{"total":12,"items":[{"citing_arxiv_id":"2606.18288","ref_index":123,"ref_count":2,"confidence":0.9,"is_internal_anchor":false,"paper_title":"A Knowledge Theory of Capital:The Value of Natural and Artificial Intelligence, Volume 1","primary_cat":"econ.GN","submitted_at":"2026-06-12T21:25:41+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Extends Adam Smith's capital theory to modern knowledge economies by treating knowledge as governable stock and introducing concepts for its creation, enclosure, feedback, and loss.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.10113","ref_index":67,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Emotion Profiling in LLM-Based Literary Translation: Systematic Shifts Across MT and Post-Editing","primary_cat":"cs.CL","submitted_at":"2026-06-08T19:46:00+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"LLM translations introduce model-specific statistically significant emotional fingerprints that limit preservation of author voice, with post-editing providing partial alignment to human norms.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.00334","ref_index":28,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Isolating LLM Lexical Bias: A Curation-Free Triangulated Metric for Preference-Stage Learning","primary_cat":"cs.CL","submitted_at":"2026-05-29T20:19:49+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Introduces a triangulation-based metric to quantify lexical shifts attributable to preference tuning without requiring manual curation of examples.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.20602","ref_index":1,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Self-Training Doesn't Flatten Language -- It Restructures It: Surface Markers Amplify While Deep Syntax Dies","primary_cat":"cs.CL","submitted_at":"2026-05-20T01:44:47+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Self-training restructures language by amplifying surface markers and collapsing deep syntax according to structural depth rather than frequency, as evidenced by correlations across multiple models and a human fine-tuning control.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.17193","ref_index":16,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Multi-LLM Systems Exhibit Robust Semantic Collapse","primary_cat":"cs.MA","submitted_at":"2026-05-16T23:29:42+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Closed-loop multi-LLM systems exhibit robust semantic collapse across model families and interventions, consistent with intrinsic properties of autoregressive generation.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.02236","ref_index":4,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Perturbation Dose Responses in Recursive LLM Loops: Raw Switching, Stochastic Floors, and Persistent Escape under Append, Replace, and Dialog Updates","primary_cat":"cs.AI","submitted_at":"2026-05-04T05:16:43+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"In 30-step recursive LLM loops, append-mode persistent escape from source basins reaches 50% near 400 tokens under full history but plateaus below 50% under tail-clip memory policy, while replace-mode switching largely reflects state reset.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.08115","ref_index":19,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Alice v1: Distillation-Enhanced Video Generation Surpassing Closed-Source Models","primary_cat":"cs.GR","submitted_at":"2026-04-27T23:37:33+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Alice v1 is an open video model that surpasses its teacher and closed-source systems like Veo3 and Sora2 in quality while running 7x faster through specialized distillation.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.15786","ref_index":3,"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":2,"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":"baseline","top_context_polarity":"baseline","context_text":", a collection of texts without available wa- termarking). We create a custom set of tests to better understand these limitations for our use case, and choose the most suitable detector, as reported below. We compare the four above-mentioned detec- tors across five dimensions: (1) text length sensi- tivity,testingdetectionperformanceontextsrang- ing from1to500words; (2) HTML robustness, comparing detection accuracy on identical AI- generated text in plain versus HTML-embedded formats; (3) model family, evaluating detection of outputs from GPT-4o, Claude, and Gemini; (4) model version, testing across OpenAI model versionsfromdavinci-002toGPT-4o; and(5)mul- tilingual robustness. The full results of this ro- bustness analysis are reported in Appendix A."},{"citing_arxiv_id":"2507.03933","ref_index":1,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Losing our Tail, Again: (Un)Natural Selection & Multilingual LLMs","primary_cat":"cs.CL","submitted_at":"2025-07-05T07:36:49+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Position paper warns that model collapse in self-consuming multilingual LLM training loops risks flattening linguistic diversity and cultural nuance.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2506.06024","ref_index":2,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"On Inverse Problems, Parameter Estimation, and Domain Generalization","primary_cat":"cs.IT","submitted_at":"2025-06-06T12:15:02+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"A theoretical framework for parameter estimation in inverse problems shows inversion does not necessarily improve accuracy per the data processing inequality and reveals a vulnerability in domain generalization via the Double Meaning Theorem.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2503.08223","ref_index":36,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices","primary_cat":"cs.DC","submitted_at":"2025-03-11T09:41:29+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":2.0,"formal_verification":"none","one_line_summary":"Position paper claiming that distributed training across massive edge devices can overcome data depletion and centralized compute monopolies in LLM scaling.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"grading model performance across generations [ 34, 35]. Second, synthetic data quality remains inherently unverifiable in open-domain contexts. While formal domains like mathematics allow algo- rithmic validation, natural language lacks objective evaluation standards. The absence of ground-truth verification creates self-referential quality assessments, compromising reliability [36, 20]. Finally, synthetic data struggles to replicate human linguistic diversity. Current methods disproportionately replicate dominant language patterns while underrepresenting cultural nuances and low-frequency expressions. This homogeneity limits their utility for training robust general-purpose models [37]. These persistent challenges underscore that synthetic data alone cannot sustainably address the"}],"limit":50,"offset":0}