United States

Multi-objective evolutionary algorithms (MOEAs) are widely used for searching optimal solutions in complex multi-component applications. Traditional MOEAs for multi-component deep learning (MCDL) systems face challenges in enhancing the search efficiency while maintaining the diversity. To combat these, this paper proposes $\mu$MOEA, the first LLM-empowered adaptive evolutionary search methodology to detect safety violations in MCDL systems. Inspired by the context-understanding ability of Large Language Models (LLMs), $\mu$MOEA promotes the LLM to comprehend the optimization problem and generate an initial population tailed to evolutionary objectives. Subsequently, it employs adaptive selection and variation techniques to iteratively produce offspring, balancing the evolutionary efficiency and diversity.
During the evolutionary process, to navigate away from the local optima,  $\mu$MOEA integrates the evolutionary experience back into the LLM. This utilization harnesses the LLM&#39;s quantitative reasoning prowess to generate differential seeds, breaking away from current optimal solutions. We evaluate the effectiveness of \name in finding safety violations of MCDL systems, and compare its performance with state-of-the-art MOEA methods. Experimental results show that \name can significantly improve the efficiency and diversity of the multi-objective evolutionary search.

AAAI 2025

An LLM-Empowered Adaptive Evolutionary Algorithm for Multi-Component Deep Learning Systems

evolutionary learning

ECAI2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI, including:




**An extensive programme of workshops, tutorials, and invited speakers**

**The Prestigious Applications of Intelligent Systems conference (PAIS)**

**The Frontiers in AI track sessions**

**The Lab To Market event**

**The Women in AI time**

**The EU Challenges forum**

… and many more!

ECAI 2020

ECAI 2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI.

Multi-objective evolutionary algorithms (MOEAs) are widely used for searching optimal solutions in complex multi-component applications. Traditional MOEAs for multi-component deep learning (MCDL) systems face challenges in enhancing the search efficiency while maintaining the diversity. To combat these, this paper proposes $\mu$MOEA, the first LLM-empowered adaptive evolutionary search methodology to detect safety violations in MCDL systems. Inspired by the context-understanding ability of Large Language Models (LLMs), $\mu$MOEA promotes the LLM to comprehend the optimization problem and generate an initial population tailed to evolutionary objectives. Subsequently, it employs adaptive selection and variation techniques to iteratively produce offspring, balancing the evolutionary efficiency and diversity.
During the evolutionary process, to navigate away from the local optima,  $\mu$MOEA integrates the evolutionary experience back into the LLM. This utilization harnesses the LLM's quantitative reasoning prowess to generate differential seeds, breaking away from current optimal solutions. We evaluate the effectiveness of \name in finding safety violations of MCDL systems, and compare its performance with state-of-the-art MOEA methods. Experimental results show that \name can significantly improve the efficiency and diversity of the multi-objective evolutionary search.

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.



We focus on the medication recommendation problem aiming to recommend accurate medications for a patient’s current visit. Most existing methods for this problem utilize the patient’s current health status, medications prescribed at her past visits, and an Electronic Health Records (EHR) graph which represents whether medications have been co-prescribed. However, we point out their two limitations: (1) they have difficulty in utilizing only the medications which have been prescribed in health status similar to the patient’s current health status, regardless of whether they are prescribed at her past visits or at other patients’ visits; (2) for two medications that have ever been co-prescribed, their EHR graph does not consider the degree to which one medication is prescribed together when the other is prescribed. To address these two limitations, we propose a novel medication recommendation framework, named HI-DR (pronounced as ‘Hi Doctor’), composed of following two core ideas: (Idea 1) Health status-aware attentIon; (Idea 2) an electronic health recorDs gRaph+. Extensive experiments on real-world datasets demonstrate the significant superiority of HI-DR (up to 18.69% higher accuracy than the best competitor) and the effectiveness of two core ideas in HI-DR.

HI-DR: Exploiting Health Status-Aware Attention and an EHR Graph+ for Effective Medication Recommendation

Understanding how humans cooperatively utilize semantic knowledge to explore unfamiliar environments and decide on navigation directions is critical for house service multi-robot systems. Previous methods primarily focused on single-robot centralized planning strategies, which severely limited exploration efficiency. Recent research has considered decentralized planning strategies for multiple robots, assigning separate planning models to each robot, but these approaches often overlook communication costs. In this work, we propose Multimodal Chain-of-Thought Co-Navigation (MCoCoNav), a modular approach that utilizes multimodal Chain-of-Thought to plan collaborative semantic navigation for multiple robots. MCoCoNav combines visual perception with Vision Language Models (VLMs) to evaluate exploration value through probabilistic scoring, thus reducing time costs and achieving stable outputs. Additionally, a global semantic map is used as a communication bridge, minimizing communication overhead while integrating observational results. Guided by scores that reflect exploration trends, robots utilize this map to assess whether to explore new frontier points or revisit history nodes. Experiments on HM3D_v0.2 and MP3D demonstrate the effectiveness of our approach.

