pwc-archive/papers-with-abstracts huggingface dataset:
Membership Inference Attacks Against Vision-Language Models
Authors: Yuke Hu, Zheng Li, Zhihao Liu, Yang Zhang, Zhan Qin, Kui Ren, Chun Chen
Date: 2025-01-27 00:00:00
Tasks: Inference Attack, Membership Inference Attack

Vision-Language Models (VLMs), built on pre-trained vision encoders and large language models (LLMs), have shown exceptional multi-modal understanding and dialog capabilities, positioning them as catalysts for the next technological revolution. However, while most VLM research focuses on enhancing multi-modal interaction, the risks of data misuse and leakage have been largely unexplored. This prompts the need for a comprehensive investigation of such risks in VLMs. In this paper, we conduct the first analysis of misuse and leakage detection in VLMs through the lens of membership inference attack (MIA). In specific, we focus on the instruction tuning data of VLMs, which is more likely to contain sensitive or unauthorized information. To address the limitation of existing MIA methods, we introduce a novel approach that infers membership based on a set of samples and their sensitivity to temperature, a unique parameter in VLMs. Based on this, we propose four membership inference methods, each tailored to different levels of background knowledge, ultimately arriving at the most challenging scenario. Our comprehensive evaluations show that these methods can accurately determine membership status, e.g., achieving an AUC greater than 0.8 targeting a small set consisting of only 5 samples on LLaVA.
DarkMind: Latent Chain-of-Thought Backdoor in Customized LLMs
Authors: Zhen Guo, Reza Tourani
Date: 2025-01-24 00:00:00
Tasks: Backdoor Attack

With the growing demand for personalized AI solutions, customized LLMs have become a preferred choice for businesses and individuals, driving the deployment of millions of AI agents across various platforms, e.g., GPT Store hosts over 3 million customized GPTs. Their popularity is partly driven by advanced reasoning capabilities, such as Chain-of-Thought, which enhance their ability to tackle complex tasks. However, their rapid proliferation introduces new vulnerabilities, particularly in reasoning processes that remain largely unexplored. We introduce DarkMind, a novel backdoor attack that exploits the reasoning capabilities of customized LLMs. Designed to remain latent, DarkMind activates within the reasoning chain to covertly alter the final outcome. Unlike existing attacks, it operates without injecting triggers into user queries, making it a more potent threat. We evaluate DarkMind across eight datasets covering arithmetic, commonsense, and symbolic reasoning domains, using five state-of-the-art LLMs with five distinct trigger implementations. Our results demonstrate DarkMind effectiveness across all scenarios, underscoring its impact. Finally, we explore potential defense mechanisms to mitigate its risks, emphasizing the need for stronger security measures.
Algorithmic Inheritance: Surname Bias in AI Decisions Reinforces Intergenerational Inequality
Authors: Pat Pataranutaporn, Nattavudh Powdthavee, Pattie Maes
Date: 2025-01-23 00:00:00
Tasks: Decision Making, Fairness

Surnames often convey implicit markers of social status, wealth, and lineage, shaping perceptions in ways that can perpetuate systemic biases and intergenerational inequality. This study is the first of its kind to investigate whether and how surnames influence AI-driven decision-making, focusing on their effects across key areas such as hiring recommendations, leadership appointments, and loan approvals. Using 72,000 evaluations of 600 surnames from the United States and Thailand, two countries with distinct sociohistorical contexts and surname conventions, we classify names into four categories: Rich, Legacy, Normal, and phonetically similar Variant groups. Our findings show that elite surnames consistently increase AI-generated perceptions of power, intelligence, and wealth, which in turn influence AI-driven decisions in high-stakes contexts. Mediation analysis reveals perceived intelligence as a key mechanism through which surname biases influence AI decision-making process. While providing objective qualifications alongside surnames mitigates most of these biases, it does not eliminate them entirely, especially in contexts where candidate credentials are low. These findings highlight the need for fairness-aware algorithms and robust policy measures to prevent AI systems from reinforcing systemic inequalities tied to surnames, an often-overlooked bias compared to more salient characteristics such as race and gender. Our work calls for a critical reassessment of algorithmic accountability and its broader societal impact, particularly in systems designed to uphold meritocratic principles while counteracting the perpetuation of intergenerational privilege.
NAR db status Version 2 and miRNAverse: Over Two Years of Manual Meta-Registry Curation and Updates
Authors: Marcel Friedrichs, Cassandra Königs
Date: 2025-01-30 00:00:00

Previously, we reported on a new meta-registry for NAR published databases focusing on high-quality annotations regarding database availability and longevity. With over two years of continued manual curation, here, we report on recent updates and additions. Furthermore, the available annotations as well as the underlying database structure have been unified with the miRNAverse meta-registry. This allows for more in-depth insights as well as easier curation and future developments shared across both meta-registries. NAR db status currently provides annotations for 2,082 databases and miRNAverse for 194 databases. With the oldest annotation revision from June 2022 and the newest from January 2025, NAR db status spans two and a half years of continued manual curation. NAR db status is available at https://nardbstatus.de and miRNAverse at https://mirnaverse.de.
GestureLSM: Latent Shortcut based Co-Speech Gesture Generation with Spatial-Temporal Modeling
Authors: Pinxin Liu, Luchuan Song, Junhua Huang, Haiyang Liu, Chenliang Xu
Date: 2025-01-31 00:00:00
Tasks: Denoising, Gesture Generation

Generating full-body human gestures based on speech signals remains challenges on quality and speed. Existing approaches model different body regions such as body, legs and hands separately, which fail to capture the spatial interactions between them and result in unnatural and disjointed movements. Additionally, their autoregressive/diffusion-based pipelines show slow generation speed due to dozens of inference steps. To address these two challenges, we propose GestureLSM, a flow-matching-based approach for Co-Speech Gesture Generation with spatial-temporal modeling. Our method i) explicitly model the interaction of tokenized body regions through spatial and temporal attention, for generating coherent full-body gestures. ii) introduce the flow matching to enable more efficient sampling by explicitly modeling the latent velocity space. To overcome the suboptimal performance of flow matching baseline, we propose latent shortcut learning and beta distribution time stamp sampling during training to enhance gesture synthesis quality and accelerate inference. Combining the spatial-temporal modeling and improved flow matching-based framework, GestureLSM achieves state-of-the-art performance on BEAT2 while significantly reducing inference time compared to existing methods, highlighting its potential for enhancing digital humans and embodied agents in real-world applications. Project Page: https://andypinxinliu.github.io/GestureLSM
Drag Your Gaussian: Effective Drag-Based Editing with Score Distillation for 3D Gaussian Splatting
Authors: Yansong Qu, Dian Chen, Xinyang Li, Xiaofan Li, Shengchuan Zhang, Liujuan Cao, Rongrong Ji
Date: 2025-01-30 00:00:00
Tasks: 3DGS, 3D scene Editing

Recent advancements in 3D scene editing have been propelled by the rapid development of generative models. Existing methods typically utilize generative models to perform text-guided editing on 3D representations, such as 3D Gaussian Splatting (3DGS). However, these methods are often limited to texture modifications and fail when addressing geometric changes, such as editing a character's head to turn around. Moreover, such methods lack accurate control over the spatial position of editing results, as language struggles to precisely describe the extent of edits. To overcome these limitations, we introduce DYG, an effective 3D drag-based editing method for 3D Gaussian Splatting. It enables users to conveniently specify the desired editing region and the desired dragging direction through the input of 3D masks and pairs of control points, thereby enabling precise control over the extent of editing. DYG integrates the strengths of the implicit triplane representation to establish the geometric scaffold of the editing results, effectively overcoming suboptimal editing outcomes caused by the sparsity of 3DGS in the desired editing regions. Additionally, we incorporate a drag-based Latent Diffusion Model into our method through the proposed Drag-SDS loss function, enabling flexible, multi-view consistent, and fine-grained editing. Extensive experiments demonstrate that DYG conducts effective drag-based editing guided by control point prompts, surpassing other baselines in terms of editing effect and quality, both qualitatively and quantitatively. Visit our project page at https://quyans.github.io/Drag-Your-Gaussian.
A New Statistical Approach to the Performance Analysis of Vision-based Localization
Authors: Haozhou Hu, Harpreet S. Dhillon, R. Michael Buehrer
Date: 2025-01-30 00:00:00

Many modern wireless devices with accurate positioning needs also have access to vision sensors, such as a camera, radar, and Light Detection and Ranging (LiDAR). In scenarios where wireless-based positioning is either inaccurate or unavailable, using information from vision sensors becomes highly desirable for determining the precise location of the wireless device. Specifically, vision data can be used to estimate distances between the target (where the sensors are mounted) and nearby landmarks. However, a significant challenge in positioning using these measurements is the inability to uniquely identify which specific landmark is visible in the data. For instance, when the target is located close to a lamppost, it becomes challenging to precisely identify the specific lamppost (among several in the region) that is near the target. This work proposes a new framework for target localization using range measurements to multiple proximate landmarks. The geometric constraints introduced by these measurements are utilized to narrow down candidate landmark combinations corresponding to the range measurements and, consequently, the target's location on a map. By modeling landmarks as a marked Poisson point process (PPP), we show that three noise-free range measurements are sufficient to uniquely determine the correct combination of landmarks in a two-dimensional plane. For noisy measurements, we provide a mathematical characterization of the probability of correctly identifying the observed landmark combination based on a novel joint distribution of key random variables. Our results demonstrate that the landmark combination can be identified using ranges, even when individual landmarks are visually indistinguishable.
Deeply Optimizing the SAT Solver for the IC3 Algorithm
Authors: Yuheng Su, Qiusong Yang, Yiwei Ci, Yingcheng Li, Tianjun Bu, ZiYu Huang
Date: 2025-01-24 00:00:00

The IC3 algorithm, also known as PDR, is a SAT-based model checking algorithm that has significantly influenced the field in recent years due to its efficiency, scalability, and completeness. It utilizes SAT solvers to solve a series of SAT queries associated with relative induction. In this paper, we introduce several optimizations for the SAT solver in IC3 based on our observations of the unique characteristics of these SAT queries. By observing that SAT queries do not necessarily require decisions on all variables, we compute a subset of variables that need to be decided before each solving process while ensuring that the result remains unaffected. Additionally, noting that the overhead of binary heap operations in VSIDS is non-negligible, we replace the binary heap with buckets to achieve constant-time operations. Furthermore, we support temporary clauses without the need to allocate a new activation variable for each solving process, thereby eliminating the need to reset solvers. We developed a novel lightweight CDCL SAT solver, GipSAT, which integrates these optimizations. A comprehensive evaluation highlights the performance improvements achieved by GipSAT. Specifically, the GipSAT-based IC3 demonstrates an average speedup of 3.61 times in solving time compared to the IC3 implementation based on MiniSat.
Implications of zero-growth economics analysed with an agent-based model
Authors: Dylan C. Terry-Doyle, Adam B. Barrett
Date: 2025-01-31 00:00:00

