PACO: Parts and Attributes of Common Objects

Vignesh Ramanathan, Anmol Kalia, Vladan Petrovic, Yi Wen, Baixue Zheng, Baishan Guo, Rui Wang, Aaron Marquez, Rama Kovvuri, Abhishek Kadian, Amir Mousavi, Yiwen Song, Abhimanyu Dubey, Dhruv Mahajan

Object models are gradually progressing from predicting just category labels to providing detailed descriptions of object instances. This motivates the need for large datasets which go beyond traditional object masks and provide richer annotations such as part masks and attributes. Hence, we introduce PACO: Parts and Attributes of Common Objects. It spans 75 object categories, 456 object-part categories and 55 attributes across image (LVIS) and video (Ego4D) datasets. We provide 641K part masks annotated across 260K object boxes, with roughly half of them exhaustively annotated with attributes as well. We design evaluation metrics and provide benchmark results for three tasks on the dataset: part mask segmentation, object and part attribute prediction and zero-shot instance detection. Dataset, models, and code are open-sourced at https://github.com/facebookresearch/paco.

Towards Seeing Bones at Radio Frequency

Yiwen Song, Hongyang Li, Kuang Yuan, Ran Bi, Swarun Kumar

Wireless sensing literature has long aspired to achieve X-ray-like vision at radio frequencies. Yet, state-of-the-art wireless sensing literature has yet to generate the archetypal X-ray image: one of the bones beneath flesh. In this paper, we explore MCT, a penetration-based RF-imaging system for imaging bones at mm-resolution, one that significantly exceeds prior penetration-based RF imaging literature. Indeed the long wavelength, significant attenuation and complex diffraction that occur as RF propagates through flesh, have long limited imaging resolution (to several centimeters at best). We address these concerns through a novel penetration-based synthetic aperture algorithm, coupled with a learning-based pipeline to correct for diffraction-induced artifacts. A detailed evaluation of meat models demonstrates a resolution improvement from sub-decimeter to sub-centimeter over prior art in RF penetrative imaging.

PaperOrchestra: A Multi-Agent Framework for Automated AI Research Paper Writing

Yiwen Song, Yale Song, Tomas Pfister, Jinsung Yoon

Synthesizing unstructured research materials into manuscripts is an essential yet under-explored challenge in AI-driven scientific discovery. Existing autonomous writers are rigidly coupled to specific experimental pipelines, and produce superficial literature reviews. We introduce PaperOrchestra, a multi-agent framework for automated AI research paper writing. It flexibly transforms unconstrained pre-writing materials into submission-ready LaTeX manuscripts, including comprehensive literature synthesis and generated visuals, such as plots and conceptual diagrams. To evaluate performance, we present PaperWritingBench, the first standardized benchmark of reverse-engineered raw materials from 200 top-tier AI conference papers, alongside a comprehensive suite of automated evaluators. In side-by-side human evaluations, PaperOrchestra significantly outperforms autonomous baselines, achieving an absolute win rate margin of 50%-68% in literature review quality, and 14%-38% in overall manuscript quality.

VQQA: An Agentic Approach for Video Evaluation and Quality Improvement

Yiwen Song, Tomas Pfister, Yale Song

Despite rapid advancements in video generation models, aligning their outputs with complex user intent remains challenging. Existing test-time optimization methods are typically either computationally expensive or require white-box access to model internals. To address this, we present VQQA (Video Quality Question Answering), a unified, multi-agent framework generalizable across diverse input modalities and video generation tasks. By dynamically generating visual questions and using the resulting Vision-Language Model (VLM) critiques as semantic gradients, VQQA replaces traditional, passive evaluation metrics with human-interpretable, actionable feedback. This enables a highly efficient, closed-loop prompt optimization process via a black-box natural language interface. Extensive experiments demonstrate that VQQA effectively isolates and resolves visual artifacts, substantially improving generation quality in just a few refinement steps. Applicable to both text-to-video (T2V) and image-to-video (I2V) tasks, our method achieves absolute improvements of +11.57% on T2V-CompBench and +8.43% on VBench2 over vanilla generation, significantly outperforming state-of-the-art stochastic search and prompt optimization techniques.

Demand Adaptive Multi-Objective Electric Taxi Fleet Dispatching with Carbon Emission Analysis

Yiwen Song, Ningning Sun, Huimiao Chen

As a foreseeable future mode of transport with lower emissions and higher efficiencies, electric vehicles have received worldwide attention. For convenient centralized management, taxis are considered as the fleet with electrification priority. In this work, we focus on the study on electric taxis dispatching, with consideration of picking up customers and recharging, based on real world traffic data of a large number of taxis in Beijing. First, the assumed electric taxi charging stations are located using the K mean method. Second, based on the station locations and the order demands, which are in form of origin-destination pairs and extracted from the trajectory data, a dispatching strategy as well as the simulation framework is developed with consideration of reducing customer waiting time, mitigating electric taxi charging congestion, and balancing order number distribution among electric taxis. The proposed method models the electric taxi charging behaviors temporally discretely from the aspects of charging demands and availability of chargers, and further incorporates a centralized and intelligent fleet dispatching platform, which is capable of handling taxi service requests and arranging electric taxis' recharging in real time. The methodology in this paper is readily applicable to dispatching of different types of electric vehicle fleet with similar dataset available. Among the method, we use queueing theory to model the electric vehicle charging station waiting phenomena and include this factor into dispatching platform. Carbon emission is also surveyed and analyzed.

Ultra-Wide Bandgap Ga$_2$O$_3$-on-SiC MOSFETs

Yiwen Song, Arkka Bhattacharyya, Anwarul Karim, Daniel Shoemaker, Hsien-Lien Huang, Saurav Roy, Craig McGray, Jacob H. Leach, Jinwoo Hwang, Sriram Krishnamoorthy, Sukwon Choi

Ulta-wide bandgap semiconductors based on $β$-Ga$_2$O$_3$ offer the potential to achieve higher power switching performance, efficiency, and lower manufacturing cost than today's wide bandgap power semiconductors. However, the most critical challenge to the commercialization of Ga$_2$O$_3$ electronics is overheating, which impacts the device's performance and reliability. We fabricated a Ga$_2$O$_3$/4H-SiC composite wafer using a fusion-bonding method. A low temperature ($\le$ 600 $^{\circ}$C) epitaxy and device processing approach based on low-temperature (LT) metalorganic vapor phase epitaxy is developed to grow a Ga$_2$O$_3$ epitaxial channel layer on the composite wafer and subsequently fabricate into Ga$_2$O$_3$ power MOSFETs. This LT approach is essential to preserve the structural integrity of the composite wafer. These LT-grown epitaxial Ga$_2$O$_3$ MOSFETs deliver high thermal performance (56% reduction in channel temperature), high voltage blocking capabilities up to 2.45 kV, and power figures of merit of $\sim$ 300 MW/cm$^2$, which is a record high for any heterogeneously integrated Ga$_2$O$_3$ devices reported to date. This work is the first realization of multi-kilovolt homoepitaxial Ga$_2$O$_3$ power MOSFETs fabricated on a composite substrate with high heat transfer performance which delivers state-of-the-art power density values while running much cooler than those on native substrates. Thermal characterization and modeling results reveal that a Ga$_2$O$_3$/diamond composite wafer with a reduced Ga$_2$O$_3$ thickness ($\sim$ 1 $μ$m) and thinner bonding interlayer ($<$ 10 nm) can reduce the device thermal impedance to a level lower than today's GaN-on-SiC power switches.

ToMoBrush: Exploring Dental Health Sensing using a Sonic Toothbrush

Kuang Yuan, Mohamed Ibrahim, Yiwen Song, Guoxiang Deng, Suvendra Vijayan, Robert Nerone, Akshay Gadre, Swarun Kumar

Early detection of dental disease is crucial to prevent adverse outcomes. Today, dental X-rays are currently the most accurate gold standard for dental disease detection. Unfortunately, regular X-ray exam is still a privilege for billions of people around the world. In this paper, we ask: "Can we develop a low-cost sensing system that enables dental self-examination in the comfort of one's home?" This paper presents ToMoBrush, a dental health sensing system that explores using off-the-shelf sonic toothbrushes for dental condition detection. Our solution leverages the fact that a sonic toothbrush produces rich acoustic signals when in contact with teeth, which contain important information about each tooth's status. ToMoBrush extracts tooth resonance signatures from the acoustic signals to characterize varied dental health conditions of the teeth. We evaluate ToMoBrush on 19 participants and dental-standard models for detecting common dental problems including caries, calculus, and food impaction, achieving a detection ROC-AUC of 0.90, 0.83, and 0.88 respectively. Interviews with dental experts validate ToMoBrush's potential in enhancing at-home dental healthcare.

Emu: Enhancing Image Generation Models Using Photogenic Needles in a Haystack

Xiaoliang Dai, Ji Hou, Chih-Yao Ma, Sam Tsai, Jialiang Wang, Rui Wang, Peizhao Zhang, Simon Vandenhende, Xiaofang Wang, Abhimanyu Dubey, Matthew Yu, Abhishek Kadian, Filip Radenovic, Dhruv Mahajan, Kunpeng Li, Yue Zhao, Vladan Petrovic, Mitesh Kumar Singh, Simran Motwani, Yi Wen, Yiwen Song, Roshan Sumbaly, Vignesh Ramanathan, Zijian He, Peter Vajda, Devi Parikh

Training text-to-image models with web scale image-text pairs enables the generation of a wide range of visual concepts from text. However, these pre-trained models often face challenges when it comes to generating highly aesthetic images. This creates the need for aesthetic alignment post pre-training. In this paper, we propose quality-tuning to effectively guide a pre-trained model to exclusively generate highly visually appealing images, while maintaining generality across visual concepts. Our key insight is that supervised fine-tuning with a set of surprisingly small but extremely visually appealing images can significantly improve the generation quality. We pre-train a latent diffusion model on $1.1$ billion image-text pairs and fine-tune it with only a few thousand carefully selected high-quality images. The resulting model, Emu, achieves a win rate of $82.9\%$ compared with its pre-trained only counterpart. Compared to the state-of-the-art SDXLv1.0, Emu is preferred $68.4\%$ and $71.3\%$ of the time on visual appeal on the standard PartiPrompts and our Open User Input benchmark based on the real-world usage of text-to-image models. In addition, we show that quality-tuning is a generic approach that is also effective for other architectures, including pixel diffusion and masked generative transformer models.

LLM-Based Multi-Agent Blackboard System for Information Discovery in Data Science

Alireza Salemi, Mihir Parmar, Palash Goyal, Yiwen Song, Jinsung Yoon, Hamed Zamani, Tomas Pfister, Hamid Palangi

Advances in large language models (LLMs) have created new opportunities in data science, but their deployment is often limited by the challenge of finding relevant data in large data lakes. Existing methods struggle with this: both single- and multi-agent systems are quickly overwhelmed by large, heterogeneous files, and master-slave multi-agent systems rely on a rigid central controller that requires precise knowledge of each sub-agent's capabilities, which is not possible in large-scale settings where the main agent lacks full observability over sub-agents' knowledge and competencies. We propose a novel multi-agent paradigm inspired by the blackboard architecture for traditional AI models. In our framework, a central agent posts requests to a shared blackboard, and autonomous subordinate agents - either responsible for a partition of the data lake or retrieval from the web - volunteer to respond based on their capabilities. This design improves scalability and flexibility by removing the need for a central coordinator to know each agent's expertise or internal knowledge. We evaluate the approach on three benchmarks that require data discovery: KramaBench and modified versions of DSBench and DA-Code. Results show that the blackboard architecture substantially outperforms strong baselines, achieving 13%-57% relative improvements in end-to-end success and up to a 9% relative gain in data discovery F1 over the best baseline.

ScholarPeer: A Context-Aware Multi-Agent Framework for Automated Peer Review

Palash Goyal, Mihir Parmar, Yiwen Song, Hamid Palangi, Tomas Pfister, Jinsung Yoon

Automated peer review has evolved from simple text classification to structured feedback generation. However, current state-of-the-art systems still struggle with "surface-level" critiques: they excel at summarizing content but often fail to accurately assess novelty and significance or identify deep methodological flaws because they evaluate papers in a vacuum, lacking the external context a human expert possesses. In this paper, we introduce ScholarPeer, a search-enabled multi-agent framework designed to emulate the cognitive processes of a senior researcher. ScholarPeer employs a dual-stream process of context acquisition and active verification. It dynamically constructs a domain narrative using a historian agent, identifies missing comparisons via a baseline scout, and verifies claims through a multi-aspect Q&A engine, grounding the critique in live web-scale literature. We evaluate ScholarPeer on DeepReview-13K and the results demonstrate that ScholarPeer achieves significant win-rates against state-of-the-art approaches in side-by-side evaluations and reduces the gap to human-level diversity.

Watch and Learn: Learning to Use Computers from Online Videos

Chan Hee Song, Yiwen Song, Palash Goyal, Yu Su, Oriana Riva, Hamid Palangi, Tomas Pfister

Computer-using agents (CUAs) must plan task workflows across diverse and evolving applications, yet progress is limited by the lack of large-scale, high-quality training data. Existing datasets are narrow, static, and costly to annotate, while synthetic data often yields oversimplified or misaligned behaviors. We present Watch & Learn (W&L), a framework that converts readily available Internet videos of human computer use into executable UI trajectories at scale. Instead of directly generating actions or relying on handcrafted heuristics, we cast trajectory annotation as an inverse dynamics problem that predicts user actions from consecutive screen states, which simplifies learning and generalizes across domains. Through a task-aware retrieval and labeling pipeline, W&L yields over 53K high-quality trajectories that enhance CUAs both as in-context exemplars and as supervised training data. On OSWorld, it consistently improves general-purpose and specialized CUAs, while on WindowsAgentArena it achieves state-of-the-art performance among 7B-scale models under the 15-step limit. These results show that web-scale human demonstration videos can serve as a practical and scalable foundation for advancing real-world CUAs.

PLAN-TUNING: Post-Training Language Models to Learn Step-by-Step Planning for Complex Problem Solving

Mihir Parmar, Palash Goyal, Xin Liu, Yiwen Song, Mingyang Ling, Chitta Baral, Hamid Palangi, Tomas Pfister

Recently, decomposing complex problems into simple subtasks--a crucial part of human-like natural planning--to solve the given problem has significantly boosted the performance of large language models (LLMs). However, leveraging such planning structures during post-training to boost the performance of smaller open-source LLMs remains underexplored. Motivated by this, we introduce PLAN-TUNING, a unified post-training framework that (i) distills synthetic task decompositions (termed "planning trajectories") from large-scale LLMs and (ii) fine-tunes smaller models via supervised and reinforcement-learning objectives designed to mimic these planning processes to improve complex reasoning. On GSM8k and the MATH benchmarks, plan-tuned models outperform strong baselines by an average $\sim7\%$. Furthermore, plan-tuned models show better generalization capabilities on out-of-domain datasets, with average $\sim10\%$ and $\sim12\%$ performance improvements on OlympiadBench and AIME 2024, respectively. Our detailed analysis demonstrates how planning trajectories improves complex reasoning capabilities, showing that PLAN-TUNING is an effective strategy for improving task-specific performance of smaller LLMs.

Multiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme One

Yiwen Song, Qianyue Hao, Qingmin Liao, Jian Yuan, Yong Li

Model ensemble is a useful approach in reinforcement learning (RL) for training effective agents. Despite wide success of RL, training effective agents remains difficult due to the multitude of factors requiring careful tuning, such as algorithm selection, hyperparameter settings, and even random seed choices, all of which can significantly influence an agent's performance. Model ensemble helps overcome this challenge by combining multiple weak agents into a single, more powerful one, enhancing overall performance. However, existing ensemble methods, such as majority voting and Boltzmann addition, are designed as fixed strategies and lack a semantic understanding of specific tasks, limiting their adaptability and effectiveness. To address this, we propose LLM-Ens, a novel approach that enhances RL model ensemble with task-specific semantic understandings driven by large language models (LLMs). Given a task, we first design an LLM to categorize states in this task into distinct 'situations', incorporating high-level descriptions of the task conditions. Then, we statistically analyze the strengths and weaknesses of each individual agent to be used in the ensemble in each situation. During the inference time, LLM-Ens dynamically identifies the changing task situation and switches to the agent that performs best in the current situation, ensuring dynamic model selection in the evolving task condition. Our approach is designed to be compatible with agents trained with different random seeds, hyperparameter settings, and various RL algorithms. Extensive experiments on the Atari benchmark show that LLM-Ens significantly improves the RL model ensemble, surpassing well-known baselines by up to 20.9%. For reproducibility, our code is open-source at https://anonymous.4open.science/r/LLM4RLensemble-F7EE.

