Singapore

Point cloud quality assessment faces a critical disconnect: existing methods operate on a flawed single-perception paradigm, while human observers evaluate quality through dual cognitive streams: **technical rationality** and **semantic sensibility**. This fundamental mismatch routinely produces catastrophic assessment failures in real-world scenarios where technical and semantic signals conflict. To address this, we introduce Dual-Stream Perception PCQA (DSP-PCQA), the first framework that explicitly models this perceptual duality through parallel networks thoroughly mirroring the human cognitive pathway. DSP-PCQA introduces three key innovations: (1) a **Decoupled Focus Enhancer (DFE)** that surgically isolates technical and semantic information using two targeted transformations; (2) a **Context &amp; Attribute Correlation Awareness (CACA)** module that captures the dynamic, non-linear relationships between different views and sub-models characteristic of human visual processing; and (3) an **Exchange-based Perceptual Injection (EPI)** module that strategically transfers information between perception streams, simulating how humans integrate multiple perceptual dimensions. Extensive evaluations show DSP-PCQA dramatically outperforms state-of-the-art methods across multiple datasets. Most importantly, our method resolves the perceptual discord that plagues existing approaches, maintaining high accuracy even in the challenging boundary cases where technical quality and semantic significance diverge wildly, precisely where conventional methods fail catastrophically.

AAAI 2026

DSP-PCQA: Integrating Multiple Perception Preferences for Point Cloud Quality Assessment

multi-modal vision.

3d computer vision

applications

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.

Point cloud quality assessment faces a critical disconnect: existing methods operate on a flawed single-perception paradigm, while human observers evaluate quality through dual cognitive streams: **technical rationality** and **semantic sensibility**. This fundamental mismatch routinely produces catastrophic assessment failures in real-world scenarios where technical and semantic signals conflict. To address this, we introduce Dual-Stream Perception PCQA (DSP-PCQA), the first framework that explicitly models this perceptual duality through parallel networks thoroughly mirroring the human cognitive pathway. DSP-PCQA introduces three key innovations: (1) a **Decoupled Focus Enhancer (DFE)** that surgically isolates technical and semantic information using two targeted transformations; (2) a **Context & Attribute Correlation Awareness (CACA)** module that captures the dynamic, non-linear relationships between different views and sub-models characteristic of human visual processing; and (3) an **Exchange-based Perceptual Injection (EPI)** module that strategically transfers information between perception streams, simulating how humans integrate multiple perceptual dimensions. Extensive evaluations show DSP-PCQA dramatically outperforms state-of-the-art methods across multiple datasets. Most importantly, our method resolves the perceptual discord that plagues existing approaches, maintaining high accuracy even in the challenging boundary cases where technical quality and semantic significance diverge wildly, precisely where conventional methods fail catastrophically.

poster

Prefix adders are widely used in compute-intensive applications for their high speed. However, designing these adders for an optimal area-delay trade-off is challenging due to strict design rules and an exponentially large design space. We introduce PrefixGPT, a generative pre-trained Transformer (GPT) that directly generates optimized prefix adders from scratch. Our approach represents an adder’s topology as a sequence of two-dimensional coordinates and applies a legality mask during generation, ensuring every design is valid by construction. To efficiently generate the sequence, PrefixGPT features a customized decoder-only architecture that adapts the standard Transformer model for spatial coordinate prediction. The model is trained in two stages: it is first pre-trained on a corpus of randomly synthesized valid prefix adders to learn the design rules and then fine-tuned to navigate the design space for optimized design quality. Compared with existing works, PrefixGPT not only finds a new optimal design with a 7.7% improved area-delay product (ADP) but also exhibits superior exploration quality, lowering the average ADP by up to 79.1% and slashing its standard deviation by over 94%. This demonstrates the potential of GPT-style models to first master complex hardware design principles and then apply them for more efficient design optimization. To ensure reproducibility and facilitate future research, all of our code, data, and models are publicly available at XXXXX.

PrefixGPT: Prefix Adder Optimization by a Generative Pre-trained Transformer

With the rapid growth of the Internet of Things (IoT), integrating artificial intelligence (AI) on extremely weak embedded devices has garnered significant attention, enabling improved real-time performance and enhanced data privacy.
However, the resource limitations of such devices and unreliable network conditions necessitate error-resilient device-edge collaboration systems.
Traditional approaches often focus on bit-level transmission correctness, which can be inefficient under dynamic channel conditions. 
In contrast, we propose SemanticNN, a semantic codec that tolerates bit-level errors in pursuit of semantic-level correctness, enabling compressive and resilient collaborative inference offloading under strict computational and communication constraints.
It incorporates a Bit Error Rate (BER)-aware decoder %to sense the dynamically changing BERs that adapts to dynamic channel conditions and a Soft Quantization (SQ)-based encoder to learn compact representations.
Building on this architecture, we introduce Feature-augmentation Learning, a novel training strategy that enhances offloading efficiency.
Furthermore, to address the mismatch between encoder and decoder capabilities caused by asymmetric computational resources,
we propose eXplainable AI (XAI)-based Asymmetry Compensation, which improves semantic fidelity during decoding.
We conduct extensive experiments on STM32 using three models and six public datasets across image classification and object detection tasks.
Experimental results demonstrate that, under varying transmission error rates, SemanticNN significantly reduces feature transmission volume by 56.82–344.83× while maintaining superior inference accuracy. Code is available at https://anonymous.4open.science/r/semanticnn/.

SemanticNN: Compressive and Error-Resilient Semantic Offloading for Extremely Weak Devices

Electromyography (EMG)-based gesture recognition has emerged as a promising approach for human-computer interaction. However, its performance is often limited by the scarcity of labeled EMG data, significant cross-user variability, and poor generalization to unseen gestures. To address these challenges, we propose SeqEMG-GAN, a conditional, sequence-driven generative framework that synthesizes high-fidelity EMG signals from hand joint angle sequences. Our method introduces a context-aware architecture composed of an angle encoder, a dual-layer context encoder featuring the novel Ang2Gist unit, a deep convolutional EMG generator, and a discriminator, all jointly optimized via adversarial learning. By conditioning on joint kinematic trajectories, SeqEMG-GAN is capable of generating semantically consistent EMG sequences, even for previously unseen gestures, thereby enhancing data diversity and physiological plausibility. Experimental results show that classifiers trained solely on synthetic data experience only a slight accuracy drop (from 57.77% to 55.71%). In contrast, training with a combination of real and synthetic data significantly improves accuracy to 60.53%, outperforming real-only training by 2.76%. These findings demonstrate the effectiveness of our framework, also achieves the state-of-art performance in augmenting EMG datasets and enhancing gesture recognition performance for applications such as neural robotic hand control, AI/AR glasses, and gesture-based virtual gaming systems.

New Synthetic Goldmine: Hand Joint Angle-Driven EMG Data Generation Framework for Micro-Gesture Recognition

Graphical User Interface (GUI) task automation constitutes a critical frontier in artificial intelligence research. While effective GUI agents synergistically integrate planning and grounding capabilities, current methodologies exhibit two fundamental limitations: (1) insufficient exploitation of cross-model synergies, and (2) over-reliance on synthetic data generation without sufficient utilization. To address these challenges, we propose Co-EPG, a self-iterative training framework for Co-Evolution of Planning and Grounding. Co-EPG establishes an iterative positive feedback loop: through this loop, the planning model explores superior strategies under grounding-based reward guidance via Group Relative Policy Optimization (GRPO), generating diverse data to optimize the grounding model. Concurrently, the optimized Grounding model provides more effective rewards for subsequent GRPO training of the planning model, fostering continuous improvement. Co-EPG thus enables iterative enhancement of agent capabilities through self-play optimization and training data distillation. On the Multimodal-Mind2Web and AndroidControl benchmarks, our framework outperforms existing state-of-the-art methods after just three iterations without requiring external data. The agent consistently improves with each iteration, demonstrating robust self-enhancement capabilities. This work establishes a novel training paradigm for GUI agents, shifting from isolated optimization to an integrated, self-driven co-evolution approach.

Co-EPG: A Framework for Co-Evolution of Planning and Grounding in Autonomous GUI Agents

In this paper, we explore the transferability of SSL by addressing two central questions: (i) what is the representation transferability of SSL, and (ii) how can we effectively model this transferability? Transferability is defined as the ability of a representation learned from one task to support the objective of another. Inspired by the meta-learning paradigm, we construct multiple SSL tasks within each training batch to support explicitly modeling transferability. Based on empirical evidence and causal analysis, we find that although introducing task-level information improves transferability, it is still hindered by task conflict. To address this issue, we propose a Task Conflict Calibration (TC$^2$) method to alleviate the impact of task conflict. Specifically, it first splits batches to create multiple SSL tasks, infusing task-level information. Next, it uses a factor extraction network to produce causal generative factors for all tasks and a weight extraction network to assign dedicated weights to each sample, employing data reconstruction, orthogonality, and sparsity to ensure effectiveness. Finally, TC$^2$
calibrates sample representations during SSL training and integrates into the pipeline via a two-stage bi-level optimization framework to boost the transferability of learned representations. Experimental results on multiple downstream tasks demonstrate that our method consistently improves the transferability of SSL models.

Exploring Transferability of Self-Supervised Learning by Task Conflict Calibration

Knowledge distillation from Artificial Neural Networks (ANNs) to Spiking Neural Networks (SNNs) is a prominent training paradigm. However, its efficacy is fundamentally limited by a spectral mismatch: SNNs, with their intrinsic low-pass filtering characteristics, struggle to learn high-frequency details from their ANN teachers, creating a bottleneck in knowledge transfer at both the feature and logit levels. To address this, we propose Bi-Spectrum Distillation (BSD), a novel framework that mitigates the mismatch from two complementary perspectives. First, at the feature level, our Spectral Residual Distillation (SRD) enhances the student SNN's features with a parameter-efficient, learnable filter that adaptively compensates for high-frequency information loss, which transforms the student's output to better match the teacher's rich spectral target. Second, at the logits level, our Spectral Semantic Distillation (SSD) enhances fine-grained classification by distilling high-frequency components from teacher-ordered logits. Extensive experiments on CIFAR-10/100, ImageNet, and CIFAR10-DVS demonstrate that BSD achieves new state-of-the-art performance across both CNN and Transformer-based SNNs, validating its effectiveness and broad applicability.

Bi-Spectrum Distillation: Addressing Spectral Mismatch in ANN-SNN Knowledge Transfer

Effective agent coordination is crucial in cooperative Multiagent Reinforcement Learning (MARL). While recent advances have significantly improved cooperation by modeling agent interactions through various graph structures, most existing approaches primarily focus on homogeneous agents. Despite the ubiquity of heterogeneous agents, constructing a comprehensive graph that captures their diverse attributes and relationships from scratch is notoriously labor-intensive for both humans and agents, which makes policy learning extremely challenging. To tackle this difficulty, we propose a novel method that utilizes a fuzzy human attention-guided graph to model inter-agent relationships. Instead of learning the graph entirely from scratch, we incorporate abstract human attention, with its uncertainty captured through fuzzy logic, to guide the graph development process. To further accommodate the varying attributes and objectives of heterogeneous agents while maintaining their learning capabilities, the attention-guided graph is fine-tuned through a hyper-network. Our proposed approach is end-to-end trainable and agnostic to specific MARL methods. Empirical evaluations conducted on challenging heterogeneous scenarios from the StarCraft Multiagent Challenge (SMAC) and SMACv2 validate the effectiveness of the proposed method.

Reinforcement Learning with Fuzzy Human Attention-Guided Graph for Heterogeneous Multiagent Systems

Diffusion-based Neural Combinatorial Optimization (NCO) has demonstrated effectiveness in solving NP-complete (NPC) problems by learning discrete diffusion models for solution generation, eliminating hand-crafted domain knowledge. Despite their success, existing NCO methods face significant challenges in both cross-scale and cross-problem generalization, and high training costs compared to traditional solvers. While recent studies on diffusion models have introduced training-free guidance approaches that leverage pre-defined guidance functions for conditional generation, such methodologies have not been extensively explored in combinatorial optimization. To bridge this gap, we propose a training-free inference time adaptation framework (DIFU-Ada) that enables both the zero-shot cross-problem transfer and cross-scale generalization capabilities of diffusion-based NCO solvers without requiring additional training. We provide theoretical analysis that helps understanding the cross-problem transfer capability. Our experimental results demonstrate that a diffusion solver, trained exclusively on the Traveling Salesman Problem (TSP), can achieve competitive zero-shot transfer performance across different problem scales on TSP variants, such as Prize Collecting TSP (PCTSP) and the Orienteering Problem (OP), through inference time adaptation.

Boosting Cross-problem Generalization in Diffusion-Based Neural Combinatorial Solver via Inference Time Adaptation

Nucleus detection in histopathology whole slide images (WSIs) is crucial for a broad spectrum of clinical applications. The gigapixel size of WSIs necessitates the use of sliding window methodology for nucleus detection. However, mainstream methods process each sliding window independently, which overlooks broader contextual information and easily leads to inaccurate predictions. To address this limitation, recent studies additionally crop a large Filed-of-View (LFoV) patch centered on each sliding window to extract contextual features. However, such methods substantially increase whole-slide inference latency. In this work, we propose an effective and efficient context-aware nucleus detection approach. Specifically, instead of using lFoV patches, we aggregate contextual clues from off-the-shelf features of historically visited sliding windows, which greatly enhances the inference efficiency. Moreover, compared to lFoV patches used in previous works, the sliding window patches have higher magnification and provide finer-grained tissue details, thereby enhancing the classification accuracy. To develop the proposed context-aware model, we utilize annotated patches along with their surrounding unlabeled patches for training. Beyond exploiting high-level tissue context from these surrounding regions, we design a post-training strategy that leverages abundant unlabeled nucleus samples within them to enhance the model's context adaptability. Extensive experimental results on three challenging benchmarks demonstrate the superiority of our method. Code will be available soon.

Towards Effective and Efficient Context-aware Nucleus Detection in Histopathology Whole Slide Images

The success of large-scale pre-training paradigm, exemplified by Large Language Models (LLMs), has inspired the development of Time Series Foundation Models (TSFMs). However, their application to financial candlestick (K-line) data remains limited, often underperforming non-pre-trained architectures. Moreover, existing TSFMs often overlook crucial downstream tasks such as volatility prediction and synthetic data generation. To address these limitations, we propose Kronos, a unified, scalable pre-training framework tailored to financial K-line modeling. Kronos introduces a specialized tokenizer that discretizes continuous market information into token sequences, preserving both price dynamics and trade activity patterns. We pre-train Kronos using an autoregressive objective on a massive, multi-market corpus of over 12 billion K-line records from 45 global exchanges, enabling it to learn nuanced temporal and cross-asset representations. Kronos excels in a zero-shot setting across a diverse set of financial tasks. On benchmark datasets, Kronos boosts price series forecasting RankIC by 93% over the leading TSFM and 87% over the best non-pre-trained baseline. It also achieves a 9% lower MAE in volatility forecasting and a 22% improvement in generative fidelity for synthetic K-line sequences. These results establish Kronos as a robust, versatile foundation model for end-to-end financial time series analysis.

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