The ever-approaching limits of the Earth's biosphere and the potentially catastrophic consequences caused by climate change have begun to call into question the endless growth of the economy. There is increasing interest in the prospects of zero economic growth from the degrowth and post-growth literature. In particular, the question arises as to whether a zero-growth trajectory in a capitalist system with interest-bearing debt can be economically stable. There have been several answers to this question using macroeconomic models; some find a zero-growth trajectory is stable, while other models show an economic breakdown. However, the capitalist system in a period of growth is not guaranteed to be stable. Hence, a more appropriate methodology is to compare the relative stability between a growth and zero-growth scenario on the same model. Such a question has not yet been answered at any disaggregated level. It's important to investigate the consequences of zero-growth on market share instability and concentration, bankruptcy rates, income distribution, and credit network risk. To answer such questions, we develop a macroeconomic agent-based model incorporating Minskyan financial dynamics. The growth and zero-growth scenarios are accomplished by changing an average productivity growth parameter for the firms in the model. The model results showed that real GDP growth rates were more stable in the zero-growth scenario, there were fewer economic crises, lower unemployment rates, a higher wage share of output for workers, and capital firm and bank market shares were relatively more stable. Some of the consequences of zero-growth were a higher rate of inflation than in the growth scenario, increased market concentration for both firms and banks, and a higher level of financial risk in the credit network.
Distributed Observer Design for Tracking Platoon of Connected and Autonomous Vehicles
Authors: Mohammadreza Doostmohammadian, Hamid R. Rabiee
Date: 2025-01-31 00:00:00
Tasks: Autonomous Vehicles

Intelligent transportation systems (ITS) aim to advance innovative strategies relating to different modes of transport, traffic management, and autonomous vehicles. This paper studies the platoon of connected and autonomous vehicles (CAV) and proposes a distributed observer to track the state of the CAV dynamics. First, we model the CAV dynamics via an LTI interconnected system. Then, a consensus-based strategy is proposed to infer the state of the CAV dynamics based on local information exchange over the communication network of vehicles. A linear-matrix-inequality (LMI) technique is adopted for the block-diagonal observer gain design such that this gain is associated in a distributed way and locally to every vehicle. The distributed observer error dynamics is then shown to follow the structure of the Kronecker matrix product of the system dynamics and the adjacency matrix of the CAV network. The notions of survivable network design and redundant observer scheme are further discussed in the paper to address resilience to link and node failure. Finally, we verify our theoretical contributions via numerical simulations.
Fully Distributed and Quantized Algorithm for MPC-based Autonomous Vehicle Platooning Optimization
Authors: Mohammadreza Doostmohammadian, Alireza Aghasi, Hamid R. Rabiee
Date: 2025-01-31 00:00:00
Tasks: Model Predictive Control, Quantization

Intelligent transportation systems have recently emerged to address the growing interest for safer, more efficient, and sustainable transportation solutions. In this direction, this paper presents distributed algorithms for control and optimization over vehicular networks. First, we formulate the autonomous vehicle platooning framework based on model-predictive-control (MPC) strategies and present its objective optimization as a cooperative quadratic cost function. Then, we propose a distributed algorithm to locally optimize this objective at every vehicle subject to data quantization over the communication network of vehicles. In contrast to most existing literature that assumes ideal communication channels, log-scale data quantization over the network is addressed in this work, which is more realistic and practical. In particular, we show by simulation that the proposed log-quantized algorithm reaches optimal convergence with less residual and optimality gap. This outperforms the existing literature considering uniform quantization which leads to a large optimality gap and residual.
One Stack, Diverse Vehicles: Checking Safe Portability of Automated Driving Software
Authors: Vladislav Nenchev
Date: 2025-01-30 00:00:00
Tasks: Diversity

Integrating an automated driving software stack into vehicles with variable configuration is challenging, especially due to different hardware characteristics. Further, to provide software updates to a vehicle fleet in the field, the functional safety of every affected configuration has to be ensured. These additional demands for dependability and the increasing hardware diversity in automated driving make rigorous automatic analysis essential. This paper addresses this challenge by using formal portability checking of adaptive cruise controller code for different vehicle configurations. Given a formal specification of the safe behavior, models of target configurations are derived, which capture relevant effects of sensors, actuators and computing platforms. A corresponding safe set is obtained and used to check if the desired behavior is achievable on all targets. In a case study, portability checking of a traditional and a neural network controller are performed automatically within minutes for each vehicle hardware configuration. The check provides feedback for necessary adaptations of the controllers, thus, allowing rapid integration and testing of software or parameter changes.
STAMP: Scalable Task And Model-agnostic Collaborative Perception
Authors: Xiangbo Gao, Runsheng Xu, Jiachen Li, Ziran Wang, Zhiwen Fan, Zhengzhong Tu
Date: 2025-01-24 00:00:00
Tasks: Autonomous Driving

Perception is crucial for autonomous driving, but single-agent perception is often constrained by sensors' physical limitations, leading to degraded performance under severe occlusion, adverse weather conditions, and when detecting distant objects. Multi-agent collaborative perception offers a solution, yet challenges arise when integrating heterogeneous agents with varying model architectures. To address these challenges, we propose STAMP, a scalable task- and model-agnostic, collaborative perception pipeline for heterogeneous agents. STAMP utilizes lightweight adapter-reverter pairs to transform Bird's Eye View (BEV) features between agent-specific and shared protocol domains, enabling efficient feature sharing and fusion. This approach minimizes computational overhead, enhances scalability, and preserves model security. Experiments on simulated and real-world datasets demonstrate STAMP's comparable or superior accuracy to state-of-the-art models with significantly reduced computational costs. As a first-of-its-kind task- and model-agnostic framework, STAMP aims to advance research in scalable and secure mobility systems towards Level 5 autonomy. Our project page is at https://xiangbogaobarry.github.io/STAMP and the code is available at https://github.com/taco-group/STAMP.
Towards Adaptive Self-Improvement for Smarter Energy Systems
Authors: Alexander Sommer, Peter Bazan, Jonathan Fellerer, Behnam Babaeian, Reinhard German
Date: 2025-01-31 00:00:00
Tasks: Code Generation, Decision Making

This paper introduces a hierarchical framework for decision-making and optimization, leveraging Large Language Models (LLMs) for adaptive code generation. Instead of direct decision-making, LLMs generate and refine executable control policies through a meta-policy that guides task generation and a base policy for operational actions. Applied to a simplified microgrid scenario, the approach achieves up to 15 percent cost savings by iteratively improving battery control strategies. The proposed methodology lays a foundation for integrating LLM-based tools into planning and control tasks, offering adaptable and scalable solutions for complex systems while addressing challenges of uncertainty and reproducibility.
A parallelizable variant of HCA*
Authors: Sreenivasan Ganti, Visnu Srinivasan, Pallavi Ramicetty, Shravan Mohan, Milind Savagaonkar, Shubhashis Sengupta
Date: 2025-01-31 00:00:00
Tasks: Multi-Agent Path Finding

This paper presents a parallelizable variant of the well-known Hierarchical Cooperative A* algorithm (HCA*) for the multi-agent path finding (MAPF) problem. In this variant, all agents initially find their shortest paths disregarding the presence of others. This is done using A*. Then an intersection graph (IG) is constructed; each agent is a node and two nodes have an edge between them if the paths of corresponding agents collide. Thereafter, an independent set is extracted with the aid of an approximation algorithm for the maximum independent set problem. The paths for the agents belonging to independent set are fixed. The rest of agents now again find their shortest paths, this time ensuring no collision with the prior agents. Space-time A*, which is a crucial component of HCA*, is used here. These iterations continue until no agents are left. Since the tasks of finding shortest paths for the agents in any iteration are independent of each other, the proposed algorithm can be parallelized to a large extent. In addition to this, the task of determining the IG can also be done in parallel by dividing the map into sections and with each agent focusing on a particular section. The parallelism does come at a cost of communication between the agents and the server. This is accounted for in the simulations. As an added advantage, the user need not make a choice for the priority order. It is observed, empirically, that the proposed algorithm outperforms HCA* in terms of the computation time and the cost value in many cases. Simulations are provided for corroboration.