pwc-archive/papers-with-abstracts huggingface dataset:
Independent Learning in Performative Markov Potential Games
Authors: Rilind Sahitaj, Paulius Sasnauskas, Yiğit Yalın, Debmalya Mandal, Goran Radanović
Date: 2025-04-29 00:00:00

Performative Reinforcement Learning (PRL) refers to a scenario in which the deployed policy changes the reward and transition dynamics of the underlying environment. In this work, we study multi-agent PRL by incorporating performative effects into Markov Potential Games (MPGs). We introduce the notion of a performatively stable equilibrium (PSE) and show that it always exists under a reasonable sensitivity assumption. We then provide convergence results for state-of-the-art algorithms used to solve MPGs. Specifically, we show that independent policy gradient ascent (IPGA) and independent natural policy gradient (INPG) converge to an approximate PSE in the best-iterate sense, with an additional term that accounts for the performative effects. Furthermore, we show that INPG asymptotically converges to a PSE in the last-iterate sense. As the performative effects vanish, we recover the convergence rates from prior work. For a special case of our game, we provide finite-time last-iterate convergence results for a repeated retraining approach, in which agents independently optimize a surrogate objective. We conduct extensive experiments to validate our theoretical findings.
Towards LLM-Centric Multimodal Fusion: A Survey on Integration Strategies and Techniques
Authors: Jisu An, Junseok Lee, Jeoungeun Lee, Yongseok Son
Date: 2025-06-05 00:00:00
Tasks: cross-modal alignment, Large Language Model, Multimodal Large Language Model, Representation Learning

The rapid progress of Multimodal Large Language Models(MLLMs) has transformed the AI landscape. These models combine pre-trained LLMs with various modality encoders. This integration requires a systematic understanding of how different modalities connect to the language backbone. Our survey presents an LLM-centric analysis of current approaches. We examine methods for transforming and aligning diverse modal inputs into the language embedding space. This addresses a significant gap in existing literature. We propose a classification framework for MLLMs based on three key dimensions. First, we examine architectural strategies for modality integration. This includes both the specific integration mechanisms and the fusion level. Second, we categorize representation learning techniques as either joint or coordinate representations. Third, we analyze training paradigms, including training strategies and objective functions. By examining 125 MLLMs developed between 2021 and 2025, we identify emerging patterns in the field. Our taxonomy provides researchers with a structured overview of current integration techniques. These insights aim to guide the development of more robust multimodal integration strategies for future models built on pre-trained foundations.
Does ChatGPT Comprehend the Place Value in Numbers When Solving Math Word Problems?
Authors: Jisu An, Junseok Lee, Gahgene Gweon
Date: 2023-06-03 00:00:00
Proceeding: The 24th International Conference on Artificial Intelligence in Education 2023 6
Tasks: Math, Math Word Problem Solving

With the recent advancements in GPT, there’s been a growing trend to integrate GPT in solving math word problems using strategies such as “Chain-of-Thought”(CoT) and “Program-of-Thought”(PoT). Based on the observation that CoT tends to yield lower accuracy than PoT when large numbers are involved, we conducted two experiments to examine whether chatGPT understands place values in numbers. In the first experiment, to examine whether GPT can correctly order numbers based on an understanding of place values, we order source and permutation multiplets that contain 3-6 numbers in base 1000. In the second experiment, We examine whether GPT based models that utilize English expressions (CoT_Eng and PoT_Eng), rather than numerical expressions (CoT_Num and PoT_Num) can yield better performance in solving math word problems. The results of the first experiment showed that the ordering accuracy for the permutation multiplets (6 elements = 60.5%) was lower than that of source multiplets (6 elements = 96.8%). The results of the second experiment showed that accuracy increased when information about the place value was provided explicitly in the format of English expression (79.9% in CoT, 82% in PoT) compared to numerical expression(76.8% in CoT, 80% in PoT). The observations from both experiments suggest that the concept of place value isn’t adequately integrated when numbers are represented as tokens in gpt3.5-turbo. Thus, research on training models to understand the concept of place value in numbers would be a possible direction to pursue as future research.
Implementing Lightweight Intrusion Detection System on Resource Constrained Devices
Authors: Charles Stolz, Fuhao Li, Jielun Zhang
Date: 2024-10-28 00:00:00
Proceeding: 2024 Cyber Awareness and Research Symposium (CARS) 2024 10
Tasks: Anomaly Detection, Intrusion Detection, Network Intrusion Detection

