GenAI4ELab/papercli-papers huggingface dataset:
A Vision for Reinventing Credible Elections with Artificial Intelligence
Authors: Biplav Srivastava
Venue: AAAI 2025 (main)

Abstract: In this blue sky paper, we seek to stimulate the research community to pursue important new as well as existing (unsolved) AI problems in the context of a challenging, often ignored, socio-sensitive application domain. We outline the key challenges in conducting elections credibly in leading democracies around the world today and identify our vision of a path forward with an overarching goal to increase voter participation with a two-pronged approach of AI-lead technological innovations and interdisciplinary community building. On the technology front, we envisage the need to transform Collation and Distribution of election information, and promote its Comprehensibility for users understanding and trust (CDC). On the community front, we need to invigorate the multi-disciplinary community consisting of, but not limited to, researchers in AI, security, journalism, political science, sociology, and business, to PROMote AI's Safe usage for Elections (PROMISE) with best-practices. This work is informed by our interdisciplinary research as well as experience in conducting three workshops at leading AI conferences and the AI Magazine special issue on AI and Elections.
Incentives for Early Arrival
Authors: Dengji Zhao
Venue: AAAI 2025 (main)

Abstract: Incentives for Early Arrival (I4EA) is a novel concept for online cooperative games introduced in an award-winning paper by Ge et al. (2024). The aim of I4EA is to encourage players to join a collaboration as soon as they become aware of it, a new study with significant real-world applications, including data collection and venture capital finance. This paper provides an in-depth discussion of I4EA and highlights its importance across various domains.
Embodied AI for Smart Robotic Cells in Manufacturing Applications
Authors: Satyandra K. Gupta
Venue: AAAI 2025 (main)

Abstract: Many manufacturing companies are facing an acute shortage of qualified workers. Deploying robotic cells is a potential solution to address this challenge. Historically robots have been deployed only in mass production applications in manufacturing. A large fraction of manufacturing is classified as high-mix manufacturing where a large variety of products are produced. Manually programming robots is not a viable solution in high-mix manufacturing applications. Robotic cells need to be powered by embodied AI to make them useful in high-mix manufacturing applications. This paper aims to build a bridge between smart manufacturing and AI communities to enable AI researchers to develop methods and tool that can be successfully deployed to realize smart robotic cells for high-mix manufacturing applications. This paper highlights key requirements for developing embodied AI for powering robotic cells for high-mix manufacturing applications. It also makes the case for approaches that combine model-based and data-driven methods to meet the needs of embodied AI in manufacturing applications and describes the role of generative AI approaches in smart manufacturing applications. Finally, it describes how AI can be used to enhance digital twins and augment human-machine interfaces in manufacturing applications.
Trustworthy AI Meets Educational Assessment: Challenges and Opportunities
Authors: Sheng Li
Venue: AAAI 2025 (main)

Abstract: Artificial intelligence (AI) has made substantial impacts in numerous fields, including education. Within education, learning and assessment are two key areas. Although many AI techniques have been applied to improve teaching and learning, their potential in educational assessment remains underexplored. This paper explores the intersection of AI and educational assessment and presents a rich landscape of challenges and opportunities, especially in the context of trustworthy AI, including fairness, transparency, accountability, explainability, and robustness. We will begin by outlining the foundations of trustworthy AI and educational assessment. Next, we will delve into the application of trustworthy AI for various assessment tasks, such as test item generation, test design, and automated scoring. In addition, the talk will also discuss how insights from educational measurement theory, such as item response theory (IRT) and validity frameworks, can inform the development and evaluation of trustworthy AI models. These frameworks help ensure that AI systems in education are not only accurate, but also equitable and aligned with educational goals. Finally, we will highlight future research directions, focusing on the integration of ethical AI principles into educational technology and the need for interdisciplinary collaboration to tackle the emerging challenges in this field. The aim is to foster a new generation of AI-powered educational tools that are both innovative and trustworthy, ultimately contributing to a more equitable and more effective educational landscape.
Enhancing Decision Making Through the Integration of Large Language Models and Operations Research Optimization
Authors: Segev Wasserkrug; Leonard Boussioux; Dick den Hertog; Farzaneh Mirzazadeh; Ş. İlker Birbil; Jannis Kurtz; Donato Maragno
Venue: AAAI 2025 (main)

Abstract: Many critical business and societal decisions in areas such as supply chain and healthcare involve numerous potential actions, complex constraints, and goals that can be modeled as objective functions. Mathematical optimization, a core area in Operations Research (OR), provides robust, mathematically grounded methodologies to address such decisions and has shown tremendous benefits in many applications. However, its application requires the creation of accurate and efficient optimization models, necessitating rare expertise and considerable time, creating a barrier to widespread adoption in decision-making. Thus, it is a long-standing goal to make these capabilities widely accessible.

The advent of Large Language Models (LLMs) has made advanced Artificial Intelligence (AI) capabilities widely accessible through natural language. LLMs can accelerate expert work in creating formal models like computer programs, and emerging research indicates they can also speed up the development of optimization models by OR experts. We, therefore, propose integrating and advancing LLM and optimization modeling to empower organizational decision-makers to model and solve such complex problems without requiring deep expertise in optimization.

In this work, we present our vision for democratizing optimization modeling for organizational decision-making by such a combination of LLMs and optimization modeling. We identify a set of fundamental requirements for the vision's implementation and describe the state of the art through a literature survey and some experimentation. We show that a) LLMs already provide substantial novel capabilities relevant to realizing this vision, but that b) major research challenges remain to be addressed. We also propose possible research directions to overcome these gaps. We would like this work to serve as a call to action to bring together the LLM and OR optimization modeling communities to pursue this vision, thereby enabling much more widespread improved decision-making and increasing by orders of magnitude the benefits AI and OR can bring to enterprises and society.
Learning Hierarchical Task Knowledge for Planning
Authors: Pat Langley
Venue: AAAI 2025 (main)

Abstract: In this paper, I review approaches for acquiring hierarchical knowledge
to improve the effectiveness of planning systems. First I note some
benefits of such hierarchical content and the advantages of learning
over manual construction. After this, I consider alternative paradigms
for encoding and acquiring plan expertise before turning to hierarchical
task networks. I specify the inputs to HTN learners and three subproblems
they must address: identifying hierarchical structure, unifying method
heads, and finding method conditions. Finally, I pose seven challenges
the community should pursue so that techniques for learning HTNs can
reach their full potential.
Why AI Is WEIRD and Shouldn't Be This Way: Towards AI for Everyone, with Everyone, by Everyone
Authors: Rada Mihalcea; Oana Ignat; Longju Bai; Angana Borah; Luis Chiruzzo; Zhijing Jin; Claude Kwizera; Joan Nwatu; Soujanya Poria; Thamar Solorio
Venue: AAAI 2025 (main)

Abstract: This paper presents a vision for creating AI systems that are inclusive at every stage of development, from data collection to model design and evaluation. We address key limitations in the current AI pipeline and its WEIRD* representation, such as lack of data diversity, biases in model performance, and narrow evaluation metrics. We also focus on the need for diverse representation among the developers of these systems, as well as incentives that are not skewed toward certain groups. We highlight opportunities to develop  AI systems that are for everyone (with diverse stakeholders in mind), with everyone (inclusive of diverse data and annotators), and by everyone (designed and developed by a globally diverse  workforce).


*WEIRD = an acronym coined by Joseph Henrich to highlight the coverage limitations of many psychological studies, referring to populations that are Western, Educated, Industrialized, Rich, and Democratic; while we do not fully adopt this term for AI, as its current scope does not perfectly align with the WEIRD dimensions, we believe that today's AI has a similarly "weird" coverage, particularly in terms of who is involved in its development and who benefits from it.
What Do Machine Learning Researchers Mean by “Reproducible”?
Authors: Edward Raff; Michel Benaroch; Sagar Samtani; Andrew L. Farris
Venue: AAAI 2025 (main)

Abstract: The concern that Artificial Intelligence (AI) and Machine Learning (ML) are entering a "reproducibility crisis" has spurred significant research in the past few years. Yet with each paper, it is often unclear what someone means by "reproducibility". Our work attempts to clarify the scope of "reproducibility" as displayed by the community at large. In doing so, we propose to refine the research to eight general topic areas. In this light, we see that each of these areas contains many works that do not advertise themselves as being about "reproducibility", in part because they go back decades before the matter came to broader attention.
Things Machine Learning Models Know That They Don’t Know
Authors: Salvatore Ruggieri; Andrea Pugnana
Venue: AAAI 2025 (main)

Abstract: This paper surveys Machine Learning approaches to build predictive models that know what they don't know. The consequential action of this knowledge can consist of abstaining from providing an output (rejection), deferring to another model (dynamic model selection), deferring to a human expert (learning to defer), or informing the user (uncertainty estimation).  We formally state the problems each approach solves and point to key references. We discuss open issues that deserve investigation from the scientific community.
Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes
Authors: Chen Tang; Ben Abbatematteo; Jiaheng Hu; Rohan Chandra; Roberto Martín-Martín; Peter Stone
Venue: AAAI 2025 (main)

Abstract: Reinforcement learning (RL), particularly its combination with deep neural networks referred to as deep RL (DRL), has shown tremendous promise across a wide range of applications, suggesting its potential for enabling the development of sophisticated robotic behaviors. Robotics problems, however, pose fundamental difficulties for the application of RL, stemming from the complexity and cost of interacting with the physical world. These challenges notwithstanding, recent advances have enabled DRL to succeed at some real-world robotic tasks. However, state-of-the-art DRL solutions’ maturity varies significantly across robotic applications. In this talk, I will review the current progress of DRL in real-world robotic applications based on our recent survey paper (with Tang, Abbatematteo, Hu, Chandra, and Martı́n-Martı́n), with a particular focus on evaluating the real-world successes achieved with DRL in realizing several key robotic competencies, including locomotion, navigation, stationary manipulation, mobile manipulation, human-robot interaction, and multi-robot interaction. The analysis aims to identify the key factors underlying those exciting successes, reveal underexplored areas, and provide an overall characterization of the status of DRL in robotics. I will also highlight several important avenues for future work, emphasizing the need for stable and sample-efficient real-world RL paradigms, holistic approaches for discovering and integrating various competencies to tackle complex long-horizon, open-world tasks, and principled development and evaluation procedures. The talk is designed to offer insights for RL practitioners and roboticists toward harnessing RL’s power to create generally capable real-world robotic systems.
Efficient Robot Learning via Interaction with Humans
Authors: Erdem Bıyık
Venue: AAAI 2025 (main)

Abstract: In many human-robot collaboration and multi-agent tasks, it is vital to model the partners and estimate their objectives to efficiently collaborate/interact with them. While learning from demonstrations is the most common approach for this, it is very data-hungry, which we cannot afford in many settings including robotics, and demonstrations are unreliable in a surprisingly large number of domains, including those we think humans perform reasonably well, e.g., driving. In this talk, I will start with introducing comparison-based feedback and explain why it does not suffer from most of the problems that demonstrations have, but is still data-hungry. To address this problem, I will propose comparative language based feedback and active learning techniques, which will result in (1) a new type of human feedback, and (2) an active querying algorithm that optimizes the information the AI agent will elicit from the human. I will conclude the talk by discussing what other types of human feedback exist, e.g., interventions or hand gestures, and how we can incorporate them into the existing learning algorithms.
Realizing AI for Impact: Towards Participatory Human-AI Collaboration for Water Conservation and Reproductive Health
Authors: Elizabeth Bondi-Kelly
Venue: AAAI 2025 (main)

Abstract: AI has immense potential for positive social impact, including in domains ranging from conservation to health. However, it can be challenging to account for human collaborations and real-world uncertainties when deploying such systems, which can lead to critical errors. Therefore, my research focuses on developing new methods in multi-agent systems and machine learning, including methods for participatory design of AI, human-AI collaboration, and uncertainty quantification, to develop safe, impactful AI systems, particularly in the domains of water conservation and reproductive health.
AI Governance and Lessons Learned as an AI Policy Advisor in the United States Senate
Authors: Serena Booth
Venue: AAAI 2025 (main)

Abstract: This talk examines the intersection of artificial intelligence and policymaking, focusing on legislative and regulatory frameworks in the United States. It explores the role of key federal agencies, existing technology-agnostic laws affecting AI, and gaps in regulatory oversight that require legislative intervention. Consumer protection laws are analyzed for their relevance to AI governance, particularly in financial services. The discussion also highlights the implications for AI research, emphasizing the importance of interdisciplinary collaboration between computer scientists and policymakers to ensure responsible AI development that aligns with democratic values and societal interests.
Leveraging Human Input to Enable Robust, Interactive, and Aligned AI Systems
Authors: Daniel S. Brown
Venue: AAAI 2025 (main)

Abstract: Ensuring that AI systems do what we, as humans, actually want them to do, is one of the biggest open research challenges in AI alignment and safety. My research seeks to directly address this challenge by enabling AI systems to interact with humans to learn aligned and robust behaviors. The way in which robots and other AI systems behave is often the result of optimizing a reward function. However, manually designing good reward functions is highly challenging and error prone, even for domain experts. Consider trying to write down a reward function that describes good driving behavior or how you like your bed made in the morning. While reward functions for these tasks are difficult to manually specify, human feedback in the form of demonstrations or preferences are often much easier to obtain. However, human data is often difficult to interpret, due to ambiguity and noise. Thus, it is critical that AI systems take into account epistemic uncertainty over the human's true intent. My talk will give an overview of my lab's progress along the following fundamental research areas: (1) efficiently maintaining uncertainty over human intent, (2) directly optimizing behavior to be robust to uncertainty over human intent, and (3) actively querying for additional human input to reduce uncertainty over human intent.
Representation-driven Option Discovery in Reinforcement Learning
Authors: Marlos C. Machado
Venue: AAAI 2025 (main)

Abstract: The ability to reason at multiple levels of temporal abstraction is a fundamental aspect of intelligence. In reinforcement learning (RL), this attribute is often modelled through temporally extended courses of actions called options. In this talk, I will introduce a general framework for option discovery, which uses the agent's representation to discover useful options. By leveraging these options to generate a rich stream of experience, the agent can improve its representations and learn more effectively. This representation-driven option discovery approach creates a virtuous cycle of refinement, continuously improving both the representation and options, and it is particularly effective for problems where agents need to operate at varying levels of abstraction to succeed.
Open-World Multimodal Understanding and Generation with Efficiently Finetuned Foundation Models
Authors: Long Chen
Venue: AAAI 2025 (main)

Abstract: With the astonishing ability of different pretrained foundation models (e.g., large language models (LLMs), vision-language models, diffusion models), today’s AI research and development tendency has been revolutionized. In this talk, I will answer two questions: Q1: How can we efficiently train or fine-tune foundation models? Q2: How can we build strong open-world multimodal understanding and generation models with these pretrained foundation models?
Breaking the Resource Monopoly from Industries: Sustainable and Reliable LLM Serving by Recycling Outdated and Resource-Constrained GPUs
Authors: Tianlong Chen
Venue: AAAI 2025 (main)

Abstract: In recent years, Large Language Model (LLM) agents, exemplified by models like ChatGPT, and PaLM, have showcased remarkable prowess in various tasks, owing to their vast number of parameters and emergent in-context learning capabilities. People expect the wide usage of LLM serving at edge hardware, personal devices, and organization/enterprise IT infrastructures to revolutionize global access to information, communication, automation, and creativity. However, due to the extreme large-scale LLM parameters (LLaMA 3.1 contains 405 billion of 2 or 4 bytes floating point numbers), the LLM serving is facing significant sustainability pressure due to its requirements on the latest high-embodied carbon hardware (e.g., GPUs, HBMs, memory, storage, and network hardware) and the high operational carbon emissions, leading to a significant and alarming increase in carbon emissions and a high barrier to their widespread deployments and practical applications in various scenarios. Companies, organizations, and institutes usually have the complete general-purpose IT infrastructure, which consists of a large amount of computing, memory, storage, and network hardware. Although these general-purpose IT infrastructures are far more than enough for existing application executions, deploying and executing the LLM for a broad spectrum of serving platforms can be challenging and difficult due to resource limitations. Purchasing the latest hardware including GPUs (e.g., Nvidia H100 or H200) will lead to considerable issues including 1) serious embodied carbon emissions during the new hardware production, 2) no explicitly lower operational carbon emissions with essential modeling and optimizations, 3) high economic and financial pressures, and 4) potentially tremendous existing hardware resource wasting. Therefore, it is a trend and becomes a must to explore how to use the existing hardware, especially outdated hardware, to collectively improve both environmental sustainability, efficiency, and reliability for LLM serving. A few pioneering examples include Microsoft’s Project Natick, Google’s TPU Pod Optimization, Alibaba’s Cloud Server Repurposing, and Facebook’s Network Hardware Reuse. In this talk, I will traverse my series of contributions with promising new directions, particularly emphasizing modularized LLM architecture (Part 1), in-storage sustainable computing (Part 2), and reliable serving against software and hardware attacks (Part 3).
Automated, Interpretable, and Scalable Scientific Machine Learning
Authors: Wuyang Chen
Venue: AAAI 2025 (main)

Abstract: Although Artificial Intelligence (AI) has transformed vision and language modeling, Scientific Machine Learning (SciML) complements data-driven AI via a knowledge-driven approach, enhancing our understanding of the physical world. My work focuses on: 1) automating scientific reasoning with language models, 2) improving geometric interpretation, 3) developing foundation models for multiphysics.
Adaptive Experimental Design to Accelerate Scientific Discovery and Engineering Design
Authors: Aryan Deshwal
Venue: AAAI 2025 (main)

Abstract: Artificial Intelligence (AI)  and Machine Learning hold immense potential to accelerate scientific discovery and engineering design. A fundamental challenge in these domains involves efficiently exploring a large space of hypotheses  using expensive experiments in a resource-efficient manner. My research focuses on developing novel adaptive experimental design methods to address this broad challenge. Specifically, I develop new probabilistic modeling and decision making tools that operate in small data settings. These approaches have yielded substantial improvements in sample-efficiency, particularly for black-box optimization over high-dimensional combinatorial spaces (e.g., sequences and graphs). This cover letter outlines key methods I have developed and their real-world sustainability applications in areas such as nano-porous materials discovery, hardware design, and additive manufacturing. Additionally, I highlight my initiatives to foster collaboration between Science/Engineering and AI communities.