Singapore

Achieving appropriate human reliance on Artificial Intelligence (AI) systems remains a central challenge in Human-Computer Interaction. Confidence scores—indicators of an AI system’s certainty in its recommendations—have been proposed as a means to help users calibrate their trust and reliance on AI Decision Support Systems (DSS). However, limited research has explored how well-calibrated versus miscalibrated confidence scores affect human decision-making.
We report a study examining the effects of confidence calibration on user reliance, decision accuracy, and perceived utility of an AI DSS. In a within-subjects experiment involving 184 participants solving logic puzzles, we found that well-calibrated confidence scores significantly improved decision accuracy (+20\%, 95\% CI: [0.18, 0.23]), whereas miscalibrated scores yielded minimal accuracy gains (+2\%, 95\% CI: [-0.01, 0.04]) and increased vulnerability to automation bias and conservatism bias. Participants were more likely to accept AI recommendations when high confidence was expressed, even when those recommendations were incorrect, resulting in errors. Conversely, miscalibrated and low-confidence recommendations increased conservatism bias, leading users to reject even accurate AI suggestions.
Perceived utility of the AI system was higher when confidence levels were high (p &lt; 0.001) and when confidence was well-calibrated (p = 0.002). These findings underscore the importance of designing AI systems with properly calibrated confidence cues to improve human-AI collaboration and mitigate reliance-related biases.

AAAI 2026

Too Sure for Our Own Good: A User Study on AI Confidence and Human Reliance

explainable ai (xai) for human understanding

human-ai collaboration

human-computer interaction

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-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
AAAI is providing you with a conference planner, which you can use to help organize your itinerary of activities. This includes talks to attend in person, talks to attend remotely, breaks with colleagues and your site seeing activities. To access this conference planner, please go to [https://aaai-2023.takemobi.io](https://aaai-2023.takemobi.io).

In order to access this site, you need to register. If you haven't already, please register [here](https://aaai.org/Conferences/AAAI-23/registration/).


AAAI 2023

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.

Achieving appropriate human reliance on Artificial Intelligence (AI) systems remains a central challenge in Human-Computer Interaction. Confidence scores—indicators of an AI system’s certainty in its recommendations—have been proposed as a means to help users calibrate their trust and reliance on AI Decision Support Systems (DSS). However, limited research has explored how well-calibrated versus miscalibrated confidence scores affect human decision-making.
We report a study examining the effects of confidence calibration on user reliance, decision accuracy, and perceived utility of an AI DSS. In a within-subjects experiment involving 184 participants solving logic puzzles, we found that well-calibrated confidence scores significantly improved decision accuracy (+20\%, 95\% CI: [0.18, 0.23]), whereas miscalibrated scores yielded minimal accuracy gains (+2\%, 95\% CI: [-0.01, 0.04]) and increased vulnerability to automation bias and conservatism bias. Participants were more likely to accept AI recommendations when high confidence was expressed, even when those recommendations were incorrect, resulting in errors. Conversely, miscalibrated and low-confidence recommendations increased conservatism bias, leading users to reject even accurate AI suggestions.
Perceived utility of the AI system was higher when confidence levels were high (p < 0.001) and when confidence was well-calibrated (p = 0.002). These findings underscore the importance of designing AI systems with properly calibrated confidence cues to improve human-AI collaboration and mitigate reliance-related biases.

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.

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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.

Time series analysis has progressed from traditional
autoregressive models to deep learning, Transformers, and
foundation models (FMs), including large language models
(LLMs) and large vision models (LVMs). These advances have
expanded model design possibilities and, notably, enabled
time series problem-solving across multiple modalities,
greatly enhancing downstream applications in domains such
as climate, agriculture, and healthcare. In this talk, I
will provide an overview of recent developments in large
FMs for time series, highlighting frameworks for
transferring knowledge from other modalities to time
series, and identifying the advantages of LVMs in
cross-modal knowledge transfer over LLMs. I will then delve
into our recent research on LVMs for time series,
discussing (1) mainstream techniques for imaging time
series (i.e., transforming time series to images); (2)
empirical findings on LVMs' effectiveness and limitations
in time series modeling; and (3) integration of LVMs in
multimodal frameworks for time series encoding. Finally,
this talk will conclude with an application of LVMs in
brain time series learning in neuroscience. The aim of the
talk is to review state-of-the-art AI techniques for time
series, highlight unique challenges, and share our recent
findings in this promising area.