The rapid growth of Internet of Things (IoT) devices has increased the risk of network intrusions, which need effective security solutions for devices with low computational capability. However, Deep Learning based Intrusion Detection Systems (IDS) often demand substantial computational resources, which are unsuitable for the resource constrained IoT devices. To tackle this issue, we propose a lightweight IDS for Raspberry Pi operation. The proposed architecture includes traffic capture, threat detection, and alerting modules, utilizing signature and anomaly-based techniques. The signature-based module detects known attacks using predefined patterns, while the machine learning-based anomaly detection module identifies new threats by monitoring deviations from normal network behavior. The evaluation results show that our proposed scheme can detect a wide range of threats with minimal computational overhead.
Test-Time Scaling of Diffusion Models via Noise Trajectory Search
Authors: Vignav Ramesh, Morteza Mardani
Date: 2025-05-24 00:00:00
Tasks: Denoising, Image Generation, Multi-Armed Bandits, Text to Image Generation, Text-to-Image Generation

The iterative and stochastic nature of diffusion models enables test-time scaling, whereby spending additional compute during denoising generates higher-fidelity samples. Increasing the number of denoising steps is the primary scaling axis, but this yields quickly diminishing returns. Instead optimizing the noise trajectory--the sequence of injected noise vectors--is promising, as the specific noise realizations critically affect sample quality; but this is challenging due to a high-dimensional search space, complex noise-outcome interactions, and costly trajectory evaluations. We address this by first casting diffusion as a Markov Decision Process (MDP) with a terminal reward, showing tree-search methods such as Monte Carlo tree search (MCTS) to be meaningful but impractical. To balance performance and efficiency, we then resort to a relaxation of MDP, where we view denoising as a sequence of independent contextual bandits. This allows us to introduce an $\epsilon$-greedy search algorithm that globally explores at extreme timesteps and locally exploits during the intermediate steps where de-mixing occurs. Experiments on EDM and Stable Diffusion reveal state-of-the-art scores for class-conditioned/text-to-image generation, exceeding baselines by up to $164\%$ and matching/exceeding MCTS performance. To our knowledge, this is the first practical method for test-time noise trajectory optimization of arbitrary (non-differentiable) rewards.
Whose Name Comes Up? Auditing LLM-Based Scholar Recommendations
Authors: Daniele Barolo, Chiara Valentin, Fariba Karimi, Luis Galárraga, Gonzalo G. Méndez, Lisette Espín-Noboa
Date: 2025-05-29 00:00:00

This paper evaluates the performance of six open-weight LLMs (llama3-8b, llama3.1-8b, gemma2-9b, mixtral-8x7b, llama3-70b, llama3.1-70b) in recommending experts in physics across five tasks: top-k experts by field, influential scientists by discipline, epoch, seniority, and scholar counterparts. The evaluation examines consistency, factuality, and biases related to gender, ethnicity, academic popularity, and scholar similarity. Using ground-truth data from the American Physical Society and OpenAlex, we establish scholarly benchmarks by comparing model outputs to real-world academic records. Our analysis reveals inconsistencies and biases across all models. mixtral-8x7b produces the most stable outputs, while llama3.1-70b shows the highest variability. Many models exhibit duplication, and some, particularly gemma2-9b and llama3.1-8b, struggle with formatting errors. LLMs generally recommend real scientists, but accuracy drops in field-, epoch-, and seniority-specific queries, consistently favoring senior scholars. Representation biases persist, replicating gender imbalances (reflecting male predominance), under-representing Asian scientists, and over-representing White scholars. Despite some diversity in institutional and collaboration networks, models favor highly cited and productive scholars, reinforcing the rich-getricher effect while offering limited geographical representation. These findings highlight the need to improve LLMs for more reliable and equitable scholarly recommendations.
LLM in the Loop: Creating the PARADEHATE Dataset for Hate Speech Detoxification
Authors: Shuzhou Yuan, Ercong Nie, Lukas Kouba, Ashish Yashwanth Kangen, Helmut Schmid, Hinrich Schutze, Michael Farber
Date: 2025-06-02 00:00:00
Tasks: 8k

Detoxification, the task of rewriting harmful language into non-toxic text, has become increasingly important amid the growing prevalence of toxic content online. However, high-quality parallel datasets for detoxification, especially for hate speech, remain scarce due to the cost and sensitivity of human annotation. In this paper, we propose a novel LLM-in-the-loop pipeline leveraging GPT-4o-mini for automated detoxification. We first replicate the ParaDetox pipeline by replacing human annotators with an LLM and show that the LLM performs comparably to human annotation. Building on this, we construct PARADEHATE, a large-scale parallel dataset specifically for hatespeech detoxification. We release PARADEHATE as a benchmark of over 8K hate/non-hate text pairs and evaluate a wide range of baseline methods. Experimental results show that models such as BART, fine-tuned on PARADEHATE, achieve better performance in style accuracy, content preservation, and fluency, demonstrating the effectiveness of LLM-generated detoxification text as a scalable alternative to human annotation.
Synergizing LLMs with Global Label Propagation for Multimodal Fake News Detection
Authors: Shuguo Hu, Jun Hu, Huaiwen Zhang
Date: 2025-05-31 00:00:00
Tasks: Fake News Detection

Large Language Models (LLMs) can assist multimodal fake news detection by predicting pseudo labels. However, LLM-generated pseudo labels alone demonstrate poor performance compared to traditional detection methods, making their effective integration non-trivial. In this paper, we propose Global Label Propagation Network with LLM-based Pseudo Labeling (GLPN-LLM) for multimodal fake news detection, which integrates LLM capabilities via label propagation techniques. The global label propagation can utilize LLM-generated pseudo labels, enhancing prediction accuracy by propagating label information among all samples. For label propagation, a mask-based mechanism is designed to prevent label leakage during training by ensuring that training nodes do not propagate their own labels back to themselves. Experimental results on benchmark datasets show that by synergizing LLMs with label propagation, our model achieves superior performance over state-of-the-art baselines.
MAS-Zero: Designing Multi-Agent Systems with Zero Supervision
Authors: Zixuan Ke, Austin Xu, Yifei Ming, Xuan-Phi Nguyen, Caiming Xiong, Shafiq Joty
Date: 2025-05-26 00:00:00
Proceeding: arXiv 2025 5
Tasks: Math, Problem Decomposition

Multi-agent systems (MAS) leveraging the impressive capabilities of Large Language Models (LLMs) hold significant potential for tackling complex tasks. However, most current MAS depend on manually designed agent roles and communication protocols. These manual designs often fail to align with the underlying LLMs' strengths and struggle to adapt to novel tasks. Recent automatic MAS approaches attempt to mitigate these limitations but typically necessitate a validation set for tuning and yield static MAS designs lacking adaptability during inference. We introduce MAS-ZERO, the first self-evolved, inference-time framework for automatic MAS design. MAS-ZERO employs meta-level design to iteratively generate, evaluate, and refine MAS configurations tailored to each problem instance, without requiring a validation set. Critically, it enables dynamic agent composition and problem decomposition through meta-feedback on solvability and completeness. Experiments across math, graduate-level QA, and software engineering benchmarks, using both closed-source and open-source LLM backbones of varying sizes, demonstrate that MAS-ZERO outperforms both manual and automatic MAS baselines, achieving a 7.44% average accuracy improvement over the next strongest baseline while maintaining cost-efficiency. These findings underscore the promise of meta-level self-evolved design for creating effective and adaptive MAS.
Dual encoding feature filtering generalized attention UNET for retinal vessel segmentation
Authors: Md Tauhidul Islam, Wu Da-Wen, Tang Qing-Qing, Zhao Kai-Yang, Yin Teng, Li Yan-Fei, Shang Wen-Yi, Liu Jing-Yu, Zhang Hai-Xian
Date: 2025-06-02 00:00:00
Tasks: Data Augmentation, Retinal Vessel Segmentation

