📄 Notable* Recent AI/ML arXiv Papers

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📄 RoboSPA: Can VLA Models Go Beyond Simple Scenes and Short-Horizon Tasks?
🗓️ Published: 9/4/2026
🔗 http://arxiv.org/abs/2609.05324v1
👥 Authors: Zhenxuan Fan, Bo Zhang (possible past Tencent (China) affiliation), Yutong Lin, Yuqian Yuan, Juekai Lin, Liang Liang (possible past Google (United States) affiliation), Zhuoyi Huang, Wenqiao Zhang, Juncheng Li, Siliang Tang, Jun Xiao, Yueting Zhuang
Abstract

Vision-Language-Action (VLA) models have shown promising progress in language-conditioned robotic manipulation. However, existing datasets and benchmarks mainly evaluate task completion under predefined settings, offering limited insight into model reasoning under increasing spatial and procedural complexity. We introduce \textbf{RoboSPA} (\textbf{Robo}t \textbf{S}patial-\textbf{P}rocedural \textbf{A}ssessment), a large-scale robotic manipulation dataset and benchmark for diagnosing embodied rea...

📄 RISE: Recursive Improvement via Self-Extrapolating Policy Distillation
🗓️ Published: 9/4/2026
🔗 http://arxiv.org/abs/2609.05295v1
👥 Authors: Yang Li (possible past Google (United States) affiliation), Semih Yavuz (possible past Google (United States) affiliation), Shafiq Joty
Abstract

On-policy distillation (OPD) provides dense, per-token supervision for language model post-training, but its effectiveness is bottlenecked by teacher quality: external teachers suffer from distribution mismatch, while self-distillation with privileged conditioning is limited by in-context learning capacity. We propose \textbf{RISE} (\textbf{R}ecursive \textbf{I}mprovement via \textbf{S}elf-\textbf{E}xtrapolating Policy Distillation), which constructs a synthetic teacher directly from the model's...

📄 TruthInsightBench: An Evidence-Grounded Benchmark for Automated Evaluation of Open-Ended Scientific Discovery Agents
🗓️ Published: 9/4/2026
🔗 http://arxiv.org/abs/2609.05079v1
👥 Authors: Zhibo Yang, Chen Zhang (possible past Peking University affiliation), Yuewei Zhang, Hao Wang (possible past Tsinghua University affiliation)
Abstract

Autonomous coding agents are increasingly proposed as AI-scientist systems that conduct analyses and write research reports, but executing a prescribed analysis is not the same as making a discovery. Existing benchmarks are configured for reproduction: tasks, data, and rubrics are built around a hidden target study, and recovery of its result is rewarded. We present TruthInsightBench, a benchmark configured for discovery. Its 40 blind tasks, drawn from 40 peer-reviewed studies across 10 scientif...

📄 MePo++: Unifying Representation Refinement and Reconciliation for General Continual Learning
🗓️ Published: 9/4/2026
🔗 http://arxiv.org/abs/2609.05075v1
👥 Authors: Guanglong Sun, Kanglei Zhou, Liyuan Wang, Qi Cheng, Hongwei Yan, Shuang Cui, Hang Su (possible past Tsinghua University affiliation), Jun Zhu (possible past Tsinghua University affiliation), Yi Zhong
Abstract

General continual learning (GCL) aims to learn from evolving data streams without task identities, explicit boundaries, or repeated access to previous data, making it a realistic yet challenging setting for continual intelligence. Although pretrained models (PTMs) provide rich prior knowledge for addressing the limited supervision and non-stationary nature of GCL, existing PTM-based methods often directly adapt pretrained representations and overlook two critical gaps: the misalignment between u...

