GenAI4ELab/papercli-papers huggingface dataset:
Aligning Generative Music AI with Human Preferences: Methods and Challenges
Authors: Dorien Herremans; Abhinaba Roy
Venue: AAAI 2026 (main)

Abstract: Recent advances in generative AI for music have achieved remarkable fidelity and stylistic diversity, yet these systems often fail to align with nuanced human preferences due to the specific loss functions they use. This paper advocates for the systematic application of preference alignment techniques to music generation, addressing the fundamental gap between computational optimization and human musical appreciation. Drawing on recent breakthroughs including MusicRL's large-scale preference learning, multi-preference alignment frameworks like diffusion-based preference optimization in DiffRhythm+, and inference-time optimization techniques like Text2midi-InferAlign, we discuss how these techniques can address music's unique challenges: temporal coherence, harmonic consistency, and subjective quality assessment. We identify key research challenges including scalability to long-form compositions, reliability amongst others in preference modelling. Looking forward, we envision preference-aligned music generation enabling transformative applications in interactive composition tools and personalized music services. This work calls for sustained interdisciplinary research combining advances in machine learning, music-theory to create music AI systems that truly serve human creative and experiential needs.
Democratizing LLM Efficiency: From Hyperscale Optimizations to Universal Deployability
Authors: Hen-Hsen Huang
Venue: AAAI 2026 (main)

Abstract: Large language models (LLMs) have become indispensable, but the most celebrated efficiency methods---mixture-of-experts (MoE), speculative decoding, and complex retrieval-augmented generation (RAG)---were built for hyperscale providers with vast infrastructure and elite teams. 
Outside that context, their benefits collapse into overhead, fragility, and wasted carbon. 
The result is that a handful of Big Tech companies benefit, while thousands of hospitals, schools, governments, and enterprises are left without viable options. 
We argue that the next frontier is not greater sophistication at scale, but robust simplicity: efficiency that thrives under modest resources and minimal expertise. We propose a new research agenda: retrofitting pretrained models with more efficient architectures without retraining, inventing lightweight fine-tuning that preserves alignment, making reasoning economical despite long chains of thought, enabling dynamic knowledge management without heavy RAG pipelines, and adopting Overhead-Aware Efficiency (OAE) as a standard benchmark.
By redefining efficiency to include adoption cost, sustainability, and fairness, we can democratize LLM deployment---ensuring that optimization reduces inequality and carbon waste rather than amplifying them.
Knowledge-Guided Machine Learning: A Paradigm Shift in AI for Science
Authors: Anuj Karpatne; Xiaowei Jia; Vipin Kumar
Venue: AAAI 2026 (main)

Abstract: As advances in artificial intelligence (AI) and machine learning (ML) continue to transform commercial applications, the scientific community is increasingly eager to harness AI/ML’s power to accelerate modeling and discovery. However, purely data-driven AI methods often lack interpretability, generalizability, and consistency with established scientific principles. Conversely, traditional process-based models embody deep scientific knowledge but suffer from limited scalability or incomplete representation of complex systems. Knowledge-guided machine learning (KGML) offers a promising path forward by integrating scientific knowledge with data-driven approaches to produce AI models that are robust, trustworthy, and capable of advancing both AI and science. This talk summarizes the foundations of KGML, outlines a taxonomy for organizing research efforts, and highlights emerging opportunities for broad scientific impact.
Complex Reasoning over Vision and Language --Leveraging Neurosymbolic AI
Authors: Parisa Kordjamshidi
Venue: AAAI 2026 (main)

