AI agents

Tool use, function calling, orchestration and the protocols that let models act rather than only answer.

15,984 stories · RSS feed

arXiv AI

Jul 14

TENET: One Step Toward Test-Driven Development for Repository-Level Code Generation

arXiv:2509. 24148v3 Announce Type: replace-cross Abstract: Test-Driven Development (TDD) is a widely adopted practice that requires developers to create and execute tests alongside implementation.

By Yiran Hu, Nan Jiang, Shanchao Liang, Yi Wu, Lin Tan

llms agents

arXiv AI

Jul 14

Learning in Blocks: A Multi Agent Debate Assisted Personalized Adaptive Learning Framework for Language Learning

arXiv:2604. 22770v2 Announce Type: replace-cross Abstract: Most digital language learning curricula rely on discrete-item quizzes that test recall rather than applied conversational proficiency.

By Nicy Scaria, Silvester John Joseph Kennedy, Deepak Subramani

llms agents benchmarks

arXiv AI

Jul 14

Vinci2: Providing Proactive Assistance in Continuous Egocentric Videos

arXiv:2607. 11523v1 Announce Type: cross Abstract: When should an intelligent assistant speak up without being asked?

By Gong Sitong, Tianyu Yan, Caixin Kang, Bo Zheng, Xiang Ruan, Huchuan Lu, Kaipeng Zhang, Yoichi Sato, Yifei Huang

rag agents benchmarks safety

arXiv AI

Jul 14

Same Stories, Different Journeys: From Social Comparison to Sensemaking in AI-Mediated Peer Career Exploration

arXiv:2607. 11039v1 Announce Type: cross Abstract: Young job seekers frequently turn to social media to compare themselves with peers and make sense of career possibilities.

By Pengping Tan, Baoquan Zhao, Zhenhui Peng

agents

arXiv AI

Jul 14

World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning

arXiv:2607. 10630v1 Announce Type: cross Abstract: Robust motion planning in dense traffic requires autonomous vehicles to interact in rare and safety-critical scenarios that are underrepresented in naturalistic driving data.

By Tong Nie, Yuewen Mei, Junlin He, Yihong Tang, Jian Sun, Wei Ma

agents reinforcement-learning fine-tuning benchmarks safety

arXiv AI

Jul 14

AutoVSR: Automatic Visual-to-Symbolic Reasoning for Symbolic Expression Generation from Circuit Schematic

arXiv:2607. 11338v1 Announce Type: new Abstract: Symbolic expressions can effectively characterize and predict circuit behavior, but deriving them directly from circuit schematics is challenging.

By Zhe Xiao, Longfei Li, Xu He, Haoying Wu, Zixing Zhang, Mingyu Liu

llms agents multimodal benchmarks

arXiv Machine Learning

Jul 14

SKooP: Symmetric Koopman Predictions for Faster and More Generalizable Legged Robot Locomotion with Reinforcement Learning

arXiv:2607. 11624v1 Announce Type: cross Abstract: Reinforcement learning (RL) algorithms classically suffer from poor sample efficiency.

By Evelyn D'Elia, Weishu Zhan, Giulio Turrisi, Giulio Romualdi, Giuseppe L'Erario, Raffaello Camoriano, Wei Pan, Daniele Pucci

agents reinforcement-learning robotics benchmarks

arXiv AI

Jul 14

Agents Don't Just Agree, They Remember: Benchmarking Persistent Sycophancy in Stateful Personal Agents

arXiv:2607. 10526v1 Announce Type: new Abstract: Stateful personal agents increasingly maintain long-term user profiles, episodic memories, and reusable skills.

By Xutao Mao, Liangjie Zhao, Leyao Wang, Rui Qian, Qiang Huang, Wentao Wang, Bo Han, Xiang Zheng, Cong Wang

agents benchmarks safety

arXiv AI

Jul 14

Measure the Sim-to-Real Gap: Designing an Affordable Real-World Benchmark Platform for Reinforcement Learning in AIoT Systems

arXiv:2607. 10309v1 Announce Type: new Abstract: Reinforcement learning (RL) is commonly employed to enhance the performance of autonomous systems, including the Autonomous Internet of Things (AIoT).

By Rongping Zhou, Omid Tavallaie, Shuaijun Chen, Albert Y. Zomaya

agents reinforcement-learning robotics benchmarks safety

arXiv AI

Jul 14

BatteryLake: Agentic, Physics-Grounded Curation of Heterogeneous Battery Aging Data and Benchmarking

arXiv:2607. 09762v1 Announce Type: new Abstract: Public battery aging datasets are a critical asset for advanced health management, but their practical use is often limited by inconsistent formats, unclear schemas, and metadata scattered across repositories and publications.

By Tianwen Zhu, Hao Wang, Yonggang Wen

llms agents benchmarks

arXiv AI

Jul 14

VehAnchor: Metadata-Free Metric Scale Recovery from Vehicle Cues in Aerial Imagery

arXiv:2603. 04277v2 Announce Type: replace-cross Abstract: Autonomous aerial robots operating in GPS-denied or communication-degraded environments frequently lose access to camera metadata and telemetry, leaving onboard perception systems unable to recover the absolute metric scale of the scene.

By Yifei Chen, Chenqian Le, Jiayi Cheng, Xupeng Chen

llms agents computer-vision robotics multimodal benchmarks safety

arXiv AI

Jul 14

The evolution of AI from image interpretation toward scientific inference in nanoparticle electron microscopy

arXiv:2607. 10388v1 Announce Type: cross Abstract: Artificial intelligence (AI) is transforming electron microscopy by enabling quantitative analysis of increasingly large and complex datasets for nanoparticle characterization.

By Evropi Toulkeridou, Jiafei Li, Leonardo Lari, Panagiotis Grammatikopoulos

llms agents computer-vision multimodal benchmarks

arXiv AI

Jul 14

STEC: Evidence Compression for Deep Search in Open-domain Multi-Hop QA

arXiv:2607. 10795v1 Announce Type: new Abstract: In open-domain multi-hop question answering (QA), LLM-based search agents offer a promising approach to knowledge-intensive QA by combining retrieval with reasoning.

By Xinkang Li, Rong Jiang, Xin Song, Ye Wang, Yue Han, Changjian Li

llms agents nlp benchmarks

arXiv AI

Jul 14

AI YOU Town: Make Friends and Money with Your Digital Twin

arXiv:2607. 10539v1 Announce Type: new Abstract: Existing approaches to infer user traits and generate responses consistent with a persona rely on static prompting.

By Yan Lin, Yuyang Dai, Jiahui Geng, Yuxia Wang

llms agents safety

arXiv AI

Jul 14

Tool-Adaptive LLM Reranker

arXiv:2607. 10555v1 Announce Type: cross Abstract: Generative Large Language Models (LLMs) have revolutionized information retrieval, yet their strictly parametric nature frequently leads to severe factual hallucinations when confronted with complex queries beyond their epistemic boundaries.

By Zichuan Liu, Ruijin Hua

llms rag agents reinforcement-learning efficiency benchmarks safety

arXiv AI

Jul 14

Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories

arXiv:2607. 10601v1 Announce Type: new Abstract: Large Language Model (LLM) agents are commonly trained from expert trajectories using supervised fine-tuning (SFT), which treats multi-turn agent behavior as ordinary text imitation.

By Yixiong Chen, Alan Yuille

llms agents reinforcement-learning fine-tuning

arXiv AI

Jul 14

Filtering Harmful Actions Isn't Enough: Phantom Transfer in Agentic SDF

arXiv:2607. 10750v1 Announce Type: new Abstract: Synthetic data is widely used to train large language models because it is inexpensive to generate and easy to control.

By Chinmayi Dixit

llms agents reinforcement-learning fine-tuning benchmarks safety

arXiv Machine Learning

Jul 14

Two Confounds in Cross-Model Value Comparison: Response Determinism and the Access Harness

arXiv:2607. 10202v1 Announce Type: new Abstract: Cross-model comparisons read divergence in value dispositions as evidence that language models hold individuated values.

By Hong-In Won, Jinseok Jang, Hyoseop Kim

llms agents

arXiv Machine Learning

Jul 14

Adversarially Guided Diffusion for LiDAR Range Image Synthesis

arXiv:2607. 09787v1 Announce Type: cross Abstract: LiDAR semantic segmentation is a key perception task in autonomous driving, where false predictions can affect downstream planning and safety-critical decision-making.

By Stavros Bouras, Antonios Makris, Alexandros Gkillas, Aris S. Lalos, Konstantinos Tserpes

agents diffusion computer-vision robotics safety

arXiv AI

Jul 14

MAG: A Web-Agent Benchmark and Harness for Multimodal Action and Guide Generation

arXiv:2607. 10079v1 Announce Type: new Abstract: Digital Adoption Platforms (DAPs) are embedded overlays widely used on web systems to guide users through operations inside a page, helping them get started with unfamiliar interfaces quickly.

By Chengguang Gan, Hanjun Wei, Yunhao Liang, Zhixi Cai, Qinghao Zhang, Shiwen Ni

llms agents multimodal benchmarks

Get the daily briefing