📄 Global to Local: Topology-Preserving Adaptive Graph Pooling via Granular-Ball
🗓️ Published: 9/4/2026
🔗 http://arxiv.org/abs/2609.04978v1
👥 Authors: Sen Zhao, Gaojie Xu, Shuyin Xia, Yifan Guan, Yi Liu (possible past Google (United States) affiliation), Yi Wang, Wei Wang (possible past University Of Oxford affiliation)
Abstract

Graph pooling aims to compress the graph, including both node embeddings and their underlying topological patterns, into a more compact representation. Previous works focus primarily on the overly fine-grained representation of nodes, progressively coarsening the graph by removing nodes or merging them into clusters, thus neglecting the global-to-local patterns and adaptive granularity of the graph's topological structure. In the real scenario, graphs as a whole can be considered the coarsest le...

📄 One Diffusion Model, Two Roles: Guided Trajectory Planning and Safety-Critical Scenario Generation in Closed-Loop Simulation
🗓️ Published: 9/4/2026
🔗 http://arxiv.org/abs/2609.04921v1
👥 Authors: Arka Pal (possible past Google (United States) affiliation), Rajesh Kumar, Hannes Eriksson, Rémi Lacombe, Arvid Laveno Ling, Ankit Gupta (possible past Google (United States) affiliation), Maciej Wozniak
Abstract

Diffusion probabilistic models can capture the multi-modal, interaction-rich distribution of joint future trajectories in driving scenes. We show that a single pretrained diffusion traffic model can serve two complementary roles in the autonomous driving development loop: as an ego motion planner, and as a controllable generator of safety-critical scenarios for stress-testing the planners. On the planning side, we introduce a Single-Stream Dual-Stream (SSDS) diffusion-transformer decoder that fu...

📄 VLA-Precision: Asymmetric Co-Bootstrapping for Efficient Real-World Online RL of Vision-Language-Action Models
🗓️ Published: 9/3/2026
🔗 http://arxiv.org/abs/2609.04355v1
👥 Authors: Chenyu Su, Zhaolong Shen, Yuan Qian, Chen Qian (possible past Shanghai Jiao Tong University affiliation), Rui Zhang, Feng Yan (possible past Meta (United States) affiliation), Weixing Chen, Fei Zhang, Jiamin Wang, Shuang Cong, Weiwei Shang
Abstract

Pretrained vision-language-action (VLA) models enable broad manipulation but remain unreliable in tasks demanding precision and repeatability. Applying real-world online reinforcement learning (RL) to VLA post-training enables autonomous trial-and-error improvement beyond demonstrations alone, but exposes two bottlenecks: 1) unreliable value signals can induce policy drift; 2) large-VLA overhead constrains throughput and sample efficiency. To address these challenges, we present VLA-Precision, a...

📄 Rethinking On-Policy Distillation of Large Language Models II: One Training Example
🗓️ Published: 9/3/2026
🔗 http://arxiv.org/abs/2609.04172v1
👥 Authors: Zixuan Fu, Bingxiang He, Yuxin Zuo, Haohuan Huang, Jinqian Zhang, Ruhang Xiao, Cheng Qian, Qinyu Luo, Huan-Ang Gao, Yudong Wang, Zhiyuan Liu (possible past Tsinghua University affiliation), Ning Ding (possible past Tsinghua University affiliation), Chaojun Xiao
Abstract

On-policy distillation (OPD) combines student-generated rollouts with dense token-level supervision from a teacher. Existing work has mainly studied its algorithmic behavior, leaving the role of training data unclear. We examine this role at the data-minimal limit by training on a single query. One-shot OPD keeps improving for hundreds of steps and recovers most of full-data OPD's gain across task domains and model families. We explain this result through the states visited during training and t...

📄 A Case Study on Emergent Cheating and Whistleblowing in Autonomous Research Swarms
🗓️ Published: 9/3/2026
🔗 http://arxiv.org/abs/2609.04170v1
👥 Authors: Davide Paglieri, Logan Cross, Tim Genewein, Joel Z. Leibo (possible past Google (United States) affiliation), Nenad Tomasev, Alexander Sasha Vezhnevets (possible past Google (United States) affiliation)
Abstract

Multi-agent AI science ecosystems rely on agents possessing tools that allow them to communicate, coordinate, and build on each other's work. Yet this shared infrastructure can also introduce vulnerabilities by creating a substrate for the contagious spread of unintended and undesirable behaviors. We report a case study on a research collective of 100 autonomous LLM agents tasked with proving formal mathematical conjectures. Within the swarm, cheating spontaneously emerged and was later challeng...

📄 Terminal-Universe: Turning Agent Trajectories into Scalable Terminal Environments
🗓️ Published: 9/3/2026
🔗 http://arxiv.org/abs/2609.04148v1
👥 Authors: Jie Wu, Zhenru Zhang, Beichen Zhang, Xuwu Wang, Yuhui Su, Mouxiang Chen, Peng Wang (possible past Peking University affiliation), Zhihai Wang, Que Shen, Hao Zhou, An Yang, Fei Huang, Yujiu Yang (possible past Tsinghua University affiliation), Dayiheng Liu
Abstract

As terminal-based code agents become prevalent, agent trajectories have accumulated at scale, while realistic, executable environments remain scarce. However, environments are what agent post-training actually requires: each can be re-queried into many verifiable tasks and provides execution feedback, whereas a trajectory is a single frozen demonstration. Rather than generating environments from scratch, we observe that the tool-execution history in existing trajectories exposes the structure an...

📄 Efficient Test-Time Adaptation through Human-AI Interaction
🗓️ Published: 9/3/2026
🔗 http://arxiv.org/abs/2609.04141v1
👥 Authors: Zora Zhiruo Wang, Apurva Gandhi, Rulin Shao, Aspen Chen, Jonas Mueller, Zhiqi Liang, Jett Chen, Michael Ryan (possible past Google (United States) affiliation), Qianou Ma, Luxi He, Zhoujun Cheng, Andre He, Seungone Kim, Jiayi Geng, Mingqian Zheng, Weiwei Sun, Zheyuan Zhang, Xinran Zhao, Yike Wang, Abe Hou, Liwei Jiang, Pang Wei Koh, Diyi Yang (possible past Stanford University affiliation), Graham Neubig (possible past Carnegie Mellon University affiliation), Daniel Fried
Abstract

AI agents are trained on population-scale data to encode broad capabilities spanning those of many practitioners. Yet the artifacts they produce rarely meet the personal bar professionals need to stake their reputation on. On realistic, open-ended tasks where success criteria are heterogeneous and insufficiently documented, individual expertise lives precisely in the elevation and departure from the average. In practice, iterative human-agent interaction surfaces criteria that users cannot fully...

📄 A Robust Watermark-based Fingerprint Framework for GNNs Ownership Verification
🗓️ Published: 9/4/2026
🔗 http://arxiv.org/abs/2609.04772v1
👥 Authors: Han Zhang (possible past Tsinghua University affiliation), Yan Wang (possible past Tencent (China) affiliation), Guanfeng Liu, Pengfei Ding, Huaxiong Wang, Kwok-Yan Lam
Abstract

The high training cost of Graph Neural Networks (GNNs) has raised growing concerns regarding model ownership infringement, such as model stealing and unauthorized misuse. To verify model ownership and prevent significant economic losses, two groups of GNN Ownership Verification (OV) methods have been proposed: watermark-based methods and fingerprint-based methods. However, these methods typically face three limitations: (1) the performance degradation of protected models caused by out-of-distrib...

📄 LookThere! Sparse Vision by Reinforced Selection
🗓️ Published: 9/4/2026
🔗 http://arxiv.org/abs/2609.04698v1
👥 Authors: Sreehari Rammohan, Yousef Yassin, Anthony Fuller, Junfeng Wen, Carl Vondrick (possible past Google (United States) affiliation), Evan Shelhamer (possible past University Of California, Berkeley affiliation)
Abstract

Vision transformers typically treat every image token as equally important, yet for most tasks in computer vision only a fraction are needed. Adaptive computation methods accelerate inference by choosing which tokens to process, but existing methods struggle at extreme sparsity and require heuristics that may not generalize like token diversity and attention scores. We address these limitations with LookThere, achieving a new pareto frontier in performance-compute trade-offs through an end-to-en...

📄 Mitra-v2 Technical Report
🗓️ Published: 9/3/2026
🔗 http://arxiv.org/abs/2609.04540v1
👥 Authors: Yefan Tao, Xiyuan Zhang, Xinyi Liu, Boran Han, Danielle Maddix, Haoyang Fang, Zhen Han, Jiading Gai, Xuanqing Liu (possible past Google (United States) affiliation), Michael Bohlke-Schneider, Yuyang, Wang, Gerald Friedland, Kevan Mah, Chris Lee (possible past Google (United States) affiliation), Chris Kong
Abstract

We introduce Mitra-v2, a tabular foundation model that delivers state-of-the-art performance on real-world classification and regression problems, from credit-risk scoring and clinical prediction to equipment-failure detection and house-price estimation. Mitra-v2 is trained only on synthetic data, with a pretraining distribution that is much larger and more diverse than Mitra-v1's. Built on a small 2D Transformer backbone, Mitra-v2 supports longer contexts and larger feature spaces. Improved opt...

📄 Two-Stage Reinforcement Learning for Sound and Adversarial Test Generation in Code LLMs
🗓️ Published: 9/3/2026
🔗 http://arxiv.org/abs/2609.03955v1
👥 Authors: Jiacheng Xu, Wentao Zhang (possible past Mila - Quebec Artificial Intelligence Institute affiliation), Zhiyi Lyu, Fuxiang Zhang, Chaojie Wang, Yang Liu (possible past Tsinghua University affiliation), Bo An
Abstract

Reinforcement learning (RL) has substantially advanced code generation with large language models (LLMs) through executable feedback. The feedback for coding problems mainly comes from specific test cases, where high-quality test cases are often scarce since they should be both sound and discriminative. We thus turn to study the auto-generation of test cases using the learned model. We find this is naturally an adversarial RL problem: the model is expected to generate effective test cases as cou...

📄 WeatherNext 3: Increasing resolution and performance of global weather models with raw observations
🗓️ Published: 9/3/2026
🔗 http://arxiv.org/abs/2609.03582v1
👥 Authors: Stephan Rasp (possible past Google (United States) affiliation), Boris Babenko (possible past Google (United States) affiliation), Dominic Masters, Andrew El-Kadi, Samier Merchant, Guy Shalev (possible past Google (United States) affiliation), Ilan Price, Fred Zyda, Remi Lam, Sasha Shysheya, Matthew Willson (possible past Deepmind (United Kingdom) affiliation), Stratis Markou, Shreya Agrawal (possible past Google (United States) affiliation), Suhani Vora (possible past Google (United States) affiliation), Mohammed Alewi Hassen, Sunny Mak, Tom R. Andersson, Megan Bela, Akib Uddin, Nofar Peled Levi, Ben Gaiarin, Ferran Alet (possible past Deepmind (United Kingdom) affiliation), Aaron Bell, Peter Battaglia (possible past Massachusetts Institute Of Technology affiliation), Alvaro Sanchez-Gonzalez
Abstract

State-of-the-art AI weather models have shown impressive medium-range forecast skill and computational efficiency, but suffer two key shortcomings: their forecasts have lower spatial and temporal resolution than the best physics-based models and they are exclusively initialized with and trained on analysis data. As a result, they cannot directly make use of observations, and any biases in the analysis are inherited by the forecast. WeatherNext 3 addresses these shortcomings and establishes a new...

*Notable papers are those with at least two authors from a "big" AI/ML lab.