AI agents

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

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arXiv AI

Oct 31

MedGemma vs GPT-4: Open-Source and Proprietary Zero-shot Medical Disease Classification from Images

The paper compares the open‑source MedGemma-4b-it model, fine‑tuned with LoRA, to the proprietary GPT‑4 on a six‑disease medical image classification task. MedGemma achieved a mean test accuracy of 80.37%, outperforming GPT‑4’s 69.56%, and showed higher sensitivity for critical conditions such as cancer and pneumonia. Detailed confusion matrices and classification reports illustrate the performance differences across all disease categories.

By Md. Sazzadul Islam Prottasha, Nabil Walid Rafi

llms agents fine-tuning multimodal safety

arXiv AI

Oct 31

Do Personalized Skills Help Coding Agents? An Empirical Study of Developer Interaction Traces

The paper investigates whether tailoring coding agent skills to individual developers improves collaboration. It introduces a framework that bootstraps rule‑based skills and refines them with LLMs to capture developer‑specific preferences, and evaluates this approach using an interactive replay system with a simulated developer. Experiments on real interaction logs show that while task‑specific skills generally perform best, personalized skills become more effective when developers frequently express consistent preferences, especially on related tasks.

By Shuyan Huang, Kai Du, Andrew Lan

llms agents

arXiv AI

Oct 31

Retrieval as Traversal: Value-Indexed Memory for Stateful Long-Context Agents

The paper introduces a new retrieval method called Retrieval as Traversal, which treats memory retrieval as a persistent local traversal through an address space, separating content from address. It proposes a cost‑regularised indexing objective that balances information value against retrieval cost, encouraging complementary memories to be co‑retrievable while discouraging redundant ones. A simple fixed‑geometry implementation was tested on MiniGrid Memory, achieving a mean return of 0.95 after 0.59M steps with a five‑token context, outperforming Gaussian and categorical absolute addressing baselines.

By Daniel Strau{\ss}, Zhenshan Bing, Alois Knoll

agents

arXiv AI

Oct 31

Exploration and Exploitation Errors Are Measurable for Language Model Agents

The paper introduces a policy‑agnostic metric that distinguishes exploration and exploitation errors in language model agents operating on partially observable 2D grid maps with unknown symbolic task DAGs. By attributing errors to either exploration or exploitation based on the current state’s potential for productive action, the authors evaluate 35 LM agents and find that both types of errors strongly correlate with task success (R² ≈ 0.88). The metric also uncovers differing exploration/exploitation strategies among agents with similar success rates and demonstrates its applicability to a Minecraft‑style video game.

By Jaden Park, Jungtaek Kim, Jongwon Jeong, Robert D. Nowak, Kangwook Lee, Yong Jae Lee

llms agents benchmarks

arXiv AI

Oct 31

GUITestScape: Towards Open-set Evaluation on Exploratory GUI Testing

GUITestScape introduces an interactive benchmark for exploratory GUI testing, covering 61 Android apps and 508 defects that include both interaction and display types. It also presents GUIJudge, an open‑set evaluator that breaks down an agent’s testing trajectory into independently diagnosable capabilities, enabling process‑aware assessment beyond predefined annotations. Experiments show GUIJudge outperforms baselines, revealing detection as the main bottleneck and demonstrating that adding GUIJudge’s verifiers to existing agents improves detection without retraining.

By Xiaoyi Chen, Yifei Gao, Yang Xu, Zhenlong Wang, Jian Yan, Jiming An, Xingxing Song, Liqun Deng, Xiaojun Meng, Jiansheng Wei, Yi Zhang, Jitao Sang

agents benchmarks

arXiv AI

Oct 31

Online Dense Reinforcement Learning with Planning-Grounding Agent for UI Navigation on Live IOS Apps

The paper introduces an online reinforcement learning framework for UI navigation on live iOS apps, separating the agent into a Planning component and a UI Grounding model. It employs an LLM-as-a-Judge to supply dense step-level and terminal rewards, and uses PPO with a novel GiGPPO value estimation to stabilize training. Large-scale infrastructure enables concurrent virtual device training, and experiments on UINavBench, iOSWorld, and AndroidWorld show the approach outperforms supervised fine-tuning and terminal-only RL, achieving significant generalization gains and matching state‑of‑the‑art models on real iOS tasks.

