Research radar

arXiv preprints, filtered for developer impact.

Concise summaries of new AI papers, ranked for how likely they are to change how production systems are built. arXiv collection remains rate-limited.

Papers tracked

1,327

1 arXiv sources

Top radar

http://arxiv.org/abs/2607.13941v1

Published 24h

by paper date

Rate limit

1/3s

arXiv policy

Paper	Authors	arXiv ID	Categories	Published	Code	Radar	Summary
GPU-Parallel Linearization Error Bounds for Real-Time Robust Optimal Control of Nonlinear and Neural Network Dynamics	Jeffrey Fang, Keyi Shen, Anutam Srinivasan	http://arxiv.org/abs/2607.01203v1	eess.SY, cs.AI	Jul 1, 2026	None	RDR64	This paper studies real-time robust optimal control for uncertain nonlinear systems, where li...
Decision-Aware Training for Sample-Based Generative Models	Kornelius Raeth, Nicole Ludwig	http://arxiv.org/abs/2607.01171v1	cs.LG, stat.ML	Jul 1, 2026	None	RDR64	Sample-based generative models are increasingly used for probabilistic forecasting in high-st...
AdaJEPA: An Adaptive Latent World Model	Ying Wang, Oumayma Bounou, Yann LeCun	http://arxiv.org/abs/2606.32026v1	cs.LG, cs.AI	Jun 30, 2026	None	RDR64	Latent world models enable planning from high-dimensional observations by predicting future s...
Random Reshuffling Dominates Stochastic Gradient Descent	Zijian Liu	http://arxiv.org/abs/2606.32005v1	math.OC, cs.LG	Jun 30, 2026	None	RDR74	Stochastic Gradient Descent ($\textsf{SGD}$) is one of the most classical optimization algori...
Radial Suppression Accelerates Algorithmic Generalization: A Geometric Analysis of Delayed Generalization	Srijan Tiwari, Aditya Chauhan, Manjot Singh	http://arxiv.org/abs/2606.32000v1	cs.LG, cs.AI	Jun 30, 2026	None	RDR64	Why do neural networks memorize algorithmic training data long before they generalize? We pre...
Pessimism's Paradox: Conservative Offline Training Amplifies Reward Hacking During Online Adaptation in Reasoning Models	Subramanyam Sahoo, Aman Chadha, Vinija Jain	http://arxiv.org/abs/2606.30627v1	cs.LG, cs.AI	Jun 29, 2026	None	RDR64	Conservative offline training is widely advocated as a safe foundation for subsequent online...
KnowsTFM: Knowledge-Informed Fine-Tuning of Small Tabular Foundation Models	Boshko Koloski, Xiangjian Jiang, Senja Pollak	http://arxiv.org/abs/2606.30258v1	cs.LG, cs.AI	Jun 29, 2026	None	RDR65	Tabular foundation models have advanced deep learning for tabular data by delivering strong d...
G-RRM: Guiding Symbolic Solvers with Recurrent Reasoning Models	Timo Bertram, Sidhant Bhavnani, Richard Freinschlag	http://arxiv.org/abs/2607.02491v1	cs.AI	Jul 2, 2026	None	RDR61	In this work, we focus on SE-RRMs, a symbol-equivariant instantiation of RRMs that exhibits i...
Neural Certificate Pricing for Combinatorial Optimization Problems	Jingyi Chen, Xinyuan Zhang, Xinwu Qian	http://arxiv.org/abs/2607.01185v1	cs.LG	Jul 1, 2026	None	RDR63	Combinatorial optimization (CO) problems are difficult because certifiable discrete structure...
Defending Against Harmful Supervision Hidden in Benign Samples	Bang An, Yibo Yang, Dandan Guo	http://arxiv.org/abs/2606.30263v1	cs.CR, cs.AI	Jun 29, 2026	None	RDR64	Existing defenses are effective when harmful content is explicitly mixed into downstream fine...