pwc-archive/papers-with-abstracts huggingface dataset:
Magma: A Foundation Model for Multimodal AI Agents
Authors: Jianwei Yang, Reuben Tan, Qianhui Wu, Ruijie Zheng, Baolin Peng, Yongyuan Liang, Yu Gu, Mu Cai, Seonghyeon Ye, Joel Jang, Yuquan Deng, Lars Liden, Jianfeng Gao
Date: 2025-02-18 00:00:00
Proceeding: CVPR 2025 1
Tasks: Autonomous Web Navigation, Image to text, model, Robot Manipulation

We present Magma, a foundation model that serves multimodal AI agentic tasks in both the digital and physical worlds. Magma is a significant extension of vision-language (VL) models in that it not only retains the VL understanding ability (verbal intelligence) of the latter, but is also equipped with the ability to plan and act in the visual-spatial world (spatial-temporal intelligence) and complete agentic tasks ranging from UI navigation to robot manipulation. To endow the agentic capabilities, Magma is pretrained on large amounts of heterogeneous datasets spanning from images, videos to robotics data, where the actionable visual objects (e.g., clickable buttons in GUI) in images are labeled by Set-of-Mark (SoM) for action grounding, and the object movements (e.g., the trace of human hands or robotic arms) in videos are labeled by Trace-of-Mark (ToM) for action planning. Extensive experiments show that SoM and ToM reach great synergy and facilitate the acquisition of spatial-temporal intelligence for our Magma model, which is fundamental to a wide range of tasks as shown in Fig.1. In particular, Magma creates new state-of-the-art results on UI navigation and robotic manipulation tasks, outperforming previous models that are specifically tailored to these tasks. On image and video-related multimodal tasks, Magma also compares favorably to popular large multimodal models that are trained on much larger datasets. We make our model and code public for reproducibility at https://microsoft.github.io/Magma.
HOMIE: Humanoid Loco-Manipulation with Isomorphic Exoskeleton Cockpit
Authors: Qingwei Ben, Feiyu Jia, Jia Zeng, Junting Dong, Dahua Lin, Jiangmiao Pang
Date: 2025-02-18 00:00:00
Tasks: Imitation Learning

Generalizable humanoid loco-manipulation poses significant challenges, requiring coordinated whole-body control and precise, contact-rich object manipulation. To address this, this paper introduces HOMIE, a semi-autonomous teleoperation system that combines a reinforcement learning policy for body control mapped to a pedal, an isomorphic exoskeleton arm for arm control, and motion-sensing gloves for hand control, forming a unified cockpit to freely operate humanoids and establish a data flywheel. The policy incorporates novel designs, including an upper-body pose curriculum, a height-tracking reward, and symmetry utilization. These features enable the system to perform walking and squatting to specific heights while seamlessly adapting to arbitrary upper-body poses. The exoskeleton, by eliminating the reliance on inverse dynamics, delivers faster and more precise arm control. The gloves utilize Hall sensors instead of servos, allowing even compact devices to achieve 15 or more degrees of freedom and freely adapt to any model of dexterous hands. Compared to previous teleoperation systems, HOMIE stands out for its exceptional efficiency, completing tasks in half the time; its expanded working range, allowing users to freely reach high and low areas as well as interact with any objects; and its affordability, with a price of just $500. The system is fully open-source, demos and code can be found in our https://homietele.github.io/.
Towards a Design Guideline for RPA Evaluation: A Survey of Large Language Model-Based Role-Playing Agents
Authors: Chaoran Chen, Bingsheng Yao, Ruishi Zou, Wenyue Hua, Weimin Lyu, Yanfang Ye, Toby Jia-Jun Li, Dakuo Wang
Date: 2025-02-18 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

