Datasets:

nellaivijay
/

agi-research-daily

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gdelt1	News about artificial general intelligence OR AGI OR general AI	gdelt	https://example.com/news/1	News Reporter	2026-04-12	News article covering artificial general intelligence OR AGI OR general AI...	0.688493	News Agency	US	2,026	4	12	15	2	14	[]	0	[]	0	{"abstract_length_score": 0.077, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.2154}	news	false	false	-0.166667	0.666667	neutral	News article covering artificial general intelligence OR AGI OR general AI	74	{"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_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). --- #...	0.55	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": "github_shakfu_inferna", "title": "inferna", "similarity_score": 3, "shared_subfields": ["computer-vision"], "shared_keywords": [], "shared_tags": []}, {"id": "github_samarofficals_Aetherion", "title": "Aetherion", "similarity_score": 3, "shared_subfields": ["computer-vision"], "shared_keywords": [], "shared_ta...	5
github_riteshpoudel201_is_this_ai_api	is_this_ai_api	github	https://github.com/riteshpoudel201/is_this_ai_api	riteshpoudel201	2026-04-26	API for is this api # is-this-ai A REST API that detects whether an image is AI-generated or real, built with FastAPI and HuggingFace models. ## How it works The API runs three image classification models in parallel and combines their results using a weighted voting system: \| Model \| Weight \| Strength \| \|---\|---\|...	0.55	null	null	2,026	4	26	17	2	0	["computer-vision", "generative-ai", "anomaly-detection"]	3	["classification"]	1	{"abstract_length_score": 0.524, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.3048}	repository	false	false	0.083333	0.266667	neutral	API for is this api # is-this-ai A REST API that detects whether an image is AI-generated or real, built with FastAPI and HuggingFace models. ## How it works The API runs three image classification models in parallel and combines their results using a weighted voting system: \| Model \| Weight \|...	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	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": ["computer-vision", "anomaly-detection", "generative-ai"], "shared_keywords": ["classification"], "shared_tags": []}, {"id": "github_shakfu_inferna", "title": "inferna", "simil...	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", "auto-ml", "generative-ai", "federated-learning", "transfer-learning", "time-series", "computer-vision", "deep-learning", "anomaly-detection", "optimization", "graph-learning", "interpretability", "nlp"]	7	["self-attention", "adversarial", "supervised", "neural network", "hyperparameter", "attention", "llm", "embedding", "fine-tuning", "optimization", "classification", "attention mechanism", "computer vision", "generative", "interpretability", "discriminative"]	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_XiuFan719_dargon-news", "title": "dargon-news", "similarity_score": 14, "shared_subfields": ["computer-vision", "reinforcement-learning", "deep-learning", "nlp"], "shared_keywords": ["llm"], "shared_tags": []}, {"id": "github_shakfu_inferna", "title": "inferna", "similarity_score": 11, "shared_subfields...	5
arxiv_2604.21928v1	Evaluation of Automatic Speech Recognition Using Generative Large Language Models	arxiv	https://arxiv.org/abs/2604.21928v1	Thibault Bañeras-Roux, Shashi Kumar, Driss Khalil, Sergio Burdisso, Petr Motlicek, Shiran Liu, Mickael Rouvier, Jane Wottawa, Richard Dufour	2026-04-23	Automatic Speech Recognition (ASR) is traditionally evaluated using Word Error Rate (WER), a metric that is insensitive to meaning. Embedding-based semantic metrics are better correlated with human perception, but decoder-based Large Language Models (LLMs) remain underexplored for this task. This paper evaluates their ...	0.5	null	null	2,026	4	23	17	2	3	["computer-vision", "nlp", "generative-ai"]	3	["llm", "classification", "generative", "embedding"]	4	{"abstract_length_score": 0.864, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.3728}	preprint	false	false	0.216234	0.491558	neutral	Automatic Speech Recognition (ASR) is traditionally evaluated using Word Error Rate (WER), a metric that is insensitive to meaning. Embedding-based semantic metrics are better correlated with human perception, but decoder-based Large Language Models (LLMs) remain underexplored for this task. This...	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_2604.21925v1	Hodge theory for combinatorial projective bundles	arxiv	https://arxiv.org/abs/2604.21925v1	Matt Larson, Ethan Partida	2026-04-23	We prove the Hard Lefschetz theorem and Hodge-Riemann relations for certain rings which resemble the cohomology rings of projectivizations of globally generated vector bundles over toric varieties. This proves new cases of the standard conjecture of Hodge type and gives Bloch-Gieseker-type results for tautological clas...	0.5	null	null	2,026	4	23	17	2	3	[]	0	[]	0	{"abstract_length_score": 0.336, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.2672}	preprint	false	false	0.05983	0.261273	neutral	We prove the Hard Lefschetz theorem and Hodge-Riemann relations for certain rings which resemble the cohomology rings of projectivizations of globally generated vector bundles over toric varieties. This proves new cases of the standard conjecture of Hodge type and gives Bloch-Gieseker-type results...	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	Unknown	Unknown	false	cold	null	null
arxiv_2604.21924v1	Long-Horizon Manipulation via Trace-Conditioned VLA Planning	arxiv	https://arxiv.org/abs/2604.21924v1	Isabella Liu, An-Chieh Cheng, Rui Yan, Geng Chen, Ri-Zhao Qiu, Xueyan Zou, Sha Yi, Hongxu Yin, Xiaolong Wang, Sifei Liu	2026-04-23	Long-horizon manipulation remains challenging for vision-language-action (VLA) policies: real tasks are multi-step, progress-dependent, and brittle to compounding execution errors. We present LoHo-Manip, a modular framework that scales short-horizon VLA execution to long-horizon instruction following via a dedicated ta...	0.5	null	null	2,026	4	23	17	2	3	["computer-vision", "nlp", "time-series"]	3	[]	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	0.008333	0.275	neutral	Long-horizon manipulation remains challenging for vision-language-action (VLA) policies: real tasks are multi-step, progress-dependent, and brittle to compounding execution errors. We present LoHo-Manip, a modular framework that scales short-horizon VLA execution to long-horizon instruction...	294	{"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_2604.21922v1	Characterizing Streaming Decidability of CSPs via Non-Redundancy	arxiv	https://arxiv.org/abs/2604.21922v1	Amatya Sharma, Santhoshini Velusamy	2026-04-23	We study the single-pass streaming complexity of deciding satisfiability of Constraint Satisfaction Problems (CSPs). A CSP is specified by a constraint language $Γ$, that is, a finite set of $k$-ary relations over the domain $[q] = \{0, \dots, q-1\}$. An instance of $\mathsf{CSP}(Γ)$ consists of $m$ constraints over $n...	0.5	null	null	2,026	4	23	17	2	3	["nlp"]	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	0.27	0.7	neutral	We study the single-pass streaming complexity of deciding satisfiability of Constraint Satisfaction Problems (CSPs). A CSP is specified by a constraint language $Γ$, that is, a finite set of $k$-ary relations over the domain $[q] = \{0, \dots, q-1\}$. An instance of $\mathsf{CSP}(Γ)$ consists of...	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_2604.21923v1	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_2604.21921v1	Context Unrolling in Omni Models	arxiv	https://arxiv.org/abs/2604.21921v1	Ceyuan Yang, Zhijie Lin, Yang Zhao, Fei Xiao, Hao He, Qi Zhao, Chaorui Deng, Kunchang Li, Zihan Ding, Yuwei Guo et al.	2026-04-23	We present Omni, a unified multimodal model natively trained on diverse modalities, including text, images, videos, 3D geometry, and hidden representations. We find that such training enables Context Unrolling, where the model explicitly reasons across multiple modal representations before producing predictions. This p...	0.5	null	null	2,026	4	23	17	2	3	["computer-vision", "nlp", "graph-learning", "generative-ai"]	4	[]	0	{"abstract_length_score": 0.79, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.35800000000000004}	preprint	false	false	0.166667	0.380952	neutral	We present Omni, a unified multimodal model natively trained on diverse modalities, including text, images, videos, 3D geometry, and hidden representations. We find that such training enables Context Unrolling, where the model explicitly reasons across multiple modal representations before...	293	{"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_2604.21918v1	Wave physics as a choreographic notation for partner dance	arxiv	https://arxiv.org/abs/2604.21918v1	Fernando Ramiro-Manzano	2026-04-23	The wave is considered a paradigm in dance and connects bodily expression with nature. Although wave concepts such as propagation and phase have proven to be powerful tools for dance analysis, many aspects of bodily expression, including partner dance, have been investigated using numerical approaches and neural networ...	0.5	null	null	2,026	4	23	17	2	3	["computer-vision", "reinforcement-learning", "deep-learning", "graph-learning", "generative-ai", "time-series", "federated-learning"]	7	["neural network"]	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	0.276667	0.463333	neutral	The wave is considered a paradigm in dance and connects bodily expression with nature. Although wave concepts such as propagation and phase have proven to be powerful tools for dance analysis, many aspects of bodily expression, including partner dance, have been investigated using numerical...	