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Articles 241 - 270 of 9003

Full-Text Articles in Computer Sciences

Artem: Enhancing Large Language Model Agents With Spatial-Temporal Episodic Memory, Cassandra Hui Ming Tan, Budhitama Subagdja, Ah-Hwee Tan Jan 2026

Artem: Enhancing Large Language Model Agents With Spatial-Temporal Episodic Memory, Cassandra Hui Ming Tan, Budhitama Subagdja, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Current large language models (LLMs) exhibit significant deficiencies in episodic memory tasks including encoding, storing, and retrieving specific information from temporally dependent events over a long period of time. Recent approaches to handle memory tasks in LLMs, such as in-context learning, retrieval-augmented generation (RAG), and fine-tuning, may resolve the long-term retention issues, but are still inadequate to handle tasks requiring chronological awareness of the stored information. We introduce Agentic Retrieval with Temporal-Episodic Memory (ARTEM), a hybrid LLM-based agent architecture integrating LLMs with a self-organizing neural network named Spatial-Temporal Episodic Memory (STEM), designed to handle episodic memory tasks. Our approach employs …

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Fluid Agency In Ai Systems: A Case For Functional Equivalence In Copyright, Patent, And Tort, Anirban Mukherjee, Hannah H. Chang Jan 2026

Fluid Agency In Ai Systems: A Case For Functional Equivalence In Copyright, Patent, And Tort, Anirban Mukherjee, Hannah H. Chang

Research Collection Lee Kong Chian School Of Business

Modern Artificial Intelligence (AI) systems exhibit fluid agency in multi-step workflows: lacking human-like consciousness or culpability, yet they display behavior that is (i) stochastic (probabilistic and path‑dependent), (ii) dynamic (co‑evolving with user interaction), and (iii) adaptive (able to reorient across contexts). These properties generate valuable outputs but collapse attribution, irreducibly entangling human and machine inputs. Doctrines that assume traceable provenance—authorship, inventorship, and liability—fracture under this unmappability, yielding ownership gaps and moral “crumple zones.”This Article argues that only functional equivalence stabilizes doctrine under unmappability: Where provenance is indeterminate, legal frameworks should treat human and AI contributions as equivalent for allocating rights …

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Llamoco: Instruction Tuning Of Large Language Models For Optimization Code Generation, Zeyuan Ma, Yue-Jiao Gong, Hongshu Guo, Jiacheng Chen, Yining Ma, Zhiguang Cao Jan 2026

Llamoco: Instruction Tuning Of Large Language Models For Optimization Code Generation, Zeyuan Ma, Yue-Jiao Gong, Hongshu Guo, Jiacheng Chen, Yining Ma, Zhiguang Cao

Research Collection School Of Computing and Information Systems

Recently, combining the strength of large language models (LLMs) and Evolutionary Computation (EC) has shown promising results for addressing optimization problems. It typically involves either iterative next-step solution seeking or directly prompting LLMs to generate critical optimization codes. However, these methods often suffer from low computational efficiency, high sensitivity to prompt design, and a lack of domain-specific knowledge. We introduce LLaMoCo, the first instruction-tuning framework designed to adapt LLMs for solving optimization problems in a code-to-code manner. LLaMoCo features a comprehensive instruction set that includes code-style problem descriptions as input prompts and robust optimization codes from expert EC optimizers as …

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Tempo: Training-Time Equilibration Of Modalities For Per-Sample Optimization In Multimodal Sentiment, Yi Zhao, Erik Cambria, Xiaosong E, Xianxun Zhu Jan 2026

Tempo: Training-Time Equilibration Of Modalities For Per-Sample Optimization In Multimodal Sentiment, Yi Zhao, Erik Cambria, Xiaosong E, Xianxun Zhu

Research Collection School Of Computing and Information Systems

Multimodal sentiment models often become over-reliant on the “easiest” modality (typically text), leading to three coupled sub-problems: (i) representation-level dominance, where weaker modalities contribute little to the fused representation; (ii) optimization-level dominance, where the strongest modality drives most gradient updates and suppresses learning in others; and (iii) robustness degradation, where audio or vision fail under noise or missing inputs at test time. We present TEMPO, a plug-and-play training framework that mitigates these issues by rebalancing learning pressure across modalities while leaving inference unchanged. For each mini-batch, TEMPO estimates relative modality strength and applies two synchronized, training-only controls: selective forward attenuation …

