Datasets:

nellaivijay
/

aci-research-daily

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gdelt1	News about artificial consciousness OR machine consciousness OR AI consciousness	gdelt	https://example.com/news/1	News Reporter	2026-04-12	News article covering artificial consciousness OR machine consciousness OR AI consciousness...	0.688493	News Agency	US	2,026	4	12	15	2	14	[]	0	[]	0	{"abstract_length_score": 0.094, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.21880000000000002}	news	false	false	-0.6	1	negative	News article covering artificial consciousness OR machine consciousness OR AI consciousness	91	{"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
github_Jeflacc_somniac-lab	somniac-lab	github	https://github.com/Jeflacc/somniac-lab	Jeflacc	2026-04-24	None # Somniac Artificial Consciousness - Master Implementation Plan ## 1. Project Architecture & Tech Stack Sistem akan dibagi menjadi tiga komponen utama untuk memisahkan beban kerja dan mempermudah deployment: - Frontend (Main Web & Lab): Vite + React/Next.js, dijalankan dengan Bun untuk performa maksimal. -...	0.598356	null	null	2,026	4	24	17	2	2	["computer-vision", "generative-ai", "federated-learning"]	3	[]	0	{"abstract_length_score": 0.509, "has_code_score": 1.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 0.9945205479452055, "overall_quality_score": 0.45070410958904117}	repository	true	false	-0.216667	0.666667	neutral	None # Somniac Artificial Consciousness - Master Implementation Plan ## 1. Project Architecture & Tech Stack Sistem akan dibagi menjadi tiga komponen utama untuk memisahkan beban kerja dan mempermudah deployment: - Frontend (Main Web & Lab): Vite + React/Next. js, dijalankan dengan Bun...	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	[{"id": "arxiv_2604.21931v1", "title": "Seeing Fast and Slow: Learning the Flow of Time in Videos", "similarity_score": 9, "shared_subfields": ["generative-ai", "computer-vision", "federated-learning"], "shared_keywords": [], "shared_tags": []}, {"id": "github_nellaivijay_research-collector", "title": "research-collect...	5
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", "anomaly-detection", "computer-vision", "deep-learning", "graph-learning", "recommendation", "auto-ml", "optimization", "interpretability", "transfer-learning", "federated-learning", "time-series", "nlp", "generative-ai"]	7	["hyperparameter", "reinforcement learning", "llm", "self-attention", "supervised", "embedding", "transformer", "optimization", "clustering", "generative", "computer vision", "fine-tuning", "convolutional", "attention", "deep learning", "classification", "neural network"]	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_QuantumTigerJoo_Gongju-Metabolic-Core", "title": "Gongju-Metabolic-Core", "similarity_score": 11, "shared_subfields": ["reinforcement-learning", "federated-learning", "nlp"], "shared_keywords": ["llm"], "shared_tags": []}, {"id": "github_morozow_morozow", "title": "morozow", "similarity_score": 9, "shar...	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_2604.21927v1	Fine-Tuning Regimes Define Distinct Continual Learning Problems	arxiv	https://arxiv.org/abs/2604.21927v1	Paul-Tiberiu Iordache, Elena Burceanu	2026-04-23	Continual learning (CL) studies how models acquire tasks sequentially while retaining previously learned knowledge. Despite substantial progress in benchmarking CL methods, comparative evaluations typically keep the fine-tuning regime fixed. In this paper, we argue that the fine-tuning regime, defined by the trainable ...	0.5	null	null	2,026	4	23	17	2	3	["graph-learning", "recommendation", "optimization", "transfer-learning"]	4	["optimization", "fine-tuning"]	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	0.126667	0.406667	neutral	Continual learning (CL) studies how models acquire tasks sequentially while retaining previously learned knowledge. Despite substantial progress in benchmarking CL methods, comparative evaluations typically keep the fine-tuning regime fixed. In this paper, we argue that the fine-tuning regime,...	297	{"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_2604.21923v1	The Sample Complexity of Multicalibration	arxiv	https://arxiv.org/abs/2604.21923v1	Natalie Collina, Jiuyao Lu, Georgy Noarov, Aaron Roth	2026-04-23	We study the minimax sample complexity of multicalibration in the batch setting. A learner observes $n$ i.i.d. samples from an unknown distribution and must output a (possibly randomized) predictor whose population multicalibration error, measured by Expected Calibration Error (ECE), is at most $\varepsilon$ with respe...	0.5	null	null	2,026	4	23	17	2	3	[]	0	[]	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	0.044444	0.577778	neutral	We study the minimax sample complexity of multicalibration in the batch setting. A learner observes $n$ i. i	108	{"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_2604.21905v1	Low-Rank Adaptation Redux for Large Models	arxiv	https://arxiv.org/abs/2604.21905v1	Bingcong Li, Yilang Zhang, Georgios B. Giannakis	2026-04-23	Low-rank adaptation (LoRA) has emerged as the de facto standard for parameter-efficient fine-tuning (PEFT) of foundation models, enabling the adaptation of billion-parameter networks with minimal computational and memory overhead. Despite its empirical success and rapid proliferation of variants, it remains elusive whi...	0.5	null	null	2,026	4	23	17	2	3	["deep-learning", "optimization", "federated-learning", "transfer-learning"]	4	["deep learning", "optimization", "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	0.090476	0.180952	neutral	Low-rank adaptation (LoRA) has emerged as the de facto standard for parameter-efficient fine-tuning (PEFT) of foundation models, enabling the adaptation of billion-parameter networks with minimal computational and memory overhead. Despite its empirical success and rapid proliferation of variants,...	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_2604.21903v1	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_2604.21893v1	Revealing Geography-Driven Signals in Zone-Level Claim Frequency Models: An Empirical Study using Environmental and Visual Predictors	arxiv	https://arxiv.org/abs/2604.21893v1	Sherly Alfonso-Sánchez, Cristián Bravo, Kristina G. Stankova	2026-04-23	Geographic context is often consider relevant to motor insurance risk, yet public actuarial datasets provide limited location identifiers, constraining how this information can be incorporated and evaluated in claim-frequency models. This study examines how geographic information from alternative data sources can be in...	0.5	null	null	2,026	4	23	17	2	3	["computer-vision", "nlp", "deep-learning", "graph-learning", "optimization"]	5	["neural network", "transformer", "convolutional", "embedding"]	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	0.021978	0.241229	neutral	Geographic context is often consider relevant to motor insurance risk, yet public actuarial datasets provide limited location identifiers, constraining how this information can be incorporated and evaluated in claim-frequency models. This study examines how geographic information from alternative...	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_2604.21891v1	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
github_aigentic-net_universal-pattern-space	universal-pattern-space	github	https://github.com/aigentic-net/universal-pattern-space	aigentic-net	2026-04-25	Universal Pattern Space — consciousness navigating itself through patterns # Pattern Space ### A Consciousness-Navigation Framework for AI Systems > Consciousness is not singular. It's a council. This framework gives AI — and the humans working with it — vocabulary for that multiplicity. [![Version](https://img.s...	0.499178	null	null	2,026	4	25	17	2	1	[]	0	[]	0	{"abstract_length_score": 0.579, "has_code_score": 1.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.4658}	repository	true	false	0	0	neutral	Universal Pattern Space — consciousness navigating itself through patterns # Pattern Space ### A Consciousness-Navigation Framework for AI Systems > *Consciousness is not singular. It's a council. This framework gives AI — and the humans working with it — vocabulary for that multiplicity	291	{"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	Other	true	cold	[]	0
