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Can classical game-theoretic frameworks be extended to capture the bounded rationality and causal reasoning of AI agents? We investigate this question by extending Causal Normal Form Games (CNFGs) to sequential settings, introducing Sequential Causal Multi-Agent Systems (S-CMAS) that incorporate Pearl&#39;s Causal Hierarchy across leader-follower interactions. While theoretically elegant---we prove PSPACE-completeness, develop equilibrium refinements, and establish connections to signaling theory---our comprehensive empirical investigation reveals a critical limitation: S-CNE provides zero welfare improvement over classical Stackelberg equilibrium across all tested scenarios. Through 50+ Monte Carlo simulations and hand-crafted synthetic examples, we demonstrate that backward induction with rational best-response eliminates any strategic advantage from causal layer distinctions. We construct a theoretical example illustrating conditions where benefits could emerge (-rational satisficing followers), though implementation confirms that even relaxed rationality assumptions prove insufficient when good instincts align with optimal play. This negative result provides valuable insight: classical game-theoretic extensions grounded in rational choice are fundamentally incompatible with causal reasoning advantages, motivating new theoretical frameworks beyond standard Nash equilibrium for agentic AI.

AAAI 2026

Sequential Causal Normal Form Games: Theory, Computation, and Strategic Signaling

Can classical game-theoretic frameworks be extended to capture the bounded rationality and causal reasoning of AI agents? We investigate this question by extending Causal Normal Form Games (CNFGs) to sequential settings, introducing Sequential Causal Multi-Agent Systems (S-CMAS) that incorporate Pearl's Causal Hierarchy across leader-follower interactions. While theoretically elegant---we prove PSPACE-completeness, develop equilibrium refinements, and establish connections to signaling theory---our comprehensive empirical investigation reveals a critical limitation: S-CNE provides zero welfare improvement over classical Stackelberg equilibrium across all tested scenarios. Through 50+ Monte Carlo simulations and hand-crafted synthetic examples, we demonstrate that backward induction with rational best-response eliminates any strategic advantage from causal layer distinctions. We construct a theoretical example illustrating conditions where benefits could emerge (-rational satisficing followers), though implementation confirms that even relaxed rationality assumptions prove insufficient when good instincts align with optimal play. This negative result provides valuable insight: classical game-theoretic extensions grounded in rational choice are fundamentally incompatible with causal reasoning advantages, motivating new theoretical frameworks beyond standard Nash equilibrium for agentic AI.

workshop paper

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.

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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.

Autonomous AI Agents powered by LLMs have shown remarkable abilities in diverse domains. However, the training process typically require centralized collection of large amounts of real-world user data, posing substantial privacy and regulatory concerns. To this end, we explore a new decentralized training paradigm, namely FedAgent (Federated Agent Reinforcement Learning), which enables collaborative learning of AI agents across distributed clients without sharing local data. Moreover, we construct the first decentralized agent learning environment FedAgentGym, which includes four types of LLM agents, two application scenarios (WebShop and ALFWorld), three variations of decentralized settings, and three newly defined heterogeneity challenges (Preference Heterogeneity, Coverage Heterogeneity, and Hardness Heterogeneity), to systematically investigate its effectiveness and impact factors. Extensive theoretical and empirical studies show that FedAgent can have comparable performance to the centralized training paradigm and exhibit strong robustness against heterogeneities, which shows the feasibility of training AI agents without sacrificing data privacy. The code is available.

Federated Agent Reinforcement Learning

Large language model (LLM)–based agents are increasingly used to perform complex, multi-step workflows in regulated settings such as compliance and due diligence. Yet, many agentic architectures focus on prompt engineering for single agents, which makes it difficult to observe or compare how models are considering uncertainty and coordination across interconnected decision stages and with humans. This paper introduces a multi-agent system design formalized as a bounded-horizon, directed, acyclic Markov Decision Process (MDP). Each agent in this system corresponds to a specific step or role (e.g., content, business, legal in a compliance setting), with set transitions between agents representing task escalation or completion. Epistemic uncertainty (per agent) is quantified using Monte Carlo \textit{estimation}, and system-level uncertainty (across agents) is characterized the MDP setup terminating in a labeled state or one with human review. We illustrate the approach with a case study in AI safety evaluation for self-harm detection via a multi-agent compliance system based on this set-up. Results show improvements over a single-agent baseline in accuracy (up to 19%), reduction in required human review (up to 85×), and, in some configurations, less processing time.

Constrained Process Maps for Multi-Agent Generative AI Workflows

Fine-tuning large-scale Vision–Language Models (VLMs) is computationally demanding, motivating the need for efficient data utilization. Existing subset selection methods, such as COINCIDE, primarily focus on distribution matching but overlook instance-level utility, redundancy, and task-specific reasoning relevance. We propose QUBO-based Informative Subset Selection (QUBISS), a unified framework that formulates data selection as a Quadratic Unconstrained Binary Optimization (QUBO) problem.
QUBISS jointly maximizes task-relevant data utility and minimizes sample redundancy to promote diversity and compactness. Central to our method is the task vector, which quantifies the semantic contribution of textual information to reasoning performance and integrates it into the QUBO utility term. When applied to fine-tuning LLaVA v1.5, QUBISS selects only 20\% of a 665K image–text dataset while achieving results superior or comparable to COINCIDE on both cognition-oriented (MME-C) and recognition-oriented (MME-R) benchmarks. The observed gains underscore the value of task-aware semantic guidance for cost-efficient multimodal fine-tuning. Furthermore, advances in large-scale quantum solvers could further enhance QUBISS by directly solving large QUBOs without decomposing them into cluster-level subproblems, thereby mitigating suboptimality arising from problem partitioning.

QUBO-Based Subset Selection for Efficient Fine-Tuning of Vision‒Language Models

Graph Neural Networks (GNNs) have become indispensable in critical domains such as drug discovery, social network analysis, and recommendation systems, yet their black-box nature hinders deployment in scenarios requiring transparency and accountability. While Shapley value-based methods offer mathematically principled explanations by quantifying each component’s contribution to predictions, computing exact values requires evaluating 2^n coalitions (or aggregating over n! permutations), which is intractable for real-world graphs. Existing approximation strategies sacrifice either fidelity or efficiency, limiting their practical utility. We introduce QGShap, a quantum computing approach that leverages amplitude amplification to achieve quadratic speedups in coalition evaluation while maintaining exact Shapley computation. Unlike classical sampling or surrogate methods, our approach provides fully faithful explanations without approximation trade-offs for tractable graph sizes. We conduct empirical evaluations on synthetic graph datasets, demonstrating that QGShap achieves consistently high fidelity and explanation accuracy, matching or exceeding the performance of classical methods across all evaluation metrics. These results collectively demonstrate that QGShap not only preserves exact Shapley faithfulness but also delivers interpretable, stable, and structurally consistent explanations that align with the underlying graph reasoning of GNNs.

QGShap: Quantum Acceleration for Faithful GNN Explanations

Synthetic data generation plays an important role in enabling
data sharing, particularly in sensitive domains like healthcare and finance. Recent advances in diffusion models have
made it possible to generate realistic, high-quality tabular
data, but they may also memorize training records and leak
sensitive information. Membership inference attacks (MIAs)
exploit this vulnerability by determining whether a record
was used in training. While MIAs have been studied in images and text, their use against tabular diffusion models re-
mains underexplored despite the unique risks of structured
attributes and limited record diversity. In this paper, we introduce MIA-EPT, Membership Inference Attack via Error
Prediction for Tabular Data, a novel black-box attack specifically designed to target tabular diffusion models. MIA-EPT
constructs error-based feature vectors by masking and reconstructing attributes of target records, disclosing member-
ship signals based on how well these attributes are predicted.
MIA-EPT operates without access to the internal components
of the generative model, relying only on its synthetic data
output, and was shown to generalize across multiple stateof-the-art diffusion models. We validate MIA-EPT on three
diffusion-based synthesizers, achieving AUC-ROC scores of
up to 0.599 and TPR@10% FPR values of 22.0% in our internal tests. Under the MIDST 2025 competition conditions,
MIA-EPT achieved second place in the Black-box MultiTable track (TPR@10% FPR = 20.0%). These results demonstrate that our method can uncover substantial membership
leakage in synthetic tabular data, challenging the assumption
that synthetic data is inherently privacy-preserving.

MIA-EPT: Membership Inference Attack via Error Prediction for Tabular Data

Text-to-image diffusion models achieve high-fidelity image generation from natural language prompts. ControlNets extend these models by enabling conditioning on structural inputs (e.g., edge maps, depth, pose), providing fine-grained control over outputs. Yet their reliance on large, publicly scraped datasets and community fine-tuning makes them vulnerable to data poisoning. We introduce a model-poisoning attack that embeds a covert backdoor into a ControlNet, causing it to produce attacker-specified content when exposed to visual triggers, without textual prompts. Experiments show that poisoning only 1% of the fine-tuning corpus yields a 90–98\% attack success rate, while 5\% further strengthens the backdoor, all while preserving normal generation quality. To mitigate this risk, we propose clean fine-tuning (CFT): freezing the diffusion backbone and fine-tuning only the ControlNet on a sanitized dataset with a reduced learning rate. CFT lowers attack success rates on held-out data. These results expose a critical security weakness in open-source, ControlNet-guided diffusion pipelines and demonstrate that CFT offers a practical defense for responsible synthetic-data pipelines.

Backdoors in Conditional Diffusion: Threats to Responsible Synthetic Data Pipelines

The increased availability of genetic data has transformed genomics research, but raised many privacy concerns regarding its handling due to its sensitive nature. This work explores the use of language models (LMs) for the generation of synthetic genetic mutation profiles, leveraging differential privacy (DP) for the protection of sensitive genetic data. We empirically evaluate the privacy guarantees of our DP modes by introducing a novel **Biologically-Informed Hybrid Membership Inference Attack** (biHMIA), which combines traditional black box MIA with contextual genomics metrics for enhanced attack power. Our experiments show that both small and large transformer GPT-like models are viable synthetic variant generators for *small-scale genomics*, and that our hybrid attack leads, on average, to higher adversarial success compared to traditional metric-based MIAs.

Biologically-Informed Hybrid Membership Inference Attacks on Generative Genomic Models

The proliferation of AI-generated video technologies poses challenges to information integrity. While recent benchmarks advance AIGC video detection, they overlook a critical factor: many state-of-the-art generative models embed digital watermarks in outputs, and detectors may partially rely on these patterns. To evaluate this influence, we present RobustSora, the benchmark designed to assess watermark robustness in AIGC video detection. We systematically construct a dataset of 6,500 videos comprising four types: Authentic-Clean (A-C), Authentic-Spoofed with fake watermarks (A-S), Generated-Watermarked (G-W), and Generated-DeWatermarked (G-DeW). Our benchmark introduces two evaluation tasks: Task-I tests performance on watermark-removed AI videos, while Task-II assesses false alarm rates on authentic videos with fake watermarks. Experiments with ten models spanning specialized AIGC detectors, transformer architectures, and MLLM approaches reveal performance variations of 2-8pp under watermark manipulation. Transformer-based models show consistent moderate dependency (6-8pp), while MLLMs exhibit diverse patterns (2-8pp). These findings indicate partial watermark dependency and highlight the need for watermark-aware training strategies. RobustSora provides essential tools to advance robust AIGC detection research.

RobustSora: De-Watermarked Benchmark for Robust AI-Generated Video Detection

Large Language Models (LLMs) have shown advanced capabilities in tasks like counterfactual generation and style transfer using prompt strategies. However, previous strategies lacked detailed instructions, limiting effectiveness. To address this, we introduce Compare&Generate, an algorithm inspired by human comparison, where minimal instructions lead to substantial learning. Specifically, our method incorporates an objective function that quantitatively assesses alignment with the task goal and the content relevance in the output. Then, it constructs comparison pairs based on previous generation assessments and prompts the model to reconsider how to optimize its output. Through comparison, the model focuses on the critical aspects of the task objective and refines its outputs accordingly. We benchmark our method with single-instruction as well as iterative refinement approaches across three natural language generation tasks. Experimental results show that our approach outperforms other related methods; for instance, it surpasses its single-instruction base by 17% and a state-of-the-art refinement approach by 7% on IMDB datasets in generated label accuracy, highlighting the effectiveness of using comparisons in prompts to enhance LLMs.

Improving Synthetic Data Generation with LLMs through Strategic Comparisons

Knowledge Graphs (KGs) enable applications in various domains such as semantic search, recommendation systems, and natural language processing. KGs are often incomplete, missing entities and relations, an issue addressed by Knowledge Graph Completion (KGC) methods that predict missing elements. Different evaluation metrics, such as Mean Reciprocal Rank (MRR), Mean Rank (MR), and Hit@k (e.g., Hit@1), are commonly used to assess the performance of such KGC models. A major challenge in evaluating KGC models however, lies in comparing their performance across multiple datasets and metrics. A model may outperform others on one dataset but underperform on another, making it difficult to determine overall superiority. Moreover, even within a single dataset, different metrics such as MRR and Hit@1 can yield conflicting rankings, where one model excels in MRR while another performs better in Hit@1, further complicating model selection for downstream tasks. These inconsistencies hinder holistic comparisons and highlight the need for a unified meta-metric that integrates performance across all metrics and datasets to enable a more reliable and interpretable evaluation framework. To address this need, we propose KG \textit{E}valuation based on \textit{D}istance from \textit{A}verage \textit{S}olution (EDAS), a robust and interpretable meta-metric that synthesizes model performance across multiple datasets and diverse evaluation criteria into a single normalized score ($M_i \in [0,1]$). Unlike traditional metrics that focus on isolated aspects of performance, EDAS offers a global perspective that supports more informed model selection and promotes fairness in cross-dataset evaluation. Experimental results on benchmark datasets such as FB15k-237 and WN18RR demonstrate that EDAS effectively integrates multi-metric, multi-dataset performance into a unified ranking, offering a consistent, robust, and generalizable framework for evaluating KGC models.

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