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Purified Zero-Shot Sketch-Based Image Retrieval, Yang Zhou, Jingru Yang, Jin Wang, Kaixiang Huang, Guodong Lu, Shengfeng He Jan 2026

Purified Zero-Shot Sketch-Based Image Retrieval, Yang Zhou, Jingru Yang, Jin Wang, Kaixiang Huang, Guodong Lu, Shengfeng He

Research Collection School Of Computing and Information Systems

Sketches, as a new solution in multimedia systems that can replace natural language, are characterized by sparse visual cues such as simple strokes that differ significantly from natural images containing complex elements such as background, foreground, and texture. This misalignment poses substantial challenges for zero-shot sketch-based image retrieval (ZS-SBIR). Prior approaches match sketches to full images and tend to overlook redundant elements in natural images, leading to model distraction and semantic ambiguity. To address this issue, we introduce a distraction-agnostic framework, purified cross-domain matching (PuXIM), which operates on a straightforward principle: masking and matching. We devise a visual-cross-linguistic (VxL) sampler …

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Portfoliopilot: An Agentic Platform For Financial Portfolio Management Algorithm Development And Evaluation, Jared Chan Xu Yang, Haokai Ma, Yunshan Ma Jan 2026

Portfoliopilot: An Agentic Platform For Financial Portfolio Management Algorithm Development And Evaluation, Jared Chan Xu Yang, Haokai Ma, Yunshan Ma

Research Collection School Of Computing and Information Systems

Developing new portfolio-management algorithms typically demands substantial programming effort, limiting rapid experimentation and excluding finance professionals without coding skills. Current robo-advisory tools offer pre-built but rigid strategies, restricting customization and experimentation. We introduce PortfolioPilot, an open-source, agentic platform that enables users to generate bespoke portfolio through natural-language descriptions. Leveraging the Anthropic Claude API, PortfolioPilot dynamically synthesizes executable TypeScript algorithms that run in the frontend with security validation. The system integrates real-time backtesting with historical market data, classical optimization algorithms (Markowitz, LSTM, ARIMA), and interactive performance visualizations.

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Potent But Stealthy: Rethink Profile Pollution Against Sequential Recommendation Via Bi-Level Constrained Reinforcement Paradigm, Jiajie Su, Zihan Nan, Yunshan Ma, Xiaobo Xia, Xiaohua Feng, Weiming Liu, Xiang Chen, Xiaolin Zheng, Chaochao Chen Jan 2026

Potent But Stealthy: Rethink Profile Pollution Against Sequential Recommendation Via Bi-Level Constrained Reinforcement Paradigm, Jiajie Su, Zihan Nan, Yunshan Ma, Xiaobo Xia, Xiaohua Feng, Weiming Liu, Xiang Chen, Xiaolin Zheng, Chaochao Chen

Research Collection School Of Computing and Information Systems

Sequential Recommenders, which exploit dynamic user intents through interaction sequences, are vulnerable to adversarial attacks. While existing attacks primarily rely on data poisoning, they require large-scale user access or fake profiles, thus lacking practicality. In this paper, we focus on the Profile Pollution Attack that subtly contaminates partial user interactions to induce targeted mispredictions. Previous PPA methods suffer from two limitations, i.e., i) overreliance on sequence horizon impact restricts fine-grained perturbations on item transitions, and ii) holistic modifications cause detectable distribution shifts. To address these challenges, we propose a constrained reinforcement driven attack CREAT that synergizes a bi-level optimization framework …

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Choice-Based Crowdshipping For Next-Day Delivery Services: A Dynamic Task Display Problem, Alp Arslan, Firat Kilci, Shih-Fen Cheng, Archan Misra Jan 2026

Choice-Based Crowdshipping For Next-Day Delivery Services: A Dynamic Task Display Problem, Alp Arslan, Firat Kilci, Shih-Fen Cheng, Archan Misra

