Singapore

Existing battery State of Health (SOH) prediction approaches often struggle to provide both accurate predictions and reliable uncertainty estimates. This paper presents a novel Multi-Task Learning (MTL) framework that jointly tackles SOH prediction and provides a proxy metric for uncertainty through a unified architecture. The framework combines a Physics-Informed Neural Network (PINN) for SOH prediction with a deep autoencoding Gaussian mixture model for uncertainty modeling. Particularly, the energy score from the Gaussian mixture model serves as a proxy metric for uncertainty, where a higher score indicates potential prediction unreliability. Moreover, to enhance task-specific learning, we employ a multi-head attention mechanism that adaptively captures distinct feature relationships. 
Our experiments show improvements in prediction performance compared to the state-of-the-art baseline.
A comprehensive evaluation on six XJTU battery benchmark datasets demonstrates that our framework achieves a prediction accuracy of 99.50% (MAPE: 0.0050) while providing reliable uncertainty quantification through the proxy metric.

AAAI 2026

Physics-Informed Multi-Task Learning for Battery State of Health Prediction with Uncertainty Quantification

battery

prediction

uncertainty

### Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

Welcome to the 2025 meeting of the Nations of the Americas Chapter of the Association for Computational Linguistics! I am proud to help organize the first NAACL conference to carry the new name of our organization, one that emphasizes inclusion for all of the Americas. I am also pleased to welcome you to Albuquerque, New Mexico, a state whose unique blend of cultural influences will make for an excellent backdrop for NAACL 2025, especially with this year’s special theme on NLP in a Multicultural World. 
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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.

The core of efficient on-demand ride-pooling lies in solving the Ride-Pool Matching Problem (RMP), which involves assigning multiple customer requests to single vehicles under various service constraints (e.g., pickup windows, detour allowances, and vehicle occupancy). A significant missed opportunity in most current RMP approaches is the assumption that passengers must be picked up and dropped off exactly at their requested locations. Allowing passengers to walk even short distances to meet vehicles could unlock substantial improvements in ride-pooling operations.

Building upon the limitations of existing Ride-Pool Matching Problem (RMP) solutions that neglect passenger walkability, this paper introduces a novel matching method that strategically incorporates flexible pickup and drop-off locations. Our approach simultaneously determines the optimal assignment of vehicles to requests (one vehicle to potentially multiple requests and each request to at most one vehicle), identifies advantageous meeting points for passengers, and plans efficient vehicle routes. This comprehensive optimization respects all service constraints and considers the long-term implications of routing decisions. To achieve this integrated solution, we first employ a tree-based approach to enumerate all feasible pairings between passengers and vehicles. Subsequently, we calculate an optimal route for each of these feasible matches. Finally, we evaluate the quality of all possible assignments and select the most advantageous matching for implementation.

In our experimental evaluation on city-scale taxi datasets, we demonstrate that our method improves the number of served requests by up to 13\% and reduces the average vehicle travel distance by up to 21\%. By serving more passengers with less driving distance, our approach achieves greater efficiency in a more sustainable manner — using fewer resources to deliver better service and creating a win-win outcome for all stakeholders, including customers, drivers, the aggregator, and the environment.

Optimizing Ride-Pooling Operations with Extended Pickup and Drop-Off Flexibility

Recommendation systems shape much of what people, including
youth, encounter online, influencing their exposure to
information and ideas. Understanding their workings and
potential downsides, such as filter bubbles, is
increasingly important. At the same time, Minecraft remains
one of the most popular and accessible game platforms among
students worldwide, making it a promising medium for AI
literacy outreach. Building on a previous in-person
augmented reality application called Beetrap, in which
players, acting as bees, pollinate flowers and see how
similar choices narrow their environment, we created
Beetrap-MC, a Minecraft-based version aimed at broader
reach and accessibility. Unlike the original
facilitator-led group workshop, Beetrap-MC is designed for
students’ individual playthroughs. We conducted a study
with nine middle school participants, using
pre-/post-assessments and qualitative interviews to
evaluate its effectiveness. Results showed significant
learning gains in key AI concepts, such as understanding
filter bubbles and their consequences. We also discuss key
differences in design, usability, and outcomes between
Beetrap-MC and the original, reflecting on trade-offs in
adapting a group-based embodied experience into a shorter,
self-guided digital format.