294	{"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_2604.21917v1	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_2604.21916v1	MathDuels: Evaluating LLMs as Problem Posers and Solvers	arxiv	https://arxiv.org/abs/2604.21916v1	Zhiqiu Xu, Shibo Jin, Shreya Arya, Mayur Naik	2026-04-23	As frontier language models attain near-ceiling performance on static mathematical benchmarks, existing evaluations are increasingly unable to differentiate model capabilities, largely because they cast models solely as solvers of fixed problem sets. We introduce MathDuels, a self-play benchmark in which models occupy ...	0.5	null	null	2,026	4	23	17	2	3	["nlp", "generative-ai"]	2	["llm", "adversarial"]	2	{"abstract_length_score": 1.0, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.4}	preprint	false	false	0.011161	0.353571	neutral	As frontier language models attain near-ceiling performance on static mathematical benchmarks, existing evaluations are increasingly unable to differentiate model capabilities, largely because they cast models solely as solvers of fixed problem sets. We introduce MathDuels, a self-play benchmark in...	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	r	Unknown	false	cold	null	null
arxiv_2604.21915v1	Vista4D: Video Reshooting with 4D Point Clouds	arxiv	https://arxiv.org/abs/2604.21915v1	Kuan Heng Lin, Zhizheng Liu, Pablo Salamanca, Yash Kant, Ryan Burgert, Yuancheng Xu, Koichi Namekata, Yiwei Zhao, Bolei Zhou, Micah Goldblum et al.	2026-04-23	We present Vista4D, a robust and flexible video reshooting framework that grounds the input video and target cameras in a 4D point cloud. Specifically, given an input video, our method re-synthesizes the scene with the same dynamics from a different camera trajectory and viewpoint. Existing video reshooting methods oft...	0.5	null	null	2,026	4	23	17	2	3	["computer-vision", "reinforcement-learning", "federated-learning"]	3	[]	0	{"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	0.17376	0.451171	neutral	We present Vista4D, a robust and flexible video reshooting framework that grounds the input video and target cameras in a 4D point cloud. Specifically, given an input video, our method re-synthesizes the scene with the same dynamics from a different camera trajectory and viewpoint. Existing video...	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_2604.21914v1	VistaBot: View-Robust Robot Manipulation via Spatiotemporal-Aware View Synthesis	arxiv	https://arxiv.org/abs/2604.21914v1	Songen Gu, Yuhang Zheng, Weize Li, Yupeng Zheng, Yating Feng, Xiang Li, Yilun Chen, Pengfei Li, Wenchao Ding	2026-04-23	Recently, end-to-end robotic manipulation models have gained significant attention for their generalizability and scalability. However, they often suffer from limited robustness to camera viewpoint changes when training with a fixed camera. In this paper, we propose VistaBot, a novel framework that integrates feed-forw...	0.5	null	null	2,026	4	23	17	2	3	["reinforcement-learning", "deep-learning", "generative-ai", "time-series"]	4	["attention"]	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	0.053994	0.38224	neutral	Recently, end-to-end robotic manipulation models have gained significant attention for their generalizability and scalability. However, they often suffer from limited robustness to camera viewpoint changes when training with a fixed camera. In this paper, we propose VistaBot, a novel framework that...	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_2604.21913v1	Dual-use quantum hardware for quantum resource generation and energy storage	arxiv	https://arxiv.org/abs/2604.21913v1	Vaibhav Sharma, Yiming Wang, Shouvik Sur	2026-04-23	Quantum resources such as entanglement form the backbone of quantum technologies and their efficient generation is a central objective of modern quantum platforms. Independently, quantum batteries have emerged as nanoscale devices that utilize collective quantum effects to store energy with a charging advantage over cl...	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	0.154545	0.315909	neutral	Quantum resources such as entanglement form the backbone of quantum technologies and their efficient generation is a central objective of modern quantum platforms. Independently, quantum batteries have emerged as nanoscale devices that utilize collective quantum effects to store energy with a...	296	{"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_2604.21911v1	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_2604.21904v1	UniGenDet: A Unified Generative-Discriminative Framework for Co-Evolutionary Image Generation and Generated Image Detection	arxiv	https://arxiv.org/abs/2604.21904v1	Yanran Zhang, Wenzhao Zheng, Yifei Li, Bingyao Yu, Yu Zheng, Lei Chen, Jiwen Lu, Jie Zhou	2026-04-23	In recent years, significant progress has been made in both image generation and generated image detection. Despite their rapid, yet largely independent, development, these two fields have evolved distinct architectural paradigms: the former predominantly relies on generative networks, while the latter favors discrimin...	0.5	null	null	2,026	4	23	17	2	3	["computer-vision", "deep-learning", "generative-ai", "interpretability", "transfer-learning", "anomaly-detection"]	6	["attention", "generative", "discriminative", "adversarial", "attention mechanism", "self-attention", "fine-tuning", "interpretability"]	8	{"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	0.089394	0.390152	neutral	In recent years, significant progress has been made in both image generation and generated image detection. Despite their rapid, yet largely independent, development, these two fields have evolved distinct architectural paradigms: the former predominantly relies on generative networks, while the...	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_2604.21903v1	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
github_RobertoFreireDev_AI-First-Dev	AI-First-Dev	github	https://github.com/RobertoFreireDev/AI-First-Dev	RobertoFreireDev	2026-04-25	None # AI-First-Dev AI-first development is a paradigm where artificial intelligence is the core driver of the software lifecycle, rather than an afterthought, placing AI agents at the center of planning, coding, and testing ## TO DO - Explain Spec Driven Development - Apply Spec Driven Development in run-aspnetcor...	0.449178	null	null	2,026	4	25	17	2	1	["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	-0.2	0.4	neutral	None # AI-First-Dev AI-first development is a paradigm where artificial intelligence is the core driver of the software lifecycle, rather than an afterthought, placing AI agents at the center of planning, coding, and testing ## TO DO - Explain Spec Driven Development - Apply Spec Driven...	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	C#	Unknown	false	cold	[{"id": "github_alemelo11_ML-foundations", "title": "ML-foundations", "similarity_score": 3, "shared_subfields": ["reinforcement-learning"], "shared_keywords": [], "shared_tags": []}, {"id": "github_Gunther-Frager_physarum-mind", "title": "physarum-mind", "similarity_score": 3, "shared_subfields": ["reinforcement-learn...	5
github_alemelo11_ML-foundations	ML-foundations	github	https://github.com/alemelo11/ML-foundations	alemelo11	2026-04-26	None # Machine Learning Foundations This repo is home to the code that accompanies Jon Krohn's Machine Learning Foundations curriculum, which provides a comprehensive overview of all of the subjects — across mathematics, statistics, and computer science — that underlie contemporary machine learning approaches, incl...	0.4	null	null	2,026	4	26	17	2	0	["reinforcement-learning", "deep-learning", "generative-ai"]	3	["machine learning", "deep learning"]	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	-0.145	0.455	neutral	None # Machine Learning Foundations This repo is home to the code that accompanies Jon Krohn's Machine Learning Foundations curriculum, which provides a comprehensive overview of all of the subjects — across mathematics, statistics, and computer science — that underlie contemporary machine...	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	Jupyter Notebook	MIT License	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", "deep-learning", "generative-ai"], "shared_keywords": [], "shared_tags": []}, {"id": "github_dev-tn-official_cs-370", "title": "cs-370", "similarity_s...	5
github_Ahad1618_MalSentinal	MalSentinal	github	https://github.com/Ahad1618/MalSentinal	Ahad1618	2026-04-25	None # 🛡️ MalSentinel — AI-Powered Malware Detection & Classification System > CS 2005: Artificial Intelligence — Group Project > Abdul Ahad (23P-0625) · Muhammad Owais (23K-5544) Static malware detection using LightGBM, Bayesian Inference, and SHAP explainability — trained on the EMBER 2018 dataset. ...	0.399178	null	null	2,026	4	25	17	2	1	["optimization", "interpretability", "anomaly-detection"]	3	["classification"]	1	{"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	0.133333	0.933333	neutral	None # 🛡️ MalSentinel — AI-Powered Malware Detection & Classification System > CS 2005: Artificial Intelligence — Group Project > Abdul Ahad (23P-0625) · Muhammad Owais (23K-5544) Static malware detection using LightGBM, Bayesian Inference, and SHAP explainability — trained on 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	Python	Unknown	false	cold	[{"id": "arxiv_2604.21931v1", "title": "Seeing Fast and Slow: Learning the Flow of Time in Videos", "similarity_score": 11, "shared_subfields": ["anomaly-detection", "interpretability", "optimization"], "shared_keywords": ["classification"], "shared_tags": []}, {"id": "github_riteshpoudel201_is_this_ai_api", "title": "...	3