Research Collection School Of Computing and Information Systems

This paper studies integrating the crowd workforce into next-day home delivery services. In this setting, both crowd drivers and contract drivers collaborate in making deliveries. Crowd drivers have limited capacity and can choose not to deliver if the presented tasks do not align with their preferences. The central question addressed is: How can the platform minimize the total task fulfilment cost, which includes payouts to crowd drivers and additional payouts to contract drivers for delivering the unselected tasks by customizing task displays to crowd drivers? To tackle this problem, we formulate it as a finite-horizon Stochastic Decision Problem, capturing crowd …

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Can We Read Ai’S Mind? A Quest For Transparency, Santhosh Kumar Ravindran, Estera Kot, Fiona Fui-Hoon Nah Jan 2026

Can We Read Ai’S Mind? A Quest For Transparency, Santhosh Kumar Ravindran, Estera Kot, Fiona Fui-Hoon Nah

Research Collection School Of Computing and Information Systems

Although Artificial Intelligence (AI) systems are playing an increasing role in critical domains such as healthcare, finance, and autonomous systems, their decision-making processes remain largely opaque. This paper examines the challenges of AI transparency, addressing the “black box” problem using Explainable AI (XAI) techniques such as SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME). It also examines the ethical, regulatory, and societal implications of AI opacity and proposes a Comprehensive AI Observability (CAO) Framework that integrates deep explainability, provenance tracking, and real-time monitoring to enhance AI accountability. By bridging technical solutions with governance structures, this research emphasizes the …

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Ai Systems For Physicians: A Review From Socio-Technical And Human-Computer Interaction Perspectives, Wu Jiaqi Young, Fiona Fui-Hoon Nah Jan 2026

Ai Systems For Physicians: A Review From Socio-Technical And Human-Computer Interaction Perspectives, Wu Jiaqi Young, Fiona Fui-Hoon Nah

Research Collection School Of Computing and Information Systems

The adoption of artificial intelligence (AI) in healthcare is accelerating, yet successful implementations of physician-facing AI systems remain limited and uneven. This paper presents a literature review of 40 peer-reviewed studies published between November 2022 and November 2024, spanning clinical, technical, and human-computer interaction (HCI) domains. Anchored in a socio-technical perspective, the review examines our existing understanding of how technical design, user expertise, and organizational factors shape the effectiveness of AI systems in real-world clinical settings. Our analysis identifies two meta-themes: (1) context as a dynamic, multi-level influence that actively reshapes AI system behavior, and (2) trust as an emergent …

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Thinkmatter: Panoramic-Aware Instructional Semantics For Monocular Vision-And-Language Navigation, Guangzhao Dai, Shuo Wang, Hao Zhao, Bin Zhu, Qianru Sun, Xiangbo Shu Jan 2026

Thinkmatter: Panoramic-Aware Instructional Semantics For Monocular Vision-And-Language Navigation, Guangzhao Dai, Shuo Wang, Hao Zhao, Bin Zhu, Qianru Sun, Xiangbo Shu

Research Collection School Of Computing and Information Systems

Vision-and-Language Navigation in continuous environments (VLN-CE) requires an embodied robot to navigate the target destination following the natural language instruction. Most existing methods use panoramic RGB-D cameras for 360° observation of environments. However, these methods struggle in real-world applications because of the higher cost of panoramic RGB-D cameras. This paper studies a low-cost and practical VLN-CE setting, e.g., using monocular cameras of limited field of view, which means “Look Less” for visual observations and environment semantics. In this paper, we propose a ThinkMatter framework for monocular VLN-CE, where we motivate monocular robots to “Think More” by 1) generating novel views …

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Memoryart: Enhancing Llms Via Multi-Memory Models With Adaptive Resonance Theory For Healthcare Agents, Renke Dai, Hebin Hu, Jiahui Zhang, Yilin Kang, Ah-Hwee Tan Jan 2026

