Singapore

High-quality information set abstraction remains a core challenge in solving large-scale imperfect-information extensive-form games (IIEFGs) — such as no-limit Texas Hold’em — where the finite nature of spatial resources hinders strategy solving over the full game. State-of-the-art AI methods rely on pre-trained discrete clustering for abstraction, yet their hard classification irreversibly loses critical information: specifically, the quantifiable subtle differences between information sets — vital for strategy solving — thereby compromising the quality of such solving. Inspired by the word embedding paradigm in natural language processing, this paper proposes the Embedding CFR algorithm, a novel approach for solving strategies in IIEFGs within an embedding space. The algorithm pre-trains and embeds features of isolated information sets into an interconnected low-dimensional continuous space, where the resulting vectors more precisely capture both the distinctions and connections between information sets. Embedding CFR presents a strategy-solving process driven by regret accumulation and strategy updates within this embedding space, with accompanying theoretical analysis verifying its capacity to reduce cumulative regret. Experiments on poker show that with the same spatial overhead, Embedding CFR achieves significantly faster exploitability convergence compared to cluster-based abstraction algorithms, confirming its effectiveness. Furthermore, to our knowledge, it is the first algorithm in poker AI that pre-trains information set abstractions through low-dimensional embedding for strategy solving.

AAAI 2026

No-Regret Strategy Solving in Imperfect-Information Games via Pre-Trained Embedding

technical 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.

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.<br><br>

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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.

Drug shortages pose critical risks to patient care and healthcare systems worldwide, yet the effectiveness of regulatory interventions remains poorly understood due to fundamental information asymmetries in pharmaceutical supply chains. We present **ShortageSim**, the first Large Language Model (LLM)-based multi-agent simulation framework that captures the complex, strategic interactions between drug manufacturers, institutional buyers, and regulatory agencies in response to shortage alerts. Unlike traditional game-theoretic models that assume perfect rationality and complete information, **ShortageSim** leverages LLMs to simulate bounded-rational decision-making under uncertainty. Through a sequential production game spanning multiple quarters, we model how FDA announcements—both reactive alerts about existing shortages and proactive warnings about potential disruptions—propagate through the supply chain and influence capacity investment and procurement decisions. 
Our experiments on historical shortage events reveal that **ShortageSim** reduces the resolution-lag percentage for discontinued-disclosed cases by 83\%, bringing simulated durations more aligned to ground truth than the zero-shot baseline.
We will open-source **ShortageSim** and a dataset of 2,925 FDA shortage events, providing a novel computational framework for designing and testing interventions in complex, information-scarce supply chains.

ShortageSim: Simulating Drug Shortages Under Information Asymmetry

Clustering is a fundamental task in unsupervised learning, but most existing methods heavily rely on hyperparameters such as the number of clusters or other sensitive settings, limiting their applicability in real-world scenarios. To address this long-standing challenge, we propose a novel and fully parameter-free clustering framework via Self-supervised Consensus Maximization, named SCMax. Our framework performs hierarchical agglomerative clustering and cluster evaluation in a single, integrated process. At each step of agglomeration, it creates a new, structure-aware data representation through a self-supervised learning task guided by the current clustering structure. We then introduce a nearest neighbor consensus score, which measures the agreement between the nearest neighbor-based merge decisions suggested by the original representation and the self-supervised one. The moment at which consensus maximization occurs can serve as a criterion for determining the optimal number of clusters. Extensive experiments on multiple datasets demonstrate that the proposed framework outperforms existing clustering approaches designed for scenarios with an unknown number of clusters.

Parameter-Free Clustering via Self-Supervised Consensus Maximization

Drug combinations are widely used in modern medicine but may cause severe adverse drug reactions. Therefore, making effective drug-drug interactions (DDI) prediction is crucial for pharmacovigilance. Existing DDI prediction models are typically built from a structural perspective, assuming that drugs with similar molecular structures may exhibit similar interactions. However, such approaches overlook the biological mechanisms underlying DDI in the human body. This not only weakens the generalization ability of the model, but also makes its interpretability less convincing. Inspired by this, we propose a new method called PC-DDI. Unlike structure-based models, PC-DDI utilizes pharmacophores as basic unit, and designs a complete pharmacophore feature processing framework. It further constructs a pharmacophore-based bipartite graph to model interactions between pharmacophores. This approach allows us to explore the underlying mechanisms of DDI from a functional perspective. We also design a spatial attention weight graph convolution module to optimize the message passing process by integrating pharmacophore position features with node features. Furthermore, we apply causal inference to identify key pharmacophores in pharmacophore bipartite graph, enhancing the interpretability. Compared with the SOTA, PC-DDI achieves an accuracy improvement of 1.84% under the transductive setting and consistently outperforms others in all other experiments, demonstrating its superior generalization capability and overall performance in DDI prediction tasks.

Closer to Biological Mechanism: Drug-Drug Interaction Prediction from the Perspective of Pharmacophore

Counterfactual regret minimization (CFR) is a family of algorithms for effectively solving imperfect-information games. To enhance CFR's applicability in large games, researchers use neural networks to approximate its behavior. However, existing methods are mainly based on vanilla CFR and struggle to effectively integrate more advanced CFR variants. In this work, we propose an efficient model-free neural CFR algorithm, overcoming the limitations of existing methods in approximating advanced CFR variants. At each iteration, it collects variance-reduced sampled advantages based on a value network, fits cumulative advantages by bootstrapping, and applies discounting and clipping operations to simulate the update mechanisms of advanced CFR variants. Experimental results show that, compared with model-free neural algorithms, it exhibits faster convergence in typical imperfect-information games and demonstrates stronger adversarial performance in a large poker game.

Deep (Predictive) Discounted Counterfactual Regret Minimization

Selecting an appropriate look-back horizon remains a fundamental challenge in time series forecasting (TSF), particularly in federated learning scenarios where data is decentralized, heterogeneous, and often non-independent. While recent work has explored horizon selection by preserving forecasting-relevant information in an intrinsic space, these approaches are primarily restricted to centralized and independently distributed settings. This paper presents a principled framework for adaptive horizon selection in federated time series forecasting through an intrinsic space formulation. We introduce a synthetic data generator that captures essential temporal structures in client data, including autoregressive dependencies, seasonality, and trend, while incorporating client-specific heterogeneity. Building on this model, we define a transformation that maps time series windows into an intrinsic representation space with well-defined geometric and statistical properties. We then derive a decomposition of the forecasting loss into a Bayesian term, which reflects irreducible uncertainty, and an approximation term, which accounts for finite-sample effects and limited model capacity. Our analysis shows that while increasing the look-back horizon improves the identifiability of deterministic patterns, it also increases approximation error due to higher model complexity and reduced sample efficiency. We prove that the total forecasting loss is minimized at the smallest horizon where the irreducible loss starts to saturate, while the approximation loss continues to rise. This work provides a rigorous theoretical foundation for adaptive horizon selection for time series forecasting in federated learning.

Optimal Look-back Horizon for Time Series Forecasting in Federated Learning

Generalization to unseen environments is a significant challenge in the field of robotics and control. In this work, we focus on contextual reinforcement learning, where agents act within environments with varying contexts, such as self-driving cars or quadrupedal robots that need to operate in different terrains or weather conditions than they were trained for. We tackle the critical task of generalizing to out-of-distribution (OOD) settings, without access to explicit context information at test time. Recent work has addressed this problem by training a context encoder and a history adaptation module in separate stages. While promising, this two-phase approach is cumbersome to implement and train. We simplify the methodology and introduce SPARC: single-phase adaptation for robust control. We test SPARC on varying contexts within the high-fidelity racing simulator Gran Turismo 7 and wind-perturbed MuJoCo environments, and find that it achieves reliable and robust OOD generalization.

Out-of-Distribution Generalization with a SPARC: Racing 100 Unseen Vehicles with a Single Policy

Retrieval-Augmented Generation (RAG) critically depends on effective query expansion to retrieve relevant information. However, existing expansion methods adopt uniform strategies that overlook user-specific semantics, ignoring individual expression styles, preferences, and historical context. In practice, identical queries in text can express vastly different intentions across users. This representational rigidity limits the ability of current RAG systems to generalize effectively in personalized settings. Specifically, we identify two core challenges for personalization: 1) user expression styles are inherently diverse, making it difficult for standard expansions to preserve personalized intent. 2) user corpora induce heterogeneous semantic structures—varying in topical focus and lexical organization—which hinders the effective anchoring of expanded queries within the user’s corpora space. To address these challenges, we propose Personalize Before Retrieve (PBR), a framework that incorporates user-specific signals into query expansion prior to retrieval. PBR consists of two components: P-PRF, which generates stylistically aligned pseudo feedback using user history for simulating user expression style, and P-Anchor, which performs graph-based structure alignment over user corpora to capture its structure. Together, they produce personalized query representations tailored for retrieval. Experiments on two personalized benchmarks show that PBR consistently outperforms strong baselines, with up to 10% gains on PersonaBench across retrievers. Our findings demonstrate the value of modeling personalization before retrieval to close the semantic gap in user-adaptive RAG systems.

Personalize Before Retrieve: LLM-based Personalized Query Expansion for User-Centric Retrieval

Recent advances in controllable text-to-image (T2I) generation have shown promising results in natural image generation. However, controllable remote sensing (RS) T2I generation remains a challenging task due to the unique characteristics and requirements of geospatial data. Existing methods struggle to effectively integrate diverse spatial control condition (e.g., edge maps, segmentation masks) into a coherent generation process. They often fail to model the complex spatial relationships among different geographic elements and maintain semantic consistency with textual descriptions, which are typically vague or incomplete in RS applications. Additionally,
constrained by the small scale, low description quality, and limited scene variety of existing datasets, these models tend
to produce outputs with structurally inconsistent layouts and visually unrealistic content. To address these issues, we propose
Any2RSI, a flexible framework for controllable RS T2I generation that supports the flexible combination of various control
conditions. At its core, Any2RSI introduces a Cross-Modal Multi-Control Adapter capable of extracting modality-agnostic
embeddings from heterogeneous inputs, enabling precise spatial guidance. Furthermore, to overcome the limitations of sparse and
ambiguous textual prompts commonly found in RS tasks, we design a Vision Language Model (VLM)-Empowered Enriched
Description Generation module. This module enhances input descriptions by integrating cross-modal semantic information,
generating richer and more accurate textual representations that guide the generation of semantically coherent images. Finally, to
mitigate the data scarcity in RS T2I generation task, we construct RST2I-110K, a new large-scale, multi-scene dataset containing
over 115,000 high quality RS images paired with detailed textual descriptions. Extensive experiments on both existing and newly
proposed datasets demonstrate that Any2RSI achieves state-of-the-art performance, significantly improving both the realism and
structural accuracy of generated RS imagery.

Any2RSI: Controllable Remote Sensing Text-to-Image Generation via Any Control and Enriched Description

Human artists can continuously refine their coarse sketches during artistic creation. This is quiet different from existing autoregressive generation, where a token is determined once sampled.
Aiming to flexibly refine the generated contents, this paper presents a Self-Calibrated AutoregressioN (SCAN) model capable of self-evaluating and refining generation quality without regenerating the entire image.
We unify image token generation and quality evaluation into a single autoregressive model, formulating both tasks as categorical prediction problems. 
During inference, the model first generates a coarse initial image, then iteratively refines the lowest-quality patches until satisfactory image quality is achieved. 
Experimental results demonstrate that SCAN effectively handles diverse real-world generation errors and achieves a promising balance between image quality and speed. For example, SCAN-XL achieves an FID of 2.10 and an IS of 326.1, surpassing the LlamaGen-XL by 1.29 (+38\%) in FID and 99.0 (+43.6\%) in IS, with a 5.6× speedup (19.76s → 3.56s).
Compared to recent works, SCAN improves FID and speed by +18.3\% and +23\% over VAR-d20, and by +7\% and +46\% over RandAR-XL. 
Code and models will be released to facilitate further exploration in self-calibrated content generation.

SCAN: Self-Calibrated AutoregressioN for High-Quality Visual Generation

Reinforcement learning (RL) has emerged as a powerful paradigm for aligning large language models (LLMs) with human preferences, particularly in reasoning-intensive tasks. However, existing RL methods such as Group Relative Policy Optimization (GRPO) often suffer from group collapse—where all sampled completions are either correct or incorrect—leading to vanishing gradient signals and ineffective alignment. Furthermore, many approaches focus solely on final answer correctness, overlooking the alignment of intermediate reasoning steps and penalizing output length in a rigid manner. 

To address these issues, we propose \textbf{TAPO}, a novel RL framework for improving reasoning alignment in LLMs via enhanced optimization signals. TAPO introduces three key techniques: (1) \textit{Dynamic Teacher Injection (DTI)} selectively injects high-quality or adversarial completions into training groups, restoring learning signal in collapsed scenarios; (2) \textit{Perturbed Answer Injection (PAI)} introduces partially correct completions to enable finer-grained contrastive supervision between answer-level and reasoning-level alignment; and (3) \textit{InfoLen-Aware Reward Shaping} penalizes responses based on semantic redundancy and overlength, encouraging outputs that are both informative and concise.

Extensive experiments across diverse reasoning benchmarks demonstrate that TAPO achieves superior alignment with desired reasoning behavior, significantly outperforming GRPO baselines. Ablation studies further confirm the individual impact of each technique on improving signal quality and alignment fidelity.

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ShortageSim: Simulating Drug Shortages Under Information Asymmetry

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