{"total":10,"items":[{"citing_arxiv_id":"2605.20279","ref_index":6,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"The Economics of Model Collapse: Equilibrium, Welfare, and Optimal Provenance Subsidies in Synthetic Data Markets","primary_cat":"econ.GN","submitted_at":"2026-05-19T04:41:39+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":8.0,"formal_verification":"none","one_line_summary":"Introduces the Synthetic Data Contamination Equilibrium and derives closed-form optimal provenance subsidies s* = KL(q||p)/(2 kappa) plus watermark strengths to mitigate model collapse, validated by OLS matching structural predictions on C4 data.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.07724","ref_index":78,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Curated Synthetic Data Doesn't Have to Collapse: A Theoretical Study of Generative Retraining with Pluralistic Preferences","primary_cat":"cs.LG","submitted_at":"2026-05-08T13:27:23+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Recursive generative retraining with pluralistic preferences converges to a stable diverse distribution that satisfies a weighted Nash bargaining solution.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.04127","ref_index":11,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Position: the Stochastic Parrot in the Coal Mine. Model Collapse is a Threat to Low-Resource Communities","primary_cat":"cs.LG","submitted_at":"2026-05-05T15:42:31+00:00","verdict":null,"verdict_confidence":null,"novelty_score":null,"formal_verification":null,"one_line_summary":null,"context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.26855","ref_index":6,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Cognitive Atrophy and Systemic Collapse in AI-Dependent Software Engineering","primary_cat":"cs.SE","submitted_at":"2026-04-29T16:20:25+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"LLM integration in software engineering builds epistemological debt that erodes mental models and homogenizes code via recursive training, risking systemic fragility as illustrated by 2026 Amazon outages.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.25110","ref_index":43,"ref_count":2,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Knowledge Distillation Must Account for What It Loses","primary_cat":"cs.LG","submitted_at":"2026-04-28T01:32:46+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Knowledge distillation evaluations must report lost teacher capabilities via a Distillation Loss Statement rather than relying solely on task scores.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.15786","ref_index":19,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Filter Babel: The Challenge of Synthetic Media to Authenticity and Common Ground in AI-Mediated Communication","primary_cat":"cs.HC","submitted_at":"2026-04-17T07:42:35+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Filter Babel explores a future of AI-personalized private experiences that may erode common ground in communication while supporting individual identity and selfhood.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.26965","ref_index":12,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"The Impact of AI-Generated Text on the Internet","primary_cat":"cs.CY","submitted_at":"2026-04-14T16:06:14+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"By mid-2025 roughly 35% of new websites are AI-generated or AI-assisted, correlating with lower semantic diversity and higher positive sentiment but showing no significant drop in factual accuracy or stylistic diversity.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"directions.Computational Linguistics, 51(1): 275-338, 2025. S. Xing, J. Hong, Y. Wang, R. Chen, Z. Zhang, A. Grama, Z. Tu, and Z. Wang. Llms can get\" brain rot\"!arXiv preprint arXiv:2510.13928, 2025. X. Yu, M. Haroon, E. Menchen-Trevino, and M. Wojcieszak. Nudging recommendation algo- rithms increases news consumption and diver- sity on YouTube.PNAS nexus, 3(12):pgae518, 2024. J. Zhang, S. Yu, D. Chong, A. Sicilia, M. R. Tomz, C. D. Manning, and W. Shi. Verbal- ized sampling: How to mitigate mode col- lapse and unlock LLM diversity.arXiv preprint arXiv:2510.01171, 2025. Y. Zhang and T. Zhang. The impact of genera- tive AI on content platforms: A two-sided mar- ket analysis with multi-dimensional quality het-"},{"citing_arxiv_id":"2604.08554","ref_index":16,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Drift and selection in LLM text ecosystems","primary_cat":"cs.CL","submitted_at":"2026-03-15T08:28:38+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Recursive LLM text generation drives public corpora toward shallow equilibria via drift unless normative selection for quality sustains deeper structure with a bounded divergence.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2508.16168","ref_index":35,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"FuXi-TC: A generative framework integrating deep learning and physics-based models for improved tropical cyclone forecasts","primary_cat":"physics.ao-ph","submitted_at":"2025-08-22T07:43:32+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"FuXi-TC combines the FuXi global DL model with a diffusion generative framework to downscale and improve TC intensity and precipitation forecasts, matching ECMWF skill while being faster and generalizing zero-shot to North Atlantic hurricanes.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2504.12501","ref_index":274,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Reinforcement Learning from Human Feedback","primary_cat":"cs.LG","submitted_at":"2025-04-16T21:36:46+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":2.0,"formal_verification":"none","one_line_summary":"The book introduces the origins, mathematical setup, and optimization stages of RLHF including reward modeling, reinforcement learning, rejection sampling, and direct alignment algorithms.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}