The paper introduces the expected sample complexity metric for stochastic multi‑armed bandits and analyzes it within a new framework called approximately correct in expectation (ACE). It demonstrates that ACE guarantees lead to almost sure convergence to the optimal expected reward, contrasts this with high‑probability guarantees, and shows how to translate ACE bounds into explicit expected regret bounds. The authors prove that deterministic algorithms cannot achieve favorable ACE bounds, propose an explore‑then‑ε‑greedy algorithm when ε is known, analyze Thompson sampling when ε is unknown, and provide nearly matching lower bounds that establish tightness and a performance separation between the two regimes.
By Nadav Sukenik, Nadav Merlis
The paper presents a robotic platform that enables closed‑loop autonomous navigation of liquid droplets on an open surface using model‑based reinforcement learning. A two‑axis tilting board with a silicone oil film moves the droplet while an overhead camera supplies real‑time feedback, and a policy is trained in only 50–150 physical episodes without simulation or analytical models. The system demonstrates zero‑shot transfer to unseen geometries, autonomously discovers an oscillatory depinning strategy to release stuck droplets, and completes the full training pipeline in under 90 minutes.
By Rajneesh Anand, Mayuresh V. Kothare
The paper introduces a scalable heterogeneous Graph Neural Network framework that automatically generates and evaluates shared roadmaps for multi‑agent path planning in continuous environments. By representing waypoints, agent locations, and task locations as distinct nodes, the GNN learns from expert solver trajectories to identify critical points and prune redundant connections, producing a compact, coordination‑aware roadmap. Experiments show the approach can reduce planning effort, achieving at least a 40% reduction in runtime and graph size for dense roadmaps.
By Brandon Ho, Nikola Rogers, Seung-Kyum Choi
Kernel Autoresearch (Kernaut) is a new approach that treats kernel design as open‑ended model discovery, using coding agents to write kernel programs while construction contracts guarantee validity. The method maintains a quality‑diversity archive and novelty screening to encourage functionally distinct kernels, which are shown to generalize to unseen tasks and outperform meta‑learned deep kernels on black‑box optimization families. Additionally, kernels discovered from enzyme‑kinetic rate laws achieve lower error than tuned ARD and deep kernel baselines, and a human‑refined version further improves predictive accuracy and optimization regret.
By Richard Cornelius Suwandi, Feng Yin, Kevin Murphy
The paper introduces CARE, a method for certifying accelerated inference in vision‑language‑action models. CARE uses paired rollouts on a calibration set to guarantee that the risk of acceleration‑induced failure stays below a user‑specified budget, selecting the fastest certified accelerator or falling back to the reference policy. Experiments on LIBERO suites show that CARE achieves 9.0–10.8× speedups while preserving at least 85.8% of reference‑solved episodes with 95% confidence, and it outperforms non‑guaranteed selectors in both budget adherence and rollout efficiency.
By Rui Liu, Tong Zheng, Jindong Gu, Zhipeng Wang
The paper introduces Joint On-Policy Learning and Teaching (JOLT), a method that trains a single policy to act both as a privileged teacher and an unprivileged student. It derives a condition ensuring the teacher’s distillation update aligns positively with the student’s reward gradient, leading to a KL‑regularized teacher objective that matches the student’s current capabilities. Experiments show JOLT improves training efficiency and performance across various tasks, with additional gains when student rewards are incorporated.
By Randy Ardywibowo, Arnav Dalal, Jiantao Jiao
CADFather is an autonomous system that reconstructs editable CAD models from 3D meshes by coordinating vision‑language assistants, learned and algorithmic tools, and numerical optimization. It evaluates and refines candidate CAD programs, maintaining alternatives for each part and preserving the best valid result throughout reconstruction. The system uses pretrained models without additional training and is evaluated on multiple benchmark datasets for reconstruction quality and execution validity.
By Gennadiy Savrasov, Maksim Elistratov, Nikita Gavrilov, Albert Garifullin, Oleg Pavlov, Soslan Kabisov, Vladimir Frolov, Anton Konushin, Andrey Kuznetsov, Dmitrii Zhemchuzhnikov
SearchWorld introduces a recurrent state‑space world model that couples explicit spatial memory with value‑guided imagination for UAV object search. The model maintains bird’s‑eye‑view exploration and obstacle memory, decodes a task‑aware spatial value layer, and uses a cognition‑action network to improve policy through imagined rollouts without a separate critic. Experiments on UAV‑ON show a 23.8% success rate, surpassing the best published agent (19.5%) and achieving 35.5% oracle success while remaining robust on unseen scenes.
