📄 Notable* Recent AI/ML arXiv Papers

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📄 Harness-of-Harness: Multi-Day Autonomous Software Development with Continual Improvement
🗓️ Published: 9/1/2026
🔗 http://arxiv.org/abs/2609.01481v1
👥 Authors: Haoyang Yan, Min-Le Su, Hangfan Zhang, Zhanhao Li, Chen Zhang (possible past Peking University affiliation), Shao Zhang, Yang Chen (possible past Tencent (China) affiliation), Lei Bai, Shuyue Hu
Abstract

This paper studies autonomous software development, in which LLM-based coding agents transform high-level requirements into complete, functional, and usable software systems without human intervention. We introduce Harness-of-Harness (HoH), a framework that enables coding agents to continually improve software during autonomous development. HoH operates on existing coding-agent harnesses, and organizes their executions into iterative planning-coding-testing loops. To sustain improvement across l...

📄 EmbodiedSkills: A Unified Framework for Orchestrating, Training, and Deploying VLA Agents
🗓️ Published: 9/1/2026
🔗 http://arxiv.org/abs/2609.01281v1
👥 Authors: Wei Wang (possible past University Of Oxford affiliation), Wenqiao Zhang, Yutong Lin, Yuqian Yuan, Tianwei Lin (possible past Baidu (China) affiliation), Jinhao Mao, Zhenxuan Fan, Mingjian Gao, Yang Dai, Wentong Li, Zheqi Lv, Zheng Dong, Yingjie Niu, Jiaqi Zhu, Jun Xiao, Chao Li (possible past Baidu (China) affiliation), Yueting Zhuang
Abstract

Vision-language-action (VLA) models map visual observations and language instructions directly to robot actions, but long-horizon tasks require more than action prediction. An agent must coordinate perception, planning, execution, progress verification, and recovery as the physical state evolves. An action prediction or a model-generated skill decision does not, by itself, guarantee that the proposed operation is valid in the current state or that its outcome will be verified. We propose Embodie...

📄 From Language to Behavior: Scaling Sequence Transformers for Industrial Recommendation Ranking with Rec-Native Designs
🗓️ Published: 9/1/2026
🔗 http://arxiv.org/abs/2609.01240v1
👥 Authors: Jie Chen (possible past Tencent (China) affiliation), Xiangqian Yu, Yanchao Lian, Tan Lu, Run Yang, Zhengchun Shang, Xing Wang (possible past Tencent (China) affiliation), Cheng Chen (possible past Google (United States) affiliation), Ke Hu (possible past Google (United States) affiliation), Qiang Li, Tianjiu Yin, Xiaobing Liu (possible past Google (United States) affiliation)
Abstract

Scaling Transformers has driven large gains in language modeling, but transplanting this to behavior-sequence modeling in production ranking is challenging: recommendation differs in signal quality, where behavior sequences are noisy, temporally irregular, and sparsely supervised, and in computation asymmetry, where each request scores many candidates against one shared user history under tight latency budgets. We propose ReST, a recommendation-native Transformer scaling framework. For signal qu...

📄 ARISE-RL: Agentic Rubric-Grounded Iterative Self-Evolution with Reinforcement Learning
🗓️ Published: 9/1/2026
🔗 http://arxiv.org/abs/2609.01058v1
👥 Authors: Fanrui Zhang, Ruixue Ding, Qiang Zhang (possible past Tsinghua University affiliation), Xi Chen (possible past University Of California, Berkeley affiliation), Boli Chen, Shihang Wang, Qiuchen Wang, Hongmin Zhan, Jinxin Bian, Li Xingchao, Peijin Zheng, Hao Cheng (possible past Tencent (China) affiliation), Pengjun Xie, Kaipeng Zhang (possible past Tencent (China) affiliation), Jiawei Liu, Zheng-Jun Zha
Abstract

Training open-ended agents via reinforcement learning (RL) is hindered by the lack of verifiable gold answers and scalable rubrics. Moreover, even near the model's capability boundary, long-horizon open-ended agentic tasks often yield brittle and unstable rewards, resulting in weak or noisy rollout contrast that obscures fine-grained optimization signals for group-based policy learning. To address these challenges, we propose ARISE-RL, a novel full-cycle self-evolution framework that couples a t...