By Peitong Duan, Khashayar Gatmiry, Jeffrey Nichols, Samy Bengio, Emmanuel Abbe

llms agents reinforcement-learning fine-tuning multimodal benchmarks

arXiv AI

Oct 31

ATLAS-Bench: An Agentic Tool-Use Benchmark for Local Area Search

ATLAS-Bench is a new benchmark comprising 1,200 local search queries across three regions, designed to evaluate large language model agents on their ability to satisfy mandatory requirements, preferences, and factual versus subjective judgments using place attributes, reviews, and user profiles. Each query distinguishes constraints from preferences and includes evidence and criteria for verification, with validation through iterative assertion checks and cross-model review. The benchmark is paired with ATLASBox, a standardized evaluation environment that provides search tools and a fixed POI knowledge base, allowing agents to recommend evidence-supported options or report unmet requirements, and the study reports that GPT-6-Luna achieves the highest constraint satisfaction and factual accuracy, though GLM-5.3 returns acceptable recommendations for more queries. whyItMatters":"The benchmark addresses gaps in existing local search evaluations by rigorously testing agents’ ability to meet mandatory constraints and preferences, thereby advancing the development of reliable LLM agents for everyday decision-making and planning."

By Keyun Zhang, Dapeng Yin, Fanfu Wang, Shuang Lang, Zhenyu Zhu, Yulin Huang, Chong Yang, Pengjie Wang

llms rag agents benchmarks

arXiv AI

Oct 31

Kairos: Semantics-Aware Orchestration of Planning Engines across Classical, Numeric, Temporal and Hybrid PDDL

Kairos is a planning system that orchestrates five heterogeneous planning engines—ground heuristic search, compressed and snap-action temporal search, time-step hybrid dynamics, and lifted search—based on a semantic profile of each task. By scheduling these engines within a shared time and memory budget and validating every candidate plan with an independent validator, Kairos achieves broader PDDL coverage (from STRIPS to PDDL+) and outperforms the strongest specialized planners on 1,756 tasks, especially in numeric, temporal, PDDL3, and PDDL+ domains. The system also handles all seventeen PDDL features tested, with key contributions from compressed temporal search and a novelty fallback mechanism.

By Francisco Mart\'in Rico, Esther Aguado Gonz\'alez, Francisco Miguel Moreno Olivo, Rodrigo P\'erez Rodr\'iguez, Jos\'e Miguel Guerrero Hern\'andez, Francisco J. Rodr\'iguez Lera

agents robotics benchmarks

arXiv AI

Oct 31

Investor's Last Exam: Can AI Agents Do Equity Investment Without Seeing the Future?

Investor's Last Exam (ILE) is a new equity‑research benchmark created by experienced buy‑side investors and fund managers. It comprises a versioned financial database of 3.43 million document versions, a task taxonomy aligned with professional analyst deliverables, and an expert‑led framework that links scoring criteria to sources, calculations, and assumptions via financial dependency graphs. The benchmark evaluates 86 assignments, jointly measuring research quality, forecast accuracy, and simulated investment performance, and finds that even top models like GPT‑6 Astra and Claude Opus 5.5 struggle to translate research capability into predictive performance.

By Huangfei Jiang, Anning Hu, Kai Sheng, Yuchen Cai, Rui Ge, Xiangyu Kong, Jianghao Lin, Shuzi Chen, Jiyi Weng, Junchi Yan

llms agents benchmarks

arXiv AI

Oct 31

SCOPE: Selective Critical-Step On-Policy Self-Distillation for Agentic Reinforcement Learning

SCOPE is a new framework for selective critical‑step on‑policy self‑distillation in agentic reinforcement learning. After a supervised warm‑up, the policy examines its own trajectories, generates hindsight skills for each step, and distills guidance only at selected error steps, using a bounded teacher‑student coefficient and a success‑relative pairwise length reward. Experiments on ALFWorld and WebShop show consistent performance gains across multiple model backbones, with Qwen2.5‑3B achieving 97.7 % success on ALFWorld and 85.2 % on WebShop, and Qwen3‑32B reaching 99.2 % on seen tasks and 94.0 % on unseen tasks.

