Singapore

Recent advances in Time Series Foundation Models (TSFMs) have fundamentally revolutionized general time series analysis across domains like finance, retail, weather, and power. However, how to unlock the hidden capacity of general-purpose TSFMs for wearable activity recognition still remains largely unexplored, given severe sensor annotation scarcity and highly heterogeneous sensor data. To address these challenges, we propose DeepSenseMoE—a novel multi-scale convolution-based Mixture of Experts (MoE) module for parameter-efficient fine-tuning of general-purpose TSFMs to sensor-based activity recognition. DeepSenseMoE integrates three key innovations: (1) Multi-scale convolutional experts with different filter sizes responsible for capturing varying sensor contexts; (2) Shared-expert isolation mechanism compressing common activity knowledge into a single shared expert while reducing redundancy among routed experts; and (3) Hierarchical supervised contrastive alignment guiding experts to further learn discriminative activity features. Extensive experiments on three challenging HAR benchmarks demonstrate DeepSenseMoE&#39;s superiority, achieving up to 9.5% accuracy gains over state-of-the-art under few-shot and full-supervised settings, with only &lt;1% additional trainable parameters. We hope that this work may establish a solid foundation to accelerate development and deployment of powerful TSFMs in data-scarce wearable activity recognition tasks while reducing the reliance on labeled sensor data. Code will be released.

AAAI 2026

DeepSenseMoE: Harnessing Power of Time Series Foundation Models for Few-Shot Human Activity Recognition

hai: applications

sensor networks & smart cities

app: internet of things

hai: human-computer interaction

Recent advances in Time Series Foundation Models (TSFMs) have fundamentally revolutionized general time series analysis across domains like finance, retail, weather, and power. However, how to unlock the hidden capacity of general-purpose TSFMs for wearable activity recognition still remains largely unexplored, given severe sensor annotation scarcity and highly heterogeneous sensor data. To address these challenges, we propose DeepSenseMoE—a novel multi-scale convolution-based Mixture of Experts (MoE) module for parameter-efficient fine-tuning of general-purpose TSFMs to sensor-based activity recognition. DeepSenseMoE integrates three key innovations: (1) Multi-scale convolutional experts with different filter sizes responsible for capturing varying sensor contexts; (2) Shared-expert isolation mechanism compressing common activity knowledge into a single shared expert while reducing redundancy among routed experts; and (3) Hierarchical supervised contrastive alignment guiding experts to further learn discriminative activity features. Extensive experiments on three challenging HAR benchmarks demonstrate DeepSenseMoE's superiority, achieving up to 9.5% accuracy gains over state-of-the-art under few-shot and full-supervised settings, with only <1% additional trainable parameters. We hope that this work may establish a solid foundation to accelerate development and deployment of powerful TSFMs in data-scarce wearable activity recognition tasks while reducing the reliance on labeled sensor data. Code will be released.

poster

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.<br><br>

To access this event page, you need to log in with the **email address you registered with**. <br>Access credentials will be sent to your email from Underline -  subject line "Welcome to AAAI 2026". Please be sure to check your spam email folder if you do not see an email confirmation right away.

Please log in

To access this event page, you are required to register.
Please complete your registration to continue.

We recommend reading [**the registration information**](https://aaai.org/conference/aaai/aaai-26/registration/) first.

**Online Registration Form**: https://aaai.getregistered.net/conference-2026 

Registration Required

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.

Recent camera-based 3D semantic scene completion (SSC) methods have increasingly explored leveraging temporal cues to enrich the features of the current frame. However, while these approaches primarily focus on enhancing in-frame regions, they often struggle to reconstruct critical out-of-frame areas near the sides of the ego-vehicle, although previous frames commonly contain valuable contextual information about these unseen regions. To address this limitation, we propose the Current-Centric Contextual 3D Fusion (C3DFusion) module, which generates hidden region-aware 3D feature geometry by explicitly aligning 3D-lifted point features from both current and historical frames. C3DFusion performs enhanced temporal fusion through two complementary techniques—historical context blurring and current-centric feature densification—which suppress noise from inaccurately warped historical point features by attenuating their scale, and enhance current point features by increasing their volumetric contribution. Simply integrated into standard SSC architectures, C3DFusion demonstrates strong effectiveness, significantly outperforming state-of-the-art methods on the SemanticKITTI and SSCBench-KITTI-360 datasets. Furthermore, it exhibits robust generalization, achieving notable performance gains when applied to other baseline models.

Towards Temporal Fusion Beyond the Field of View for Camera-based Semantic Scene Completion

Remote sensing imagery presents vast, inherently unstructured spatial data, necessitating sophisticated reasoning to interpret complex user intents and contextual relationships beyond simple recognition tasks. In this paper, we aim to construct an Earth observation workflow to handle complex queries by reasoning about spatial context and user intent. As a reasoning workflow, it should autonomously explore and construct its own inference paths, rather than being confined to predefined ground‑truth sequences.
Ideally, its architecture ought to be unified yet generalized, possessing capabilities to perform diverse reasoning tasks through one model without requiring additional fine-tuning.
Existing remote sensing approaches rely on supervised fine-tuning paradigms and task‑specific heads, limiting both autonomous reasoning and unified generalization.
To this end, we propose RemoteReasoner, a unified workflow for geospatial reasoning. The design of RemoteReasoner integrates a multi-modal large language model (MLLM) for interpreting user instructions and localizing targets, together with task transformation strategies that enable multi-granularity tasks, including object-, region-, and pixel-level. 
In contrast to existing methods, our framework is trained with reinforcement learning (RL) to endow the MLLM sufficient reasoning autonomy. 
At the inference stage, our transformation strategies enable diverse task output formats without requiring task-specific decoders or further fine-tuning. Experiments demonstrated that RemoteReasoner achieves state-of-the-art (SOTA) performance across multi-granularity reasoning tasks. Furthermore, it retains the MLLM's inherent generalization capability, demonstrating robust performance on unseen tasks and out-of-distribution categories. 
The model and code will be publicly available once our paper is accepted.

RemoteReasoner: Towards Unifying Geospatial Reasoning Workflow

We study an online linear regression setting in which the observed feature vectors are corrupted by noise and the learner can pay to reduce the noise level. In practice, this may happen for several reasons: for example, because features can be measured more accurately using more expensive equipment, or because data providers can be incentivized to release less private features. Assuming feature vectors are drawn i.i.d. from a fixed but unknown distribution, we measure the learner's regret against the linear predictor minimizing a notion of loss that combines the prediction error and payment. When the mapping between payments and noise covariance is known, we prove that the rate $\sqrt{T}$ is optimal for regret if logarithmic factors are ignored. When the noise covariance is unknown, we show that the optimal regret rate becomes of order $T^{2/3}$ (ignoring log factors). Our analysis leverages matrix martingale concentration, showing that the empirical loss uniformly converges to the expected one for all payments and linear predictors.

Online Linear Regression with Paid Stochastic Features

Model merging has emerged as an efficient technique for expanding large language models (LLMs) by integrating specialized expert models. However, it also introduces a new threat: model merging stealing, where free-riders exploit models through unauthorized model merging.
Unfortunately, existing defense mechanisms fail to provide effective protection. Specifically, we identify three critical protection properties that existing methods fail to simultaneously satisfy: (1) proactively preventing unauthorized merging; (2) ensuring compatibility with general open-source settings; (3) achieving high security with negligible performance loss.
To address the above issues, we propose MergeBarrier, a plug-and-play defense that proactively prevents unauthorized merging. The core design of MergeBarrier is to disrupt the Linear Mode Connectivity (LMC) between the protected model and its homologous counterparts, thereby eliminating the low-loss path required for effective model merging.
Extensive experiments show that MergeBarrier effectively prevents model merging stealing with negligible accuracy loss.

Do Not Merge My Model! Safeguarding Open-Source LLMs Against Unauthorized Model Merging

Recent advances in audio-driven portrait animation have demonstrated impressive capabilities. However, existing methods struggle to align with fine-grained human preferences across multiple dimensions, such as motion naturalness, lip-sync accuracy, and visual quality. This is due to the difficulty of optimizing among competing preference objectives, which often conflict with one another, and the scarcity of large-scale, high-quality datasets with multidimensional preference annotations. To address these, we first introduce Talking-Critic, a multimodal reward model that learns human-aligned reward functions to quantify how well generated videos satisfy multidimensional expectations. Leveraging this model, we curate Talking-NSQ, a large-scale multidimensional human preference dataset containing 410K preference pairs. Finally, we propose Timestep-Layer adaptive multi-expert Preference Optimization (TLPO), a novel framework for aligning diffusion-based portrait animation models with fine-grained, multidimensional preferences. TLPO decouples preferences into specialized expert modules, which are then fused across timesteps and network layers, enabling comprehensive, fine-grained enhancement across all dimensions without mutual interference. Experiments demonstrate that Talking-Critic significantly outperforms existing methods in aligning with human preference ratings. Meanwhile, TLPO achieves substantial improvements over baseline models in lip-sync accuracy, motion naturalness, and visual quality, exhibiting superior performance in both qualitative and quantitative evaluations.

FantasyTalking2: Timestep-Layer Adaptive Preference Optimization for Audio-Driven Portrait Animation

Cell clustering is crucial for uncovering cellular heterogeneity in single-cell RNA sequencing (scRNA-seq) data by identifying cell types and marker genes. Despite its importance, benchmarks for scRNA-seq clustering methods remain fragmented, often lacking standardized protocols and failing to incorporate recent advances in artificial intelligence. To fill these gaps, we present scCluBench, a comprehensive benchmark of clustering algorithms for scRNA-seq data. First, scCluBench provides 36 scRNA-seq datasets collected from diverse public sources, covering multiple tissues, which are uniformly processed and standardized to ensure consistency for systematic evaluation and downstream analyses. To evaluate performance, we collect and reproduce a range of scRNA-seq clustering methods, including traditional, deep learning-based, graph-based, and biological foundation models. We comprehensively evaluate each method both quantitatively and qualitatively, using core performance metrics as well as visualization analyses. Furthermore, we construct representative downstream biological tasks, such as marker gene identification and cell type annotation, to further assess the practical utility. scCluBench then investigates the performance differences and applicability boundaries of various clustering models across diverse analytical tasks, systematically assessing their robustness and scalability in real-world scenarios. Overall, scCluBench offers a standardized and user-friendly benchmark for scRNA-seq clustering, with curated datasets, unified evaluation protocols, and transparent analyses, facilitating informed method selection and providing valuable insights into model generalizability and application scope. All datasets, code, and supplementary resources for scCluBench are available after publication.

scCluBench: Comprehensive Benchmarking of Clustering Algorithms for Single-Cell RNA Sequencing

Large language model (LLM)-based recommender systems have achieved high-quality performance by bridging the discrepancy between the item space and the language space through item tokenization. However, existing item tokenization methods typically require training separate models for each item domain, limiting generalization. Moreover, the diverse distributions and semantics across item domains make it difficult to construct a unified tokenization that preserves domain-specific information. To address these challenges, we propose **UniTok**, a **Uni**fied item **Tok**enization framework that integrates our own mixture-of-experts (MoE) architecture with a series of codebooks to convert items into discrete tokens, enabling scalable tokenization while preserving semantic information across *multiple item domains*. Specifically, items from different domains are first projected into a unified latent space through a shared encoder. They are then routed to *domain-specific* experts to capture the *unique* semantics, while a *shared* expert, which is always active, encodes common knowledge transferable across domains. Additionally, to mitigate *semantic imbalance* across domains, we present a mutual information calibration mechanism, which guides the model towards retaining similar levels of semantic information for each domain. Comprehensive experiments on wide-ranging real-world datasets demonstrate that the proposed UniTok framework is **(a) highly effective:** achieving up to 51.89\% improvements over strong benchmarks, **(b) theoretically sound:** showing the analytical validity of our architectural design and optimization; and **(c) highly generalizable:** demonstrating robust performance across diverse domains without requiring per-domain retraining, a capability not supported by existing baselines.

Tokenize Once, Recommend Anywhere: Unified Item Tokenization for Multi-domain LLM-based Recommendation

Automated planning involves finding a sequence of actions that changes the world from an initial state to a final state with goals satisfied. The general problem is PSPACE-hard. Nevertheless, many restricted variants are NP-complete or even in P. Existing complexity work focuses mostly on plan existence, or plan with minimal plan length. Little is known about optimization variants that aim to satisfy as many goal conditions as possible. In this paper, we aim to fill this gap by providing a first inapproximability study of goal-maximization using the classical STRIPS formalism. For MAXPLANSAT and its length-bounded counterpart MAXPLANSAT(K), we prove tight constant-factor lower bounds. More specifically, through performing $L$-reductions from MAXE3SAT and MAX3DM, we show several of these problems are inapproximable by a constant factor, unless P=NP.

Inapproximability of STRIPS Planning

This paper examines a variety of classical optimization problems, including well-known minimization tasks and more general variational inequalities. We consider a stochastic formulation of these problems and, unlike most previous work, we take into account the complex Markov nature of the noise. We also consider the geometry of the problem in an arbitrary non-Euclidean setting and propose four methods based on the Mirror Descent iteration technique. The theoretical analysis is provided for smooth and convex minimization problems and variational inequalities with Lipschitz and monotone operators. The convergence guarantees obtained are optimal for first-order stochastic methods, as evidenced by the lower bound estimates provided in this paper. In order to validate the theoretical results, we present the relevant numerical experiments on various reinforcement learning tasks.

Methods for Optimization Problems with Markovian Stochasticity and Non-Euclidean Geometry

Multi-task learning (MTL) is critical in real-world applications such as autonomous driving and robotics, enabling simultaneous handling of diverse tasks. However, obtaining fully annotated data for all tasks is impractical due to labeling costs. Existing methods for partially labeled MTL typically rely on predictions from unlabeled tasks, making it difficult to establish reliable task associations and potentially leading to negative transfer and suboptimal performance. To address these issues, we propose a prototype-based knowledge retrieval framework that achieves robust MTL instead of relying on predictions from unlabeled tasks. Our framework consists of two key components: (1) a task prototype embedding task-specific characteristics and quantifying task associations, and (2) a knowledge retrieval transformer that adaptively refines feature representations based on these associations. To achieve this, we introduce an association knowledge generating (AKG) loss to ensure the task prototype consistently captures task-specific characteristics. Extensive experiments demonstrate the effectiveness of our framework, highlighting its potential for robust multi-task learning, even when only a subset of tasks is annotated.

Content not yet available

Next from AAAI 2026

Towards Temporal Fusion Beyond the Field of View for Camera-based Semantic Scene Completion

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES