pwc-archive/papers-with-abstracts huggingface dataset:
Beyond Matryoshka: Revisiting Sparse Coding for Adaptive Representation
Authors: Tiansheng Wen, Yifei Wang, Zequn Zeng, Zhong Peng, Yudi Su, Xinyang Liu, Bo Chen, Hongwei Liu, Stefanie Jegelka, Chenyu You
Date: 2025-03-03 00:00:00
Tasks: Representation Learning, Retrieval

Many large-scale systems rely on high-quality deep representations (embeddings) to facilitate tasks like retrieval, search, and generative modeling. Matryoshka Representation Learning (MRL) recently emerged as a solution for adaptive embedding lengths, but it requires full model retraining and suffers from noticeable performance degradations at short lengths. In this paper, we show that sparse coding offers a compelling alternative for achieving adaptive representation with minimal overhead and higher fidelity. We propose Contrastive Sparse Representation (CSR), a method that sparsifies pre-trained embeddings into a high-dimensional but selectively activated feature space. By leveraging lightweight autoencoding and task-aware contrastive objectives, CSR preserves semantic quality while allowing flexible, cost-effective inference at different sparsity levels. Extensive experiments on image, text, and multimodal benchmarks demonstrate that CSR consistently outperforms MRL in terms of both accuracy and retrieval speed-often by large margins-while also cutting training time to a fraction of that required by MRL. Our results establish sparse coding as a powerful paradigm for adaptive representation learning in real-world applications where efficiency and fidelity are both paramount. Code is available at https://github.com/neilwen987/CSR_Adaptive_Rep
Language-Assisted Feature Transformation for Anomaly Detection
Authors: Eunggu Yun, Heonjin Ha, Yeongwoo Nam, Bryan Dongik Lee
Date: 2025-03-03 00:00:00
Tasks: Anomaly Detection

This paper introduces LAFT, a novel feature transformation method designed to incorporate user knowledge and preferences into anomaly detection using natural language. Accurately modeling the boundary of normality is crucial for distinguishing abnormal data, but this is often challenging due to limited data or the presence of nuisance attributes. While unsupervised methods that rely solely on data without user guidance are common, they may fail to detect anomalies of specific interest. To address this limitation, we propose Language-Assisted Feature Transformation (LAFT), which leverages the shared image-text embedding space of vision-language models to transform visual features according to user-defined requirements. Combined with anomaly detection methods, LAFT effectively aligns visual features with user preferences, allowing anomalies of interest to be detected. Extensive experiments on both toy and real-world datasets validate the effectiveness of our method.
Leveraging Compute-in-Memory for Efficient Generative Model Inference in TPUs
Authors: Zhantong Zhu, Hongou Li, Wenjie Ren, Meng Wu, Le Ye, Ru Huang, Tianyu Jia
Date: 2025-03-01 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

With the rapid advent of generative models, efficiently deploying these models on specialized hardware has become critical. Tensor Processing Units (TPUs) are designed to accelerate AI workloads, but their high power consumption necessitates innovations for improving efficiency. Compute-in-memory (CIM) has emerged as a promising paradigm with superior area and energy efficiency. In this work, we present a TPU architecture that integrates digital CIM to replace conventional digital systolic arrays in matrix multiply units (MXUs). We first establish a CIM-based TPU architecture model and simulator to evaluate the benefits of CIM for diverse generative model inference. Building upon the observed design insights, we further explore various CIM-based TPU architectural design choices. Up to 44.2% and 33.8% performance improvement for large language model and diffusion transformer inference, and 27.3x reduction in MXU energy consumption can be achieved with different design choices, compared to the baseline TPUv4i architecture.
POPGym Arcade: Parallel Pixelated POMDPs
Authors: Zekang Wang, Zhe He, Borong Zhang, Edan Toledo, Steven Morad
Date: 2025-03-03 00:00:00
Tasks: counterfactual, Imitation Learning, Q-Learning

