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IJCAI 2026

Generalizing Bayesian Human-AI Collaboration: Theory and Application in Data-Scarce Environments

Peng Liu, Hailong Sun, Chung-Piaw Teo, Mabel Chou

Combining predictions from heterogeneous classifiers—such as in-house deep learning models, human experts, and large language models (LLMs)—is a key challenge, especially in data-scarce environments such as humanitarian operations. We propose a flexible Bayesian framework to effectively fuse these diverse inputs. By integrating classifier logits with auxiliary human feedback (e.g., confidence, image clarity) using an ordered probit process, our model generalizes prior work to accommodate the real-world properties of these classifiers. We validate our framework on a challenging product recognition task in food bank operations, an environment defined by data scarcity and an inexperienced volunteer workforce. Our combined model significantly outperforms standalone ResNet, human, and LLM-based approaches, demonstrating the practical benefits of fusing these heterogeneous signal sources.

IJCAI 2026

ThinFormer: Channel Sparse Transformer for Efficient HRW Object Detection

Wenxi Li, Kunpeng Liu, Moran Liu, Shuyang Liu, Chenyang Lyu, Haozhe Lin, Yuchen Guo

Object detection in high-resolution wide (HRW) shots presents unique challenges due to the extreme sparsity of objects and the variability in sparsity ratios across images. Conventional detectors, designed for close-up settings like MS COCO, struggle to generalize to these scenarios, leading to inefficient computation and degraded performance. While prior work has focused on spatial sparsity, the potential of sparsity in the channel dimension remains largely underexplored. In this paper, we propose ThinFormer, a dynamic and efficient framework tailored for object detection in HRW shots. ThinFormer leverages sparsity in both the spatial domain and the channel domain, the latter enabled by a novel local feed-forward network (local FFN) that exploits the duality between spatial and channel representations. To adapt to varying levels of scene complexity, we introduce a dynamic sparsity ratio estimator, trained in a self-supervised manner, which guides selective activation of channels based on semantic richness. Extensive experiments on the PANDA benchmark demonstrate that ThinFormer significantly reduces computational cost while maintaining competitive detection performance compared to state-of-the-art methods. Our results highlight the effectiveness of embracing multi-domain sparsity for scalable and robust object detection in complex, real-world HRW imagery. Code and supplementary material are available at https://github.com/LiuKunpeng03/Thinformer.

IJCAI 2026

Learning to Harvest: VR-Guided Expert Behaviour Capture for Decision Modelling in Agricultural Robots

Yining Lang, Zhaoxin Li, Xiujuan Chai

While deep learning has significantly enhanced robotic perception in agriculture, autonomous decision-making in dense and occluded environments remains a persistent challenge. This paper proposes a VR-based expert motion capture framework to bridge this gap by integrating high-fidelity virtual environments with human expertise. By reconstructing a realistic tomato greenhouse in Unity and utilizing 27-point motion capture suits, we captured multi-dimensional expert demonstration data encompassing trajectories, postures, and velocities. In the VR environment, ``hard cases" featuring frequent collisions can be selectively generated at a higher frequency, allowing experts to provide concentrated demonstrations of specialized postures that markedly accelerate algorithmic learning efficiency. Crucially, the virtual framework enables multiple experts to repetitively harvest identical targets to match optimal trajectories—a process precluded in reality by the destructive nature of harvesting. Moreover, it bypasses biological growth and seasonal constraints, facilitating continuous, high-frequency experimentation independent of crop maturation. These expert data were subsequently integrated into a multi-task reinforcement learning model as experience replay and reward templates. Experimental results demonstrate that our expert-driven algorithm significantly outperforms baseline methods in harvesting efficiency, collision avoidance, and stability, establishing VR-guided motion capture as a robust and scalable paradigm for advancing agricultural robotics.

IJCAI 2026

LISTEN to Your Preferences: An LLM Framework for Multi-Objective Selection

Adam S. Jovine, Tinghan Ye, Francis Bahk, Jingjing Wang, Matthew Ford, David B. Shmoys, Peter I. Frazier

Human experts often struggle to select the best option from a large set of items with multiple competing objectives, a process bottlenecked by the difficulty of formalizing complex, implicit preferences. To address this, we introduce LISTEN (LLM-based Iterative Selection with Trade-off Evaluation from Natural-language), an agentic LLM-based framework that treats the LLM as a decision-making agent capable of iteratively refining its internal preference model and taking actions (e.g., proposing utilities or selecting candidates) to maximize alignment with a user's implicit goals. To operate within LLM constraints like context windows and inference costs, we propose two iterative algorithms: LISTEN-U, which uses the LLM to refine a parametric utility function, and LISTEN-T, a non-parametric method that performs tournament-style selections over small batches of solutions. Evaluated on diverse tasks including flight booking, shopping, and exam scheduling, our results show LISTEN-U excels when preferences are parametrically aligned (a property we measure with a novel concordance metric), while LISTEN-T offers more robust performance overall. This work explores a promising direction for steering complex multi-objective decisions directly with natural language, reducing the cognitive burden of traditional preference elicitation. Code is available at https://github.com/AdamJovine/LISTEN.

