{"total":18,"items":[{"citing_arxiv_id":"2606.27021","ref_index":129,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"SMR: Scheduler with Multi-Channel Map-Encoded Reinforcement Learning for Radio Telescopes","primary_cat":"astro-ph.IM","submitted_at":"2026-06-25T13:32:47+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"SMR uses multi-channel map-encoded reinforcement learning to achieve roughly 10% better time utilization than greedy baselines for single-dish radio telescope scheduling.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.26406","ref_index":20,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Beyond Feedforward Networks: Reentry Neural Systems as the Fundamental Basis of Subjecthood and Intrinsic Safety of Next-Generation AGI","primary_cat":"cs.LG","submitted_at":"2026-06-24T21:54:02+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"A cycle-based reentry architecture is proposed to guarantee self-model emergence, self-preservation, and prompt-injection immunity in AGI via a D-I loop and a new S-measure of integrated information.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.23286","ref_index":31,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Transfer learning-based method for automated ewaste recycling in smart cities","primary_cat":"cs.CV","submitted_at":"2026-06-22T13:00:39+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":2.0,"formal_verification":"none","one_line_summary":"Transfer learning with fine-tuned AlexNet achieves 98% accuracy classifying smartphone e-waste into 12 classes on a small dataset via hyperparameter tuning and augmentation.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.22515","ref_index":25,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Biological Sex Determination in Cadavers Using Deep Learning Algorithms from Computed Tomography Images of Pelvis and Skull","primary_cat":"cs.CV","submitted_at":"2026-06-21T14:07:37+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Deep learning models on standardized 2D CT projections of pelvis and skull from 141 cadavers reach 95.65% patient-level accuracy for biological sex determination.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.17603","ref_index":31,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Expanding SPHERE-JEPA: A Family of Statistical Regularizers for the Hypersphere","primary_cat":"cs.LG","submitted_at":"2026-06-16T07:10:05+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Derives deterministic MMD, KSD, and KL objectives with rotationally invariant kernels on the hypersphere, yielding more stable SSL training and dataset-dependent geometry in learned representations.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.11672","ref_index":4,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Can Open-Source LLM Agents Replace Static Application Security Testing Tools? 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[28] S. Hochreiter, J. Schmidhuber, Long Short-Term Memory, Neural Computation 9 (1997) 1735-1780. doi:10.1162/neco.1997.9.8.1735. [29] A. Krizhevsky, I. Sutskever, G. E. Hinton, ImageNet Classification with Deep Convolutional Neural Networks, Communications of the ACM 60 (2017) 84-90. doi:10.1145/3065386. [30] Y. LeCun, et al., Gradient-based Learning Applied to Document Recognition, Proceedings of the IEEE 86 (1998) 2278-2324. doi:10.1109/5.726791. [31] T. Mikolov, et al., Efficient Estimation of Word Representations in Vector Space, 2013. doi:10.48550/ arXiv.1301.3781.arXiv:1301.3781, version 3. [32] A. Vaswani, et al., Attention Is All You Need, 2017."},{"citing_arxiv_id":"2604.27870","ref_index":1,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Parameter-Efficient Architectural Modifications for Translation-Invariant CNNs","primary_cat":"cs.CV","submitted_at":"2026-04-30T13:52:42+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Strategic insertion of Global Average Pooling layers in VGG-16 reduces trainable parameters by 98%, maintains 66.4% ImageNet Top-1 accuracy, doubles translation robustness, and yields superior Spearman correlations in perceptual IQA tasks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.23593","ref_index":54,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"When AI reviews science: Can we trust the referee?","primary_cat":"cs.AI","submitted_at":"2026-04-26T08:03:32+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"AI peer review systems are vulnerable to prompt injections, prestige biases, assertion strength effects, and contextual poisoning, as demonstrated by a new attack taxonomy and causal experiments on real conference submissions.","context_count":1,"top_context_role":"background","top_context_polarity":"unclear","context_text":"(2025). Llms as meta-reviewers' assistants: A case study. Proc. Conf. North Am. Chapter Assoc. Comput. Linguist. Hum. Lang. Technol. 