Datasets:

nellaivijay
/

asi-research-daily

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gdelt1	News about artificial super intelligence OR superintelligent AI OR ASI	gdelt	https://example.com/news/1	News Reporter	2026-04-12	News article covering artificial super intelligence OR superintelligent AI OR ASI...	0.688493	News Agency	US	2,026	4	12	15	2	14	[]	0	[]	0	{"abstract_length_score": 0.084, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.21680000000000002}	news	false	false	-0.133333	0.833333	neutral	News article covering artificial super intelligence OR superintelligent AI OR ASI	81	{"completeness_score": 85.0, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 92.5, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	Unknown Author	Unknown	Unknown	false	cold	[]	0
arxiv_2604.21931v1	Seeing Fast and Slow: Learning the Flow of Time in Videos	arxiv	https://arxiv.org/abs/2604.21931v1	Yen-Siang Wu, Rundong Luo, Jingsen Zhu, Tao Tu, Ali Farhadi, Matthew Wallingford, Yu-Chiang Frank Wang, Steve Marschner, Wei-Chiu Ma	2026-04-23	How can we tell whether a video has been sped up or slowed down? How can we generate videos at different speeds? Although videos have been central to modern computer vision research, little attention has been paid to perceiving and controlling the passage of time. In this paper, we study time as a learnable visual conc...	0.5	null	null	2,026	4	23	17	2	3	["reinforcement-learning", "time-series", "generative-ai", "anomaly-detection", "deep-learning", "transfer-learning", "auto-ml", "computer-vision", "nlp", "graph-learning", "optimization", "interpretability", "recommendation", "federated-learning"]	7	["attention", "generative", "fine-tuning", "clustering", "computer vision", "architecture search", "deep learning", "llm", "transformer", "adversarial", "optimization", "hyperparameter", "self-attention", "classification", "supervised", "neural network", "reinforcement learning"]	3	{"abstract_length_score": 1.0, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.4}	preprint	false	false	0.002685	0.344815	neutral	How can we tell whether a video has been sped up or slowed down. How can we generate videos at different speeds. Although videos have been central to modern computer vision research, little attention has been paid to perceiving and controlling the passage of time	263	{"completeness_score": 92.5, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 96.25, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	Unknown Author	Unknown	Unknown	false	cold	[{"id": "github_Resp94_Infinity-Squad", "title": "Infinity-Squad", "similarity_score": 9, "shared_subfields": ["reinforcement-learning", "generative-ai", "computer-vision"], "shared_keywords": [], "shared_tags": []}, {"id": "github_YouMind-OpenLab_awesome-gpt-image-2", "title": "awesome-gpt-image-2", "similarity_score"...	5
arxiv_2604.21917v1	CrossCommitVuln-Bench: A Dataset of Multi-Commit Python Vulnerabilities Invisible to Per-Commit Static Analysis	arxiv	https://arxiv.org/abs/2604.21917v1	Arunabh Majumdar	2026-04-23	We present CrossCommitVuln-Bench, a curated benchmark of 15 real-world Python vulnerabilities (CVEs) in which the exploitable condition was introduced across multiple commits - each individually benign to per-commit static analysis - but collectively critical. We manually annotate each CVE with its contributing commit ...	0.5	null	null	2,026	4	23	17	2	3	["reinforcement-learning", "anomaly-detection"]	2	[]	0	{"abstract_length_score": 1.0, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.4}	preprint	false	false	0.060938	0.457812	neutral	We present CrossCommitVuln-Bench, a curated benchmark of 15 real-world Python vulnerabilities (CVEs) in which the exploitable condition was introduced across multiple commits - each individually benign to per-commit static analysis - but collectively critical. We manually annotate each CVE with its...	302	{"completeness_score": 92.5, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 96.25, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	Unknown Author	python	Unknown	false	cold	null	null
arxiv_2604.21911v1	When Prompts Override Vision: Prompt-Induced Hallucinations in LVLMs	arxiv	https://arxiv.org/abs/2604.21911v1	Pegah Khayatan, Jayneel Parekh, Arnaud Dapogny, Mustafa Shukor, Alasdair Newson, Matthieu Cord	2026-04-23	Despite impressive progress in capabilities of large vision-language models (LVLMs), these systems remain vulnerable to hallucinations, i.e., outputs that are not grounded in the visual input. Prior work has attributed hallucinations in LVLMs to factors such as limitations of the vision backbone or the dominance of the...	0.5	null	null	2,026	4	23	17	2	3	["computer-vision", "nlp", "reinforcement-learning", "graph-learning", "optimization", "transfer-learning"]	6	["optimization", "fine-tuning"]	2	{"abstract_length_score": 1.0, "has_code_score": 1.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.55}	preprint	true	false	0.119898	0.461224	neutral	Despite impressive progress in capabilities of large vision-language models (LVLMs), these systems remain vulnerable to hallucinations, i. e. , outputs that are not grounded in the visual input	193	{"completeness_score": 92.5, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 96.25, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	Unknown Author	Unknown	Unknown	false	cold	null	null
arxiv_2604.21910v1	From Research Question to Scientific Workflow: Leveraging Agentic AI for Science Automation	arxiv	https://arxiv.org/abs/2604.21910v1	Bartosz Balis, Michal Orzechowski, Piotr Kica, Michal Dygas, Michal Kuszewski	2026-04-23	Scientific workflow systems automate execution -- scheduling, fault tolerance, resource management -- but not the semantic translation that precedes it. Scientists still manually convert research questions into workflow specifications, a task requiring both domain knowledge and infrastructure expertise. We propose an a...	0.5	null	null	2,026	4	23	17	2	3	["nlp", "reinforcement-learning", "graph-learning", "generative-ai", "optimization", "federated-learning"]	6	["llm", "optimization"]	2	{"abstract_length_score": 1.0, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.4}	preprint	false	false	0.1	0.4	neutral	Scientific workflow systems automate execution -- scheduling, fault tolerance, resource management -- but not the semantic translation that precedes it. Scientists still manually convert research questions into workflow specifications, a task requiring both domain knowledge and infrastructure...	296	{"completeness_score": 92.5, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 96.25, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	Unknown Author	Unknown	Unknown	false	cold	null	null
