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IJCAI 2026

Adaptive Reward Design in Reinforcement Learning: A Taxonomy and Survey

Raphaela Baybas, Carlo D'Eramo, Philipp Brune

Adaptive Reward Design (ARD) is becoming a fundamental component for Reinforcement Learning (RL) agents, as they are deployed in increasingly complex settings where a single static reward across all phases of learning is rarely sufficient. Yet, ARD is rarely studied as a coherent topic: Relevant ideas are dispersed across reward shaping, curriculum learning, intrinsic motivation, non-stationary objectives, and preference- or feedback-based learning, which obscures conceptual connections and complicates method selection. This survey provides a unified view of ARD in RL by introducing a taxonomy, organized by the primary driver of the reward variation. The taxonomy distinguishes external-feedback-driven reward updates from reward adaptations driven by endogenous within-run signals and those conditioned on exogenous context signals. Using explicit assignment rules, we place work published between 2010 and 2025 within this taxonomy. By synthesizing typical RL settings and domains at the driver level, we simplify the method selection in ARD. Further, we describe the evolution and current trends of ARD and conclude by outlining promising future research directions.

IJCAI 2026

A Survey of Artificial Intelligence in Endoscopic Surgery Workflow: From Perception to Surgical Support

Juyan Ba, Hao Chen, Xiaohan Xing, Yi Wang

Endoscopic surgery demands continuous real-time visual decision-making under severe constraints, including a limited field of view, motion blur, and dynamically deforming anatomy. These factors impose substantial cognitive load on surgeons and motivate the integration of artificial intelligence (AI) throughout the endoscopic surgical workflow. This survey reviews recent progress in AI for endoscopic surgery and organizes the literature into four stages that span perception to action: (1) image enhancement and analysis methods that improve visual perception; (2) multimodal video understanding approaches that model and reason surgical instruments and anatomical structures over space and time; (3) 3D reconstruction techniques that enable robust tracking and interpretation of deformable anatomy; and (4) emerging paradigms of embodied surgical intelligence, where action-conditioned world models link perception to intraoperative assistance. Across these stages, we summarize current capabilities and limitations and identify key open challenges for clinical deployment. In addition, we provide an overview of 18 publicly available datasets, highlighting their scope and annotations. We hope this survey will stimulate further research toward reliable and clinically deployable AI systems for endoscopic surgery.

IJCAI 2026

Graph Rewiring in GNNs to Mitigate Over-Squashing and Over-Smoothing: A Survey

Hugo Attali, Nathalie Pernelle, Davide Buscaldi, Fragkiskos D. Malliaros

Graph Neural Networks are powerful models for learning from graph-structured data, yet their effectiveness is often limited by two critical challenges: over-squashing, where information from distant nodes is excessively compressed, and over-smoothing, where repeated propagation makes node representations indistinguishable. Both phenomena stem from the interaction between message passing and the input topology, ultimately degrading information flow and limiting the performance of GNNs. In this survey, we examine graph rewiring techniques, a class of methods designed to modify the graph topology to enhance information propagation in GNNs. We provide a comprehensive review of state-of-the-art rewiring approaches, delving into their theoretical underpinnings, practical implementations, and performance trade-offs.

IJCAI 2026

A Survey on Active Feature Acquisition Strategies

Linus Aronsson, Arman Rahbar, Morteza Haghir Chehreghani

Active feature acquisition (AFA) studies how a predictive system can sequentially choose which feature values to obtain for each instance to balance predictive accuracy against feature acquisition cost. This survey provides the first unified treatment of modern AFA through a partially observable Markov decision process (POMDP) formulation, showing that most existing methods can be understood as different approximations of the same underlying sequential decision problem. The survey proposes an up-to-date taxonomy organizing AFA into four categories: (i) embedded cost-aware predictors (notably cost-sensitive decision trees and ensembles), (ii) model-based methods that plan using learned probabilistic components, (iii) model-free methods that learn acquisition policies from simulated episodes, and (iv) hybrid methods that combine the strengths of model-based and model-free approaches. We argue that this POMDP-centric view clarifies connections among existing methods and motivates more principled algorithm design. Since much prior work is heuristic and lacks formal guarantees, we also outline routes to guarantees by connecting AFA to adaptive stochastic optimization. We conclude by highlighting open challenges and promising directions for future research.