By Yatai Ji, Zhengqiu Zhu, Yong Zhao, Yue Hu, Fanglong Yao, Chen Gao, Pengfei Zhu, Quanjun Yin
arXiv:2610.09690v1 Announce Type: cross
Abstract: Autonomous underwater vehicles (AUVs) commonly rely on inertial navigation systems (INS) aided by Doppler velocity logs (DVLs) for reliable underwate...
By Arup Kumar Sahoo, Itzik Klein
arXiv:2610.09770v1 Announce Type: cross
Abstract: Deep learning has made rapid advances in weather forecasting: autoregressive models trained on atmospheric reanalyses now rival dynamical models acro...
By Tom Beucler, J. David Neelin, Hui Su, Shivanshi Asthana, Chris Bretherton, Will Chapman, Costa Christopoulos, Spencer K. Clark, Aditya Grover, Ignacio Lopez-Gomez, Tapio Schneider, Adam Subel, Oliver Watt-Meyer
arXiv:2610.09858v1 Announce Type: cross
Abstract: Large language model agents tackle multi-step tasks by interleaving reasoning and tool calls with observations from the environment. Prior work has s...
By Sergei Polezhaev, Barys Liskavets, Ori Press, Alexander Golubev
arXiv:2610.10140v1 Announce Type: cross
Abstract: Artificial intelligence (AI) is becoming an increasingly useful tool across the experimental sciences, including angle-resolved photoemission spectro...
By Sandy Adhitia Ekahana, Aalok Tiwari, Pratik Saud, Aaron Bostwick, Chris Jozwiak, Eli Rotenberg, Jyoti Katoch
arXiv:2610.10179v1 Announce Type: cross
Abstract: Search agents enable Large Language Models (LLMs) to iteratively retrieve and use information for complex multi-hop questions. Reinforcement Learning...
By Wenyu Huang, Xinyu Hou, Pavlos Vougiouklis, Ruofei Lai, Jeff Z. Pan
arXiv:2610.10362v1 Announce Type: cross
Abstract: We study decentralized adaptive sensing, where multiple agents choose measurements from evolving local beliefs while exchanging information over a co...
By Theodoros Tsiligkaridis
RSIGym is a new agent-native research environment built on Everything as a Service (EaaS) that exposes training, inference, rollout, evaluation, and sandbox execution through reusable services. It supports shared budget and permission controls for Data, Harness, and Joint improvement tracks, allowing agents to investigate individual interventions and jointly optimize data, training settings, and execution harnesses within the same environment. Using RSIGym, the Opus 5 model achieved the highest RSI-Index of 0.4809 across five benchmarks, improving performance on software engineering, terminal interaction, mathematics, scientific reasoning, and skill-based tasks.
By Fanqing Meng, Lingxiao Du, Haocheng Lu, Qiguang Chen, Ziqi Zhao, Zijian Wu, Jiayuan Zhuo, Mengkang Hu, Michael Qizhe Shieh
arXiv:2605.09504v3 Announce Type: replace-cross
Abstract: We show that the instruments used to measure AI offensive capability fail in two ways: (i) they overstate harm, and (ii) they credit the mode...
By Michael Alexander Riegler, Finn Schwall, Annika Willoch Olstad, Birk Sebastian Frostelid Torpmann-Hagen, Sushant Gautam, Klas H. Pettersen, Inga Str\"umke
arXiv:2610.10378v1 Announce Type: new
Abstract: Document-level text simplification involves transformations that go beyond sentence-internal edits, addressing discourse coherence, anaphora resolution...
By Meeri-Ly Muru, Eduard Barbu
arXiv:2610.10426v1 Announce Type: new
Abstract: Terminal-agent capability depends jointly on model weights and the runtime harness that formats prompts, binds tools, and handles error recovery. Exist...
By Jixuan Chen, Jiaxin Zhang, Qinyuan Ye, Yada Pruksachatkun, Haoxiang Zhang, Jingming Zhuo, Yifan Zhang, Yutong Dai, Juntao Tan, Xiangyu Peng, Silvio Savarese, Zeyuan Chen, Lianhui Qin, Chien-Sheng Wu
arXiv:2610.09079v1 Announce Type: cross
Abstract: Large language models equipped with development environments have moved code generation toward repository-scale construction, yet building complete r...
By Haibo Jin, Peng Kuang, Xucheng Yu, Jerry Wang, Dehao Wu, Haohan Wang
arXiv:2610.09237v1 Announce Type: cross
Abstract: Scientific studies of language agents need behavioral variables that support hypotheses across tasks and models. We formulate this research problem a...
By Tianqiang Yan