United States

Health and longevity are topics of great interest, leading to an exploration of the Japanese concept of ikigai, known for its impact on a fulfilling, extended life. Ikigai levels are dynamic, changing with personal growth and life situations, but traditional assessment methods are time-consuming, discouraging frequent tracking. In this paper, we propose Personalized Optimization and Wellbeing Enhancement Recommendation (POWER), which integrates an ikigai simulator to pre- dict ikigai levels from profile information and a hobby recommender that uses reinforcement learning to adapt recommendations based on continuous user feedback. Our methods, validated through both offline data and an online user study, effectively capture and enhance ikigai.

AAAI 2025

The POWER of Ikigai: Optimizing Life Fulfillment with an Integrated User Simulator and Adaptive Hobby Recommender

Welcome to the largest national, multi-specialty research event **featuring research from 750 medical students, residents and fellows, and international medical graduates.**


#### Event Details
The 2022 American Medical Association Research Challenge includes a poster symposium hall and a semifinals hall with the top scored research. Log in at any time throughout the event to:
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Ricardo Correa, MD, EdD
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Stuart M. Greenstein, MD
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Jessica Li, MD/PhD Candidate
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Charles Lopresto, DO

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AMA Research Challenge 2022

Welcome to the largest national, multi-specialty research event featuring research from 750 medical students, residents and fellows, and international medical graduates.


technical paper

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.



In bounded-suboptimal heuristic search, the aim is to find a solution path within a given quality bound of the optimal solution as quickly as possible, which is crucial when computational resources are limited. Recent research has demonstrated Weighted A* variants such as XDP that find bounded suboptimal solutions without needing to perform state re-expansions; they work by shifting where the suboptimality in the search is allowed. However, the suboptimality distribution is fixed before the search begins. This paper introduces a new framework that allows suboptimality to be dynamically distributed at runtime, based on the properties of the search. Experiments show this dynamic policy consistently outperforms existing algorithms across a diverse set of domains, particularly those with dynamic costs.

Suboptimal Search with Dynamic Distribution of Suboptimality

Despite the significant impact of visual events on human cognition, understanding events in videos remains a challenging task for AI due to their complex structures, semantic hierarchies, and dynamic evolution. To address this, we propose the task of video event understanding that extracts event scripts and makes predictions with these scripts from videos.
To support this task, we introduce VidEvent, a large-scale dataset containing over 23,000 well-labeled events, featuring detailed event structures, broad hierarchies, and logical relations extracted from movie recap videos. The dataset was created through a meticulous annotation process, ensuring high-quality and reliable event data.
We also provide comprehensive baseline models offering detailed descriptions of their architecture and performance metrics. These models serve as benchmarks for future research, facilitating comparisons and improvements.
Our analysis of VidEvent and the baseline models highlights the dataset's potential to advance video event understanding and encourages the exploration of innovative algorithms and models.

VidEvent: A Large Dataset for Understanding Dynamic Evolution of Events in Videos

Character-based dialogue (aka role-playing) enables users to freely customize characters for interaction, which often relies on LLMs, raising the need to evaluate LLMs’ character customization capability. However, existing benchmarks fail to ensure a robust evaluation as they often only involve a single character category or evaluate limited dimensions. Moreover, the sparsity of character features in responses makes feature-focused generative evaluation both ineffective and inefficient. To address these issues, we propose \textsc{CharacterBench}, the largest bilingual generative benchmark, with 22,859 human-annotated samples covering 3,956 characters from 25 detailed character categories. We define 11 dimensions of 6 aspects, classified as sparse and dense dimensions based on whether character features evaluated by specific dimensions manifest in each response. We enable effective and efficient evaluation by crafting tailored queries for each dimension to induce characters’ responses related to specific dimensions. Further, we develop CharacterJudge model for cost-effective and stable evaluations. Experiments show its superiority over SOTA automatic judges (e.g., GPT-4) and our benchmark’s potential to optimize LLMs’ character customization.

CharacterBench: Benchmarking Character Customization of Large Language Models

We study reasoning about relative position, orientation and distance of moving objects in 2D space. We first construct a new hybrid calculus HOPA by augmenting qualitative distance and quantitative constraints into Oriented Point Relation Algebra (OPRA). Then we develop a framework for consistency checking and reasoning with HOPA using Answer Set Programming. This framework can also explain source of inconsistency, infer new knowledge and generate a layout of objects and their orientation in the discrete space. The framework is capable of reasoning with (un)certain, heterogenous and presumed information. We evaluate efficiency and scalability of our method by computational experiments, and illustrate its applications with sample scenarios from robotic perception and marine navigation.

Hybrid Reasoning About Relative Position and Orientation of Objects and Navigating Agents Using Answer Set Programming

Consider the scenario where multiple agents have to move in an optimal way through a network, each one towards their ending position, and while avoiding collisions. By optimal, we mean as fast as possible, which is evaluated by a measure known as the makespan of the proposed solution. This is the setting studied in the \textsc{Multiagent Path Finding} problem. In this work we additionally provide the agents with a way to communicate with each other. Due to size constraints, it is reasonable to assume that the range of the communication of each agent will be limited. What should be the trajectories of the agents to, additionally, maintain a backbone of communication? In this work we study this \textsc{Multiagent Path Finding with Communication Constraint} problem under the parameterized complexity framework.

Our main contribution is three exact algorithms that are efficient when considering particular structures for the input network. We provide such algorithms for the case when the communication range and the number of agents (the makespan resp.) is provided in the input and the network has a tree topology, or bounded maximum degree (has a tree-like topology, i.e., bounded treewidth resp.). We complement these results by showing that it is highly unlikely to construct efficient algorithms when considering the number of agents as part of the input, even if the makespan is $3$ and the communication range is $1$.

Exact Algorithms for Multiagent Path Finding with Communication Constraints on Tree-Like Structures

With Large Language Model (LLM) agents taking on more evaluation responsibilities in decision-making, it is essential to recognize their possible biases to guarantee fair and trust- worthy AI-supported decisions. This study is the first to thoroughly examine the choice-supportive bias in LLM agents, a cognitive bias that is known to impact human decisionmaking and evaluation. We conduct experiments across 19 open/unopen-source LLM models in five scenarios at maximum, employing both memory-based and evaluation-based tasks adapted and redesigned from human cognitive studies. Our findings show that LLM agents may exhibit biased attribution or evaluation that supports their initial choices, and such bias may persist even if contextual hallucination is not observable. Key findings show that bias manifestation can differ greatly depending on prompt construction and context preservation, and the bias may be mitigated in larger models. Significantly, we observe that the bias increases when the agents perceive they are in control. Our extensive study involving 301 well-educated humans shows that, despite bias, certain LLM agents can still perform better than humans in similar evaluation tasks. This research contributes to the growing area of AI psychology, and the findings underscore the importance of addressing cognitive biases in LLM Agent systems, with wide-ranging implications spanning from improving AI-assisted decision-making to advancing AI safety and ethics.

LLM Agents Can Be Choice-Supportive Biased Evaluators: An Empirical Study

Model editing aims to correct outdated or erroneous knowledge in large models without costly retraining. Recent research discovered that the mid-layer representation of the subject's final token in a prompt has a strong influence on factual predictions, and developed Large Language Model (LLM) editing techniques based on this observation. However, for Vision-LLMs (VLLMs), how visual representations impact the predictions from a decoder-only language model remains largely unexplored. To the best of our knowledge, model editing for VLLMs has not been extensively studied in the literature. In this work, we employ the contribution allocation and noise perturbation methods to measure the contributions of visual representations for token predictions. Our attribution analysis shows that visual representations in mid-to-later layers that are highly relevant to the prompt contribute significantly to predictions. Based on these insights, we propose *VisEdit*, a novel model editor for VLLMs that effectively corrects knowledge by editing intermediate visual representations in regions important to the edit prompt. We evaluated *VisEdit* using multiple VLLM backbones and public VLLM editing benchmark datasets. The results show the superiority of *VisEdit* over the strong baselines adapted from existing state-of-the-art editors for LLMs.

Attribution Analysis Meets Model Editing: Advancing Knowledge Correction in Vision Language Models with VisEdit

Spatially Resolved Transcriptomics (SRT) has become an indispensable tool in various fields, including tumor micro-environment identification, neurobiology, and the study of complex tissue architecture.  However, the accuracy of these insights is often compromised by noise in spatial transcriptomics data due to technical limitations. While recent advancements in denoising methods have shown some promise, they frequently fall short by neglecting spatial features, overlooking the variability in noise levels among genes, and relying heavily on external histological images for supplementary information. In our study, we propose DUSTED, a Dual-Attention Enhanced Spatial Transcriptomics Denoiser, designed to address these challenges. Built on a graph autoencoder framework, DUSTED utilizes gene channel attention and graph attention mechanisms to simultaneously consider spatial features and noise variability in gene expression data. Additionally, it integrates prior biological knowledge, ensuring a more accurate fit for gene expression distributions. Benchmark tests using simulated datasets demonstrate that DUSTED outperforms existing methods. Furthermore, in real-world applications with the HOCWTA and DLPFC datasets, DUSTED excels in enhancing the correlation between gene and protein expression, recovering spatial gene expression patterns, and improving clustering results. These improvements underscore its potential impact on advancing our understanding of tumor micro-environments, neural tissue organization, and other biologically significant areas.

DUSTED: Dual-Attention Enhanced Spatial Transcriptomics Denoiser

Traditional recurrent neural network architectures, such as long short-term memory neural networks (LSTM), have historically held a prominent role in time series forecasting (TSF) tasks. While the recently introduced sLSTM for Natural Language Processing (NLP) introduces exponential gating and memory mixing that are beneficial for long term sequential learning, its potential short memory issue is a barrier to applying sLSTM directly in TSF. To address this, we propose a simple yet efficient algorithm named P-sLSTM, which is built upon sLSTM by incorporating patching and channel independence. These modifications substantially enhance sLSTM's performance in TSF, achieving state-of-the-art results. Furthermore, we provide theoretical justifications for our design, and conduct extensive comparative and analytical experiments to fully validate the efficiency and superior performance of our model.

Unlocking the Power of LSTM for Long Term Time Series Forecasting

In the context of reinforcement learning from human feedback (RLHF), the reward function is generally derived from maximum likelihood estimation of a random utility model based on pairwise comparisons made by humans. The problem of learning a reward function is one of preference aggregation that, we argue, largely falls within the scope of social choice theory. From this perspective, we can evaluate different aggregation methods via established axioms, examining whether these methods meet or fail well-known standards. We demonstrate that both the Bradley-Terry-Luce Model and its broad generalizations fail to meet basic axioms. In response, we develop novel rules for learning reward functions with strong axiomatic guarantees. A key innovation from the standpoint of social choice is that our problem has a linear structure, which greatly restricts the space of feasible rules and leads to a new paradigm that we call linear social choice.

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