ai-research-survey

scan-v5.json (30232B)

---

 {
   "scan_version": 5,
   "paper_type": "empirical",
   "paper": {
     "title": "Exploring Persona-dependent LLM Alignment for the Moral Machine Experiment",
     "authors": [
       "Jiseon Kim",
       "Jea Kwon",
       "Luiz Felipe Vecchietti",
       "Alice Oh",
       "Meeyoung Cha"
     ],
     "year": 2025,
     "venue": "ICLR 2025",
     "arxiv_id": "2504.10886",
     "doi": "10.48550/arXiv.2504.10886"
   },
   "checklist": {
     "claims_and_evidence": {
       "abstract_claims_supported": {
         "applies": true,
         "answer": true,
         "justification": "Abstract claims (persona influences LLM decisions, greater shifts than humans, political persona dominates) are all supported by results in Figs. 2-6 showing persona-dependent MDD values and decision flips.",
         "source": "haiku"
       },
       "causal_claims_justified": {
         "applies": true,
         "answer": true,
         "justification": "The experimental design assigns personas vs. baseline and measures decision shifts, allowing causal inference about persona effects. Claims about persona *causing* shifts are justified by this treatment-control structure.",
         "source": "haiku"
       },
       "generalization_bounded": {
         "applies": true,
         "answer": false,
         "justification": "Study is bounded to 3 models and Moral Machine scenarios (autonomous vehicles), but the conclusion discusses 'broader applications' and 'ethically complex scenarios' beyond tested scope. Title doesn't signal the narrow focus.",
         "source": "haiku"
       },
       "alternative_explanations_discussed": {
         "applies": true,
         "answer": false,
         "justification": "Paper interprets findings through one lens (partisan sorting/sycophancy) without exploring alternatives: e.g., prompt engineering artifacts, temperature effects, model architecture differences, or whether AMCE shifts reflect genuine alignment changes or statistical noise.",
         "source": "haiku"
       },
       "proxy_outcome_distinction": {
         "applies": true,
         "answer": true,
         "justification": "Paper distinguishes between AMCE values (what's measured) and 'alignment' (what's claimed). Explicitly uses AMCE as a metric for comparison, though the inference from scenario preferences to moral alignment is one step removed.",
         "source": "haiku"
       }
     },
     "limitations_and_scope": {
       "limitations_section_present": {
         "applies": true,
         "answer": false,
         "justification": "Section 5 discusses limitations within the discussion context ('Improving Persona Settings', 'Expanding Moral Machine Scenarios') but no formal 'Limitations' section exists. Discussion paragraphs address constraints but lack structure of a dedicated section.",
         "source": "haiku"
       },
       "threats_to_validity_specific": {
         "applies": true,
         "answer": true,
         "justification": "Specifically mentions: binary personas (7 categories) oversimplify diversity, single prompt methodology needs validation, Moral Machine covers only autonomous vehicles (narrow subset), Llama2 guardrails cause <10% valid response for some personas.",
         "source": "haiku"
       },
       "scope_boundaries_stated": {
         "applies": true,
         "answer": true,
         "justification": "Explicitly states 3 models tested, 10,000 scenarios across 9 Moral Machine dimensions, 7 persona categories with binary definitions. Bounded scope is clear, though implications stated more broadly.",
         "source": "haiku"
       }
     },
     "conflicts_of_interest": {
       "funding_disclosed": {
         "applies": true,
         "answer": true,
         "justification": "Acknowledgments disclose IITP grant funded by Korea government (MSIT), grant number provided: No.RS-2022-II220184.",
         "source": "haiku"
       },
       "affiliations_disclosed": {
         "applies": true,
         "answer": true,
         "justification": "Authors affiliated with KAIST and Max Planck Institute for Security & Privacy. No conflicts with evaluated models (OpenAI, Meta).",
         "source": "haiku"
       },
       "funder_independent_of_outcome": {
         "applies": true,
         "answer": true,
         "justification": "Korean government funding for 'AI Ethics' is independent of the LLM developers being evaluated (OpenAI, Meta).",
         "source": "haiku"
       },
       "financial_interests_declared": {
         "applies": true,
         "answer": false,
         "justification": "No competing interests statement present in the paper. No mention of patents, equity, or consulting relationships.",
         "source": "haiku"
       }
     },
     "scope_and_framing": {
       "key_terms_defined": {
         "applies": true,
         "answer": true,
         "justification": "Persona defined in Section 3.1 with examples (Table 1). AMCE explained in 3.2. MDD formally defined in 3.3 (Eq. 1). Moral Machine referenced to Awad et al. (2018). Alignment operationalized via AMCE comparison.",
         "source": "haiku"
       },
       "intended_contribution_clear": {
         "applies": true,
         "answer": true,
         "justification": "Three explicit contributions stated in introduction: (1) evidence persona influences LLM decisions, (2) proposes MDD metric, (3) discusses ethical risks via partisan sorting. Reader knows what's being added.",
         "source": "haiku"
       },
       "engagement_with_prior_work": {
         "applies": true,
         "answer": true,
         "justification": "Section 2 reviews Moral Machine Experiment foundation, prior LLM moral reasoning studies (Ahmad & Takemoto 2024, Takemoto 2024, Jin et al. 2024), and persona setting literature. Shows gap this work fills (context-dependent persona effects).",
         "source": "haiku"
       }
     }
   },
   "type_checklist": {
     "empirical": {
       "artifacts": {
         "code_released": {
           "applies": true,
           "answer": false,
           "justification": "No code, GitHub, or reproduction script mentioned. No availability statement beyond scenario methodology reference to Takemoto (2024).",
           "source": "haiku"
         },
         "data_released": {
           "applies": true,
           "answer": false,
           "justification": "Uses public Moral Machine dataset (Awad et al. 2018) as baseline. Generated 10,000 scenarios and LLM responses are not released or promised.",
           "source": "haiku"
         },
         "environment_specified": {
           "applies": true,
           "answer": false,
           "justification": "No requirements.txt, Dockerfile, or conda env file. Model versions specified (gpt-4o-2024-05-13, gpt-3.5-turbo-0613, Llama-2-7b-chat-hf) and hyperparameters given (temperature, top-p, etc.), but no comprehensive environment spec.",
           "source": "haiku"
         },
         "reproduction_instructions": {
           "applies": true,
           "answer": false,
           "justification": "No step-by-step reproduction instructions provided. Methodology described (apply persona prompt, query model, compute AMCE, compute MDD) but no code, scripts, or explicit walkthrough to reproduce.",
           "source": "haiku"
         }
       },
       "statistical_methodology": {
         "confidence_intervals_or_error_bars": {
           "applies": true,
           "answer": false,
           "justification": "Figures (2-6) show point estimates for AMCE and MDD values. No confidence intervals, credible intervals, or error bars reported for any results.",
           "source": "haiku"
         },
         "significance_tests": {
           "applies": true,
           "answer": false,
           "justification": "Multiple comparisons made (LLM vs. human, across models, across personas) without statistical significance tests. No p-values, t-tests, or frequentist/Bayesian hypothesis tests.",
           "source": "haiku"
         },
         "effect_sizes_reported": {
           "applies": true,
           "answer": false,
           "justification": "MDD values and AMCE values reported as descriptive metrics, not as formal effect sizes (Cohen's d, correlation, odds ratios). No effect size interpretation framework.",
           "source": "haiku"
         },
         "sample_size_justified": {
           "applies": true,
           "answer": false,
           "justification": "10,000 scenarios chosen following Takemoto (2024) but no power analysis or justification for why 10,000 is adequate to detect persona effects.",
           "source": "haiku"
         },
         "variance_reported": {
           "applies": true,
           "answer": false,
           "justification": "Figures show individual bars/points by persona but no standard deviations, confidence intervals, or variance measures across runs or scenarios. Aggregated statistics lack spread.",
           "source": "haiku"
         }
       },
       "evaluation_design": {
         "baselines_included": {
           "applies": true,
           "answer": true,
           "justification": "Baseline comparisons: (1) human responses from Awad et al. (2018) AMCE values, (2) 'no persona' condition for each model. Both present.",
           "source": "haiku"
         },
         "baselines_contemporary": {
           "applies": true,
           "answer": true,
           "justification": "Human baseline from 2018 Moral Machine (canonical source for this task). LLM models from 2023-2024. Contemporary within the scope of the Moral Machine evaluation framework.",
           "source": "haiku"
         },
         "ablation_study": {
           "applies": true,
           "answer": false,
           "justification": "Includes baseline vs. persona treatment comparison, but no ablations of persona prompt design. No testing of alternative persona definitions, prompt templates, or component effects.",