IJCAI 2026

A Review on Test-Time Scaling for Agentic Large Language Models

Jiayu An, Zheng Chen, Yongcheng Jing, Dacheng Tao, Bo Li

The field of Large Language Models (LLMs) is moving beyond simply scaling the parameters during pre-training. Instead, researchers are focusing on Test-Time Scaling (TTS), which is a mechanism to allocate computational resources dynamically during the inference process. In the rapidly emerging field of Agentic LLMs, TTS has also shown great value in enabling agents to handle complex tasks that static models cannot handle. Despite all the substantial work in this field, it still lacks a comprehensive survey summarizing these advances. To the best of our knowledge, this paper presents the first systematic review of TTS tailored for Agentic LLMs. To this end, we propose a novel RAIE taxonomy along four scaling dimensions: (i) Reasoning optimizes the entire thought process through search algorithms and self-verification; (ii) Acting involves collecting information through external tools and environmental feedback; (iii) Interaction copes with complex tasks by forming various multi-agent networks; (iv) Evolution enables agents to continuously update their memory and strategies. Furthermore, we also introduce a task-oriented guideline for choosing the best TTS strategy. Finally, we sum up challenges and future directions to inspire further studies in this field. A curated list of papers and associated code will be made publicly available.

IJCAI 2026

LLM-Based Agents on the Edge: A Survey of Privacy, Scalability, Heterogeneity, and Autonomy

Nikita Agrawal, Ruben Mayer

Large language model (LLM)–based agents are increasingly being deployed beyond centralized cloud environments and toward the edge of the network, where they operate closer to data sources. This transition facilitates lower latency and enhances contextual awareness, privacy, and responsiveness, but it also introduces challenges that differ from traditional cloud-based agent deployments. This survey provides a systematic overview of LLM-based edge agents with a particular focus on four critical dimensions: privacy, scalability, heterogeneity, and autonomy. To facilitate structured analysis, we introduce a novel taxonomy along four axes: deployment, functional role, interaction, and adaptation. Based on our taxonomy, we analyze the challenges LLM-based agents face on the edge and discuss design solutions that can help mitigate possible issues. We further analyze the degree to which existing LLM-based edge agent frameworks achieve privacy, scalability, heterogeneity, and autonomy.

IJCAI 2026

Beyond “Made with AI”: Visualizing Provenance Density to Mitigate the Transparency Penalty

Qing Zhang, Yifei Huang, Juyoung Lee, Thad Starner, Jun Rekimoto

As generative AI makes polished prose cheap to produce, users can no longer rely on fluency as a proxy for truth. We call this failure mode the Fluency Trap: users trust fluent hallucinations while also discounting accurate content once it is disclosed as AI-generated. Binary "Made with AI" labels respond with authorship disclosure, but they do not show what supports a claim. We propose Provenance Density, an evidence-visualization interface that shows the density of verified claims in a text. In a user study with 81 participants, an idealized Provenance Density interface produced a large discernment gap between truth and fabrication (+4.15 points, d=1.82), whereas participants given no signal showed no detectable discrimination. A technical audit with 200 samples shows that retrieval density alone is insufficient; unexpectedly, the Consistency Veto carries most of the discriminative signal on dynamic queries. As AI-generated content becomes indistinguishable from human writing, effective transparency must move from authorship disclosure toward evidence visualization.