arxiv_2604.21903v1	A Scale-Adaptive Framework for Joint Spatiotemporal Super-Resolution with Diffusion Models	arxiv	https://arxiv.org/abs/2604.21903v1	Max Defez, Filippo Quarenghi, Mathieu Vrac, Stephan Mandt, Tom Beucler	2026-04-23	Deep-learning video super-resolution has progressed rapidly, but climate applications typically super-resolve (increase resolution) either space or time, and joint spatiotemporal models are often designed for a single pair of super-resolution (SR) factors (upscaling spatial and temporal ratio between the low-resolution...	0.5	null	null	2,026	4	23	17	2	3	["computer-vision", "nlp", "deep-learning", "generative-ai", "time-series", "auto-ml"]	6	["attention", "hyperparameter"]	2	{"abstract_length_score": 1.0, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.4}	preprint	false	false	-0.018824	0.331473	neutral	Deep-learning video super-resolution has progressed rapidly, but climate applications typically super-resolve (increase resolution) either space or time, and joint spatiotemporal models are often designed for a single pair of super-resolution (SR) factors (upscaling spatial and temporal ratio...	296	{"completeness_score": 92.5, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 96.25, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	Unknown Author	Unknown	Unknown	false	cold	null	null
arxiv_2604.21901v1	GiVA: Gradient-Informed Bases for Vector-Based Adaptation	arxiv	https://arxiv.org/abs/2604.21901v1	Neeraj Gangwar, Rishabh Deshmukh, Michael Shavlovsky, Hancao Li, Vivek Mittal, Lexing Ying, Nickvash Kani	2026-04-23	As model sizes continue to grow, parameter-efficient fine-tuning has emerged as a powerful alternative to full fine-tuning. While LoRA is widely adopted among these methods, recent research has explored vector-based adaptation methods due to their extreme parameter efficiency. However, these methods typically require s...	0.5	null	null	2,026	4	23	17	2	3	["computer-vision", "nlp", "generative-ai", "optimization", "transfer-learning"]	5	["classification", "fine-tuning"]	2	{"abstract_length_score": 0.995, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.399}	preprint	false	false	-0.007051	0.578846	neutral	As model sizes continue to grow, parameter-efficient fine-tuning has emerged as a powerful alternative to full fine-tuning. While LoRA is widely adopted among these methods, recent research has explored vector-based adaptation methods due to their extreme parameter efficiency. However, these...	295	{"completeness_score": 92.5, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 96.25, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	Unknown Author	Unknown	Unknown	false	cold	null	null
arxiv_2604.21896v1	Nemobot Games: Crafting Strategic AI Gaming Agents for Interactive Learning with Large Language Models	arxiv	https://arxiv.org/abs/2604.21896v1	Chee Wei Tan, Yuchen Wang, Shangxin Guo	2026-04-23	This paper introduces a new paradigm for AI game programming, leveraging large language models (LLMs) to extend and operationalize Claude Shannon's taxonomy of game-playing machines. Central to this paradigm is Nemobot, an interactive agentic engineering environment that enables users to create, customize, and deploy L...	0.5	null	null	2,026	4	23	17	2	3	["computer-vision", "nlp", "reinforcement-learning", "generative-ai", "transfer-learning"]	5	["llm", "reinforcement learning", "fine-tuning"]	3	{"abstract_length_score": 1.0, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.4}	preprint	false	false	0.042267	0.396517	neutral	This paper introduces a new paradigm for AI game programming, leveraging large language models (LLMs) to extend and operationalize Claude Shannon's taxonomy of game-playing machines. Central to this paradigm is Nemobot, an interactive agentic engineering environment that enables users to create,...	299	{"completeness_score": 92.5, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 96.25, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	Unknown Author	Unknown	Unknown	false	cold	null	null
arxiv_2604.21891v1	A Multi-Stage Warm-Start Deep Learning Framework for Unit Commitment	arxiv	https://arxiv.org/abs/2604.21891v1	Muhy Eddin Za'ter, Anna Van Boven, Bri-Mathias Hodge, Kyri Baker	2026-04-23	Maintaining instantaneous balance between electricity supply and demand is critical for reliability and grid instability. System operators achieve this through solving the task of Unit Commitment (UC),ca high dimensional large-scale Mixed-integer Linear Programming (MILP) problem that is strictly and heavily governed b...	0.5	null	null	2,026	4	23	17	2	3	["nlp", "deep-learning"]	2	["deep learning", "transformer", "attention", "self-attention"]	4	{"abstract_length_score": 1.0, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.4}	preprint	false	false	0.033971	0.509414	neutral	Maintaining instantaneous balance between electricity supply and demand is critical for reliability and grid instability. System operators achieve this through solving the task of Unit Commitment (UC),ca high dimensional large-scale Mixed-integer Linear Programming (MILP) problem that is strictly...	300	{"completeness_score": 92.5, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 96.25, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	Unknown Author	Unknown	Unknown	false	cold	null	null
arxiv_2604.21889v1	TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale	arxiv	https://arxiv.org/abs/2604.21889v1	Jun Wang, Ziyin Zhang, Rui Wang, Hang Yu, Peng Di, Rui Wang	2026-04-23	Real-time detection and mitigation of technical anomalies are critical for large-scale cloud-native services, where even minutes of downtime can result in massive financial losses and diminished user trust. While customer incidents serve as a vital signal for discovering risks missed by monitoring, extracting actionabl...	0.5	null	null	2,026	4	23	17	2	3	["computer-vision", "nlp", "reinforcement-learning", "graph-learning", "anomaly-detection"]	5	["llm", "clustering"]	2	{"abstract_length_score": 1.0, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.4}	preprint	false	false	0.09369	0.536964	neutral	Real-time detection and mitigation of technical anomalies are critical for large-scale cloud-native services, where even minutes of downtime can result in massive financial losses and diminished user trust. While customer incidents serve as a vital signal for discovering risks missed by monitoring,...	302	{"completeness_score": 92.5, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 96.25, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	Unknown Author	rust	Unknown	false	cold	null	null
arxiv_2604.21886v1	The Dyson Minds 2025 Workshop: SETI around Black Holes	arxiv	https://arxiv.org/abs/2604.21886v1	Olivia Curtis, Van Hunter Adams, Daniel Angerhausen, Joseph Bates, Anamaria Berea, Steven J. Dick, Martin Elvis, Sunil P. Khatri, Richard Linares, Manushaqe Muco et al.	2026-04-23	The Dyson Minds 2025 Workshop, held at the Center for Brains, Minds & Machines at MIT and organized by Penn State, MIT, and The Ultraintelligence Foundation, brought together researchers in astrophysics, engineering, artificial intelligence, computer science, and philosophy to examine "Dyson Minds" -- large-scale post-...	0.5	null	null	2,026	4	23	17	2	3	["reinforcement-learning", "generative-ai", "time-series", "recommendation", "interpretability", "federated-learning", "anomaly-detection"]	7	[]	0	{"abstract_length_score": 1.0, "has_code_score": 0.0, "has_doi_score": 1.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.55}	preprint	false	true	0.081772	0.500313	neutral	The Dyson Minds 2025 Workshop, held at the Center for Brains, Minds & Machines at MIT and organized by Penn State, MIT, and The Ultraintelligence Foundation, brought together researchers in astrophysics, engineering, artificial intelligence, computer science, and philosophy to examine "Dyson Minds"...	302	{"completeness_score": 85.0, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 92.5, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	Unknown Author	Unknown	Unknown	false	cold	null	null
arxiv_2604.21885v1	A Multimodal Text- and Graph-Based Approach for Open-Domain Event Extraction from Documents	arxiv	https://arxiv.org/abs/2604.21885v1	Praval Sharma	2026-04-23	Event extraction is essential for event understanding and analysis. It supports tasks such as document summarization and decision-making in emergency scenarios. However, existing event extraction approaches have limitations: (1) closed-domain algorithms are restricted to predefined event types and thus rarely generaliz...	0.5	null	null	2,026	4	23	17	2	3	["nlp", "reinforcement-learning", "deep-learning", "graph-learning"]	4	["attention", "llm"]	2	{"abstract_length_score": 1.0, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.4}	preprint	false	false	0.119505	0.671703	neutral	Event extraction is essential for event understanding and analysis. It supports tasks such as document summarization and decision-making in emergency scenarios. However, existing event extraction approaches have limitations: (1) closed-domain algorithms are restricted to predefined event types and...	301	{"completeness_score": 92.5, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 96.25, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	Unknown Author	swift	Unknown	false	cold	null	null
arxiv_2604.21879v1	Addressing Image Authenticity When Cameras Use Generative AI	arxiv	https://arxiv.org/abs/2604.21879v1	Umar Masud, Abhijith Punnappurath, Luxi Zhao, David B. Lindell, Michael S. Brown	2026-04-23	The ability of generative AI (GenAI) methods to photorealistically alter camera images has raised awareness about the authenticity of images shared online. Interestingly, images captured directly by our cameras are considered authentic and faithful. However, with the increasing integration of deep-learning modules into...	0.5	null	null	2,026	4	23	17	2	3	["computer-vision", "nlp", "graph-learning", "generative-ai"]	4	["generative"]	1	{"abstract_length_score": 1.0, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.4}	preprint	false	false	0.083135	0.572619	neutral	The ability of generative AI (GenAI) methods to photorealistically alter camera images has raised awareness about the authenticity of images shared online. Interestingly, images captured directly by our cameras are considered authentic and faithful. However, with the increasing integration of...	296	{"completeness_score": 92.5, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 96.25, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	Unknown Author	Unknown	Unknown	false	cold	null	null
arxiv_2604.21878v1	Gradual Voluntary Participation: A Framework for Participatory AI Governance in Journalism	arxiv	https://arxiv.org/abs/2604.21878v1	Matilde Barbini, Stefano Sorrentino, Daniel Gatica-Perez	2026-04-23	The integration of AI into journalism challenges participatory design (PD), particularly with respect to stakeholder influence, workplace perceptions, and organizational dynamics. Traditional PD assumes that users can shape technologies, yet AI systems resist influence due to opaque data, fixed architectures, and inacc...	0.5	null	null	2,026	4	23	17	2	3	["reinforcement-learning", "generative-ai", "interpretability", "federated-learning"]	4	[]	0	{"abstract_length_score": 1.0, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.4}	preprint	false	false	0.048333	0.371667	neutral	The integration of AI into journalism challenges participatory design (PD), particularly with respect to stakeholder influence, workplace perceptions, and organizational dynamics. Traditional PD assumes that users can shape technologies, yet AI systems resist influence due to opaque data, fixed...	298	{"completeness_score": 92.5, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 96.25, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	Unknown Author	rust	Unknown	false	cold	null	null
arxiv_2604.21864v1	FAccT-Checked: A Narrative Review of Authority Reconfigurations and Retention in AI-Mediated Journalism	arxiv	https://arxiv.org/abs/2604.21864v1	Stefano Sorrentino, Matilde Barbini, Daniel Gatica-Perez	2026-04-23	Building on recent interpretivist approaches, we conduct a critical narrative review across journalism studies, human-computer interaction, and FAccT scholarship, conceptualizing editorial authority as the conjunction of decision rights, epistemic warrant, and responsibility. We provide a comprehensive theoretical fram...	0.5	null	null	2,026	4	23	17	2	3	["nlp", "reinforcement-learning", "generative-ai", "federated-learning"]	4	["llm"]	1	{"abstract_length_score": 1.0, "has_code_score": 0.0, "has_doi_score": 1.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.55}	preprint	false	true	0.062698	0.267989	neutral	Building on recent interpretivist approaches, we conduct a critical narrative review across journalism studies, human-computer interaction, and FAccT scholarship, conceptualizing editorial authority as the conjunction of decision rights, epistemic warrant, and responsibility. We provide a...	292	{"completeness_score": 92.5, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 96.25, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	Unknown Author	Unknown	Unknown	false	cold	null	null
arxiv_2604.21863v1	Replay-buffer engineering for noise-robust quantum circuit optimization	arxiv	https://arxiv.org/abs/2604.21863v1	Akash Kundu, Sebastian Feld	2026-04-23	Deep reinforcement learning (RL) for quantum circuit optimization faces three fundamental bottlenecks: replay buffers that ignore the reliability of temporal-difference (TD) targets, curriculum-based architecture search that triggers a full quantum-classical evaluation at every environment step, and the routine discard...	0.5	null	null	2,026	4	23	17	2	3	["reinforcement-learning", "time-series", "optimization"]	3	["reinforcement learning", "optimization", "architecture search"]	3	{"abstract_length_score": 1.0, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.4}	preprint	false	false	0.225	0.35	neutral	Deep reinforcement learning (RL) for quantum circuit optimization faces three fundamental bottlenecks: replay buffers that ignore the reliability of temporal-difference (TD) targets, curriculum-based architecture search that triggers a full quantum-classical evaluation at every environment step,...	299	{"completeness_score": 92.5, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 96.25, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	Unknown Author	Unknown	Unknown	false	cold	null	null
arxiv_2604.21860v1	Transient Turn Injection: Exposing Stateless Multi-Turn Vulnerabilities in Large Language Models	arxiv	https://arxiv.org/abs/2604.21860v1	Naheed Rayhan, Sohely Jahan	2026-04-23	Large language models (LLMs) are increasingly integrated into sensitive workflows, raising the stakes for adversarial robustness and safety. This paper introduces Transient Turn Injection(TTI), a new multi-turn attack technique that systematically exploits stateless moderation by distributing adversarial intent across ...	0.5	null	null	2,026	4	23	17	2	3	["nlp", "reinforcement-learning", "federated-learning"]	3	["llm", "adversarial"]	2	{"abstract_length_score": 1.0, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.4}	preprint	false	false	0.09831	0.528646	neutral	Large language models (LLMs) are increasingly integrated into sensitive workflows, raising the stakes for adversarial robustness and safety. This paper introduces Transient Turn Injection(TTI), a new multi-turn attack technique that systematically exploits stateless moderation by distributing...	296	{"completeness_score": 92.5, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 96.25, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	Unknown Author	Unknown	Unknown	false	cold	null	null
arxiv_2604.21854v1	Bounding the Black Box: A Statistical Certification Framework for AI Risk Regulation	arxiv	https://arxiv.org/abs/2604.21854v1	Natan Levy, Gadi Perl	2026-04-23	Artificial intelligence now decides who receives a loan, who is flagged for criminal investigation, and whether an autonomous vehicle brakes in time. Governments have responded: the EU AI Act, the NIST Risk Management Framework, and the Council of Europe Convention all demand that high-risk systems demonstrate safety b...	0.5	null	null	2,026	4	23	17	2	3	["federated-learning"]	1	[]	0	{"abstract_length_score": 1.0, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.4}	preprint	false	false	0.00625	0.444444	neutral	Artificial intelligence now decides who receives a loan, who is flagged for criminal investigation, and whether an autonomous vehicle brakes in time. Governments have responded: the EU AI Act, the NIST Risk Management Framework, and the Council of Europe Convention all demand that high-risk systems...	302	{"completeness_score": 92.5, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 96.25, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	Unknown Author	Unknown	Unknown	false	cold	null	null
arxiv_2604.21853v1	Generative artificial intelligence reduces social welfare through model collapse	arxiv	https://arxiv.org/abs/2604.21853v1	Fabian Baumann, Erol Akçay, Joshua B. Plotkin	2026-04-23	Generative artificial intelligence (genAI) is rapidly reshaping how knowledge and culture are produced and consumed. Yet generative models are vulnerable to model collapse: when trained on data generated by earlier versions of themselves, their outputs can lose diversity and accuracy. This creates a social dilemma, bec...	0.5	null	null	2,026	4	23	17	2	3	["reinforcement-learning", "graph-learning", "generative-ai", "time-series", "interpretability"]	5	["generative"]	1	{"abstract_length_score": 1.0, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.4}	preprint	false	false	-0.047059	0.381303	neutral	Generative artificial intelligence (genAI) is rapidly reshaping how knowledge and culture are produced and consumed. Yet generative models are vulnerable to model collapse: when trained on data generated by earlier versions of themselves, their outputs can lose diversity and accuracy. This creates...	301	{"completeness_score": 85.0, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 92.5, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	Unknown Author	Unknown	Unknown	false	cold	null	null
arxiv_2604.21840v1	TraceScope: Interactive URL Triage via Decoupled Checklist Adjudication	arxiv	https://arxiv.org/abs/2604.21840v1	Haolin Zhang, William Reber, Yuxuan Zhang, Guofei Gu, Jeff Huang	2026-04-23	Modern phishing campaigns increasingly evade snapshot-based URL classifiers using interaction gates (e.g., checkbox/slider challenges), delayed content rendering, and logo-less credential harvesters. This shifts URL triage from static classification toward an interactive forensics task: an analyst must actively navigat...	0.5	null	null	2,026	4	23	17	2	3	["computer-vision", "nlp", "reinforcement-learning", "federated-learning"]	4	["llm", "classification"]	2	{"abstract_length_score": 1.0, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.4}	preprint	false	false	0.095833	0.5125	neutral	Modern phishing campaigns increasingly evade snapshot-based URL classifiers using interaction gates (e. g. , checkbox/slider challenges), delayed content rendering, and logo-less credential harvesters	200	{"completeness_score": 92.5, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 96.25, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	Unknown Author	Unknown	Unknown	false	cold	null	null
github_nikhillpandeyy_placement-prediction	placement-prediction	github	https://github.com/nikhillpandeyy/placement-prediction	nikhillpandeyy	2026-04-26	None # 🎓 Employment Potential Evaluator using ANN/MLP Academic Project: Evaluating Academic & Employment Potential using Artificial Neural Networks (MLP) with API Integration A full-stack web application that predicts student placement outcomes using Machine Learning, trained on 45,000 real student records with...	0.35	null	null	2,026	4	26	17	2	0	["deep-learning"]	1	["machine learning", "neural network"]	2	{"abstract_length_score": 0.509, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.3018}	repository	false	false	-0.028571	0.514286	neutral	None # 🎓 Employment Potential Evaluator using ANN/MLP Academic Project: Evaluating Academic & Employment Potential using Artificial Neural Networks (MLP) with API Integration A full-stack web application that predicts student placement outcomes using Machine Learning, trained on 45,000 real...	301	{"completeness_score": 85.0, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 92.5, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	GitHub User	JavaScript	Unknown	false	cold	[{"id": "arxiv_2604.21931v1", "title": "Seeing Fast and Slow: Learning the Flow of Time in Videos", "similarity_score": 5, "shared_subfields": ["deep-learning"], "shared_keywords": ["neural network"], "shared_tags": []}]	1
github_Resp94_Infinity-Squad	Infinity-Squad	github	https://github.com/Resp94/Infinity-Squad	Resp94	2026-04-24	None # 👾 BMAD: Business Management AI Developer > Transforme seu terminal em um escritório virtual habitado por uma equipe completa de Inteligência Artificial. Bem-vindo ao BMAD! Imagine que você está jogando um RPG clássico de GameBoy Advance, mas em vez de derrotar monstros, seus personagens estão constru...	0.348356	null	null	2,026	4	24	17	2	2	["computer-vision", "reinforcement-learning", "generative-ai"]	3	[]	0	{"abstract_length_score": 0.509, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 0.9945205479452055, "overall_quality_score": 0.30070410958904115}	repository	false	false	-0.183333	0.433333	neutral	None # 👾 BMAD: Business Management AI Developer > Transforme seu terminal em um escritório virtual habitado por uma equipe completa de Inteligência Artificial. Bem-vindo ao BMAD. Imagine que você está jogando um RPG clássico de GameBoy Advance, mas em vez de derrotar monstros, seus...	297	{"completeness_score": 85.0, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 92.5, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	GitHub User	HTML	Unknown	false	cold	[{"id": "arxiv_2604.21931v1", "title": "Seeing Fast and Slow: Learning the Flow of Time in Videos", "similarity_score": 9, "shared_subfields": ["reinforcement-learning", "generative-ai", "computer-vision"], "shared_keywords": [], "shared_tags": []}, {"id": "github_YouMind-OpenLab_awesome-gpt-image-2", "title": "awesome...	5
