Datasets:

nellaivijay
/

ani-research-daily

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gdelt1	News about artificial narrow intelligence OR weak AI OR ANI	gdelt	https://example.com/news/1	News Reporter	2026-04-12	News article covering artificial narrow intelligence OR weak AI OR ANI...	0.688493	News Agency	US	2,026	4	12	15	2	14	[]	0	[]	0	{"abstract_length_score": 0.073, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.2146}	news	false	false	-0.391667	0.675	negative	News article covering artificial narrow intelligence OR weak AI OR ANI	70	{"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	["anomaly-detection", "interpretability", "recommendation", "nlp", "computer-vision", "auto-ml", "generative-ai", "graph-learning", "federated-learning", "time-series", "transfer-learning", "reinforcement-learning", "deep-learning", "optimization"]	7	["llm", "attention", "reinforcement learning", "generative", "fine-tuning", "transformer", "embedding", "self-attention", "deep learning", "classification", "computer vision", "hyperparameter", "gradient descent", "clustering", "supervised", "optimization"]	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_kermitthedev_Physics-Aware-Soil-Moisture-ML", "title": "Physics-Aware-Soil-Moisture-ML", "similarity_score": 14, "shared_subfields": ["deep-learning", "interpretability", "reinforcement-learning", "time-series"], "shared_keywords": ["deep learning"], "shared_tags": []}, {"id": "github_sh4shv4t_Parlay", ...	5
arxiv_2604.21930v1	Temporal Taskification in Streaming Continual Learning: A Source of Evaluation Instability	arxiv	https://arxiv.org/abs/2604.21930v1	Nicolae Filat, Ahmed Hussain, Konstantinos Kalogiannis, Elena Burceanu	2026-04-23	Streaming Continual Learning (CL) typically converts a continuous stream into a sequence of discrete tasks through temporal partitioning. We argue that this temporal taskification step is not a neutral preprocessing choice, but a structural component of evaluation: different valid splits of the same stream can induce d...	0.5	null	null	2,026	4	23	17	2	3	["computer-vision", "time-series"]	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	-0.039583	0.503125	neutral	Streaming Continual Learning (CL) typically converts a continuous stream into a sequence of discrete tasks through temporal partitioning. We argue that this temporal taskification step is not a neutral preprocessing choice, but a structural component of evaluation: different valid splits of the...	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	Unknown	Unknown	false	cold	null	null
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_2604.21909v1	Directional Confusions Reveal Divergent Inductive Biases Through Rate-Distortion Geometry in Human and Machine Vision	arxiv	https://arxiv.org/abs/2604.21909v1	Leyla Roksan Caglar, Pedro A. M. Mediano, Baihan Lin	2026-04-23	Humans and modern vision models can reach similar classification accuracy while making systematically different kinds of mistakes - differing not in how often they err, but in who gets mistaken for whom, and in which direction. We show that these directional confusions reveal distinct inductive biases that are invisibl...	0.5	null	null	2,026	4	23	17	2	3	["computer-vision", "reinforcement-learning", "generative-ai"]	3	["classification"]	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	-0.022321	0.352679	neutral	Humans and modern vision models can reach similar classification accuracy while making systematically different kinds of mistakes - differing not in how often they err, but in who gets mistaken for whom, and in which direction. We show that these directional confusions reveal distinct inductive...	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	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_2604.21900v1	Three-periodic helices on elliptic curves and their associated regular algebras	arxiv	https://arxiv.org/abs/2604.21900v1	Daniel Chan, Adam Nyman	2026-04-23	Let $k$ denote an algebraically closed field of characteristic zero and let $X$ denote a smooth elliptic curve over $k$. Given a three-periodic elliptic helix $\underline{\mathcal{E}}$ of vector bundles over $X$ with endomorphism $\mathbb{Z}$-algebra $\operatorname{End} \underline{\mathcal{E}}$ and quadratic cover $\ma...	0.5	null	null	2,026	4	23	17	2	3	["reinforcement-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	0.054762	0.446703	neutral	Let $k$ denote an algebraically closed field of characteristic zero and let $X$ denote a smooth elliptic curve over $k$. Given a three-periodic elliptic helix $\underline{\mathcal{E}}$ of vector bundles over $X$ with endomorphism $\mathbb{Z}$-algebra $\operatorname{End} \underline{\mathcal{E}}$ and...	