Role-Playing Agent (RPA) is an increasingly popular type of LLM Agent that simulates human-like behaviors in a variety of tasks. However, evaluating RPAs is challenging due to diverse task requirements and agent designs. This paper proposes an evidence-based, actionable, and generalizable evaluation design guideline for LLM-based RPA by systematically reviewing 1,676 papers published between Jan. 2021 and Dec. 2024. Our analysis identifies six agent attributes, seven task attributes, and seven evaluation metrics from existing literature. Based on these findings, we present an RPA evaluation design guideline to help researchers develop more systematic and consistent evaluation methods.
Towards Adaptive Feedback with AI: Comparing the Feedback Quality of LLMs and Teachers on Experimentation Protocols
Authors: Kathrin Seßler, Arne Bewersdorff, Claudia Nerdel, Enkelejda Kasneci
Date: 2025-02-18 00:00:00

Effective feedback is essential for fostering students' success in scientific inquiry. With advancements in artificial intelligence, large language models (LLMs) offer new possibilities for delivering instant and adaptive feedback. However, this feedback often lacks the pedagogical validation provided by real-world practitioners. To address this limitation, our study evaluates and compares the feedback quality of LLM agents with that of human teachers and science education experts on student-written experimentation protocols. Four blinded raters, all professionals in scientific inquiry and science education, evaluated the feedback texts generated by 1) the LLM agent, 2) the teachers and 3) the science education experts using a five-point Likert scale based on six criteria of effective feedback: Feed Up, Feed Back, Feed Forward, Constructive Tone, Linguistic Clarity, and Technical Terminology. Our results indicate that LLM-generated feedback shows no significant difference to that of teachers and experts in overall quality. However, the LLM agent's performance lags in the Feed Back dimension, which involves identifying and explaining errors within the student's work context. Qualitative analysis highlighted the LLM agent's limitations in contextual understanding and in the clear communication of specific errors. Our findings suggest that combining LLM-generated feedback with human expertise can enhance educational practices by leveraging the efficiency of LLMs and the nuanced understanding of educators.
Efficient Machine Translation Corpus Generation: Integrating Human-in-the-Loop Post-Editing with Large Language Models
Authors: Kamer Ali Yuksel, Ahmet Gunduz, Abdul Baseet Anees, Hassan Sawaf
Date: 2025-02-18 00:00:00
Tasks: Machine Translation, Translation

This paper introduces an advanced methodology for machine translation (MT) corpus generation, integrating semi-automated, human-in-the-loop post-editing with large language models (LLMs) to enhance efficiency and translation quality. Building upon previous work that utilized real-time training of a custom MT quality estimation metric, this system incorporates novel LLM features such as Enhanced Translation Synthesis and Assisted Annotation Analysis, which improve initial translation hypotheses and quality assessments, respectively. Additionally, the system employs LLM-Driven Pseudo Labeling and a Translation Recommendation System to reduce human annotator workload in specific contexts. These improvements not only retain the original benefits of cost reduction and enhanced post-edit quality but also open new avenues for leveraging cutting-edge LLM advancements. The project's source code is available for community use, promoting collaborative developments in the field. The demo video can be accessed here.
UXAgent: An LLM Agent-Based Usability Testing Framework for Web Design
Authors: Yuxuan Lu, Bingsheng Yao, Hansu Gu, Jing Huang, Jessie Wang, Yang Li, Jiri Gesi, Qi He, Toby Jia-Jun Li, Dakuo Wang
Date: 2025-02-18 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

Usability testing is a fundamental yet challenging (e.g., inflexible to iterate the study design flaws and hard to recruit study participants) research method for user experience (UX) researchers to evaluate a web design. Recent advances in Large Language Model-simulated Agent (LLM-Agent) research inspired us to design UXAgent to support UX researchers in evaluating and reiterating their usability testing study design before they conduct the real human subject study. Our system features an LLM-Agent module and a universal browser connector module so that UX researchers can automatically generate thousands of simulated users to test the target website. The results are shown in qualitative (e.g., interviewing how an agent thinks ), quantitative (e.g., # of actions), and video recording formats for UX researchers to analyze. Through a heuristic user evaluation with five UX researchers, participants praised the innovation of our system but also expressed concerns about the future of LLM Agent-assisted UX study.
Explainable AI-Driven Neural Activity Analysis in Parkinsonian Rats under Electrical Stimulation
Authors: Jibum Kim, Hanseul Choi, Gaeun Kim, Sunggu Yang, Eunha Baeg, Donggue Kim, Seongwon Jin, Sangwon Byun
Date: 2025-02-18 00:00:00
Tasks: Explainable artificial intelligence, Explainable Artificial Intelligence (XAI)