We present the POPGym Arcade, a collection of hardware-accelerated, pixel-based environments with shared observation and action spaces. Each environment includes fully and partially observable variants, enabling counterfactual studies on partial observability. We also introduce mathematical tools for analyzing policies under partial observability, which reveal how agents recall past information to make decisions. Our analysis shows (1) that controlling for partial observability is critical and (2) that agents with long-term memory learn brittle policies that struggle to generalize. Finally, we demonstrate that recurrent policies can be "poisoned" by old, out-of-distribution observations, with implications for sim-to-real transfer, imitation learning, and offline reinforcement learning.
CoInD: Enabling Logical Compositions in Diffusion Models
Authors: Sachit Gaudi, Gautam Sreekumar, Vishnu Boddeti
Date: 2025-03-03 00:00:00
Tasks: Attribute

How can we learn generative models to sample data with arbitrary logical compositions of statistically independent attributes? The prevailing solution is to sample from distributions expressed as a composition of attributes' conditional marginal distributions under the assumption that they are statistically independent. This paper shows that standard conditional diffusion models violate this assumption, even when all attribute compositions are observed during training. And, this violation is significantly more severe when only a subset of the compositions is observed. We propose CoInD to address this problem. It explicitly enforces statistical independence between the conditional marginal distributions by minimizing Fisher's divergence between the joint and marginal distributions. The theoretical advantages of CoInD are reflected in both qualitative and quantitative experiments, demonstrating a significantly more faithful and controlled generation of samples for arbitrary logical compositions of attributes. The benefit is more pronounced for scenarios that current solutions relying on the assumption of conditionally independent marginals struggle with, namely, logical compositions involving the NOT operation and when only a subset of compositions are observed during training.
FABG : End-to-end Imitation Learning for Embodied Affective Human-Robot Interaction
Authors: Yanghai Zhang, Changyi Liu, Keting Fu, Wenbin Zhou, Qingdu Li, Jianwei Zhang
Date: 2025-03-03 00:00:00
Tasks: Gesture Recognition, Imitation Learning

This paper proposes FABG (Facial Affective Behavior Generation), an end-to-end imitation learning system for human-robot interaction, designed to generate natural and fluid facial affective behaviors. In interaction, effectively obtaining high-quality demonstrations remains a challenge. In this work, we develop an immersive virtual reality (VR) demonstration system that allows operators to perceive stereoscopic environments. This system ensures "the operator's visual perception matches the robot's sensory input" and "the operator's actions directly determine the robot's behaviors" - as if the operator replaces the robot in human interaction engagements. We propose a prediction-driven latency compensation strategy to reduce robotic reaction delays and enhance interaction fluency. FABG naturally acquires human interactive behaviors and subconscious motions driven by intuition, eliminating manual behavior scripting. We deploy FABG on a real-world 25-degree-of-freedom (DoF) humanoid robot, validating its effectiveness through four fundamental interaction tasks: expression response, dynamic gaze, foveated attention, and gesture recognition, supported by data collection and policy training. Project website: https://cybergenies.github.io
On Generalization Across Environments In Multi-Objective Reinforcement Learning
Authors: Jayden Teoh, Pradeep Varakantham, Peter Vamplew
Date: 2025-03-02 00:00:00
Tasks: Decision Making, Multi-Objective Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Sequential Decision Making

Real-world sequential decision-making tasks often require balancing trade-offs between multiple conflicting objectives, making Multi-Objective Reinforcement Learning (MORL) an increasingly prominent field of research. Despite recent advances, existing MORL literature has narrowly focused on performance within static environments, neglecting the importance of generalizing across diverse settings. Conversely, existing research on generalization in RL has always assumed scalar rewards, overlooking the inherent multi-objectivity of real-world problems. Generalization in the multi-objective context is fundamentally more challenging, as it requires learning a Pareto set of policies addressing varying preferences across multiple objectives. In this paper, we formalize the concept of generalization in MORL and how it can be evaluated. We then contribute a novel benchmark featuring diverse multi-objective domains with parameterized environment configurations to facilitate future studies in this area. Our baseline evaluations of state-of-the-art MORL algorithms on this benchmark reveals limited generalization capabilities, suggesting significant room for improvement. Our empirical findings also expose limitations in the expressivity of scalar rewards, emphasizing the need for multi-objective specifications to achieve effective generalization. We further analyzed the algorithmic complexities within current MORL approaches that could impede the transfer in performance from the single- to multiple-environment settings. This work fills a critical gap and lays the groundwork for future research that brings together two key areas in reinforcement learning: solving multi-objective decision-making problems and generalizing across diverse environments. We make our code available at https://github.com/JaydenTeoh/MORL-Generalization.
Precise Localization of Memories: A Fine-grained Neuron-level Knowledge Editing Technique for LLMs
Authors: Haowen Pan, Xiaozhi Wang, Yixin Cao, Zenglin Shi, Xun Yang, Juanzi Li, Meng Wang
Date: 2025-03-03 00:00:00
Tasks: knowledge editing

Knowledge editing aims to update outdated information in Large Language Models (LLMs). A representative line of study is locate-then-edit methods, which typically employ causal tracing to identify the modules responsible for recalling factual knowledge about entities. However, we find these methods are often sensitive only to changes in the subject entity, leaving them less effective at adapting to changes in relations. This limitation results in poor editing locality, which can lead to the persistence of irrelevant or inaccurate facts, ultimately compromising the reliability of LLMs. We believe this issue arises from the insufficient precision of knowledge localization. To address this, we propose a Fine-grained Neuron-level Knowledge Editing (FiNE) method that enhances editing locality without affecting overall success rates. By precisely identifying and modifying specific neurons within feed-forward networks, FiNE significantly improves knowledge localization and editing. Quantitative experiments demonstrate that FiNE efficiently achieves better overall performance compared to existing techniques, providing new insights into the localization and modification of knowledge within LLMs.
Patterning Silver Nanowire Network via the Gibbs-Thomson Effect
Authors: Hongteng Wang, Haichuan Li, Yijia Xin, Weizhen Chen, Haogen Liu, Ying Chen, Yaofei Chen, Lei Chen, Yunhan Luo, Zhe Chen, Gui-Shi Liu
Date: 2025-03-03 00:00:00

As transparent electrodes, patterned silver nanowire (AgNW) networks suffer from noticeable pattern visibility, which is an unsettled issue for practical applications such as display. Here, we introduce a Gibbs-Thomson effect (GTE)-based patterning method to effectively reduce pattern visibility. Unlike conventional top-down and bottom-up strategies that rely on selective etching, removal, or deposition of AgNWs, our approach focuses on fragmenting nanowires primarily at the junctions through the GTE. This is realized by modifying AgNWs with a compound of diphenyliodonium nitrate and silver nitrate, which aggregates into nanoparticles at the junctions of AgNWs. These nanoparticles can boost the fragmentation of nanowires at the junctions under an ultralow temperature (75{\deg}C), allow pattern transfer through a photolithographic masking operation, and enhance plasmonic welding during UV exposure. The resultant patterned electrodes have trivial differences in transmittance ({\Delta}T = 1.4%) and haze ({\Delta}H = 0.3%) between conductive and insulative regions, with high-resolution patterning size down to 10 {\mu}m. To demonstrate the practicality of this novel method, we constructed a highly transparent, optoelectrical interactive tactile e-skin using the patterned AgNW electrodes.
CLEA: Closed-Loop Embodied Agent for Enhancing Task Execution in Dynamic Environments
Authors: Mingcong Lei, Ge Wang, Yiming Zhao, Zhixin Mai, Qing Zhao, Yao Guo, Zhen Li, Shuguang Cui, Yatong Han, Jinke Ren
Date: 2025-03-02 00:00:00
Tasks: Task Planning

Large Language Models (LLMs) exhibit remarkable capabilities in the hierarchical decomposition of complex tasks through semantic reasoning. However, their application in embodied systems faces challenges in ensuring reliable execution of subtask sequences and achieving one-shot success in long-term task completion. To address these limitations in dynamic environments, we propose Closed-Loop Embodied Agent (CLEA) -- a novel architecture incorporating four specialized open-source LLMs with functional decoupling for closed-loop task management. The framework features two core innovations: (1) Interactive task planner that dynamically generates executable subtasks based on the environmental memory, and (2) Multimodal execution critic employing an evaluation framework to conduct a probabilistic assessment of action feasibility, triggering hierarchical re-planning mechanisms when environmental perturbations exceed preset thresholds. To validate CLEA's effectiveness, we conduct experiments in a real environment with manipulable objects, using two heterogeneous robots for object search, manipulation, and search-manipulation integration tasks. Across 12 task trials, CLEA outperforms the baseline model, achieving a 67.3% improvement in success rate and a 52.8% increase in task completion rate. These results demonstrate that CLEA significantly enhances the robustness of task planning and execution in dynamic environments.
SePer: Measure Retrieval Utility Through The Lens Of Semantic Perplexity Reduction
Authors: Lu Dai, Yijie Xu, Jinhui Ye, Hao liu, Hui Xiong
Date: 2025-03-03 00:00:00
Tasks: RAG, Retrieval, Retrieval-augmented Generation