github_YouMind-OpenLab_awesome-gpt-image-2	awesome-gpt-image-2	github	https://github.com/YouMind-OpenLab/awesome-gpt-image-2	YouMind-OpenLab	2026-04-16	🚀 World's largest GPT Image 2 prompt library, updated daily — 2000+ curated prompts with preview images, 16 languages. OpenAI's next-gen image model with pixel-perfect text rendering, cross-image consistency, and commercial-grade illustration. Free & open source. <a href="https://youmind.com/gpt-image-2-prompts"> ...	0.341781	null	null	2,026	4	16	16	2	10	["computer-vision", "nlp", "reinforcement-learning"]	3	["gpt"]	1	{"abstract_length_score": 0.768, "has_code_score": 1.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 0.9726027397260274, "overall_quality_score": 0.49812054794520544}	repository	true	false	0.28	0.56	neutral	🚀 World's largest GPT Image 2 prompt library, updated daily — 2000+ curated prompts with preview images, 16 languages. OpenAI's next-gen image model with pixel-perfect text rendering, cross-image consistency, and commercial-grade illustration. Free & open source	262	{"completeness_score": 85.0, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 92.5, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	GitHub User	TypeScript	Other	true	cold	[{"id": "arxiv_2604.21931v1", "title": "Seeing Fast and Slow: Learning the Flow of Time in Videos", "similarity_score": 9, "shared_subfields": ["reinforcement-learning", "computer-vision", "nlp"], "shared_keywords": [], "shared_tags": []}, {"id": "github_Resp94_Infinity-Squad", "title": "Infinity-Squad", "similarity_sc...	5
github_GWaman2007_community-relief-portal	community-relief-portal	github	https://github.com/GWaman2007/community-relief-portal	GWaman2007	2026-04-12	AI-powered disaster response platform with real-time geospatial deduplication, multi-tenant RBAC, and closed-loop volunteer dispatch. # 🌍 Community Relief Portal Autonomous AI Deduplication & Real-Time Geospatial Volunteer Routing ![Hackathon Submission](https://img.shields.io/badge/Status-Hackathon_Ready-emeral...	0.338493	null	null	2,026	4	12	15	2	14	[]	0	[]	0	{"abstract_length_score": 0.638, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 0.9616438356164384, "overall_quality_score": 0.3199287671232877}	repository	false	false	0.3125	0.85	positive	AI-powered disaster response platform with real-time geospatial deduplication, multi-tenant RBAC, and closed-loop volunteer dispatch. # 🌍 Community Relief Portal Autonomous AI Deduplication & Real-Time Geospatial Volunteer Routing. [Hackathon Submission](https://img	270	{"completeness_score": 85.0, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 92.5, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	GitHub User	TypeScript	Unknown	false	cold	[]	0
github_jprando_testa-habilidade-ai-typescript	testa-habilidade-ai-typescript	github	https://github.com/jprando/testa-habilidade-ai-typescript	jprando	2026-04-26	O teste de fogo para IAs geradoras de código: avaliando modelos (LLMs) contra armadilhas de concorrência e Event Loop. # 🤖 LLM Concurrency Benchmark: O Teste de Fogo em TypeScript Este repositório existe para separar os modelos seniores dos juniores. O objetivo não é resolver um algoritmo de faculdade, mas sim um pr...	0.3	null	null	2,026	4	26	17	2	0	["nlp"]	1	["llm"]	1	{"abstract_length_score": 0.623, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.3246}	repository	false	false	0.083333	0.516667	neutral	O teste de fogo para IAs geradoras de código: avaliando modelos (LLMs) contra armadilhas de concorrência e Event Loop. # 🤖 LLM Concurrency Benchmark: O Teste de Fogo em TypeScript Este repositório existe para separar os modelos seniores dos juniores. O objetivo não é resolver um algoritmo de...	296	{"completeness_score": 85.0, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 92.5, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	GitHub User	TypeScript	Unknown	false	cold	[{"id": "arxiv_2604.21931v1", "title": "Seeing Fast and Slow: Learning the Flow of Time in Videos", "similarity_score": 5, "shared_subfields": ["nlp"], "shared_keywords": ["llm"], "shared_tags": []}, {"id": "github_alissonrangel_miniguia-estudos-notebooklm", "title": "miniguia-estudos-notebooklm", "similarity_score": 3...	4
github_alissonrangel_miniguia-estudos-notebooklm	miniguia-estudos-notebooklm	github	https://github.com/alissonrangel/miniguia-estudos-notebooklm	alissonrangel	2026-04-26	Repositório para o desafio de projeto da DIO para estudos com o NotebookLM # Repositório para estudos no NotebookLM sobre cibersegurança e ataques cibernéticos ## Contexto e Objetivo - O assunto escolhido para estudos com o NotebookLM foi: - Conceitos de cibersegurança e ataques cibernéticos - Objetivos: - C...	0.3	null	null	2,026	4	26	17	2	0	["nlp"]	1	[]	0	{"abstract_length_score": 0.579, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.3158}	repository	false	false	0	0	neutral	Repositório para o desafio de projeto da DIO para estudos com o NotebookLM # Repositório para estudos no NotebookLM sobre cibersegurança e ataques cibernéticos ## Contexto e Objetivo - O assunto escolhido para estudos com o NotebookLM foi: - Conceitos de cibersegurança e ataques...	289	{"completeness_score": 85.0, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 92.5, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	GitHub User	Unknown	Unknown	false	cold	[{"id": "arxiv_2604.21931v1", "title": "Seeing Fast and Slow: Learning the Flow of Time in Videos", "similarity_score": 3, "shared_subfields": ["nlp"], "shared_keywords": [], "shared_tags": []}, {"id": "github_jprando_testa-habilidade-ai-typescript", "title": "testa-habilidade-ai-typescript", "similarity_score": 3, "sh...	4
github_sanika-patil1204_AWS-Project-3	AWS-Project-3	github	https://github.com/sanika-patil1204/AWS-Project-3	sanika-patil1204	2026-04-26	None <br/> <p align="center"> <img src="https://raw.githubusercontent.com/donnemartin/data-science-ipython-notebooks/master/images/aws.png"> </p> <br/> # Awesome AWS [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awe...	0.3	null	null	2,026	4	26	17	2	0	["computer-vision"]	1	[]	0	{"abstract_length_score": 0.509, "has_code_score": 1.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.45180000000000003}	repository	true	false	0.355	0.595	positive	None <br/> <p align="center"> <img src="https://raw. githubusercontent. com/donnemartin/data-science-ipython-notebooks/master/images/aws	139	{"completeness_score": 85.0, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 92.5, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	GitHub User	Python	Other	true	cold	[{"id": "arxiv_2604.21931v1", "title": "Seeing Fast and Slow: Learning the Flow of Time in Videos", "similarity_score": 3, "shared_subfields": ["computer-vision"], "shared_keywords": [], "shared_tags": []}, {"id": "github_Resp94_Infinity-Squad", "title": "Infinity-Squad", "similarity_score": 3, "shared_subfields": ["co...	4
