United States

There has been tremendous progress in the past decade in the
field of quantified Boolean formulas (QBF), both in practical
solving as well as in creating a theory of corresponding proof
systems and their proof complexity analysis. Both for solving
and for proof complexity, it is important to have interesting
formula families on which we can test solvers and gauge the
strength of the proof systems. There are currently few such
formula families in the literature.
We initiate a general programme how to transform computationally
hard problems (located in the polynomial hierarchy)
into QBFs hard for the main QBF resolution systems Q-Res
and QU-Res that relate to core QBF solvers. We illustrate
this general approach on three problems from graph theory
and logic. This yields QBF families that are provably hard for
Q-Res and QU-Res (without any complexity assumptions).

AAAI 2025

Computationally Hard Problems Are Hard for QBF Proof Systems Too

satisfiability

We are pleased to announce the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI-25), which will be held in Philadelphia, Pennsylvania at the Pennsylvania Convention Center from February 25 to March 4, 2025.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

### [Invited Speakers](https://aaai.org/conference/aaai/aaai-25/aaai-25-invited-speakers/)

Register [here](https://aaai.org/conference/aaai/aaai-25/registration/)

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.



technical paper

Present bias, the tendency to overvalue immediate rewards while undervaluing future ones, is a well-known barrier to achieving long-term goals. As artificial intelligence and behavioral economics increasingly focus on this phenomenon, the need for robust mathematical models to predict behavior and guide effective interventions has become crucial. However, existing models are constrained by their reliance on the discreteness of time and limited discount functions. This study introduces a novel continuous-time mathematical model for agents influenced by present bias.
Using the variational principle, we model human behavior, where individuals repeatedly act according to a sequence of states that minimize their perceived cost.
Our model not only retains analytical tractability but also accommodates various discount functions. Using this model, we consider intervention optimization problems under exponential and hyperbolic discounting and theoretically derive optimal intervention strategies, offering new insights into managing present-biased behavior.

A Continuous-time Tractable Model for Present-biased Agents

The advancement in multimodal research has increased focus on Emotion Recognition in Conversations (ERC), targeting accurately identifying emotional changes. Methods based on graph convolution can better capture the dynamic changes of emotions and improve the accuracy and robustness of emotion recognition. However, existing methods do not distinguish the interaction patterns of a conversation, which results in limiting their ability to model contextual emotional relationships. In this paper, we propose a Dynamic Interactive Bimodal HyperGraph Convolutional Networks (DIB-HGCN), which creatively constructs two types of sub-hypergraphs, i.e., the monologic sub-hypergraph and the dialogic sub-hypergraph, for modeling emotion relationships of different interaction patterns. The monologic sub-hypergraph is used to explore the contextual consistent emotions during the speaker's monologue interactions, while the dialogic sub-hypergraph focuses on capturing the emotional transfers in the dialogic interactions. Meanwhile, the single window partitioning mechanism fails to accommodate the distinct emotional velocity variations across the two interaction patterns. Therefore, we set up dynamic windows in the monologic interactions to fully utilize the information of sentence nodes with consistent emotions, and we add fragment windows to the dialogic interactions to prevent information interference caused by frequent emotional transfers. The experimental results show that our proposed method outperforms existing methods on two benchmark multimodal ERC datasets.

Dynamic Interactive Bimodal Hypergraph Networks for Emotion Recognition in Conversations

We introduce Neural Conjugate Flows (NCF), a class of neural-network architectures equipped with exact flow structure. By leveraging topological conjugation, we prove that these networks are not only naturally isomorphic to a continuous group, but are also universal approximators for flows of ordinary differential equation (ODEs). Furthermore, topological properties of these flows can be enforced by the architecture in an interpretable manner. We demonstrate in numerical experiments how this topological group structure leads to concrete computational gains over other physics informed neural networks in estimating and extrapolating latent dynamics of ODEs, while training up to five times faster than other flow-based architectures.

Neural Conjugate Flows: A Physics-Informed Architecture with Flow Structure

Gaussian Splatting (GS) has emerged as a crucial technique for representing discrete volumetric radiance fields. It leverages unique parametrization to mitigate computational demands in scene optimization. This work introduces Topology-Aware 3D Gaussian Splatting (Topology-GS), which addresses two key limitations in current approaches: compromised pixel-level structural integrity due to incomplete initial geometric coverage, and inadequate feature-level integrity from insufficient topological constraints during optimization. To overcome these limitations, Topology-GS incorporates a novel interpolation strategy, Local Persistent Voronoi Interpolation (LPVI), and a topology-focused regularization term based on persistent barcodes, named PersLoss. LPVI utilizes persistent homology to guide adaptive interpolation, enhancing point coverage in low-curvature areas while preserving topological structure. PersLoss aligns the visual perceptual similarity of rendered images with ground truth by constraining distances between their topological features. Comprehensive experiments on three novel-view synthesis benchmarks demonstrate that Topology-GS outperforms existing methods in terms of PSNR, SSIM, and LPIPS metrics, while maintaining efficient memory usage. This study pioneers the integration of topology with 3D-GS, laying the groundwork for future research in this area.