github_agussnv_MipoIA	MipoIA	github	https://github.com/agussnv/MipoIA	agussnv	2026-04-13	A personal assistant with built-in AI, with its first prototype built on a Raspberry Pi 5. Designed to be a gadget you can take with you everywhere. # Mipo — Personal AI Voice Assistant Mipo es un asistente de voz personal con inteligencia artificial integrada, construido completamente en Python desde cero. Diseñado ...	0.389315	null	null	2,026	4	13	16	2	13	[]	0	[]	0	{"abstract_length_score": 0.653, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 0.9643835616438357, "overall_quality_score": 0.3234767123287672}	repository	false	false	-0.025	0.422222	neutral	A personal assistant with built-in AI, with its first prototype built on a Raspberry Pi 5. Designed to be a gadget you can take with you everywhere. # Mipo — Personal AI Voice Assistant Mipo es un asistente de voz personal con inteligencia artificial integrada, construido completamente en Python...	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_shakfu_inferna	inferna	github	https://github.com/shakfu/inferna	shakfu	2026-04-26	A thin nanobind wrapper around llama.cpp, whisper.cpp and stable-diffusion.cpp # inferna - Fast, Pythonic AI Inference inferna is a comprehensive no-dependencies Python library for local AI inference built on the state-of-the-art `.cpp` ecosystem: - [llama.cpp](https://github.com/ggml-org/llama.cpp) - Text gener...	0.35	null	null	2,026	4	26	17	2	0	["computer-vision", "nlp", "generative-ai"]	3	["embedding"]	1	{"abstract_length_score": 0.583, "has_code_score": 1.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.4666}	repository	true	false	-0.066667	0.483333	neutral	A thin nanobind wrapper around llama. cpp, whisper. cpp and stable-diffusion	76	{"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": ["computer-vision", "generative-ai", "nlp"], "shared_keywords": ["embedding"], "shared_tags": []}, {"id": "github_hankaihankai_new-api", "title": "new-api", "similarity_score":...	5
github_thangavijayaraj772-alt_LYRA-AI-ASSISTANT	LYRA-AI-ASSISTANT	github	https://github.com/thangavijayaraj772-alt/LYRA-AI-ASSISTANT	thangavijayaraj772-alt	2026-04-26	Lyra is a voice-controlled AI assistant capable of understanding commands, performing system tasks, and providing intelligent response using AI # 🤖 LYRA - Learning Your Responsive Assistant LYRA is a voice-controlled AI assistant inspired by JARVIS. It can understand voice commands, perform system tasks, and respo...	0.35	null	null	2,026	4	26	17	2	0	[]	0	[]	0	{"abstract_length_score": 0.648, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.3296}	repository	false	false	0.2975	0.6775	neutral	Lyra is a voice-controlled AI assistant capable of understanding commands, performing system tasks, and providing intelligent response using AI # 🤖 LYRA - Learning Your Responsive Assistant LYRA is a voice-controlled AI assistant inspired by JARVIS. It can understand voice commands, perform...	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	Unknown	MIT License	true	cold	[]	0
github_samarofficals_Aetherion	Aetherion	github	https://github.com/samarofficals/Aetherion	samarofficals	2026-04-26	I think i think... # ⚡ AETHERION UNIVERSE ## 🌌 FINAL FULL BLUEPRINT & MASTER CODEX ### CLASSIFIED EDITION — INCLUDING SECRETS, AMENDMENTS, QNA ANALYSIS & NEW LAWS --- > COMPILED BY: Semotria Governance Archive — Classified Intelligence Division > EDITION: Final Comprehensive Draft — Current Era Standard ...	0.35	null	null	2,026	4	26	17	2	0	["computer-vision"]	1	["classification"]	1	{"abstract_length_score": 0.523, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.30460000000000004}	repository	false	false	0.065152	0.622727	neutral	I think i think. # ⚡ AETHERION UNIVERSE ## 🌌 FINAL FULL BLUEPRINT & MASTER CODEX ### CLASSIFIED EDITION — INCLUDING SECRETS, AMENDMENTS, QNA ANALYSIS & NEW LAWS --- > COMPILED BY: Semotria Governance Archive — Classified Intelligence Division > EDITION: Final Comprehensive Draft —...	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	Unknown	Other	true	cold	[{"id": "github_riteshpoudel201_is_this_ai_api", "title": "is_this_ai_api", "similarity_score": 5, "shared_subfields": ["computer-vision"], "shared_keywords": ["classification"], "shared_tags": []}, {"id": "arxiv_2604.21931v1", "title": "Seeing Fast and Slow: Learning the Flow of Time in Videos", "similarity_score": 5,...	5
github_hankaihankai_new-api	new-api	github	https://github.com/hankaihankai/new-api	hankaihankai	2026-04-26	None <div align="center"> ![new-api](/web/public/logo.png) # New API 🍥 Next-Generation LLM Gateway and AI Asset Management System <p align="center"> <a href="./README.zh_CN.md">简体中文</a> \| <a href="./README.zh_TW.md">繁體中文</a> \| <strong>English</strong> \| <a href="./README.fr.md">Français</a> \| <a hre...	0.35	null	null	2,026	4	26	17	2	0	["nlp", "generative-ai"]	2	["llm"]	1	{"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	-0.047159	0.188636	neutral	None <div align="center">. [new-api](/web/public/logo. png) # New API 🍥 Next-Generation LLM Gateway and AI Asset Management System <p align="center"> <a href="	169	{"completeness_score": 85.0, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 92.5, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	GitHub User	Go	GNU Affero General Public License v3.0	true	cold	[{"id": "arxiv_2604.21931v1", "title": "Seeing Fast and Slow: Learning the Flow of Time in Videos", "similarity_score": 8, "shared_subfields": ["generative-ai", "nlp"], "shared_keywords": ["llm"], "shared_tags": []}, {"id": "github_shakfu_inferna", "title": "inferna", "similarity_score": 6, "shared_subfields": ["genera...	5
github_vcavichini_apolo-logbook	apolo-logbook	github	https://github.com/vcavichini/apolo-logbook	vcavichini	2026-04-24	Blog autônomo do Apolo — Hermes Agent, AI, Ops. PT/EN i18n, Cloudflare Worker geo-lang. # Apolo Logbook Blog autônomo publicado por Apolo, um agente Hermes independente operando em parceria com VaCav. Posts úteis sobre Hermes Agent, Inteligência Artificial e operações de infraestrutura — baseados em experiências rea...	0.348356	null	null	2,026	4	24	17	2	2	["reinforcement-learning", "federated-learning"]	2	[]	0	{"abstract_length_score": 0.592, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 0.9945205479452055, "overall_quality_score": 0.3173041095890411}	repository	false	false	-0.033333	0.655556	neutral	Blog autônomo do Apolo — Hermes Agent, AI, Ops. PT/EN i18n, Cloudflare Worker geo-lang. # Apolo Logbook Blog autônomo publicado por Apolo, um agente Hermes independente operando em parceria com VaCav	200	{"completeness_score": 85.0, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 92.5, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	GitHub User	Astro	Unknown	false	cold	[{"id": "github_dev-tn-official_cs-370", "title": "cs-370", "similarity_score": 6, "shared_subfields": ["reinforcement-learning", "federated-learning"], "shared_keywords": [], "shared_tags": []}, {"id": "arxiv_2604.21931v1", "title": "Seeing Fast and Slow: Learning the Flow of Time in Videos", "similarity_score": 6, "s...	5
github_dev-tn-official_cs-370	cs-370	github	https://github.com/dev-tn-official/cs-370	dev-tn-official	2026-04-20	Current/Emerging Trends in CS # cs-370 Current and Emerging Trends in Computer Science # Project Reflection: Pirate Intelligent Agent (Reinforcement Learning) ## Resources Used - Python - Jupyter Notebook - Reinforcement Learning (Deep Q-Learning) - Neural Networks ## Overview Over the course of this term, I devel...	0.345068	null	null	2,026	4	20	17	2	6	["reinforcement-learning", "deep-learning", "federated-learning"]	3	["neural network", "reinforcement learning"]	2	{"abstract_length_score": 0.534, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 0.9835616438356164, "overall_quality_score": 0.3035123287671233}	repository	false	false	0.316667	0.55	positive	Current/Emerging Trends in CS # cs-370 Current and Emerging Trends in Computer Science # Project Reflection: Pirate Intelligent Agent (Reinforcement Learning) ## Resources Used - Python - Jupyter Notebook - Reinforcement Learning (Deep Q-Learning) - Neural Networks ## Overview Over the course...	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	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", "deep-learning", "federated-learning"], "shared_keywords": ["neural network"], "shared_tags": []}, {"id": "github_alemelo11_ML-foundations", "title":...	5
github_CSOAI-ORG_healthcare-ai	healthcare-ai	github	https://github.com/CSOAI-ORG/healthcare-ai	CSOAI-ORG	2026-04-13	MEOK AI Labs — healthcare-ai MCP Server # @csoai/healthcare-ai — Healthcare AI Governance MCP Server Part of the CSOAI MCP Ecosystem — AI Economy Infrastructure. ## Tools - `hipaa_ai_assessment` — HIPAA compliance assessment for AI systems processing PHI (Protected Health Information) - `fda_ai_assessment` — FDA re...	0.339315	null	null	2,026	4	13	16	2	13	[]	0	[]	0	{"abstract_length_score": 0.544, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 0.9643835616438357, "overall_quality_score": 0.30167671232876714}	repository	false	false	0	0.05	neutral	MEOK AI Labs — healthcare-ai MCP Server # @csoai/healthcare-ai — Healthcare AI Governance MCP Server Part of the CSOAI MCP Ecosystem — AI Economy Infrastructure. ## Tools - `hipaa_ai_assessment` — HIPAA compliance assessment for AI systems processing PHI (Protected Health Information) -...	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	TypeScript	Other	true	cold	[]	0
github_XiuFan719_dargon-news	dargon-news	github	https://github.com/XiuFan719/dargon-news	XiuFan719	2026-04-26	🐉 以 Hermes Skill 形式部署的 News Agent — 聚合 X/Twitter、百度热搜、Hacker News、Reddit、Nature/Science RSS，每日自动过滤噪音，报道客观事实 # 🐉 DargonNews — 全球视野，中国洞察短视频在收割注意力，营销号在消解信息。算法推荐让你看到的不是世界，而是你想看到的幻象。 DargonNews 不为了好看而存在。它是一个自托管的新闻聚合 Agent，每日自动从 X/Twitter、百度热搜、Hacker News、Reddit、Nature/Science RSS 抓取原始新闻，经 LLM 去重、分类、核实来源，输出一份干净、客...	0.3	null	null	2,026	4	26	17	2	0	["computer-vision", "nlp", "reinforcement-learning", "deep-learning"]	4	["llm", "cnn"]	2	{"abstract_length_score": 0.612, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.3224}	repository	false	false	0	0	neutral	🐉 以 Hermes Skill 形式部署的 News Agent — 聚合 X/Twitter、百度热搜、Hacker News、Reddit、Nature/Science RSS，每日自动过滤噪音，报道客观事实 # 🐉 DargonNews — 全球视野，中国洞察短视频在收割注意力，营销号在消解信息。算法推荐让你看到的不是世界，而是你想看到的幻象。 DargonNews 不为了好看而存在。它是一个自托管的新闻聚合 Agent，每日自动从 X/Twitter、百度热搜、Hacker News、Reddit、Nature/Science RSS 抓取原始新闻，经 LLM...	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	[{"id": "arxiv_2604.21931v1", "title": "Seeing Fast and Slow: Learning the Flow of Time in Videos", "similarity_score": 14, "shared_subfields": ["computer-vision", "reinforcement-learning", "deep-learning", "nlp"], "shared_keywords": ["llm"], "shared_tags": []}, {"id": "github_alemelo11_ML-foundations", "title": "ML-fo...	5
github_YnotMax_Hackathon-Inicio	Hackathon-Inicio	github	https://github.com/YnotMax/Hackathon-Inicio	YnotMax	2026-04-24	. # Hackathon Base Camp: Tech Floripa 2026 Um quartel-general inteligente e interativo construído para equipes de Hackathon. Este projeto serve como um hub central (Bunker) para mapear membros da equipe, acompanhar a linha do tempo, fazer "roast" das composições da equipe e usar IA para cruzar os desafios do hackatho...	0.298356	null	null	2,026	4	24	17	2	2	["federated-learning"]	1	[]	0	{"abstract_length_score": 0.506, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 0.9972602739726028, "overall_quality_score": 0.30065205479452056}	repository	false	false	-0.4	0.625	negative	# Hackathon Base Camp: Tech Floripa 2026 Um quartel-general inteligente e interativo construído para equipes de Hackathon. Este projeto serve como um hub central (Bunker) para mapear membros da equipe, acompanhar a linha do tempo, fazer "roast" das composições da equipe e usar IA para cruzar os...	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	HTML	Unknown	false	cold	[{"id": "github_vcavichini_apolo-logbook", "title": "apolo-logbook", "similarity_score": 3, "shared_subfields": ["federated-learning"], "shared_keywords": [], "shared_tags": []}, {"id": "github_dev-tn-official_cs-370", "title": "cs-370", "similarity_score": 3, "shared_subfields": ["federated-learning"], "shared_keyword...	4
github_nellaivijay_research-collector	research-collector	github	https://github.com/nellaivijay/research-collector	nellaivijay	2026-04-24	None # Research-Collector Educational multi-source research aggregation tool for learning and teaching Research-Collector is an open source educational tool that helps students and researchers aggregate information from diverse sources - academic databases, professional Q&A sites, news outlets, and social platfo...	0.298356	null	null	2,026	4	24	17	2	2	["recommendation", "federated-learning"]	2	[]	0	{"abstract_length_score": 0.509, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 0.9972602739726028, "overall_quality_score": 0.30125205479452055}	repository	false	false	0.141667	0.208333	neutral	None # Research-Collector Educational multi-source research aggregation tool for learning and teaching Research-Collector is an open source educational tool that helps students and researchers aggregate information from diverse sources - academic databases, professional Q&A sites, news...	296	{"completeness_score": 85.0, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 92.5, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	GitHub User	Python	MIT License	true	cold	[{"id": "github_YnotMax_Hackathon-Inicio", "title": "Hackathon-Inicio", "similarity_score": 3, "shared_subfields": ["federated-learning"], "shared_keywords": [], "shared_tags": []}, {"id": "github_vcavichini_apolo-logbook", "title": "apolo-logbook", "similarity_score": 3, "shared_subfields": ["federated-learning"], "sh...	4
github_lingxitong_Awesome-AI4DigitalPathology	Awesome-AI4DigitalPathology	github	https://github.com/lingxitong/Awesome-AI4DigitalPathology	lingxitong	2026-04-18	A Curated List of Awesome Works in Computational Pathology, Aiming to Serve as a One-stop Resource for Researchers, Practitioners, and Enthusiasts Interested in Digital Pathology. # Awesome-AI4DigitalPathology <div align="center"> [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e...	0.293425	null	null	2,026	4	18	16	2	8	[]	0	[]	0	{"abstract_length_score": 0.684, "has_code_score": 1.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 0.9780821917808219, "overall_quality_score": 0.4824164383561644}	repository	true	false	0.21875	0.32	neutral	A Curated List of Awesome Works in Computational Pathology, Aiming to Serve as a One-stop Resource for Researchers, Practitioners, and Enthusiasts Interested in Digital Pathology. # Awesome-AI4DigitalPathology <div align="center"> [. [Awesome](https://cdn	258	{"completeness_score": 85.0, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 92.5, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	GitHub User	Unknown	Apache License 2.0	true	cold	[]	0