Beetrap-MC: A Minecraft-Based AI Literacy Tool for Teaching Filter Bubbles to Middle School Students

Monitoring Earth's evolving land covers requires methods capable of detecting changes across a wide range of categories and contexts. Existing change detection methods are hindered by their dependency on predefined classes, reducing their effectiveness in open-world applications. To address this issue, we introduce open-vocabulary change detection (OVCD), a novel task that bridges vision and language to detect changes across any category. Considering the lack of high-quality data and annotation, we propose two training-free frameworks, M-C-I and I-M-C, which leverage and integrate off-the-shelf foundation models for the OVCD task. The insight behind the M-C-I framework is to discover all potential changes and then classify these changes, while the insight of I-M-C framework is to identify all targets of interest and then determine whether their states have changed. Based on these two frameworks, we instantiate to obtain several methods, e.g., SAM-DINOv2-SegEarth-OV, Grounding-DINO-SAM2-DINO, etc. Extensive evaluations on 5 benchmark datasets demonstrate the superior generalization and robustness of our OVCD methods over existing supervised and unsupervised methods. To support continued exploration, we release DynamicEarth, a dedicated codebase designed to advance research and application of OVCD.

DynamicEarth: How Far Are We from Open-Vocabulary Change Detection?

Graph Neural Networks (GNNs) have emerged as a dominant paradigm for graph classification. Specifically, most existing GNNs mainly rely on the message passing strategy between neighbor nodes, where the expressivity is limited by the 1-dimensional Weisfeiler-Lehman (1-WL) test. Although a number of $k$-WL-based GNNs have been proposed to overcome this limitation, their computational cost increases rapidly with $k$, significantly restricting the practical applicability. Moreover, since the $k$-WL models mainly operate on node tuples, these $k$-WL-based GNNs cannot retain fine-grained node- or edge-level semantics required by attribution methods (e.g., Integrated Gradients), leading to the less interpretable problem. To overcome the above shortcomings, in this paper, we propose a novel Line Graph Aggregation Network (LGAN), that constructs a line graph from the induced subgraph centered at each node to perform the higher-order aggregation. We theoretically prove that the LGAN not only possesses the greater expressive power than the 2-WL, but also has lower time complexity under injective aggregation assumptions. Empirical evaluations on benchmarks demonstrate that the LGAN outperforms state-of-the-art $k$-WL-based GNNs, while offering better interpretability.

LGAN: An Efficient High-Order Graph Neural Network via the Line Graph Aggregation

Massive multi-modality datasets are fundamental to the success of large video-language models. However, existing datasets often focus on providing textual descriptions for visual content, treating audio, particularly music, as weakly related information. This overlooks the inherent semantic correlation between visual narratives and musical scores, limiting the development of models for fine-grained cross-modal understanding and generation. To address this gap, we introduce VMChill, a large-scale, fine-grained multimodal video dataset. We leverage trailers as our data source, as they are professionally edited to create a strong synergy between visual pacing, scene transitions, and background music for narrative and emotional impact. Our dataset comprises over 20 million video clips derived from more than 27.1k hours of high-resolution trailer videos.
To annotate this data, we propose a systematic multimodal captioning framework. This framework first employs specialized unimodal models to extract descriptive features from multiple perspectives, including visual content, motion dynamics, and musical attributes (e.g., genre, instruments, mood). Subsequently, a large language model (LLM) is utilized to adaptively fuse these diverse descriptions into a single, coherent, and rich multimodal caption. This process yields VMChill-2M, a high-quality subset of 2 million clips with detailed multimodal annotations, and VMChill-Test, a manually refined test set for evaluation. We conduct extensive experiments on downstream tasks, including video understanding and generation, to establish benchmarks and demonstrate the dataset's quality. The results validate that VMChill effectively enhances model performance, highlighting its potential to facilitate future research in fine-grained multimodal learning. We will release the dataset, annotation codebase, and processing pipelines to support community research.

VMChill: A Dataset for Fine-Grained Visual-Musical Synergy

High-resolution Earth Observation technologies present unprecedented opportunities for geospatial analysis, yet traditional 2D aerial-view semantic segmentation remains limited by its inability to model spatial relationships and handle object occlusions. While 3D Aerial-view Segmentation (3DAS) has emerged to address these limitations, existing methods predominantly rely on 2D discriminative models pre-trained on natural scenes. These models struggle to accurately recognize aerial-view imagery, resulting in suboptimal performance due to significant domain discrepancies. This paper introduces ID-Splat, a novel object-centric framework that directly leverages multi-view object identities without discriminative information to enhance 3D semantic understanding. ID-Splat implements a two-stage process: first, Mask-object Tracking combines SAM and Point Tracking to establish robust and consistent object identities across multi-view aerial images; second, Object Integration & Propagation assigns these identities to 3D Gaussian Splatting (3DGS) points, enabling complete 3D segmentation through semantic propagation. Experimental results on the 3D-AS dataset demonstrate that ID-Splat significantly outperforms existing methods, particularly under sparse supervision conditions. ID-Splat also achieves state-of-the-art performance while reducing the need for extensive labeled data by effectively leveraging the inherent 3D structure.