Enhancing Multi-Robot Semantic Navigation Through Multimodal Chain-of-Thought Score Collaboration

The primary objective of Optical Chemical Structure Recognition is to identify chemical structure images into corresponding markup sequences. However, the complex two-dimensional structures of molecules, particularly those with rings and multiple branches, present significant challenges for current end-to-end methods to learn one-dimensional markup directly. To overcome this limitation, we propose a novel Ring-Free Language (RFL), which utilizes a divide-and-conquer strategy to describe chemical structures in a hierarchical form. RFL allows complex molecular structures to be decomposed into multiple parts, ensuring both uniqueness and conciseness while enhancing readability. This approach significantly reduces the learning difficulty for recognition models. Leveraging RFL, we propose a universal Molecular Skeleton Decoder (MSD), which comprises a skeleton generation module that progressively predicts the molecular skeleton and individual rings, along with a branch classification module for predicting branch information. Experimental results demonstrate that the proposed RFL and MSD can be applied to various mainstream methods, achieving superior performance compared to state-of-the-art approaches in both printed and handwritten scenarios. The code will be made publicly available.

RFL: Simplifying Chemical Structure Recognition with Ring-Free Language

In the rapidly evolving landscape of large language models (LLMs) for medical applications, ensuring the reliability and accuracy of these models in clinical settings is paramount. Existing benchmarks often focus on fixed-format tasks like multiple-choice QA, which fail to capture the complexity of real-world clinical diagnostics. Moreover, traditional evaluation metrics and LLM-based evaluators struggle with misalignment, often providing oversimplified assessments that do not adequately reflect human judgment. To address these challenges, we introduce HDCEval, a **H**ierarchical **D**ivide-and-**C**onquer **Eval**uation framework tailored for fine-grained alignment in medical evaluation. HDCEval is built on a set of fine-grained medical evaluation guidelines developed in collaboration with professional doctors, encompassing Patient Question Relevance, Medical Knowledge Correctness, and Expression. The framework decomposes complex evaluation tasks into specialized subtasks, each evaluated by expert models trained through Attribute-Driven Token Optimization (ADTO) on a meticulously curated preference dataset. This hierarchical approach ensures that each aspect of the evaluation is handled with expert precision, leading to a significant improvement in alignment with human evaluators.

Hierarchical Divide-and-Conquer for Fine-Grained Alignment in LLM-Based Medical Evaluation

Mixed Integer Linear Program (MILP) solvers are mostly built upon a Branch-and-Bound (B&B) algorithm, where the efficiency of traditional solvers heavily depends on hand-crafted heuristics for branching. The past few years have witnessed the increasing popularity of data-driven approaches to learn these heuristics automatically. However, the success of these methods heavily relies on the availability of high-quality demonstrations, which requires either the development of near-optimal heuristics or a time-consuming sampling process. This paper averts this challenge by proposing Suboptimal-Demonstration-Guided Reinforcement Learning (SORREL) for learning to branch. SORREL selectively learns from suboptimal demonstrations based on value estimation. It utilizes suboptimal demonstrations through both offline reinforcement learning on the demonstrations generated by suboptimal heuristics and self-imitation learning on past good experiences sampled by itself. Our experiments demonstrate its advanced performance in both branching quality and training efficiency over previous methods for various MILP problems.

SORREL: Suboptimal-Demonstration-Guided Reinforcement Learning for Learning to Branch

Beginning with Witkowski et al. (2021), recent work on forecasting competitions has addressed incentive problems with the common winner-take-all mechanism. Frongillo et al. (2021) propose a competition mechanism based on Multiplicative Weights, an online learning algorithm. They show that their mechanism selects an $\epsilon$-optimal forecaster with high probability using only $O(\log(n)/\epsilon^2)$ events. These works, together with all prior work on this problem thus far, assume that events are independent. We prove the first accuracy and approximate truthfulness guarantees for forecasting competitions with correlated events. To quantify correlation, we introduce a notion of block correlation, which allows each event to be strongly correlated with up to $b$ others and weakly correlated with the rest. We show that under distributions with this correlation, the Multiplicative Weights mechanism retains its $\epsilon$-optimal guarantee using $O(b^2 \log(n)/\epsilon^2)$ events. Our proof involves a novel concentration bound for correlated random variables which may be of broader interest.

Forecasting Competitions with Correlated Events

Assignment problems are a classic combinatorial optimization problem in which a group of agents must be assigned to a group of tasks such that maximum utility is achieved while satisfying assignment constraints. Given the utility of each agent completing each task, polynomial-time algorithms exist to solve a single assignment problem in its simplest form. However, in many modern-day applications such as satellite constellations, power grids, and mobile robot scheduling, assignment problems unfold over time, with the utility for a given assignment depending heavily on the state of the system. We apply multi-agent reinforcement learning to this problem, learning the value of assignments by bootstrapping from the known polynomial-time greedy solver and then learning from further experience. We then choose assignments using a distributed optimal assignment mechanism rather than by selecting them directly. We demonstrate that this algorithm is theoretically justified and avoids pitfalls experienced by other RL algorithms in this setting. Finally, we show that our algorithm significantly outperforms other methods in the literature, even while scaling to realistic scenarios with hundreds of agents and tasks.

Multi Agent Reinforcement Learning for Sequential Satellite Assignment Problems

Multi-modality pre-training paradigm that aligns protein sequences and biological descriptions has learned general protein representations and achieved promising performance in various downstream applications. However, these works were still unable to replicate the extraordinary success of language-supervised visual foundation models due to the ineffective usage of aligned protein-text paired data and the lack of an effective function-informed pre-training paradigm. To address these issues, this paper curates a large-scale protein-text paired dataset called ProtAnno with a property-driven sampling strategy, and introduces a novel function-informed protein pre-training paradigm. Specifically, the sampling strategy determines sampling probability based on the confidence and coverage of property annotations, balancing the data quality and data quantity in face of large-scale noisy data. Furthermore, motivated by significance of the protein specific functional mechanism, the proposed paradigm explicitly model protein static and dynamic functional segments by two segment-wise pre-training objectives, injecting fine-grained information in a function-informed manner. Leveraging all these innovations, we develop ProtCLIP, a multi-modality foundational model that comprehensively represents function-aware protein embeddings. On 22 different protein benchmarks within 5 types, including protein functionality classification, mutation effect prediction, cross-modality transformation, and protein-protein interaction prediction, our ProtCLIP consistently achieves the state-of-the-art performance, with remarkable improvements of 75% on average in five cross-modality transformation tasks, 59.9% in GO-CC and 39.7% in GO-BP protein function prediction. The experimental results demonstrate the extraordinary potential of ProtCLIP serving as the protein multi-modality foundation model.

ProtCLIP: Function-Informed Protein Multi-Modal Learning

In multi-agent reinforcement learning, centralized training with decentralized execution (CTDE) methods typically assume that agents make decisions based on their local observations independently, which may not lead to a correlated joint policy with coordination. Coordination can be explicitly encouraged during training and individual policies can be trained to imitate the correlated joint policy. However, this may lead to an $\textit{asymmetric learning failure}$ due to the observation mismatch between the joint and individual policies. Inspired by the concept of correlated equilibrium, we introduce a $\textit{strategy modification}$ called AgentMixer that allows agents to correlate their policies. AgentMixer combines individual partially observable policies into a joint fully observable policy non-linearly. To enable decentralized execution, we introduce $\textit{Individual-Global-Consistency}$ to guarantee mode consistency during joint training of the centralized and decentralized policies and prove that AgentMixer converges to an $\epsilon$-approximate Correlated Equilibrium. In the Multi-Agent MuJoCo, SMAC-v2, Matrix Game, and Predator-Prey benchmarks, AgentMixer outperforms or matches state-of-the-art methods.

AgentMixer: Multi-Agent Correlated Policy Factorization

Existing semantic segmentation methods face challenges when processing input images degraded by raindrops on the lens or windshield. Unlike other adverse conditions such as fog and nighttime, which degrade visual quality, raindrops not only impair visual appearances but also introduce misleading occlusion, leading to significant performance drops in current models. The novelty of our approach lies in our two-stage, dual teacher-student framework. We tackle the complex problem of raindrop degradation by dividing it into two distinct challenges: degraded visual appearance and raindrop occlusion. These challenges are then addressed individually in two stages, utilizing two pairs of teacher-student networks. This division enables the networks to develop specialized expertise in handling each aspect of raindrop degradation, enabling their collaboration to achieve superior performance. In the first stage, one teacher-student pair focuses on learning to extract information from visual degraded areas. Building on this, the second teacher-student pair focuses specially on the raindrop occlusion. As such, unlike the existing methods, our approach employs a collaborative approach to decompose and address raindrop-induced degradations. In the second stage, we introduce a mask-based recovery technique to identify and rectify areas that likely contain misleading information, thus further refining the predictions. Additionally, this stage encourages both pairs to expand knowledge by swapping their specialized expertise. Our method achieves a performance of 60.3 mIoU on Rainy WCity and 72.8 mIoU on ACDC Rainy, representing an improvement of +4.4 mIoU and +2.3 mIoU over the existing state-of-the-art methods, respectively.

Downloads

Next from AAAI 2025

HI-DR: Exploiting Health Status-Aware Attention and an EHR Graph+ for Effective Medication Recommendation

Similar lecture

Many-Objective Problems Are Not Always Difficult for Pareto Dominance-Based Evolutionary Algorithms

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES