Singapore

Adversarial attacks remain a major challenge for deep
learning models, as they can undermine both performance and
reliability in practical applications such as image
recognition. Although evolutionary algorithms (EAs) have
proven effective in optimizing complex systems, their use
for directly enhancing model robustness for adversarial
defense has been limited. In this study, we introduce
ResNet-GA, a method that applies evolutionary deep learning
(EDL) to develop ResNet-like networks specifically designed
to resist different forms of adversarial perturbations. The
approach evolves network architectures with a genetic
algorithm (GA), adapting the Residual Blocks at every stage
in ResNet according to the needs of each dataset and attack
type. Experimental results show that ResNet-GA strengthens
model robustness beyond standard baselines, highlighting
the value of iterative evolutionary design for building
more dependable deep learning systems under various
adversarial conditions.

AAAI 2026

ResNet-GA: Evolutionary Deep Learning Models for Adversarial Defense (Student Abstract)

technical paper

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.

Logit Knowledge Distillation has gained substantial
research interest in recent years due to its simplicity and
lack of requirement for intermediate feature alignment;
however, it suffers from limited interpretability in its
decision-making process. To address this, we propose
implicit Clustering Distillation (iCD): a simple and
effective method that mines and transfers interpretable
structural knowledge from logits, without requiring
ground-truth labels or feature-space alignment. iCD
leverages Gram matrices over decoupled local logit
representations to enable student models to learn latent
semantic structural patterns. Extensive experiments on
benchmark datasets demonstrate the effectiveness of iCD
across diverse teacher-student architectures, with
particularly strong performance in fine-grained
classification tasks---achieving a peak improvement of
+5.08% over the baseline.

iCD: An Implicit Clustering Distillation Method for Structural Information Mining (Student Abstract)

End-to-end (E2E) autonomous driving must maintain global consistency while preserving local precision. However, existing E2E approaches rarely achieve both goals simultaneously. Therefore, we propose a multimodal coarse-to-local transformer (MC2L-Transformer), which is composed of a hierarchical transformer architecture. Multimodal inputs are fused into a shared embedding, and global waypoints are produced. Local refinement is then utilized to capture fine interactions around the vehicle. Furthermore, a temporal encoder summarizes recent context, and navigation target and velocity are embedded to guide route- and speed-aware decoding. We evaluate in CARLA, and the results show lower collision and off-route rates even under sudden events. These results indicate that combining a coarse-to-local hierarchical transformer with a lightweight temporal context provides a practical step toward reliable E2E autonomous driving.

Multimodal Coarse-to-Local Transformer for End-to-End Autonomous Driving (Student Abstract)

The consideration of channel correlation is crucial for
improving the performance of multivariate time series
forecasting. However, existing models fail to capture it in
homogeneous and highly correlated channels. In this work,
we introduce CompRestacking (Compression Restacking), a
strikingly intuitive and effective method to address this
problem. The approach consists of three main components:
(1) PCC-Restacking for correlation-aware channel ordering,
(2) Temporal embedding for time encoding, and (3)
Aggregation compression for compact token generation.
CompRestacking consistently outperforms in experiment
results. The results demonstrate that CompRestacking
leverages strong channel correlations for improved
performance.

CompRestacking: Capturing Channel Dependency in Highly Correlated Multivariate Time Series Data (Student Abstract)

Large language models (LLMs) have become indispensable, but the most celebrated efficiency methods---mixture-of-experts (MoE), speculative decoding, and complex retrieval-augmented generation (RAG)---were built for hyperscale providers with vast infrastructure and elite teams. 
Outside that context, their benefits collapse into overhead, fragility, and wasted carbon. 
The result is that a handful of Big Tech companies benefit, while thousands of hospitals, schools, governments, and enterprises are left without viable options. 
We argue that the next frontier is not greater sophistication at scale, but robust simplicity: efficiency that thrives under modest resources and minimal expertise. We propose a new research agenda: retrofitting pretrained models with more efficient architectures without retraining, inventing lightweight fine-tuning that preserves alignment, making reasoning economical despite long chains of thought, enabling dynamic knowledge management without heavy RAG pipelines, and adopting Overhead-Aware Efficiency (OAE) as a standard benchmark.
By redefining efficiency to include adoption cost, sustainability, and fairness, we can democratize LLM deployment---ensuring that optimization reduces inequality and carbon waste rather than amplifying them.

Democratizing LLM Efficiency: From Hyperscale Optimizations to Universal Deployability

Large language models (LLMs) have the potential to address social and behavioral determinants of health by transforming labor intensive workflows in resource-constrained settings. Creating LLM-based applications that serve the needs of underserved communities requires a deep understanding of their local context, but it is often the case that neither LLMs nor their developers possess this local expertise, and the experts in these communities often face severe time/resource constraints.
This creates a disconnect: how can one engage in meaningful co-design of an LLM-based application for an under-resourced community when the communication channel between the LLM developer and domain expert is constrained? 
We explored this question through a real-world case study, in which our data science team sought to partner with social workers at a safety net hospital to build an LLM application that summarizes patients' social needs. 
Whereas prior works focus on the challenge of prompt tuning, we found that the most critical challenge in this setting is the careful and precise specification of *what* information to surface to providers so that the LLM application is accurate, comprehensive, and verifiable.
Here we present a novel co-design framework for settings with limited access to domain experts, in which the summary generation task is first decomposed into individually-optimizable attributes and then each attribute is efficiently refined and validated through a multi-tier cascading approach.

When the Domain Expert Has No Time and the LLM Developer Has No Clinical Expertise: Real-World Lessons from LLM Co-Design in a Safety-Net Hospital

Cognitive diagnosis (CD), inferring student knowledge mastery based on historical response records, is crucial for personalized educational services such as adaptive practice and learning path planning. Existing CD models were built based on the assumption that student's response data is integral, overlooking the nonrandom missingness of data caused by student answering exercises selectively. 
This missingness generally leads to biased and incomplete observations, where confounders, such as selection bias and exposure bias, significantly undermine the accuracy of student knowledge modeling.
To address missingness, we propose a Debiased Cognitive Diagnosis (DBCD) framework through the perspective of counterfactual modeling to remove exogenous confounders from the response data.
Specifically, the proposed DBCD achieves debiasing for CD by applying the idea of contrastive learning 
to constrain the model's prediction distributions on both factual and counterfactual data.
For a student, the factual data is his/her original response records, while the counterfactual data is generated by sampling the same number of exercises from all exercises of each concept through a similarity-based counterfactual sampling strategy.
Considering the difficulty of directly removing the exogenous confounders for student, we devise a β-Variational Autoencoder to model their exogenous confounders within the latent representations of knowledge proficiency by leveraging exercise priors and student response patterns. 
Then, the learned representations are further combined with the vanilla student's ability embedding 
via a gating mechanism-based fusion for final diagnosis prediction of the model.
Extensive experiments on three real-world educational datasets demonstrate that the proposed DBCD effectively mitigates confounders and even outperforms existing methods, thereby validating the feasibility and effectiveness of the DBCD framework.

Debiased Cognitive Diagnosis: A Contrastive Counterfactual Modeling Method via Variational Autoencoder

As one of the primary causes of visual impairment, Diabetic Retinopathy (DR) requires accurate and robust grading to facilitate timely diagnosis and intervention. Different from conventional DR grading methods that utilize single-view images, recent clinical studies have revealed that multi-view fundus images can significantly enhance DR grading performance by expanding the field of view (FOV). However, there is a long-tailed distribution problem in fundus image analysis, i.e., a high prevalence of mild DR grades and a low prevalence of rare ones (e.g., cases of high severity), which presents a significant challenge to developing a unified model capable of detecting rare or unseen DR grades not encountered during training. In this paper, we propose ProME-DR, a Prompt-driven zero-shot DR grading framework, which leverages prompt Matching and Emulating to recognize the unseen DR categories and views beyond the training set. ProME-DR disentangles the training process into two stages to learn generalized knowledge for novel DR disease grading. Initially, ProME-DR leverages two sets of prompt units to capture semantic and inter-view consistency knowledge via a split-and-mask manner, gathering instance-level DR visual clues. Subsequently, it constructs a concept-aware emulator to generate context prompt units, linking extensible knowledge learned from the previously seen DR attributes for zero-shot DR grading. Extensive experiments conducted on eight datasets and various scenarios confirm the superiority of ProME-DR.

