Singapore

Large-scale pre-trained vision-language models (VLMs) like CLIP show exceptional performance and zero-shot generalization. However, their reliability may be severely undermined by a critical vulnerability to subtle adversarial perturbations. Our work reveals a critical cross-modal vulnerability: visual-only perturbations induce substantial, synchronous shifts in decision attribution maps across both image and text. This phenomenon signifies a fundamental disruption of the VLM&#39;s internal logic, as it alters both the model&#39;s perceptual focus and its decision rationale. To counter this vulnerability, we introduce Cross-modal Bidirectional Attribution guided Few-shot Adversarial Prompt Tuning (CBA-FAPT), a novel method that leverages the model&#39;s internal decision rationale as a regularizer for robust learning. Our framework&#39;s core mechanism is the alignment of a novel bidirectional attribution map. This map is a unique fusion of two components. It combines forward feature attention to capture the model&#39;s perceptual focus. It also incorporates backward decision gradients to act as a proxy for the model&#39;s decision rationale, quantifying how each feature influences the final outcome. We enforce consistency on this bidirectional map between clean and adversarial examples. This approach corrects the model&#39;s internal logic on two fronts and effectively restores its adversarial robustness. Comprehensive experiments on 11 datasets demonstrate that CBA-FAPT outperforms the state-of-the-art, establishing a superior trade-off between robust and natural accuracy.

AAAI 2026

Stabilizing Cross-Modal Bidirectional Attribution: Few-Shot Adversarial Prompt Tuning for Robust Vision-Language Models

cv: adversarial attacks & robustness nlp: safety and robustness nlp: prompt engineering / prompting

The purpose of the AAAI conference is to promote research in artificial intelligence (AI) and scientific exchange among AI researchers, practitioners, scientists, and engineers in affiliated disciplines. 

AAAI 2021

Large-scale pre-trained vision-language models (VLMs) like CLIP show exceptional performance and zero-shot generalization. However, their reliability may be severely undermined by a critical vulnerability to subtle adversarial perturbations. Our work reveals a critical cross-modal vulnerability: visual-only perturbations induce substantial, synchronous shifts in decision attribution maps across both image and text. This phenomenon signifies a fundamental disruption of the VLM's internal logic, as it alters both the model's perceptual focus and its decision rationale. To counter this vulnerability, we introduce Cross-modal Bidirectional Attribution guided Few-shot Adversarial Prompt Tuning (CBA-FAPT), a novel method that leverages the model's internal decision rationale as a regularizer for robust learning. Our framework's core mechanism is the alignment of a novel bidirectional attribution map. This map is a unique fusion of two components. It combines forward feature attention to capture the model's perceptual focus. It also incorporates backward decision gradients to act as a proxy for the model's decision rationale, quantifying how each feature influences the final outcome. We enforce consistency on this bidirectional map between clean and adversarial examples. This approach corrects the model's internal logic on two fronts and effectively restores its adversarial robustness. Comprehensive experiments on 11 datasets demonstrate that CBA-FAPT outperforms the state-of-the-art, establishing a superior trade-off between robust and natural accuracy.

technical paper

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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.

With the rise of generative artificial intelligence
(GenAI), academic dishonesty in classrooms has skyrocketed,
yet the existing solutions for detecting such dishonesty
often fall short. Standard "AI detectors" merely analyze
one text at a time, failing to account for students'
previous writings, which risks erroneous predictions.
Meanwhile, existing token-based authorship verification
(AV) models fail to analyze the nuances in writing styles
that truly distinguish authorship. To fill this existing
gap, we propose a novel AV framework that combines
token-level stylometric features (e.g., POS tag patterns)
with handcrafted stylistic features (e.g., sentence
structure variation) to construct a comprehensive feature
set. Using both benchmark corpora and real-world high
school student essays, we trained multiple machine learning
classifiers using the proposed feature set. Our initial
experiments show that our approach outperforms the standard
token-only baselines by over 25%, while offering
interpretable, style-based insights. These preliminary
results highlight the importance of nuanced stylistic
features and suggest that a holistic AV system can provide
educators with more reliable and transparent detection
tools. Looking ahead, we plan to extend this work with
large language models and multi-agent approaches to further
enhance robustness and adaptability.

Style-First Authorship Verification for Academic Integrity in the Generative AI Era (Student Abstract)

We study the problem of \emph{Constrained Online Convex Optimization with Memory} (\texttt{COCO‑M}), where both the loss and constraints depend on a window of $m$ past decisions of the learner ($m$ memory length). This setting extends the previously-studied unconstrained OCO-M framework and captures a range of practical problems arising, for instance, in the control of constrained dynamical systems and in scheduling problems with reconfiguration budgets, among others. For this new problem, we propose the first algorithms that achieve sublinear regret and cumulative constraint‑violation (CCV) over time‑varying constraints, both with and without predictions about the missing loss and constraint functions. In the absence of predictions, our approach achieves regret $\mathcal{O}(m^{3/2} \sqrt{T \log T})$ and CCV $\mathcal{O}\big(T^{3/4}\vee\! m^{3/2} \sqrt{T \log T}\big)$ using an intuitive adaptive penalty method. When short-horizon ''untrusted'' predictions are available, we reinterpret the problem as an instance of online learning with delayed feedback, and design an optimistic algorithm with regret $\mathcal{O}\!\bigl(\sqrt{\mathcal{E}_T(f)}\,\vee\,\log T\bigr)$ and CCV $\mathcal{O}\!\bigl((\sqrt{\mathcal{E}_T(g)}+m)\log T\bigr)$. These bounds depend on the accumulated prediction errors of the losses and constraints ($\mathcal{E}_T(f)$ and $\mathcal{E}_T(g)$), and improve directly with the predictions accuracy while maintaining order-optimal convergence when they fail. Our results close the gap between classical COCO and memory-dependent COCO, and create a new versatile learning toolbox with diverse applications.

Constrained Online Convex Optimization with Memory and Predictions

We depart from Uncapacitated Facility Location and by assuming that the connection costs of agents to facilities are congestion dependent, we define a novel problem, namely, Facility Location for Congesting (Selfish) Commuters. The connection costs of agents to facilities come as a result of how the agents commute to reach the facilities in an underlying network with cost functions on the edges. Inapproximability results follow from the related literature and thus approximate solutions is all we can hope for. For when the cost functions are nondecreasing we employ in a novel way an approximate version of Caratheodory’s Theorem (Barman 2018) to show how approximate solutions for different versions of the problem can be derived. For when the cost functions are nonincreasing we show how this problem generalizes the Cost-Distance problem (Meyerson, Munagala, and Plotkin 2008) and provide an algorithm that for this more general case achieves the same approximation guarantees.

Facility Location for Congesting Commuters and Generalizing the Cost-Distance Problem

While vision-language foundation models (VLMs) achieve remarkable performance when fine-tuned on downstream in-distribution (ID) data, this process compromises their generalization ability on out-of-distribution (OOD) data that deviate from the downstream tasks due to overfitting. To address this, we propose ProLoG, a new adaptation method that effectively fine-tunes VLMs on downstream tasks while achieving high OOD performance. Specifically, we design a unique integration of prompt tuning and LoRA, offering a robust hybrid platform to improve performance. During training, we propose an augmentation-based regularization loss that enhances the generalization of our hybrid network by using augmented image features aligned with LLM-generated texts containing key attributes of each class. By leveraging our hybrid design, we also introduce an adaptive inference strategy that flexibly applies trained prompts and LoRA based on a task similarity score to effectively handle both ID and OOD data. Experimental results demonstrate that our proposed method outperforms existing works on various datasets, confirming its advantages.

ProLoG: Hybrid Prompt and LoRA Based Adaptation of Vision-Language Models for OOD Generalization

We present ReCAD, a reinforcement learning framework that bootstraps pretrained large models (PLMs) to generate precise parametric computer-aided design (CAD) models from multimodal inputs, by leveraging their inherent generation capabilities. With just access to simple functional interfaces (e.g., point coordinates), our approach enables the emergence of complex CAD operations (e.g., pattern replication} and mirror). This stands in contrast to previous methods, which typically rely on knowledge injected through supervised fine-tuning (SFT), offer limited support for editability, and fail to fully exploit the strong generative priors of PLMs. Specifically, the ReCAD framework begins by fine-tuning vision-language models (VLMs) to equip them with basic CAD model generation capabilities, where we first rewrite hardcoded CAD scripts into parameterized code, which is then leveraged to generate accurate textual descriptions for supervision. Then, we propose a novel reinforcement learning strategy that incorporates parameterized code as guidance to enhance the model’s reasoning on challenging questions, and further employ a curriculum-based hierarchical primitive learning process to progressively teach structured and compositional skills under a unified reward function that ensures both geometric accuracy and semantic fidelity.
ReCAD establishes a new state-of-the-art on both text-to-CAD and image-to-CAD tasks, significantly improving geometric accuracy across in- and out-of-distribution settings. In the in-distribution setting, ReCAD reduces the mean Chamfer Distance (CD) from 73.47 to 29.61, and in the out-of-distribution setting, mean CD drops from 272.06 to 80.23, outperforming all existing baselines by a substantial margin.

ReCAD: Reinforcement Learning Enhanced Parametric CAD Model Generation with Vision-Language Models

Chain of Thought (CoT) reasoning has demonstrated remarkable deep reasoning capabilities in both large language models (LLMs) and multimodal large language models (MLLMs). However, its reliability is often undermined by the accumulation of errors in intermediate steps. This paper proposes a novel approach to calibrating CoT reasoning accuracy by leveraging the model’s internal cognition of truthfulness. Our findings suggest that the model implicitly tracks the evolving veracity of intermediate steps throughout the dynamic, progressive reasoning process. We train a confidence predictor to quantify the model’s internal cognition of truthfulness at each reasoning step, enabling dynamic selection of the most plausible reasoning path through beam search. Experimental results demonstrate that our method significantly outperforms the state-of-the-art baselines (e.g., Self-Consistency, and PRM Guided Search) across the mathematical, symbolic, and commonsense reasoning tasks, exhibiting superior accuracy and reliability in both unimodal and multimodal settings. This study proposes a novel path toward improving the reliability of CoT reasoning, demonstrating strong potential for wide-ranging applications.

Deep Hidden Cognition Facilitates Reliable Chain-of-Thought Reasoning

Offline policy learning from logged data is a critical paradigm for enabling effective decision-making without costly online exploration. However, its application has been largely confined to single-objective problems, a stark contrast to real-world scenarios where decision-making inherently involves navigating multiple, often conflicting, objectives. This paper introduces a comprehensive framework for Offline Multi-Objective Bandits (OffMOB), providing a principled solution to the fundamental challenge of learning Pareto-optimal policies from a static dataset. Our core contribution is a novel algorithm that uniquely integrates the pessimism principle with multi-objective optimization to safely learn from off-policy data. Crucially, our approach transcends the primary limitation of scalarization techniques, which are restricted to finding a single policy for a pre-defined preference. Instead, OffMOB directly approximates the entire Pareto front, learning a single, flexible policy model capable of generating an optimal action for any desired trade-off. To rigorously evaluate performance, we introduce the Tchebycheff sub-optimality metric and establish the first finite-sample generalization bounds for this problem class, proving that our algorithm converges to the true Pareto front under practical data coverage assumptions. Extensive experiments on complex benchmarks demonstrate that OffMOB significantly outperforms existing methods, identifying the complete set of optimal trade-offs where naive extensions and single-objective methods fail.

Offline Multi-Objective Bandits: From Logged Data to Pareto-Optimal Policies

Zero-shot event extraction (ZSEE) remains a significant challenge for large language models (LLMs) due to the need for complex reasoning and domain-specific understanding. Direct prompting often yields incomplete or structurally invalid outputs—such as misclassified triggers, missing arguments, and schema violations. To address these limitations, we present Agent-Event-Coder (AEC), a novel multi-agent framework that treats event extraction like software engineering: as a structured, iterative code-generation process. AEC decomposes ZSEE into specialized subtasks—retrieval, planning, coding, and verification—each handled by a dedicated LLM agent. Event schemas are represented as executable class definitions, enabling deterministic validation and precise feedback via a verification agent. This programming-inspired approach allows for systematic disambiguation and schema enforcement through iterative refinement. By leveraging collaborative agent workflows, AEC enables LLMs to produce precise, complete, and schema-consistent extractions in zero-shot settings. Experiments across five diverse domains and six LLMs demonstrate that AEC consistently outperforms prior zero-shot baselines, showcasing the power of treating event extraction like code generation.

Extracting Events Like Code: A Multi-Agent Programming Framework for Zero-Shot Event Extraction

Modern ETL (Extract, Transform, Load) tools offer graphical, no-code interfaces for workflow creation but still require users to manually identify transformation functions and configure their properties, which is time-consuming and demands prior expertise. We present the research and engineering foundations of the IBM DataStage Assistant, a deployed capability that generates complete multi-stage ETL flows directly from natural language (NL) descriptions. Our framework infers transformation functions, their properties, and transformer expressions, enabling novices to discover relevant functions and allowing experts to bypass manual configuration. The proposed framework achieves a prediction accuracy of 96.4% for flow predictions, 87.0% for properties, and 83.6% for transformer expressions. We also show a document exploration module that uses retrieval-augmented generation (RAG) over product documentation to answer tool-specific questions in NL. Implemented in IBM DataStage, this approach supports iterative, in-environment workflow design and reduces context switching. In initial studies, it achieves up to 90% time savings for novices and 50% for experts.

From Natural Language to Executable ETL Flows: The IBM DataStage Assistant

Self-supervised learning (SSL) is an emerging paradigm that
exploits supervisory signals generated from the data
itself, and many recent studies have leveraged SSL to
conduct graph anomaly detection. However, we empirically
found that three important factors can substantially impact
detection performance across datasets: (1) the specific SSL
strategy employed; (2) the tuning of the strategy’s
hyperparameters; and (3) the allocation of combination
weights when using multiple strategies. Most SSL-based
graph anomaly detection methods circumvent these issues by
arbitrarily or selectively (i.e., guided by label
information) choosing SSL strategies, hyperparameter
settings, and combination weights. While an arbitrary
choice may lead to subpar performance, using label
information in an unsupervised setting is label information
leakage and leads to severe overestimation of a method’s
performance. Leakage has been criticized as “one of the top
ten data mining mistakes", yet many recent studies on
SSL-based graph anomaly detection have been using label
information to select hyperparameters. To mitigate this
issue, we propose to use an internal evaluation strategy
(with theoretical analysis) to select hyperparameters in
SSL for unsupervised anomaly detection. We perform
extensive experiments using 10 recent SSL-based graph
anomaly detection algorithms on various benchmark datasets,
demonstrating both the prior issues with hyperparameter
selection and the effectiveness of our proposed strategy.

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Style-First Authorship Verification for Academic Integrity in the Generative AI Era (Student Abstract)

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