Singapore

Recent advances in Deep Learning (DL) have improved multivariate time series (MTS) classification and regression by capturing complex patterns, but their lack of transparency hinders decision-making. Explainable AI (XAI) methods offer partial insights, yet often fall short of conveying the full decision space. Counterfactual Explanations (CE) provide a promising alternative, but current approaches typically prioritize either accuracy or sparsity -- rarely both -- limiting their practical value. To address this, we propose CONFETTI, a novel multi-objective CE method for MTS. CONFETTI uses Class Activation Maps (CAMs) to identify key subsequences, locates a counterfactual target, and optimally modifies the time series to balance prediction confidence and sparsity. This method provides actionable insights with minimal changes, improving interpretability, and decision support. CONFETTI is evaluated on seven MTS datasets from the UEA archive, demonstrating its effectiveness in various domains. CONFETTI consistently outperforms state-of-the-art CE methods in its optimization objectives, and in six other metrics from the literature, achieving, for example, at least 10% higher confidence while improving sparsity in at least 40%.

AAAI 2026

Counterfactual eXplainable AI (XAI) Method for Deep Learning-Based Multivariate Time Series Classification

hai: explainable ai (xai) for human understanding

so: metareasoning and metaheuristics

and causality

krr: action

ml: time-series/data streams

change

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.

poster

Retrieval-Augmented Generation (RAG) systems enhance Large Language Models (LLMs) by retrieving relevant documents from external corpora before generating responses. This approach significantly expands LLM capabilities by leveraging vast, up-to-date external knowledge. However, this reliance on external knowledge makes RAG systems vulnerable to corpus poisoning attacks that manipulate generated outputs via poisoned document injection. Existing poisoning attack strategies typically treat the retrieval and generation stages as disjointed, limiting their effectiveness. We propose Joint-GCG, the first framework to unify gradient-based attacks across both retriever and generator models through three innovations: (1) Cross-Vocabulary Projection for aligning embedding spaces, (2) Gradient Tokenization Alignment for synchronizing token-level gradient signals, and (3) Adaptive Weighted Fusion for dynamically balancing attacking objectives. Evaluations demonstrate that Joint-GCG achieves at most 25% and an average of 5% higher attack success rate than previous methods across multiple retrievers and generators. While optimized under a white-box assumption, the generated poisons show unprecedented transferability to unseen models. Joint-GCG's innovative unification of gradient-based attacks across retrieval and generation stages fundamentally reshapes our understanding of vulnerabilities within RAG systems.

Joint-GCG: Unified Gradient-Based Poisoning Attacks on Retrieval-Augmented Generation Systems

Certified defenses aim to provide provable robustness against attacks. Models certified against adversarial attacks provide $l_p$-bounded guarantees on the absence of adversarial manipulation of inputs for predicted labels. We study the potential for malicious exploitation of certification frameworks to better understand the limits of guarantee provisions. The objective is to not only mislead a classifier but also manipulate the certification process to generate robustness certificate guarantee for an adversarial input—*certificate spoofing*. A recent study in ICLR demonstrated crafting large perturbations can shift inputs far into regions cable of generating a certificate for an incorrect class. Our study investigates if perturbations needed to cause a misclassification and yet coax a certified model into issuing deceptive, large robustness radii can still be imperceptible. We explore the idea of region-focused adversarial examples to demonstrate imperceptible perturbations capable of spoofing certificates and achieving certification radii larger than the source class—*ghost certificates*. Extensive evaluations with *ImageNet* demonstrate the ability to effectively bypass state-of-the-art certified defenses. Our work raises new questions regarding the safe deployment of systems with certified defenses and current robustness verification methods, while underscoring the need to invest efforts to better understand and appreciate the robustness guarantees of certified models.

Certified but Fooled! Breaking Certified Defenses with Ghost Certificates

Narrative comprehension on long stories and novels has been a challenging domain attributed to their intricate plotlines and entangled, often evolving relations among characters and entities. Given the LLM's diminished reasoning over extended context and its high computational cost, retrieval-based approaches remain a pivotal role in practice. However, traditional RAG methods could fall short due to their stateless, single-step retrieval process, which often overlooks the dynamic nature of capturing interconnected relations within long-range context. In this work, we propose ComoRAG, holding the principle that narrative reasoning is not a one-shot process, but a dynamic, evolving interplay between new evidence acquisition and past knowledge consolidation, analogous to human cognition on reasoning with memory-related signals in the brain. Specifically, when encountering a reasoning impasse, ComoRAG undergoes iterative reasoning cycles while interacting with a dynamic memory workspace. In each cycle, it generates probing queries to devise new exploratory paths, then integrates the retrieved evidence of new aspects into a global memory pool, thereby supporting the emergence of a coherent context for the query resolution. Across four challenging long-context narrative benchmarks (200K+ tokens), ComoRAG outperforms strong RAG baselines with consistent relative gains up to 11% compared to the strongest baseline. Further analysis reveals that ComoRAG is particularly advantageous for complex queries requiring global comprehension, offering a principled, cognitively motivated paradigm for retrieval-based stateful reasoning.