Cross-Modal Knowledge Transfer in Time Series AI via Large Vision Models

Artificial Intelligence Generated Content (AIGC) assisting image production triggers academic controversy widely in journalism, while attracts increasingly consideration by media agencies. Key issues involve misinformation, authenticity, semantic fidelity and interpretability. Now, Most AIGC tools operate as opaque “black boxes,” which make them difficult to meet the dual demands of content accuracy and semantic alignment in journalistic contexts, even cause ethical, sociotechnical and trusting dilemma. In this paper, the authors explore feasible pathways for controllable image production in the typical scenario of journalism – special coverage, and conduct two experiments based on coverage projects of China’s top media agency: (1) Experiment 1 focuses on cross-platform adaptability by applying standardized prompts to three coverage scenes, showing the significant disparities in semantic alignment, cultural specificity, and visual realism, which are largely influenced by training corpus bias and platform-level filtering policies. (2) In experiment 2, a human-in-the-loop modular pipeline is developed that combines high-precision segmentation (SAM, GroundingDINO), semantic alignment (BrushNet) and style regulating (Style-LoRA, Prompt-to-Prompt). It ensures the editorial fidelity by CLIP-based semantic scoring, NSFW/OCR/YOLO-based content filtering, and the embedding of verifiable content credentials. The traceable deployment of modular pipeline keeps the consistency of semantic representation and interpretation when producing images. Consequently, the authors propose a human-AI collaboration mechanism of AIGC assisted image production for special coverage, and corresponding evaluation principles is suggested in three main criteria: Character Identity Stability (CIS), Cultural Expression Accuracy (CEA) and User-Public Appropriateness (U-PA). This paper calls for systemic upgrades involving human-in-the-loop modelling, stylistic co-creation, and trust labelling mechanisms, and provides a practical framework for controllable and reliable AIGC deployment in image production of journalism.

A Human-AI Collaboration Mechanism Study on AIGC Assisted Image Production for Special Coverage

Diffusion models have demonstrated remarkable success in image generation, yet a persistent challenge remains: the bias between model predictions and the target distribution. In this paper, we propose a Bidirectional Noise Injection framework for enhancing diffusion models, implemented via Coordinated Input-Output Perturbation (CIOP). Our approach mitigates this bias by randomly applying synchronized noise injection to both the model inputs and the prediction targets during the training stage. This stochastic, synchronized noise injected acts as a smoothing mechanism that effectively reduces the 2-Wasserstein distance between the predicted and target distributions, as substantiated by our theoretical analysis based on optimal transport theory. Extensive experiments on multiple benchmark datasets and various generative tasks demonstrate that our method improves generation quality and training efficiency without incurring additional computational cost. Furthermore, the design of CIOP enables seamless integration with existing diffusion model improvements and advanced frameworks, thereby broadening its applicability. These results highlight the potential of Bidirectional Noise Injection via CIOP to alleviate bias in diffusion-based generative models across a wide range of settings.

Bidirectional Noise Injection: Enhancing Diffusion Models via Coordinated Input-Output Perturbation

Linear Temporal Logic on Finite Traces (LTLf) is a popular logic to express declarative specifications in Artificial Intelligence (AI). 
The recent call for explainable AI tools has made relevant the problem of computing efficiently minimal unsatisfiable cores (MUCs) and minimal correction sets (MCSs) of LTLf formulas. Recent work has focused on extraction of MUCs on formulas in conjunctive form.
In this paper, we present a method that operates on arbitrary formulas and computes a more refined notion of MUCs, as introduced by Schuppan, along with the corresponding notion of MCSs. Experiments show that our system, based on Answer Set Programming, outperforms available tools.

Computing Syntax Tree-based Minimal Unsatisfiable Cores of LTLf Formulas

Large language models (LLMs) have shown promise in medical question answering, yet they often overlook the domain-specific expertise that professionals depend on—such as the clinical subject areas (e.g., trauma, airway) and the certification level (e.g., EMT, Paramedic). Existing approaches typically apply general-purpose prompting or retrieval strategies without leveraging this structured context, limiting performance in high-stakes settings. We address this gap with EMSQA, an 24.3K-question multiple-choice dataset spanning 10 clinical subject areas and 4 certification levels, accompanied by curated, subject area-aligned knowledge bases (40K documents and 2M tokens). Building on EMSQA, we introduce (i) Expert-CoT, a prompting strategy that conditions chain-of-thought (CoT) reasoning on specific clinical subject area and certification level, and (ii) ExpertRAG, a retrieval-augmented generation pipeline that grounds responses with subject area-aligned documents and real-world patient data. Experiments on 4 LLMs show that CoT-Expert improves up to 2.05\% over vanilla CoT prompting. Additionally, combining CoT-Expert with ExpertRAG yields up to a 4.67\% accuracy gain over standard RAG baselines. Notably, the 32B expertise-augmented LLMs pass all the computer-adaptive EMS certification simulation exams.

Expert-Guided Prompting and Retrieval-Augmented Generation for Emergency Medical Service Question Answering

Modeling genomic sequences faces two unsolved challenges: the information density varies widely across different regions, while there is no clearly defined minimum vocabulary unit. Relying on either four primitive bases or independently designed DNA tokenizers, existing approaches with naive masked language modeling pre-training often fail to adapt to the varying complexities of genomic sequences.
Leveraging Token Merging techniques, this paper introduces a hierarchical architecture that jointly optimizes a dynamic genomic tokenizer and latent Transformers with context-aware pre-training tasks. As for network structures, the tokenization module automatically chunks adjacent bases into words by stacking multiple layers of the differentiable token merging blocks with local-window constraints, then a Latent Encoder captures the global context of these merged words by full-attention blocks. Symmetrically employing a Latent Decoder and a Local Decoder, MergeDNA learns with two pre-training tasks: Merged Token Reconstruction simultaneously trains the dynamic tokenization module and adaptively filters important tokens, while Adaptive Masked Token Modeling learns to predict these filtered tokens to capture informative contents. Extensive experiments show that MergeDNA achieves superior performance on three popular DNA benchmarks and several multi-omics tasks with fine-tuning or zero-shot evaluation, outperforming typical tokenization methods and large-scale DNA foundation models.

MergeDNA: Context-Aware Genome Modeling with Dynamic Tokenization Through Token Merging

Lithium-ion (Li-ion) batteries are the major type of battery used in a variety of everyday applications, including electric vehicles (EVs), mobile devices, and energy storage systems. Predicting the Remaining Useful Life (RUL) of Li-ion batteries is crucial for ensuring their reliability, safety, and cost-effectiveness in battery-powered systems. The materials used for the battery cathodes and their designs play a significant role in determining the degradation rates and RUL, as they lead to distinct electrochemical reactions. Unfortunately, RUL prediction models often overlook the cathode materials and designs to simplify the model-building process, ignoring the effects of these electrochemical reactions. Other reasons are that specifications related to battery materials may not always be readily available, and a battery might consist of a mix of different materials. As a result, the predictive models that are developed often lack generalizability. To tackle these challenges, this paper proposes a novel material-based Mixture-of-Experts (MoE) approach for predicting the RUL of batteries, specifically addressing the complexities associated with heterogeneous battery chemistries. The MoE is integrated into a probabilistic framework, called \textit{Multiple Non-crossing Quantile Mixture-of-Experts for Probabilistic Prediction (RUL-QMoE)}, which accommodates battery operational conditions and enables uncertainty quantification. The RUL-QMoE model integrates specialized expert networks for five battery types: LFP, NCA, NMC, LCO, and NMC-LCO, within a gating mechanism that dynamically assigns relevance based on the battery's input features. Furthermore, by leveraging non-crossing quantile regression, the proposed RUL-QMoE produces coherent and interpretable predictive distributions of the battery's RUL, enabling robust uncertainty quantification in the battery's RUL prediction. Trained on seven real-world datasets, the proposed RUL-QMoE achieves strong predictive performance across all battery types, with MAE = 65 (cycles), MAPE = 9.59\%, RMSE = 100 (cycles), and $R^2=96.84\%$. Compared to traditional models like XGBoost, Random Forest, CNN, and LSTM, the proposed RUL-QMoE model consistently delivers lower RMSE and superior probabilistic insights, including survival probabilities and prediction intervals. The model has been integrated into our Battery AI platform in collaboration with Toyota Motor Engineering \& Manufacturing North America, Inc., as part of a broader Battery Foundation Model initiative. This RUL-QMoE model will serve future Toyota EVs' users and battery system designers.