Large Language Models (LLMs) have demonstrated improved generation performance by incorporating externally retrieved knowledge, a process known as retrieval-augmented generation (RAG). Despite the potential of this approach, existing studies evaluate RAG effectiveness by 1) assessing retrieval and generation components jointly, which obscures retrieval's distinct contribution, or 2) examining retrievers using traditional metrics such as NDCG, which creates a gap in understanding retrieval's true utility in the overall generation process. To address the above limitations, in this work, we introduce an automatic evaluation method that measures retrieval quality through the lens of information gain within the RAG framework. Specifically, we propose Semantic Perplexity (SePer), a metric that captures the LLM's internal belief about the correctness of the retrieved information. We quantify the utility of retrieval by the extent to which it reduces semantic perplexity post-retrieval. Extensive experiments demonstrate that SePer not only aligns closely with human preferences but also offers a more precise and efficient evaluation of retrieval utility across diverse RAG scenarios.
HiMo: High-Speed Objects Motion Compensation in Point Clouds
Authors: Qingwen Zhang, Ajinkya Khoche, Yi Yang, Li Ling, Sina Sharif Mansouri, Olov Andersson, Patric Jensfelt
Date: 2025-03-02 00:00:00
Tasks: Autonomous Vehicles, Motion Compensation, Scene Flow Estimation, Semantic Segmentation

LiDAR point cloud is essential for autonomous vehicles, but motion distortions from dynamic objects degrade the data quality. While previous work has considered distortions caused by ego motion, distortions caused by other moving objects remain largely overlooked, leading to errors in object shape and position. This distortion is particularly pronounced in high-speed environments such as highways and in multi-LiDAR configurations, a common setup for heavy vehicles. To address this challenge, we introduce HiMo, a pipeline that repurposes scene flow estimation for non-ego motion compensation, correcting the representation of dynamic objects in point clouds. During the development of HiMo, we observed that existing self-supervised scene flow estimators often produce degenerate or inconsistent estimates under high-speed distortion. We further propose SeFlow++, a real-time scene flow estimator that achieves state-of-the-art performance on both scene flow and motion compensation. Since well-established motion distortion metrics are absent in the literature, we introduce two evaluation metrics: compensation accuracy at a point level and shape similarity of objects. We validate HiMo through extensive experiments on Argoverse 2, ZOD, and a newly collected real-world dataset featuring highway driving and multi-LiDAR-equipped heavy vehicles. Our findings show that HiMo improves the geometric consistency and visual fidelity of dynamic objects in LiDAR point clouds, benefiting downstream tasks such as semantic segmentation and 3D detection. See https://kin-zhang.github.io/HiMo for more details.
Compare different SG-Schemes based on large least square problems
Authors: Ramkrishna Acharya
Date: 2025-03-03 00:00:00

This study reviews some of the popular stochastic gradient-based schemes based on large least-square problems. These schemes, often called optimizers in machine learning play a crucial role in finding better parameters of a model. Hence this study focuses on viewing such optimizers with different hyper-parameters and analyzing them based on least square problems. Codes that produced results in this work are available on https://github.com/q-viper/gradients-based-methods-on-large-least-square.
LADDER: Self-Improving LLMs Through Recursive Problem Decomposition
Authors: Toby Simonds, Akira Yoshiyama
Date: 2025-03-02 00:00:00
Tasks: Problem Decomposition, reinforcement-learning, Reinforcement Learning

We introduce LADDER (Learning through Autonomous Difficulty-Driven Example Recursion), a framework enabling LLMs to autonomously improve their problem-solving capabilities through self-guided learning. By recursively generating and solving progressively simpler variants of complex problems, LADDER enables models to progressively learn through reinforcement learning how to solve harder problems. This self-improvement process is guided by verifiable reward signals, allowing the model to assess its solutions. Unlike prior approaches requiring curated datasets or human feedback, LADDER leverages the model's own capabilities to easier variants of sample questions. We demonstrate LADDER's effectiveness on mathematical integration tasks, where it improves a Llama 3B model's accuracy from 1\% to 82\% on undergraduate-level problems and enables a 7B parameter model to achieve state-of-the-art performance (70\%) on the MIT Integration Bee examination for it's model size. We also introduce TTRL (Test-Time Reinforcement Learning), a method that generates variants of test problems at inference time and applies reinforcement learning to further improve performance. By further creating and solving related problems during testing, TTRL enables the 7B model to achieve a score of 85\%, surpassing o1. These results showcase how strategic self-directed learning can achieve significant capability improvements without relying on architectural scaling or human supervision.
WAVIE: A Modular and Open-Source Python Implementation for Fully Automated Digitisation of Paper Electrocardiograms
Authors: Mathilde A Verlyck, Joshua R Dillon, Stephen A Creamer, Debbie Zhao
Date: 2024-12-20 00:00:00
Proceeding: Computing in Cardiology 2024 12
Tasks: ECG Digitization, object-detection, Object Detection

The electrocardiogram (ECG) is a ubiquitous tool for the assessment of heart disease. While considerable effort has been directed at ECG digitization to facilitate artificial intelligence applications, limitations persist in the generalizability of existing methods. As part of the ‘Digitization and Classification of ECG Images: The George B. Moody PhysioNet Challenge 2024’, we present WAVIE, a fully-automated, modular, and open-source framework for ECG digitization to handle the heterogeneity of real-world data. Using the PTB-XL dataset, synthetic paper ECGs were generated with known variations and artifacts. Our team, wavie ABI, developed a three-stage framework consisting of deep-learning models for orientation correction, object detection, and waveform extraction. Inference on the hidden test set for the digitisation task produced a mean signal-to-noise ratio (SNR) of 5.469 (ranked 3rd of 16 teams). WAVIE provides a comprehensive and generalizable baseline that can be reconfigured and fine-tuned for specific ECG digitisation tasks, ensuring adaptability for future research applications.
AI-Driven Relocation Tracking in Dynamic Kitchen Environments
Authors: Arash Nasr Esfahani, Hamed Hosseini, Mehdi Tale Masouleh, Ahmad Kalhor, Hedieh Sajedi
Date: 2025-03-03 00:00:00
Tasks: 2D Object Detection, 3D Reconstruction, Indoor Localization, Navigate, Object, Object Tracking, Optical Flow Estimation

As smart homes become more prevalent in daily life, the ability to understand dynamic environments is essential which is increasingly dependent on AI systems. This study focuses on developing an intelligent algorithm which can navigate a robot through a kitchen, recognizing objects, and tracking their relocation. The kitchen was chosen as the testing ground due to its dynamic nature as objects are frequently moved, rearranged and replaced. Various techniques, such as SLAM feature-based tracking and deep learning-based object detection (e.g., Faster R-CNN), are commonly used for object tracking. Additionally, methods such as optical flow analysis and 3D reconstruction have also been used to track the relocation of objects. These approaches often face challenges when it comes to problems such as lighting variations and partial occlusions, where parts of the object are hidden in some frames but visible in others. The proposed method in this study leverages the YOLOv5 architecture, initialized with pre-trained weights and subsequently fine-tuned on a custom dataset. A novel method was developed, introducing a frame-scoring algorithm which calculates a score for each object based on its location and features within all frames. This scoring approach helps to identify changes by determining the best-associated frame for each object and comparing the results in each scene, overcoming limitations seen in other methods while maintaining simplicity in design. The experimental results demonstrate an accuracy of 97.72%, a precision of 95.83% and a recall of 96.84% for this algorithm, which highlights the efficacy of the model in detecting spatial changes.