IJCAI 2026

HFFN-ID: A Hierarchical Feature Fusion Network with Bi-Phase Subject ID Modulation for EEG Mel-Spectrogram Reconstruction

Chi Huang, Zhaohu Liu, Yong Peng, Wanzeng Kong

High-fidelity reconstruction of mel-spectrograms from EEG signals remains a formidable challenge, primarily due to the inherent inter-subject variability of neural patterns and the semantic gap between heterogeneous feature representations of these two modalities. To alleviate both issues, this paper proposes a Hierarchical Feature Fusion Network with bi-phase subject IDentifier modulation (HFFN-ID) for reconstructing mel-spectrograms from EEG signals. The primary improvements of the HFFN-ID framework are from two aspects, a Binary-Phase Subject-ID Modulation (BiPSM) mechanism for explicit subject conditioning and a Condition-guided Hierarchical Fusion (CHF) component for dynamic multi-layer feature synthesis, which respectively aim to address the problems of inter-subject variability in neural patterns and heterogeneous cross-modality feature fusion. Moreover, a speech envelope feature-based pre-training strategy is incorporated to initialize the parameter space, inspired by the shared low-level representations across different speech features. On the SparrKULee dataset, HFFN-ID establishes a new benchmark with a pearson correlation coefficient of 0.0723, representing a substantial 44% relative improvement over existing baseline. Importantly, HFFN-ID is highly efficient, achieving a 38.7% parameter reduction and a 2.5× training speedup. These results highlight the effectiveness of subject-conditioned and hierarchically fused architectures for advancing high-fidelity neural speech decoding.

IJCAI 2026

Cross-Modal Dynamic Hypergraph Computation via Functional-Structural Brain Network for Brain Disorder Diagnosis

Jingxi Feng, Heming Xu, Rundong Xue, Junhao Cai, Xudong Chen, Dong Zhang, Shaoyi Du

Cross-modal brain networks characterize the complex connections between different brain regions from both functional and structural perspectives, which is of significant importance for brain network analysis and the diagnosis of brain diseases. However, existing methods have failed to fully exploit the complementary information between functional and structural brain networks, neglecting the guiding role of topology in the transmission of functional modal information, as well as the potential associations of cross-modal high-order information. To address these challenges, this paper proposes a cross-modal dynamic hypergraph computing (CDHGC) framework for brain disease diagnosis and an in-depth analysis of coupled functional-structural brain networks. The CDHGC comprises two key modules: the topology-guided dynamic hypergraph generation module, which combines the topology-constrained KNN and topology-guided centroid sampling methods to uncover high-order correlations among brain regions across modalities based on topological structure. This module dynamically optimizes the hypergraph structure during training to explore potential associations of cross-modal information. The cross-modal hypergraph convolution module efficiently aggregates information from functional-structural brain networks through a cross-modal attention mechanism during message-passing, resulting in high-order joint representations. Experiments conducted on the ADNI and ABIDE datasets demonstrate that the proposed method outperforms current state-of-the-art approaches. Interpretability analysis reveals that the proposed method can discover multi-modal biomarkers associated with brain diseases.

IJCAI 2026

MindCopilot: Towards Formalizing and Evaluating Granular Human-LLM Co-Writing

Youqing Fang, Yinhao Tang, Yanan SUN, Jiangning Liu, Ziyi Wang, Xun Zhao, Bin Liu, Weiming Zhang, Kuikun Liu, Wenwei Zhang, Kai Chen

Recent writing assistants are increasingly shifting from passive, prompt-driven interaction to proactive, suggestion-based completion, which integrates localized continuations into the writing flow and reduces coordination burden. However, existing evaluations simply focus on output quality, failing to capture how users accept, edit, or repair suggestions in real-time interaction, and thus obscuring the true usability of proactive co-writing systems. To address this gap, we adopt a sequential, behavior-centered view of interactive writing and formalize co-writing as a Human-in-the-Loop Markov Decision Process, modeling writing as an interaction shaped by user acceptance and editing decisions. Based on this formulation, we introduce the Co-Writing Fidelity Suite, an interaction-aware metric suite that captures both user–assistant alignment and cognitive editing effort, including Hierarchical Acceptance Rate and Knowledge-aware Editing Distance. We conduct a large-scale simulation study across 16 writing domains, using 1,688 controlled continuation queries sampled from different writing stages. Our analysis reveals systematic effects of interaction structure on acceptance behavior and editing cost. A follow-up user study with 30 participants confirms that these behavioral patterns align with real user experience. Together, our findings demonstrate that interaction-aware evaluation provides insights beyond output-only metrics and informs the design of more effective proactive writing assistants.