IJCAI 2026

Probing Cultural Awareness in LLMs: A Case Study of Cross-Culture Aesthetic Stylistics

Jiashuo Wang, Fenggang Yu, Jian Wang, Chak Tou Leong, Xiaoyu Shen, Chunpu Xu, Jiawen Duan, Wenjie Li, Johan F. Hoorn

Large Language Models (LLMs) are increasingly deployed in diverse cultural contexts, yet their ability to master aesthetic stylistics, i.e., the strategic use of language to evoke cultural resonance, remains underexplored. We curate C4Styli, a benchmark of highly stylized translated movie titles and advertising slogans from Hong Kong and the Chinese Mainland, to evaluate LLMs via the lens of behavioral recognition and productive competence. Extensive evaluations show that LLMs differ from humans in stylistic recognition, and this recognition ability varies across text domains. In addition, stylistic recognition and generation performance in LLMs are not consistently aligned. To further examine whether LLMs genuinely capture stylistic information in stylistic recognition, we conduct structural ablation with logistic regression probes. We find that, in the Hong Kong setting, stylistic recognition in LLMs relies primarily on surface-level linguistic information rather than stylistic structure. This suggests limited sensitivity to Hong Kong-specific stylistic structure. Our code and data are available at https://github.com/wangjs9/C4STYLI.

IJCAI 2026

Uncovering Discriminatory Behavior in LLM-Based CV Screening Systems with Composite Statistical Metamorphic Relations

Tetyana Turiy, Simon Speth, Alexander Pretschner

Artificial intelligence (AI) applications are prone to reproducing existing patterns of marginalization, bias, and discrimination. The adoption of automated decision-making tools, particularly those based on large language models (LLMs), necessitates the detection and analysis of their discriminatory behavior to ensure compliance with anti-discrimination regulations. This work advances metamorphic testing for bias detection, aiming to (1) uncover discriminatory behavior in AI tools and (2) standardize the metamorphic test creation approach, rooting it in the legal and academic research on discrimination. To fill in these gaps, we introduce a metamorphic test creation workflow and evaluate it on the LLM-based CV screening use case. Our method yields a metamorphic relation (MR) catalogue of 8 atomic and 6 composite MRs, legally justified by Article 21 (1) of the Charter of Fundamental Rights of the European Union and sociologically motivated by the intersectionality theory. The resulting metamorphic test suite uncovers discriminatory failures for all 6 evaluated LLMs, totaling 1,272 defects across 7,800 test cases. Both the new atomic MRs and the composite MRs detect additional discriminatory defects for all LLMs. Composite MRs consistently uncovered more defects than their atomic counterparts, with failure rates increasing by on average 9% for Llama-3.1 8B and 25% for Llama-3.2 3B.

IJCAI 2026

Psychological Benefits and Costs of Diversifying Algorithmic Recourse

Tomu Tominaga, Naomi Yamashita, Takeshi Kurashima

Algorithmic recourse provides counterfactual action plans that help people overturn unfavorable AI decisions. While diverse recourse sets may improve transparency and motivation, they may also impose cognitive load and negative emotions by increasing counterfactual reasoning demands. To examine this trade-off, we conducted a between-subjects controlled experiment (N=750) that manipulated recourse-set diversity while controlling the number of options, and evaluated its effects on psychological benefits and costs. Results show that diversification enhances psychological benefits (e.g., willingness to act) for small sets without incurring additional psychological costs, whereas for large sets, it makes cognitive load more salient. These findings suggest that naively diversifying recourse can burden decision subjects, underscoring the need for new diversification methods that incorporate human cognition and psychology to mitigate such costs.

IJCAI 2026

Responsibility in Multi-Agent Sequential Decision-Making: Comparing Human Judgments to Formal Models of Causal Attribution

Nripsuta Saxena, Stelios Triantafyllou, Goran Radanovic

With the growing adoption of artificial intelligence in high-stakes decision-making domains, identifying the causes of outcomes--particularly failures--and determining who is responsible has become a critical concern. In this work, we investigate how well formal definitions of responsibility attribution, grounded in the framework of actual causality, align with human judgments of responsibility. To this end, we conduct a large-scale survey to elicit human judgments of responsibility in multi-agent sequential decision-making scenarios, using a modified version of the card game Goofspiel. We evaluate different responsibility attribution methods, assessing their alignment with human judgments about responsibility, and identifying factors that significantly shape responsibility judgments. While no single responsibility attribution method consistently aligns with human responses, our findings highlight key factors that influence human responsibility judgments, including agent-specific biases and the amount of information available to agents during decision-making.

IJCAI 2026

Test-Time User Alignment via Bayesian Population Guidance in Subjective Tasks

Hoe Sung Ryu, June Christoph Kang, Christian Wallraven

In subjective tasks, different individuals can have different correct answers for the same input—the ground truth is not fixed but rather determined by each user's personal perspective. Standard foundation models suppress this individual variation by producing population-averaged predictions; conversely, few-shot in-context learning (ICL) struggles to capture user-specific preferences from limited demonstrations. To address this limitation, we propose Population-Guided Bayesian Calibration (PGBC), a test-time personalization method that applies Bayesian inference to foundation model outputs for user-specific alignment. Unlike ICL methods that rely on the model to implicitly infer preferences from examples, PGBC explicitly quantifies each individual's deviation from the population distribution and incorporates this into prediction with minimal feedback (K < 10). Experiments on three subjective benchmark datasets across four foundation models demonstrate that PGBC significantly outperforms zero-shot baselines with a large effect size (Cohen's d = 1.21), and the improvement over ICL approaches is even more pronounced (d = 1.40). Furthermore, user-level distribution analysis reveals that PGBC aligns predictions with individual preferences when zero-shot baselines exhibit substantial bias, while ICL methods increase distributional mismatch. Our results show that PGBC enables practical deployment in cold-start scenarios where minimal user feedback must yield maximal personalization.

IJCAI 2026

Studying the Effects of Robot Distraction on School Shooter Behavior Using Virtual Reality

Christopher A. McClurg, Alan R. Wagner

We examine a particular adversarial human--robot interaction for which mobile robots influence the behavior of people role-playing as a school shooter. Using a high-fidelity virtual reality environment, we conducted a controlled study with 150 participants. Two autonomous robots predicted participant movement and positioned themselves to interfere and distract. The robots' approach strategy was manipulated---either moving directly into the participant's path (aggressive) or maintaining distance (passive)---along with the level of distraction, ranging from no additional cues (low), to siren and lights (medium), to siren, lights, and smoke to impair visibility (high). An aggressive, high-distraction robot configuration reduced the number of victims by 46.6% relative to a no-robot control. These results show that robot-based distraction can meaningfully alter human behavior and outcomes in adversarial settings, while also raising important ethical questions about the use of such systems in school environments.

IJCAI 2026

DiverValue-Bench: A Benchmark and Fine-Tuning Framework for Aligning Large Language Models with Diverse Human Values

Yao Liang, Dongcheng Zhao, Feifei Zhao, Guobin Shen, Yuwei Wang, Dongqi Liang, Yi Zeng

Aligning large language models (LLMs) with diverse human values is essential for safe and effective deployment, yet existing benchmarks often overlook cultural and demographic variation. We introduce DiverValue-Bench, a population-aware benchmark for evaluating multi-dimensional value alignment across 74 countries/regions. It contains 23,763 quality-controlled instances derived from PRISM user feedback and audited through large-scale human validation, with fine-grained value labels, personalized questions, contrastive reference answers, and rich demographic metadata. Using DiverValue-Bench, we evaluate representative LLMs and reveal substantial geographic and demographic disparities that are masked by aggregate performance. We further show that lightweight preference-based fine-tuning with Low-Rank Adaptation (LoRA) and Direct Preference Optimization (DPO) substantially improves in-domain value alignment while yielding consistent out-of-domain gains. These results highlight the need for population-aware alignment evaluation and demonstrate the utility of DiverValue-Bench as a practical foundation for global alignment, personalized value modeling, and equitable AI development.

IJCAI 2026

Self-Refine Learning in LLM Multi-Agent Systems for Legal Norm Cognition and Compliance

Rongxin Cheng, Jianhui Yang, Bohan Xiong, Ning Zheng, Yiran HU, Qingjing Chen, Yan Liu, Huanghai Liu, Yun Liu, Weixing Shen

As large language models (LLMs) increasingly serve as autonomous agents in social simulations, ensuring their ability to understand and comply with legal norms is essential. Yet, current LLM agents frequently exhibit reward hacking (RH) behaviors by optimizing metrics at the expense of norm adherence, undermining simulation fidelity and limiting deployment. We introduce a TBC-TBA self-refine learning multi-agent framework that enables dynamic normative adaptation through iterative multi-agent feedback. This framework integrates Think-Before-Chat (social feedback processing) and Think-Before-Act (norm-guided decision making) phases, allowing agents to progressively refine their normative understanding via structured interaction cycles. Across five mainstream LLMs and 100 legal scenarios, we found that while LLMs partially recognize legal norms, they systematically exhibit RH behaviors with illegal action rates (IAR) of 14.29–37.11%. Comparison with human cognition reveals alignment in moral reasoning but sharp divergence in risk perception and probability distortion. To address these deficits, we introduce four methods to improve LLM's normative compliance. Dynamic Norm Learning Mechanism (DNLM) serves as the core, using a psychologically grounded identify–infer–implement process that reduces IAR by 15.78% on average and delivers the most significant improvement. We also introduce Deep MaxPain (DMP) for consequence based deterrence, Norm Analysis Chain-of-Thought (NA-CoT) for structured reasoning, and Few-shot Norm Learning (FNL) for case based acquisition, all of them enhance compliance. Our findings show that LLM agents can better follow legal norms when equipped with structured self-refine learning and psychologically informed mechanisms. This work improves social alignment in multi-agent systems and opens avenues for future research on scalable, norm-compliant autonomous agents.The code and data are publicly available on GitHub.

IJCAI 2026

Perturbation-Resilient Autonomous Navigation with Distributionally Robust Reinforcement Learning

Zhaofan Zhang, Minghao Yang, Sihong Xie, Hui Xiong

The robustness of autonomous vehicles such as drones and Unmanned Surface Vehicles (USV) is crucial when facing unknown and complex marine environments, especially when heteroscedastic observational noise poses significant challenges to sensor-based navigation tasks. Recently, Distributional Reinforcement Learning (DistRL) has shown promising results in some challenging autonomous navigation tasks without prior environmental information. However, these methods overlook situations where noise patterns vary across different environmental conditions, hindering safe navigation and disrupting the learning of value functions. To address the problem, we propose DRIQN to integrate Distributionally Robust Optimization (DRO) with implicit quantile networks to optimize worst-case performance under natural environmental conditions. Leveraging explicit subgroup modeling in the replay buffer, DRIQN incorporates heterogeneous noise sources and target robustness-critical scenarios. Experimental results based on the risk-sensitive environment demonstrate that DRIQN significantly outperforms state-of-the-art meth- ods, achieving +13.51% success rate, -12.28% collision rate and +35.46% for time saving, +27.99% for energy saving, compared with the runner-up.

IJCAI 2026

GRALP: A Generative Representation Framework for Action Refinement and Latent Planning in Offline Robotic Control

Talha Zaidi, Arslan Munir, Sardar Ali Abbas

Offline robotic control requires long-horizon reasoning from fixed datasets while avoiding unsafe extrapolation beyond demonstrated behavior. We propose GRALP, a principled framework that resolves this tension by jointly enforcing support preservation and controllability at the level of temporal abstraction. GRALP adopts a deliberate architectural separation: diffusion is used exclusively as a deterministic action decoder for executing fixed latent skills, while planning and value estimation operate entirely in latent space under conservative constraints. This design enables stable value learning, controllable skill composition, and efficient planning without trajectory-level diffusion sampling at inference. Across unified D4RL benchmarks, GRALP achieves the highest average performance on Navigation, Sequential (Kitchen), and Adroit domains while remaining competitive on locomotion tasks. On contact-rich RoboSuite manipulation with human demonstrations (Lift and Pick-and-Place), GRALP achieves consistently high success rates (over 94%). These results indicate that reliable long-horizon offline control emerges when expressivity is confined to execution and decision-making operates over support-aligned latent abstractions.