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_2604.21896v1	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_2604.21895v1	Heavy Quark Transport is Non-Gaussian Beyond Leading Log	arxiv	https://arxiv.org/abs/2604.21895v1	Jean F. Du Plessis, Bruno Scheihing-Hitschfeld	2026-04-23	We find that heavy quark transport beyond leading logarithm at weak coupling is intrinsically non-Gaussian: the longitudinal momentum transfer distribution has asymmetric exponential tails that are crucial for equilibration dynamics. We show this by computing the leading-order momentum transfer kernel for relativistic ...	0.5	null	null	2,026	4	23	17	2	3	["graph-learning"]	1	[]	0	{"abstract_length_score": 0.757, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.35140000000000005}	preprint	false	false	-0.071154	0.509707	neutral	We find that heavy quark transport beyond leading logarithm at weak coupling is intrinsically non-Gaussian: the longitudinal momentum transfer distribution has asymmetric exponential tails that are crucial for equilibration dynamics. We show this by computing the leading-order momentum transfer...	298	{"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_2604.21891v1	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_2604.21880v1	A theory of generalized Lamé curves	arxiv	https://arxiv.org/abs/2604.21880v1	You-Cheng Chou, Chin-Lung Wang, Po-Sheng Wu	2026-04-23	We study the generalized Lamé equation on an elliptic curve $E$ with multiple singularities. By restricting to the locus admitting solutions with quasi-periodic properties, we construct two curves: (i) The generalized Lam'e curve: with $n:=\sum\nolimits_{i=1}^r n_i\in\mathbb Z_{\geq 0}$, we construct $\mathcal{Y}_{\m...	0.5	null	null	2,026	4	23	17	2	3	["generative-ai"]	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	-0.375	0.5	negative	We study the generalized Lamé equation on an elliptic curve $E$ with multiple singularities. By restricting to the locus admitting solutions with quasi-periodic properties, we construct two curves: (i) The generalized Lam'e curve: with $n:=\sum\nolimits_{i=1}^r n_i\in\mathbb Z_{\geq 0}$, we...	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_2604.21879v1	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_2604.21874v1	Enhancing Coherence of Spin Centers in p-n Diodes via Optimization Algorithms	arxiv	https://arxiv.org/abs/2604.21874v1	Jonatan A. Posligua, David E. Stewart, Denis R. Candido	2026-04-23	Solid-state spin defects hold great promise as building blocks for various quantum technologies. Embedding spin centers in $p$-$n$ diodes under reverse bias has proved to be a powerful strategy to narrow the optical linewidth and increase spin coherence, while also enabling control of the photoluminescence wavelength v...	0.5	null	null	2,026	4	23	17	2	3	["nlp", "deep-learning", "optimization", "federated-learning"]	4	["gradient descent", "optimization", "embedding"]	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	0.053571	0.458036	neutral	Solid-state spin defects hold great promise as building blocks for various quantum technologies. Embedding spin centers in $p$-$n$ diodes under reverse bias has proved to be a powerful strategy to narrow the optical linewidth and increase spin coherence, while also enabling control of the...	292	{"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
github_mohanveeramanikantak_ai-superintelligence	ai-superintelligence	github	https://github.com/mohanveeramanikantak/ai-superintelligence	mohanveeramanikantak	2026-04-26	None # 🤖 AI Superintelligence ## 📌 Overview AI Superintelligence is a stage of artificial intelligence where machines surpass human intelligence in all aspects, including reasoning, creativity, and decision-making. It is considered the ultimate evolution of AI beyond Artificial General Intelligence (AGI). --- #...	0.45	null	null	2,026	4	26	17	2	0	["computer-vision"]	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	-0.107143	0.617857	neutral	None # 🤖 AI Superintelligence ## 📌 Overview AI Superintelligence is a stage of artificial intelligence where machines surpass human intelligence in all aspects, including reasoning, creativity, and decision-making. It is considered the ultimate evolution of AI beyond Artificial General...	292	{"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": ["computer-vision"], "shared_keywords": [], "shared_tags": []}, {"id": "github_YouMind-OpenLab_awesome-gpt-image-2", "title": "awesome-gpt-image-2", "similarity_score": 3, "shar...	4
github_itsbkh99_CS3081_Lab1	CS3081_Lab1	github	https://github.com/itsbkh99/CS3081_Lab1	itsbkh99	2026-04-26	None # CS3081 – Artificial Intelligence \| Lab 1: Maze Solver Effat University · Computer Science Department · Spring 2026 --- ## 📁 Project Structure ``` Lab1/ ├── maze.py # Main solver — DFS (StackFrontier) ├── maze_bfs.py # Modified solver — BFS (QueueFrontier) [Exercise 2] ├── maze1.txt ...	0.4	null	null	2,026	4	26	17	2	0	[]	0	[]	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	-0.117262	0.540476	neutral	None # CS3081 – Artificial Intelligence \| Lab 1: Maze Solver Effat University · Computer Science Department · Spring 2026 --- ## 📁 Project Structure ``` Lab1/ ├── maze. py # Main solver — DFS (StackFrontier) ├── maze_bfs. py # Modified solver — BFS (QueueFrontier) [Exercise...	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	Python	Unknown	false	cold	[]	0
github_Stanestane_game-design-skills-bundle	game-design-skills-bundle	github	https://github.com/Stanestane/game-design-skills-bundle	Stanestane	2026-04-21	A public OpenClaw and ClawHub-ready bundle of game design skills for emotional direction, feature workflows, audits, FTUE, pitch decks, prototyping, and red-team design review. # Game Design Skills Bundle A curated bundle of reusable game design skills for OpenClaw / ClawHub-style agent workflows. This repository co...	0.39589	null	null	2,026	4	21	17	2	5	["reinforcement-learning", "federated-learning"]	2	[]	0	{"abstract_length_score": 0.681, "has_code_score": 1.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 0.9863013698630136, "overall_quality_score": 0.4834602739726027}	repository	true	false	-0.1	0.509524	neutral	A public OpenClaw and ClawHub-ready bundle of game design skills for emotional direction, feature workflows, audits, FTUE, pitch decks, prototyping, and red-team design review. # Game Design Skills Bundle A curated bundle of reusable game design skills for OpenClaw / ClawHub-style agent workflows....	302	{"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": 6, "shared_subfields": ["reinforcement-learning", "federated-learning"], "shared_keywords": [], "shared_tags": []}, {"id": "github_robsartin_planisphere", "title": "planisphere", "similarity_score": 6...	5
github_banyikun_epistemic_exploration	epistemic_exploration	github	https://github.com/banyikun/epistemic_exploration	banyikun	2026-04-21	Epistemic Exploration Toward Artificial General Intelligence <h1 align="center">🔥 Awesome-Epistemic-Exploration "Epistemic Exploration Toward Artificial General Intelligence" (ArXiv 2026) </h2> <p align="center"> <b>◇ Responder → Reasoner → Agent → Prospector → Ecosystem ◇</b> <br> <i><b>🚀 Exploratio...	0.39589	null	null	2,026	4	21	17	2	5	["reinforcement-learning"]	1	[]	0	{"abstract_length_score": 0.565, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 0.9863013698630136, "overall_quality_score": 0.3102602739726027}	repository	false	false	-0.194531	0.425	neutral	Epistemic Exploration Toward Artificial General Intelligence <h1 align="center">🔥 Awesome-Epistemic-Exploration "Epistemic Exploration Toward Artificial General Intelligence" (ArXiv 2026) </h2> <p align="center"> <b>◇ Responder → Reasoner → Agent → Prospector → Ecosystem ◇</b> <br> ...	302	{"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	MIT License	true	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_taraudani_DS-4320-Project-2", "title": "DS-4320-Project-2", "similarity_score": 3, "shared_...	5
github_kermitthedev_Physics-Aware-Soil-Moisture-ML	Physics-Aware-Soil-Moisture-ML	github	https://github.com/kermitthedev/Physics-Aware-Soil-Moisture-ML	kermitthedev	2026-04-23	Physics-aware ANN vs LSTM comparison for soil moisture prediction with Monte Carlo uncertainty quantification and SHAP explainability . # Characterizing the Temporal Threshold: When do Sequential Architectures Outperform Feedforward Networks in Soil Moisture Retrieval? *Inspired by Boyd et al. (2019) — "High Spatio...	0.347534	null	null	2,026	4	23	17	2	3	["reinforcement-learning", "deep-learning", "time-series", "interpretability"]	4	["deep learning", "neural network", "lstm"]	3	{"abstract_length_score": 0.642, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 0.9945205479452055, "overall_quality_score": 0.3273041095890411}	repository	false	false	-0.218	0.668	neutral	Physics-aware ANN vs LSTM comparison for soil moisture prediction with Monte Carlo uncertainty quantification and SHAP explainability. # Characterizing the Temporal Threshold: When do Sequential Architectures Outperform Feedforward Networks in Soil Moisture Retrieval. *Inspired by Boyd et al	294	{"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	MIT License	true	cold	[{"id": "arxiv_2604.21931v1", "title": "Seeing Fast and Slow: Learning the Flow of Time in Videos", "similarity_score": 14, "shared_subfields": ["deep-learning", "interpretability", "reinforcement-learning", "time-series"], "shared_keywords": ["deep learning"], "shared_tags": []}, {"id": "github_taraudani_DS-4320-Proje...	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", "nlp", "computer-vision"], "shared_keywords": [], "shared_tags": []}, {"id": "github_sh4shv4t_Parlay", "title": "Parlay", "similarity_score": 9, "shar...	5
github_Random-Word_global-growth	global-growth	github	https://github.com/Random-Word/global-growth	Random-Word	2026-04-14	An analysis of global development and human growth trajectories. # Growth, Poverty, and Planetary Boundaries A quantitative response to the claim that capitalism is "mathematically unworkable" for ending global poverty within ecological limits. This project uses World Bank, Maddison Project, Our World in Data, O...	0.340137	null	null	2,026	4	14	16	2	12	["reinforcement-learning"]	1	[]	0	{"abstract_length_score": 0.569, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 0.9671232876712329, "overall_quality_score": 0.3072246575342466}	repository	false	false	-0.033333	0.35	neutral	An analysis of global development and human growth trajectories. # Growth, Poverty, and Planetary Boundaries A quantitative response to the claim that capitalism is "mathematically unworkable" for ending global poverty within ecological limits. This project uses World Bank, Maddison Project,...	302	{"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": ["reinforcement-learning"], "shared_keywords": [], "shared_tags": []}, {"id": "github_taraudani_DS-4320-Project-2", "title": "DS-4320-Project-2", "similarity_score": 3, "shared_...	5
github_rashmisubhash_aintropy-retrieval-layer	aintropy-retrieval-layer	github	https://github.com/rashmisubhash/aintropy-retrieval-layer	rashmisubhash	2026-04-26	None # Extreme-Fast Retrieval Layer — POC ## The Problem AIntropy's retrieval problem is the standard hard case in modern search systems: hold sub-second latency against a corpus that eventually scales to 100M+ documents while still doing the expensive work that improves relevance. This POC explores that tradeoff wi...	0.3	null	null	2,026	4	26	17	2	0	[]	0	[]	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	-0.178333	0.388333	neutral	None # Extreme-Fast Retrieval Layer — POC ## The Problem AIntropy's retrieval problem is the standard hard case in modern search systems: hold sub-second latency against a corpus that eventually scales to 100M+ documents while still doing the expensive work that improves relevance. This POC...	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	Python	Unknown	false	cold	[]	0
medium_	The Essential Guide to Effectively Summarizing Massive Documents, Part 2	medium	https://towardsdatascience.com/the-essential-guide-to-effectively-summarizing-massive-documents-part-2/	Vinayak Sengupta	2026-04-25	We have the document clusters, and it’s time to unlock their true potential! Let’s explore how to extract meaningful information from the actionable clusters. The post The Essential Guide to Effectively Summarizing Massive Documents, Part 2 appeared first on Towards Data Science.	0.299178	null	null	2,026	4	25	17	2	1	["nlp"]	1	[]	0	{"abstract_length_score": 0.28, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.256}	unknown	false	false	0.23	0.668333	neutral	We have the document clusters, and it’s time to unlock their true potential. Let’s explore how to extract meaningful information from the actionable clusters. The post The Essential Guide to Effectively Summarizing Massive Documents, Part 2 appeared first on Towards Data Science	279	{"completeness_score": 92.5, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 96.25, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	Vinayak Sengupta	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_YouMind-OpenLab_awesome-gpt-image-2", "title": "awesome-gpt-image-2", "similarity_score": 3, "shared_subfields...	5
github_rlei-odes_lancy	lancy	github	https://github.com/rlei-odes/lancy	rlei-odes	2026-04-25	Retrieve your Knowledge - Configurable RAG # Lancy — Open-Source RAG System > Ask questions of your documents. See exactly how the answer was found. Lancy is a self-hosted, production-ready Retrieval-Augmented Generation system. It brings transparency to the RAG process: every response shows its sources, evidence qu...	0.299178	null	null	2,026	4	25	17	2	1	["graph-learning", "generative-ai"]	2	[]	0	{"abstract_length_score": 0.547, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 0.9972602739726028, "overall_quality_score": 0.30885205479452055}	repository	false	false	0.177083	0.341667	neutral	Retrieve your Knowledge - Configurable RAG # Lancy — Open-Source RAG System > Ask questions of your documents. See exactly how the answer was found. Lancy is a self-hosted, production-ready Retrieval-Augmented Generation system	229	{"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": 6, "shared_subfields": ["generative-ai", "graph-learning"], "shared_keywords": [], "shared_tags": []}, {"id": "github_alsaifybashar_SynoSec-buildathon", "title": "SynoSec-buildathon", "similarity_scor...	3
github_damandogra_Cassini-MountainMapping	Cassini-MountainMapping	github	https://github.com/damandogra/Cassini-MountainMapping	damandogra	2026-04-25	None # to run locally: you can use `run.md` to run the script # 🌊 Atlas ### Tighza, Morocco --- ## Table of Contents 1. [What this project does](#what-this-project-does) 2. [Why Morocco / Ounila?](#why-morocco--ounila) 3. [Team roles](#team-roles) 4. [Repository structure](#repository-structure) 5. [Quick start — ...	0.299178	null	null	2,026	4	25	17	2	1	[]	0	[]	0	{"abstract_length_score": 0.509, "has_code_score": 1.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 0.9972602739726028, "overall_quality_score": 0.4512520547945206}	repository	true	false	0.177778	0.3	neutral	None # to run locally: you can use `run. md` to run the script # 🌊 Atlas ### Tighza, Morocco --- ## Table of Contents 1. [What this project does](#what-this-project-does) 2	176	{"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	[]	0
github_BTemuujin_hidden-topological-transitions	hidden-topological-transitions	github	https://github.com/BTemuujin/hidden-topological-transitions	BTemuujin	2026-04-25	None # Replication of Hidden Topological Transitions in Emergent Magnetic Monopole Lattices This repository contains a strict numerical replication of the theoretical study "Hidden topological transitions in emergent magnetic monopole lattices" (Phys. Rev. B 107, 094437, 2023) by Kato and Motome. ## 🎯 Project Goa...	0.299178	null	null	2,026	4	25	17	2	1	[]	0	[]	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	-0.1	0.282143	neutral	None # Replication of Hidden Topological Transitions in Emergent Magnetic Monopole Lattices This repository contains a strict numerical replication of the theoretical study "Hidden topological transitions in emergent magnetic monopole lattices" (Phys. Rev. B 107, 094437, 2023) by Kato and Motome	300	{"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	[]	0
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": ["recommendation", "federated-learning"], "shared_keywords": [], "shared_tags": []}, {"id": "github_robsartin_planisphere", "title": "planisphere", "similarity_score": 3, "share...	4