Retinal blood vessel segmentation is crucial for diagnosing ocular and cardiovascular diseases. Although the introduction of U-Net in 2015 by Olaf Ronneberger significantly advanced this field, yet issues like limited training data, imbalance data distribution, and inadequate feature extraction persist, hindering both the segmentation performance and optimal model generalization. Addressing these critical issues, the DEFFA-Unet is proposed featuring an additional encoder to process domain-invariant pre-processed inputs, thereby improving both richer feature encoding and enhanced model generalization. A feature filtering fusion module is developed to ensure the precise feature filtering and robust hybrid feature fusion. In response to the task-specific need for higher precision where false positives are very costly, traditional skip connections are replaced with the attention-guided feature reconstructing fusion module. Additionally, innovative data augmentation and balancing methods are proposed to counter data scarcity and distribution imbalance, further boosting the robustness and generalization of the model. With a comprehensive suite of evaluation metrics, extensive validations on four benchmark datasets (DRIVE, CHASEDB1, STARE, and HRF) and an SLO dataset (IOSTAR), demonstrate the proposed method's superiority over both baseline and state-of-the-art models. Particularly the proposed method significantly outperforms the compared methods in cross-validation model generalization.
Activation by Interval-wise Dropout: A Simple Way to Prevent Neural Networks from Plasticity Loss
Authors: Sangyeon Park, Isaac Han, Seungwon Oh, Kyung-Joong Kim
Date: 2025-02-03 00:00:00
Tasks: Atari Games, Continual Learning, image-classification, Image Classification

Plasticity loss, a critical challenge in neural network training, limits a model's ability to adapt to new tasks or shifts in data distribution. This paper introduces AID (Activation by Interval-wise Dropout), a novel method inspired by Dropout, designed to address plasticity loss. Unlike Dropout, AID generates subnetworks by applying Dropout with different probabilities on each preactivation interval. Theoretical analysis reveals that AID regularizes the network, promoting behavior analogous to that of deep linear networks, which do not suffer from plasticity loss. We validate the effectiveness of AID in maintaining plasticity across various benchmarks, including continual learning tasks on standard image classification datasets such as CIFAR10, CIFAR100, and TinyImageNet. Furthermore, we show that AID enhances reinforcement learning performance in the Arcade Learning Environment benchmark.
How Neural Networks Organize Concepts: Introducing Concept Trajectory Analysis for Deep Learning Interpretability
Authors: Andrew Smigaj
Date: 2025-06-01 00:00:00
Proceeding: Independent Research 2025 6
Tasks: Bias Detection

We present Concept Trajectory Analysis (CTA), an interpretability method that tracks how neural networks organize concepts by following their paths through clustered activation spaces across layers. Applying CTA to GPT-2 with 1,228 single-token words revealed that the model organizes language primarily by grammatical function rather than semantic meaning. We found that 48.5% of words converge to grammatical highways where nouns—whether animals, objects, or abstracts—travel together, while maintaining semantic distinctions at finer scales (χ2 = 95.90, p < 0.0001). 
    CTA combines geometric clustering with trajectory tracking to quantify how concepts flow through networks. Our method introduces windowed analysis to identify phase transitions (semantic→grammatical in GPT-2) and leverages LLMs to generate interpretable cluster labels. In medical AI, CTA exposed how a heart disease model stratifies patients through risk pathways, revealing demographic biases (male overprediction in Path 4, 83% male composition). 
   By making neural organization visible and quantifiable, CTA provides actionable insights for model debugging, bias detection, and scientific understanding of deep learning. Our open-source implementation enables researchers to apply CTA to any neural network, advancing interpretable AI across domains.
QiMeng: Fully Automated Hardware and Software Design for Processor Chip
Authors: Rui Zhang, Yuanbo Wen, Shuyao Cheng, Di Huang, Shaohui Peng, Jiaming Guo, Pengwei Jin, Jiacheng Zhao, Tianrui Ma, Yaoyu Zhu, Yifan Hao, Yongwei Zhao, Shengwen Liang, Ying Wang, Xing Hu, Zidong Du, Huimin Cui, Ling Li, Qi Guo, Yunji Chen
Date: 2025-06-05 00:00:00

Processor chip design technology serves as a key frontier driving breakthroughs in computer science and related fields. With the rapid advancement of information technology, conventional design paradigms face three major challenges: the physical constraints of fabrication technologies, the escalating demands for design resources, and the increasing diversity of ecosystems. Automated processor chip design has emerged as a transformative solution to address these challenges. While recent breakthroughs in Artificial Intelligence (AI), particularly Large Language Models (LLMs) techniques, have opened new possibilities for fully automated processor chip design, substantial challenges remain in establishing domain-specific LLMs for processor chip design. In this paper, we propose QiMeng, a novel system for fully automated hardware and software design of processor chips. QiMeng comprises three hierarchical layers. In the bottom-layer, we construct a domain-specific Large Processor Chip Model (LPCM) that introduces novel designs in architecture, training, and inference, to address key challenges such as knowledge representation gap, data scarcity, correctness assurance, and enormous solution space. In the middle-layer, leveraging the LPCM's knowledge representation and inference capabilities, we develop the Hardware Design Agent and the Software Design Agent to automate the design of hardware and software for processor chips. Currently, several components of QiMeng have been completed and successfully applied in various top-layer applications, demonstrating significant advantages and providing a feasible solution for efficient, fully automated hardware/software design of processor chips. Future research will focus on integrating all components and performing iterative top-down and bottom-up design processes to establish a comprehensive QiMeng system.
hdl2v: A Code Translation Dataset for Enhanced LLM Verilog Generation
Authors: Charles Hong, Brendan Roberts, Huijae An, Alex Um, Advay Ratan, Yakun Sophia Shao
Date: 2025-06-05 00:00:00
Tasks: Code Generation, Code Translation, Data Augmentation, Knowledge Distillation

Large language models (LLMs) are playing an increasingly large role in domains such as code generation, including hardware code generation, where Verilog is the key language. However, the amount of publicly available Verilog code pales in comparison to the amount of code available for software languages like Python. In this work, we present hdl2v ("HDL-to-Verilog"), a dataset which seeks to increase the amount of available human-written Verilog data by translating or compiling three other hardware description languages - VHDL, Chisel, and PyMTL3 - to Verilog. Furthermore, we demonstrate the value of hdl2v in enhancing LLM Verilog generation by improving performance of a 32 billion-parameter open-weight model by up to 23% (pass@10) in VerilogEvalV2, without utilizing any data augmentation or knowledge distillation from larger models. We also show hdl2v's ability to boost the performance of a data augmentation-based fine-tuning approach by 63%. Finally, we characterize and analyze our dataset to better understand which characteristics of HDL-to-Verilog datasets can be expanded upon in future work for even better performance.
The Cost of Dynamic Reasoning: Demystifying AI Agents and Test-Time Scaling from an AI Infrastructure Perspective
Authors: Jiin Kim, Byeongjun Shin, Jinha Chung, Minsoo Rhu
Date: 2025-06-04 00:00:00
Tasks: AI Agent, Large Language Model

Large-language-model (LLM)-based AI agents have recently showcased impressive versatility by employing dynamic reasoning, an adaptive, multi-step process that coordinates with external tools. This shift from static, single-turn inference to agentic, multi-turn workflows broadens task generalization and behavioral flexibility, but it also introduces serious concerns about system-level cost, efficiency, and sustainability. This paper presents the first comprehensive system-level analysis of AI agents, quantifying their resource usage, latency behavior, energy consumption, and datacenter-wide power consumption demands across diverse agent designs and test-time scaling strategies. We further characterize how AI agent design choices, such as few-shot prompting, reflection depth, and parallel reasoning, impact accuracy-cost tradeoffs. Our findings reveal that while agents improve accuracy with increased compute, they suffer from rapidly diminishing returns, widening latency variance, and unsustainable infrastructure costs. Through detailed evaluation of representative agents, we highlight the profound computational demands introduced by AI agent workflows, uncovering a looming sustainability crisis. These results call for a paradigm shift in agent design toward compute-efficient reasoning, balancing performance with deployability under real-world constraints.