Parkinson's disease (PD) is a neurodegenerative disorder characterized by motor dysfunction and abnormal neural oscillations. These symptoms can be modulated through electrical stimulation. Traditional neural activity analysis in PD has typically relied on statistical methods, which often introduce bias owing to the need for expert-driven feature extraction. To address this limitation, we explore an explainable artificial intelligence (XAI) approach to analyze neural activity in Parkinsonian rats receiving electrical stimulation. Electrocorticogram (ECoG) signals were collected before and after electrical stimulation using graphene-based electrodes that enable less-invasive monitoring and stimulation in PD. EEGNet, a convolutional neural network, classified these ECoG signals into pre- and post-stimulation states. We applied layer-wise relevance propagation, an XAI technique, to identify key neural inputs contributing to the model's decisions, incorporating the spatial electrode information matched to the cortex map. The XAI analysis highlighted area-specific importance in beta and gamma frequency bands, which could not be detected through mean comparison analyses relying on feature extraction. These findings demonstrate the potential of XAI in analyzing neural dynamics in neurodegenerative disorders such as PD, suggesting that the integration of graphene-based electrodes with advanced deep learning models offers a promising solution for real-time PD monitoring and therapy.
SPEX: Scaling Feature Interaction Explanations for LLMs
Authors: Justin Singh Kang, Landon Butler, Abhineet Agarwal, Yigit Efe Erginbas, Ramtin Pedarsani, Kannan Ramchandran, Bin Yu
Date: 2025-02-19 00:00:00

Large language models (LLMs) have revolutionized machine learning due to their ability to capture complex interactions between input features. Popular post-hoc explanation methods like SHAP provide marginal feature attributions, while their extensions to interaction importances only scale to small input lengths ($\approx 20$). We propose Spectral Explainer (SPEX), a model-agnostic interaction attribution algorithm that efficiently scales to large input lengths ($\approx 1000)$. SPEX exploits underlying natural sparsity among interactions -- common in real-world data -- and applies a sparse Fourier transform using a channel decoding algorithm to efficiently identify important interactions. We perform experiments across three difficult long-context datasets that require LLMs to utilize interactions between inputs to complete the task. For large inputs, SPEX outperforms marginal attribution methods by up to 20% in terms of faithfully reconstructing LLM outputs. Further, SPEX successfully identifies key features and interactions that strongly influence model output. For one of our datasets, HotpotQA, SPEX provides interactions that align with human annotations. Finally, we use our model-agnostic approach to generate explanations to demonstrate abstract reasoning in closed-source LLMs (GPT-4o mini) and compositional reasoning in vision-language models.
Generative Video Semantic Communication via Multimodal Semantic Fusion with Large Model
Authors: Hang Yin, Li Qiao, Yu Ma, Shuo Sun, Kan Li, Zhen Gao, Dusit Niyato
Date: 2025-02-19 00:00:00
Tasks: Semantic Communication, Video Reconstruction

Despite significant advancements in traditional syntactic communications based on Shannon's theory, these methods struggle to meet the requirements of 6G immersive communications, especially under challenging transmission conditions. With the development of generative artificial intelligence (GenAI), progress has been made in reconstructing videos using high-level semantic information. In this paper, we propose a scalable generative video semantic communication framework that extracts and transmits semantic information to achieve high-quality video reconstruction. Specifically, at the transmitter, description and other condition signals (e.g., first frame, sketches, etc.) are extracted from the source video, functioning as text and structural semantics, respectively. At the receiver, the diffusion-based GenAI large models are utilized to fuse the semantics of the multiple modalities for reconstructing the video. Simulation results demonstrate that, at an ultra-low channel bandwidth ratio (CBR), our scheme effectively captures semantic information to reconstruct videos aligned with human perception under different signal-to-noise ratios. Notably, the proposed ``First Frame+Desc." scheme consistently achieves CLIP score exceeding 0.92 at CBR = 0.0057 for SNR > 0 dB. This demonstrates its robust performance even under low SNR conditions.
Deep-Unfolded Massive Grant-Free Transmission in Cell-Free Wireless Communication Systems
Authors: Gangle Sun, Mengyao Cao, Wenjin Wang, Wei Xu, Christoph Studer
Date: 2025-02-19 00:00:00