📄 User Representation via Cross Multi-source Behavior Pre-training for Mobile Games
🗓️ Published: 9/1/2026
🔗 http://arxiv.org/abs/2609.01057v1
👥 Authors: Chengqi Yang, Yiran Qiao, Feng Liu, Xingyu Lou, Zijun Zhou, Xiaoyun Mo, Changwang Zhang (possible past Tencent (China) affiliation), Jiayuan Xu, Jun Wang (possible past Tencent (China) affiliation), Xiang Ao
Abstract

User representation pre-training has become a fundamental paradigm for alleviating data sparsity in downstream personalization tasks. However, existing studies predominantly focus on single-app or app-level behaviors, overlooking the inherently cross-source and multi-granular nature of user activities on mobile devices. At the device level, user intent emerges from complex interactions among heterogeneous behavior sources and hierarchical action structures, posing challenges that cannot be addre...

📄 Figures as Programs: Recursive Generation of Editable Scientific Figures
🗓️ Published: 9/1/2026
🔗 http://arxiv.org/abs/2609.01006v1
👥 Authors: Yepeng Liu, Dasen Dai, Chengzhi Liu, Yiren Song, Hai Ci, Yu Zhang (possible past Google (United States) affiliation), Qi Zhang (possible past Tencent (China) affiliation), Mike Zheng Shou (possible past National University Of Singapore affiliation), Xin Eric Wang, Yuheng Bu
Abstract

Scientific methodology figures are essential for communicating complex methods clearly, yet creating them remains labor-intensive and typically requires multiple rounds of refinement. Recent image-generation models can synthesize visually appealing raster figures, but producing a human-satisfactory result in a single generation step remains difficult. Moreover, precise edits to raster figures are challenging for both humans and models. We formulate scientific figure generation as recursive SVG p...

📄 Polished but Unresolved: Identifying Late-Stage Pressure States in Long-Horizon Tool-Use Agents
🗓️ Published: 9/1/2026
🔗 http://arxiv.org/abs/2609.00823v1
👥 Authors: Haoyang Chen, Yi Liu (possible past Google (United States) affiliation), Jianzhi Shao, Xiaozhou Xu, Zhe Sun (possible past Tsinghua University affiliation), Wei Hu
Abstract

Long-horizon tool-use agents need not only to search and plan, but also to decide when to finalize. We study late-stage pressure states, in which an agent is biased toward submitting a final answer that appears complete and polished while key constraints remain unresolved. We first train a linear probe to show that this pressure state is identifiable from the agent's hidden states. Then, we use activation interventions along this pressure direction and find that shifting the hidden states change...

📄 ContextPipe: Database-Inspired Context Assembly for Long-Horizon Agents
🗓️ Published: 9/1/2026
🔗 http://arxiv.org/abs/2609.00749v1
👥 Authors: Peng Xu (possible past Google (United States) affiliation), Zuyu Zhang, Yuze Sun, Feng Tian, Long Wang, Chen Zhang (possible past Peking University affiliation)
Abstract

Long-horizon large language model (LLM) agents require context assembly: the runtime must decide what to include in each prompt, in what order, and when to compact history under a hard context-window budget and a byte-sensitive prompt cache. In production agentic systems, this logic is scattered across prompt builders, ad hoc compaction routines, cache-break workarounds, and per-provider shims. We argue that context assembly is structurally isomorphic to query execution in a relational database:...

📄 ChatDev 2.0: A No-Code Multi-Agent Platform for Developing Everything
🗓️ Published: 9/1/2026
🔗 http://arxiv.org/abs/2609.00714v1
👥 Authors: Yufan Dang, Shu Yao, Bowen Lai, Chenting Xu, Ruijie Shi, Wai-Shing Leung, Huatao Li, Chen Qian (possible past Shanghai Jiao Tong University affiliation), Zhiyuan Liu (possible past Tsinghua University affiliation)
Abstract

Large language model (LLM)-based multi-agent systems (MAS) have shown strong potential for solving complex tasks, yet their development forces a tradeoff: code frameworks are expressive but engineering-intensive, while no-code builders simplify authoring but constrain agent interactions to author-defined workflows. We present ChatDev 2.0: DevAll (hereafter DevAll), a no-code platform for building, executing, and inspecting heterogeneous MAS that delivers both high expressiveness and ease of use....

📄 EM^2Mem: Event-Centric Multimodal Memory for Large Language Models
🗓️ Published: 9/1/2026
🔗 http://arxiv.org/abs/2609.00551v1
👥 Authors: Yijun Chen, Yaqi Zheng, Yanya Li, Boyi Xiao, Buqiang Xu, Shuofei Qiao, Jizhan Fang, Xinle Deng, Yunzhi Yao, Xuehai Wang, Liuxin Zhang, Hui Li (possible past Baidu (China) affiliation), Huajun Chen (possible past Alibaba Group (China) affiliation), Shumin Deng (possible past Alibaba Group (China) affiliation)
Abstract

Multimodal memory offers a scalable interface for long-video question answering, but existing methods often retrieve captions, frames, transcripts, summaries, or graph facts as isolated fragments. Although searchable, such fragments are not generation-ready: language models must reconstruct cross-modal and temporal alignments at inference time, when context is limited and attribution is difficult. We propose EM^2Mem, an event-centric multimodal memory framework that binds heterogeneous evidence ...

📄 ISO-RAG: Isoperimetric Noise Control for Retrieval-Augmented Generation
🗓️ Published: 9/1/2026
🔗 http://arxiv.org/abs/2609.00513v1
👥 Authors: Siyuan Zhang, Hanchen Wang (possible past University Of Cambridge affiliation), Dong Wen, Ying Zhang (possible past Tencent (China) affiliation), Wenjie Zhang
Abstract

Retrieval-Augmented Generation (RAG) mitigates large language models (LLMs) hallucinations, yet conventional dense retrieval struggles with the complex reasoning paths of multi-hop question answering (QA). Graph-based RAG captures multi-step relationships but suffers from severe semantic drift and high online latency due to noisy global graph traversals. Thus, we propose ISO-RAG (ISOperimetric Retrieval-Augmented Generation), a geometry-aware RAG framework. By projecting the underlying knowledge...

📄 EvoFlint: An Evolutionary Atlas of Multi-Turn LLM Vulnerabilities
🗓️ Published: 8/31/2026
🔗 http://arxiv.org/abs/2609.00487v1
👥 Authors: Feitong Qiao, Liren Peng, Shiming Ren, Aishwarya Jadhav, Arghavan Bahadorinejad, Marinette Chen, Muhan Zhang (possible past Meta (United States) affiliation), Abdulaziz Suria, Gennevi Lu, Anish Das Sarma (possible past Stanford University affiliation)
Abstract

Frontier language models that refuse harmful single-turn prompts often comply when the same intent is reached gradually over many turns, making multi-turn attacks one of the least understood failure modes of large language models. Most automated red-teaming methods treat this as a generation problem: produce attacks that break the model. We argue it is better framed as a search problem: discover, organize, and iteratively refine a diverse archive of attack strategies, producing a structured map ...

📄 ProbeMatchDTI: Probe-Driven Multi-Scale Biochemical Pattern Matching for Drug-Target Interaction Prediction
🗓️ Published: 9/2/2026
🔗 http://arxiv.org/abs/2609.02549v1
👥 Authors: Quan Hao, Mengyue Fan, Zifan Dong, Youru Li, Jianduo Zhao, Lechuan Xu, Hao Zhang (possible past Tencent (China) affiliation), Fei Xia (possible past Stanford University affiliation), Jigang Wang, Chong Qiu, Liguo Zhang
Abstract

Drug-target interaction (DTI) prediction is an important task in AI-driven drug discovery. Although recent biochemical representation learning methods have improved DTI prediction, their passive feature aggregation tends to favor dominant molecular patterns while suppressing weak yet binding-relevant signals, such as functional groups and residue-context patterns, limiting the modeling of multi-scale biochemical correspondences. To address this issue, we propose ProbeMatchDTI, a pattern-probe-dr...