Abstract: Recent research highlights the lack of reliability of large language models (LLMs) in tasks requiring complex reasoning. While they can produce impressively fluent text in response to prompts, they can fail on  basic reasoning skills, such as recognizing that left is the opposite of right. They struggle even more with grounding such concepts in real-world contexts involving perception and action. Addressing real-world problems, however, typically requires models composed of multiple interdependent learners, with strong capabilities for composition and reasoning. In this talk, I will discuss the reasoning challenges of LLMs and discuss how symbolic representations can enhance neural models by enabling Spatial and Compositional Reasoning over complex linguistic structures, grounding language in visual perception, integrating multiple modalities, and dealing with uncertainty. I will overview recent research in Neurosymbolic (NeSy) modeling and emphasize the need for community-driven libraries to advance this direction. As part of this effort, I will introduce the DomiKnowS framework developed by my team, which combines symbolic and sub-symbolic representations to tackle complex, AI-complete problems, integrating symbolic and logical knowledge seamlessly into deep models and LLMs through a range of underlying algorithms.
The Tatort Test of Intelligence: Towards Narrative Comprehension as a Benchmark for AI
Authors: Stefan Kramer; Lennart Baur; Lars Reinhardt
Venue: AAAI 2026 (main)

Abstract: We propose—somewhat tongue-in-cheek, yet with serious implications—a new test for artificial intelligence: the ability to watch a 90-minute episode of the long-running German crime drama Tatort, and to explain every relevant detail. This involves reconstructing the evolving social network of characters, identifying their beliefs, desires, and intentions, and, crucially, determining who committed the crime. We argue that this task integrates narrative understanding, common-sense reasoning, social cognition, and theory of mind—and thus provides a uniquely challenging benchmark for AI.
System L: Toward System 2-Style Legal Reasoning
Authors: Chuanyi Li; Yi Feng; Vincent Ng
Venue: AAAI 2026 (main)

Abstract: Dual-system theory distinguishes between fast, intuitive System 1 and slow, deliberative System 2. While this dichotomy describes many forms of reasoning, it oversimplifies the reality of expert legal reasoning. Legal reasoning is not merely a process of slow, logical deliberation. It is intrinsically normative, embedding precedent analysis, statutory interpretation, policy balancing, and social values. This paper envisions a reasoning architecture for legal reasoning, System L (Legal System 2), which extends traditional System 2 by integrating domain-specific normative frameworks in a structured manner. Using the IRAC (Issue–Rule–Application–Conclusion) structure as a backbone model, System L represents a blueprint for the next generation of cognitive and AI systems capable of human-like legal reasoning.
Rethinking AI: From Functions to Functors
Authors: Sridhar Mahadevan
Venue: AAAI 2026 (main)

Abstract: We propose a new theoretical foundation for artificial intelligence (AI) and machine learning (ML), building on ideas in pure mathematics relating to categories and functors. 
This paper builds on our AAAI 2025 tutorial Thinking with Functors: Category Theory for A(G)I, which provides background material. 
In addition, our recent papers on intuitionistic j-do calculus in Topos Causal Models} and GAIA: Categorical Foundations of Generative AI,  illustrate how to generalize well-known formalisms in AI, 
such as causal inference and deep learning, to a category-theoretic setting.
Responsible Mechanism Design
Authors: Pavel Naumov
Venue: AAAI 2026 (main)

Abstract: Traditionally, the goal of mechanism design was to promote
socially desirable behaviour of rational agents, to achieve
fairness, or to promote efficiency. I would like to suggest
a new subfield of mechanism design, Responsible Mechanism
Design, focused on achieving individual accountability of
agents for their contributions to the outcome of collective
decisions.
Is Word Sense Disambiguation Dead in the LLM Era?
Authors: Roberto Navigli
Venue: AAAI 2026 (main)

Abstract: Word Sense Disambiguation (WSD) has been a central challenge since the earliest proposals for Machine Translation (MT), most famously Weaver's 1949 memorandum. Classical systems treated WSD as an explicit task, grounded in lexical resources and annotated data. Recently, however, Large Language Models (LLMs) have blurred the boundary between disambiguation and general language understanding, leading some to suggest that WSD might be obsolete. This paper surveys the role of WSD in the LLM era, drawing on recent studies of encoder-based sense separation and disambiguation, and decoder-based definition selection and generation, as well as multilingual evaluation. Closed-source instruction-tuned LLMs now achieve performance comparable to specialized WSD systems, yet systematic weaknesses remain: non-predominant senses are often misclassified and disambiguation biases in MT persist. We argue that WSD is not "dead" but redefined as a diagnostic lens for assessing lexical-semantic competence, robustness, and interpretability in LLMs.
Why Isn’t Relational Learning Taking Over the World?
Authors: David Poole
Venue: AAAI 2026 (main)

Abstract: Artificial intelligence seems to be taking over the world with systems that model pixels, words, and phonemes.  The world is arguably made up, not of pixels, words, and phonemes but of entities (objects, things, including events) with properties and relations among them. Surely we should model these, not the perception or description of them. You might suspect that concentrating on modeling words and pixels is because all of the (valuable) data in the world is in terms of text and images. If you look into almost any company you will find their most valuable data is in spreadsheets, databases and other relational formats. These are not the form that are studied in introductory machine learning, but are full of product numbers, student numbers, transaction numbers and other identifiers that can't be interpreted naively as numbers. The field that studies this sort of data has various names including relational learning, statistical relational AI, and many others. This paper explains why relational learning is not taking over the world -- except in a few cases with restricted relations -- and what needs to be done to bring it to it's rightful prominence.
10 Open Challenges Steering the Future of Vision-Language-Action Models
Authors: Soujanya Poria; Navonil Majumder; Chia-Yu Hung; Amir Ali Bagherzadeh; Chuan Li; Kenneth Kwok; Ziwei Wang; Cheston Tan; Jiajun Wu; David Hsu
Venue: AAAI 2026 (main)

Abstract: Due to their ability of follow natural language
instructions,
vision-language-action (VLA) models are increasingly preva-
lent in the embodied AI arena, following the widespread suc-
cess of their precursors—LLMs and VLMs. In this paper,
we discuss 10 principal milestones in the ongoing develop-
ment of VLA models—multimodality, reasoning, data, eval-
uation, cross-robkot action generalization, efficiency,
whole-
body coordination, safety, agents, and coordination with hu-
mans. Furthermore, we discuss the emerging trends of us-
ing spatial understanding, modeling world dynamics, post
training, and data synthesis—all aiming to reach these mile-
stones. Through these discussions, we hope to bring
attention
to the research avenues that may accelerate the development
of VLA models into wider acceptability.
Implications for AI Research: Applying Lessons from the Expert Systems Boom and Bust to the Current Large-Language Model Boom
Authors: Ted Senator
Venue: AAAI 2026 (main)

Abstract: AI is currently in the midst of a boom, mostly due to the
success and predominance of large language models and
associated models for other perceptual tasks such as
computer
vision. Yet AI has experienced several booms and
busts over the past 75 years. While the booms are driven by
commercial potential, the following busts affect not only
commercial investment but also research funding and
trends. This paper examines the expert systems boom of the
1980s and the following AI winter, identifies similarities,
analogs, and differences with the current boom, and projects
potential outcomes and directions for AI research that may
follow when the current enthusiasm wanes based on these
similarities, analogs, and differences. The presentation is
distinct from currently active discussions and debates about
the potential and limitations of large models such as
whether
problems such as hallucination will be solved, whether they
can reason, or whether they will achieve AGI; rather, it
examines
previous AI techniques and how they evolved once
their capabilities and limitations became well understood.
Scaling Up AI Alignment
Authors: Aarti Singh
Venue: AAAI 2026 (main)

Abstract: From expert AI systems of the 1970s to self-supervised
systems of the 2020s, the pendulum of AI development has
swung from heavy reliance on human feedback to no or
minimal reliance in the last 50 years. Self-supervised
approaches have contributed significantly to the success
and scalable development of AI. However, today we are at a
tipping point where the future of AI, and whether so-ciety
ends up benefiting from this technology in the long run,
depends critically on the subsequent AI develop-ment
aligning with human goals and values. Realizing this, there
has been ramping up of efforts to align AI models with
human expectations and values. Human feedback, however,
remains limited and difficult to elicit. Thus, a key
question lingers – how can we scale up alignment of AI
systems with individual expectations and societal norms?
This talk and paper provides an overview and perspective on
efforts at answering this question.
Voting in Divisible Settings: A Survey
Authors: Warut Suksompong; Nicholas Teh
Venue: AAAI 2026 (main)

Abstract: Voting is one of the most prominent applications of preference aggregation and computational social choice. While much of the literature focuses on models involving discrete candidates, there has been a growing interest in voting over divisible resources, such as budget, space, and time. In this survey, we review existing work on voting in divisible settings, including fundamental models of budget aggregation, fair mixing, and cake sharing. We also establish connections among these models, highlight unifying themes across different frameworks, and suggest directions for future research.
Right Looks, Wrong Reasons: Compositional Fidelity in Text-to-Image Generation
Authors: Mayank Vatsa; Aparna Bharati; Richa Singh
Venue: AAAI 2026 (main)

Abstract: The architectural blueprint of today’s leading text-to-image models contains a fundamental flaw: an inability to handle logical composition. This survey investigates this breakdown across three core primitives—negation, counting, and spatial relations. Our analysis reveals a dramatic performance collapse: models that are accurate on single primitives fail precipitously when these are combined, exposing severe interference. We trace this failure to three key factors. First, training data show a near-total absence of explicit negations. Second, continuous attention architectures are fundamentally unsuitable for discrete logic. Third, evaluation metrics reward visual plausibility over constraint satisfaction. By analyzing recent benchmarks and methods, we show that current solutions and simple scaling cannot bridge this gap. Achieving genuine compositionality, we conclude, will require fundamental advances in representation and reasoning rather than incremental adjustments to existing architectures.
Towards Trustworthy Multimodal AI Systems
Authors: Chirag Agarwal
Venue: AAAI 2026 (main)

Abstract: Machine learning models have become ubiquitous in the last decade, and with their increasing use in critical applications (e.g., healthcare, financial systems, and crime forecasting), it is vital to ensure that ML developers and practitioners understand and trust their decisions. This problem has become paramount in the era of frontier models, which are developed by training billion-parameter models on broad, uncurated datasets and extensive computing. In this talk, we will first explore the (un)reliability of existing multimodal explainability techniques in large language and multimodal models and understand the robustness and safety implications of Mechanistic Interpretability tools. Next, we will delve into two complementary threads: i) domain-specific safety and related trustworthy evaluation that surfaces risks missed by generic red-teaming, focusing on multilingual and distribution-shifted settings; and ii) methods that explicitly train and assess reasoning in medical LLMs.
Towards Agents That Exhibit Human-Like Autonomy in Complex Environments
Authors: Rohan Chandra
Venue: AAAI 2026 (main)

Abstract: Deploying intelligent, autonomous agents e.g. autonomous vehicles and robots, in the real world has been a longstanding goal in robotics and artificial intelligence (AI). We have already begun to witness the emergence of vacuum robots in our homes, service robots in warehouses, and even self-driving cars on our way to work. These environments are often dense, constrained, and unstructured, with heterogeneous agents, each with their own unique behaviors and objectives. While agents today are designed to navigate these environments safely, their overly conservative nature often leads to slow and jerky motion (frequent stopping and freezing), lack of social compliance (not giving way to other people, blocking doorways and intersection), and poor adaptability across diverse complex environments (failure due to sudden accidents e.g. liquid spills). In other words, these robots often fail to capture the essence of human-like autonomy, which involves the ability to take calculated risks, even in complex environments. In this talk, I will describe my vision for a paradigm shift in the way intelligent physical agents navigate highly dense, heterogeneous, constrained, and unstructured environments using human-like autonomy.
Beyond Neuron-Level Sparsity: Achieving Faithful and Interpretable LLMs with Mixture of Decoders
Authors: Grigorios Chrysos
Venue: AAAI 2026 (main)

Abstract: As large language models (LLMs) scale, ensuring interpretability and privacy becomes critical. This talk addresses these interconnected challenges with novel approaches to model specialization and safety. First, we tackle the dense, distributed nature of LLM representations by casting Mixture-of-Experts (MoE) as a tensor decomposition, enabling specialized experts in a factorized space. Second, we argue that current neuron-level sparsity methods create a severe accuracy-sparsity trade-off, and we propose a paradigm shift to layer-level sparsity with the Mixture of Decoders (MxD). We explain how MxD uses tensor factorization to expand dense layers into thousands of specialized, full-rank sublayers, demonstrating how it significantly outperforms alternatives in preserving model faithfulness and performance across LLMs up to 3B parameters. Finally, we address privacy in open-weight models by proposing a scalable and certifiable algorithm that induces maximal uncertainty on protected instances, proving tight bounds that characterize the resulting privacy-utility tradeoff.
Bridging Public Health with Clinical Decisions from a Data Centric Perspective
Authors: Jiaming Cui
Venue: AAAI 2026 (main)

Abstract: Public health and clinical decisions are intertwined. Public
health crises place a high burden on healthcare facilities, forcing them to make decisions such as maintaining quality verses
treating more people. Meanwhile, sub-optimal clinical decisions also cause downstream effects on communities. For ex-
ample, discharging patients too early may increase disease
spread. Motivated by this, we bring a data-centric perspective
to bridge clinical decisions within the context of infectious
diseases for public health. This work addresses multiple challenges arising from effectively utilizing rich clinical datasets
and issues stemming from the complexity of disease spread
dynamics in healthcare facilities. We will cover methods
developed to address these challenges with better designed
models to optimize disease surveillance and control policies
and new techniques for end-to-end learning with mechanistic
models. We will conclude by discussing emerging challenges
and opportunities at the intersection of machine learning, scientific modeling, and clinical decision-making for computer
scientists, epidemiologists, and computational biologists.
Towards Human-centered Proactive Conversational AI
Authors: Yang Deng
Venue: AAAI 2026 (main)

Abstract: Conversational AI agents are envisioned to provide social support or functional service to human users via natural language interactions. The popularity of conversational AI has grown unprecedentedly with the advent of ChatGPT, which showcases exceptional proficiency in the capabilities of context understanding and response generation with large language models (LLMs). However, typical conversational systems are built to follow instructions, which means that the conversation is led by the user, and the system simply follows the user’s instructions or intents. My research endows the conversational AI with the capabilities of creating or controlling the conversation to achieve the conversational goals by taking initiative and anticipating impacts on themselves or human users, namely Proactive Conversational AI. I will also highlight the importance of moving towards building human-centered proactive conversational AI that emphasize human needs and expectations, and that considers ethical and social implications of these agents, rather than solely focusing on technological capabilities.
Unlocking the Power of Large Multimodal Models for Robot Learning: Robustness, Generalization, and Opportunities
Authors: Mingyu Ding
Venue: AAAI 2026 (main)

Abstract: Large multimodal models (LMMs) have revolutionized AI by
demonstrating remarkable capabilities in vision, language,
audio, and other domains, particularly in understanding and
generalization tasks. Yet, moving beyond passive
understanding to active interaction requires embodied
agents, such as robots, that can harness the capabilities
of AI models to act within the physical world. My core
research aims to build embodied agents that reason about
and interact with the physical world with human-like
commonsense. Specifically, I design algorithms and
representations that enable robots to perceive their
environment, reason about physical properties, and plan
long-horizon actions for both manipulation and locomotion.
These advances are grounded in the integration of
large-scale AI models with embodied control.

I organize this agenda into three stages: (1) injecting
actions into LMMs to form vision–language–action (VLA)
models; (2) learning from human motion and contact to
enrich physical reasoning; and (3) advancing whole-body
robot loco-manipulation guided by LMMs toward embodied
artificial general intelligence (AGI). The talk details
recent advances in leveraging LMMs for robot learning,
emphasizing the promise of robust generalization across
diverse environments, tasks, and modalities. I will
highlight contributions at the intersection of perception,
reasoning, and control, and outline open challenges and
future opportunities toward enabling humanoid robots that
can robustly understand, interact, and collaborate with
humans in complex real-world settings.