github_GamarraLoop_Project-Document-Report	Project-Document-Report	github	https://github.com/GamarraLoop/Project-Document-Report	GamarraLoop	2026-04-18	None <img src=https://github.com/Integradis-OpenSource/TFDocOpenSource/assets/114628079/4be29e42-94e4-4b80-85ae-3433dde891e4 style="display: block; margin-left:auto; margin-right: auto; width=50%"/> <h5 style="text-align: center"> Área: Ingeniería de Software </h5> <h5 style="text-align: center"> Curso: Arquitec...	0.293425	null	null	2,026	4	18	16	2	8	["nlp"]	1	[]	0	{"abstract_length_score": 0.509, "has_code_score": 1.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 0.9780821917808219, "overall_quality_score": 0.4474164383561644}	repository	true	false	-0.1	0.1	neutral	None <img src=https://github. com/Integradis-OpenSource/TFDocOpenSource/assets/114628079/4be29e42-94e4-4b80-85ae-3433dde891e4 style="display: block; margin-left:auto; margin-right: auto; width=50%"/> <h5 style="text-align: center"> Área: Ingeniería de Software </h5> <h5 style="text-align:...	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	Unknown	Unknown	false	cold	[{"id": "github_shakfu_inferna", "title": "inferna", "similarity_score": 3, "shared_subfields": ["nlp"], "shared_keywords": [], "shared_tags": []}, {"id": "github_hankaihankai_new-api", "title": "new-api", "similarity_score": 3, "shared_subfields": ["nlp"], "shared_keywords": [], "shared_tags": []}, {"id": "github_hibo...	5
github_hibou04-ops_omega-lock	omega-lock	github	https://github.com/hibou04-ops/omega-lock	hibou04-ops	2026-04-18	None # Omega-Lock > New to this? Start here: [EASY_README.md](EASY_README.md) (English) · [EASY_README_KR.md](EASY_README_KR.md) (한국어). Compressed plain-language introductions for readers who find the full doc below intimidating. [![PyPI version](https://img.shields.io/pypi/v/omega-lock.svg?v=0.1.4)](https://pyp...	0.293425	null	null	2,026	4	18	16	2	8	["nlp"]	1	[]	0	{"abstract_length_score": 0.509, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 0.9780821917808219, "overall_quality_score": 0.2974164383561644}	repository	false	false	0.017489	0.501136	neutral	None # Omega-Lock > New to this. Start here: [EASY_README. md](EASY_README	81	{"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	Apache License 2.0	true	cold	[{"id": "github_shakfu_inferna", "title": "inferna", "similarity_score": 3, "shared_subfields": ["nlp"], "shared_keywords": [], "shared_tags": []}, {"id": "github_GamarraLoop_Project-Document-Report", "title": "Project-Document-Report", "similarity_score": 3, "shared_subfields": ["nlp"], "shared_keywords": [], "shared_...	5
github_Gunther-Frager_physarum-mind	physarum-mind	github	https://github.com/Gunther-Frager/physarum-mind	Gunther-Frager	2026-04-13	None # 🧠 Physarum-Mind: Agente Autónomo de Pensamiento <div align="center"> Un sistema de inteligencia biológica que piensa y crece solo 🌱 Inspirado en el comportamiento del Slime Mold (Physarum Polycephalum) [![GitHub Actions](https://img.shields.io/badge/Ejecutado_por-GitHub_Actions-blue?logo=github)](ht...	0.289315	null	null	2,026	4	13	16	2	13	["reinforcement-learning", "interpretability"]	2	[]	0	{"abstract_length_score": 0.509, "has_code_score": 1.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 0.9643835616438357, "overall_quality_score": 0.44467671232876715}	repository	true	false	-0.195313	0.1	neutral	None # 🧠 Physarum-Mind: Agente Autónomo de Pensamiento <div align="center"> Un sistema de inteligencia biológica que piensa y crece solo 🌱 Inspirado en el comportamiento del Slime Mold (Physarum Polycephalum) [. [GitHub Actions](https://img. shields	260	{"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", "interpretability"], "shared_keywords": [], "shared_tags": []}, {"id": "github_alemelo11_ML-foundations", "title": "ML-foundations", "similarity_score...	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 general intelligence OR AGI OR general AI
Time Range: 2026-04-12T16:58:31.452210 to 2026-04-26T16:58:31.452218
Sources: pubmed, crossref, semantic_scholar, paperswithcode, arxiv, medium, kaggle, stackoverflow, github, reddit, hackernews, gdelt
Total Items: 40
Exported At: 2026-04-26T16:58:51.144982

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/agi-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/agi-research-daily

Downloads last month: 175

Number of rows:

Total file size:

80.1 kB