Towards Zero-Shot Diabetic Retinopathy Grading: Learning Generalized Knowledge via Prompt-Driven Matching and Emulating

Learning multimodal representation is a fundamental task that supports a wide range of applications such as visual-text retrieval. While pioneering approaches \textit{e.g.,} CLIP paves the way by learning separated encoders for different modalities, they struggle to model complex interactions between modalities, resulting in inferior vision and language representation. Recently, researchers have begun to leverage powerful Large Vision-Language Models (LVLMs) for unimodal or multimodal encoding, showing substantial improvement over separated encoder methods. However, we find that directly adapting LVLMs to embedding models suffers from insufficient visual representation and coarse multimodal alignment. To address these issues, we propose a simple yet effective Fine-grained Alignment Matters (FAM) method to achieve fine-grained vision-language embedding learning with LVLMs. First, to close the gap between the pure generation and multimodal embedding using LVLMs, we propose Multi-granularity Aligned Contrastive (MAC) to explicitly learn and align fine-grained modality representations at multiple granularity levels using image-text pairs. Second, to mitigate the insufficiency of visual representation during adapting LVLMs to downstream embedding tasks, we propose a Vision Embedding Inversion Training (VEIN) strategy to encourage the extracted embeddings to preserve fine-grained visual features. Extensive experiments demonstrate the effectiveness of our method, which achieves superior performance on various downstream multimodal datasets.

FAM: Fine-Grained Alignment Matters in Multimodal Embedding Learning with Large Vision-Language Models

Neural Radiance Fields (NeRF) have shown remarkable capabilities
for photorealistic novel view synthesis. One major deficiency of
NeRF is that dense inputs are typically required, and the rendering
quality will drop drastically given sparse inputs. In this paper, we
highlight the effectiveness of rendered semantics from dense novel
views, and show that rendered semantics can be treated as a more
robust form of augmented data than rendered RGB. Our method
enhances NeRF’s performance by incorporating guidance derived
from the rendered semantics. The rendered semantic guidance encompasses
two levels: the supervision level and the feature level.
The supervision-level guidance incorporates a bi-directional verification
module that decides the validity of each rendered semantic
label, while the feature-level guidance integrates a learnable codebook
that encodes semantic-aware information, which is queried
by each point via the attention mechanism to obtain semanticrelevant
predictions. The overall semantic guidance is embedded
into a self-improved pipeline.We also introduce a more challenging
sparse-input indoor benchmark, where the number of inputs is
limited to as few as 6. Experiments demonstrate the effectiveness
of our method and it exhibits superior performance compared to
existing approaches.

Empowering Sparse-Input Neural Radiance Fields with Dual-Level Semantic Guidance from Dense Novel Views

Vision-Language-Action (VLA) models typically bridge the gap between perceptual and action spaces by pre-training a Vision-Language Model (VLM) on robotic data. While this approach greatly enhances performance, it also incurs significant training costs. In this paper, we investigate how to effectively bridge vision-language (VL) representations to action (A). We introduce VLA-Adapter, a novel paradigm designed to reduce the reliance of VLA models on large-scale VLMs and extensive pre-training. To this end, we first systematically analyze the effectiveness of various VL conditions and present key findings on which conditions are essential for bridging perception and action spaces. Based on these insights, we propose a lightweight Policy module with Bridge Attention, which autonomously injects the optimal condition into the action space. In this way, our method achieves high performance using only a 0.5B-parameter backbone, without any robotic data pre-training. Extensive experiments on both simulated and real-world robotic benchmarks demonstrate that VLA-Adapter not only achieves state-of-the-art performance, but also offers the fast inference speed reported to date. Furthermore, thanks to the proposed advanced bridging paradigm, VLA-Adapter enables the training of a powerful VLA model in just 8 hours on a single consumer-grade GPU, significantly lowering the barrier to deploying VLA models.

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