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.

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http://arxiv.org/abs/2609.38132v1

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Paper	Authors	arXiv ID	Categories	Published	Code	Radar	Summary
Achieving an $O(1/N)$ Optimality Gap in Average-Reward Weakly-Coupled MDPs	Yige Hong, Xiangcheng Zhang, Qiaomin Xie	http://arxiv.org/abs/2609.38132v1	cs.LG, math.OC	Sep 29, 2026	None	Score64	We study average-reward weakly-coupled Markov decision processes (WCMDPs), where a WCMDP cons...
X-Reset: Scaling Object-Centric Reinforcement Learning via Cross-Embodiment Resets	Prithwish Dan, Chenyang Ma, Wei Zhan	http://arxiv.org/abs/2609.35715v1	cs.LG, cs.AI	Sep 28, 2026	None	Score72	Reinforcement learning (RL) in simulation can train dexterous manipulation policies without r...
Mind the RefGAP: Correcting Reference Attention in Diffusion-Based Visual Editing	Yanan Wang, Shengcai Liao, Guangyi Liu	http://arxiv.org/abs/2609.35708v1	cs.CV	Sep 28, 2026	None	Score69	Reference-guided diffusion editors struggle to faithfully reproduce user-provided references....
Steering Language Model Goals with Value Transplant	Pengcheng Jiang, Fabien Roger	http://arxiv.org/abs/2609.34056v1	cs.LG, cs.CL	Sep 28, 2026	None	Score64	Reasoning models often act as if they pursue goals, but their efforts are not always directed...
Singularities of Non-negative Matrix Factorization and their application to Bayesian inference	Naoki Hayashi, Yota Maeda, Yasushi Esaki	http://arxiv.org/abs/2609.34043v1	stat.ML, cs.LG	Sep 28, 2026	None	Score64	Non-negative matrix factorization (NMF) is a singular statistical model whose Bayesian asympt...
Counterfactual Video Generation Enables Scalable Humanoid Loco-Manipulation	Zihan Wang, Zhen Wu, Pieter Abbeel	http://arxiv.org/abs/2609.38172v1	cs.RO, cs.CV	Sep 29, 2026	None	Score68	Teaching humanoids loco-manipulation skills, such as carrying diverse objects, via visual imi...
PowerSim: Differentiable Physics Simulation and Rendering with Power Diagrams	Trong-Tung Nguyen, Anand Bhattad	http://arxiv.org/abs/2609.38153v1	cs.CV	Sep 29, 2026	None	Score71	We introduce PowerSim, a method to bring physically grounded, differentiable dynamics to Powe...
LIFT: Layout-In-Future Video Generation under Large Viewpoint Change via On-Policy Self-Distillation	Shengxiang Ji, Boyang Wang, Haiyang Xu	http://arxiv.org/abs/2609.38146v1	cs.CV	Sep 29, 2026	None	Score71	We introduce LIFT, a unified image-to-video generation framework that complements camera cont...
The Statistical Cost of Causal Discovery with Feedback	Sunmin Oh, Seungsu Han, Gunwoong Park	http://arxiv.org/abs/2609.34050v1	stat.ML, cs.LG	Sep 28, 2026	None	Score64	What determines the unavoidable sample cost of learning cyclic causal structure? For cyclic l...
Lagrangian--Hamiltonian Flows for Video Prediction and Image Generation: A Symplectic Perspective	Jiawei Hu	http://arxiv.org/abs/2609.35710v1	cs.CV	Sep 28, 2026	None	Score60	We introduce LHFM, a geometric framework for learning image dynamics. Drawing on structures c...