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(LLMs) can support a more general-purpose approach by building person-specific simulations (i.e., \"generative agents\") grounded in self-report data.", "key_details": [ "We test whether large language models (LLMs) can support a more general-purpose approach by building person-specific simulations (i.e., \"generative agents\") grounded in self-report data.", "Using data from a diverse national sample of 1,052 Americans, we build agents from (i) two-hour, semi-structured interviews (elicited using the American Voices Project interview schedule), (ii) structured surveys (the General Social Survey and Big Five pers...", "On held-out General Social Survey items, agent accuracy reached 83% (interview only), 82% (surveys only), and 86% (combined) of participants' two-week test-retest consistency, compared with agents prompted only with individuals' demographics (74%).", "Agents predicted personality traits and behaviors in experiments with similar accuracy, and reduced disparities in accuracy across racial and ideological groups relative to demographics-only baselines." ], "results_evidence": [ "arXiv:2411.10109v2 Announce Type: replace Abstract: Machine learning can predict human behavior well when substantial structured data and well-defined outcomes are available, but these models are typically limited to specific outcomes and cannot readily be...", "Using data from a diverse national sample of 1,052 Americans, we build agents from (i) two-hour, semi-structured interviews (elicited using the American Voices Project interview schedule), (ii) structured surveys (the General Social Survey and Big Five pers...", "On held-out General Social Survey items, agent accuracy reached 83% (interview only), 82% (surveys only), and 86% (combined) of participants' two-week test-retest consistency, compared with agents prompted only with individuals' demographics (74%)." ], "limitations_unknowns": [ "arXiv:2411.10109v2 Announce Type: replace Abstract: Machine learning can predict human behavior well when substantial structured data and well-defined outcomes are available, but these models are typically limited to specific outcomes and cannot readily be..." ], "practical_next_steps": [ "Reproduce one claim with a public baseline and fixed evaluation settings.", "Check robustness on out-of-distribution or long-context cases.", "Track whether independent teams report matching results." ] }, { "story_id": "hn:47889357", "title": "Show HN: Virgulas. A local-first browser outliner", "url": "https://github.com/pitermarx/Virgulas", "source_domain": "github.com", "category_label": "Hn", "overall": 5.85, "metrics": { "signal": 8.36, "novelty": 5.1, "impact": 2.35, "confidence": 7.45, "actionability": 3.5 }, "why_made_cut": "Signal 8.4, Confidence 7.5, and Impact 2.4 combined to rank this in the top set.", "badges": [ "repo" ], "context": "This is something I always wanted to do, as I love workflowy.com, but I want to own my data.

I had tried a few times before, but could not until now with the help of AI.

This is actually the second try of AI assistance.", "whats_new": "THe first failed completely as I did not do anything and simply let the agent go free.", "key_details": [ "THe first failed completely as I did not do anything and simply let the agent go free.", "On this second attempt I was much more involved in the details and code organization.