ComoRAG: A Cognitive-Inspired Memory-Organized RAG for Stateful Long Narrative Reasoning

Most existing spatial reasoning benchmarks focus on static or globally observable environments, failing to capture the challenges of long-horizon reasoning and memory utilization under partial observability and dynamic changes. We introduce two dynamic spatial benchmarks—locally observable maze navigation and match-2 elimination—that systematically evaluate models' abilities in spatial understanding and adaptive planning when local perception, environment feedback, and global objectives are tightly coupled. Each action triggers structural changes in the environment, requiring continuous update of cognition and strategy. We further propose a subjective experience-based memory mechanism for cross-task experience transfer and validation. Experiments show that our benchmarks reveal key limitations of mainstream models in dynamic spatial reasoning and long-term memory, providing a comprehensive platform for future methodological advances.

EvoEmpirBench: Dynamic Spatial Reasoning with Agent-ExpVer

Aerial-Ground Person Re-IDentification (AGPReID) aims to extract identity-discriminative representations from heterogeneous perspectives across different platforms in complex real-world environments. However, existing methods primarily focus on visual appearance modeling and make insufficient use of semantic attribute priors, which limits their ability to bridge the aerial-ground view gap. To address this limitation, we propose a Semantic-driven Visual Progressive Refinement framework for AGPReID (SVPR-ReID), which effectively leverages textual attribute priors to guide the extraction of fine-grained visual cues. Specifically, we design a View-Decoupled Feature Extractor that incorporates view-aware textual prompts to decouple view-invariant identity features. Then, to alleviate inter-class ambiguity, we propose an Attribute-Scattered Mixture-of-Experts module that integrates attribute semantics into the visual space, thereby improving discrimination among visually similar pedestrians. Finally, we design a Context-Vision Progressive Refinement module for progressive refinement of attribute and view-invariant features, obtaining robust cross-view identity representations. In particular, we contribute a comprehensive benchmark for AGPReID, named CP2108, which contains 142,817 images of 2,108 identities annotated with 22 attributes. Notably, it includes 191 identities captured across different times, enabling both short- and long-term Re-ID evaluation, addressing the limitation of existing datasets that focus only on short-term scenarios. Extensive experimental results validate the effectiveness of our SVPR-ReID on four AGPReID datasets.

Semantic-Driven Visual Progressive Refinement for Aerial-Ground Person ReID: A Challenging Large-Scale Benchmark

Mathematical Expression Recognition (MER) has made significant progress in recognizing the simple expression, but the robust recognition of the complex mathematical expression (CMER) with numerous tokens and multiple lines remains a formidable challenge. In this paper, we first introduce CMER-Bench, a carefully constructed benchmark that categorizes expressions into three difficulty levels: normal, moderate, and complex. Leveraging CMER-Bench, we conduct a comprehensive evaluation of existing expert MER models and general-purpose multimodal large language models (MLLMs). The results reveal that while current methods perform well on normal and moderate expressions, their performance degrades significantly when handling complex mathematical expressions. In response, and considering that existing public training datasets are primarily composed of simple samples, we propose CMER-17M, a large-scale dataset specifically designed for the recognition of complex mathematical expressions. This dataset provides rich and diverse samples to support the development of accurate and robust CMER models. Furthermore, to address the challenges posed by the spatial structure of complex expressions, we introduce a novel expression representation called SML, which explicitly models the hierarchical and spatial structure of mathematical content beyond \LaTeX{} format. Based on the SML representation, we propose a specialized model named CMERNet, built upon an encoder-decoder architecture and trained on CMER-17M. Experimental results show that CMERNet, with only 0.1 billion parameters, significantly outperforms all existing expert models and MLLMs on CMER-Bench, particularly in the complex level. All data and code will be released.

Complex Mathematical Expression Recognition: Benchmark, Large-Scale Dataset and Strong Baseline

The release of open-weight large language models (LLMs) creates a tension between advancing accessible research and preventing misuse, such as malicious fine-tuning to elicit harmful content. Current safety measures struggle to preserve the general capabilities of the LLM while resisting a determined adversary with full access to the model's weights and architecture, who can use full-parameter fine-tuning to erase existing safeguards. To address this, we introduce AntiDote, a bi-level optimization procedure for training LLMs to be resistant to such tampering. AntiDote involves an auxiliary adversary hypernetwork that learns to generate malicious Low-Rank Adaptation (LoRA) weights conditioned on the defender model's internal activations. The defender LLM is then trained with an objective to nullify the effect of these adversarial weight additions, forcing it to maintain its safety alignment. We validate this approach against a diverse suite of 52 red-teaming attacks, including jailbreak prompting, latent space manipulation, and direct weight-space attacks. AntiDote is upto 27.4\% more robust against adversarial attacks compared to both tamper-resistance and unlearning baselines. Crucially, this robustness is achieved with a minimal trade-off in utility, incurring a performance degradation of upto less than 0.5\% across capability benchmarks including MMLU, HellaSwag, and GSM8K. Our work offers a practical and compute efficient methodology for building open-weight models where safety is a more integral and resilient property.

AntiDote: Bi-level Adversarial Training for Tamper-Resistant LLMs

Machine unlearning, which enables a model to forget specific data, is crucial for ensuring data privacy and model reliability. However, its effectiveness can be severely undermined in real-world scenarios where models learn unintended biases from spurious correlations within the data. This paper investigates the unique challenges of unlearning from such biased models. We identify a novel phenomenon we term "shortcut unlearning," where models exhibit an "easy to learn, yet hard to forget" tendency. Specifically, models struggle to forget easily-learned, bias-aligned samples; instead of forgetting the class attribute, they unlearn the bias attribute, which can paradoxically improve accuracy on the class intended to be forgotten. To address this, we propose CUPID, a new unlearning framework inspired by the observation that samples with different biases exhibit distinct loss landscape sharpness. Our method first partitions the forget set into causal- and bias-approximated subsets based on sample sharpness, then disentangles model parameters into causal and bias pathways, and finally performs a targeted update by routing refined causal and bias gradients to their respective pathways. Extensive experiments on biased datasets including Waterbirds, BAR, and Biased NICO++ demonstrate that our method achieves state-of-the-art forgetting performance and effectively mitigates the shortcut unlearning problem.

Easy to Learn, Yet Hard to Forget: Towards Robust Unlearning Under Bias

LLM's code generation capabilities have yielded substantial improvements in the effectiveness of programming tasks. However, LLM-generated code still suffers from compilation and runtime errors. Existing offline preference optimization methods primarily focus on enhancing LLMs' coding abilities using pass/fail signals in the preference data, overlooking the deep-level error types in the failed codes. To address this, we propose Adaptively Progressive Preference Optimization (AP2O) for coding (i.e., AP2O-Coder), a method that guides LLMs adaptively and methodically to reduce code errors for code generation. Specifically, we construct an error notebook from failed codes and progressively optimize the LLM to correct errors type by type. Furthermore, we adaptively replay error types to tailor to the LLM's changing weaknesses throughout the training process. Through extensive experiments on both code and general LLMs (Llama, Qwen, and DeepSeek series) with parameters ranging from 0.5B to 34B, our AP2O-Coder improves code generation performance by up to 3% in pass@k while using less preference data. The code is in the supplementary material.

AP2O-Coder: Adaptively Progressive Preference Optimization for Reducing Compilation and Runtime Errors in LLM-Generated Code

The rapid advancement of generative models has produced highly realistic synthetic images, posing significant challenges to digital media authenticity. Existing AI-generated image detectors often fail to generalize to images from unseen generators when crossing architectural boundaries (i.e., Generative Adversarial Networks (GANs) vs. Diffusion Models (DMs)). We hypothesize that this generalization gap arises from fundamental differences in how these architectures generate images. In this work, we provide the first theoretical analysis explaining why GANs and DMs produce fundamentally different artifacts through the lens of the manifold hypothesis. We prove that GANs produce characteristic boundary artifacts from partial manifold coverage, while DMs exhibit over-smoothing and unique noise patterns due to the need for complete coverage. Motivated by this theoretical finding, we propose a novel semi-supervised detection approach called Triarchy Detector (TriDetect) that enhances standard binary classification with an architecture-aware clustering loss. Specifically, instead of producing binary classification heads, the architecture-aware classifier generates distinct logits for both real images and multiple fake clusters. To prevent the problem of cluster collapse in unsupervised learning scenarios, we implement balanced cluster assignment through the Sinkhorn-Knopp algorithm. Furthermore, we design a cross-view consistency mechanism to ensure that the model learns discriminative features that capture architectural patterns rather than image statistics. By learning to recognize architectural patterns that persist across different generators within the same family, our method achieves superior generalization to unseen generators.

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