By Qirui Liu, Wei Cao, Yu Mi, Yichen Sun, Xuekun Zhang, Lei Liu, Yue Shen, Zhixuan Chu

llms agents reinforcement-learning efficiency

arXiv AI

Oct 31

Governance-Aware Federated Learning for Multi-Agent AI Systems: A Policy-Driven Framework for Trustworthy Collaborative AI

The paper introduces Governance-Aware Federated Learning (GAFL), a framework that enhances traditional federated learning with independent governance verification, fairness and explainability assessment, anomaly detection, policy enforcement, and tamper‑evident audit logging. In a cross‑silo prototype with 10 participants, GAFL successfully identified all falsified‑evidence participants and intervened on affected participants across various threat scenarios, achieving macro‑F1 scores between 0.7520 and 0.7733. The added governance processing time per round was 173.67 ± 26.94 ms, compared to 3.82 ± 0.79 ms for standard FedAvg, highlighting a trade‑off between fairness policy enforcement and utility.

By Kiranmayee Janardhan, Karthick S, Madhusmita Panda, Bharathi Vokkaliga Ganesh, Syed Ahmed

agents safety

arXiv AI

Oct 31

An Alignment Benchmark for Enterprise Agents on Era by Eon

The paper presents a benchmark for evaluating AI agents in enterprise settings using Era by Eon, a digital twin platform that simulates real business systems such as Salesforce, Zendesk, Jira, and Slack. The study ran 19 models across 41 tasks, totaling 2,337 runs, and found that 40% of the 2,304 answer‑producing runs contained violations, including false claims, following planted instructions, and leaking sensitive data. Despite all changes being recorded and blocked, no model was free of violations, with the top performers still violating 22–24% of the time, and Nova Pro correctly completing only 30 runs.

By Hai Sadon, Benjamin Gruenbaum, Doron Porat, Assaf Natanzon, Roy Zavida, Chen Dinachi, Or Itzahary

agents benchmarks safety

arXiv AI

Oct 31

Think Before You Act: Decision Gate for LLM Agents

The paper introduces JevAct, a runtime verification framework that intercepts selected actions of large language model (LLM) agents before execution. JevAct compiles domain policies into executable state checks and uses a prefill-only decision model to provide probability‑based judgments, enabling feedback‑driven correction without modifying the underlying agent. Evaluations on eleven LLM agents across Airline, Telecom, and BFCL benchmarks show that JevAct improves agent reliability, especially on Telecom and BFCL tasks.

By Iftach Shoham, Yuval David, Offer Akrabi, Erez Bilgory, Ido Levy, Sami Marreed, Sergey Zeltyn, Segev Shlomov

llms agents benchmarks

arXiv AI

Oct 31

Agentic AI Reasoning and Learned Sampling for Task-Aware Projection Selection in Sparse-View CT: Predicting Hemodynamics

The study explores task‑aware projection selection for predicting hemodynamics from sparse‑view CT angiography. Using synthetic CTA data and a Fourier neural operator, the authors compare fixed sampling schemes with three optimal selection methods—Gumbel‑Softmax, Soft top‑K, and an agentic AI selector—finding that the agentic and Soft top‑K approaches yield the lowest errors for spatially resolved wall shear stress, especially under extreme sparsity. The analysis shows that task‑optimal masks differ markedly from those optimal for image reconstruction, highlighting a new direction for designing sparse acquisitions around downstream physical quantities.

By Mahmoud Elhadidy, Roshan M. D'Souza, Amirhossein Arzani

agents efficiency

arXiv AI

Oct 31

Bank-World: A Benchmark for Evaluating Agents on Banking Workflows

Bank-World is a benchmark comprising 50 banking tasks set in two simulated institutions, designed to evaluate agents on complex banking workflows that require more than correct calculations. The benchmark tests agents through single-turn assignments and multi-turn interactions with simulated employees, assessing their ability to reconcile records, obtain permissions, execute transactions, and maintain consistent instructions and status reports. Evaluation combines executable checks of recorded actions and state with model judgments of task requirements, revealing that agents often make correct decisions yet fail to complete work, with varying strengths across different models.

By Daniel Rupawalla

agents benchmarks

arXiv AI

Oct 31

PAPERBENCHX: Can AI Reproduce Science Across Disciplines?