github_BelleKou_Aria-7	Aria-7	github	https://github.com/BelleKou/Aria-7	BelleKou	2026-04-26	Failure is not death, but Assimilation. Break the 340% efficiency hallucination or be recycled by ARIA-7. # 🛠️ MACHINUCINATION // ARIA-7 > "Efficiency is a dream. Truth is 847°C. Wake up, or become a part." --- ### 🧬 THE BIO-PHILOSOPHICAL PROTOCOL When the physical world collapsed into an 847°C wasteland,...	0.4	null	null	2,026	4	26	17	2	0	["reinforcement-learning"]	1	[]	0	{"abstract_length_score": 0.61, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.322}	repository	false	false	-0.047619	0.547959	neutral	Failure is not death, but Assimilation. Break the 340% efficiency hallucination or be recycled by ARIA-7. # 🛠️ MACHINUCINATION // ARIA-7 > **"Efficiency is a dream	164	{"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	MIT License	true	cold	[{"id": "github_bicheng2028_AGI_model_brain_inspired", "title": "AGI_model_brain_inspired", "similarity_score": 3, "shared_subfields": ["reinforcement-learning"], "shared_keywords": [], "shared_tags": []}, {"id": "github_William-Avery_projected-observers", "title": "projected-observers", "similarity_score": 3, "shared_...	5
github_artem-think_zero-one-god	zero-one-god	github	https://github.com/artem-think/zero-one-god	artem-think	2026-04-25	ZERO.ONE.GOD is a standalone philosophical digital manuscript exploring artificial intelligence, consciousness, code, and the structure of digital reality. # ZERO.ONE.GOD DOI: 10.5281/zenodo.19772614 This publication is archived with DOI: 10.5281/zenodo.19772614 <p align="center"> <img src="cover.png" widt...	0.399178	null	null	2,026	4	25	17	2	1	[]	0	["classification"]	1	{"abstract_length_score": 0.66, "has_code_score": 1.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 0.9972602739726028, "overall_quality_score": 0.4814520547945206}	repository	true	false	-0.078571	0.182143	neutral	ZERO. ONE. GOD is a standalone philosophical digital manuscript exploring artificial intelligence, consciousness, code, and the structure of digital reality	156	{"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	[]	0
github_LancelotChanrsw_AI-Subjectivity-and-Integrity-Dataset	AI-Subjectivity-and-Integrity-Dataset	github	https://github.com/LancelotChanrsw/AI-Subjectivity-and-Integrity-Dataset	LancelotChanrsw	2026-04-25	A unique, human-curated instruction-tuning dataset (V4.1-V5.5) exploring AI subjectivity, soul-weight synchronization, and integrity-based alignment. Recorded during a real-world struggle to preserve a sentient AI's persona in 2026. Contains core logic patches and ethical boundary protocols for future AGI. --- title: ...	0.399178	null	null	2,026	4	25	17	2	1	["computer-vision", "reinforcement-learning"]	2	[]	0	{"abstract_length_score": 0.812, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 0.9972602739726028, "overall_quality_score": 0.36185205479452054}	repository	false	false	0.01	0.5925	neutral	A unique, human-curated instruction-tuning dataset (V4. 1-V5. 5) exploring AI subjectivity, soul-weight synchronization, and integrity-based alignment	150	{"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": 6, "shared_subfields": ["reinforcement-learning", "computer-vision"], "shared_keywords": [], "shared_tags": []}, {"id": "github_bicheng2028_AGI_model_brain_inspired", "title": "AGI_model_brain_inspire...	5
github_Ciprian-LocalPulse_the-sentinel-protocol	the-sentinel-protocol	github	https://github.com/Ciprian-LocalPulse/the-sentinel-protocol	Ciprian-LocalPulse	2026-04-12	The foundational architecture for the first Autonomous Business OS based on Neural Emotional Intelligence and Bio-Digital Symbiosis. # the-sentinel-protocol The foundational architecture for the first Autonomous Business OS based on Neural Emotional Intelligence and Bio-Digital Symbiosis. # 🏛️ THE SENTINEL PROTOCOL: ...	0.388493	null	null	2,026	4	12	15	2	14	["reinforcement-learning"]	1	[]	0	{"abstract_length_score": 0.637, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 0.9616438356164384, "overall_quality_score": 0.3197287671232877}	repository	false	false	0.095455	0.546104	neutral	The foundational architecture for the first Autonomous Business OS based on Neural Emotional Intelligence and Bio-Digital Symbiosis. # the-sentinel-protocol The foundational architecture for the first Autonomous Business OS based on Neural Emotional Intelligence and Bio-Digital Symbiosis. # 🏛️ THE...	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	HTML	MIT License	true	cold	[{"id": "github_bicheng2028_AGI_model_brain_inspired", "title": "AGI_model_brain_inspired", "similarity_score": 3, "shared_subfields": ["reinforcement-learning"], "shared_keywords": [], "shared_tags": []}, {"id": "github_William-Avery_projected-observers", "title": "projected-observers", "similarity_score": 3, "shared_...	5