ID-Splat: Propagating Object Identities for Segmenting 3D Aerial-view Scenes

Continual Learning with Pre-trained Models holds great promise for efficient adaptation across sequential tasks. However, most existing approaches freeze PTMs and rely on auxiliary modules like prompts or adapters, limiting model plasticity and leading to suboptimal generalization when facing significant distribution shifts. While full fine-tuning can improve adaptability, it risks disrupting crucial pre-trained knowledge. In this paper, we propose Mutual Information-guided Sparse Tuning (MIST), a plug-and-play method that selectively updates a small subset of PTM parameters, less than 5%, based on sensitivity to mutual information objectives. MIST enables effective task-specific adaptation while preserving generalization. To further reduce interference, we introduce strong sparsity regularization by randomly dropping gradients during tuning, resulting in fewer than 0.5% of parameters being updated per step. Applied before standard freeze-based methods, MIST consistently boosts performance across diverse continual learning benchmarks. Experiments show that integrating our method into multiple baselines yields significant performance gains. Our code is available at \url{https://github.com/zhwhu/MIST}.

Sparse Tuning Enhances Plasticity in PTM-based Continual Learning

Gaussian process regression (GPR) is a popular nonparametric Bayesian method that provides predictive uncertainty estimates and is widely used in safety-critical applications. While prior research has introduced various uncertainty bounds, most existing approaches 
require access to specific input features and rely on posterior mean and variance estimates or tuning hyperparameters. These limitations hinder robustness and fail to capture the model’s global behavior in expectation.
To address these limitations, we propose a chaining-based framework for estimating upper and lower bounds on the expected extreme values over unseen data, without requiring access to specific input locations. We provide kernel-specific refinements for commonly used kernels such as RBF and Matérn, in which our bounds are tighter than generic constructions. We further improve numerical tightness by avoiding analytical relaxations. 
In addition to global estimation, we also develop a novel method for local uncertainty quantification at specified inputs. This approach leverages chaining geometry through partition diameters, adapting to local structure without relying on posterior variance scaling. 
Our experimental results validate the theoretical findings and demonstrate that our method outperforms existing approaches on both synthetic and real-world datasets.

Practical Global and Local Bounds in Gaussian Process Regression via Chaining

This paper introduces **STAR-1**, a high-quality, just-1k-scale *safety* dataset specifically designed for large reasoning models (LRMs) like DeepSeek-R1. Built on three core principles --- diversity, deliberative reasoning, and rigorous filtering --- STAR-1 aims to address the critical needs for safety alignment in LRMs. Specifically, we begin by integrating existing open-source safety datasets from diverse sources. Then, we curate safety policies to generate policy-grounded deliberative reasoning samples. Lastly, we apply a GPT-4o-based safety scoring system to select training examples aligned with best practices. Experimental results show that fine-tuning LRMs with STAR-1 leads to an average 40% improvement in safety performance across four benchmarks, while only incurring a marginal decrease (e.g., an average of 1.1%) in reasoning ability measured across five reasoning tasks. Extensive ablation studies further validate the importance of our design principles in constructing STAR-1 and analyze its efficacy across both LRMs and traditional LLMs.

STAR-1: Safer Alignment of Reasoning LLMs with 1K Data

We study almost envy-freeness in house allocation, where
$m$ houses are to be allocated among $n$ agents so that
every agent receives exactly one house. An envy-free
allocation need not exist, and therefore we may have to
settle for relaxations. We study different aggregate
measures of envy as markers of fairness. In particular, we
define the amount of envy experienced by an agent $a$
w.r.t. an allocation to be the number of agents that agent
$a$ envies under that allocation. ~\cite{KMS2021} studied
the problem of minimizing the number of envious agents and
showed that it is NP-Complete to find allocations that
minimize the number of envious agents, even for binary
utilities, and this quantity is hard to approximate for
general utilities. In this paper, we explore envy
minimization in house allocation from a broader perspective
and prove algorithmic results not only for minimizing the
number of envious agents but for two other measures of envy
as well---minimizing the amount of maximum envy experienced
by any agent and minimizing the amount of total envy
experienced by all the agents put together.
We prove a host of algorithmic and hardness results. We
also suggest practical approaches for these problems via
integer linear program (ILP) formulations and report the
findings of our experimental evaluation of ILPs. Finally,
we study the price of fairness, which quantifies the loss
of welfare we must suffer due to the fairness requirements,
and present tight bounds as well as algorithms that
simultaneously optimize both welfare and fairness.

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