Grant-free transmission and cell-free communication are vital in improving coverage and quality-of-service for massive machine-type communication. This paper proposes a novel framework of joint active user detection, channel estimation, and data detection (JACD) for massive grant-free transmission in cell-free wireless communication systems. We formulate JACD as an optimization problem and solve it approximately using forward-backward splitting. To deal with the discrete symbol constraint, we relax the discrete constellation to its convex hull and propose two approaches that promote solutions from the constellation set. To reduce complexity, we replace costly computations with approximate shrinkage operations and approximate posterior mean estimator computations. To improve active user detection (AUD) performance, we introduce a soft-output AUD module that considers both the data estimates and channel conditions. To jointly optimize all algorithm hyper-parameters and to improve JACD performance, we further deploy deep unfolding together with a momentum strategy, resulting in two algorithms called DU-ABC and DU-POEM. Finally, we demonstrate the efficacy of the proposed JACD algorithms via extensive system simulations.
A new pathway to generative artificial intelligence by minimizing the maximum entropy
Authors: Mattia Miotto, Lorenzo Monacelli
Date: 2025-02-18 00:00:00
Tasks: Benchmarking

Generative artificial intelligence revolutionized society. Current models are trained by minimizing the distance between the produced data and the training set. Consequently, development is plateauing as they are intrinsically data-hungry and challenging to direct during the generative process. To overcome these limitations, we introduce a paradigm shift through a framework where we do not fit the training set but find the most informative yet least noisy representation of the data simultaneously minimizing the entropy to reduce noise and maximizing it to remain unbiased via adversary training. The result is a general physics-driven model, which is data-efficient and flexible, permitting to control and influence the generative process. Benchmarking shows that our approach outperforms variational autoencoders. We demonstrate the methods effectiveness in generating images, even with limited training data, and its unprecedented capability to customize the generation process a posteriori without any fine-tuning or retraining
Evidence of Replica Symmetry Breaking under the Nishimori conditions in epidemic inference on graphs
Authors: Alfredo Braunstein, Louise Budzynski, Matteo Mariani, Federico Ricci-Tersenghi
Date: 2025-02-18 00:00:00
Tasks: Bayesian Inference

In Bayesian inference, computing the posterior distribution from the data is typically a non-trivial problem, which usually requires approximations such as mean-field approaches or numerical methods, like the Monte Carlo Markov Chain. Being a high-dimensional distribution over a set of correlated variables, the posterior distribution can undergo the notorious replica symmetry breaking transition. When it happens, several mean-field methods and virtually every Monte Carlo scheme can not provide a reasonable approximation to the posterior and its marginals. Replica symmetry is believed to be guaranteed whenever the data is generated with known prior and likelihood distributions, namely under the so-called Nishimori conditions. In this paper, we break this belief, by providing a counter-example showing that, under the Nishimori conditions, replica symmetry breaking arises. Introducing a simple, geometrical model that can be thought of as a patient zero retrieval problem in a highly infectious regime of the epidemic Susceptible-Infectious model, we show that under the Nishimori conditions, there is evidence of replica symmetry breaking. We achieve this result by computing the instability of the replica symmetric cavity method toward the one step replica symmetry broken phase. The origin of this phenomenon -- replica symmetry breaking under the Nishimori conditions -- is likely due to the correlated disorder appearing in the epidemic models.
SearchRAG: Can Search Engines Be Helpful for LLM-based Medical Question Answering?
Authors: Yucheng Shi, Tianze Yang, Canyu Chen, Quanzheng Li, Tianming Liu, Xiang Li, Ninghao Liu
Date: 2025-02-18 00:00:00
Tasks: Medical Question Answering, Question Answering, RAG, Retrieval-augmented Generation

Large Language Models (LLMs) have shown remarkable capabilities in general domains but often struggle with tasks requiring specialized knowledge. Conventional Retrieval-Augmented Generation (RAG) techniques typically retrieve external information from static knowledge bases, which can be outdated or incomplete, missing fine-grained clinical details essential for accurate medical question answering. In this work, we propose SearchRAG, a novel framework that overcomes these limitations by leveraging real-time search engines. Our method employs synthetic query generation to convert complex medical questions into search-engine-friendly queries and utilizes uncertainty-based knowledge selection to filter and incorporate the most relevant and informative medical knowledge into the LLM's input. Experimental results demonstrate that our method significantly improves response accuracy in medical question answering tasks, particularly for complex questions requiring detailed and up-to-date knowledge.
A Neural Difference-of-Entropies Estimator for Mutual Information
Authors: Haoran Ni, Martin Lotz
Date: 2025-02-18 00:00:00

Estimating Mutual Information (MI), a key measure of dependence of random quantities without specific modelling assumptions, is a challenging problem in high dimensions. We propose a novel mutual information estimator based on parametrizing conditional densities using normalizing flows, a deep generative model that has gained popularity in recent years. This estimator leverages a block autoregressive structure to achieve improved bias-variance trade-offs on standard benchmark tasks.
Symmetric Rank-One Quasi-Newton Methods for Deep Learning Using Cubic Regularization
Authors: Aditya Ranganath, Mukesh Singhal, Roummel Marcia
Date: 2025-02-17 00:00:00
Tasks: Computational Efficiency

Stochastic gradient descent and other first-order variants, such as Adam and AdaGrad, are commonly used in the field of deep learning due to their computational efficiency and low-storage memory requirements. However, these methods do not exploit curvature information. Consequently, iterates can converge to saddle points or poor local minima. On the other hand, Quasi-Newton methods compute Hessian approximations which exploit this information with a comparable computational budget. Quasi-Newton methods re-use previously computed iterates and gradients to compute a low-rank structured update. The most widely used quasi-Newton update is the L-BFGS, which guarantees a positive semi-definite Hessian approximation, making it suitable in a line search setting. However, the loss functions in DNNs are non-convex, where the Hessian is potentially non-positive definite. In this paper, we propose using a limited-memory symmetric rank-one quasi-Newton approach which allows for indefinite Hessian approximations, enabling directions of negative curvature to be exploited. Furthermore, we use a modified adaptive regularized cubics approach, which generates a sequence of cubic subproblems that have closed-form solutions with suitable regularization choices. We investigate the performance of our proposed method on autoencoders and feed-forward neural network models and compare our approach to state-of-the-art first-order adaptive stochastic methods as well as other quasi-Newton methods.x
Decentralized Planning Using Probabilistic Hyperproperties
Authors: Francesco Pontiggia, Filip Macák, Roman Andriushchenko, Michele Chiari, Milan Češka
Date: 2025-02-19 00:00:00

Multi-agent planning under stochastic dynamics is usually formalised using decentralized (partially observable) Markov decision processes ( MDPs) and reachability or expected reward specifications. In this paper, we propose a different approach: we use an MDP describing how a single agent operates in an environment and probabilistic hyperproperties to capture desired temporal objectives for a set of decentralized agents operating in the environment. We extend existing approaches for model checking probabilistic hyperproperties to handle temporal formulae relating paths of different agents, thus requiring the self-composition between multiple MDPs. Using several case studies, we demonstrate that our approach provides a flexible and expressive framework to broaden the specification capabilities with respect to existing planning techniques. Additionally, we establish a close connection between a subclass of probabilistic hyperproperties and planning for a particular type of Dec-MDPs, for both of which we show undecidability. This lays the ground for the use of existing decentralized planning tools in the field of probabilistic hyperproperty verification.