AAAI 2026 Papers

Large language models are widely used, but aligning them with societal values remains challenging. Current approaches often rely on human annotations, which are hard to scale, or synthetic data produced by models that may themselves be misaligned, making it difficult to capture genuine public opinion. This limits scalability and introduces demographic biases that reduce the representativeness and fairness of model behavior. We introduce a novel approach to pluralistic alignment through behavioral learning, grounded in the psychological principle that actions (behavior) have strong consistency with opinions. Specifically, we present ALPHA50M, a dataset of over 50 million samples derived from 1.5 million real-world advertisements, incorporating rich behavioral signals inferred from demographic engagement patterns. Models trained on this data achieve state-of-the-art zero-shot performance on diverse alignment benchmarks spanning cultural reasoning, political views, and social values. We also propose two new benchmarks: OpinionQA-XL, which covers surveys across 100+ societal topics, and GSS, which evaluates temporal opinion shift modeling over decades. Our results demonstrate that learning from behavioral signals, derived from observed human actions, enables models to align with diverse demographic opinions, capture underlying social and cultural norms, and generalize to new topics and surveys beyond training data. This behavioral learning paradigm offers a scalable and demographically broad alternative to existing alignment techniques.

Test-time scaling has significantly improved large language model (LLM) performance, enabling deeper reasoning to solve complex problems. However, this increased reasoning capability also leads to excessive token generation and unnecessary problem-solving attempts. We introduce "Don't Reason Bench (DNR Bench)", a new benchmark designed to evaluate LLMs’ ability to robustly understand tricky reasoning triggers and avoid unnecessary generation. DNR Bench consists of 150 adversarially designed prompts that are easy for humans to understand and respond to, but surprisingly not for many recent prominent LLMs. DNR Bench tests models' abilities across different capabilities, such as instruction adherence, hallucination avoidance, redundancy filtering, and unanswerable question recognition. We evaluate reasoning LLMs (RLMs), including DeepSeek-R1, OpenAI O3-mini, and Claude-3.7-sonnet, and compare them against a powerful non-reasoning model, such as GPT-4o. Our experiments reveal that RLMs generate up to 70x more tokens than necessary, often failing at tasks that simpler non-reasoning models handle efficiently with higher accuracy. Our findings underscore the need for more effective training and inference strategies in RLMs.

Large language models are increasingly influencing human moral decisions, yet current approaches focus primarily on evaluating rather than actively steering their moral decisions. We formulate this as an out-of-distribution moral alignment problem, where LLM agents must learn to apply consistent moral reasoning frameworks to scenarios beyond their training distribution. We introduce Moral-Reason-QA, a novel dataset extending 680 human-annotated, high-ambiguity moral scenarios with framework-specific reasoning traces across utilitarian, deontological, and virtue ethics, enabling systematic evaluation of moral generalization in realistic decision contexts. Our learning approach employs Group Relative Policy Optimization with composite rewards that simultaneously optimize decision alignment and framework-specific reasoning processes to facilitate learning of the underlying moral frameworks. Experimental results demonstrate successful generalization to unseen moral scenarios, with softmax-normalized alignment scores improving by +0.757 for utilitarian and +0.450 for deontological frameworks when tested on out-of-distribution evaluation sets. The experiments also reveal training challenges and promising directions that inform future research. These findings establish that LLM agents can be systematically trained to internalize and apply specific moral frameworks to novel situations, providing a critical foundation for AI safety as language models become more integrated into human decision-making processes.

Although large language models (LLMs) are increasingly trained using human feedback for safety and alignment with human values, alignment decisions often overlook human social diversity. This study examines how incorporating pluralistic values affects LLM behavior by systematically evaluating demographic variation and design parameters in the alignment pipeline. We collect alignment data from US and German participants (N = 1,095 participants, 27,375 ratings) who rated LLM responses across five dimensions: Toxicity, Emotional Awareness (EA), Sensitivity, Stereotypical Bias, and Helpfulness. We fine-tuned multiple Large Language Models and Large Reasoning Models using preferences from different social groups while varying rating scales, disagreement handling methods, and optimization techniques. The results revealed systematic demographic effects: male participants rated responses 18% less toxic than female participants; conservative and Black participants rated responses 27.9% and 44% higher on EA than liberal and White participants, respectively. Models fine-tuned on group-specific preferences exhibited distinct behaviors. Technical design choices showed strong effects: the preservation of rater disagreement achieved roughly 53% greater toxicity reduction than majority voting, and 5-point scales yielded about 22% more reduction than binary formats; and Direct Preference Optimization (DPO) consistently outperformed Group Relative Policy Optimization (GRPO) in multi-value optimization. These findings represent a preliminary step in answering a critical question: How should alignment balance expert-driven and user-driven signals to ensure both safety and fair representation?

As AI systems become more capable, it is important that their decisions are understandable and aligned with human expectations. A key challenge is the lack of interpretability in deep models. Existing methods such as GradCAM generate heatmaps but provide limited conceptual insight, while prototype-based approaches offer example-based explanations but often rely on rigid region selection and lack semantic consistency. To address these limitations, we propose PCMNet, a Part-Prototypical Concept Mining Network that learns human-comprehensible prototypes from meaningful regions without extra supervision. By clustering these into concept groups and extracting concept activation vectors, PCMNet provides structured, concept-level explanations and enhances robustness under occlusion and adversarial conditions, which are both critical for building reliable and aligned AI systems. Experiments across multiple benchmarks show that PCMNet outperforms state-of-the-art methods in interpretability, stability, and robustness. This work contributes to AI alignment by enhancing transparency, controllability, and trustworthiness in modern AI systems.

Present day LLMs face the challenge of managing affordance-based safety risks—situations where outputs inadvertently facilitate harmful actions due to overlooked logical implications. Traditional safety solutions, such as scalar outcome-based reward models, parameter tuning, or heuristic decoding strategies, lack the granularity and proactive nature needed to reliably detect and intervene during subtle yet crucial reasoning steps. Addressing this fundamental gap, we introduce AURA, an innovative, multi-layered framework centered around Process Reward Models (PRMs), providing comprehensive, step level evaluations across logical coherence and safety-awareness. Our framework seamlessly combines introspective self-critique, fine-grained PRM assessments, and adaptive safety-aware decoding to dynamically and proactively guide models toward safer reasoning trajectories. Empirical evidence clearly demonstrates that this approach significantly surpasses existing methods, significantly improving the logical integrity and affordance-sensitive safety of model outputs. This research represents a pivotal step toward safer, more responsible, and contextually aware AI, setting a new benchmark for alignment-sensitive applications.

TSP is a classic and extensively studied problem with numerous real-world applications in artificial intelligence and operations research. It is well-known that TSP admits a constant approximation ratio on metric graphs but becomes NP-hard to approximate within any computable function f(n) on general graphs. This disparity highlights a significant gap between the results on metric graphs and general graphs. Recent research has introduced some parameters to measure the ``distance'' of general graphs from being metric and explored FPT approximation algorithms parameterized by these parameters. Two commonly studied parameters are p, the number of vertices in triangles violating the triangle inequality, and q, the minimum number of vertices whose removal results in a metric graph. In this paper, we present improved FPT approximation algorithms with respect to these two parameters. For p, we propose an FPT algorithm with a 1.5-approximation ratio, improving upon the previous ratio of 2.5. For q, we significantly enhance the approximation ratio from 11 to 3, advancing the state of the art in both cases.

The MOEA/D is the most popular decomposition-based evolutionary algorithm to solve multi-objective optimization problems. However, among the two common decomposition approaches, weighted-sum and Tchebycheff, the existing theoretical research almost exclusively focus on the latter one. In this first complete mathematical runtime analysis for the MOEA/D using the original weighted-sum decomposition, we show that this variant of the algorithm solves the classic ONEMINMAX benchmark considerably faster than both the MOEA/D with Tchebycheff decomposition and many other classic algorithms such as the NSGA-II, NSGA-III, SMS-EMOA, and SPEA2. More precisely, we show that already a logarithmic number of subproblems suffices for the algorithm to be efficient, and then typically O(n log^2 n) function evaluations suffice to compute the full Pareto front. This beats the other algorithms by a factor of Θ(n / log n). For a second benchmark, the ONEJUMPZEROJUMP problem, we show a speed-up by a factor of Θ(n). Overall, this work shows that a further development of the weighted-sum approach might be fruitful.

To relieve intensive human-expertise required to design optimization algorithms, recent Meta-Black-Box Optimization (MetaBBO) researches leverage generalization strength of meta-learning to train neural network-based algorithm design policies over a predefined training problem set, which automates the adaptability of the low-level optimizers on unseen problem instances. Currently, a common training problem set choice in existing MetaBBOs is well-known benchmark suites CoCo-BBOB. Although such choice facilitates the MetaBBO's development, problem instances in CoCo-BBOB are more or less limited in diversity, raising the risk of overfitting of MetaBBOs, which might further results in poor generalization. In this paper, we propose an instance generation approach, termed as LSRE, which could generate diverse training problem instances for MetaBBOs to learn more generalizable policies. LSRE first trains an autoencoder which maps high-dimensional problem features into a 2-dimensional latent space. Uniform-grid sampling in this latent space leads to hidden representations of problem instances with sufficient diversity. By leveraging a genetic-programming approach to search function formulas with minimal L2-distance to these hidden representations, LSRE reverse engineers a diversified problem set, termed as Diverse-BBO. We validate the effectiveness of LSRE by training various MetaBBOs on Diverse-BBO and observe their generalization performances on either synthetic or realistic scenarios. Extensive experimental results underscore the superiority of Diverse-BBO to existing training set choices in MetaBBOs. Further ablation studies not only demonstrate the effectiveness of design choices in LSRE, but also reveal interesting insights on instance diversity and MetaBBO's generalization.

In Bi-Objective Search (BOS), the task is to compute the Pareto-optimal frontier of paths in a graph with two cost values per edge. Recent work introduced a general BOS framework that classifies search nodes and studies how ordering functions affect expansion order. In this paper, we continue this line of research. We further refine the classes of nodes and show that many nodes that were added to the open list and are classified as never-expand nodes still need to be extracted and further examined. Additionally, we introduce a method that enables constant-time dominance checks for the MIN and MAX ordering functions. This allows a practical usage of these ordering functions, as we demonstrate in our experimental section.

Recent advancements in bidirectional heuristic search have yielded significant theoretical insights and novel algorithms. While most previous work has concentrated on optimal search methods, this paper focuses on bounded-suboptimal bidirectional search, where a bound on the suboptimality of the solution cost is specified. We build upon the state-of-the-art optimal bidirectional search algorithm, BAE*, designed for consistent heuristics, and introduce several variants of BAE* specifically tailored for the bounded-suboptimal context. Through experimental evaluation, we compare the performance of these new variants against other bounded-suboptimal bidirectional algorithms as well as the standard weighted A* algorithm. Our results demonstrate that each algorithm excels under distinct conditions, highlighting the strengths and weaknesses of each approach.

Evolutionary Algorithms (EAs) have become the most popular tool for solving widely-existed multi-objective optimization problems. In Multi-Objective EAs (MOEAs), there is increasing interest in using an archive to store non-dominated solutions generated during the search. This approach can 1) mitigate the effects of population oscillation, a common issue in many MOEAs, and 2) allow for the use of smaller, more practical population sizes. In this paper, we analytically show that the archive can even further help MOEAs through reusing its solutions during the process of new solution generation. We first prove that using a small population size alongside an archive (without incorporating archived solutions in the generation process) may fail on certain problems, as the population may remove previously discovered but promising solutions. We then prove that reusing archive solutions can overcome this limitation, resulting in at least a polynomial speedup on the expected running time. Our analysis focuses on the well-established SMS-EMOA algorithm applied to the commonly studied OneJumpZeroJump problem as well as one of its variants. We also show that reusing archive solutions can be better than using a large population size directly. Finally, we validate our theoretical findings through experiments on well-known practical optimization problems.

Greedy search methods such as Greedy Best-First Search (GBFS) and Enforced Hill-Climbing (EHC) often struggle when faced with Uninformed Heuristic Regions (UHRs) like heuristic local minima or plateaus. In this work, we theoretically and empirically compare two popular methods for escaping UHRs: breadth-first search (BrFS) and restarting random walks (RRWs). First, we derive the expected runtime of escaping a UHR using BrFS and RRWs, based on properties of the UHR and the random walk procedure, and then use these results to identify when RRWs will be faster in expectation than BrFS. Next, we evaluate these methods for escaping UHRs by comparing standard EHC, which uses BrFS to escape UHRs, to variants of EHC called EHC-RRW, which use RRWs for that purpose. EHC-RRW is shown to have strong expected runtime guarantees in cases where EHC has previously been shown to be effective. Finally, we run experiments with these approaches on PDDL planning benchmarks to better understand their relative effectiveness for escaping UHRs.