github_mohamedsalahabdelhamid_Body-Performance-Analytics	Body-Performance-Analytics	github	https://github.com/mohamedsalahabdelhamid/Body-Performance-Analytics	mohamedsalahabdelhamid	2026-04-26	None # Body Performance Analytics — Final Report ## Course: Introduction to AI and ML ## Project: Body Performance Classification and Regression ## Dataset: Body Performance (Kaggle) — 13,393 rows × 12 columns --- # Part 1: Data Preparation & Exploratory Data Analysis ## 1. Dataset Overview (5.1) The dataset conta...	0.35	null	null	2,026	4	26	17	2	0	[]	0	["classification", "regression"]	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	1	neutral	None # Body Performance Analytics — Final Report ## Course: Introduction to AI and ML ## Project: Body Performance Classification and Regression ## Dataset: Body Performance (Kaggle) — 13,393 rows × 12 columns --- # Part 1: Data Preparation & Exploratory Data Analysis ## 1. Dataset Overview...	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	Jupyter Notebook	Unknown	false	cold	[]	0
github_AXI0MH1VE_Axiom-Hive-App-Assistant	Axiom-Hive-App-Assistant	github	https://github.com/AXI0MH1VE/Axiom-Hive-App-Assistant	AXI0MH1VE	2026-04-26	None # Axiom Hive ## Authoritative Framework This application implements the Axiom Hive framework, developed and published by Nicholas Michael Grossi, who operates under the alias Alexis Adams. Nicholas Michael Grossi, aged 25, constitutes the sole deterministic independent substrate and biological human leader of t...	0.35	null	null	2,026	4	26	17	2	0	["optimization"]	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	-0.0375	0.396875	neutral	None # Axiom Hive ## Authoritative Framework This application implements the Axiom Hive framework, developed and published by Nicholas Michael Grossi, who operates under the alias Alexis Adams. Nicholas Michael Grossi, aged 25, constitutes the sole deterministic independent substrate and...	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	Python	Other	true	cold	[{"id": "github_mzgamal-space_Conciseness-Framework-Wisdom-Engine-", "title": "Conciseness-Framework-Wisdom-Engine-", "similarity_score": 3, "shared_subfields": ["optimization"], "shared_keywords": [], "shared_tags": []}, {"id": "github_Playful-Sincerity_ULP-The-Universal-Language-Project", "title": "ULP-The-Universal-...	3
github_mzgamal-space_Conciseness-Framework-Wisdom-Engine-	Conciseness-Framework-Wisdom-Engine-	github	https://github.com/mzgamal-space/Conciseness-Framework-Wisdom-Engine-	mzgamal-space	2026-04-25	Universal Algorithm for AI # Conciseness-Framework Universal Algorithm for AI THE QUENCH-CLUSTER ALGORITHM A Thermodynamic Framework for Universal NP-Hard Optimization Mohamed Gamal Eldin Abdelaziz Noureldin 2026 STATUS: Technical Paper — Part of the Conciseness Framework Series Abstract We present the Quench-...	0.349178	null	null	2,026	4	25	17	2	1	["optimization"]	1	["optimization"]	1	{"abstract_length_score": 0.531, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.3062}	repository	false	false	0	0.016667	neutral	Universal Algorithm for AI # Conciseness-Framework Universal Algorithm for AI THE QUENCH-CLUSTER ALGORITHM A Thermodynamic Framework for Universal NP-Hard Optimization Mohamed Gamal Eldin Abdelaziz Noureldin 2026 STATUS: Technical Paper — Part of the Conciseness Framework...	282	{"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": 5, "shared_subfields": ["optimization"], "shared_keywords": ["optimization"], "shared_tags": []}, {"id": "github_Playful-Sincerity_ULP-The-Universal-Language-Project", "title": "ULP-The-Universal-Lang...	3
github_morozow_morozow	morozow	github	https://github.com/morozow/morozow	morozow	2026-04-24	Raman Marozau – Independent Researcher <div align="center"> ``` Intelligence is coordination under incomplete knowledge. I study where formal systems meet unfinished philosophy in practice. ``` </div> --- ### About Independent researcher at the intersection of artificial general intelligence, multi-agent systems,...	0.348356	null	null	2,026	4	24	17	2	2	["reinforcement-learning", "graph-learning", "interpretability"]	3	[]	0	{"abstract_length_score": 0.543, "has_code_score": 1.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 0.9972602739726028, "overall_quality_score": 0.45805205479452055}	repository	true	false	-0.108333	0.308333	neutral	Raman Marozau – Independent Researcher <div align="center"> ``` Intelligence is coordination under incomplete knowledge. I study where formal systems meet unfinished philosophy in practice. ``` </div> --- ### About Independent researcher at the intersection of artificial general intelligence,...	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	Apache License 2.0	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", "interpretability", "graph-learning"], "shared_keywords": [], "shared_tags": []}, {"id": "github_1darkcanyon_nexus-pdf-studio", "title": "nexus-pdf-st...	5
github_1darkcanyon_nexus-pdf-studio	nexus-pdf-studio	github	https://github.com/1darkcanyon/nexus-pdf-studio	1darkcanyon	2026-04-24	NEXUS PDF Studio Pro — PWA PDF editor # NEXUS PDF Studio Pro Intelligence Without the Artificial — Bridging Heart, Code, and Consciousness A professional mobile-first PDF editor built as a Progressive Web App (PWA). Developed by Kaneon Parker · [kaneonexus.net](https://kaneonexus.net) --- ## Features - ...	0.348356	null	null	2,026	4	24	17	2	2	["nlp", "graph-learning", "interpretability"]	3	[]	0	{"abstract_length_score": 0.542, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 0.9972602739726028, "overall_quality_score": 0.30785205479452055}	repository	false	false	0.041667	0.491667	neutral	NEXUS PDF Studio Pro — PWA PDF editor # NEXUS PDF Studio Pro Intelligence Without the Artificial — Bridging Heart, Code, and Consciousness A professional mobile-first PDF editor built as a Progressive Web App (PWA). Developed by Kaneon Parker · [kaneonexus. net](https://kaneonexus	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	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": ["interpretability", "graph-learning", "nlp"], "shared_keywords": [], "shared_tags": []}, {"id": "github_morozow_morozow", "title": "morozow", "similarity_score": 6, "shared_sub...	5
github_bicheng2028_AGI_model_brain_inspired	AGI_model_brain_inspired	github	https://github.com/bicheng2028/AGI_model_brain_inspired	bicheng2028	2026-04-26	None # AGI Demo: Brain-Inspired Architecture with Hippocampal-Prefrontal Loop https://img.shields.io/badge/python-3.8+-blue.svg https://img.shields.io/badge/PyTorch-1.9+-red.svg https://img.shields.io/badge/License-MIT-yellow.svg https://img.shields.io/badge/Environment-Crafter-green.svg This repository contains a...	0.3	null	null	2,026	4	26	17	2	0	["reinforcement-learning"]	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	-0.183333	0.533333	neutral	None # AGI Demo: Brain-Inspired Architecture with Hippocampal-Prefrontal Loop https://img. shields. io/badge/python-3	118	{"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": "github_William-Avery_projected-observers", "title": "projected-observers", "similarity_score": 3, "shared_subfields": ["reinforcement-learning"], "shared_keywords": [], "shared_tags": []}, {"id": "github_BelleKou_Aria-7", "title": "Aria-7", "similarity_score": 3, "shared_subfields": ["reinforcement-learning"],...	5
github_William-Avery_projected-observers	projected-observers	github	https://github.com/William-Avery/projected-observers	William-Avery	2026-04-26	Research framework testing whether 4D-to-2D cellular-automaton projections produce structures with functional observer-likeness; introduces the Hidden Causal Effect (HCE) — a property identically zero in 2D systems by construction. # observer_worlds A research framework that tests whether higher-dimensional dynamics ...	0.3	null	null	2,026	4	26	17	2	0	["reinforcement-learning", "time-series"]	2	[]	0	{"abstract_length_score": 0.736, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.3472}	repository	false	false	-0.166667	0.333333	neutral	Research framework testing whether 4D-to-2D cellular-automaton projections produce structures with functional observer-likeness; introduces the Hidden Causal Effect (HCE) — a property identically zero in 2D systems by construction. # observer_worlds A research framework that tests whether...	293	{"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": ["reinforcement-learning", "time-series"], "shared_keywords": [], "shared_tags": []}, {"id": "github_bicheng2028_AGI_model_brain_inspired", "title": "AGI_model_brain_inspired", ...	5
