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IJCAI 2026

Drowning in Degrees of Freedom: One Agent for Every Task

Steven James

We describe a research programme aimed at the construction of a single, generally intelligent agent - one competent across all tasks rather than skilled at any one. Such an agent must act in the real world through a rich sensorimotor interface, with sensors and actuators general enough for any task it may face. But that same richness makes any single task nearly impossible to learn directly. We argue that the resolution lies in abstraction, and survey our work towards this goal: discovering skills from experience, deriving compact models from those skills, and transferring both to new tasks. Skills, in particular, can be composed so that new tasks are solved with no further learning. Together, these threads work towards a single agent capable, in principle, of every task its body allows.

IJCAI 2026

Toward Trustworthy Recommender Systems in the Era of Agentic AI: From Relational User Modeling to Generative Personalization

Wenqi Fan

Recommender systems are evolving from models that predict user–item matching scores into intelligent systems that learn from relational structure, retrieve evidence, reason over user intent, and support personalized actions. This talk discusses a research agenda for trustworthy recommender systems in the era of agentic AI, organized around a transition from relational user modeling to generative personalization. We will discuss three connected components: relational user modeling with deep neural networks, generative and agentic personalization with large language models, and trustworthy foundation for reliable recommendations. The discussion revisits how social, behavioral, and knowledge relations can be integrated for user modeling; how generative models and personalized agents expand recommendation from ranking to intelligent decision support; and how trustworthiness challenges should be addressed throughout the system. The talk aims to provide a unified view of recommender systems as relational, generative, agentic, and trustworthy AI systems.

IJCAI 2026

Adaptive Experimental Design to Accelerate Scientific Discovery and Engineering Design

Aryan Deshwal

Artificial Intelligence (AI) and Machine Learning hold immense potential to accelerate scientific discovery and engineering design. A fundamental challenge in these domains involves efficiently exploring a large space of designs or hypotheses using expensive experiments in a resource-efficient manner. This paper surveys novel adaptive experimental design methods to address this broad challenge. Specifically, we discuss new probabilistic modeling and decision-making techniques that are applicable in small data settings. These approaches have shown substantial improvements in sample-efficiency, particularly for black-box optimization over high-dimensional combinatorial spaces (e.g., sequences and graphs) and a variety of goals ranging from multiobjective to multi-fidelity optimization. This paper outlines key methods and their real-world sustainability applications in areas such as nanoporous materials discovery, hardware design, surfactant design, and additive manufacturing.

IJCAI 2026

Verifiable PDE Reasoning and Modeling with Neurosymbolics

Wuyang Chen

Recent progress in Large Language Models (LLMs) has transformed text and code generation, yet models still falter on Partial Differential Equations (PDEs) where correctness, constraints, and physical consequences are critical. We explore how formal LLM reasoning can advance symbolic PDE modeling. First, our PDE-Controller formalizes informal PDEs, synthesizes solver-ready code, and plans subgoals to tackle nonconvex control via interactions with external solvers. Second, our Lean Finder accelerates PDE formalization via a semantics-aware search engine for Lean/Mathlib that retrieves relevant theorems, outperforming GPT models and gaining significant traction in the AI-for-math community. Through these efforts, we aim to design a semantics-first LLM that autoformalizes informal PDE problems into machine-checked specifications and synthesizes solver-ready code. This closes the loop between formal analysis and LLM reasoning, ultimately surpassing human heuristics across PDEs.

IJCAI 2026

Graph4LLM: A Systematic Survey of Graph-Enhanced Large Language Models

Xinyan Zhu, Cheng Yang, Qiuyu Wang, Zeyuan Guo, Yiding Wang, Zedi Liu, Chunchen Wang, Chuan Shi

Large Language Models (LLMs) excel in natural language processing (NLP) tasks. However, they suffer from inherent limitations due to their sequence-based nature, such as structural information loss and factual unreliability. Graphs, with the ability to explicitly model entities and relations, offer an effective way to address these shortcomings. To systematically synthesize the emerging research on graph-enhanced LLMs, this survey, Graph4LLM, examines how these methods integrate graphs into various stages of the LLM pipeline, including the input, model, and output phases. For each phase, we provide a detailed review of the key methods and techniques. We also introduce a wide range of application scenarios where Graph4LLM methods demonstrate significant potential. Finally, we outline the challenges and future research directions for developing more efficient and interpretable solutions.

IJCAI 2026

From Multimodal Perception to Strategic Reasoning: A Survey on AI-Generated Game Commentary

Qirui Zheng, Xingbo Wang, Keyuan Cheng, Yunlong Lu, Muhammad Asif Ali, Lingfeng Li, Yongyi Wang, Wenxin Li

The advent of artificial intelligence has propelled AI-Generated Game Commentary (AI-GGC) into a rapidly expanding research area, offering advantages such as scalable availability and personalized narration. However, existing studies remain fragmented, and a systematic survey that unifies prior efforts is still lacking. To bridge this gap, our survey introduces a unified framework that systematically organizes the AI-GGC landscape. We present a novel taxonomy focused on three core commentator capabilities: Live Observation, Strategic Analysis, and Historical Recall, and further categorize commentary into three corresponding types: Descriptive Commentary, Analytical Commentary, and Background Commentary. Building on this structure, we provide an in-depth review of methods, datasets, and evaluation metrics, analyzing their strengths and limitations. Finally, we highlight key challenges and point out promising directions for future research in AI-GGC.

IJCAI 2026

Tackling Multimodal Learning Challenges with Mixture-of-Expert: A Survey

Liangwei Zheng, Wei Emma Zhang, Olaf Maennel, Lin Yue, Weitong Chen

Mixture-of-Experts (MoE) presents a naturally compatible and scalable framework for multimodal learning, demonstrating strong adaptability across diverse modalities and tasks. Despite its growing success, a comprehensive and systematic evaluation of multimodal MoE remains lacking. Existing surveys tend to address either multimodal learning or MoE independently, overlooking the unique interplay between them. This survey fills that gap by addressing a central question: How does MoE effectively resolve multimodal challenges? We approach this from three key perspectives: (1) MoE as an Efficient Multimodal Framework: enabling scalable multimodal modeling by decoupling computational cost from parameter growth and mitigating modality redundancy through selective expert activation; (2) MoE as a Multimodal Representation Learner: integrating complementary multi-opinion expert knowledge to enrich alignment and interaction representations; and (3)MoE as a Multimodal Adapter: providing a modular and flexible mechanism to model imperfect modality data such as modality imbalance and missing modality. Through an extensive literature review, we identify critical research gaps, including interpretable routing, expert communication, modality integration, and lifelong multimodal learning. We position this survey as a foundation for future research toward interpretable, adaptive, and sustainable multimodal Mixture-of-Experts systems.

IJCAI 2026

A Survey of Joint Online-Offline Fine-tuning for Large Language Models

Taihang Zhen, Guang Yang, Chenzhang Li, Nuo Yan, Shilong Zhou, Guangyu Liu, Xiaotong Tang, Jing Huo, Boyan Wang, Junlan Feng, Yuyao Zhang

Post-training for Large Language Models (LLMs) can be mainly categorized into offline Supervised Fine-Tuning (SFT) for knowledge acquisition and online Reinforcement Fine-Tuning (RFT) for adaptive refinement. Current state-of-the-art approaches typically employ a sequential cold-start pipeline (SFT-Then-RFT). However, we argue that this disjoint transition imposes an “alignment tax", leading to catastrophic forgetting and reward hacking during the unregularized exploration phase. In this work, we advocate for Joint Online-Offline Fine-Tuning as a superior paradigm that breaks the convention of restricting offline data to SFT and online data to RFT. By integrating full offline response generation with online rollouts—particularly within the realm of Reinforcement Learning with Verifiable Rewards (RLVR)—this approach mitigates the limitations of isolated training phases. We provide the first comprehensive survey focusing specifically on the synchronization of data provenance. We introduce a novel taxonomy for related works, analyze their theoretical advantages in balancing stability with plasticity, and outline a roadmap for next-generation post-training frameworks.

IJCAI 2026

When Vision Meets Graphs: A Survey on Graph Reasoning and Learning

Xinjian Zhao, Wei Pang, Zhixuan Yu, Xiangru Jian, Xiaozhuang Song, Yaoyao Xu, Zhongkai Xue, Dingshuo Chen, Shu Wu, Philip Torr, Tianshu Yu

Graphs are a fundamental data structure underlying many problems in the natural and social sciences. Over the past decade, Graph Neural Networks (GNNs) have dominated graph machine learning, supported by solid theoretical foundations. Yet scientists often understand graph structure through vision: chemists read molecular diagrams and social scientists inspect network visualizations. Despite decades of work on graph visualization, most graph learning pipelines still treat graphs purely as symbolic structures, rarely leveraging the visual form of graphs. We argue that this gap deserves renewed attention in the era of powerful vision and vision language models. This survey provides a first systematic overview of the emerging area we term vision meets graphs, which treats visual depictions of graphs as first-class inputs for reasoning and learning. We organize existing work into three threads. Vision for Graph Reasoning studies how models can use visual depictions of graphs to understand structure and carry out multi-step reasoning. Vision for Graph Learning explores how visual features can complement or augment graph encoders beyond known limitations of message passing. Scientific Graphs examines domains where standardized depiction conventions support both reasoning and learning. Our goal is to clarify what current methods can and cannot do, and to outline a path toward foundation models that perceive and reason about graphs as scientists do.

IJCAI 2026

Multimodal Emotion Recognition with Large Language Models

Hongrui Zhang, Daiqing Wu, Yangyang Li, Kuien Liu, Yuhui Wang, Yu Zhou, Sicheng Zhao

Multimodal Emotion Recognition (MER) focuses on identifying and interpreting emotions from modality-compound inputs. Closely mirroring human cognitive processes in real-world environments, MER has drawn substantial attention from both academia and industry. Recently, a paradigm shift has been unveiled in MER, from leveraging small-scale, task-specific models to Large Language Models (LLMs). We refer to the latter as the MER-with-LLMs paradigm, which offers unprecedented generality, spurring numerous empirical attempts, even alongside speculation about their potential to achieve general emotional intelligence. However, with these new opportunities come new challenges, including the scarcity of emotionally annotated data, the affective gap both within and across modalities, and the opacity of affective interpretation. To systematically review existing research and guide future exploration, this paper categorizes prior works according to their focus on addressing these challenges into three directions: Affective Data Augmentation, Multimodal Affective Representation, and Multimodal Affective Reasoning. By thoroughly tracing the development, emerging trends, and remaining issues within each direction, this paper aims to provide a clear academic map of the MER-with-LLMs paradigm and foster its structured advancement.

IJCAI 2026

A Survey on Value Alignment in Agentic AI Systems

Wei Zeng, Hengshu Zhu, Chuan Qin, Han Wu, Yihang Cheng, Yinuo Shen, Zhe Wang, Yuyang Wang, Sirui Zhang, Xiaowei Jin, Zhenxing Wang, Feimin Zhong, Hui Xiong

With the evolution of artificial intelligence (AI) paradigms towards agentic AI, the widespread integration of large language models (LLMs) enhances system capabilities while also introducing situational risks and challenges of value misalignment, making value alignment in agentic AI systems a critical issue. This paper constructs a multi-level value framework encompassing L0 (universal values), L1 (cultural and industry values), and L2 (context-specific values). Guided by this framework, we conduct an in-depth analysis along the technical stack: at the LLM level, we examine value injection mechanisms through pretraining and post-training; at the single-agent level, we focus on representation and injecting values to agents, Profiles and memory, and planning and action; at the multi-agent level, we summarize collaborative alignment methods such as communication strategy optimization and multi-objective reinforcement learning. Following a systematic review of existing datasets and methods for multi-level alignment evaluation, we outline future research directions, including inter-agent value coordination mechanisms, high-quality scenario data sharing, game-theoretic design for value alignment in agent interaction and communication protocol alignment—aiming to establish a more systematic and dynamic evaluation framework and to promote robust and trustworthy value consensus in agentic AI systems within social collaboration.

IJCAI 2026

Towards Automated Kernel Generation in the Era of LLMs

Yang Yu, Peiyu Zang, Chi Hsu Tsai, Haiming Wu, Yixin Shen, Jialing Zhang, Haoyu Wang, Zhiyou Xiao, Jingze Shi, Yuyu Luo, Wentao Zhang, Chunlei Men, Guang Liu, Yonghua Lin

The performance of modern AI systems is fundamentally constrained by the quality of their underlying GPU kernels, which translate high-level algorithmic semantics into low-level hardware operations. Achieving near-optimal kernels requires expert-level understanding of hardware architectures and programming models, making kernel engineering a critical but notoriously time-consuming and non-scalable process. Recent advances in large language models and LLM-based agents have opened new possibilities for automating kernel generation and optimization. LLMs are well-suited to compress expert-level kernel knowledge that is difficult to formalize, while agentic systems further enable scalable optimization by casting kernel development as an iterative, feedback-driven loop. Rapid progress has been made in this area. However, the field remains fragmented and lacks a systematic perspective for LLM-driven kernel generation. This survey addresses this gap by providing a structured overview of existing approaches, spanning LLM-based approaches and agentic optimization workflows, and systematically organizing the datasets and benchmarks that underpin learning and evaluation in this domain. Moreover, key open challenges and future research directions are further outlined, aiming to establish a comprehensive reference for the next generation of automated kernel optimization. To keep track of this field, we maintain an open-source GitHub repository at https://github.com/flagos-ai/awesome-LLM-driven-kernel-generation.

IJCAI 2026

Beyond Scaling: A Survey of Data-Efficient Learning for LLM Agents

Yaqing Wang, Zhenlin Luo, Peiyao Zhao, YUNFENG CAI, Quanming Yao

LLM-based agents perceive, reason, act, and adapt through interaction. While scaling remains important, agentic progress also depends on extracting more learning signal from limited experience, including demonstrations, feedback, reasoning traces, tool-use records, and interaction trajectories. This survey develops an agent-centric view of data-efficient learning. We formulate an agentic learning loop, define data efficiency as capability improvement without proportional increases in supervision or real-environment trial-and-error, and synthesize methods into experience augmentation, agent structural design, and learning paradigms. We also summarize representative application domains and open challenges in experience reuse and cost-aware evaluation. Overall, the agentic era requires smarter ways to acquire, structure, reuse, and learn from limited experience.

IJCAI 2026

Concept Bottleneck Models for Explainable Decision Making: A Survey of Progress, Taxonomy, and Future Directions

Chunjiang Wang, Fan Li, Wenbo Hu, Rui Yan, Kun Zhang, Shaohua Kevin Zhou

Deep neural networks deliver strong performance but remain opaque, limiting their use in high-stakes domains that require transparency and human oversight. Concept Bottleneck Models (CBMs) address this gap by introducing a human-interpretable concept layer that mediates inputs and decisions, enabling semantic explanations and test-time intervention. This survey provides a unified review of CBMs organized along four dimensions: concept acquisition, concept-based decision making, concept intervention, and concept evaluation. We summarize the evolution of concept construction from manual annotation to lexicon-based mining, LLM/VLM-guided generation, and visually grounded discovery via prototypes and diffusion models; review emerging CBM architectures beyond strict bottlenecks; and consolidate evaluation and intervention protocols emphasizing faithfulness, sparsity, and intervenability, with particular relevance to high-stakes domains such as healthcare. We synthesize fragmented literature and outline key challenges and future directions for concept-based interpretable decision making.

IJCAI 2026

Towards Vision-Spatiotemporal Fusion in Traffic Forecasting: A Survey on Cross-Modal Alignment

Anna Wang, Chao Zhang, Mingwei Lin, Junbo Zhang, Zeshui Xu, Wentao Li, Pengfei ZHANG, Oscar Castillo

Traffic forecasting is evolving, with world models emerging as a powerful framework applicable to tasks such as core state, trajectory, event, and demand forecasting. These tasks involve both visual and spatiotemporal data, yet most existing methods treat them separately, hindering a unified understanding of traffic scenes in both semantic meanings and spatiotemporal dynamics. The fusion of the two modalities is critical for building models that comprehend complex traffic scenarios. However, the fusion issue faces two fundamental misalignments: semantic, where pixels conflict with traffic concepts, and geometric, which requires spatial intelligence to map 2D inputs into 3D. This survey reframes vision-spatiotemporal fusion via the unique lens of cross-modal alignment, addressing semantic and geometric failures that limit forecasting reliability. First, we categorize existing methods into three paradigms: feature-level, semantic-level, and task-level. This reveals their progression from low-level feature manipulation to high-level architectural integration. Second, we synthesize representative techniques per paradigm, highlighting geometric challenges such as cross-view association and spatial mapping. Third, we examine current datasets and benchmarks, highlighting their deficiencies in evaluating alignment. Finally, we outline future directions, including spatiotemporal intelligence for robust perception and holistic traffic world models. The unified framework establishes a reference for robust and explainable forecasting systems.

IJCAI 2026

Deep Learning and Foundation Models for Weather Prediction: A Survey

Jimeng Shi, Azam Shirali, Bowen Jin, Sizhe Zhou, Wei Hu, Rahuul Rangaraj, Zhaonan Wang, Yanzhao Wu, Leonardo Bobadilla, Upmanu Lall, Shaowen Wang, Jiawei Han, Giri Narasimhan

Numerical weather prediction (NWP) models remain the cornerstone of atmospheric sciences. Yet, deep learning (DL) is challenging this paradigm by its ability to capture intricate spatio-temporal patterns and deliver ultra-fast predictions. Analogous to the foundation models (e.g., ChatGPT) in natural language processing, foundation models in the weather/climate domain have also been developed. This paper reviews DL and foundation models for weather prediction by highlighting their strengths and limitations. In particular, we carefully examine them from the perspective of their training paradigms: deterministic predictive learning, probabilistic generative learning, and pre-training & fine-tuning. For each paradigm, we summarize the underlying model architectures, training methods, and respective features. To facilitate further study, we provide a curated repository featuring categorized papers, open-source code, and benchmark datasets. Finally, we discuss and suggest potential research directions across new tasks and models in weather data storage and management, and operational deployment, further inspiring innovations in this rapidly evolving field. GitHub: https://github.com/JimengShi/DL-Foundation-Models-Weather.