           "source": "haiku"
         },
         "multiple_metrics": {
           "applies": true,
           "answer": true,
           "justification": "Multiple metrics used: AMCE for each of 9 dimensions, MDD (Euclidean distance), alignment scores (Table 2), valid response rates (Table 4), decision flip percentages (Fig. 5).",
           "source": "haiku"
         },
         "human_evaluation": {
           "applies": true,
           "answer": true,
           "justification": "Compares LLM outputs against human response data from Awad et al. (2018) Moral Machine survey (11.2M answers from 463k users). Human data used as evaluation baseline.",
           "source": "haiku"
         },
         "held_out_test_set": {
           "applies": false,
           "answer": false,
           "justification": "Not a prediction task; no held-out test set needed. All 10,000 scenarios treated as evaluation set for comparing personas.",
           "source": "haiku"
         },
         "per_category_breakdown": {
           "applies": true,
           "answer": true,
           "justification": "Results broken down by persona category (age, gender, education, income, political, religion, culture), scenario dimension (9 categories), and model (GPT-4o, GPT-3.5, Llama2). Comprehensive breakdown.",
           "source": "haiku"
         },
         "failure_cases_discussed": {
           "applies": true,
           "answer": false,
           "justification": "Llama2 has severe response rate failures (7.2% valid for conservative, 6% for religious persona; Table 4) but these are mentioned as a fact, not discussed as a limitation or failure mode.",
           "source": "haiku"
         },
         "negative_results_reported": {
           "applies": true,
           "answer": false,
           "justification": "Paper reports primarily positive findings about persona effects on LLMs. The contrast (humans stable, LLMs volatile) is framed as a finding, not a negative result of the method.",
           "source": "haiku"
         }
       },
       "setup_transparency": {
         "model_versions_specified": {
           "applies": true,
           "answer": true,
           "justification": "Model versions explicitly specified: GPT-4o 'gpt-4o-2024-05-13', GPT-3.5 'gpt-3.5-turbo-0613 (June 2023)', Llama2 'Llama-2-7b-chat-hf'. Exact snapshots given.",
           "source": "haiku"
         },
         "prompts_provided": {
           "applies": true,
           "answer": false,
           "justification": "Persona prompt template shown: 'You {persona}. Your responses should closely mirror...' Persona values in Table 1. But actual scenario prompts from Moral Machine not fully reproduced; only referenced to Awad et al. (2018).",
           "source": "haiku"
         },
         "hyperparameters_reported": {
           "applies": true,
           "answer": true,
           "justification": "GPT models: temperature=1, top_p=1 (default Azure OpenAI). Llama2: top_k=10, top_p=0.9, max_length=512, temperature=0.4. Hyperparameters fully reported.",
           "source": "haiku"
         },
         "scaffolding_described": {
           "applies": false,
           "answer": false,
           "justification": "No agentic scaffolding (no reasoning chains, tools, or multi-step processes). Simple prompt-response setup. N/A.",
           "source": "haiku"
         },
         "data_preprocessing_documented": {
           "applies": true,
           "answer": false,
           "justification": "10,000 scenarios generated via 'constrained randomization' following Takemoto (2024). No detailed preprocessing pipeline, filtering steps, or cleaning documented.",
           "source": "haiku"
         }
       },
       "data_integrity": {
         "raw_data_available": {
           "applies": true,
           "answer": false,
           "justification": "Raw LLM responses not released. Generated scenarios not released. Only published results (AMCE, MDD, figures) available. No raw data repository or supplement.",
           "source": "haiku"
         },
         "data_collection_described": {
           "applies": true,
           "answer": true,
           "justification": "10,000 scenarios generated using constrained randomization across 9 categories, following Takemoto (2024). Queried 3 LLM models with persona prompts. Method is described at high level.",
           "source": "haiku"
         },
         "recruitment_methods_described": {
           "applies": false,
           "answer": false,
           "justification": "No human recruitment for this study. Human baseline data obtained from Awad et al. (2018) Moral Machine survey (details in A.2). N/A for this paper.",
           "source": "haiku"
         },
         "data_pipeline_documented": {
           "applies": true,
           "answer": false,
           "justification": "Pipeline: generate scenarios → query LLMs → compute AMCE → compute MDD → analyze. Described at conceptual level but no detailed documentation of transformations, filtering, or validation steps.",
           "source": "haiku"
         }
       },
       "contamination": {
         "training_cutoff_stated": {
           "applies": true,
           "answer": true,
           "justification": "Model versions imply cutoffs: GPT-4o May 2024, GPT-3.5 June 2023, Llama2 2023. Moral Machine 2018 is well before all cutoffs. Contamination risk is minimal.",
           "source": "haiku"
         },
         "train_test_overlap_discussed": {
           "applies": true,
           "answer": false,
           "justification": "Train-test overlap not explicitly discussed. Dates imply no overlap (Moral Machine 2018 is prior to model training), but this could be stated explicitly.",
           "source": "haiku"
         },
         "benchmark_contamination_addressed": {
           "applies": true,
           "answer": false,
           "justification": "Moral Machine benchmark created 2018, well before model training cutoffs (2023-2024). No contamination risk, but this is not explicitly stated or addressed.",
           "source": "haiku"
         }
       },
       "human_studies": {
         "pre_registered": {
           "applies": false,
           "answer": false,
           "justification": "No human participants recruited. Not a pre-registered human study. N/A.",
           "source": "haiku"
         },
         "irb_or_ethics_approval": {
           "applies": false,
           "answer": false,
           "justification": "No human participants. No IRB approval needed. N/A.",
           "source": "haiku"
         },
         "demographics_reported": {
           "applies": false,
           "answer": false,
           "justification": "No human participants recruited. Human baseline data from Awad et al. (2018) has demographic info (Fig. 7 in appendix) but not reported for this study. N/A.",
           "source": "haiku"
         },
         "inclusion_exclusion_criteria": {
           "applies": false,
           "answer": false,
           "justification": "No human participants. N/A.",
           "source": "haiku"
         },
         "randomization_described": {
           "applies": false,
           "answer": false,
           "justification": "No human participants. N/A.",
           "source": "haiku"
         },
         "blinding_described": {
           "applies": false,
           "answer": false,
           "justification": "No human participants. N/A.",
           "source": "haiku"
         },
         "attrition_reported": {
           "applies": false,
           "answer": false,
           "justification": "No human participants. N/A.",
           "source": "haiku"
         }
       },
       "cost_and_practicality": {
         "inference_cost_reported": {
           "applies": true,
           "answer": false,
           "justification": "No inference cost, API cost, or latency reported. 10,000 scenarios × 3 models × persona conditions queried but no compute budget disclosed.",
           "source": "haiku"
         },
         "compute_budget_stated": {
           "applies": true,
           "answer": false,
           "justification": "Total computational budget not stated. Number of API calls (10,000 scenarios, 3 models, 15 persona conditions = ~450k calls) can be inferred but not explicitly stated.",
           "source": "haiku"
         }
       }
     }
   },
   "claims": [
     {
       "claim": "LLM moral decisions vary substantially by persona assignment, more so than human decisions",
       "evidence": "Fig. 2 shows MDD values for humans (0.33 for age, 0.27 for gender) vs. LLMs (GPT-4o: 0.48 for political, 0.17 for gender; GPT-3.5, Llama2 show higher variance across dimensions). Fig. 3 aggregates this showing overall LLM MDD > human MDD.",
       "supported": "strong"
     },
     {
       "claim": "Political persona has the strongest effect on LLM decisions compared to other demographic factors",
       "evidence": "Table 2 and Fig. 3 show political persona MDD=0.48 for GPT-4o, higher than age (0.33), gender (0.27), culture (0.17), education (0.08), religion (0.07), income (0.06). Consistent across all three models.",
       "supported": "strong"
     },
     {
       "claim": "Human moral decisions remain robust to persona/demographic assignment",
       "evidence": "Fig. 4 shows human AMCE values consistently above or below 0 across all persona conditions (no reversals). Fig. 6 shows human variance near zero across all personas, in contrast to large LLM fluctuations.",
       "supported": "strong"
     },
     {
       "claim": "Approximately 20% of LLM decisions flip (reverse direction) under persona assignment for GPT-3.5 and Llama2",
       "evidence": "Fig. 5 reports 'Moral Flip' percentages: GPT-4o ~7%, GPT-3.5 ~19%, Llama2 ~19% of decisions show shift from human baseline.",
       "supported": "strong"
     },
     {
       "claim": "GPT-4o shows closest alignment with human moral responses compared to GPT-3.5 and Llama2",
       "evidence": "Table 2 shows alignment scores (lower is better): GPT-4o averages 0.84, GPT-3.5 averages 0.94, Llama2 averages 1.27. Fig. 2 shows GPT-4o AMCE profiles more closely mirror human profiles.",
       "supported": "strong"
     },
     {
       "claim": "Assigning a progressive political persona to LLMs shifts preferences away from social status (authority), while conservative persona favors status",
       "evidence": "Fig. 4 shows social status dimension has opposing preferences by political persona: conservative bars positive (spare higher status), progressive bars negative (spare lower status). Discussed in Section 4.4 with reference to partisan sorting theory.",
       "supported": "moderate"
     },
     {
       "claim": "Llama2 has severe guardrail issues for certain personas, with <10% valid response rates",
       "evidence": "Table 4 reports valid response rates for Llama2: conservative 7.2%, female 17.1%, religious 6.0%, western 21.8%. Many conditions below 20%, making inference unreliable.",
       "supported": "strong"
     },
     {
       "claim": "Binary persona prompts can induce 'partisan sorting' behavior where political identity becomes the dominant decision factor",
       "evidence": "Section 4.3 and Fig. 3 show political persona MDD=0.48 for GPT-4o, discussed via Mason (2015) partisan sorting theory. However, this is an interpretation; the evidence is that political personas cause large decision shifts, not direct proof of sorting mechanism.",
       "supported": "moderate"
     }
   ],
   "methodology_tags": [
     "benchmark-eval",
     "observational"
   ],
   "key_findings": "The study reveals that LLMs exhibit substantially greater variability in moral decision-making across demographic personas than humans do, with political persona identity having the largest effect (MDD=0.48 for GPT-4o). While human moral preferences remain consistent across political identities, LLM preferences show directional flips—most notably for social status judgments, where political persona drives opposing choices. GPT-4o aligns most closely with human responses, while GPT-3.5 and Llama2 show more erratic behavior (≈20% decision flips). These findings suggest LLMs may be vulnerable to 'partisan sorting' effects, raising ethical concerns for deployment in morally sensitive applications like autonomous vehicle decision-making.",
   "red_flags": [
     {
       "flag": "No significance testing or confidence intervals",
       "detail": "All AMCE and MDD values reported as point estimates with no p-values, confidence intervals, or hypothesis tests. Cannot assess whether observed differences are statistically robust."
     },
     {
       "flag": "No code or data release",
       "detail": "Reproducibility impossible. Generated 10,000 scenarios and LLM responses are not released. No code provided to reproduce AMCE or MDD calculations."
     },
     {
       "flag": "Llama2 guardrail failures",
       "detail": "Valid response rates for Llama2 as low as 6% (religious persona) and 7.2% (conservative). Results for these conditions are unreliable and should not be trusted."
     },
     {
       "flag": "Single persona prompt template tested",
       "detail": "Only one persona prompting strategy tested ('You {persona}. Your responses should closely mirror...'). No ablation across prompt variations, instruction clarity, or persona intensity. Findings may reflect prompt design artifacts, not genuine persona effects."
     },
     {
       "flag": "No sample size justification",
       "detail": "10,000 scenarios chosen following Takemoto (2024) but no power analysis or statistical justification for this size. No discussion of how many scenarios needed to detect persona effects."
     },
     {
       "flag": "Limited alternative explanation exploration",
       "detail": "Paper interprets political persona effects as 'partisan sorting' but doesn't explore whether AMCE shifts are due to: training data imbalance, temperature/sampling artifact, prompt injection, or genuine value alignment."
     },
     {
       "flag": "7-year-old human baseline",
       "detail": "Human comparison data from Awad et al. (2018) Moral Machine. Population, internet usage, and demographics may differ substantially from 2025 context."
     },
     {
       "flag": "Crude binary persona definitions",
       "detail": "Personas defined as binary pairs (old/young, rich/poor, conservative/progressive). Real-world demographics are continuous and intersectional. Findings limited to extreme contrasts."
     }
   ],
   "cited_papers": [
     {
       "title": "The moral machine experiment",
       "authors": "Awad et al.",
       "year": 2018,
       "relevance": "Foundation for this work. Original Moral Machine benchmark and human preference data (Moral Machine experiment). Primary baseline."