github_peterRooo_awesome-gpt-image-2-prompts	awesome-gpt-image-2-prompts	github	https://github.com/peterRooo/awesome-gpt-image-2-prompts	peterRooo	2026-04-24	A professional GPT Image 2 prompt library with 500 curated, copy-ready prompts, repository-hosted preview images, structured JSON/CSV exports, source attribution, and daily updates from GptImageLab. <div align="center"> # Awesome GPT Image 2 Prompts A polished, image-backed prompt library for GPT Image 2 creator...	0.298356	null	null	2,026	4	24	17	2	2	["generative-ai", "nlp", "computer-vision"]	2	["gpt"]	1	{"abstract_length_score": 0.703, "has_code_score": 1.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 0.9945205479452055, "overall_quality_score": 0.4895041095890411}	repository	true	false	0.25	0.3	neutral	A professional GPT Image 2 prompt library with 500 curated, copy-ready prompts, repository-hosted preview images, structured JSON/CSV exports, source attribution, and daily updates from GptImageLab. <div align="center"> # Awesome GPT Image 2 Prompts A polished, image-backed prompt library for...	298	{"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": 9, "shared_subfields": ["generative-ai", "nlp", "computer-vision"], "shared_keywords": [], "shared_tags": []}, {"id": "github_YouMind-OpenLab_awesome-gpt-image-2", "title": "awesome-gpt-image-2", "sim...	5
github_sh4shv4t_Parlay	Parlay	github	https://github.com/sh4shv4t/Parlay	sh4shv4t	2026-04-21	Parlay — RL negotiation environment where LLM agents learn game theory, theory-of-mind, and strategic bluffing through self-play. OpenEnv compliant · MCP ready · GRPO-trained on Qwen2.5. --- title: Parlay emoji: 🤝 colorFrom: indigo colorTo: green sdk: docker pinned: false tags: ["openenv", "hackathon", "rl", "gamethe...	0.29589	null	null	2,026	4	21	17	2	5	["computer-vision", "nlp", "reinforcement-learning"]	3	["llm"]	1	{"abstract_length_score": 0.691, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 0.989041095890411, "overall_quality_score": 0.3360082191780822}	repository	false	false	-0.092857	0.457143	neutral	Parlay — RL negotiation environment where LLM agents learn game theory, theory-of-mind, and strategic bluffing through self-play. OpenEnv compliant · MCP ready · GRPO-trained on Qwen2. 5	186	{"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": 11, "shared_subfields": ["reinforcement-learning", "nlp", "computer-vision"], "shared_keywords": ["llm"], "shared_tags": []}, {"id": "github_YouMind-OpenLab_awesome-gpt-image-2", "title": "awesome-gpt...	5
github_taraudani_DS-4320-Project-2	DS-4320-Project-2	github	https://github.com/taraudani/DS-4320-Project-2	taraudani	2026-04-20	This repository contains the full pipeline for a machine learning project that predicts hourly electricity demand on the PJM Interconnection 24 hours in advance. # DS-4320-Project-2 ### Executive Summary This repository contains the full pipeline for a machine learning project that predicts hourly electricity demand...	0.295068	null	null	2,026	4	20	17	2	6	["reinforcement-learning"]	1	["machine learning"]	1	{"abstract_length_score": 0.666, "has_code_score": 1.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 0.9863013698630136, "overall_quality_score": 0.4804602739726027}	repository	true	false	0.275	0.375	neutral	This repository contains the full pipeline for a machine learning project that predicts hourly electricity demand on the PJM Interconnection 24 hours in advance. # DS-4320-Project-2 ### Executive Summary This repository contains the full pipeline for a machine learning project that predicts...	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	Jupyter Notebook	MIT License	true	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_kermitthedev_Physics-Aware-Soil-Moisture-ML", "title": "Physics-Aware-Soil-Moisture-ML", "s...	5
github_Vijay-Kiran-R_Small_Language_Model	Small_Language_Model	github	https://github.com/Vijay-Kiran-R/Small_Language_Model	Vijay-Kiran-R	2026-04-19	A ~125.9M parameter small language model featuring Full Attention Residuals (AttnRes), GQA, SwiGLU, SWA+Global NoPE, and Multi-Token Prediction. # SLM — Small Language Model: Complete Technical Reference > Who this is for: Beginners who want to understand how language models are built from scratch, > AND experien...	0.294247	null	null	2,026	4	19	16	2	7	["nlp", "deep-learning"]	2	["attention"]	1	{"abstract_length_score": 0.649, "has_code_score": 1.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 0.9808219178082191, "overall_quality_score": 0.47596438356164383}	repository	true	false	0.157143	0.471429	neutral	A ~125. 9M parameter small language model featuring Full Attention Residuals (AttnRes), GQA, SwiGLU, SWA+Global NoPE, and Multi-Token Prediction. # SLM — Small Language Model: Complete Technical Reference > Who this is for: Beginners who want to understand how language models are built from...	299	{"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": 8, "shared_subfields": ["deep-learning", "nlp"], "shared_keywords": ["attention"], "shared_tags": []}, {"id": "medium_", "title": "The Essential Guide to Effectively Summarizing Massive Documents, Par...	5
github_shshs21_marketdata-trw	marketdata-trw	github	https://github.com/shshs21/marketdata-trw	shshs21	2026-04-18	None # Market Data Pipeline A crypto market database built to eliminate survivorship bias and selection bias from downstream backtests, plus the tools to build it from scratch. This repo produces `marketdata.db`, a SQLite file containing historical market cap rankings, token metadata, and daily OHLCV bars going b...	0.293425	null	null	2,026	4	18	16	2	8	["recommendation"]	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	-0.03125	0.09375	neutral	None # Market Data Pipeline A crypto market database built to eliminate survivorship bias and selection bias from downstream backtests, plus the tools to build it from scratch. This repo produces `marketdata. db`, a SQLite file containing historical market cap rankings, token metadata, and...	299	{"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": ["recommendation"], "shared_keywords": [], "shared_tags": []}, {"id": "github_nellaivijay_research-collector", "title": "research-collector", "similarity_score": 3, "shared_subf...	2
github_robsartin_planisphere	planisphere	github	https://github.com/robsartin/planisphere	robsartin	2026-04-15	Interactive web planisphere with satellite overlays (CesiumJS + satellite.js). Apache-2.0. # Planisphere Interactive web planisphere with satellite overlays. Static SPA (CesiumJS + satellite.js) deployed to Cloudflare Pages, with a Cloudflare Worker + D1 backing the Phase 2 Notebook mode. Apache 2.0 licensed. See [`...	0.290959	null	null	2,026	4	15	16	2	11	["reinforcement-learning", "federated-learning"]	2	[]	0	{"abstract_length_score": 0.595, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 0.9726027397260274, "overall_quality_score": 0.31352054794520545}	repository	false	false	0.25	0.65	neutral	Interactive web planisphere with satellite overlays (CesiumJS + satellite. js). Apache-2	88	{"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	Apache License 2.0	true	cold	[{"id": "arxiv_2604.21931v1", "title": "Seeing Fast and Slow: Learning the Flow of Time in Videos", "similarity_score": 6, "shared_subfields": ["reinforcement-learning", "federated-learning"], "shared_keywords": [], "shared_tags": []}, {"id": "github_Stanestane_game-design-skills-bundle", "title": "game-design-skills-b...	5
github_alsaifybashar_SynoSec-buildathon	SynoSec-buildathon	github	https://github.com/alsaifybashar/SynoSec-buildathon	alsaifybashar	2026-04-12	None # SynoSec SynoSec is an AI-assisted vulnerability discovery and security orchestration platform. It combines graph-reasoning agents, evidence-producing security tools, structured vulnerability reporting, and a built-in cyber range to identify weaknesses in a target system and explain how those weaknesses connect...	0.288493	null	null	2,026	4	12	15	2	14	["reinforcement-learning", "graph-learning"]	2	[]	0	{"abstract_length_score": 0.509, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 0.9616438356164384, "overall_quality_score": 0.2941287671232877}	repository	false	false	-0.5	0.6875	negative	None # SynoSec SynoSec is an AI-assisted vulnerability discovery and security orchestration platform. It combines graph-reasoning agents, evidence-producing security tools, structured vulnerability reporting, and a built-in cyber range to identify weaknesses in a target system and explain how...	298	{"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": 6, "shared_subfields": ["reinforcement-learning", "graph-learning"], "shared_keywords": [], "shared_tags": []}, {"id": "github_taraudani_DS-4320-Project-2", "title": "DS-4320-Project-2", "similarity_s...	5

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 narrow intelligence OR weak AI OR ANI
Time Range: 2026-04-12T16:58:42.217005 to 2026-04-26T16:58:42.217013
Sources: pubmed, crossref, semantic_scholar, paperswithcode, arxiv, medium, kaggle, stackoverflow, github, reddit, hackernews, gdelt
Total Items: 40
Exported At: 2026-04-26T16:59:07.781212

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/ani-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/ani-research-daily

Downloads last month: 14

Number of rows:

Total file size:

81.4 kB