IJCAI 2026

Spatial Pattern Matching: A Survey

Nicole R. Schneider, Kent O'Sullivan, Hanan Samet

Recent developments in Artificial Intelligence (AI) have led to new ways for users to search through vast information. However, users may have questions that are grounded in the real world that require spatial inference, for which language models are not well suited. Conversely, traditional spatial search methods, like spatial pattern matching, answer spatial reasoning questions correctly but are noise-intolerant, slow, and brittle. Presently, there are opportunities to integrate AI and spatial pattern matching to enable robust and flexible spatial search. This paper surveys existing spatial pattern matching methods, including the few that apply machine learning to the problem, discussing their efficiency and limitations, and describing opportunities to further enable spatial search through AI.

IJCAI 2026

A Survey on 3D Skeleton Based Person Re-Identification: Taxonomy, Advances, Challenges, and Interdisciplinary Prospects

Haocong Rao, Chunyan Miao

Person re-identification via 3D skeletons is an important emerging research area that attracts increasing attention within the pattern recognition community. With distinctive advantages across various application scenarios, numerous 3D skeleton based person re-identification (SRID) methods with diverse skeleton modeling and learning paradigms have been proposed in recent years. In this paper, we provide a comprehensive review and analysis of recent SRID advances. First of all, we define the SRID task and provide an overview of its origin and major advancements. Secondly, we formulate a systematic taxonomy that organizes existing methods into three categories centered on hand-crafted, sequence-based, and graph-based modeling. Then, we elaborate on the representative models along these three types with an illustration of foundational mechanisms. Meanwhile, we provide an overview of mainstream supervised, self-supervised, and unsupervised SRID learning paradigms and corresponding common methods. A thorough evaluation of state-of-the-art SRID methods is further conducted over various types of benchmarks and protocols to compare their effectiveness, efficiency, and key properties. Finally, we present the key challenges and prospects to advance future research, and highlight interdisciplinary applications of SRID with a case study. A curated collection of valuable resources is available at https://github.com/Kali-Hac/3D-SRID-Survey.

IJCAI 2026

A Comprehensive Survey of Deep Learning for Multivariate Time Series Forecasting: A Channel Strategy Perspective

Xiangfei Qiu, Hanyin Cheng, Xingjian Wu, Junkai Lu, Jilin Hu, Chenjuan Guo, Christian S. Jensen, Bin Yang

Multivariate Time Series Forecasting (MTSF) plays a crucial role across diverse fields, ranging from economics to energy to traffic. In recent years, deep learning has demonstrated outstanding performance in MTSF tasks. In MTSF, modeling the correlations among different channels is critical, as leveraging information from other related channels can significantly improve the prediction accuracy of a specific channel. This study systematically reviews the channel modeling strategies for time series and proposes a taxonomy organized into three hierarchical levels: the strategy perspective, the mechanism perspective, and the characteristic perspective. On this basis, we provide a structured analysis of these methods and conduct an in-depth examination of the advantages and limitations of different channel strategies. Finally, we summarize and discuss some future research directions to provide useful research guidance. Moreover, we maintain an up-to-date GitHub repository which includes all the papers discussed in the survey.

IJCAI 2026

Knowledge-Guided 3D CT Generation: A Conditioning-Centric Taxonomy

Francesca Pia Panaccione, Eugenio Lomurno, Matteo Matteucci

Controllable generation guided by external knowledge is a key requirement in modern generative deep learning applications, enabling the synthesis of samples with explicit constraints on semantic content, structural properties, and variability. In 3D Computed Tomography (CT), such control is essential for clinical applications, including data augmentation, privacy-preserving data sharing, and the simulation of specific anatomical or pathological scenarios. While research on conditional 3D CT generation has expanded rapidly, the diversity of existing approaches makes systematic comparison difficult and obscures fundamental design choices. In this survey, we propose a conditioning-centric taxonomy that organizes the literature along three orthogonal dimensions: the type of external knowledge (K), the knowledge integration paradigm (I), and the generative architecture (A). This factorization defines an explicit design space (K x I x A) that provides a unified perspective on prior work. Using this framework, we systematize existing methods, identify dominant trends and recurring design patterns, and highlight underexplored regions of the design space that point toward promising directions for future research.

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