Singapore

Terrestrial ecosystems constitute a major component of the global carbon sink and play a critical role in regulating the global carbon cycle. 
Although process-based models such as the Ecosystem Demography (ED) model have been developed to simulate these dynamics and widely adopted in research and applications, they remain computationally intensive and are not well suited for large-scale (e.g., global) projections at high spatial and temporal resolution, or under wide-range of future scenarios. AI-based emulators of process-based physical models have emerged as promising directions to accelerate the computation, with encouraging success in fields such as weather forecasting (e.g., Google&#39;s GraphCast and NVIDIA&#39;s FourCastNet). There are several challenges to develop emulators for ecosystem processes, including error accumulation over long-sequences, single-step initial conditions, and high-dimensional environment conditions. Existing works often rely on time-series patterns in look-back windows and are not well-suited for the problem with single-step initial conditions. Moreover, they often do not consider uncertainty, making it hard to know when the approximations are highly confident and when the results may need to be updated, e.g., by the process-based models. To address these limitations, we introduce EcoDiffusion, a conditional diffusion framework tailored for ecosystem dynamics emulation to effectively condition on large volumes environment variables and generate non-autoregressive forecasts at multiple scales with uncertainty-awareness. We carried out testing cases at locations distributed over the globe under different scenarios and EcoDiffusion demonstrated significant improvements over existing models.

AAAI 2026

EcoDiffusion: Uncertainty-Aware Emulation of Ecosystem Processes with Conditional Diffusion for Long Sequences with Single-Step Initialization

ml: deep generative models & autoencoders; ml: applications

ml: time-series/data streams

Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop.

The NAML workshop is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team.

The goals of NAML are:

To spread awareness of current machine learning research relevant to Naval applications

To connect machine learning researchers with experts in military requirements

To build and strengthen collaborations within the defense research community


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NAML 2022

Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop. The NAML workshop is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team.

The goals of NAML are:

To spread awareness of current machine learning research relevant to Naval applications

To connect machine learning researchers with experts in military requirements

To build and strengthen collaborations within the defense research community



Terrestrial ecosystems constitute a major component of the global carbon sink and play a critical role in regulating the global carbon cycle. 
Although process-based models such as the Ecosystem Demography (ED) model have been developed to simulate these dynamics and widely adopted in research and applications, they remain computationally intensive and are not well suited for large-scale (e.g., global) projections at high spatial and temporal resolution, or under wide-range of future scenarios. AI-based emulators of process-based physical models have emerged as promising directions to accelerate the computation, with encouraging success in fields such as weather forecasting (e.g., Google's GraphCast and NVIDIA's FourCastNet). There are several challenges to develop emulators for ecosystem processes, including error accumulation over long-sequences, single-step initial conditions, and high-dimensional environment conditions. Existing works often rely on time-series patterns in look-back windows and are not well-suited for the problem with single-step initial conditions. Moreover, they often do not consider uncertainty, making it hard to know when the approximations are highly confident and when the results may need to be updated, e.g., by the process-based models. To address these limitations, we introduce EcoDiffusion, a conditional diffusion framework tailored for ecosystem dynamics emulation to effectively condition on large volumes environment variables and generate non-autoregressive forecasts at multiple scales with uncertainty-awareness. We carried out testing cases at locations distributed over the globe under different scenarios and EcoDiffusion demonstrated significant improvements over existing models.

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>

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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.

Assistive robotics is an important subarea of robotics that focuses on the well-being of people with disabilities. A robotic guide dog is an assistive quadruped robot for assisting visually impaired people in obstacle avoidance and navigation. Enabling language capabilities on robotic guide dogs goes beyond naively adding an existing dialog system onto a mobile robot. The novel challenges include grounding language to the dynamically changing environment and improving spatial awareness for the human handler. To address those challenges, we develop a novel dialog system for robotic guide dogs that uses large language models to verbalize both navigational plans and scenes. The goal is to enable verbal communication for collaborative decision-making within the handler-robot team. In experiments, we performed a human study to evaluate different verbalization strategies, and a simulation study to evaluate the efficiency and accuracy in navigation tasks.

From Woofs to Words: Towards Intelligent Robotic Guide Dogs with Verbal Communication

Traditional language-conditioned manipulation agent adaptation to new manipulation skills leads to catastrophic forgetting of old skills, limiting dynamic scene practical deployment. In this paper, we propose SkillsCrafter, a novel robotic manipulation framework designed to continually learn multiple skills while reducing catastrophic forgetting of old skills. Specifically, we propose a Manipulation Skills Adaptation to retain the old skills knowledge while inheriting the shared knowledge between new and old skills to facilitate learning of new skills. Meanwhile, we perform the singular value decomposition on the diverse skill instructions to obtain common skill semantic subspace projection matrices, thereby recording the essential semantic space of skills. To achieve forget-less and generalization manipulation, we propose a Skills Specialization Aggregation to compute inter-skills similarity in skill semantic subspaces, achieving aggregation of the previously learned skill knowledge for any new or unknown skill. Extensive simulator and real-world experiments demonstrate the effectiveness and superiority of our SkillsCrafter.

Lifelong Language-Conditioned Robotic Manipulation Learning

In autonomous driving, vision-centric 3D object detection recognizes and localizes 3D objects from RGB images. However, due to high annotation costs and diverse outdoor scenes, training data often fails to cover all possible test scenarios, known as the out-of-distribution (OOD) issue. Training-free image editing offers a promising solution for improving model robustness by training data enhancement without any modifications to pre-trained diffusion models. Nevertheless, inversion-based methods often suffer from limited effectiveness and inherent inaccuracies, while recent rectified-flow-based approaches struggle to preserve objects with accurate 3D geometry. In this paper, we propose DriveFlow, a Rectified Flow Adaptation method for training data enhancement in autonomous driving based on pre-trained Text-to-Image flow models. Based on frequency decomposition, DriveFlow introduces two strategies to adapt noise-free editing paths derived from text-conditioned velocities. 1) High-Frequency Foreground Preservation: DriveFlow incorporates a high-frequency alignment loss for foreground to maintain precise 3D object geometry. 2) Dual-Frequency Background Optimization: DriveFlow also conducts dual-frequency optimization for background, balancing editing flexibility and semantic consistency. Comprehensive experiments validate the effectiveness and efficiency of DriveFlow, demonstrating comprehensive performance improvements on all categories across OOD scenarios. Code will be released.

DriveFlow: Rectified Flow Adaptation for Robust 3D Object Detection in Autonomous Driving

In recent years, Large Vision-Language Models (LVLMs) have significantly advanced multimodal tasks. However, their inference requires intensive processing of numerous visual tokens and incurs substantial computational overhead. Existing methods typically compress visual tokens either at the input stage or in early model layers, ignoring variations across tasks and depths. To address these limitations, we introduce **TOP-RL**, a **Task-Optimized Progressive** token pruning framework based on **Reinforcement Learning**. TOP-RL formulates visual token pruning as a multi-stage Markov Decision Process (MDP). It employs an agent trained with dense and fine-grained reward signals to progressively generate differentiable binary masks. This enables TOP-RL to adaptively select crucial visual tokens tailored to each task, effectively balancing accuracy and computational efficiency. Extensive experiments on leading multimodal datasets and advanced LVLMs validate that TOP-RL effectively learns task-optimized pruning policies, significantly boosting inference efficiency while preserving robust performance. For instance, LLaVA-NeXT equipped with TOP-RL achieves a **1.9$\times$** speedup in inference time and a **9.3$\times$** reduction in FLOPs, with **96%** performance preserved.

TOP-RL: Task-Optimized Progressive Token Pruning with Reinforcement Learning for Vision Language Models

While deep learning (DL) has demonstrated significant success in recommender systems, it suffers from high computational complexity and poor scalability. In this work, we demonstrate, from an information-theoretic perspective, the redundancy of existing DL-based recommender models in two aspects: (1) Feature Redundancy. We show that many features are highly mutually correlated, noisy, or weakly predictive of user-item interaction labels. (2) Structural Redundancy. We further show that a large proportion of parameters in the dense layers contribute minimally to overall performance, indicating significant redundancy within the model architecture. To address these challenges, we propose REACTION (paRameter-Efficient LeArning for recommendaTION), an information-theoretic framework designed to reduce model complexity without sacrificing performance. REACTION consists of two core components: Adaptive Feature Extraction (AFE) leverages mutual information to project high-dimensional sparse features into a compact, informative subspace. This adaptively filters noisy or weak features, reduces embedding parameters, and preserves implicit feature interactions without explicit high-order computation. 
Dynamic Tower Fusion (DTF) bridges the representational gap between dual-tower expressiveness and single-tower efficiency. It facilitates rich cross-tower interactions during training, then merges the towers into a unified, low-latency single tower for inference. 
Extensive experiments on four large-scale benchmarks demonstrate that REACTION not only outperforms existing methods in accuracy but also achieves a drastic reduction in both model parameters and inference costs, thus establishing a new paradigm for efficient and scalable recommendation systems.

REACTION: Parameter-Efficient Learning for Recommendation

Domain Generalization (DG) has been recently explored to enhance the generalizability of Point Cloud Classification (PCC) models toward unseen domains. Prior works are based on convolutional networks, Transformer or Mamba architectures, either suffering from limited receptive fields or high computational cost, or insufficient long-range dependency modeling. RWKV, as an emerging architecture, possesses superior linear complexity, global receptive fields, and long-range dependency. In this paper, we present the first work that studies the generalizability of RWKV models in DG PCC. We find that directly applying RWKV to DG PCC encounters two significant challenges: RWKV's fixed direction token shift methods, like Q-Shift, introduce spatial distortions when applied to unstructured point clouds, weakening local geometric modeling and reducing robustness. In addition, the Bi-WKV attention in RWKV amplifies slight cross-domain differences in $key$ distributions through exponential weighting, leading to attention shifts and degraded generalization. To this end, we propose PointDGRWKV, the first RWKV-based framework tailored for DG PCC. It introduces two key modules to enhance spatial modeling and cross-domain robustness, while maintaining RWKV's linear efficiency. In particular, we present Adaptive Geometric Token Shift to model local neighborhood structures to improve geometric context awareness. In addition, Cross-Domain $key$ feature Distribution Alignment is designed to mitigate attention drift by aligning $key$ feature distributions across domains. Extensive experiments on multiple benchmarks demonstrate that PointDGRWKV achieves state-of-the-art performance on DG PCC.

PointDGRWKV: Generalizing RWKV-like Architecture to Unseen Domains for Point Cloud Classification

Expressive Human Pose and Shape Estimation (EHPS) plays a crucial role in various AR/VR applications and has witnessed significant progress in recent years. However, current state-of-the-art methods still struggle with accurate parameter estimation for facial and hand regions and exhibit limited generalization to wild images. To address these challenges, we present CoEvoer, a novel one-stage synergistic cross-dependency transformer framework tailored for upper-body EHPS. CoEvoer enables explicit feature-level interaction across different body parts, allowing for mutual enhancement through contextual information exchange. Specifically, larger and more easily estimated regions such as the torso provide global semantics and positional priors to guide the estimation of finer, more complex regions like the face and hands. Conversely, the localized details captured in facial and hand regions help refine and calibrate adjacent body parts. To the best of our knowledge, CoEvoer is the first framework designed specifically for upper-body EHPS, with the goal of capturing the strong coupling and semantic dependencies among the face, hands, and torso through joint parameter regression. Extensive experiments demonstrate that CoEvoer achieves state-of-the-art performance on upper-body benchmarks and exhibits strong generalization capability even on unseen wild images.

CoEvoer: Collaborative Evolution Transformer for Upper-Body Expressive Human Pose and Shape Estimation

Referring Audio-Visual Segmentation (Ref-AVS) aims to segment target objects in audible videos based on given reference expressions.
Prior works typically rely on learning latent embeddings via multimodal fusion to prompt a tunable SAM/SAM2 decoder for segmentation, which requires strong pixel-level supervision and lacks interpretability. From a novel perspective of explicit reference understanding, we propose TGS-Agent, which decomposes the task into a Think-Ground-Segment process, mimicking the human reasoning procedure by first identifying the referred object through multimodal analysis, followed by coarse-grained grounding and precise segmentation.
To this end, we first propose Ref-Thinker, a multimodal language model capable of reasoning over textual, visual, and auditory cues.
We construct an instruction-tuning dataset with explicit object-aware think-answer chains for Ref-Thinker fine-tuning. The object description inferred by Ref-Thinker is used as an explicit prompt for Grounding-DINO and SAM2, which perform grounding and segmentation without relying on pixel-level supervision. Additionally, we introduce R\textsuperscript{2}-AVSBench, a new benchmark with linguistically diverse and reasoning-intensive references for better evaluating model generalization. Our approach achieves state-of-the-art results on both standard Ref-AVSBench and proposed R\textsuperscript{2}-AVSBench.

Think Before You Segment: An Object-aware Reasoning Agent for Referring Audio-Visual Segmentation

Recent advances in long video understanding typically mitigate visual redundancy through visual token pruning based on attention distribution. However, while existing methods employ post-hoc low-response token pruning in decoder layers, they overlook the input-level semantic correlation between visual tokens and instructions (query). In this paper, we propose QuoTA, an ante-hoc training-free modular that extends existing large video-language models (LVLMs) for visual token assignment based on query-oriented frame-level importance assessment. The query-oriented token selection is crucial as it aligns visual processing with task-specific requirements, optimizing token budget utilization while preserving semantically relevant content. Specifically, (i) QuoTA strategically allocates frame-level importance scores based on query relevance, enabling one-time visual token assignment before cross-modal interactions in decoder layers, (ii) we decouple the query through Chain-of-Thoughts reasoning to facilitate more precise LVLM-based frame importance scoring, and (iii) QuoTA offers a plug-and-play functionality that extends to existing LVLMs. Extensive experimental results demonstrate that implementing QuoTA with LLaVA-Video-7B yields an average performance improvement of 3.2% across six benchmarks (including Video-MME and MLVU) while operating within an identical visual token budget as the baseline.

QuoTA: Query-oriented Token Assignment via CoT Query Decouple for Long Video Comprehension

Existing text-video retrieval methods mainly focus on single-modal video content (i.e., visual entities), often overlooking heterogeneous scene text that is ubiquitous in human environments. Although scene text in videos provides fine-grained semantics for cross-modal retrieval, effectively utilizing it presents two key challenges: (1) Temporally dense scene text disrupts sync with sparse video frames, obstructing video understanding; (2) Redundant scene text and irrelevant video frames hinder the learning of discriminative temporal clues for retrieval. To address them, we propose a temporal scene-text calibrating and distilling (TCD) network for text-video retrieval. Specifically, we first design a window-OCR captioner that aggregates dense scene text into OCR captions to facilitate feature interaction. Next, we devise a heterogeneous semantics calibration module that leverages scene text as a self-supervised signal to temporally align window-level OCR captions and frame-level video features. Further, we introduce a context-guided temporal clue distillation module to learn the complementary and relevant details between scene text and video modalities, thereby obtaining discriminative temporal clues for retrieval. Extensive experiments show that our TCD achieves state-of-the-art performance on three scene-text related benchmarks. Demo is available at the anonymous link https://tcd365.github.io.

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