Topology-Aware 3D Gaussian Splatting: Leveraging Persistent Homology for Optimized Structural Integrity

Multimodal Sentiment Analysis (MSA) stands as a critical research frontier, seeking to comprehensively unravel human emotions by amalgamating text, audio, and visual data. Yet, discerning subtle emotional nuances within audio and video expressions poses a formidable challenge, particularly when emotional polarities across various segments appear similar. In this paper, our objective is to spotlight emotion-relevant attributes of audio and visual modalities to facilitate multimodal fusion in the context of nuanced emotional shifts in visual-audio scenarios. To this end, we introduce DEVA, a progressive fusion framework founded on textual sentiment descriptions aimed at accentuating emotional features of visual-audio content. DEVA employs an Emotional Description Generator (EDG) to transmute raw audio and visual data into textualized sentiment descriptions, thereby amplifying their emotional characteristics. These descriptions are then integrated with the source data to yield richer, enhanced features. Furthermore, DEVA incorporates the Text-guided Progressive Fusion Module (TPF), leveraging varying levels of text as a core modality guide. This module progressively fuses visual-audio minor modalities to alleviate disparities between text and visual-audio modalities. Experimental results on widely-used sentiment analysis benchmark datasets, including MOSI, MOSEI, and CH-SIMS, underscore significant enhancements compared to state-of-the-art models. Moreover, fine-grained emotion experiments corroborate the robust sensitivity of DEVA to subtle emotional variations.

Enriching Multimodal Sentiment Analysis Through Textual Emotional Descriptions of Visual-Audio Content

Large language models (LLMs) have demonstrated strong capabilities in language understanding and generation, and their potential in educational contexts is increasingly being explored. One promising area is learnersourcing, where students engage in creating their own educational content, such as multiple-choice questions. A critical step in this process is generating effective explanations for the solutions to these questions, as such explanations aid in peer understanding and promote deeper conceptual learning. However, students often find it difficult to craft high-quality explanations due to limited understanding or gaps in their subject knowledge. To support this task, we introduce ``ILearner-LLM,'' a framework that uses iterative enhancement with LLMs to improve generated explanations. The framework combines an explanation generation model and an explanation evaluation model fine-tuned using student preferences for quality, where feedback from the evaluation model is fed back into the generation model to refine the output. Our experiments with LLaMA2-13B and GPT-4 using five large datasets from the PeerWise MCQ platform show that ILearner-LLM produces explanations of higher quality that closely align with those written by students. Our findings represent a promising approach for enriching the learnersourcing experience for students and for leveraging the capabilities of large language models for educational applications.

Exploring Iterative Enhancement for Improving Learnersourced Multiple-Choice Question Explanations with Large Language Models

Assessing students' responses, especially natural language responses, is a major challenge in education. In general, in education contexts, automatically evaluating what learners do or say is important as it enables personalized instruction, e.g., based on what the learner knows tailored tasks and feedback are given to the learner. Recently, deep learning techniques led to state-of-the-art methods in NLP such as transformer-based methods which resulted in significant performance improvements for many NLP tasks such as text classification and question answering. However, there is not much work exploring such methods for assessing students' free answers, particularly in the context of code comprehension, which brings additional challenges as the student explanations include code references as well. This paper explores the potential of applying automated assessments methods using transformers to code comprehension. We fine-tuned pre-trained transformer models, including BERT, RoBERTa, CodeBERT, and SciBERT, to see how well they can automatically judge students' responses to code comprehension tasks. Our results demonstrate that these models can significantly enhance the accuracy and reliability of automated assessments, offering insights into how the latest NLP techniques can be leveraged in computer science education to support personalized learning experiences.

Automated Assessment of Student Self-Explanation in Code Comprehension Using Pre-Trained Language Models

We present a novel Automatic Target Recognition (ATR) system using open-vocabulary object detection and classification models. A primary advantage of this approach is that target classes can be defined just before runtime by a non-technical end user, using either a few natural language text descriptions of the target, or a few image exemplars, or both. Nuances in the desired targets can be expressed in natural language, which is useful for unique targets with little or no training data. We also implemented a novel combination of several techniques to improve performance, such as leveraging the additional information in the sequence of overlapping frames to perform tubelet identification (i.e., sequential bounding box matching), bounding box re-scoring, and tubelet linking. Additionally, we developed a technique to visualize the aggregate output of many overlapping frames as a mosaic of the area scanned during the aerial surveillance or reconnaissance, and a kernel density estimate (or heatmap) of the detected targets. We initially applied this ATR system to the use case of detecting and clearing unexploded ordinance on airfield runways and we are currently extending our research to other real-world applications.

An Application-Agnostic Automatic Target Recognition System Using Vision Language Models

Humans have a strong intuitive understanding of the physical world. Through observations and interactions with the environment, we build mental models that predict how the world would change if we applied a specific action (i.e., intuitive physics). My research draws on these human insights to develop model-based RL agents that learn from their interactions and build predictive models that generalize widely across a range of objects made with different materials. The core idea behind my research is to introduce novel representations and integrate structural priors into learning systems to model dynamics at different levels of abstraction. I will discuss how such structures can make model-based planning algorithms more effective, helping robots accomplish complex manipulation tasks (e.g., manipulating an object pile, shaping deformable foam into a target configuration, and making a dumpling from dough using various tools).

Learning Structured World Models From and For Physical Interactions

Language is highly structured, with syntactic and semantic structures, to some extent, agreed upon by speakers. With implicit or explicit awareness of such structures, humans can learn and use language efficiently and generalize to sentences that contain unseen words. Motivated by human language learning, in this presentation, I will introduce a family of machine learning tasks that learns language structures through grounding, where distant supervision from other data sources (i.e., grounds), including but not limited to different modalities (e.g., vision), execution results of programs, and other languages, are used to guide the learning of language structures. I will demonstrate the potential of this task formulation, advocate for its adoption through three schemes, and discuss the possibility of the general language learning problem through grounding.

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A Continuous-time Tractable Model for Present-biased Agents

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