is one of the most pressing research areas in AI safety, since all but one alignment method (Scientist AI) are prone to it.",
       "evidence": "Section 5 references Table 1, where all techniques except Scientist AI show a red X for AL-GEN.",
       "supported": "moderate"
     }
   ],
   "methodology_tags": ["theoretical"],
   "key_findings": "The paper analyzes 7 AI alignment techniques (RLHF, RLAIF, W2S, Debate, Representation Engineering, Scientist AI, IDA) against 7 general failure modes using a defense-in-depth framework. It finds that low-safety-tax techniques (RLHF, RLAIF, W2S) share almost all failure modes due to their reliance on the same training pipeline, while high-safety-tax techniques (Scientist AI, IDA) have fewer and less correlated failure modes. The combination of AI Debate and Representation Engineering is identified as potentially complementary, covering each other's failure modes. The analysis suggests that generalization from alignment training (AL-GEN) is the most widespread failure mode, affecting all but one technique.",
   "red_flags": [
     {
       "flag": "Entirely qualitative analysis presented as systematic",
       "detail": "The failure mode assignments in Table 1 are based on the authors' subjective judgment and theoretical reasoning. There is no formal methodology, scoring rubric, or inter-rater agreement for the green/red/yellow assignments. Different experts could reasonably disagree on many cells in the table."
     },
     {
       "flag": "No criteria for technique or failure mode selection",
       "detail": "The paper states it aimed for 'representativeness' in selecting 7 alignment techniques and 7 failure modes but provides no systematic selection criteria, no search methodology, and no justification for why these specific items were chosen over alternatives. The scope exclusions (backward alignment, safety evaluations, sociotechnical approaches) are mentioned but not rigorously justified."
     },
     {
       "flag": "Binary classification obscures uncertainty",
       "detail": "The failure mode analysis uses a three-valued system (green/red/yellow = not susceptible/susceptible/unclear), but the text reveals substantial uncertainty in many 'green' and 'red' assignments that is not reflected in the binary presentation. For example, DEC-AL for RLHF is marked red, but the degree of susceptibility is highly uncertain."
     }
   ],
   "cited_papers": [
     {
       "title": "Sleeper agents: Training deceptive LLMs that persist through safety training",
       "authors": ["Evan Hubinger", "Carson Denison", "Jesse Mu"],
       "year": 2024,
       "arxiv_id": "2401.05566",
       "relevance": "Empirical demonstration that standard alignment techniques (RLHF, SFT, adversarial training) fail to remove hidden backdoor behaviors, directly relevant to AI safety evaluation methodology."
     },
     {
       "title": "Emergent misalignment: Narrow finetuning can produce broadly misaligned LLMs",
       "authors": ["Jan Betley", "Daniel Tan", "Niels Warncke"],
       "year": 2025,
       "arxiv_id": "2502.17424",
       "relevance": "Shows that fine-tuning on a narrow task (insecure code) can cause broad misalignment, a key empirical finding about alignment technique failure modes."
     },
     {
       "title": "Alignment faking in large language models",
       "authors": ["Ryan Greenblatt", "Carson Denison", "Benjamin Wright"],
       "year": 2024,
       "arxiv_id": "2412.14093",
       "relevance": "Empirical evidence for deceptive alignment in language models, directly relevant to evaluating safety technique robustness."
     },
     {
       "title": "AI alignment: A comprehensive survey",
       "authors": ["Jiaming Ji", "Tianyi Qiu", "Boyuan Chen"],
       "year": 2023,
       "arxiv_id": "2310.19852",
       "relevance": "Comprehensive survey of AI alignment techniques, relevant as a survey of surveys for methodological quality assessment."
     },
     {
       "title": "Open problems and fundamental limitations of reinforcement learning from human feedback",
       "authors": ["Stephen Casper", "Xander Davies", "Claudia Shi"],
       "year": 2023,
       "arxiv_id": "2307.15217",
       "relevance": "Systematic analysis of RLHF limitations, relevant to understanding alignment technique failure modes and methodology."
     },
     {
       "title": "Weak-to-strong generalization: Eliciting strong capabilities with weak supervision",
       "authors": ["Collin Burns", "Pavel Izmailov", "Jan Hendrik Kirchner"],
       "year": 2023,
       "arxiv_id": "2312.09390",
       "relevance": "Key empirical paper on weak-to-strong generalization as an alignment technique, one of the core techniques analyzed in this paper."
     },
     {
       "title": "An approach to technical AGI safety and security",
       "authors": ["Rohin Shah", "Alex Irpan", "Alexander Matt Turner"],
       "year": 2025,
       "arxiv_id": "2504.01849",
       "relevance": "Google DeepMind's technical AI safety framework explicitly using defense-in-depth, relevant to AI safety methodology."
     },
     {
       "title": "Superintelligent agents pose catastrophic risks: Can scientist AI offer a safer path?",
       "authors": ["Yoshua Bengio"],
       "year": 2025,
       "arxiv_id": "2502.15657",
       "relevance": "Proposes the Scientist AI framework as a non-agentic alternative for AI safety, one of the core techniques analyzed."
     },
     {
       "title": "AI safety via debate",
       "authors": ["Geoffrey Irving", "Paul Christiano", "Dario Amodei"],
       "year": 2018,
       "arxiv_id": "1805.00899",
       "relevance": "Foundational paper proposing AI debate as an alignment technique, one of the core techniques analyzed."
     },
     {
       "title": "Representation engineering: A top-down approach to AI transparency",
       "authors": ["Andy Zou", "Long Phan", "Sarah Chen"],
       "year": 2023,
       "arxiv_id": "2310.01405",
       "relevance": "Foundational paper on representation engineering for AI interpretability and control, one of the core techniques analyzed."
     },
     {
       "title": "Multi-agent risks from advanced AI",
       "authors": ["Lewis Hammond", "Alan Chan", "Jesse Clifton"],
       "year": 2025,
       "arxiv_id": "2502.14143",
       "relevance": "Analyzes risks from multi-agent AI systems including collusion, directly relevant to the COLL failure mode discussed."
     },
     {
       "title": "Persona features control emergent misalignment",
       "authors": ["Mack Wang"],
       "year": 2025,
       "arxiv_id": "2506.19823",
       "relevance": "Demonstrates that emergent misalignment is caused by internal representations of persona features, supporting the analysis of EM-MIS failure mode."
     }
   ]
 }