IJCAI 2026

Boosting Knowledge Transfer and Retention with Brain-inspired Multi-View Incremental Learning

Yuhong Chen, Zihan Fang, Huifeng Yin, Yujie Wu, Qi Xu, Lei Deng, Shiping Wang, Mingkun Xu

Traditional multi-view learning models are primarily designed for static datasets with fixed views. However, in dynamic incremental view environments, this approach inevitably leads to view forgetting, where the introduction of new views weakens previously acquired knowledge. In contrast, the human brain exhibits remarkable memory retention and knowledge transfer capabilities when receiving objects described from different perspectives, with past experience further supplementing new insights. Inspired by underlying neural processing mechanisms, we propose a novel view incremental learning framework named Hebbian View Orthogonal Projection (HVOP). HVOP constructs a knowledge transfer space, where gradient updates are projected onto the orthogonal complement of historical representations, thereby mitigating interference between old and new views. By further incorporating recursive lateral connections and Hebbian learning rules, the proposed model imparts brain-like dynamic adaptability to the learning process, enhancing knowledge transfer and integration, thereby enabling stable knowledge transfer under evolving views. We validate HVOP on node classification tasks, demonstrating its superior performance in both knowledge retention and transfer compared to traditional methods. The results highlight the efficacy of biologically inspired mechanisms in mitigating the view forgetting phenomenon.

IJCAI 2026

FedGLoRA: Grassmann-Manifold Federated Learning via Dual LoRA for Large EEG Models

Qianyu Chen, Yihao Zhong, Runxuan Tang, Tianyi Zhang, Jing Liu, Ziyu Jia, Chenyu Liu

Large EEG Models (LEMs) are drawing increasing attention in EEG, as large-scale pretraining yields transferable representations that improve generalization. As EEG research moves to real-world deployment, objectives and paradigms diversify, yielding increasingly heterogeneous and unevenly scaled datasets across institutions. This evolution induces distribution drift and scale imbalance, making static pretrained LEMs brittle and requiring adaptation to newly collected data. Under the constraints of the high cost of retraining and the privacy sensitivity of newly collected data, federated learning offers a practical route to update LEMs via decentralized parameter updates. Yet standard federated fine-tuning is unstable for pretrained LEMs, suffering backbone drift, misaligned updates under non-IID heterogeneity, and scale imbalance. We propose FedGLoRA: we freeze the backbone and communicate only LoRA adapters via a dual-branch design, with a shared global branch and a private on-device branch. FedGLoRA aggregates global updates on a Grassmann subspace with a Grassmann-proximal constraint, and uses bidirectional distillation to mitigate imbalance. Experiments on motor imagery and emotion recognition show consistent gains and improved stability over strong baselines.

IJCAI 2026

CoFL: Consensus Driven Human-AI Collaborative Federated Learning

Zeyuan Cai, Yao Zhang, Zhiwen Yu, Jiaqi Liu, Yuchang Sun, Chenhao Ma, Yilin Zhao

Federated learning (FL) has emerged as a distributed machine learning paradigm due to its privacy-preserving advantages. Most FL studies assume offline labeled datasets are available at clients. In practice, however, client data often arrive without labels in a streaming manner, making label acquisition a crucial problem. Existing solutions typically rely on human experts or artificial intelligence (AI) models for annotation. Nevertheless, due to the subjectivity among human experts and the inherent limitations of AI models, the labels they provide are often unreliable. In this work, we propose CoFL, a novel consensus driven human-AI collaborative FL method, which utilizes the complementarity between humans and AI models to produce more reliable labels. Moreover, CoFL leverages cross-client consensus among human experts to further enhance the collaboration process. Experiments on three datasets demonstrate that CoFL consistently outperforms baseline methods under various settings. The code is available at https://github.com/huaiguang233/CoFL.

IJCAI 2026

Incentive-Compatible Diffusion Combinatorial Auctions

Haotian Zhu, Miao Li, Bin Li, Dengji Zhao

The diffusion auction is an emerging auction model which leverages social networks to recruit more buyers, thereby improving auction revenue and allocation efficiency. Previous studies on diffusion auctions have primarily focused on the single-parameter domain, where each buyer's valuation function is represented by a single private value. However, due to the complex interdependencies between allocations and network structures, few studies have examined the combinatorial auction domain, in which buyers' valuation functions exhibit multidimensionality. In this paper, we fully characterize implementable diffusion mechanisms within the combinatorial auction domain for the first time. Based on the characterization, we identify a class of allocation policies for each of which the optimal payment, namely the one maximizing the seller's revenue, can be derived in closed form. Furthermore, we apply the established theory to design a practical diffusion combinatorial auction, named the Exhausted-Reference Pricing Mechanism, which is incentive-compatible, individually rational, and weakly budget balanced.

IJCAI 2026

A Truthful Multiunit Profit-Optimal Mechanism for Synthesizing Social Laws

Jun Wu, Jian Huang, Chongjun Wang

Social Law Synthesis (SLS) in strategic environments is a novel multi-unit mechanism design problem, spanning modeling to computational challenges. We derive a method to specify the problem succinctly, reduce payment determination to allocation determination, and design an integer linear programming (ILP)-based algorithm that further reduces allocation to a polynomial-time ILP formulation. This offloads intractability to powerful ILP solvers, yielding a truthful, individually rational, and profit-optimal mechanism.

IJCAI 2026

The Price of Proportional Representation in Temporal Voting

Nicholas Teh

We study proportional representation in the temporal voting model, where collective decisions are made repeatedly over time over a fixed horizon. Prior work has extensively investigated how proportional representation axioms from multiwinner voting (e.g., justified representation (JR) and its variants) can be adapted, satisfied, and verified in this setting. However, much less is understood about their interaction with social welfare. In this work, we quantify the efficiency cost of enforcing proportionality. We formalize the welfare-proportionality tension via the worst-case ratio between the maximum achievable utilitarian welfare and the maximum welfare attainable subject to a proportionality axiom. We show that imposing proportional representation in the temporal setting can incur a growing, yet sublinear, welfare loss as the number of voters or rounds increases. We further identify a clean separation among axioms: for JR, the welfare loss diminishes as the time horizon grows and vanishes asymptotically, whereas for stronger axioms this conflict persists even with many rounds. Moreover, we prove that welfare maximization under each axiom is NP-complete and APX-hard, even under static preferences and bounded-degree approvals, and provide fixed-parameter algorithms under several natural structural parameters.

IJCAI 2026

Connected EF1 Allocations Exist in Discrete Chore Cutting

Ankang Sun, Bo Li

In this paper, we prove the existence of an envy-free up to one item (EF1) division for a discrete chore. Our approach builds upon the powerful Simmons-Su framework, which leverages Sperner’s lemma to guarantee the existence of a simplex corresponding to a sequence of similar fractional divisions, ensuring that each agent is satisfied with a different bundle. For allocations of goods, existing works have introduced rounding techniques that convert an envy-free fractional division into a connected integral EF1 division for any number of agents with monotone valuations. However, the analogous problem for chores has remained unresolved, and existing rounding techniques fail due to the asymmetric definitions of EF1 for goods and chores. To overcome this asymmetry, we refine the existing rounding techniques and show that connected EF1 divisions exist for a discrete chore.

IJCAI 2026

Beyond Homogeneous Adversaries: Stackelberg Security Games with Mixed Quantal Response

Hoang Giang Pham, Tien Mai, Thuy Anh Ta, Minh Hoàng Hà

The quantal response (QR) model is widely used in Stackelberg security games (SSGs) to capture boundedly rational adversaries. Existing work on SSGs under QR, however, almost exclusively assumes a homogeneous attacker population, ignoring heterogeneity in attacker preferences and rationality. We study SSG with mixed quantal response attackers, where the follower population consists of multiple discrete attacker types, each following a type-specific QR model. The defender allocates limited resources across targets, while an attacker drawn from this heterogeneous population observes the defender’s strategy and attacks a single target. This results in a highly non-convex equilibrium computation problem. We develop a polynomial-time approximation scheme (PTAS) for this setting when the number of attacker types is bounded, based on an exponential cone programming formulation combined with a carefully designed Branch-and-Bound procedure. Experiments demonstrate that our approach outperforms standard gradient-based methods and that explicitly modeling attacker heterogeneity yields significant gains over traditional SSG models with a single QR attacker.

IJCAI 2026

Proportional Selection in Networks

Georgios Papasotiropoulos, Oskar Skibski, Piotr Skowron, Tomasz Wąs

We address the problem of selecting k representative nodes from a network, aiming to simultaneously achieve two objectives: identifying the most influential nodes and ensuring that the selection proportionally reflects the diversity within the network. We propose a general approach to accomplish this by combining ideas from network science and computational social choice. Notably, our algorithms depend only on the connections between nodes and do not utilize any additional information that would explicitly identify groups of nodes. We analyze them theoretically, and demonstrate their effectiveness through a series of experiments.

IJCAI 2026

Do Not Discretize, Optimize: Almost Greedy Fictitious Play

Evangelos Markakis, Christodoulos Santorinaios

Our work evolves around Fictitious Play, one of the first iterative methods that is known to converge to a Nash equilibrium in zero-sum games. In recent years, there has been a revived interest, due to applications in various machine learning problems, which has motivated a line of work on its convergence properties and on proposing new variants of the initial algorithm. Our paper is along this direction and introduces one new variant, which we refer to as Almost Greedy Fictitious Play. The proposed algorithm greedily attempts to find the optimal stepsize at each iteration but its search space is constrained and includes almost all the line between the cumulative mixed strategy and the current best response. Our main result is that the method achieves an instance dependent convergence rate of O(1/T) with respect to the duality gap. This matches the rate of Continuous Fictitious Play, and offers an alternative to discretization. We complement our theoretical findings with experiments that demonstrate the effectiveness of the method.

IJCAI 2026

The Complexity of Tournament Fixing: Subset FAS Number and Acyclic Neighborhoods

Yuxi Liu, Junqiang Peng, Mingyu Xiao

The Tournament Fixing Problem (TFP) asks whether a knockout tournament can be scheduled to guarantee that a given player v* wins. Although TFP is NP-hard in general, it is known to be fixed-parameter tractable (FPT) when parameterized by the feedback arc/vertex set number, or the in/out-degree of v*. However, it remained open whether TFP is FPT with respect to the subset FAS number of v* --- the minimum number of arcs intersecting all cycles containing v* --- a parameter that is never larger than the aforementioned ones. In this paper, we resolve this question negatively by proving that TFP stays NP-hard even when the subset FAS number of v* is constant ≥ 1 and either the subgraph induced by the in-neighbors D[N_{in}(v*)] or the out-neighbors D[N_{out}(v*)] is acyclic. Conversely, when both D[N_{in}(v*)] and D[N_{out}(v*)] are acyclic, we show that TFP becomes FPT parameterized by the subset FAS number of v*. Furthermore, we provide sufficient conditions under which v* can win even when this parameter is unbounded.

IJCAI 2026

Deterministic Implementation in Single-Item Auctions

Yan Liu, Zeyu Ren, Pingzhong Tang, Zihe Wang, Yulong Zeng, Jie Zhang

Deterministic auctions are attractive in practice due to their transparency, simplicity, and ease of implementation, motivating a sharper understanding of when they can attain the same outcomes as randomized mechanisms. We study deterministic implementation in single-item auctions under two notions of outcomes: (revenue, welfare) pairs and interim allocations. For (revenue, welfare) pairs, we show a separation in discrete settings: there exists a pair implementable by a deterministic Bayesian incentive-compatible (BIC) auction but not by any deterministic dominant-strategy incentive-compatible (DSIC) auction. For continuous atomless priors, we identify conditions under which deterministic DSIC auctions are equivalent to randomized BIC auctions in terms of achievable outcomes. For interim allocations, under a strict monotonicity condition, we establish a deterministic analogue of Border's theorem for two bidders, providing a necessary and sufficient condition for deterministic DSIC implementability. Using this characterization, we exhibit an interim allocation implementable by a randomized BIC auction but not by any deterministic DSIC auction.

IJCAI 2026

Eliminating Envy in Multigraphs Through Subsidy

Bo Li, Ankang Sun, Shiji Xing

Envy-free (EF) allocation is a fundamental problem at the intersection of theoretical computer science and economics. When resources are indivisible, achieving an EF allocation is often impossible. A common remedy is to compensate agents with subsidies. Brustle et al. proved that a total subsidy of n-1 is both necessary and sufficient to guarantee EF in the worst case, where n is the number of agents and each agent has additive valuations with marginal values of at most 1 per item. In this paper, we consider a constrained setting, namely graph orientation, that was recently introduced by Christodoulou et al. In this model, agents correspond to vertices in a multigraph, and resources correspond to edges, with each edge allocated to one of its two incident agents. Despite extensive study of the graph orientation model, it remains unclear how much subsidy is sufficient to guarantee EF. We show that a total subsidy of n/2 is always sufficient to guarantee EF in any multigraph, halving the subsidy required in the unconstrained setting, and provide a polynomial-time algorithm to compute such an allocation. We show that this bound is tight, even for simple graphs.

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