📄 Humanoid Safe Stop via Learned Stoppability Value
🗓️ Published: 9/2/2026
🔗 http://arxiv.org/abs/2609.02358v1
👥 Authors: Junfeng Long, Pieter Abbeel (possible past University Of California, Berkeley affiliation), Koushil Sreenath, Roberto Horowitz, Guanya Shi, C. Karen Liu (possible past Stanford University affiliation)
Abstract

Humanoid robots responding to emergency stop commands typically execute a fixed maneuver, without reasoning about whether a safe stop is actually feasible from the current state. We cast emergency stopping as a reach-avoid problem and propose Safe-Stop, a task-agnostic framework that pairs a learned stop policy with learned stoppability estimators. The estimators are complementary: a stop-probability estimator supervised by the actual outcomes of the fixed stop policy, and a reach-avoidance esti...

📄 TC-Next: Zero-Shot Multimodal Cyclone Forecasting
🗓️ Published: 9/2/2026
🔗 http://arxiv.org/abs/2609.02085v1
👥 Authors: Zhe Wang (possible past Deepmind (United Kingdom) affiliation), Sijie Chen, Yiming Luo, Daehyun Kim (possible past Samsung (South Korea) affiliation), Chien-Yi Chang
Abstract

We present TropicalCycloneNext (TC-Next), a multimodal deep learning model that forecasts tropical cyclone track and intensity at $6$-$24$ h leads by leveraging a foundation model's forecast fields of atmospheric kinematic and thermodynamic fields and GridSat infrared satellite imagery. Trained only on GraphCast forecasts over the Western Pacific (WP), yet reliant only on generic atmospheric variables, TC-Next on GraphCast lowers track error by $15$-$44\%$ and intensity error by a factor of $3$-...

📄 Train What You Deploy: Closing the MLP Reachability Gap in Low-Rank Clone Distillation
🗓️ Published: 9/2/2026
🔗 http://arxiv.org/abs/2609.02006v1
👥 Authors: Wenhui Chen, Zhifeng Li (possible past Tencent (China) affiliation), Jie Zhou (possible past Tsinghua University affiliation), Navan Preet Singh, Madalina Ciobanu, Chenghua Wang, Qingqing Mao, Ritankar Das
Abstract

A compressed student has two shapes that need not agree: the weight it deploys at inference and the weight family its training can reach. We show that a state-of-the-art weight-inheritance distiller, Low-Rank Clone (LRC), deploys a full-width student MLP but ties training to a teacher-induced slice, leaving 62.5-81.4% of each deployed matrix's independent linear degrees of freedom unreachable-paid for at inference, never trainable. Our principle is one line: train what you deploy. From the ident...

📄 A Survey on Self-Improving Test-Time Intelligence: Feedback-Driven Adapting, Learning, and Scaling at Inference
🗓️ Published: 9/1/2026
🔗 http://arxiv.org/abs/2609.01679v1
👥 Authors: Shuaicheng Niu, Guohao Chen, Yaofo Chen, Zhiquan Wen, Jinwu Hu, Zeshuai Deng (possible past Baidu (China) affiliation), Deyu Chen, Shuhai Zhang, Renjie Chen, Zihao Lian, Shoukai Xu, Gang Dai, Yunbei Zhang, Wei Luo (possible past Baidu (China) affiliation), Yifan Zhang, Mingkui Tan (possible past Baidu (China) affiliation), Cheng Deng (possible past Tencent (China) affiliation)
Abstract

The ability of AI systems to improve their behavior during deployment is becoming increasingly important. As inference moves beyond the static execution of a fixed trained model, a growing body of work studies how models can refine their behavior on the fly by exploiting test-time information and additional computation. These developments have largely evolved along two directions: methods that modify the model's state using test-time signals, and methods that improve predictions through extra in...

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