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Automatic Heuristic Design (AHD) is an active research area due to its utility in solving complex search and NP-hard combinatorial optimization problems in the real world. The recent advancements in Large Language Models (LLMs) introduce new possibilities by coupling LLMs with evolutionary computation to automatically generate heuristics, known as LLM-based Evolutionary Program Search (LLM-EPS). While previous LLM-EPS studies obtained great performance on various tasks, there is still a gap in understanding the properties of heuristic search spaces and achieving a balance between exploration and exploitation, which is a critical factor in large heuristic search spaces. In this study, we address this gap by proposing two diversity measurement metrics and perform an analysis on previous LLM-EPS approaches, including FunSearch, EoH, and ReEvo. Results on black-box AHD problems reveal that while EoH demonstrates higher diversity than FunSearch and ReEvo, its objective score is unstable. Conversely, ReEvo&#39;s reflection mechanism yields good objective scores but fails to optimize diversity effectively. With this finding in mind, we introduce HSEvo, an adaptive LLM-EPS framework that maintains a balance between diversity and convergence with a harmony search algorithm. Through experimentation, we find that HSEvo achieved high diversity indices and good objective scores while remaining cost-effective. These results underscore the importance of balancing exploration and exploitation and understanding heuristic search spaces in designing frameworks in LLM-EPS.

AAAI 2025

HSEvo: Elevating Automatic Heuristic Design with Diversity-Driven Harmony Search and Genetic Algorithm Using LLMs

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

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-26 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.

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AAAI 2026

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

Automatic Heuristic Design (AHD) is an active research area due to its utility in solving complex search and NP-hard combinatorial optimization problems in the real world. The recent advancements in Large Language Models (LLMs) introduce new possibilities by coupling LLMs with evolutionary computation to automatically generate heuristics, known as LLM-based Evolutionary Program Search (LLM-EPS). While previous LLM-EPS studies obtained great performance on various tasks, there is still a gap in understanding the properties of heuristic search spaces and achieving a balance between exploration and exploitation, which is a critical factor in large heuristic search spaces. In this study, we address this gap by proposing two diversity measurement metrics and perform an analysis on previous LLM-EPS approaches, including FunSearch, EoH, and ReEvo. Results on black-box AHD problems reveal that while EoH demonstrates higher diversity than FunSearch and ReEvo, its objective score is unstable. Conversely, ReEvo's reflection mechanism yields good objective scores but fails to optimize diversity effectively. With this finding in mind, we introduce HSEvo, an adaptive LLM-EPS framework that maintains a balance between diversity and convergence with a harmony search algorithm. Through experimentation, we find that HSEvo achieved high diversity indices and good objective scores while remaining cost-effective. These results underscore the importance of balancing exploration and exploitation and understanding heuristic search spaces in designing frameworks in LLM-EPS.

poster

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.



Prompt optimization automatically optimizes prompting expressions, unlocking the full potential of LLMs in downstream tasks. However, current prompt optimization methods are costly to train and lack sufficient interpretability. This paper proposes enhancing LLMs' reasoning performance by eliciting their causal inference ability from prompting instructions to correct answers. Specifically, we introduce the Self-Causal Instruction Enhancement (SCIE) method, which enables LLMs to generate high-quality, low-quantity observational data, then estimate the causal effect based on this data, and ultimately generate instructions with the optimized causal effect. In SCIE, the instructions are treated as the treatment, and proxy features are used to process natural language, establishing causal relationships through proxy treatments between instructions and downstream tasks. Additionally, we propose applying Object-Relational (OR) principles, where the uncovered causal relationships are treated as the inheritable class across task objects, ensuring low-cost reusability. Extensive experiments demonstrate that our method effectively generates instructions that enhance reasoning performance with reduced training cost, leveraging interpretable textual features to provide clear and actionable insights.

Eliciting Causal Abilities in Large Language Models for Reasoning Tasks

The super-alignment problem of how humans can effectively supervise super-human AI has garnered increasing attention. Recent research has focused on investigating the weak-to-strong generalization (W2SG) scenario as an analogy for super-alignment. This scenario examines how a pre-trained strong model, supervised by an aligned weak model, can outperform its weak supervisor. Despite good progress, current W2SG methods face two main issues: 1) The annotation quality is limited by the knowledge scope of the weak model; 2) It is risky to position the strong model as the final corrector.

To tackle these issues, we propose a ``Strong Empowered and Aligned Weak Mastered'' (SEAM) framework for weak annotations in W2SG. This framework can leverage the vast intrinsic knowledge of the pre-trained strong model to empower the annotation and position the aligned weak model as the annotation master. Specifically, the pre-trained strong model first generates principle fast-and-frugal trees for samples to be annotated, encapsulating rich sample-related knowledge. Then, the aligned weak model picks informative nodes based on the tree's information distribution for final annotations. Experiments on six datasets for preference tasks in W2SG scenarios validate the effectiveness of our proposed method.

Strong Empowered and Aligned Weak Mastered Annotation for Weak-to-Strong Generalization

With the rising use of neural networks across various application domains, it becomes increasingly important to ensure that they do not exhibit dangerous or undesired behaviour. In light of this, several neural network robustness verification algorithms have been developed, among which methods based on Branch and Bound (BaB) constitute the current state of the art. However, these algorithms still require immense computational resources. In this work, we seek to reduce this cost by leveraging running time prediction techniques, thereby allowing for more efficient resource allocation and use. 
Towards this end, we present a novel method that dynamically predicts whether a verification instance can be solved in the remaining time budget available to the verification algorithm. We introduce features describing BaB-based verification instances and use these to construct running time, and more specifically, timeout prediction models. We leverage these models to terminate runs on instances early in the verification process that would otherwise result in a timeout. Overall, using our method, we were able to reduce the total running time by 64% on average compared to the standard verification procedure, while certifying a comparable number of instances.

Dynamic Algorithm Termination for Branch-and-Bound-based Neural Network Verification

Large language models (LLMs) have significantly advanced the field of automated code generation. However, a notable research gap exists in the evaluation of social biases that may be present in the code produced by LLMs. To solve this issue, we propose a novel fairness framework, i.e., Solar, to assess and mitigate the social biases of LLM-generated code. Specifically, Solar can automatically generate test cases for quantitatively uncovering social biases of the auto-generated code by LLMs. To quantify the severity of social biases in generated code, we develop a dataset that covers a diverse set of social problems. We applied Solar and the crafted dataset to four state-of-the-art LLMs for code generation. Our evaluation reveals severe bias in the LLM-generated code from all the subject LLMs. Furthermore, we explore several strategies for bias mitigation,  including Chain-of-Thought (CoT) prompting, combining positive role-playing with CoT prompting and iterative prompting. Our experiments show that iterative prompting can effectively reduce social bias in LLM-generated code by up to 90%. Solar is highly extensible to evaluate new social problems.

Bias Unveiled: Investigating Social Bias in LLM-Generated Code

Multi-objective preference alignment of large language models (LLMs) is critical for developing personalizable, helpful and harmless AI systems. However, optimizing model outputs in the presence of diverse objectives, while also allowing for varying the relative weights of these objectives at inference time presents a significant challenge. Existing approaches are either computationally expensive to train or do not exhibit sufficient steerability at inference time. This paper introduces the Multi-Objective Online DPO (MO-ODPO) algorithm, designed to robustly and efficiently align model behaviors with multiple, potentially conflicting human preferences. Our approach incorporates a prompt conditioning mechanism, allowing for easy adjustment of user preferences at test time while only training a single policy, ensuring adaptive and personalized model performance. Experimental results on two popular benchmarks demonstrate the efficacy of MO-ODPO in achieving Pareto-optimal performance as well as good inference-time steerability over current state-of-the-art approaches.

Robust Multi-Objective Preference Alignment with Online DPO

We present a Reinforcement Learning Platform for Adversarial Black-box untargeted and targeted attacks that allows users to select from various distortion filters to create adversarial examples. The platform uses a Reinforcement Learning agent to add minimum distortion to input images while still causing misclassification by the target model. The agent uses a novel dual-action method to explore the input image at each step to identify sensitive regions for adding distortions while removing noises that have less impact on the target model. This dual action leads to faster and more efficient convergence of the attack. The platform can also be used to measure the robustness of image classification models against specific distortion types. Also, retraining the model with adversarial samples significantly improved robustness when evaluated on benchmark datasets. The proposed platform outperforms state-of-the-art methods in terms of the average number of queries required to cause misclassification. This advances trustworthiness with a positive social impact.

Reinforcement Learning Platform for Adversarial Black-box Attacks with Custom Distortion Filters

Existing work on the alignment problem has focused mainly on (1) qualitative descriptions of the alignment problem; (2) attempting to align AI actions with human interests by focusing on value specification and learning; and/or (3) focusing on a single agent or on humanity as a monolith. Recent sociotechnical approaches highlight the need to understand complex misalignment among multiple human and AI agents. We address this gap by adapting a computational social science model of human contention to the alignment problem. Our model quantifies misalignment in large, diverse agent groups with potentially conflicting goals across various problem areas. Misalignment scores in our framework depend on the observed agent population, the domain in question, and conflict between agents' weighted preferences. Through simulations, we demonstrate how our model captures intuitive aspects of misalignment across different scenarios. We then apply our model to two case studies, including an autonomous vehicle setting, showcasing its practical utility. Our approach offers enhanced explanatory power for complex sociotechnical environments and could inform the design of more aligned AI systems in real-world applications.

Quantifying Misalignment Between Agents: Towards a Sociotechnical Understanding of Alignment

The advent of large language models (LLMs) has sparked significant interest in using natural language for preference learning. However, existing methods often suffer from high computational burdens, taxing human supervision, and lack of interpretability. To address these issues, we introduce MAPLE, a framework for large language model-guided Bayesian active preference learning. MAPLE leverages LLMs to model the distribution over preference functions, conditioning it on both natural language feedback and conventional preference learning feedback, such as pairwise trajectory rankings. MAPLE also employs active learning to systematically reduce uncertainty in this distribution and incorporates a language-conditioned active query selection mechanism to identify informative and easy-to-answer queries, thus reducing human burden. We evaluate MAPLE's sample efficiency and preference inference quality across two benchmarks, including a real-world vehicle route planning benchmark using OpenStreetMap data. Our results demonstrate that MAPLE accelerates the learning process and effectively improves humans' ability to answer queries.

MAPLE: A Framework for Active Preference Learning Guided by Large Language Models

Significant efforts have been made to analyze the political stance or bias in news articles, especially as political polarization intensifies over the years. Recent advancements in machine learning have enabled researchers to develop various bias prediction models, which typically learn features not only from the text of the news articles but also from external knowledge. However, when training these models, the political bias label assigned to a news article is often based solely on the news source that published it. This approach can be problematic, as a news outlet with a particular political stance might publish an article that reflects a different political perspective.

To address this issue, we first find out distinct text patterns that pertain to a particular news source (or a publisher), which are barely meaningful to predict the political bias of its news article. Then, we conduct comprehensive experiments to investigate (i) whether the prior models trained to predict the bias can also predict its source and (ii) whether the prior models change prediction results if a distinct pattern of a source with a different political stance is inserted to a news article. 
Our experimental results reveal that all the prior models are prone to predict the source even if they are trained to predict the bias.
We finally suggest a new deep learning model for political bias prediction that avoids learning the source-indicative patterns.

Political Bias Prediction Models Focus on Source Cues, Not Semantics

The success of the reward model in distinguishing between responses with subtle safety differences depends critically on the high-quality preference dataset, which should capture the fine-grained nuances of harmful and harmless responses. This motivates the need to develop the datasets involving preference margins, which accurately quantify how harmless one response is compared to another. In this paper, we take the first step to propose an effective and cost-efficient framework to promote the margin-enhanced preference dataset development. Our framework, Legend, Leverages rEpresentation enGineering to annotate preferENce Datasets. It constructs the specific direction within the LLM's embedding space that represents safety. By leveraging this safety direction, Legend can then leverage the semantic distances of paired responses along this direction to annotate margins automatically. We experimentally demonstrate our effectiveness in both reward modeling and harmless alignment for LLMs. Legend also stands out for its efficiency, requiring only the inference time rather than additional training. This efficiency allows for easier implementation and scalability, making Legend particularly valuable for practical applications in aligning LLMs with safe conversations.

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