pwc-archive/papers-with-abstracts huggingface dataset:
Proceedings 40th International Conference on Logic Programming
Authors: Pedro Cabalar, Francesco Fabiano, Martin Gebser, Gopal Gupta, Theresa Swift
Date: 2025-02-11 00:00:00
Tasks: Inductive logic programming, Probabilistic Programming, Prompt Engineering

Since the first conference In Marseille in 1982, the International Conference on Logic Programming (ICLP) has been the premier international event for presenting research in logic programming. These proceedings include technical communications about, and abstracts for presentations given at the 40th ICLP held October 14-17, in Dallas Texas, USA. The papers and abstracts in this volume include the following areas and topics. Formal and operational semantics: including non-monotonic reasoning, probabilistic reasoning, argumentation, and semantic issues of combining logic with neural models. Language design and programming methodologies such as answer set programming. inductive logic programming, and probabilistic programming. Program analysis and logic-based validation of generated programs. Implementation methodologies including constraint implementation, tabling, Logic-based prompt engineering, and the interaction of logic programming with LLMs.
KIMAs: A Configurable Knowledge Integrated Multi-Agent System
Authors: Zitao Li, Fei Wei, Yuexiang Xie, Dawei Gao, Weirui Kuang, Zhijian Ma, Bingchen Qian, Yaliang Li, Bolin Ding
Date: 2025-02-13 00:00:00
Tasks: Management, RAG, Retrieval, Retrieval-augmented Generation

Knowledge-intensive conversations supported by large language models (LLMs) have become one of the most popular and helpful applications that can assist people in different aspects. Many current knowledge-intensive applications are centered on retrieval-augmented generation (RAG) techniques. While many open-source RAG frameworks facilitate the development of RAG-based applications, they often fall short in handling practical scenarios complicated by heterogeneous data in topics and formats, conversational context management, and the requirement of low-latency response times. This technical report presents a configurable knowledge integrated multi-agent system, KIMAs, to address these challenges. KIMAs features a flexible and configurable system for integrating diverse knowledge sources with 1) context management and query rewrite mechanisms to improve retrieval accuracy and multi-turn conversational coherency, 2) efficient knowledge routing and retrieval, 3) simple but effective filter and reference generation mechanisms, and 4) optimized parallelizable multi-agent pipeline execution. Our work provides a scalable framework for advancing the deployment of LLMs in real-world settings. To show how KIMAs can help developers build knowledge-intensive applications with different scales and emphases, we demonstrate how we configure the system to three applications already running in practice with reliable performance.
Exact Leader Estimation: A New Approach for Distributed Differentiation
Authors: Rodrigo Aldana-Lopez, David Gomez-Gutierrez, Elio Usai, Hernan Haimovich
Date: 2025-02-13 00:00:00

A novel strategy aimed at cooperatively differentiating a signal among multiple interacting agents is introduced, where none of the agents needs to know which agent is the leader, i.e. the one producing the signal to be differentiated. Every agent communicates only a scalar variable to its neighbors; except for the leader, all agents execute the same algorithm. The proposed strategy can effectively obtain derivatives up to arbitrary $m$-th order in a finite time under the assumption that the $(m+1)$-th derivative is bounded. The strategy borrows some of its structure from the celebrated homogeneous robust exact differentiator by A. Levant, inheriting its exact differentiation capability and robustness to measurement noise. Hence, the proposed strategy can be said to perform robust exact distributed differentiation. In addition, and for the first time in the distributed leader-observer literature, sampled-data communication and bounded measurement noise are considered, and corresponding steady-state worst-case accuracy bounds are derived. The effectiveness of the proposed strategy is verified numerically for second- and fourth-order systems, i.e., for estimating derivatives of up to first and third order, respectively.
AIDE: Agentically Improve Visual Language Model with Domain Experts
Authors: Ming-Chang Chiu, Fuxiao Liu, Karan Sapra, Andrew Tao, Yaser Jacoob, Xuezhe Ma, Zhiding Yu, Guilin Liu
Date: 2025-02-13 00:00:00
Tasks: Knowledge Distillation, Language Modeling, Language Modelling, MME