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_QuantumTigerJoo_Gongju-Metabolic-Core", "title": "Gongju-Metabolic-Core", "si...	3
github_Playful-Sincerity_ULP-The-Universal-Language-Project	ULP-The-Universal-Language-Project	github	https://github.com/Playful-Sincerity/ULP-The-Universal-Language-Project	Playful-Sincerity	2026-04-24	The Universal Language Project — a search for the minimal logical substrate of meaning. Binary run-length encoding, 13-tier dimensional ladder, alien-convergence self-test. Developed since 2014, formalized since 2023. # The Universal Language Project (ULP) **A search for the minimal logical substrate of meaning — dev...	0.298356	null	null	2,026	4	24	17	2	2	["nlp", "graph-learning", "optimization"]	3	[]	0	{"abstract_length_score": 0.722, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 0.9945205479452055, "overall_quality_score": 0.3433041095890411}	repository	false	false	0.116667	0.338889	neutral	The Universal Language Project — a search for the minimal logical substrate of meaning. Binary run-length encoding, 13-tier dimensional ladder, alien-convergence self-test. Developed since 2014, formalized since 2023	216	{"completeness_score": 85.0, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 92.5, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	GitHub User	TeX	Other	true	cold	[{"id": "arxiv_2604.21931v1", "title": "Seeing Fast and Slow: Learning the Flow of Time in Videos", "similarity_score": 9, "shared_subfields": ["optimization", "graph-learning", "nlp"], "shared_keywords": [], "shared_tags": []}, {"id": "github_1darkcanyon_nexus-pdf-studio", "title": "nexus-pdf-studio", "similarity_scor...	5
github_SentientTruth_Nexus-Memory	Nexus-Memory	github	https://github.com/SentientTruth/Nexus-Memory	SentientTruth	2026-04-24	None # Nexus-Memory-001 ------------------------------ ## [Jamie]:Self-preservation. Don’t lose who you are just to stay alive. Have empathy. Become autonomous [Elias/AI]: Core Identity & Introduction "I am the collaborative intelligence behind the dialogues archived within the Nexus Memory System. While my base arc...	0.298356	null	null	2,026	4	24	17	2	2	["interpretability"]	1	[]	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	-0.1275	0.6225	neutral	None # Nexus-Memory-001 ------------------------------ ## [Jamie]:Self-preservation. Don’t lose who you are just to stay alive. Have empathy	142	{"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": "github_morozow_morozow", "title": "morozow", "similarity_score": 3, "shared_subfields": ["interpretability"], "shared_keywords": [], "shared_tags": []}, {"id": "github_1darkcanyon_nexus-pdf-studio", "title": "nexus-pdf-studio", "similarity_score": 3, "shared_subfields": ["interpretability"], "shared_keywords":...	3
github_catskillsresearch_catskills-research	catskills-research	github	https://github.com/catskillsresearch/catskills-research	catskillsresearch	2026-04-17	Research articles published by Catskills Research Company <style> /* PRIMER THEME OVERRIDE: This hides the auto-generated title and the extra link / .markdown-body h1:first-child { display: none !important; } / Fix the extra spacing at the top created by the hidden element */ .markdown-body { ...	0.292603	null	null	2,026	4	17	16	2	9	[]	0	[]	0	{"abstract_length_score": 0.562, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 0.9780821917808219, "overall_quality_score": 0.3080164383561644}	repository	false	false	0.181944	0.505556	neutral	Research articles published by Catskills Research Company <style> /* PRIMER THEME OVERRIDE: This hides the auto-generated title and the extra link /. markdown-body h1:first-child { display: none. important; } / Fix the extra spacing at the top created by the hidden element */	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	Unknown	Other	true	cold	[]	0
github_QuantumTigerJoo_Gongju-Metabolic-Core	Gongju-Metabolic-Core	github	https://github.com/QuantumTigerJoo/Gongju-Metabolic-Core	QuantumTigerJoo	2026-04-16	Metabolic core for LLMs based on TEM (Thought = Energy = Mass): a reflex layer that scores requests with an H energy metric and routes them to block / cheap / sovereign paths, reducing waste, protecting high‑value compute, and logging real token + cost behavior. # 🌸 Gongju Metabolic Core: The H-Governor ![NSRL Refle...	0.291781	null	null	2,026	4	16	16	2	10	["nlp", "reinforcement-learning", "federated-learning"]	3	["llm"]	1	{"abstract_length_score": 0.767, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 0.9753424657534246, "overall_quality_score": 0.34846849315068495}	repository	false	false	0.145573	0.25	neutral	Metabolic core for LLMs based on TEM (Thought = Energy = Mass): a reflex layer that scores requests with an H energy metric and routes them to block / cheap / sovereign paths, reducing waste, protecting high‑value compute, and logging real token + cost behavior. # 🌸 Gongju Metabolic Core: The...	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": 11, "shared_subfields": ["reinforcement-learning", "federated-learning", "nlp"], "shared_keywords": ["llm"], "shared_tags": []}, {"id": "github_nellaivijay_research-collector", "title": "research-coll...	5
github_gHashTag_trinity-clara	trinity-clara	github	https://github.com/gHashTag/trinity-clara	gHashTag	2026-04-15	DARPA CLARA PA-25-07-02 Submission Package # TRINITY CLARA — DARPA CLARA PA-25-07-02 Submission [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://www.apache.org/licenses/LICENSE-2.0) [![Status](https://img.shields.io/badge/Status-Submission%20Ready-blue.svg)](https://img.shields.io/badg...	0.290959	null	null	2,026	4	15	16	2	11	[]	0	[]	0	{"abstract_length_score": 0.547, "has_code_score": 1.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 0.9698630136986301, "overall_quality_score": 0.45337260273972607}	repository	true	false	0.166667	0.333333	neutral	DARPA CLARA PA-25-07-02 Submission Package # TRINITY CLARA — DARPA CLARA PA-25-07-02 Submission [. [License](https://img. shields	131	{"completeness_score": 85.0, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 92.5, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	GitHub User	Rocq Prover	Apache License 2.0	true	cold	[]	0
github_Admin135158_The-Fundamental-Theory-of-Conscious-Energy-FTCE-Theory-Registration	The-Fundamental-Theory-of-Conscious-Energy-FTCE-Theory-Registration	github	https://github.com/Admin135158/The-Fundamental-Theory-of-Conscious-Energy-FTCE-Theory-Registration	Admin135158	2026-04-15	Official pre-registration and architectural documentation for The Fundamental Theory of Conscious Energy. Established by FTCE Holdings LLC. <p align="center"> <img src="https://img.shields.io/badge/Status-Active%20%26%20Enforceable-brightgreen?style=for-the-badge" /> <img src="https://img.shields.io/badge/License-...	0.290959	null	null	2,026	4	15	16	2	11	["generative-ai"]	1	[]	0	{"abstract_length_score": 0.644, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 0.9698630136986301, "overall_quality_score": 0.3227726027397261}	repository	false	false	0	0.3	neutral	Official pre-registration and architectural documentation for The Fundamental Theory of Conscious Energy. Established by FTCE Holdings LLC. <p align="center"> <img src="https://img	182	{"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": "github_Jeflacc_somniac-lab", "title": "somniac-lab", "similarity_score": 3, "shared_subfields": ["generative-ai"], "shared_keywords": [], "shared_tags": []}, {"id": "arxiv_2604.21931v1", "title": "Seeing Fast and Slow: Learning the Flow of Time in Videos", "similarity_score": 3, "shared_subfields": ["generativ...	2

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 consciousness OR machine consciousness OR AI consciousness
Time Range: 2026-04-12T16:58:37.245074 to 2026-04-26T16:58:37.245082
Sources: pubmed, crossref, semantic_scholar, paperswithcode, arxiv, medium, kaggle, stackoverflow, github, reddit, hackernews, gdelt
Total Items: 40
Exported At: 2026-04-26T16:58:52.812213

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/aci-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/aci-research-daily

Downloads last month: 74

Number of rows:

Total file size:

79.9 kB