github_Satish-112004_Bengaluru-House-Price-Data-Project	Bengaluru-House-Price-Data-Project	github	https://github.com/Satish-112004/Bengaluru-House-Price-Data-Project	Satish-112004	2026-04-26	None # 🏠 Bangalore House Price Prediction ### End-to-End Data Preprocessing + EDA + ML Model \| Python · Pandas · Scikit-learn · Seaborn [![Python](https://img.shields.io/badge/Python-3.8%2B-3776AB?style=flat-square&logo=python&logoColor=white)](https://python.org) [![Pandas](https://img.shields.io/badge/Pandas-1.5%2...	0.3	null	null	2,026	4	26	17	2	0	["federated-learning"]	1	[]	0	{"abstract_length_score": 0.509, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.3018}	repository	false	false	0	0	neutral	None # 🏠 Bangalore House Price Prediction ### End-to-End Data Preprocessing + EDA + ML Model \| Python · Pandas · Scikit-learn · Seaborn [. [Python](https://img. shields	170	{"completeness_score": 85.0, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 92.5, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	GitHub User	Python	Unknown	false	cold	[{"id": "arxiv_2604.21931v1", "title": "Seeing Fast and Slow: Learning the Flow of Time in Videos", "similarity_score": 3, "shared_subfields": ["federated-learning"], "shared_keywords": [], "shared_tags": []}, {"id": "github_nellaivijay_research-collector", "title": "research-collector", "similarity_score": 3, "shared_...	4
github_abhi940927_AssignmentManagementSystem	AssignmentManagementSystem	github	https://github.com/abhi940927/AssignmentManagementSystem	abhi940927	2026-04-26	None <div align="center"> <img src="https://img.icons8.com/isometric/120/000000/school.png" alt="EduFlow Logo" width="120" /> <h1 align="center">EduFlow : A Learning Hub</h1> <p align="center"> <strong>An advanced, AI-powered academic platform streamlining the assignment lifecycle for both tutors and student...	0.3	null	null	2,026	4	26	17	2	0	["federated-learning"]	1	[]	0	{"abstract_length_score": 0.509, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.3018}	repository	false	false	0.039394	0.233333	neutral	None <div align="center"> <img src="https://img. icons8. com/isometric/120/000000/school	91	{"completeness_score": 85.0, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 92.5, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	GitHub User	JavaScript	Unknown	false	cold	[{"id": "arxiv_2604.21931v1", "title": "Seeing Fast and Slow: Learning the Flow of Time in Videos", "similarity_score": 3, "shared_subfields": ["federated-learning"], "shared_keywords": [], "shared_tags": []}, {"id": "github_nellaivijay_research-collector", "title": "research-collector", "similarity_score": 3, "shared_...	4
github_monum-hashmi_msafe-gc-deepfake-detection	msafe-gc-deepfake-detection	github	https://github.com/monum-hashmi/msafe-gc-deepfake-detection	monum-hashmi	2026-04-26	None # MSAFE-GC: Compression-Robust and Explainable Deepfake Face Detection <p align="center"> <img src="gradcam_figure_v2.png" alt="Grad-CAM Visualization" width="800"/> </p> <p align="center"> <img src="https://img.shields.io/badge/PyTorch-2.0+-ee4c2c?logo=pytorch&logoColor=white" /> <img src="https://img.sh...	0.3	null	null	2,026	4	26	17	2	0	["computer-vision", "interpretability", "anomaly-detection"]	3	[]	0	{"abstract_length_score": 0.509, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.3018}	repository	false	false	-0.1	0.1	neutral	None # MSAFE-GC: Compression-Robust and Explainable Deepfake Face Detection <p align="center"> <img src="gradcam_figure_v2. png" alt="Grad-CAM Visualization" width="800"/> </p> <p align="center"> <img src="https://img. shields	233	{"completeness_score": 85.0, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 92.5, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	GitHub User	Jupyter Notebook	Unknown	false	cold	[{"id": "arxiv_2604.21931v1", "title": "Seeing Fast and Slow: Learning the Flow of Time in Videos", "similarity_score": 9, "shared_subfields": ["anomaly-detection", "interpretability", "computer-vision"], "shared_keywords": [], "shared_tags": []}, {"id": "github_Resp94_Infinity-Squad", "title": "Infinity-Squad", "simil...	4
github_arunsanjeevms_MediConnect-Bengaluru	MediConnect-Bengaluru	github	https://github.com/arunsanjeevms/MediConnect-Bengaluru	arunsanjeevms	2026-04-26	None # 🚑 Smart-Aid: AI-Powered Emergency Ambulance Response System <div align="center"> ![Version](https://img.shields.io/badge/version-1.0.0-blue.svg) ![Flutter](https://img.shields.io/badge/Flutter-3.9.2-02569B?logo=flutter) ![FastAPI](https://img.shields.io/badge/FastAPI-0.104.1-009688?logo=fastapi) ![MongoDB](h...	0.3	null	null	2,026	4	26	17	2	0	["federated-learning"]	1	[]	0	{"abstract_length_score": 0.509, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.3018}	repository	false	false	-0.152588	0.05	neutral	None # 🚑 Smart-Aid: AI-Powered Emergency Ambulance Response System <div align="center">. [Version](https://img. shields	121	{"completeness_score": 85.0, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 92.5, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	GitHub User	Dart	Unknown	false	cold	[{"id": "arxiv_2604.21931v1", "title": "Seeing Fast and Slow: Learning the Flow of Time in Videos", "similarity_score": 3, "shared_subfields": ["federated-learning"], "shared_keywords": [], "shared_tags": []}, {"id": "github_nellaivijay_research-collector", "title": "research-collector", "similarity_score": 3, "shared_...	4
github_nellaivijay_research-collector	research-collector	github	https://github.com/nellaivijay/research-collector	nellaivijay	2026-04-24	None # Research-Collector Educational multi-source research aggregation tool for learning and teaching Research-Collector is an open source educational tool that helps students and researchers aggregate information from diverse sources - academic databases, professional Q&A sites, news outlets, and social platfo...	0.298356	null	null	2,026	4	24	17	2	2	["recommendation", "federated-learning"]	2	[]	0	{"abstract_length_score": 0.509, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 0.9972602739726028, "overall_quality_score": 0.30125205479452055}	repository	false	false	0.141667	0.208333	neutral	None # Research-Collector Educational multi-source research aggregation tool for learning and teaching Research-Collector is an open source educational tool that helps students and researchers aggregate information from diverse sources - academic databases, professional Q&A sites, news...	296	{"completeness_score": 85.0, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 92.5, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	GitHub User	Python	MIT License	true	cold	[{"id": "arxiv_2604.21931v1", "title": "Seeing Fast and Slow: Learning the Flow of Time in Videos", "similarity_score": 6, "shared_subfields": ["federated-learning", "recommendation"], "shared_keywords": [], "shared_tags": []}, {"id": "github_Satish-112004_Bengaluru-House-Price-Data-Project", "title": "Bengaluru-House-...	4
github_baburathod_Mana-Ooru-Jayaram-Thanda	Mana-Ooru-Jayaram-Thanda	github	https://github.com/baburathod/Mana-Ooru-Jayaram-Thanda	baburathod	2026-04-24	Mana Ooru – Jayaram Thanda is a mobile-first Digital Village Platform designed to improve communication, governance, and service accessibility for rural communities. <div align="center"> <img src="frontend/public/logo.jpg.png" alt="Mana Ooru Logo" width="150" style="border-radius: 50%" /> </div> # Mana Ooru – Jayar...	0.298356	null	null	2,026	4	24	17	2	2	[]	0	[]	0	{"abstract_length_score": 0.67, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 0.9945205479452055, "overall_quality_score": 0.33290410958904115}	repository	false	false	-0.028571	0.028571	neutral	Mana Ooru – Jayaram Thanda is a mobile-first Digital Village Platform designed to improve communication, governance, and service accessibility for rural communities. <div align="center"> <img src="frontend/public/logo. jpg	224	{"completeness_score": 85.0, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 92.5, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	GitHub User	JavaScript	Unknown	false	cold	[]	0
github_ExeDevCentral_ExePaginasweb	ExePaginasweb	github	https://github.com/ExeDevCentral/ExePaginasweb	ExeDevCentral	2026-04-24	None # ⚡ ExePaginasWeb — AI-Powered Landing Platform [![Vercel](https://img.shields.io/badge/Vercel-Deployed-000000?style=for-the-badge&logo=vercel&logoColor=white)](https://prueba-hiper-web-full-ai-video-super-b0fps6ubv.vercel.app) [![React](https://img.shields.io/badge/React-18.3-61DAFB?style=for-the-badge&logo=rea...	0.298356	null	null	2,026	4	24	17	2	2	["auto-ml"]	1	[]	0	{"abstract_length_score": 0.509, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 0.9945205479452055, "overall_quality_score": 0.30070410958904115}	repository	false	false	0	0	neutral	None # ⚡ ExePaginasWeb — AI-Powered Landing Platform [. [Vercel](https://img. shields	87	{"completeness_score": 85.0, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 92.5, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	GitHub User	TypeScript	Unknown	false	cold	[{"id": "arxiv_2604.21931v1", "title": "Seeing Fast and Slow: Learning the Flow of Time in Videos", "similarity_score": 3, "shared_subfields": ["auto-ml"], "shared_keywords": [], "shared_tags": []}]	1
github_andresperez0401_Hypetechub	Hypetechub	github	https://github.com/andresperez0401/Hypetechub	andresperez0401	2026-04-22	None # Despliegue Como parte de mejorar la experiencia de uso de la app, se desplegó con una url publica para que se pueda acceder a probarla: https://www.hypetechub.online/ api: https://api.hypetechub.online/api # Hype Tech Hub Plataforma integral construida diseñada para rankear, explorar y chatear con IA sobr...	0.296712	null	null	2,026	4	22	17	2	4	["reinforcement-learning"]	1	[]	0	{"abstract_length_score": 0.509, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 0.9917808219178083, "overall_quality_score": 0.30015616438356163}	repository	false	false	0	0	neutral	None # Despliegue Como parte de mejorar la experiencia de uso de la app, se desplegó con una url publica para que se pueda acceder a probarla: https://www. hypetechub. online/ api: https://api	196	{"completeness_score": 85.0, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 92.5, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	GitHub User	TypeScript	Unknown	false	cold	[{"id": "arxiv_2604.21931v1", "title": "Seeing Fast and Slow: Learning the Flow of Time in Videos", "similarity_score": 3, "shared_subfields": ["reinforcement-learning"], "shared_keywords": [], "shared_tags": []}, {"id": "github_Resp94_Infinity-Squad", "title": "Infinity-Squad", "similarity_score": 3, "shared_subfields...	3
github_rfnardi_Multi_Context	Multi_Context	github	https://github.com/rfnardi/Multi_Context	rfnardi	2026-04-19	None ![Neovim](https://img.shields.io/badge/Neovim-0.8+-green.svg?style=for-the-badge&logo=neovim) ![Lua](https://img.shields.io/badge/Lua-100%25-blue.svg?style=for-the-badge&logo=lua) ![Release](https://img.shields.io/badge/Version-v1.2-blue.svg?style=for-the-badge) ![License](https://img.shields.io/badge/License-MIT...	0.294247	null	null	2,026	4	19	16	2	7	["nlp"]	1	[]	0	{"abstract_length_score": 0.509, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 0.9808219178082191, "overall_quality_score": 0.29796438356164384}	repository	false	false	-0.6	1	negative	None. [Neovim](https://img. shields	35	{"completeness_score": 85.0, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 92.5, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	GitHub User	Lua	Unknown	false	cold	[{"id": "arxiv_2604.21931v1", "title": "Seeing Fast and Slow: Learning the Flow of Time in Videos", "similarity_score": 3, "shared_subfields": ["nlp"], "shared_keywords": [], "shared_tags": []}, {"id": "github_jprando_testa-habilidade-ai-typescript", "title": "testa-habilidade-ai-typescript", "similarity_score": 3, "sh...	4
github_lingxitong_Awesome-AI4DigitalPathology	Awesome-AI4DigitalPathology	github	https://github.com/lingxitong/Awesome-AI4DigitalPathology	lingxitong	2026-04-18	A Curated List of Awesome Works in Computational Pathology, Aiming to Serve as a One-stop Resource for Researchers, Practitioners, and Enthusiasts Interested in Digital Pathology. # Awesome-AI4DigitalPathology <div align="center"> [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e...	0.293425	null	null	2,026	4	18	16	2	8	[]	0	[]	0	{"abstract_length_score": 0.684, "has_code_score": 1.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 0.9780821917808219, "overall_quality_score": 0.4824164383561644}	repository	true	false	0.21875	0.32	neutral	A Curated List of Awesome Works in Computational Pathology, Aiming to Serve as a One-stop Resource for Researchers, Practitioners, and Enthusiasts Interested in Digital Pathology. # Awesome-AI4DigitalPathology <div align="center"> [. [Awesome](https://cdn	258	{"completeness_score": 85.0, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 92.5, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	GitHub User	Unknown	Apache License 2.0	true	cold	[]	0
github_italozkv_Superman_toggle_addon	Superman_toggle_addon	github	https://github.com/italozkv/Superman_toggle_addon	italozkv	2026-04-15	Addon do mod Superman: Son of Krypton # 🦸‍♂️ Kryptonian Control <p align="center"> <img src="https://img.shields.io/badge/Minecraft-1.20.1-green?style=for-the-badge&logo=mojangstudios" /> <img src="https://img.shields.io/badge/Forge-47.x-orange?style=for-the-badge" /> <img src="https://img.shields.io/badge/Sup...	0.290959	null	null	2,026	4	15	16	2	11	[]	0	[]	0	{"abstract_length_score": 0.542, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 0.9698630136986301, "overall_quality_score": 0.30237260273972605}	repository	false	false	0.05	0.25	neutral	Addon do mod Superman: Son of Krypton # 🦸‍♂️ Kryptonian Control <p align="center"> <img src="https://img. shields. io/badge/Minecraft-1	139	{"completeness_score": 85.0, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 92.5, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	GitHub User	swift	Unknown	false	cold	[]	0
github_taizone35_news	news	github	https://github.com/taizone35/news	taizone35	2026-04-15	None # news -- トレンドネタ日次レポート本リポジトリは、有名サイト・はてブ IT 人気エントリー・Hacker News・Reddit 13 サブレッドなどから日次のトレンドを収集し、興味領域と突き合わせてサマリ付きで公開するものです。 - 日次レポート本体: [ideas/daily/](ideas/daily) - 収集スキル: [.claude/skills/neta-trend-daily/SKILL.md](.claude/skills/neta-trend-daily/SKILL.md) 以下は最新日のレポートを常時コピーして表示するセクションです ( 自動同期 )。 --- # トレ...	0.290959	null	null	2,026	4	15	16	2	11	[]	0	[]	0	{"abstract_length_score": 0.509, "has_code_score": 1.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 0.9698630136986301, "overall_quality_score": 0.4457726027397261}	repository	true	false	-0.388889	0.833333	negative	None # news -- トレンドネタ日次レポート本リポジトリは、有名サイト・はてブ IT 人気エントリー・Hacker News・Reddit 13 サブレッドなどから日次のトレンドを収集し、興味領域と突き合わせてサマリ付きで公開するものです。 - 日次レポート本体: [ideas/daily/](ideas/daily) - 収集スキル: [. claude/skills/neta-trend-daily/SKILL. md](	224	{"completeness_score": 85.0, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 92.5, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	GitHub User	JavaScript	Unknown	false	cold	[]	0

Research Collector Dataset

This dataset contains research results aggregated from multiple sources by the Research-Collector tool. Each item is enriched with comprehensive metadata, ML subfield classifications, quality scores, and temporal features.

Dataset Details

Topic: artificial super intelligence OR superintelligent AI OR ASI
Time Range: 2026-04-12T16:58:38.679122 to 2026-04-26T16:58:38.679128
Sources: pubmed, crossref, semantic_scholar, paperswithcode, arxiv, medium, kaggle, stackoverflow, github, reddit, hackernews, gdelt
Total Items: 39
Exported At: 2026-04-26T16:59:07.690129

Dataset Structure

Core Fields

id: Unique identifier
title: Title of the research item
source: Source platform (e.g., pubmed, arxiv, github, reddit, stackoverflow)
url: URL to original content
author: Author(s)
published_date: Publication date (ISO 8601 format)
citations: Number of citations (if available)
upvotes: Number of upvotes (if available)
downloads: Number of downloads (if available)
comments: Number of comments (if available)
content: Content/abstract/description
score: Relevance score

Enriched Metadata Fields

metadata_year: Publication year
metadata_month: Publication month
metadata_day: Publication day
metadata_week: Week of year
metadata_quarter: Quarter of year
metadata_days_since: Days since publication
metadata_ml_subfields: ML subfield classifications (JSON array)
metadata_subfield_count: Number of ML subfields
metadata_keywords: Extracted keywords (JSON array)
metadata_keyword_count: Number of keywords
metadata_quality_scores: Quality score metrics (JSON dict)
metadata_content_type: Content type (paper, preprint, repository, discussion, qa, news)
metadata_has_code: Whether item contains code
metadata_has_doi: Whether item has DOI
metadata_sentiment_polarity: Sentiment polarity score (-1 to 1)
metadata_sentiment_subjectivity: Sentiment subjectivity score (0 to 1)
metadata_sentiment_category: Sentiment category (positive, negative, neutral)
metadata_summary: Automatic summary of content (extractive)
metadata_summary_length: Length of summary in characters
metadata_data_quality: Data quality metrics (JSON dict)
- completeness_score: Field completeness percentage (0-100)
- consistency_score: Internal consistency score (0-100)
- validity_score: Data validity score (0-100)
- overall_quality_score: Overall data quality score (0-100)
metadata_trending_score: Engagement velocity score
metadata_trending_category: Trending category (hot, warm, cool, cold)
metadata_engagement_score: Raw engagement score
metadata_related_items: Related items with similarity scores (JSON array)
metadata_related_count: Number of related items

Source-Specific Metadata

PubMed: metadata_journal, metadata_doi, metadata_mesh_terms, metadata_publication_types, metadata_abstract_length
arXiv: metadata_arxiv_id, metadata_primary_category, metadata_categories, metadata_journal_ref
GitHub: metadata_stars, metadata_forks, metadata_language, metadata_license, metadata_topics, metadata_has_readme
Reddit: metadata_subreddit, metadata_link_flair_text, metadata_upvote_ratio, metadata_total_awards, metadata_is_gilded
Stack Overflow: metadata_tags, metadata_answer_count, metadata_has_accepted_answer, metadata_view_count, metadata_owner_reputation
Semantic Scholar: metadata_citation_count, metadata_influential_citation_count, metadata_fields_of_study, metadata_has_open_access
Medium: metadata_author, metadata_publication, metadata_read_time, metadata_claps
Kaggle: metadata_votes, metadata_usability_rating, metadata_file_count

Usage Examples

from datasets import load_dataset

# Load dataset
dataset = load_dataset("nellaivijay/asi-research-daily")
train_data = dataset["train"]

# Filter by source
pubmed_items = train_data.filter(lambda x: x["source"] == "pubmed")
github_items = train_data.filter(lambda x: x["source"] == "github")

# Filter by content type
papers = train_data.filter(lambda x: x.get("metadata_content_type") == "paper")
repositories = train_data.filter(lambda x: x.get("metadata_content_type") == "repository")

# Filter by ML subfield
cv_papers = train_data.filter(lambda x: "computer-vision" in x.get("metadata_ml_subfields", []))

# Filter by quality
high_quality = train_data.filter(lambda x: x.get("metadata_quality_scores", {}).get("overall_quality_score", 0) > 0.7)

# Sort by score
sorted_items = train_data.sort("score", reverse=True)

# Filter by date
recent_items = train_data.filter(lambda x: x.get("metadata_days_since", 999) < 30)

# Filter by trending category
trending_items = train_data.filter(lambda x: x.get("metadata_trending_category") == "hot")

# Filter by data quality
high_quality = train_data.filter(lambda x: x.get("metadata_data_quality", {}).get("overall_quality_score", 0) > 0.7)

# Filter by sentiment
positive_items = train_data.filter(lambda x: x.get("metadata_sentiment_category") == "positive")

# Get related items
item_with_related = train_data[0]
related_items = item_with_related.get("metadata_related_items", [])

Data Quality Features

Standardized Dates: All dates normalized to ISO 8601 format
ML Subfield Classification: Automatic classification into 15+ ML subfields
Quality Scoring: Multi-dimensional quality assessment (abstract length, code availability, DOI, engagement, recency)
Temporal Features: Year, month, week, quarter, days since publication
Keyword Extraction: Automatic extraction of technical keywords
Content Type Detection: Automatic classification of item type
Sentiment Analysis: Sentiment polarity, subjectivity, and category classification
Automatic Summarization: Extractive summaries for quick content overview
Data Quality Metrics: Completeness, consistency, and validity scores for each item
Trending Metrics: Engagement velocity analysis with trending categories
Cross-References: Related item detection based on shared subfields, keywords, and tags
Fuzzy Deduplication: Intelligent duplicate detection with metadata merging
Metadata Completeness: Fallback logic to infer missing metadata fields

Data Sources

This dataset aggregates research from:

Academic: PubMed, arXiv, Semantic Scholar, Crossref, Papers with Code
Professional: GitHub, Stack Overflow, Kaggle
Social: Reddit, Hacker News
News: GDELT
Blogs: Medium, Towards Data Science

Limitations

Data is limited to the specified time range
Some sources may have rate limits or API restrictions
Citation counts may vary between sources
ML subfield classification is based on keyword matching and may not be perfect

Source

Generated by Research-Collector, an educational multi-source research aggregation tool.

License

MIT License

Citation

If you use this dataset, please cite the repository URL: https://huggingface.co/datasets/nellaivijay/asi-research-daily

Downloads last month: 53

Number of rows:

Total file size:

79.7 kB