Passive surveillance systems (PSS) are used to detect and track various targets by processing the electromagnetic signals they release. The study and design of the resource management algorithm for these systems revealed several phenomena and combinatorial problems with crucial theoretical properties. In this article, we first prove the completeness of the algorithm used to generate receiver settings that determine which frequency bands the PSS monitors. Next, we formulate a new optimization problem called multiple-interval coverage (MIC), which is used to determine how often each of the generated settings must be used by the PSS. We show that the MIC problem is closely related to the multicover problem, which is an extension of the well-known set cover problem. The uniqueness of MIC stems from the fact that both covered elements and covers are multiple-intervals. We propose a notation to distinguish between different variants of the problem and prove that some of them can be solved in polynomial time. Finally, we prove that the MIC problem is NP-hard even when restricted to 2-interval covers.

Evolutionary algorithms are widely used for multi-objective optimization, with NSGA-III being particularly effective for problems with more than three objectives, unlike NSGA-II. Despite its empirical success, its theoretical understanding remains limited, especially regarding runtime analysis. A central open problem concerns its population dynamics, which involve controlling the maximum number of individuals sharing the same fitness value during the exploration process. In this paper, we make a significant step towards such an understanding by proving tight runtime bounds for NSGA-III on the bi-objective OneMinMax (2-OMM) problem. We show that, for population sizes n+1 ≤ µ = O(log(n)^c (n+1)) where c

Semi-supervised learning (SSL) on graphs is critical in applications where labeled data are scarce and costly, yet existing graph-based methods often degrade under extreme label sparsity or class imbalance, yielding trivial or unstable solutions. We introduce \textbf{CombCut}, the first exact combinatorial optimization framework for multi-class graph-based semi-supervised learning that operates directly on binary one-hot assignments, without any convex relaxation or heuristic volume constraints. By employing a minorization–maximization (MM) scheme, CombCut transforms each step into a structured linear assignment problem solved efficiently via network-flow algorithms. Total unimodularity guarantees integral iterates, and our theoretical analysis establishes both monotonic ascent of the true discrete objective and convergence of every limit point to a Karush–Kuhn–Tucker (KKT) stationary solution of the original combinatorial problem. Our approach requires no hyperparameter tuning and scales near-linearly in the number of vertices. Empirical evaluation on MNIST, Fashion-MNIST, and CIFAR-10 with as few as 1–5 labels per class shows that CombCut excels in worst-case labeling scenarios, significantly outperforming state-of-the-art graph-SSL baselines and yielding more stable and accurate label propagation under severe supervision constraints.

We study the ordinal secretary problem, where a sequence of candidates arrives in uniformly random order, and the goal is to select the best candidate using only pairwise comparisons. We consider a learning-augmented setting that incorporates potentially erroneous predictions about the best candidate’s position. Our goal is to design online algorithms that balance robustness against poor predictions while having high performance when predictions are accurate. Using an optimization-based framework, we develop deterministic and randomized algorithms that extend classical strategies and explicitly model the trade-off between consistency and robustness. Also, we show the flexibility of our approach by applying it to multiple secretary problem variants, including multiple-choice and rehiring.

A suitable choice of the representation of candidate solutions is crucial for the efficiency of evolutionary algorithms and related metaheuristics. We focus on problems in permutation spaces, which are at the core of numerous practical applications of such algorithms, e.g., in scheduling and transportation. Inversion vectors (also called Lehmer codes) are an alternative representation of the permutation space S(n) compared to the classical encoding as a vector of n unique entries. In particular, they do not require any constraint handling. Using rigorous mathematical runtime analyses, we compare the efficiency of inversion vector encodings to the classical representation and give theory-guided advice on their choice. Moreover, we link the effect of local changes in the inversion code space to classical measures on permutations like the number of inversions. Finally, through experimental studies on linear ordering and quadratic assignment problems, we demonstrate the practical efficiency of inversion vector encodings.

Automated Heuristic Design (AHD) using Large Language Models (LLMs) has achieved notable success in the past two years. Despite the effectiveness of existing approaches, they only design a single heuristic to serve all problem instances, often inducing poor generalization across different distributions or sizes. To address this issue, we propose Automated Heuristic Set Design (AHSD), a new methodology for LLM-driven AHD. The aim of AHSD is to automatically design a small-sized complementary heuristic set to serve diverse problem instances, such that each problem instance could be optimized by at least one heuristic in this set. We propose Evolution of Heuristic Set (EoH-S), which realizes AHSD using an evolutionary search framework. It incorporates a complementary population management and a memetic search to design a set of heuristics. Extensive experiments on online bin packing, traveling salesman problem, and capacitated vehicle routing problem show that EoH-S consistently outperforms existing AHD methods. The resulting heuristics exhibit complementary performance across instances of varying sizes and distributions.