The enhancement of Visual Language Models (VLMs) has traditionally relied on knowledge distillation from larger, more capable models. This dependence creates a fundamental bottleneck for improving state-of-the-art systems, particularly when no superior models exist. We introduce AIDE (Agentic Improvement through Domain Experts), a novel framework that enables VLMs to autonomously enhance their capabilities by leveraging specialized domain expert models. AIDE operates through a four-stage process: (1) identifying instances for refinement, (2) engaging domain experts for targeted analysis, (3) synthesizing expert outputs with existing data, and (4) integrating enhanced instances into the training pipeline. Experiments on multiple benchmarks, including MMMU, MME, MMBench, etc., demonstrate AIDE's ability to achieve notable performance gains without relying on larger VLMs nor human supervision. Our framework provides a scalable, resource-efficient approach to continuous VLM improvement, addressing critical limitations in current methodologies, particularly valuable when larger models are unavailable to access.
Few is More: Task-Efficient Skill-Discovery for Multi-Task Offline Multi-Agent Reinforcement Learning
Authors: Xun Wang, Zhuoran Li, Hai Zhong, Longbo Huang
Date: 2025-02-13 00:00:00
Tasks: Learning to Execute, Multi-agent Reinforcement Learning, Q-Learning

As a data-driven approach, offline MARL learns superior policies solely from offline datasets, ideal for domains rich in historical data but with high interaction costs and risks. However, most existing methods are task-specific, requiring retraining for new tasks, leading to redundancy and inefficiency. To address this issue, in this paper, we propose a task-efficient multi-task offline MARL algorithm, Skill-Discovery Conservative Q-Learning (SD-CQL). Unlike existing offline skill-discovery methods, SD-CQL discovers skills by reconstructing the next observation. It then evaluates fixed and variable actions separately and employs behavior-regularized conservative Q-learning to execute the optimal action for each skill. This approach eliminates the need for local-global alignment and enables strong multi-task generalization from limited small-scale source tasks. Substantial experiments on StarCraftII demonstrates the superior generalization performance and task-efficiency of SD-CQL. It achieves the best performance on $\textbf{10}$ out of $14$ task sets, with up to $\textbf{65%}$ improvement on individual task sets, and is within $4\%$ of the best baseline on the remaining four.
SkyRover: A Modular Simulator for Cross-Domain Pathfinding
Authors: Wenhui Ma, Wenhao Li, Bo Jin, Changhong Lu, Xiangfeng Wang
Date: 2025-02-13 00:00:00
Tasks: Benchmarking

Unmanned Aerial Vehicles (UAVs) and Automated Guided Vehicles (AGVs) increasingly collaborate in logistics, surveillance, inspection tasks and etc. However, existing simulators often focus on a single domain, limiting cross-domain study. This paper presents the SkyRover, a modular simulator for UAV-AGV multi-agent pathfinding (MAPF). SkyRover supports realistic agent dynamics, configurable 3D environments, and convenient APIs for external solvers and learning methods. By unifying ground and aerial operations, it facilitates cross-domain algorithm design, testing, and benchmarking. Experiments highlight SkyRover's capacity for efficient pathfinding and high-fidelity simulations in UAV-AGV coordination. Project is available at https://sites.google.com/view/mapf3d/home.
PathFinder: A Multi-Modal Multi-Agent System for Medical Diagnostic Decision-Making Applied to Histopathology
Authors: Fatemeh Ghezloo, Mehmet Saygin Seyfioglu, Rustin Soraki, Wisdom O. Ikezogwo, Beibin Li, Tejoram Vivekanandan, Joann G. Elmore, Ranjay Krishna, Linda Shapiro
Date: 2025-02-13 00:00:00
Tasks: Decision Making, Descriptive, Diagnostic, Melanoma Diagnosis, Multiple Instance Learning, Navigate, Pathfinder, whole slide images

Diagnosing diseases through histopathology whole slide images (WSIs) is fundamental in modern pathology but is challenged by the gigapixel scale and complexity of WSIs. Trained histopathologists overcome this challenge by navigating the WSI, looking for relevant patches, taking notes, and compiling them to produce a final holistic diagnostic. Traditional AI approaches, such as multiple instance learning and transformer-based models, fail short of such a holistic, iterative, multi-scale diagnostic procedure, limiting their adoption in the real-world. We introduce PathFinder, a multi-modal, multi-agent framework that emulates the decision-making process of expert pathologists. PathFinder integrates four AI agents, the Triage Agent, Navigation Agent, Description Agent, and Diagnosis Agent, that collaboratively navigate WSIs, gather evidence, and provide comprehensive diagnoses with natural language explanations. The Triage Agent classifies the WSI as benign or risky; if risky, the Navigation and Description Agents iteratively focus on significant regions, generating importance maps and descriptive insights of sampled patches. Finally, the Diagnosis Agent synthesizes the findings to determine the patient's diagnostic classification. Our Experiments show that PathFinder outperforms state-of-the-art methods in skin melanoma diagnosis by 8% while offering inherent explainability through natural language descriptions of diagnostically relevant patches. Qualitative analysis by pathologists shows that the Description Agent's outputs are of high quality and comparable to GPT-4o. PathFinder is also the first AI-based system to surpass the average performance of pathologists in this challenging melanoma classification task by 9%, setting a new record for efficient, accurate, and interpretable AI-assisted diagnostics in pathology. Data, code and models available at https://pathfinder-dx.github.io/
QA-Expand: Multi-Question Answer Generation for Enhanced Query Expansion in Information Retrieval
Authors: Wonduk Seo, SeungHyun Lee
Date: 2025-02-12 00:00:00
Tasks: Answer Generation, Information Retrieval, Language Modeling, Language Modelling, Large Language Model, Question-Answer-Generation, Retrieval

Query expansion is widely used in Information Retrieval (IR) to improve search outcomes by enriching queries with additional contextual information. Although recent Large Language Model (LLM) based methods generate pseudo-relevant content and expanded terms via multiple prompts, they often yield repetitive, narrow expansions that lack the diverse context needed to retrieve all relevant information. In this paper, we introduce QA-Expand, a novel and effective framework for query expansion. It first generates multiple relevant questions from the initial query and subsequently produces corresponding pseudo-answers as surrogate documents. A feedback model further rewrites and filters these answers to ensure only the most informative augmentations are incorporated. Extensive experiments on benchmarks such as BEIR and TREC demonstrate that QA-Expand enhances retrieval performance by up to 13% over state-of-the-art methods, offering a robust solution for modern retrieval challenges.
Beyond Predictions: A Participatory Framework for Multi-Stakeholder Decision-Making
Authors: Vittoria Vineis, Giuseppe Perelli, Gabriele Tolomei
Date: 2025-02-12 00:00:00
Tasks: Decision Making

Conventional decision-support systems, primarily based on supervised learning, focus on outcome prediction models to recommend actions. However, they often fail to account for the complexities of multi-actor environments, where diverse and potentially conflicting stakeholder preferences must be balanced. In this paper, we propose a novel participatory framework that redefines decision-making as a multi-stakeholder optimization problem, capturing each actor's preferences through context-dependent reward functions. Our framework leverages $k$-fold cross-validation to fine-tune user-provided outcome prediction models and evaluate decision strategies, including compromise functions mediating stakeholder trade-offs. We introduce a synthetic scoring mechanism that exploits user-defined preferences across multiple metrics to rank decision-making strategies and identify the optimal decision-maker. The selected decision-maker can then be used to generate actionable recommendations for new data. We validate our framework using two real-world use cases, demonstrating its ability to deliver recommendations that effectively balance multiple metrics, achieving results that are often beyond the scope of purely prediction-based methods. Ablation studies demonstrate that our framework, with its modular, model-agnostic, and inherently transparent design, integrates seamlessly with various predictive models, reward structures, evaluation metrics, and sample sizes, making it particularly suited for complex, high-stakes decision-making contexts.
Centrally Coordinated Multi-Agent Reinforcement Learning for Power Grid Topology Control
Authors: Barbera de Mol, Davide Barbieri, Jan Viebahn, Davide Grossi
Date: 2025-02-12 00:00:00
Tasks: Decision Making, Multi-agent Reinforcement Learning

Power grid operation is becoming more complex due to the increase in generation of renewable energy. The recent series of Learning To Run a Power Network (L2RPN) competitions have encouraged the use of artificial agents to assist human dispatchers in operating power grids. However, the combinatorial nature of the action space poses a challenge to both conventional optimizers and learned controllers. Action space factorization, which breaks down decision-making into smaller sub-tasks, is one approach to tackle the curse of dimensionality. In this study, we propose a centrally coordinated multi-agent (CCMA) architecture for action space factorization. In this approach, regional agents propose actions and subsequently a coordinating agent selects the final action. We investigate several implementations of the CCMA architecture, and benchmark in different experimental settings against various L2RPN baseline approaches. The CCMA architecture exhibits higher sample efficiency and superior final performance than the baseline approaches. The results suggest high potential of the CCMA approach for further application in higher-dimensional L2RPN as well as real-world power grid settings.
WorldGUI: An Interactive Benchmark for Desktop GUI Automation from Any Starting Point
Authors: Henry Hengyuan Zhao, Kaiming Yang, Wendi Yu, Difei Gao, Mike Zheng Shou
Date: 2025-02-12 00:00:00

GUI agents have achieved outstanding performance in GUI element grounding. However, planning remains highly challenging, especially due to the sensitivity to the initial state of the environment. Specifically, slight differences in the initial state-such as the target software not being open or the interface not being in its default state, often lead to planning errors. This issue is widespread in real application scenarios, but existing benchmarks fail to evaluate it. To address this gap, we introduce WorldGUI, a comprehensive GUI benchmark containing tasks across ten widely used desktop and web applications (e.g., PowerPoint, VSCode, Acrobat), each instantiated with diverse initial states to simulate authentic human-computer interactions. Complementing this, we propose WorldGUI-Agent, a universal framework that unifies three core modules: Planner-Critic for high-level plan refinement, Step-Check for intermediate verification, and Actor-Critic for action-level optimization to proactively detect and correct errors. Experimental evaluation shows that WorldGUI-Agent outperforms the outstanding existing model (Claude-3.5 Computer Use) by 12.4% in success rate on WorldGUI, and achieves a 31.2% overall success rate on WindowsAgentArena, surpassing the prior state-of-the-art by 11.7%. Our analysis further reveals that dynamic augmentation tasks and desktop environments pose substantial hurdles, underscoring the necessity of adaptive planning and feedback-driven execution for advancing real-world GUI automation. The code and data are available at https://github.com/showlab/WorldGUI.
Symbiotic Cooperation for Web Agents: Harnessing Complementary Strengths of Large and Small LLMs
Authors: Ruichen Zhang, Mufan Qiu, Zhen Tan, Mohan Zhang, Vincent Lu, Jie Peng, Kaidi Xu, Leandro Z. Agudelo, Peter Qian, Tianlong Chen
Date: 2025-02-11 00:00:00
Tasks: Multi-Task Learning

Web browsing agents powered by large language models (LLMs) have shown tremendous potential in automating complex web-based tasks. Existing approaches typically rely on large LLMs (e.g., GPT-4o) to explore web environments and generate trajectory data, which is then used either for demonstration retrieval (for large LLMs) or to distill small LLMs (e.g., Llama3) in a process that remains decoupled from the exploration. In this paper, we propose AgentSymbiotic, an iterative framework that couples data synthesis with task-performance, yielding a "symbiotic improvement" for both large and small LLMs. Our study uncovers a complementary dynamic between LLM types: while large LLMs excel at generating high-quality trajectories for distillation, the distilled small LLMs-owing to their distinct reasoning capabilities-often choose actions that diverge from those of their larger counterparts. This divergence drives the exploration of novel trajectories, thereby enriching the synthesized data. However, we also observe that the performance of small LLMs becomes a bottleneck in this iterative enhancement process. To address this, we propose two innovations in LLM distillation: a speculative data synthesis strategy that mitigates off-policy bias, and a multi-task learning approach designed to boost the reasoning capabilities of the student LLM. Furthermore, we introduce a Hybrid Mode for Privacy Preservation to address user privacy concerns. Evaluated on the WEBARENA benchmark, AgentSymbiotic achieves SOTA performance with both LLM types. Our best Large LLM agent reaches 52%, surpassing the previous best of 45%, while our 8B distilled model demonstrates a competitive 49%, exceeding the prior best of 28%. Code will be released upon acceptance.
Human-Centric Foundation Models: Perception, Generation and Agentic Modeling
Authors: Shixiang Tang, Yizhou Wang, Lu Chen, YuAn Wang, Sida Peng, Dan Xu, Wanli Ouyang
Date: 2025-02-12 00:00:00
Tasks: Survey

