Singapore

While deep learning models have shown strong performance in simulating neural responses, they often fail to clearly separate stable visual encoding from condition-specific adaptation, which limits their ability to generalize across stimuli and individuals. We introduce the Adaptive Visual Model (AVM), a structure-preserving framework that enables condition-aware adaptation through modular subnetworks, without modifying the core representation. AVM keeps a Vision Transformer-based encoder frozen to capture consistent visual features, while independently trained modulation paths account for neural response variations driven by stimulus content and subject identity. We evaluate AVM in three experimental settings, including stimulus-level variation, cross-subject generalization, and cross-dataset adaptation, all of which involve structured changes in inputs and individuals. Across two large-scale mouse V1 datasets, AVM outperforms the state-of-the-art V1T model by approximately 2\% in predictive correlation, demonstrating robust generalization, interpretable condition-wise modulation, and high architectural efficiency. Specifically, AVM achieves a 9.1\% improvement in explained variance (FEVE) under the cross-dataset adaptation setting. These results suggest that AVM provides a unified framework for adaptive neural modeling across biological and experimental conditions, offering a scalable solution under structural constraints. Its design may inform future approaches to cortical modeling in both neuroscience and biologically inspired AI systems.

AAAI 2026

AVM: Towards Structure-Preserving Neural Response Modeling in the Visual Cortex Across Stimuli and Individuals

stimulus regulation

neural response prediction

visual cortex

Dear IPSO Virtual Conference Attendees:

Welcome to the IPSO 2023 Virtual Conference. We are delighted to bring together experts from multiple disciplines and six continents to provide a diverse perspective on pediatric cerebrovascular disease.

A few quick words about what to expect. Two important goals of this conference are to (1) foster a sense of community and (2) identify global priorities for our field. We encourage you to express your thoughts live in through the chat function to provide a global perspective on all topics. Please introduce yourself in the chat, so everyone knows where you are attending from.

We are excited to announce IPSO is able to offer 9.5 CME units for the virtual conference. If you would like to claim CME credits, please email membership@internationalpediatricstroke.org. 

We want to hear from you! Planning for IPSO 2024 Congress on March 22nd-24th in Toronto, Canada, will commence as soon as the Virtual Conference ends. Let us know your priorities through the live chat function, via email, or by submitting a symposium idea. Based on your input, the Scientific Program Committee will select the topics that we will address in greater depth when we meet again in the spring.

We hope you find the Virtual Conference both interesting and educational, and hope to see you in person in Toronto, or at another IPSO meeting in the future.

Warm regards,

Heather Fullerton, IPSO President 
Maja Steinlin, IPSO Vice-President 
Lori Jordan, IPSO Secretary & Treasurer 
Noma Dlamini, IPSO Scientific Program Committee Chair 


The International Pediatric Stroke Organization (IPSO) 2023 Virtual Conference is April 28-29th, 2023. The mission of the virtual conference is to unify the global community of professionals engaged in research and clinical care for children with cerebrovascular diseases, and chart a path forward for multinational, multidisciplinary efforts. Similar to the IPSO 2021 Virtual Conference, IPSO has partnered with Underline to host this virtual conference. 

The 2023 IPSO Virtual Conference will offer:

* A highly interactive format: brief recorded talks followed by live panel discussions with audience participation through a chat function and live audience surveys
* Optional break-out groups for IPSO committees, working groups, councils
* Poster sessions available on-demand throughout the conference with a chat function for the audience to comment for ask questions of the presenters

Disclaimer: This activity has been planned and implemented in accordance with the accreditation requirements and policies of the Accreditation Council for Continuing Medical Education (ACCME) through the joint providership of UCSF and the International Pediatric Stroke Organization (IPSO) and Hospital for Sick Children, and Vanderbilt University Medical Center. UCSF is accredited by the ACCME to provide continuing medical education for physicians.

 UCSF designates this live activity for a maximum of 9.5 AMA PRA Category 1 credit(s)™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.

Contact membership@internationalpediatricstroke.org if you would like to claim CME for the IPSO 2023 Virtual Conference. 

**About** 
Founded in February, 2019, we are an organization of health professionals, scientists and patient advocates who care about stroke and other cerebrovascular diseases in childhood. We developed out of our original collaborative network, the International Pediatric Stroke Study (IPSS), and now aim to broaden the scope of our mission beyond research.

* Our mission is to improve the lives of children worldwide with cerebrovascular disease through research, education, clinical care and advocacy.

* Our Vision is a world in which international, multidisciplinary collaborations advance the understanding, care, and outcomes of childhood cerebrovascular disease.

To access IPSO 2023 site, please register [here](https://internationalpediatricstroke.org/2023-virtual-conference/registration/). 

IPSO 2023

The mission of the International Pediatric Stroke Organization (IPSO) 2023 Virtual Conference is to unify the global community of professionals engaged in research and clinical care for children with cerebrovascular diseases, and chart a path forward for multinational, multidisciplinary efforts. 



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.

To access this event page, you need to log in with the **email address you registered with**. 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.

Reinforcement learning has emerged as a paradigm for post-training large language models, boosting their reasoning capabilities. Such approaches compute an advantage value for each sample, reflecting better or worse performance than expected, thereby yielding both positive and negative signals for training. However, existing approaches often mix them indiscriminately, especially in the early stages, leading to ambiguous guidance and limited gains. To address this issue, we propose CAPO (Curriculum Advantage Policy Optimization), an adaptive curriculum mechanism based on advantage signals. The proposed mechanism bootstraps imitation learning with positive-only advantage samples to establish robust foundations, and subsequently introduces negative signals to cultivate discriminative capabilities, thereby improving generalization across complex scenarios. Compatible with diverse optimization methods including GRPO, PPO, RLOO, and Reinforce++, our method consistently achieves stable and significant improvements in mathematical reasoning tasks, and further generalizes effectively to multimodal Graphical User Interface (GUI) reasoning scenarios, establishing itself as a versatile and robust optimization paradigm.

From Imitation to Discrimination: Toward a Generalized Curriculum Advantage Mechanism Enhancing Cross-Domain Reasoning Tasks

Recent advances in vision-language models (VLMs) and reinforcement learning (RL) have driven progress in GUI automation. However, most existing methods rely on static, one-shot visual inputs and passive perception, lacking the ability to adaptively determine when, whether, and how to observe the interface.
We present GUI-Eyes, a reinforcement learning framework for active visual perception in GUI tasks. To acquire more informative observations, the agent learns to make strategic decisions on both whether and how to invoke visual tools, such as cropping or zooming, within a two-stage reasoning process.
To support this behavior, we introduce a progressive perception strategy that decomposes the decision-making into coarse exploration and fine-grained grounding, coordinated by a two-level policy. In addition, we design a spatially continuous reward function tailored to tool usage, which integrates both location proximity and region overlap to provide dense supervision and alleviate the reward sparsity common in GUI environments.
On the ScreenSpot-Pro benchmark, GUI-Eyes-3B achieves 44.8\% grounding accuracy using only 3k labeled samples, significantly outperforming both supervised and RL-based baselines. These results highlight that tool-aware active perception, enabled by staged policy reasoning and fine-grained reward feedback, is critical for building robust and data-efficient GUI agents.

GUI-Eyes: Tool-Augmented Perception for Visual Grounding in GUI Agents

RGB-Thermal (RGBT) tracking aims to exploiting visible and thermal infrared modalities for robust all-weather tracking. However, existing RGBT trackers struggle to resolve modality discrepancies, which poses great challenges for robust feature representation. This limitation hinders effective cross-modal information propagation and fusion, which significantly reduces the tracking accuracy. To address this limitation, we propose a novel Contextual Aggregation with Deformable Alignment framework called CADTrack for RGBT Tracking. To be specific, we first deploy the Mamba-based Feature Interaction (MFI) that establishes efficient feature interaction via state space models. This interaction module can operate with linear complexity, reducing computational cost and improving feature discrimination. Then, we propose the Contextual Aggregation Module (CAM) that dynamically activates backbone layers through sparse gating based on the Mixture-of-Experts (MoE). This module can encode complementary contextual information from cross-layer features. Finally, we propose the Deformable Alignment Module (DAM) to integrate deformable sampling and temporal propagation, mitigating spatial misalignment and localization drift. With the above components, our CADTrack achieves robust and accurate tracking in complex scenarios. Extensive experiments on five RGBT tracking benchmarks verify the effectiveness of our proposed method. Our code and model will be released.

CADTrack: Learning Contextual Aggregation with Deformable Alignment for Robust RGBT Tracking

Smartphones bring significant convenience to users but also enable devices to extensively record various types of personal information.
Existing smartphone agents powered by Multimodal Large Language Models (MLLMs) have achieved remarkable performance in automating different tasks.
However, as the cost, these agents are granted substantial access to sensitive users' personal information during this operation. 
To gain a thorough understanding of the privacy awareness of these agents, we present the first large-scale benchmark encompassing 7,138 scenarios to the best of our knowledge.
In addition, for privacy context in scenarios, we annotate its type (e.g., Account Credentials), sensitivity level, and location.
We then carefully benchmark seven available mainstream smartphone agents. 
Our results demonstrate that almost all benchmarked agents show unsatisfying privacy awareness (RA), with performance remaining below 60% even with explicit hints. 
Overall, closed-source agents show better privacy ability than open-source ones, and Gemini 2.0-flash achieves the best, achieving an RA of 67%.
We also find that the agents’ privacy detection capability is highly related to scenario sensitivity level, i.e., the scenario with a higher sensitivity level is typically more identifiable. 
We hope the findings enlighten the research community to rethink the unbalanced utility-privacy tradeoff about smartphone agents.

Mind the Third Eye! Benchmarking Privacy Awareness in MLLM-powered Smartphone Agents

With the deep integration of artificial intelligence and interactive technology, Graphical User Interface (GUI) Agent, as the carrier connecting goal-oriented natural language and real-world devices, has received widespread attention from the community. Contemporary benchmarks aim to evaluate the comprehensive capabilities of GUI agents in GUI operation tasks, generally determining task completion solely by inspecting the final screen state. However, GUI operation tasks consist of multiple chained steps while not all critical information is presented in the final few pages. Although a few research has begun to incorporate intermediate steps into evaluation, accurately and automatically capturing this process information still remains an open challenge. To address this weakness, we introduce $\textbf{ProBench}$, a comprehensive mobile benchmark with over 200 challenging GUI tasks covering widely-used scenarios. 
Remaining the traditional State-related Task evaluation, we extend our dataset to include Process-related Task and design a specialized evaluation method. A newly introduced Process Provider automatically supplies accurate process information, enabling presice assessment of agent's performance. Our evaluation of advanced GUI agents reveals significant limitations for real-world GUI scenarios. These shortcomings are prevalent across diverse models, including both large-scale generalist models and smaller, GUI-specific models. A detailed error analysis further exposes several universal problems, outlining concrete directions for future improvements.

ProBench: Benchmarking GUI Agents with Accurate Process Information

Recent work has shown that machine-learned predictions can provably improve the performance of classic algorithms. In this work, we propose the first minimum-cost network flow algorithm augmented with a dual prediction. Our method is based on a classic minimum-cost flow algorithm, namely $\varepsilon$-relaxation. We provide time complexity bounds in terms of the infinity norm prediction error, which is both consistent and robust. We also prove sample complexity bounds for PAC-learning the prediction. We empirically validate our theoretical results on two applications of minimum-cost flow, i.e., traffic networks and chip escape routing, in which we learn a fixed prediction, and a featured-based neural network model to infer the prediction, respectively. Experimental results illustrate $12.74\times$ and $1.64\times$ average speedup on two applications.

Minimum-Cost Network Flow with Dual Predictions

Optimization modeling and solving are fundamental to the application of Operations Research (OR) in real-world decision making, yet the process of translating natural language problem descriptions into formal models and solver code remains highly expertise intensive. While recent advances in large language models (LLMs) have opened new opportunities for automation, the generalization ability and data efficiency of existing LLM-based methods are still limited, asmost require vast amounts of annotated or synthetic data, resulting in high costs and scalability barriers. In this work, we present OR-R1, a data-efficient training framework for automated optimization modeling and solving. OR-R1 first employs supervised fine-tuning (SFT) to help the model acquire the essential reasoning patterns for problem formulation and code generation from limited labeled data. In addition, it improves the capability and consistency through Test-Time Group Relative Policy Optimization (TGRPO). This two-stage design enables OR-R1 to leverage both scarce labeled and abundant unlabeled data for effective learning. Experiments show that OR-R1 achieves state-of-the-art performance with an average solving accuracy of 67.7\%, using only 1/10 the synthetic data required by prior methods such as ORLM, exceeding ORLM’s solving accuracy by up to 4.2\%. Remarkably, OR-R1 outperforms ORLM by over 2.4\% with just 100 synthetic samples. Furthermore, TGRPO contributes an additional 3.1\%–6.4\% improvement in accuracy, significantly narrowing the gap between single-attempt (Pass@1) and multi-attempt (Pass@8) performance from 13\% to 7\%. Extensive evaluations across diverse real-world benchmarks demonstrate that OR-R1 provides a robust, scalable, and cost-effective solution for automated OR optimization problem modeling and solving, lowering the expertise and data barriers for industrial OR applications. The source code is available in the Supplementary Material.

OR-R1: Automating Modeling and Solving of Operations Research Optimization Problem via Test-Time Reinforcement Learning

Large Language Models (LLMs) exhibit pronounced memory-bound characteristics during inference due to High Bandwidth Memory (HBM) bandwidth constraints. In this paper, we propose an L2 Cache-oriented asynchronous KV Cache prefetching method to break through the memory bandwidth bottleneck in LLM inference through computation-load overlap. By strategically scheduling idle memory bandwidth during active computation windows, our method proactively prefetches required KV Cache into GPU L2 cache, enabling high-speed L2 cache hits for subsequent accesses and effectively hiding HBM access latency within computational cycles. Extensive experiments on NVIDIA H20 GPUs demonstrate that the proposed method achieves 2.15× improvement in attention kernel efficiency and up to 1.97× end-to-end throughput enhancement, surpassing state-of-the-art baseline FlashAttention-3. Notably, our solution maintains orthogonality to existing optimization techniques and can be integrated with current inference frameworks, providing a scalable latency-hiding solution for next-generation LLM inference engines.

Accelerating LLM Inference Throughput via Asynchronous KV Cache Prefetching

Source-free domain adaptation (SFDA) aims to transfer knowledge from the source domain to an unlabeled target domain without requiring access to source data. Although previous works have focused on clustering target domain samples from continuous training, there are still some challenges: i) More source domain knowledge is forgotten with more training epochs. ii) Achieving better learning results often requires increased computational resources. To solve these problems, we propose a novel Marginal Exploration for Source-Free Domain Adaptation (ME-SFDA) method, which is a multi-scale information fusion learning based on our designed Pyramidal Atkinson-Shiffrin memory. Specifically, we design a two-step module to split samples into clustered cores and response scatters by sensory memory. Then, a novel technique is proposed for clustering samples in a hierarchical way, utilizing long-term memory to cluster cores derived from splitting the samples earlier and guide response scatters. To effectively divide samples of different classes, we propose a method that encourages unambiguous cluster assignments for the samples using multi-scale fusion information. To verify the generality of our approach, we not only discuss the UDA and SFDA tasks but also apply it to the semi-supervised domain adaptation (SSDA), which utilizes a few labeled target samples based on UDA. Extensive experiments on four standard benchmarks indicate that our approach outperforms previous SOTA methods.

ME-SFDA: Marginal Exploration with Pyramidal Atkinson-Shiffrin Memory for Source-Free Domain Adaptation

Reflection removal of a single image remains a highly challenging task due to the complex entanglement between target scenes and unwanted reflections. Despite significant progress, existing methods are hindered by the scarcity of high-quality, diverse data and insufficient restoration priors, resulting in limited generalization across various real-world scenarios. In this paper, we propose Dereflection Any Image, a comprehensive solution with an efficient data preparation pipeline and a generalizable model for robust reflection removal. First, we introduce a dataset named Diverse Reflection Removal (DRR) created by randomly rotating reflective mediums in target scenes, enabling variation of reflection angles and intensities, and setting a new benchmark in scale, quality, and diversity. Second, we propose a diffusion-based framework with one-step diffusion for deterministic outputs and fast inference. To ensure stable learning, we design a three-stage progressive training strategy, including reflection-invariant finetuning to encourage consistent outputs across varying reflection patterns that characterize our dataset. Extensive experiments show that our method achieves SOTA performance on both common benchmarks and challenging in-the-wild images, showing superior generalization across diverse real-world scenes.

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