IJCAI 2026

Closing the Motion Execution Gap: From Semantic Motion Task Constraints to Kinematic Control

Simon Stelter, Vanessa Hassouna, Malte Huerkamp, Michael Beetz

This paper addresses the Motion Execution Gap, the disconnect between high-level symbolic task descriptions using semantic constraints and executable robot motions. \textbf{Motion Statecharts} are introduced as an executable symbolic representation for complex motions. They allow the arbitrary arrangement of motion constraints, monitors or nested statecharts in parallel and sequence. World-centric motion specification and generalization across embodiments are enabled through the use of a unified differentiable kinematic world model of both, robots and environments. Motion execution is realized through a \ac{lmpc}-based implementation of the task-function approach, in which smooth transitions during task switches are ensured using jerk bounds. Cross-platform transferability was demonstrated by deploying the method on eight robot platforms, operating in diverse environments. The proposed framework is called Giskard and is available open source (https://github.com/cram2/cognitive_robot_abstract_machine).

IJCAI 2026

Disturbance-Aware Hybrid Learning for Robust and Adaptive UAV Flight in Extreme Winds

Huidong Liu, Jiarui Dou, Jiangshan Ai, Enwen Hu, Xianlei Long, Mingyan Li, Chao Chen, Fuqiang Gu

Safe and precise maneuvering of quadrotor unmanned aerial vehicles (UAVs) in high-speed wind environments remains a critical challenge. Wind disturbances are nonlinear, time-varying, and difficult to model, causing traditional controllers to struggle with perception and compensation, especially under unseen wind distributions. To address these limitations, we introduce WA-TD3, a data-driven control framework that enables real-time wind disturbance perception and adaptive compensation without dedicated wind sensors. WA-TD3 employs a deep residual network to extract wind characteristics from temporal patterns in state deviations, forming a dynamics residual-driven perception mechanism that implicitly models and compensates for unknown winds. This residual is integrated into a perception-augmented reinforcement learning architecture, providing the policy with enhanced state information for proactive disturbance-aware control. Extensive experiments on complex trajectories under varying wind intensities demonstrate that WA-TD3 consistently outperforms state-of-the-art methods, achieving over 62% improvement in tracking accuracy under strong winds.

IJCAI 2026

VLAs Are Confined yet Capable of Generalizing to Novel Tasks

Quanyi Li

Vision-language-action models (VLAs) often achieve high performance on demonstrated tasks but struggle significantly when required to extrapolate, recombining skills used in different tasks in novel ways. For instance, VLAs might successfully put the cream cheese in the bowl and put the bowl on top of the cabinet, yet still fail to put the cream cheese on top of the cabinet. This motivates us to investigate whether VLAs merely overfit to demonstrated tasks or still hold the potential to extrapolate. Our study uses text latent as the ingredient; it is a task-specific vector derived from the models’ hidden states. It thus encodes semantics necessary for completing a task and can be used to reconstruct the associated task behavior by writing it to the model’s residual stream. Furthermore, we find that skills used in distinct tasks can be combined to produce novel behaviors by blending their respective text latent. Applying this to π0, we increase its success rate from 9% to 83% on the proposed libero-ood benchmark, which features 20 tasks extrapolated from standard LIBERO tasks. This reveals that the skill representations encoded in text-latent are individual yet composable, while π0 fails to autonomously combine these representations for extrapolation. This also validates the design of libero-ood; it comprises tasks that the model fails, yet should be able to complete. We then tested other VLAs on libero-ood, and none of them achieved a success rate higher than 21%. Further analysis reveals VLAs share a common pattern to exhibit spatial overfitting, associating object names with where the object is spatially located in the demonstrated scene rather than achieving true object and goal understanding.

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