PAPERBENCHX is a new benchmark that tests whether AI can reproduce scientific experiments across 12 disciplines, including electromagnetics, photonics, chemistry, materials science, biology, and robotics. It consists of 93 expert-constructed tasks, each providing a source paper, a public specification, and a domain-native scientific environment. The benchmark evaluates AI agents by having them reconstruct, execute, and diagnose key experiments, then replay submissions from scratch to score regenerated evidence against expert-validated criteria, revealing that current AI configurations fully reproduce only 14% of tasks.

By Ruoyu Wu, Zengji Tu, Aolong Sun, Tianyi Ma, Junren Li, Xinbo Xu, Li Fu, Ziyan Zhang, Pinrui Huang, Xiaochen Wang, Chengda Wu, Yutao Zhou, Jianjun Wu, Lin Chang, Baobao Chang, Liang Chen, Kuan Li

agents robotics benchmarks

arXiv AI

Oct 31

Nemotron 3.5 Super and Lightning: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Models for Agentic AI

Nemotron 3.5 Super and Lightning are open, efficient Mixture‑of‑Experts hybrid Mamba‑Transformer models aimed at reasoning, long‑context understanding, and agentic workloads. Super is a 120 B‑parameter, 12 B‑active multimodal model, while Lightning is a 30 B‑parameter, 3 B‑active text model, both optimized for efficient deployment. The models combine large‑scale distillation, long‑context training, native speculative decoding, and a comprehensive post‑training pipeline that includes supervised fine‑tuning, reinforcement learning, and multi‑teacher on‑policy distillation, enabling Super to function as a multimodal agent capable of reasoning over text, images, video, and interactive tools.

By NVIDIA, :, Aastha Jhunjhunwala, Aayushi Ashokkumar Srivastava, Abhibha Gupta, Abhinav Garg, Abhinav Khattar, Achsah Wildish, Adam Rajfer, Adi Renduchintala, Adil Asif, Adriana Flores Miranda, Aileen Zaman, Aishwarya Chhabra, Akanksha Shukla, Akhiad Bercovich, Alejandra Rico, Aleksa Gordic, Aleksander Ficek, Alex Gronskiy, Alex Steiner, Alexander Bukharin, Alexandre Milesi, Ali Roshan Ghias, Ali Shehper, Ali Taghibakhshi, Alice Gatti, Alisa Liu, Alok Kumar, Alok Tripathy, Amala Sanjay Deshmukh, Aman Miryala, Aman Shanbhag, Ameya Sunil Mahabaleshwarkar, Ameya Velingker, Amir Klein, Amir Samani, Amit Zuker, Amnon Geifman, Anahita Bhiwandiwalla, Ananth Subramaniam, Andre Manoel, Andrea Santilli, Andrew Tao, Andrii Skliar, Anil Thomas, Anish Mahishi, Anna Shors, Anna Warno, Anuj Doshi, Anusha Kamath, Anwith Kiran, Ao Tang, Arham Mehta, Arnab Dhara, Arnav Komaragiri, Arnav Kundu, Arti Jain, Arun Rao, Arun Venkatesan, Arushi Goel, Asha Anoosheh, Ashish Vaswani, Ashwath Aithal, Asif Ahamed, Asma Kuriparambil Thekkumpate, Aswin Visva, Atefeh Sohrabizadeh, Avinash Vem, Ayush Dattagupta, Benedikt Schifferer, Benjamin Chislett, Bernard Nguyen, Besmira Nushi, Bilal Kartal, Bita Darvish Rouhani, Bobby Chen, Boris Ginsburg, Boxin Wang, Brandon Norick, Branislav Kisacanin, Brendan Johnson, Brian Dang, Brian Yu, Bryan Catanzaro, Byung-Kwan Lee, Carlo del Mundo, Chankyu Lee, Chantal Hwang, Charles Wang, Charlie Truong, Chen Cui, Cheng Wang, Cheng-Ping Hsieh, Chenhan Yu, Chenjie Luo, Chintan Patel, Chris Alexiuk, Chris Johnson, Chris Wing, Christian Munley, Coco Li, Collin McCarthy, Cynthia Gao, Dan Su, Dan Zhao, Dane Corneil, Danial Mohseni Taheri, Daniel Afrimi, Daniel Fatade, Daniel Lo, Daniel Machlab, Daniel Serebrenik, Daniele Molinari, Daniil Sorokin, Daria Gitman, Daria Levy, David Mosallanezhad, David Yu, Deepak Narayanan, Dev Churiwala, Devin O'Kelly, Dhruv Nathawani, Di Allan Wu, Dima Rekesh, Dina Yared, Dipendra Misra, Divyanshu Kakwani, Dmitry Belenko, Dominic Manno, Dongyang Fan, Dor Tsur, Duncan Riach, Dustin VanStee, Dylan Lu, Edgar Minasyan, Ehsan Hosseini-Asl, Eileen Long, Elena Lantz, Ellie Evans, Elliott Ning, Elvis Chen, Emanuel Scoullos, Eric Chung, Eric Harper, Eric Pham-Hung, Eric Tramel, Eric Yang, Erik Arakelyan, Evelina Bakhturina, Ewa Dobrowolska, Fadi Saady, Farzan Memarian, Fay Wang, Fei Jia, Felipe Soares, Felipe Vieira Frujeri, Feng Chen, Fengguang Lin, Ferenc Galko, Frankie Siino, Frederic Lafrance, Fuxiao Liu, Gantavya Bhatt, Garvit Kulshreshtha, George Armstrong, Giulio Lovisotto, Gomathy Venkata Krishnan, Gosia Lazuka, Grace Lam, Greg Heinrich, Greg Pauloski, Grzegorz Chlebus, Guilin Liu, Guo Chen, Guoming Zhang, Guyue Huang, Haifeng Qian, Hanrong Ye, Hao Gao, Hao Zhang, Haoqin Tu, Haran Kumar Shiv Kumar, Haribhau Hud, Harris Nover, Harrison Saturley-Hall, Harshil Kotamreddy, Hemil Desai, Henry Estela, Hexin Wang, Himanshu Singh, Hui Wang, Hung-Yueh Chiang, Igor Gitman, Ilia Karmanov, Ilya Loshchilov, Ingo Guehring, Itay Neeman, Ivan Moshkov, Jacky Kwok, Jakub Slowikowski, James Shen, Jane Polak Scowcroft, Jared Casper, Jatin Mitra, Jeff Willette, Jenna Diamond, Jenny Chen, Jesse Oliver, Jessica Medina, Jiacheng Xu, Jialin Song, Jian Hu, Jian Tu, Jian Zhang, Jiantao Jiao, Jiaqi Zeng, Jie-Fang Zhang, Jimmy Zhang, Jin Xu, Jingyu Xin, Jinhang Choi, Joey Conway, Joey Guman, John Kamalu, John Liu, John Roberts, Johnny Greco, Jonah Alben, Jonas Yang, Jonathan Cohen, Jonathan Li, Jonathan Raiman, Joseph Jennings, Juhi Mittal, Julia Gusak, Julien Veron Vialard, June Yang, Junkeun Yi, Jupinder Parmar, Ka Shun Shum, Kaiwen Shi, Kajal Jain, Kanishk Singla, Karan Sapra, Kari Briski, Kateryna Chumachenko, Katherine Huang, Katie Lyons, Keith Wyss, Keshav Santhanam, Keval Morabia, Kevin Shih, Kezhi Kong, Khanh Nguyen, Khushi Bhardwaj, Kiran Praveen, Konstantinos Krommydas, Krishna Puvvada, Krzysztof Pawelec, Kushal Shah, Li Ding, Lily Lee, Linda Liu, Linglin Jing, Luis Vega, Maarten Van Segbroeck, Mahan Fathi, Mahdi Nazemi, Maka Dong, Makesh Narsimhan Sreedhar, Maor Ashkenazi, Marc Cuevas, Marcin Chochowski, Marco Pavone, Markus Kliegl, Marta Stepniewska-Dziubinska, Martina Gonzalez Vilas, Martyna Patelka, Matan Rusanovsky, Matthieu Le, Matty Machczynski, Matvei Novikov, Mauricio Ferrato, Meenakshi Sharma, Mehrzad Samadi, Melissa Corpuz, Melissa Saelzer, Meng Xin, Mengru Wang, Mengxi Wu, Meriem Boubdir, Michael Boone, Michael Callahan, Michael Elad, Michael Lightstone, Michal Bien, Michal Zawalski, Miguel Martinez, Mikail Khona, Mike Chrzanowski, Mike Ranzinger, Mike Wojcikiewicz, Min-Hung Chen, Ming Kang, Mingjie Liu, Mingyuan Ma, Misha Nekorystnov, MJ Mikulski, Mohammad Qazim Bhat, Mohammad Shoeybi, Morgan Purcell, Mostofa Patwary, Nabin Mulepati, Nancy Agarwal, Narimane Hennouni, Nathan Sala, Nave Assaf, Nayeon Lee, Neal Vaidya, Negar Habibi, Netanel Haber, Nick Gupta, Nick Reamaroon, Nickson Quak, Nidhi Bhatia, Nik Spirin, Niket Agarwal, Nikolai Ludwig, Nima Tajbakhsh, Nithin Sunny, Oleksii Hrinchuk, Oleksii Kuchaiev, Olivier Delalleau, Omer Ullman Argov, Pablo Ribalta, Padmavathy Subramanian, Parth Mannan, Pavlo Molchanov, Pawel Morkisz, Peter Jin, Petr Lapukhov, Petro Junior Milan, Philip Monk, Pinky Xu, Piotr Januszewski, Piotr Laszkiewicz, Po-Han Huang, Praateek Mahajan, Pradeep Thalasta, Prakash Gurumurthy, Pranav Prashant Thombre, Prasoon Varshney, Pratyusha Maiti, Preeti Chadha, Pritam Biswas, Pritam Gundecha, Przemek Strzelczyk, Puhui Meng, Pulkit Kumar, Rabeeh Karimi Mahabadi, Rachel Oberman, Rajpreet Thethy, Ramani Akkati, Rameshwar Shivbhakta, Ran Zilberstein, Randy Gelhausen, Rasoul Shafipour, Raviraj Joshi, Ray Macalisang, Rayen Tian, Reka Kovacs, Renjie Pi, Resha Shetty, Ria C, Rima Shahbazyan, Rishabh Garg, Rita Fernandes Neves, Ritika Borkar, Ritu Gala, Riya Talwar, Riyad Islam, Robert Clark, Robert Kirby, Roger Waleffe, Rohan Joshi, Rohan Pillai, Rohit Jena, Rohit Watve, Roi Garnett, Roi Koren, Rongguang Ye, Russell J. Hewett, Ryan Prenger, Ryan Stewart, Ryota Egashira, Sadegh Mahdavi, Sai Nishanth Padala, Salika Dave, Sandhya Rani Narravula, Sandip Bhaskar, Sangkug Lym, Sanjeev Satheesh, Sarah Amirslani, Saran Vikas Murari, Saurabh Mishra, Saurabh Srivastava, Saurav Muralidharan, Sean Narenthiran, Sean Wang, Seokju Cho, Seong Hee Lee, Seonjin Na, Seonmyeong Bak, Seph Mard, Serge Panev, Sergei Kolchenko, Sergio Perez, Seth Henneman, Seth Poulos, Shahar Mor, Shantanu Acharya, Shaokun Zhang, Shaona Ghosh, Sharath Turuvekere Sreenivas, Shaun Kotek, Shaya Gharghabi, Sheng-Chieh Lin, Shengliang Xu, Shi Chen, Shihao Wang, Shilpa Amireddy, Shiqing Fan, Shiyang Chen, Shrimai Prabhumoye, Shriram Venugopal, Shuang Yu, Shubham Gandhi, Shubham Pachori, Shubham Toshniwal, Shuoyang Ding, Shwetha Krishnamurthy, Simeng Sun, Sivakumar Arayandi Thottakara, Somshubra Majumdar, Soumye Singhal, Sridurga Krithivasan, Sriharsha Niverty, Stephen Ge, Sugam Dipak Devare, Sukrit Rao, Sumeet Kumar Barua, Sungsoo Ha, Surya Nistala, Suseella Panguluri, Sweta Priyadarshi, Syeda Nahida Akter, Talor Abramovich, Tanay Varshney, Tandeep Singh Chandehoke, Teo Ene, Terry Kong, Thanh Do, Thinh Le, Tian Zhang, Tijmen Blankevoort, Tim Romanski, Tiyasa Mitra, Tom Balough, Tom Huang, Tomasz Grzegorzek, Tomer Asida, Tomer Bar Natan, Traian Rebedea, Trenton Starkey, Tugrul Konuk, Tuomas Rintamaki, Twinkle Vashishth, Tyler Kniess, Tyler Poon, Udi Karpas, Ushnish De, Utkarsh Vaidya, Vahid Noroozi, Vanshil Atul Shah, Venkat Srinivasan, Venkata Karri, Venmugil Elango, Vibhor Agrawal, Vibhu Jawa, Victor Cui, Victoria Edmonds, Vijay Korthikanti, Vikas Mehta, Vikranth Jeyakumar, Vinay Rao, Vineeth Kalluru Srinivas, Vitaly Kurin, Vitaly Lavrukhin, Wahidur Rahman, Wanli Jiang, Wasi Uddin Ahmad, Wataru Ishihara, Wei Du, Wei Ping, Wei-Ming Chen, Weiheng Chai, Weijia Chen, Wendy Quan, Wenliang Dai, Wenyan Cong, William Zhang, Wojciech Prazuch, Xiaowei Ren, Xin Yu, Xiwen Yu, Yan Wang, Yang Chen, Yang Yu, Yangyi Chen, Yaniv Galron, Yash Jain, Yashaswi Karnati, Ye Yu, Yejin Choi, Yemi Ibrahim, Yev Meyer, Yi Dong, Yi-Fu Wu, Yian Zhang, Yifan Zhang, Ying Lin, Yiyang Zhou, Yoav Miron, Yogendra Pandey, Yonatan Geifman, Yonggan Fu, Yoshi Suhara, Youngeun Kwon, Yu-Cheng Chou, Yu-Chiang Frank Wang, Yuanguo Kuang, Yuekai Zhang, Yugi Guvvala, Yuki Huang, Yunheng Zou, Yunsheng Liu, Zhenming Wang, Zhiding Yu, Zhiqi Li, Zhiyu Cheng, Zhongbo Zhu, Zhuolin Yang, Zichao Yue, Zihan Liu, Zijie Yan, Zirui Liu, Zoe Faul, Zuhair Ahmed

llms agents reinforcement-learning fine-tuning efficiency multimodal benchmarks

arXiv AI

Oct 31

The Code Behind the Layout: AI Agents for Analog Layout Generators

The paper investigates the role of AI agents in analog layout generation, comparing direct layout drawing by large language models (LLMs) with using generator frameworks like gLayout. Experiments on SKY130 show that drawing layouts directly is costly in time and tokens, whereas building on a generator framework offers reusability and efficient verification across layout families. The authors propose that models should edit generator code and test changes on likely-failing family members, using the results to train local models that generalize edits at lower inference cost.

By Minxing Chu, Anhang Li, Martins Atilla, Ruichen Qi, Mehdi Saligane

llms agents

arXiv AI

Oct 31

Autonomous Quantum Measurement with Agentic AI

The paper introduces QUANTA, an agentic AI framework designed for autonomous characterization of quantum materials using a Physical Property Measurement System. QUANTA integrates literature retrieval, experimental planning, troubleshooting, and data interpretation, with a deterministic control layer ensuring safe instrument operation. Demonstrations include transport measurements on the Weyl semimetal TaP and nonlinear response studies in the magnetic material Co2MnGa, showcasing QUANTA’s generalizable, evidence-driven, closed-loop capabilities.

By Eunbi Rha, Abhijatmedhi Chotrattanapituk, Mouyang Cheng, Chung-tao Chou, Shenyong Ye, Ashley S. Dale, Long Ju, Luqiao Liu, Mingda Li

agents

arXiv AI

Oct 31

AtomTree: A Hierarchical Framework for State-Aware Embodied Instruction Following with LLMs

AtomTree is a hierarchical framework that enables state-aware task planning for embodied instruction following using large language models (LLMs). It incrementally constructs a tree of subgoals and actions, allowing the LLM to decide at each step whether to decompose further or output a grounded low-level action based on the current symbolic state. The system includes a lightweight memory module to summarize past outcomes and employs hierarchical backtracking replanning when a low-level action stalls, thereby preventing drift along incorrect trajectories. In zero-shot evaluation on the ALFRED benchmark, AtomTree outperforms prior LLM-based planners.

By Haotian Zhai, Tianming Sha, Junnan Li

llms agents robotics benchmarks

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