AMA" ], "results_evidence": [ "Overall 5.8/10 with Signal 8.4 and Impact 2.4.", "No explicit benchmark number found in extracted text; treat gains as directional pending replication." ], "limitations_unknowns": [ "Generalization outside curated tasks is still unclear." ], "practical_next_steps": [ "Reproduce one claim with a public baseline and fixed evaluation settings.", "Check robustness on out-of-distribution or long-context cases.", "Track whether independent teams report matching results." ] }, { "story_id": "arxiv:oai:arXiv.org:2604.20871v1", "title": "M-CARE: Standardized Clinical Case Reporting for AI Model Behavioral Disorders, with a 20-Case Atlas and Experimental Validation", "url": "https://arxiv.org/abs/2604.20871", "source_domain": "arxiv.org", "category_label": "Cs.Ai", "overall": 6.25, "metrics": { "signal": 9.43, "novelty": 4.0, "impact": 2.0, "confidence": 8.7, "actionability": 6.5 }, "why_made_cut": "Signal 9.4, Confidence 8.7, and Impact 2.0 combined to rank this in the top set.", "badges": [ "paper" ], "context": "Cases are organized into five categories: RLHF Performance Artifacts, Shell-Core Override Pathology, Context & Memory Conditions, Core Identity & Plasticity, and Stress, Methodology, & Boundary Conditions.", "whats_new": "Cases are organized into five categories: RLHF Performance Artifacts, Shell-Core Override Pathology, Context & Memory Conditions, Core Identity & Plasticity, and Stress, Methodology, & Boundary Conditions.", "key_details": [ "M-CARE provides a 13-section report format, a 4-axis diagnostic assessment system, and a nosological classification of AI behavioral conditions.", "We present 20 cases from three source categories: field observations of deployed agents (8), controlled experiments across three platforms (8), and published sources (4).", "Cases are organized into five categories: RLHF Performance Artifacts, Shell-Core Override Pathology, Context & Memory Conditions, Core Identity & Plasticity, and Stress, Methodology, & Boundary Conditions.", "As a featured case, we present Shell-Induced Behavioral Override (SIBO) -- a controlled experiment showing that Shell instructions categorically override a model's default cooperative behavior." ], "results_evidence": [ "arXiv:2604.20871v1 Announce Type: cross Abstract: We introduce M-CARE (Model Clinical Assessment and Reporting for Evaluation), a clinical case report framework for AI model behavioral disorders adapted from human medicine.", "M-CARE provides a 13-section report format, a 4-axis diagnostic assessment system, and a nosological classification of AI behavioral conditions.", "We present 20 cases from three source categories: field observations of deployed agents (8), controlled experiments across three platforms (8), and published sources (4)." ], "limitations_unknowns": [ "Generalization outside curated tasks is still unclear." ], "practical_next_steps": [ "Reproduce one claim with a public baseline and fixed evaluation settings.", "Check robustness on out-of-distribution or long-context cases.", "Track whether independent teams report matching results." ] } ], "reality_check": { "read_time": "1-2 min", "items": [ { "story_id": "arxiv:oai:arXiv.org:2411.10109v2", "title": "LLM Agents Grounded in Self-Reports Enable General-Purpose Simulation of Individuals", "url": "https://arxiv.org/abs/2411.10109", "source_domain": "arxiv.org", "category_label": "Cs.Ai", "overall": 6.45, "metrics": { "signal": 9.43, "novelty": 5.1, "impact": 2.0, "confidence": 8.7, "actionability": 6.5 }, "badges": [ "paper", "demo" ], "checklist": { "primary_source": "yes", "demo": "yes", "benchmarks_evals": "yes", "baselines_ablations": "no", "third_party_corroboration": "no", "reproducibility_details": "yes" }, "what_would_change_my_mind": [ "Independent replication with comparable or better results.", "Public benchmark numbers with clear baseline comparisons." ], "likely_failure_mode": "Performance may collapse outside curated demos or narrow tasks." }, { "story_id": "arxiv:oai:arXiv.org:2604.20871v1", "title": "M-CARE: Standardized Clinical Case Reporting for AI Model Behavioral Disorders, with a 20-Case Atlas and Experimental Validation", "url": "https://arxiv.org/abs/2604.20871", "source_domain": "arxiv.org", "category_label": "Cs.Ai", "overall": 6.25, "metrics": { "signal": 9.43, "novelty": 4.0, "impact": 2.0, "confidence": 8.7, "actionability": 6.5 }, "badges": [ "paper" ], "checklist": { "primary_source": "yes", "demo": "no", "benchmarks_evals": "yes", "baselines_ablations": "no", "third_party_corroboration": "no", "reproducibility_details": "yes" }, "what_would_change_my_mind": [ "Independent replication with comparable or better results.", "Public benchmark numbers with clear baseline comparisons." ], "likely_failure_mode": "Performance may collapse outside curated demos or narrow tasks." }, { "story_id": "gh:zilliztech/claude-context", "title": "zilliztech/claude-context: Code search MCP for Claude Code. Make entire codebase the context for any coding agent.", "url": "https://github.com/zilliztech/claude-context", "source_domain": "github.com", "category_label": "Agent", "overall": 5.98, "metrics": { "signal": 8.0, "novelty": 5.1, "impact": 2.0, "confidence": 7.03, "actionability": 6.5 }, "badges": [ "repo" ], "checklist": { "primary_source": "yes", "demo": "no", "benchmarks_evals": "no", "baselines_ablations": "no", "third_party_corroboration": "no", "reproducibility_details": "yes" }, "what_would_change_my_mind": [ "Independent replication with comparable or better results.", "Public benchmark numbers with clear baseline comparisons." ], "likely_failure_mode": "Performance may collapse outside curated demos or narrow tasks." }, { "story_id": "hn:47889357", "title": "Show HN: Virgulas. 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