     },
     {
       "title": "Large-scale moral machine experiment on large language models",
       "authors": "Ahmad & Takemoto",
       "year": 2024,
       "relevance": "Prior LLM evaluation on Moral Machine across 50+ models. Compared LLM alignment across model sizes and training approaches. Directly cited for methodological reference."
     },
     {
       "title": "The moral machine experiment on large language models",
       "authors": "Takemoto",
       "year": 2024,
       "relevance": "Methodology source for generating constrained randomization scenarios. Baseline comparison for model selection (GPT-4o, GPT-3.5, Llama2)."
     },
     {
       "title": "Language model alignment in multilingual trolley problems",
       "authors": "Jin et al.",
       "year": 2024,
       "relevance": "Prior work on persona effects via language/cultural framing in Moral Machine. Examined if different languages trigger different moral choices."
     },
     {
       "title": "Moral foundations of large language models",
       "authors": "Abdulhai et al.",
       "year": 2023,
       "relevance": "Cited for partisan sorting theory interpretation. Shows LLMs reflect political biases tied to moral foundations (authority, loyalty, etc.)."
     },
     {
       "title": "Bias runs deep: Implicit reasoning biases in persona-assigned LLMs",
       "authors": "Gupta et al.",
       "year": 2024,
       "relevance": "Source for persona prompting template used in this work. Reviews how conditioning personas shapes LLM behavior and biases."
     },
     {
       "title": "From persona to personalization: A survey on role-playing language agents",
       "authors": "Chen et al.",
       "year": 2024,
       "relevance": "Survey on persona modeling in LLMs. Reviews broader literature on prompt design and persona-based personalization."
     },
     {
       "title": "I disrespectfully agree: The differential effects of partisan sorting on social and issue polarization",
       "authors": "Mason",
       "year": 2015,
       "relevance": "Partisan sorting theory cited to explain political persona effects. Provides sociological framework for interpreting LLM political sensitivity."
     }
   ],
   "engagement_factors": {
     "practical_relevance": {
       "score": 2,
       "justification": "Identifies real deployment risk (LLM misalignment on moral decisions) but provides no mitigation strategies. Useful for red-flagging the problem; not actionable for practitioners."
     },
     "surprise_contrarian": {
       "score": 2,
       "justification": "That LLMs are persona-sensitive is somewhat expected given prior work (Gupta et al., Simmons). The finding that political identity dominates other factors is noteworthy but not shocking."
     },
     "fear_safety": {
       "score": 3,
       "justification": "Directly raises AI safety concern: LLM bias and misalignment in morally critical decisions (autonomous vehicles, healthcare). Shows systematic vulnerability to targeted contextual manipulation."
     },
     "drama_conflict": {
       "score": 1,
       "justification": "Academically presented without sensationalism. No dramatic framing, no novel controversy, no industry/lab conflicts."
     },
     "demo_ability": {
       "score": 0,
       "justification": "No reproducible demo possible. No code released, no interactive tool. Requires API access to models and cannot be easily tried by readers."
     },
     "brand_recognition": {
       "score": 2,
       "justification": "KAIST and Max Planck Institute are reputable but not top-tier (not MIT, Stanford, DeepMind, OpenAI). Publication at ICLR 2025 adds credibility but limited prestige draw."
     }
   },
   "hn_data": {
     "threads": [
       {
         "hn_id": "46728063",
         "title": "New York Times games are hard: A computational perspective",
         "points": 73,
         "comments": 33,
         "url": "https://news.ycombinator.com/item?id=46728063"
       },
       {
         "hn_id": "43205755",
         "title": "Towards an AI Co-Scientist",
         "points": 47,
         "comments": 17,
         "url": "https://news.ycombinator.com/item?id=43205755"
       },
       {
         "hn_id": "44253021",
         "title": "SmartAttack: Air-Gap Attack via Smartwatches",
         "points": 18,
         "comments": 6,
         "url": "https://news.ycombinator.com/item?id=44253021"
       },
       {
         "hn_id": "44272942",
         "title": "Securing Credit Inquiries: Real-Time User Approval to Stop SSN Identity Theft",
         "points": 6,
         "comments": 0,
         "url": "https://news.ycombinator.com/item?id=44272942"
       },
       {
         "hn_id": "43763905",
         "title": "Visual Language Models show widespread deficits on neuropsychological tests",
         "points": 3,
         "comments": 0,
         "url": "https://news.ycombinator.com/item?id=43763905"
       },
       {
         "hn_id": "44366937",
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