2025:7763-7803. DOI:10.18653/v1/2025.naacl-long.395 [53] Krizhevsky A., Sutskever I. and Hinton G.E. (2017). Ima- geNet classification with deep convolutional neural networks. Commun. ACM 60:84-90. DOI:10.1145/3065386 [54] Hinton G., Deng L., Yu D., et al. (2012). Deep neural net- works for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal Process. Mag. 29:82-97. DOI:10.1109/msp.2012.2205597 [55] Devlin J., Chang M.-W., Lee K., et al. (2019). Bert: Pre-training of deep bidirectional transformers for lan- guage understanding."},{"citing_arxiv_id":"2604.14455","ref_index":32,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"AIBuildAI: An AI Agent for Automatically Building AI Models","primary_cat":"cs.AI","submitted_at":"2026-04-15T22:17:05+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"AIBuildAI uses a manager agent and three LLM sub-agents to fully automate AI model development and achieves a 63.1% medal rate on MLE-Bench, matching experienced human engineers.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.10333","ref_index":16,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Zero-shot World Models Are Developmentally Efficient Learners","primary_cat":"cs.AI","submitted_at":"2026-04-11T19:32:33+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A zero-shot visual world model trained on one child's experience achieves broad competence on physical understanding benchmarks while matching developmental behavioral patterns.","context_count":1,"top_context_role":"dataset","top_context_polarity":"use_dataset","context_text":"0388-18.2018. URL https://www.jneurosci.org/lookup/doi/10. 1523/JNEUROSCI.0388-18.2018. [15] Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. ImageNet classification with deep convolutional neural networks.Communications of the ACM, 60(6):84-90, May 2017. ISSN 0001-0782, 1557-7317. doi:10.1145/3065386. URL https://dl.acm.org/doi/10.1145/ 3065386. [16] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. ImageNet: A large-scale hierarchical image database. In2009 IEEE Conference on Computer Vision and Pattern Recognition, pages 248-255, Miami, FL, June 2009. IEEE. ISBN 978-1-4244-3992-8. doi: 10.1109/CVPR.2009.5206848. URLhttps://ieeexplore.ieee.org/document/5206848/. [17] Zhirong Wu, Yuanjun Xiong, Stella Yu, and Dahua Lin."},{"citing_arxiv_id":"2604.02473","ref_index":62,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Analyzing Reverse Address Translation Overheads in Multi-GPU Scale-Up Pods","primary_cat":"cs.DC","submitted_at":"2026-04-02T19:08:47+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Simulation study shows cold TLB misses in reverse address translation dominate latency for small collectives in multi-GPU pods, causing up to 1.4x degradation, while larger ones see diminishing returns.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"terization (IISWC). 191-202. doi:10.1109/IISWC.2018.8573483 [61] Bingyao Li, Jieming Yin, Anup Holey, Youtao Zhang, Jun Yang, and Xulong Tang. 2023. Trans-FW: Short Circuiting Page Table Walk in Multi-GPU Systems via Remote Forwarding. In2023 IEEE International Symposium on High-Performance Computer Architecture (HPCA). 456-470. doi:10.1109/HPCA56546.2023.10071054 [62] Shen Li, Yanli Zhao, Rohan Varma, Omkar Salpekar, Pieter Noordhuis, Teng Li, Adam Paszke, Jeff Smith, Brian Vaughan, Pritam Damania, and Soumith Chintala. 2020. PyTorch distributed: experiences on accelerating data parallel training. Proc. VLDB Endow.13, 12 (Aug. 2020), 3005-3018. doi:10.14778/3415478.3415530 [63] Heng Liao, Jiajin Tu, Jing Xia, Hu Liu, Xiping Zhou, Honghui Yuan, and Yuxing"},{"citing_arxiv_id":"2203.07941","ref_index":1,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Reachability In Simple Neural Networks","primary_cat":"cs.CC","submitted_at":"2022-03-15T14:25:44+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Reachability for neural networks is NP-hard for single-hidden-layer networks with output dimension 1 and weights restricted to {-1,0,1}.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2104.01952","ref_index":38,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"General Inverse Design of Thin-Film Metamaterials With Convolutional Neural Networks","primary_cat":"physics.comp-ph","submitted_at":"2021-03-29T00:56:26+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Convolutional neural networks are shown to perform inverse design of thin-film metamaterial stacks by learning the mapping from structure to ellipsometric and reflectance/transmittance spectra, with efficiency gains over traditional optimization as layer count increases.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}