The Llama 3 Herd of Models

Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Alex Vaughan, Amy Yang, Angela Fan, Anirudh Goyal, Anthony Hartshorn, Aobo Yang, Archi Mitra, Archie Sravankumar, Artem Korenev, Arthur Hinsvark, Arun Rao, Aston Zhang, Aurelien Rodriguez, Austen Gregerson, Ava Spataru, Baptiste Roziere, Bethany Biron, Binh Tang, Bobbie Chern, Charlotte Caucheteux, Chaya Nayak, Chloe Bi, Chris Marra, Chris McConnell, Christian Keller, Christophe Touret, Chunyang Wu, Corinne Wong, Cristian Canton Ferrer, Cyrus Nikolaidis, Damien Allonsius, Daniel Song, Danielle Pintz, Danny Livshits, Danny Wyatt, David Esiobu, Dhruv Choudhary, Dhruv Mahajan, Diego Garcia-Olano, Diego Perino, Dieuwke Hupkes, Egor Lakomkin, Ehab AlBadawy, Elina Lobanova, Emily Dinan, Eric Michael Smith, Filip Radenovic, Francisco Guzmán, Frank Zhang, Gabriel Synnaeve, Gabrielle Lee, Georgia Lewis Anderson, Govind Thattai, Graeme Nail, Gregoire Mialon, Guan Pang, Guillem Cucurell, Hailey Nguyen, Hannah Korevaar, Hu Xu, Hugo Touvron, Iliyan Zarov, Imanol Arrieta Ibarra, Isabel Kloumann, Ishan Misra, Ivan Evtimov, Jack Zhang, Jade Copet, Jaewon Lee, Jan Geffert, Jana Vranes, Jason Park, Jay Mahadeokar, Jeet Shah, Jelmer van der Linde, Jennifer Billock, Jenny Hong, Jenya Lee, Jeremy Fu, Jianfeng Chi, Jianyu Huang, Jiawen Liu, Jie Wang, Jiecao Yu, Joanna Bitton, Joe Spisak, Jongsoo Park, Joseph Rocca, Joshua Johnstun, Joshua Saxe, Junteng Jia, Kalyan Vasuden Alwala, Karthik Prasad, Kartikeya Upasani, Kate Plawiak, Ke Li, Kenneth Heafield, Kevin Stone, Khalid El-Arini, Krithika Iyer, Kshitiz Malik, Kuenley Chiu, Kunal Bhalla, Kushal Lakhotia, Lauren Rantala-Yeary, Laurens van der Maaten, Lawrence Chen, Liang Tan, Liz Jenkins, Louis Martin, Lovish Madaan, Lubo Malo, Lukas Blecher, Lukas Landzaat, Luke de Oliveira, Madeline Muzzi, Mahesh Pasupuleti, Mannat Singh, Manohar Paluri, Marcin Kardas, Maria Tsimpoukelli, Mathew Oldham, Mathieu Rita, Maya Pavlova, Melanie Kambadur, Mike Lewis, Min Si, Mitesh Kumar Singh, Mona Hassan, Naman Goyal, Narjes Torabi, Nikolay Bashlykov, Nikolay Bogoychev, Niladri Chatterji, Ning Zhang, Olivier Duchenne, Onur Çelebi, Patrick Alrassy, Pengchuan Zhang, Pengwei Li, Petar Vasic, Peter Weng, Prajjwal Bhargava, Pratik Dubal, Praveen Krishnan, Punit Singh Koura, Puxin Xu, Qing He, Qingxiao Dong, Ragavan Srinivasan, Raj Ganapathy, Ramon Calderer, Ricardo Silveira Cabral, Robert Stojnic, Roberta Raileanu, Rohan Maheswari, Rohit Girdhar, Rohit Patel, Romain Sauvestre, Ronnie Polidoro, Roshan Sumbaly, Ross Taylor, Ruan Silva, Rui Hou, Rui Wang, Saghar Hosseini, Sahana Chennabasappa, Sanjay Singh, Sean Bell, Seohyun Sonia Kim, Sergey Edunov, Shaoliang Nie, Sharan Narang, Sharath Raparthy, Sheng Shen, Shengye Wan, Shruti Bhosale, Shun Zhang, Simon Vandenhende, Soumya Batra, Spencer Whitman, Sten Sootla, Stephane Collot, Suchin Gururangan, Sydney Borodinsky, Tamar Herman, Tara Fowler, Tarek Sheasha, Thomas Georgiou, Thomas Scialom, Tobias Speckbacher, Todor Mihaylov, Tong Xiao, Ujjwal Karn, Vedanuj Goswami, Vibhor Gupta, Vignesh Ramanathan, Viktor Kerkez, Vincent Gonguet, Virginie Do, Vish Vogeti, Vítor Albiero, Vladan Petrovic, Weiwei Chu, Wenhan Xiong, Wenyin Fu, Whitney Meers, Xavier Martinet, Xiaodong Wang, Xiaofang Wang, Xiaoqing Ellen Tan, Xide Xia, Xinfeng Xie, Xuchao Jia, Xuewei Wang, Yaelle Goldschlag, Yashesh Gaur, Yasmine Babaei, Yi Wen, Yiwen Song, Yuchen Zhang, Yue Li, Yuning Mao, Zacharie Delpierre Coudert, Zheng Yan, Zhengxing Chen, Zoe Papakipos, Aaditya Singh, Aayushi Srivastava, Abha Jain, Adam Kelsey, Adam Shajnfeld, Adithya Gangidi, Adolfo Victoria, Ahuva Goldstand, Ajay Menon, Ajay Sharma, Alex Boesenberg, Alexei Baevski, Allie Feinstein, Amanda Kallet, Amit Sangani, Amos Teo, Anam Yunus, Andrei Lupu, Andres Alvarado, Andrew Caples, Andrew Gu, Andrew Ho, Andrew Poulton, Andrew Ryan, Ankit Ramchandani, Annie Dong, Annie Franco, Anuj Goyal, Aparajita Saraf, Arkabandhu Chowdhury, Ashley Gabriel, Ashwin Bharambe, Assaf Eisenman, Azadeh Yazdan, Beau James, Ben Maurer, Benjamin Leonhardi, Bernie Huang, Beth Loyd, Beto De Paola, Bhargavi Paranjape, Bing Liu, Bo Wu, Boyu Ni, Braden Hancock, Bram Wasti, Brandon Spence, Brani Stojkovic, Brian Gamido, Britt Montalvo, Carl Parker, Carly Burton, Catalina Mejia, Ce Liu, Changhan Wang, Changkyu Kim, Chao Zhou, Chester Hu, Ching-Hsiang Chu, Chris Cai, Chris Tindal, Christoph Feichtenhofer, Cynthia Gao, Damon Civin, Dana Beaty, Daniel Kreymer, Daniel Li, David Adkins, David Xu, Davide Testuggine, Delia David, Devi Parikh, Diana Liskovich, Didem Foss, Dingkang Wang, Duc Le, Dustin Holland, Edward Dowling, Eissa Jamil, Elaine Montgomery, Eleonora Presani, Emily Hahn, Emily Wood, Eric-Tuan Le, Erik Brinkman, Esteban Arcaute, Evan Dunbar, Evan Smothers, Fei Sun, Felix Kreuk, Feng Tian, Filippos Kokkinos, Firat Ozgenel, Francesco Caggioni, Frank Kanayet, Frank Seide, Gabriela Medina Florez, Gabriella Schwarz, Gada Badeer, Georgia Swee, Gil Halpern, Grant Herman, Grigory Sizov, Guangyi, Zhang, Guna Lakshminarayanan, Hakan Inan, Hamid Shojanazeri, Han Zou, Hannah Wang, Hanwen Zha, Haroun Habeeb, Harrison Rudolph, Helen Suk, Henry Aspegren, Hunter Goldman, Hongyuan Zhan, Ibrahim Damlaj, Igor Molybog, Igor Tufanov, Ilias Leontiadis, Irina-Elena Veliche, Itai Gat, Jake Weissman, James Geboski, James Kohli, Janice Lam, Japhet Asher, Jean-Baptiste Gaya, Jeff Marcus, Jeff Tang, Jennifer Chan, Jenny Zhen, Jeremy Reizenstein, Jeremy Teboul, Jessica Zhong, Jian Jin, Jingyi Yang, Joe Cummings, Jon Carvill, Jon Shepard, Jonathan McPhie, Jonathan Torres, Josh Ginsburg, Junjie Wang, Kai Wu, Kam Hou U, Karan Saxena, Kartikay Khandelwal, Katayoun Zand, Kathy Matosich, Kaushik Veeraraghavan, Kelly Michelena, Keqian Li, Kiran Jagadeesh, Kun Huang, Kunal Chawla, Kyle Huang, Lailin Chen, Lakshya Garg, Lavender A, Leandro Silva, Lee Bell, Lei Zhang, Liangpeng Guo, Licheng Yu, Liron Moshkovich, Luca Wehrstedt, Madian Khabsa, Manav Avalani, Manish Bhatt, Martynas Mankus, Matan Hasson, Matthew Lennie, Matthias Reso, Maxim Groshev, Maxim Naumov, Maya Lathi, Meghan Keneally, Miao Liu, Michael L. Seltzer, Michal Valko, Michelle Restrepo, Mihir Patel, Mik Vyatskov, Mikayel Samvelyan, Mike Clark, Mike Macey, Mike Wang, Miquel Jubert Hermoso, Mo Metanat, Mohammad Rastegari, Munish Bansal, Nandhini Santhanam, Natascha Parks, Natasha White, Navyata Bawa, Nayan Singhal, Nick Egebo, Nicolas Usunier, Nikhil Mehta, Nikolay Pavlovich Laptev, Ning Dong, Norman Cheng, Oleg Chernoguz, Olivia Hart, Omkar Salpekar, Ozlem Kalinli, Parkin Kent, Parth Parekh, Paul Saab, Pavan Balaji, Pedro Rittner, Philip Bontrager, Pierre Roux, Piotr Dollar, Polina Zvyagina, Prashant Ratanchandani, Pritish Yuvraj, Qian Liang, Rachad Alao, Rachel Rodriguez, Rafi Ayub, Raghotham Murthy, Raghu Nayani, Rahul Mitra, Rangaprabhu Parthasarathy, Raymond Li, Rebekkah Hogan, Robin Battey, Rocky Wang, Russ Howes, Ruty Rinott, Sachin Mehta, Sachin Siby, Sai Jayesh Bondu, Samyak Datta, Sara Chugh, Sara Hunt, Sargun Dhillon, Sasha Sidorov, Satadru Pan, Saurabh Mahajan, Saurabh Verma, Seiji Yamamoto, Sharadh Ramaswamy, Shaun Lindsay, Shaun Lindsay, Sheng Feng, Shenghao Lin, Shengxin Cindy Zha, Shishir Patil, Shiva Shankar, Shuqiang Zhang, Shuqiang Zhang, Sinong Wang, Sneha Agarwal, Soji Sajuyigbe, Soumith Chintala, Stephanie Max, Stephen Chen, Steve Kehoe, Steve Satterfield, Sudarshan Govindaprasad, Sumit Gupta, Summer Deng, Sungmin Cho, Sunny Virk, Suraj Subramanian, Sy Choudhury, Sydney Goldman, Tal Remez, Tamar Glaser, Tamara Best, Thilo Koehler, Thomas Robinson, Tianhe Li, Tianjun Zhang, Tim Matthews, Timothy Chou, Tzook Shaked, Varun Vontimitta, Victoria Ajayi, Victoria Montanez, Vijai Mohan, Vinay Satish Kumar, Vishal Mangla, Vlad Ionescu, Vlad Poenaru, Vlad Tiberiu Mihailescu, Vladimir Ivanov, Wei Li, Wenchen Wang, Wenwen Jiang, Wes Bouaziz, Will Constable, Xiaocheng Tang, Xiaojian Wu, Xiaolan Wang, Xilun Wu, Xinbo Gao, Yaniv Kleinman, Yanjun Chen, Ye Hu, Ye Jia, Ye Qi, Yenda Li, Yilin Zhang, Ying Zhang, Yossi Adi, Youngjin Nam, Yu, Wang, Yu Zhao, Yuchen Hao, Yundi Qian, Yunlu Li, Yuzi He, Zach Rait, Zachary DeVito, Zef Rosnbrick, Zhaoduo Wen, Zhenyu Yang, Zhiwei Zhao, Zhiyu Ma