RUL-QMoE: Multiple Non-crossing Quantile Mixture-of-Experts for Probabilistic Remaining Useful Life Predictions of Varying Battery Materials

Diffusion-based tabular data synthesis models have yielded promising results. However, we observe that when the data dimensionality increases, existing models tend to degenerate and may perform even worse than simpler, non-diffusion-based models. This is because limited training samples in high-dimensional space often hinder generative models from capturing the distribution accurately. To mitigate the insufficient learning signals and to stabilize training under such conditions, we propose CtrTab, a condition-controlled diffusion model that injects perturbed ground-truth samples as auxiliary inputs during training. This design introduces an implicit $L_2$ regularization on the model’s sensitivity to the control signal, improving robustness and stability in high-dimensional, low-data scenarios. Experimental results across multiple datasets show that CtrTab outperforms state-of-the-art models, with a performance gap in accuracy over 90% on average.

Towards Synthesizing High-Dimensional Tabular Data with Limited Samples

Designing effective algorithmic components remains a fundamental obstacle in tackling NP-hard combinatorial optimization problems (COPs), where solvers often rely on carefully hand-crafted strategies. Despite recent advances in using large language models (LLMs) to synthesize high-quality components, most approaches restrict the search to a single element—commonly a heuristic scoring function—thus missing broader opportunities for innovation. We introduce a broader formulation of solver design as a multi-strategy optimization problem, which seeks to jointly improve a set of interdependent components under a unified objective. To address this, we propose MOTIF—Multi-strategy Optimization via Turn-based Interactive Framework—a novel framework based on Monte Carlo Tree Search that facilitates turn-based optimization between two LLM agents. At each turn, an agent improves one component by leveraging the history of both its own and its opponent’s prior updates, promoting both competitive pressure and emergent cooperation. This structured interaction broadens the search landscape and encourages the discovery of diverse, high-performing solutions. Experiments across multiple COP domains show that MOTIF consistently outperforms state-of-the-art methods, highlighting the promise of turn-based, multi-agent prompting for fully automated solver design.

MOTIF: Multi-strategy Optimization via Turn-based Interactive Framework

We study hybrid execution in multi-agent reinforcement learning (MARL), a paradigm where agents aim to complete cooperative tasks with arbitrary communication levels at execution time by taking advantage of information-sharing among the agents. Under hybrid execution, the communication level can range from a setting in which no communication is allowed between agents (fully decentralized), to a setting featuring full communication (fully centralized), but the agents do not know beforehand which communication level they will encounter at execution time. We contribute MARO, an approach that makes use of an auto-regressive predictive model, trained in a centralized manner, to estimate missing agents' observations at execution time. We evaluate MARO on standard scenarios and extensions of previous benchmarks tailored to emphasize the impact of partial observability in MARL. Experimental results show that our method consistently outperforms relevant baselines, allowing agents to act with faulty communication while successfully exploiting shared information.

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Cross-Modal Knowledge Transfer in Time Series AI via Large Vision Models

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Cross-Modal Knowledge Transfer in Time Series AI via Large Vision Models

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Explaining Model Confidence using Counterfactuals

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