Human understanding and generation are critical for modeling digital humans and humanoid embodiments. Recently, Human-centric Foundation Models (HcFMs) inspired by the success of generalist models, such as large language and vision models, have emerged to unify diverse human-centric tasks into a single framework, surpassing traditional task-specific approaches. In this survey, we present a comprehensive overview of HcFMs by proposing a taxonomy that categorizes current approaches into four groups: (1) Human-centric Perception Foundation Models that capture fine-grained features for multi-modal 2D and 3D understanding. (2) Human-centric AIGC Foundation Models that generate high-fidelity, diverse human-related content. (3) Unified Perception and Generation Models that integrate these capabilities to enhance both human understanding and synthesis. (4) Human-centric Agentic Foundation Models that extend beyond perception and generation to learn human-like intelligence and interactive behaviors for humanoid embodied tasks. We review state-of-the-art techniques, discuss emerging challenges and future research directions. This survey aims to serve as a roadmap for researchers and practitioners working towards more robust, versatile, and intelligent digital human and embodiments modeling.
Composite Sketch+Text Queries for Retrieving Objects with Elusive Names and Complex Interactions
Authors: Prajwal Gatti, Kshitij Parikh, Dhriti Prasanna Paul, Manish Gupta, Anand Mishra
Date: 2025-02-12 00:00:00
Tasks: Contrastive Learning, Image Retrieval, Retrieval, Sketch-Based Image Retrieval

Non-native speakers with limited vocabulary often struggle to name specific objects despite being able to visualize them, e.g., people outside Australia searching for numbats. Further, users may want to search for such elusive objects with difficult-to-sketch interactions, e.g., numbat digging in the ground. In such common but complex situations, users desire a search interface that accepts composite multimodal queries comprising hand-drawn sketches of difficult-to-name but easy-to-draw objects and text describing difficult-to-sketch but easy-to-verbalize object attributes or interaction with the scene. This novel problem statement distinctly differs from the previously well-researched TBIR (text-based image retrieval) and SBIR (sketch-based image retrieval) problems. To study this under-explored task, we curate a dataset, CSTBIR (Composite Sketch+Text Based Image Retrieval), consisting of approx. 2M queries and 108K natural scene images. Further, as a solution to this problem, we propose a pretrained multimodal transformer-based baseline, STNET (Sketch+Text Network), that uses a hand-drawn sketch to localize relevant objects in the natural scene image, and encodes the text and image to perform image retrieval. In addition to contrastive learning, we propose multiple training objectives that improve the performance of our model. Extensive experiments show that our proposed method outperforms several state-of-the-art retrieval methods for text-only, sketch-only, and composite query modalities. We make the dataset and code available at our project website.
Learning Discontinuous Galerkin Solutions to Elliptic Problems via Small Linear Convolutional Neural Networks
Authors: Adrian Celaya, Yimo Wang, David Fuentes, Beatrice Riviere
Date: 2025-02-12 00:00:00

In recent years, there has been an increasing interest in using deep learning and neural networks to tackle scientific problems, particularly in solving partial differential equations (PDEs). However, many neural network-based methods, such as physics-informed neural networks, depend on automatic differentiation and the sampling of collocation points, which can result in a lack of interpretability and lower accuracy compared to traditional numerical methods. To address this issue, we propose two approaches for learning discontinuous Galerkin solutions to PDEs using small linear convolutional neural networks. Our first approach is supervised and depends on labeled data, while our second approach is unsupervised and does not rely on any training data. In both cases, our methods use substantially fewer parameters than similar numerics-based neural networks while also demonstrating comparable accuracy to the true and DG solutions for elliptic problems.