Controlling the 2D magnetism of CrBr$_3$ by van der Waals stacking engineering

Shiqi Yang, Xiaolong Xu, Bo Han, Pingfan Gu, Roger Guzman, Yiwen Song, Zhongchong Lin, Peng Gao, Wu Zhou, Jinbo Yang, Zuxin Chen, Yu Ye

The manipulation of two-dimensional (2D) magnetic order is of significant importance to facilitate future 2D magnets for low-power and high-speed spintronic devices. Van der Waals stacking engineering makes promises for controllable magnetism via interlayer magnetic coupling. However, directly examining the stacking order changes accompanying magnetic order transitions at the atomic scale and preparing device-ready 2D magnets with controllable magnetic orders remain elusive. Here, we demonstrate effective control of interlayer stacking in exfoliated CrBr$_3$ via thermally assisted strain engineering. The stable interlayer ferromagnetic (FM), antiferromagnetic (AFM), and FM-AFM coexistent ground states confirmed by the magnetic circular dichroism measurements are realized. Combined with the first-principles calculations, the atomically-resolved imaging technique reveals the correlation between magnetic order and interlay stacking order in the CrBr$_3$ flakes unambiguously. A tunable exchange bias effect is obtained in the mixed phase of FM and AFM states. This work will introduce new magnetic properties by controlling the stacking order, and sequence of 2D magnets, providing ample opportunities for their application in spintronic devices.

Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models

Fengli Xu, Qianyue Hao, Zefang Zong, Jingwei Wang, Yunke Zhang, Jingyi Wang, Xiaochong Lan, Jiahui Gong, Tianjian Ouyang, Fanjin Meng, Chenyang Shao, Yuwei Yan, Qinglong Yang, Yiwen Song, Sijian Ren, Xinyuan Hu, Yu Li, Jie Feng, Chen Gao, Yong Li

Language has long been conceived as an essential tool for human reasoning. The breakthrough of Large Language Models (LLMs) has sparked significant research interest in leveraging these models to tackle complex reasoning tasks. Researchers have moved beyond simple autoregressive token generation by introducing the concept of "thought" -- a sequence of tokens representing intermediate steps in the reasoning process. This innovative paradigm enables LLMs' to mimic complex human reasoning processes, such as tree search and reflective thinking. Recently, an emerging trend of learning to reason has applied reinforcement learning (RL) to train LLMs to master reasoning processes. This approach enables the automatic generation of high-quality reasoning trajectories through trial-and-error search algorithms, significantly expanding LLMs' reasoning capacity by providing substantially more training data. Furthermore, recent studies demonstrate that encouraging LLMs to "think" with more tokens during test-time inference can further significantly boost reasoning accuracy. Therefore, the train-time and test-time scaling combined to show a new research frontier -- a path toward Large Reasoning Model. The introduction of OpenAI's o1 series marks a significant milestone in this research direction. In this survey, we present a comprehensive review of recent progress in LLM reasoning. We begin by introducing the foundational background of LLMs and then explore the key technical components driving the development of large reasoning models, with a focus on automated data construction, learning-to-reason techniques, and test-time scaling. We also analyze popular open-source projects at building large reasoning models, and conclude with open challenges and future research directions.

Judging with Confidence: Calibrating Autoraters to Preference Distributions

Zhuohang Li, Xiaowei Li, Chengyu Huang, Guowang Li, Katayoon Goshvadi, Bo Dai, Dale Schuurmans, Paul Zhou, Hamid Palangi, Yiwen Song, Palash Goyal, Murat Kantarcioglu, Bradley A. Malin, Yuan Xue

The alignment of large language models (LLMs) with human values increasingly relies on using other LLMs as automated judges, or ``autoraters''. However, their reliability is limited by a foundational issue: they are trained on discrete preference labels, forcing a single ground truth onto tasks that are often subjective, ambiguous, or nuanced. We argue that a reliable autorater must learn to model the full distribution of preferences defined by a target population. In this paper, we propose a general framework for calibrating probabilistic autoraters to any given preference distribution. We formalize the problem and present two learning methods tailored to different data conditions: 1) a direct supervised fine-tuning for dense, probabilistic labels, and 2) a reinforcement learning approach for sparse, binary labels. Our empirical results show that finetuning autoraters with a distribution-matching objective leads to verbalized probability predictions that are better aligned with the target preference distribution, with improved calibration and significantly lower positional bias, all while preserving performance on objective tasks.

HEART: Emotionally-Driven Test-Time Scaling of Language Models

Gabriela Pinto, Palash Goyal, Mihir Parmar, Yiwen Song, Souradip Chakraborty, Zifeng Wang, Jinsung Yoon, Tomas Pfister, Hamid Palangi

Test-time scaling has significantly improved how AI models solve problems, yet current methods often get stuck in repetitive, incorrect patterns of thought. We introduce HEART, a framework that uses emotional cues to guide the model's focus, much like how feelings contribute to human decision-making. By alternating between critical tones to sharpen error detection and encouraging tones to spark new ideas, HEART helps the model break out of dead-end reasoning and find the right solution. We evaluate HEART across seven high-difficulty benchmarks--including Humanity's Last Exam, GPQA Diamond, and LiveCodeBench--demonstrating robustness across diverse models. Results show that emotion facilitates deeper reasoning, yielding consistent accuracy gains over affect-sterile baselines. These findings suggest that the next frontier in machine reasoning lies in the strategic integration of affective regulation to guide logical synthesis.

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Gheorghe Comanici, Eric Bieber, Mike Schaekermann, Ice Pasupat, Noveen Sachdeva, Inderjit Dhillon, Marcel Blistein, Ori Ram, Dan Zhang, Evan Rosen, Luke Marris, Sam Petulla, Colin Gaffney, Asaf Aharoni, Nathan Lintz, Tiago Cardal Pais, Henrik Jacobsson, Idan Szpektor, Nan-Jiang Jiang, Krishna Haridasan, Ahmed Omran, Nikunj Saunshi, Dara Bahri, Gaurav Mishra, Eric Chu, Toby Boyd, Brad Hekman, Aaron Parisi, Chaoyi Zhang, Kornraphop Kawintiranon, Tania Bedrax-Weiss, Oliver Wang, Ya Xu, Ollie Purkiss, Uri Mendlovic, Ilaï Deutel, Nam Nguyen, Adam Langley, Flip Korn, Lucia Rossazza, Alexandre Ramé, Sagar Waghmare, Helen Miller, Nathan Byrd, Ashrith Sheshan, Raia Hadsell, Sangnie Bhardwaj, Pawel Janus, Tero Rissa, Dan Horgan, Alvin Abdagic, Lior Belenki, James Allingham, Anima Singh, Theo Guidroz, Srivatsan Srinivasan, Herman Schmit, Kristen Chiafullo, Andre Elisseeff, Nilpa Jha, Prateek Kolhar, Leonard Berrada, Frank Ding, Xiance Si, Shrestha Basu Mallick, Franz Och, Sofia Erell, Eric Ni, Tejasi Latkar, Sherry Yang, Petar Sirkovic, Ziqiang Feng, Robert Leland, Rachel Hornung, Gang Wu, Charles Blundell, Hamidreza Alvari, Po-Sen Huang, Cathy Yip, Sanja Deur, Li Liu, Gabriela Surita, Pablo Duque, Dima Damen, Johnson Jia, Arthur Guez, Markus Mircea, Animesh Sinha, Alberto Magni, Paweł Stradomski, Tal Marian, Vlado Galić, Wenhu Chen, Hisham Husain, Achintya Singhal, Dominik Grewe, François-Xavier Aubet, Shuang Song, Lorenzo Blanco, Leland Rechis, Lewis Ho, Rich Munoz, Kelvin Zheng, Jessica Hamrick, Kevin Mather, Hagai Taitelbaum, Eliza Rutherford, Yun Lei, Kuangyuan Chen, Anand Shukla, Erica Moreira, Eric Doi, Berivan Isik, Nir Shabat, Dominika Rogozińska, Kashyap Kolipaka, Jason Chang, Eugen Vušak, Srinivasan Venkatachary, Shadi Noghabi, Tarun Bharti, Younghoon Jun, Aleksandr Zaks, Simon Green, Jeshwanth Challagundla, William Wong, Muqthar Mohammad, Dean Hirsch, Yong Cheng, Iftekhar Naim, Lev Proleev, Damien Vincent, Aayush Singh, Maxim Krikun, Dilip Krishnan, Zoubin Ghahramani, Aviel Atias, Rajeev Aggarwal, Christo Kirov, Dimitrios Vytiniotis, Christy Koh, Alexandra Chronopoulou, Pawan Dogra, Vlad-Doru Ion, Gladys Tyen, Jason Lee, Felix Weissenberger, Trevor Strohman, Ashwin Balakrishna, Jack Rae, Marko Velic, Raoul de Liedekerke, Oded Elyada, Wentao Yuan, Canoee Liu, Lior Shani, Sergey Kishchenko, Bea Alessio, Yandong Li, Richard Song, Sam Kwei, Orion Jankowski, Aneesh Pappu, Youhei Namiki, Yenai Ma, Nilesh Tripuraneni, Colin Cherry, Marissa Ikonomidis, Yu-Cheng Ling, Colin Ji, Beka Westberg, Auriel Wright, Da Yu, David Parkinson, Swaroop Ramaswamy, Jerome Connor, Soheil Hassas Yeganeh, Snchit Grover, George Kenwright, Lubo Litchev, Chris Apps, Alex Tomala, Felix Halim, Alex Castro-Ros, Zefei Li, Anudhyan Boral, Pauline Sho, Michal Yarom, Eric Malmi, David Klinghoffer, Rebecca Lin, Alan Ansell, Pradeep Kumar S, Shubin Zhao, Siqi Zuo, Adam Santoro, Heng-Tze Cheng, Solomon Demmessie, Yuchi Liu, Nicole Brichtova, Allie Culp, Nathaniel Braun, Dan Graur, Will Ng, Nikhil Mehta, Aaron Phillips, Patrik Sundberg, Varun Godbole, Fangyu Liu, Yash Katariya, David Rim, Mojtaba Seyedhosseini, Sean Ammirati, Jonas Valfridsson, Mahan Malihi, Timothy Knight, Andeep Toor, Thomas Lampe, Abe Ittycheriah, Lewis Chiang, Chak Yeung, Alexandre Fréchette, Jinmeng Rao, Huisheng Wang, Himanshu Srivastava, Richard Zhang, Rocky Rhodes, Ariel Brand, Dean Weesner, Ilya Figotin, Felix Gimeno, Rachana Fellinger, Pierre Marcenac, José Leal, Eyal Marcus, Victor Cotruta, Rodrigo Cabrera, Sheryl Luo, Dan Garrette, Vera Axelrod, Sorin Baltateanu, David Barker, Dongkai Chen, Horia Toma, Ben Ingram, Jason Riesa, Chinmay Kulkarni, Yujing Zhang, Hongbin Liu, Chao Wang, Martin Polacek, Will Wu, Kai Hui, Adrian N Reyes, Yi Su, Megan Barnes, Ishaan Malhi, Anfal Siddiqui, Qixuan Feng, Mihai Damaschin, Daniele Pighin, Andreas Steiner, Samuel Yang, Ramya Sree Boppana, Simeon Ivanov, Arun Kandoor, Aditya Shah, Asier Mujika, Da Huang, Christopher A. Choquette-Choo, Mohak Patel, Tianhe Yu, Toni Creswell, Jerry, Liu, Catarina Barros, Yasaman Razeghi, Aurko Roy, Phil Culliton, Binbin Xiong, Jiaqi Pan, Thomas Strohmann, Tolly Powell, Babi Seal, Doug DeCarlo, Pranav Shyam, Kaan Katircioglu, Xuezhi Wang, Cassidy Hardin, Immanuel Odisho, Josef Broder, Oscar Chang, Arun Nair, Artem Shtefan, Maura O'Brien, Manu Agarwal, Sahitya Potluri, Siddharth Goyal, Amit Jhindal, Saksham Thakur, Yury Stuken, James Lyon, Kristina Toutanova, Fangxiaoyu Feng, Austin Wu, Ben Horn, Alek Wang, Alex Cullum, Gabe Taubman, Disha Shrivastava, Chongyang Shi, Hamish Tomlinson, Roma Patel, Tao Tu, Ada Maksutaj Oflazer, Francesco Pongetti, Mingyao Yang, Adrien Ali Taïga, Vincent Perot, Nuo Wang Pierse, Feng Han, Yoel Drori, Iñaki Iturrate, Ayan Chakrabarti, Legg Yeung, Dave Dopson, Yi-ting Chen, Apoorv Kulshreshtha, Tongfei Guo, Philip Pham, Tal Schuster, Junquan Chen, Alex Polozov, Jinwei Xing, Huanjie Zhou, Praneeth Kacham, Doron Kukliansky, Antoine Miech, Sergey Yaroshenko, Ed Chi, Sholto Douglas, Hongliang Fei, Mathieu Blondel, Preethi Myla, Lior Madmoni, Xing Wu, Daniel Keysers, Kristian Kjems, Isabela Albuquerque, Lijun Yu, Joel D'sa, Michelle Plantan, Vlad Ionescu, Jaume Sanchez Elias, Abhirut Gupta, Manish Reddy Vuyyuru, Fred Alcober, Tong Zhou, Kaiyang Ji, Florian Hartmann, Subha Puttagunta, Hugo Song, Ehsan Amid, Anca Stefanoiu, Andrew Lee, Paul Pucciarelli, Emma Wang, Amit Raul, Slav Petrov, Isaac Tian, Valentin Anklin, Nana Nti, Victor Gomes, Max Schumacher, Grace Vesom, Alex Panagopoulos, Konstantinos Bousmalis, Daniel Andor, Josh Jacob, Yuan Zhang, Bill Rosgen, Matija Kecman, Matthew Tung, Alexandra Belias, Noah Goodman, Paul Covington, Brian Wieder, Nikita Saxena, Elnaz Davoodi, Muhuan Huang, Sharath Maddineni, Vincent Roulet, Folawiyo Campbell-Ajala, Pier Giuseppe Sessa, Xintian, Wu, Guangda Lai, Paul Collins, Alex Haig, Vytenis Sakenas, Xiaowei Xu, Marissa Giustina, Laurent El Shafey, Pichi Charoenpanit, Shefali Garg, Joshua Ainslie, Boone Severson, Montse Gonzalez Arenas, Shreya Pathak, Sujee Rajayogam, Jie Feng, Michiel Bakker, Sheng Li, Nevan Wichers, Jamie Rogers, Xinyang Geng, Yeqing Li, Rolf Jagerman, Chao Jia, Nadav Olmert, David Sharon, Matthew Mauger, Sandeep Mariserla, Hongxu Ma, Megha Mohabey, Kyuyeun Kim, Alek Andreev, Scott Pollom, Juliette Love, Vihan Jain, Priyanka Agrawal, Yannick Schroecker, Alisa Fortin, Manfred Warmuth, Ji Liu, Andrew Leach, Irina Blok, Ganesh Poomal Girirajan, Roee Aharoni, Benigno Uria, Andrei Sozanschi, Dan Goldberg, Lucian Ionita, Marco Tulio Ribeiro, Martin Zlocha, Vighnesh Birodkar, Sami Lachgar, Liangzhe Yuan, Himadri Choudhury, Matt Ginsberg, Fei Zheng, Gregory Dibb, Emily Graves, Swachhand Lokhande, Gabriel Rasskin, George-Cristian Muraru, Corbin Quick, Sandeep Tata, Pierre Sermanet, Aditya Chawla, Itay Karo, Yan Wang, Susan Zhang, Orgad Keller, Anca Dragan, Guolong Su, Ian Chou, Xi Liu, Yiqing Tao, Shruthi Prabhakara, Marc Wilson, Ruibo Liu, Shibo Wang, Georgie Evans, David Du, Alfonso Castaño, Gautam Prasad, Mona El Mahdy, Sebastian Gerlach, Machel Reid, Jarrod Kahn, Amir Zait, Thanumalayan Sankaranarayana Pillai, Thatcher Ulrich, Guanyu Wang, Jan Wassenberg, Efrat Farkash, Kiran Yalasangi, Congchao Wang, Maria Bauza, Simon Bucher, Ting Liu, Jun Yan, Gary Leung, Vikas Sindhwani, Parker Barnes, Avi Singh, Ivan Jurin, Jichuan Chang, Niket Kumar Bhumihar, Sivan Eiger, Gui Citovsky, Ben Withbroe, Zhang Li, Siyang Xue, Niccolò Dal Santo, Georgi Stoyanov, Yves Raimond, Steven Zheng, Yilin Gao, Vít Listík, Sławek Kwasiborski, Rachel Saputro, Adnan Ozturel, Ganesh Mallya, Kushal Majmundar, Ross West, Paul Caron, Jinliang Wei, Lluis Castrejon, Sharad Vikram, Deepak Ramachandran, Nikhil Dhawan, Jiho Park, Sara Smoot, George van den Driessche, Yochai Blau, Chase Malik, Wei Liang, Roy Hirsch, Cicero Nogueira dos Santos, Eugene Weinstein, Aäron van den Oord, Sid Lall, Nicholas FitzGerald, Zixuan Jiang, Xuan Yang, Dale Webster, Ali Elqursh, Aedan Pope, Georges Rotival, David Raposo, Wanzheng Zhu, Jeff Dean, Sami Alabed, Dustin Tran, Arushi Gupta, Zach Gleicher, Jessica Austin, Edouard Rosseel, Megh Umekar, Dipanjan Das, Yinghao Sun, Kai Chen, Karolis Misiunas, Xiang Zhou, Yixian Di, Alyssa Loo, Josh Newlan, Bo Li, Vinay Ramasesh, Ying Xu, Alex Chen, Sudeep Gandhe, Radu Soricut, Nikita Gupta, Shuguang Hu, Seliem El-Sayed, Xavier Garcia, Idan Brusilovsky, Pu-Chin Chen, Andrew Bolt, Lu Huang, Alex Gurney, Zhiying Zhang, Alexander Pritzel, Jarek Wilkiewicz, Bryan Seybold, Bhargav Kanagal Shamanna, Felix Fischer, Josef Dean, Karan Gill, Ross Mcilroy, Abhishek Bhowmick, Jeremy Selier, Antoine Yang, Derek Cheng, Vladimir Magay, Jie Tan, Dhriti Varma, Christian Walder, Tomas Kocisky, Ryo Nakashima, Paul Natsev, Mike Kwong, Ionel Gog, Chiyuan Zhang, Sander Dieleman, Thomas Jimma, Andrey Ryabtsev, Siddhartha Brahma, David Steiner, Dayou Du, Ante Žužul, Mislav Žanić, Mukund Raghavachari, Willi Gierke, Zeyu Zheng, Dessie Petrova, Yann Dauphin, Yuchuan Liu, Ido Kessler, Steven Hand, Chris Duvarney, Seokhwan Kim, Hyo Lee, Léonard Hussenot, Jeffrey Hui, Josh Smith, Deepali Jain, Jiawei Xia, Gaurav Singh Tomar, Keyvan Amiri, Du Phan, Fabian Fuchs, Tobias Weyand, Nenad Tomasev, Alexandra Cordell, Xin Liu, Jonathan Mallinson, Pankaj Joshi, Andy Crawford, Arun Suggala, Steve Chien, Nick Fernando, Mariella Sanchez-Vargas, Duncan Williams, Phil Crone, Xiyang Luo, Igor Karpov, Jyn Shan, Terry Thurk, Robin Strudel, Paul Voigtlaender, Piyush Patil, Tim Dozat, Ali Khodaei, Sahil Singla, Piotr Ambroszczyk, Qiyin Wu, Yifan Chang, Brian Roark, Chaitra Hegde, Tianli Ding, Angelos Filos, Zhongru Wu, André Susano Pinto, Shuang Liu, Saarthak Khanna, Aditya Pandey, Siobhan Mcloughlin, Qiujia Li, Sam Haves, Allan Zhou, Elena Buchatskaya, Isabel Leal, Peter de Boursac, Nami Akazawa, Nina Anderson, Terry Chen, Krishna Somandepalli, Chen Liang, Sheela Goenka, Stephanie Winkler, Alexander Grushetsky, Yifan Ding, Jamie Smith, Fan Ye, Jordi Pont-Tuset, Eric Li, Ruichao Li, Tomer Golany, Dawid Wegner, Tao Jiang, Omer Barak, Yuan Shangguan, Eszter Vértes, Renee Wong, Jörg Bornschein, Alex Tudor, Michele Bevilacqua, Tom Schaul, Ankit Singh Rawat, Yang Zhao, Kyriakos Axiotis, Lei Meng, Cory McLean, Jonathan Lai, Jennifer Beattie, Nate Kushman, Yaxin Liu, Blair Kutzman, Fiona Lang, Jingchen Ye, Praneeth Netrapalli, Pushkar Mishra, Myriam Khan, Megha Goel, Rob Willoughby, David Tian, Honglei Zhuang, JD Chen, Zak Tsai, Tasos Kementsietsidis, Arjun Khare, James Keeling, Keyang Xu, Nathan Waters, Florent Altché, Ashok Popat, Bhavishya Mittal, David Saxton, Dalia El Badawy, Michael Mathieu, Zheng Zheng, Hao Zhou, Nishant Ranka, Richard Shin, Qingnan Duan, Tim Salimans, Ioana Mihailescu, Uri Shaham, Ming-Wei Chang, Yannis Assael, Nishanth Dikkala, Martin Izzard, Vincent Cohen-Addad, Cat Graves, Vlad Feinberg, Grace Chung, DJ Strouse, Danny Karmon, Sahand Sharifzadeh, Zoe Ashwood, Khiem Pham, Jon Blanton, Alex Vasiloff, Jarred Barber, Mark Geller, Aurick Zhou, Fedir Zubach, Tzu-Kuo Huang, Lei Zhang, Himanshu Gupta, Matt Young, Julia Proskurnia, Ronny Votel, Valentin Gabeur, Gabriel Barcik, Aditya Tripathi, Hongkun Yu, Geng Yan, Beer Changpinyo, Filip Pavetić, Amy Coyle, Yasuhisa Fujii, Jorge Gonzalez Mendez, Tianhao Zhou, Harish Rajamani, Blake Hechtman, Eddie Cao, Da-Cheng Juan, Yi-Xuan Tan, Valentin Dalibard, Yilun Du, Natalie Clay, Kaisheng Yao, Wenhao Jia, Dimple Vijaykumar, Yuxiang Zhou, Xinyi Bai, Wei-Chih Hung, Steven Pecht, Georgi Todorov, Nikhil Khadke, Pramod Gupta, Preethi Lahoti, Arnaud Autef, Karthik Duddu, James Lee-Thorp, Alexander Bykovsky, Tautvydas Misiunas, Sebastian Flennerhag, Santhosh Thangaraj, Jed McGiffin, Zack Nado, Markus Kunesch, Andreas Noever, Amir Hertz, Marco Liang, Victor Stone, Evan Palmer, Samira Daruki, Arijit Pramanik, Siim Põder, Austin Kyker, Mina Khan, Evgeny Sluzhaev, Marvin Ritter, Avraham Ruderman, Wenlei Zhou, Chirag Nagpal, Kiran Vodrahalli, George Necula, Paul Barham, Ellie Pavlick, Jay Hartford, Izhak Shafran, Long Zhao, Maciej Mikuła, Tom Eccles, Hidetoshi Shimokawa, Kanav Garg, Luke Vilnis, Hanwen Chen, Ilia Shumailov, Kuang-Huei Lee, Abdelrahman Abdelhamed, Meiyan Xie, Vered Cohen, Ester Hlavnova, Dan Malkin, Chawin Sitawarin, James Lottes, Pauline Coquinot, Tianli Yu, Sandeep Kumar, Jingwei Zhang, Aroma Mahendru, Zafarali Ahmed, James Martens, Tao Chen, Aviel Boag, Daiyi Peng, Coline Devin, Arseniy Klimovskiy, Mary Phuong, Danny Vainstein, Jin Xie, Bhuvana Ramabhadran, Nathan Howard, Xinxin Yu, Gitartha Goswami, Jingyu Cui, Sam Shleifer, Mario Pinto, Chih-Kuan Yeh, Ming-Hsuan Yang, Sara Javanmardi, Dan Ethier, Chace Lee, Jordi Orbay, Suyog Kotecha, Carla Bromberg, Pete Shaw, James Thornton, Adi Gerzi Rosenthal, Shane Gu, Matt Thomas, Ian Gemp, Aditya Ayyar, Asahi Ushio, Aarush Selvan, Joel Wee, Chenxi Liu, Maryam Majzoubi, Weiren Yu, Jake Abernethy, Tyler Liechty, Renke Pan, Hoang Nguyen, Qiong, Hu, Sarah Perrin, Abhinav Arora, Emily Pitler, Weiyi Wang, Kaushik Shivakumar, Flavien Prost, Ben Limonchik, Jing Wang, Yi Gao, Timothee Cour, Shyamal Buch, Huan Gui, Maria Ivanova, Philipp Neubeck, Kelvin Chan, Lucy Kim, Huizhong Chen, Naman Goyal, Da-Woon Chung, Lu Liu, Yao Su, Anastasia Petrushkina, Jiajun Shen, Armand Joulin, Yuanzhong Xu, Stein Xudong Lin, Yana Kulizhskaya, Ciprian Chelba, Shobha Vasudevan, Eli Collins, Vasilisa Bashlovkina, Tony Lu, Doug Fritz, Jongbin Park, Yanqi Zhou, Chen Su, Richard Tanburn, Mikhail Sushkov, Mitchelle Rasquinha, Jinning Li, Jennifer Prendki, Yiming Li, Pallavi LV, Shriya Sharma, Hen Fitoussi, Hui Huang, Andrew Dai, Phuong Dao, Mike Burrows, Henry Prior, Danfeng Qin, Golan Pundak, Lars Lowe Sjoesund, Art Khurshudov, Zhenkai Zhu, Albert Webson, Elizabeth Kemp, Tat Tan, Saurabh Agrawal, Susie Sargsyan, Liqun Cheng, Jim Stephan, Tom Kwiatkowski, David Reid, Arunkumar Byravan, Assaf Hurwitz Michaely, Nicolas Heess, Luowei Zhou, Sonam Goenka, Viral Carpenter, Anselm Levskaya, Bo Wang, Reed Roberts, Rémi Leblond, Sharat Chikkerur, Stav Ginzburg, Max Chang, Robert Riachi, Chuqiao, Xu, Zalán Borsos, Michael Pliskin, Julia Pawar, Morgane Lustman, Hannah Kirkwood, Ankit Anand, Aditi Chaudhary, Norbert Kalb, Kieran Milan, Sean Augenstein, Anna Goldie, Laurel Prince, Karthik Raman, Yanhua Sun, Vivian Xia, Aaron Cohen, Zhouyuan Huo, Josh Camp, Seher Ellis, Lukas Zilka, David Vilar Torres, Lisa Patel, Sho Arora, Betty Chan, Jonas Adler, Kareem Ayoub, Jacky Liang, Fayaz Jamil, Jiepu Jiang, Simon Baumgartner, Haitian Sun, Yael Karov, Yaroslav Akulov, Hui Zheng, Irene Cai, Claudio Fantacci, James Rubin, Alex Rav Acha, Mengchao Wang, Nina D'Souza, Rohit Sathyanarayana, Shengyang Dai, Simon Rowe, Andrey Simanovsky, Omer Goldman, Yuheng Kuang, Xiaoyue Pan, Andrew Rosenberg, Tania Rojas-Esponda, Praneet Dutta, Amy Zeng, Irina Jurenka, Greg Farquhar, Yamini Bansal, Shariq Iqbal, Becca Roelofs, Ga-Young Joung, Parker Beak, Changwan Ryu, Ryan Poplin, Yan Wu, Jean-Baptiste Alayrac, Senaka Buthpitiya, Olaf Ronneberger, Caleb Habtegebriel, Wei Li, Paul Cavallaro, Aurora Wei, Guy Bensky, Timo Denk, Harish Ganapathy, Jeff Stanway, Pratik Joshi, Francesco Bertolini, Jessica Lo, Olivia Ma, Zachary Charles, Geta Sampemane, Himanshu Sahni, Xu Chen, Harry Askham, David Gaddy, Peter Young, Jiewen Tan, Matan Eyal, Arthur Bražinskas, Li Zhong, Zhichun Wu, Mark Epstein, Kai Bailey, Andrew Hard, Kamyu Lee, Sasha Goldshtein, Alex Ruiz, Mohammed Badawi, Matthias Lochbrunner, JK Kearns, Ashley Brown, Fabio Pardo, Theophane Weber, Haichuan Yang, Pan-Pan Jiang, Berkin Akin, Zhao Fu, Marcus Wainwright, Chi Zou, Meenu Gaba, Pierre-Antoine Manzagol, Wendy Kan, Yang Song, Karina Zainullina, Rui Lin, Jeongwoo Ko, Salil Deshmukh, Apoorv Jindal, James Svensson, Divya Tyam, Heri Zhao, Christine Kaeser-Chen, Scott Baird, Pooya Moradi, Jamie Hall, Qiuchen Guo, Vincent Tsang, Bowen Liang, Fernando Pereira, Suhas Ganesh, Ivan Korotkov, Jakub Adamek, Sridhar Thiagarajan, Vinh Tran, Charles Chen, Chris Tar, Sanil Jain, Ishita Dasgupta, Taylan Bilal, David Reitter, Kai Zhao, Giulia Vezzani, Yasmin Gehman, Pulkit Mehta, Lauren Beltrone, Xerxes Dotiwalla, Sergio Guadarrama, Zaheer Abbas, Stefani Karp, Petko Georgiev, Chun-Sung Ferng, Marc Brockschmidt, Liqian Peng, Christoph Hirnschall, Vikas Verma, Yingying Bi, Ying Xiao, Avigail Dabush, Kelvin Xu, Phil Wallis, Randall Parker, Qifei Wang, Yang Xu, Ilkin Safarli, Dinesh Tewari, Yin Zhang, Seungyeon Kim, Andrea Gesmundo, Mackenzie Thomas, Sergey Levi, Ahmed Chowdhury, Kanishka Rao, Peter Garst, Sam Conway-Rahman, Helen Ran, Kay McKinney, Zhisheng Xiao, Wenhao Yu, Rohan Agrawal, Axel Stjerngren, Catalin Ionescu, Jingjing Chen, Vivek Sharma, Justin Chiu, Fei Liu, Ken Franko, Clayton Sanford, Xingyu Cai, Paul Michel, Sanjay Ganapathy, Jane Labanowski, Zachary Garrett, Ben Vargas, Sean Sun, Bryan Gale, Thomas Buschmann, Guillaume Desjardins, Nimesh Ghelani, Palak Jain, Mudit Verma, Chulayuth Asawaroengchai, Julian Eisenschlos, Jitendra Harlalka, Hideto Kazawa, Don Metzler, Joshua Howland, Ying Jian, Jake Ades, Viral Shah, Tynan Gangwani, Seungji Lee, Roman Ring, Steven M. Hernandez, Dean Reich, Amer Sinha, Ashutosh Sathe, Joe Kovac, Ashleah Gill, Ajay Kannan, Andrea D'olimpio, Martin Sevenich, Jay Whang, Been Kim, Khe Chai Sim, Jilin Chen, Jiageng Zhang, Shuba Lall, Yossi Matias, Bill Jia, Abe Friesen, Sara Nasso, Ashish Thapliyal, Bryan Perozzi, Ting Yu, Anna Shekhawat, Safeen Huda, Peter Grabowski, Eric Wang, Ashwin Sreevatsa, Hilal Dib, Mehadi Hassen, Parker Schuh, Vedrana Milutinovic, Chris Welty, Michael Quinn, Ali Shah, Bangju Wang, Gabe Barth-Maron, Justin Frye, Natalie Axelsson, Tao Zhu, Yukun Ma, Irene Giannoumis, Hanie Sedghi, Chang Ye, Yi Luan, Kevin Aydin, Bilva Chandra, Vivek Sampathkumar, Ronny Huang, Victor Lavrenko, Ahmed Eleryan, Zhi Hong, Steven Hansen, Sara Mc Carthy, Bidisha Samanta, Domagoj Ćevid, Xin Wang, Fangtao Li, Michael Voznesensky, Matt Hoffman, Andreas Terzis, Vikash Sehwag, Gil Fidel, Luheng He, Mu Cai, Yanzhang He, Alex Feng, Martin Nikoltchev, Samrat Phatale, Jason Chase, Rory Lawton, Ming Zhang, Tom Ouyang, Manuel Tragut, Mehdi Hafezi Manshadi, Arjun Narayanan, Jiaming Shen, Xu Gao, Tolga Bolukbasi, Nick Roy, Xin Li, Daniel Golovin, Liviu Panait, Zhen Qin, Guangxing Han, Thomas Anthony, Sneha Kudugunta, Viorica Patraucean, Aniket Ray, Xinyun Chen, Xiaochen Yang, Tanuj Bhatia, Pranav Talluri, Alex Morris, Andrija Ražnatović, Bethanie Brownfield, James An, Sheng Peng, Patrick Kane, Ce Zheng, Nico Duduta, Joshua Kessinger, James Noraky, Siqi Liu, Keran Rong, Petar Veličković, Keith Rush, Alex Goldin, Fanny Wei, Shiva Mohan Reddy Garlapati, Caroline Pantofaru, Okwan Kwon, Jianmo Ni, Eric Noland, Julia Di Trapani, Françoise Beaufays, Abhijit Guha Roy, Yinlam Chow, Aybuke Turker, Geoffrey Cideron, Lantao Mei, Jon Clark, Qingyun Dou, Matko Bošnjak, Ralph Leith, Yuqing Du, Amir Yazdanbakhsh, Milad Nasr, Chester Kwak, Suraj Satishkumar Sheth, Alex Kaskasoli, Ankesh Anand, Balaji Lakshminarayanan, Sammy Jerome, David Bieber, Chun-Te Chu, Alexandre Senges, Tianxiao Shen, Mukund Sridhar, Ndaba Ndebele, Benjamin Beyret, Shakir Mohamed, Mia Chen, Markus Freitag, Jiaxian Guo, Luyang Liu, Paul Roit, Heng Chen, Shen Yan, Tom Stone, JD Co-Reyes, Jeremy Cole, Salvatore Scellato, Shekoofeh Azizi, Hadi Hashemi, Alicia Jin, Anand Iyer, Marcella Valentine, András György, Arun Ahuja, Daniel Hernandez Diaz, Chen-Yu Lee, Nathan Clement, Weize Kong, Drew Garmon, Ishaan Watts, Kush Bhatia, Khyatti Gupta, Matt Miecnikowski, Hugo Vallet, Ankur Taly, Edward Loper, Saket Joshi, James Atwood, Jo Chick, Mark Collier, Fotis Iliopoulos, Ryan Trostle, Beliz Gunel, Ramiro Leal-Cavazos, Arnar Mar Hrafnkelsson, Michael Guzman, Xiaoen Ju, Andy Forbes, Jesse Emond, Kushal Chauhan, Ben Caine, Li Xiao, Wenjun Zeng, Alexandre Moufarek, Daniel Murphy, Maya Meng, Nitish Gupta, Felix Riedel, Anil Das, Elijah Lawal, Shashi Narayan, Tiberiu Sosea, James Swirhun, Linda Friso, Behnam Neyshabur, Jing Lu, Sertan Girgin, Michael Wunder, Edouard Yvinec, Aroonalok Pyne, Victor Carbune, Shruti Rijhwani, Yang Guo, Tulsee Doshi, Anton Briukhov, Max Bain, Ayal Hitron, Xuanhui Wang, Ashish Gupta, Ke Chen, Cosmo Du, Weiyang Zhang, Dhruv Shah, Arjun Akula, Max Dylla, Ashyana Kachra, Weicheng Kuo, Tingting Zou, Lily Wang, Luyao Xu, Jifan Zhu, Justin Snyder, Sachit Menon, Orhan Firat, Igor Mordatch, Yuan Yuan, Natalia Ponomareva, Rory Blevins, Lawrence Moore, Weijun Wang, Phil Chen, Martin Scholz, Artur Dwornik, Jason Lin, Sicheng Li, Diego Antognini, Te I, Xiaodan Song, Matt Miller, Uday Kalra, Adam Raveret, Oscar Akerlund, Felix Wu, Andrew Nystrom, Namrata Godbole, Tianqi Liu, Hannah DeBalsi, Jewel Zhao, Buhuang Liu, Avi Caciularu, Lauren Lax, Urvashi Khandelwal, Victoria Langston, Eric Bailey, Silvio Lattanzi, Yufei Wang, Neel Kovelamudi, Sneha Mondal, Guru Guruganesh, Nan Hua, Ofir Roval, Paweł Wesołowski, Rishikesh Ingale, Jonathan Halcrow, Tim Sohn, Christof Angermueller, Bahram Raad, Eli Stickgold, Eva Lu, Alec Kosik, Jing Xie, Timothy Lillicrap, Austin Huang, Lydia Lihui Zhang, Dominik Paulus, Clement 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Clusterability as an Alternative to Anchor Points When Learning with Noisy Labels

Zhaowei Zhu, Yiwen Song, Yang Liu

The label noise transition matrix, characterizing the probabilities of a training instance being wrongly annotated, is crucial to designing popular solutions to learning with noisy labels. Existing works heavily rely on finding "anchor points" or their approximates, defined as instances belonging to a particular class almost surely. Nonetheless, finding anchor points remains a non-trivial task, and the estimation accuracy is also often throttled by the number of available anchor points. In this paper, we propose an alternative option to the above task. Our main contribution is the discovery of an efficient estimation procedure based on a clusterability condition. We prove that with clusterable representations of features, using up to third-order consensuses of noisy labels among neighbor representations is sufficient to estimate a unique transition matrix. Compared with methods using anchor points, our approach uses substantially more instances and benefits from a much better sample complexity. We demonstrate the estimation accuracy and advantages of our estimates using both synthetic noisy labels (on CIFAR-10/100) and real human-level noisy labels (on Clothing1M and our self-collected human-annotated CIFAR-10). Our code and human-level noisy CIFAR-10 labels are available at https://github.com/UCSC-REAL/HOC.