Memoryart: Enhancing Llms Via Multi-Memory Models With Adaptive Resonance Theory For Healthcare Agents, Renke Dai, Hebin Hu, Jiahui Zhang, Yilin Kang, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Though promising in healthcare consultation applications, large language models (LLMs) face critical limitations in retaining and utilizing long-term memory across multiturn interactions. In particular, existing memory enhancing paradigms are constrained by limited context windows and embedding-based retrieval, often failing to maintain task relevance and still suffering from memory prototype collapse in multi-turn healthcare consultation. To address these challenges, we propose a cognitively-inspired memory framework named MemoryART, which is grounded in Adaptive Resonance Theory (ART)—a cognitive and learning theory of how humans and animals adapt to dynamic environments. MemoryART employs three memory modules—working memory, episodic memory, and semantic memory to support …

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Benchmarking Gaslighting Negation Attacks Against Reasoning Models, Bin Zhu, Hailong Yin, Jingjing Chen, Yu Gang Jiang Jan 2026

Benchmarking Gaslighting Negation Attacks Against Reasoning Models, Bin Zhu, Hailong Yin, Jingjing Chen, Yu Gang Jiang

Research Collection School Of Computing and Information Systems

Recent advances in reasoning-centric models promise improved robustness through mechanisms such as chain-of-thought prompting and test-time scaling. However, their ability to withstand gaslighting negation attacks—adversarial prompts that confidently deny correct answers—remains underexplored. In this paper, we conduct a systematic evaluation of three state-of-the-art reasoning models, i.e., OpenAI’s o4-mini, Claude-3.7-Sonnet and Gemini-2.5-Flash, across three multimodal benchmarks: MMMU, MathVista, and CharXiv. Our evaluation reveals significant accuracy drops (25–29% on average) following gaslighting negation attacks, indicating that even top-tier reasoning models struggle to preserve correct answers under manipulative user feedback. Built upon the insights of the evaluation and to further probe this vulnerability, …

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Integrating Symbolic And Waveform Music Into Large Language Models, Teng Tu, Xiaohao Liu, Yunshan Ma, Ji Qi, Tat-Seng Chua Jan 2026

Integrating Symbolic And Waveform Music Into Large Language Models, Teng Tu, Xiaohao Liu, Yunshan Ma, Ji Qi, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Music, as a unique and integral element of human life, is characterized by its complex structures, intricate details, and the fusion of multimodal information. Recent study advance music understanding by leveraging knowledge and reasoning capabilities derived from Large Language Models (LLMs). However, they often lack compatibility and fail to fully utilize the complementary strengths of diverse representations (e.g., ABC, MIDI, Waveform). To address these limitations, we propose a unified music-language model framework, named UniMuLM, transitioning from single-representation approaches to the integration of multiple music representations for LLM. Unifying different music representation formats poses challenges such as patch integrity and boundary …

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Learning-Based Graph Shrinking For Quantum Optimization Of Constrained Combinatorial Problems, Monit Sharma, Hoong Chuin Lau Jan 2026

Learning-Based Graph Shrinking For Quantum Optimization Of Constrained Combinatorial Problems, Monit Sharma, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Graph shrinking has recently emerged as a powerful preprocessing technique for hybrid classical–quantum optimization, enabling variable and constraint reduction before quantum solving. Conventional approaches rely on Semi-Definite Programming (SDP) relaxations to compute vertex correlations, but these methods suffer from high computational overhead, instance-specific tuning, and limited generalizability. In this work, we replace the handcrafted SDP correlation stage with a reinforcement learning (RL) based correlation estimator, trained to predict merge quality directly from graph structure. We reformulate the graph shrinking process as a Markov Decision Process (MDP), design a Graph Neural Network (GNN) policy to guide vertex merging, and integrate the …

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Security-Enhanced Decentralized Conditional Privacy-Preserving Authentication In Vanets, Suqin Luo, Xinghua Li, Yinbin Miao, Xuelin Cao, Zhan Zhang, Yunwei Wang, Deng R.H. Jan 2026

Security-Enhanced Decentralized Conditional Privacy-Preserving Authentication In Vanets, Suqin Luo, Xinghua Li, Yinbin Miao, Xuelin Cao, Zhan Zhang, Yunwei Wang, Deng R.H.

Research Collection School Of Computing and Information Systems

To ensure the legitimacy of communicators while ad dressing the privacy concerns of vehicles in vehicular ad-hoc networks (VANETs), conditional privacy-preserving authentication (CPPA) schemes have been proposed. Given that existing schemes suffer from single point of failure due to centralized authorities, several distributed CPPA schemes have been proposed. However, these schemes all ignore the tight cementation between system secret keys and the authority, which could be a serious threat to system security, that the compromised authority may leak the system secret key. To address these issues, we propose a security enhanced decentralized conditional privacy-preserving authentication (DCPPA) scheme. DCPPA first introduces …

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Editorial: Special Section On Challenges And Opportunities In Retrieval-Augmented Generation For Llms: Techniques, Trends, And Applications, Philip S. Yu, Haofen Wang, Feida Zhu Jan 2026

Editorial: Special Section On Challenges And Opportunities In Retrieval-Augmented Generation For Llms: Techniques, Trends, And Applications, Philip S. Yu, Haofen Wang, Feida Zhu

Research Collection School Of Computing and Information Systems

Retrieval-Augmented Generation (RAG) represents a transformative advancement for Large Language Models (LLMs) by integrating external knowledge to substantially improve accuracy and mitigate hallucinations. As a pivotal technology in the contemporary generative Artificial Intelligence (AI) landscape, RAG addresses fundamental challenges in knowledge-intensive tasks. This special issue serves as a dedicated platform to showcase these cutting-edge advancements. It features six rigorously peer-reviewed papers that present state-of-the-art research and applications in the rapidly evolving field of RAG.

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Qualitative Study For Llm-Assisted Design Study Process: Strategies, Challenges, And Roles, Shaolun Ruan, Rui Sheng, Xiaolin Wen, Jiachen Wang, Tianyi Zhang, Yong Wang, Tim Dwyer, Jiannan Li Jan 2026

Qualitative Study For Llm-Assisted Design Study Process: Strategies, Challenges, And Roles, Shaolun Ruan, Rui Sheng, Xiaolin Wen, Jiachen Wang, Tianyi Zhang, Yong Wang, Tim Dwyer, Jiannan Li

Research Collection School Of Computing and Information Systems

Design studies aim to develop visualization solutions for real-world problems across various application domains. Recently, the emergence of large language models (LLMs) has introduced new opportunities to enhance the design study process, providing capabilities such as creative problem-solving, data handling, and insightful analysis. However, despite their growing popularity, there remains a lack of systematic understanding of how LLMs can effectively assist researchers in visualization-specific design studies. In this paper, we conducted a rnulti-stage qualitative study to fill this gap, which involved 30 design study researchers from diverse backgrounds and expertise levels. Through in-depth interviews and carefully-designed questionnaires, we investigated strategies …

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Llm-As-A-Judge For Software Engineering: Literature Review, Vision, And The Road Ahead, Junda He, Jieke Shi, Terry Yue Zhuo, Christoph Treude, Jiamou Sun, Zhenchang Xing, Xiaoning Du, David Lo Jan 2026

Llm-As-A-Judge For Software Engineering: Literature Review, Vision, And The Road Ahead, Junda He, Jieke Shi, Terry Yue Zhuo, Christoph Treude, Jiamou Sun, Zhenchang Xing, Xiaoning Du, David Lo

Research Collection School Of Computing and Information Systems

The rapid integration of Large Language Models (LLMs) into software engineering (SE) has revolutionized tasks from code generation to program repair, producing a massive volume of software artifacts. This surge in automated creation has exposed a critical bottleneck: the lack of scalable and reliable methods to evaluate the quality of these outputs. Human evaluation, while effective, is very costly and time-consuming. Traditional automated metrics like BLEU rely on high-quality references and struggle to capture nuanced aspects of software quality, such as readability and usefulness. In response, the LLM-as-a-Judge paradigm, which employs LLMs for automated evaluation, has emerged. This approach leverages …

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Dystop: Dynamic Staleness Control And Topology Construction For Asynchronous Decentralized Federated Learning, Yizhou Shi, Qianpiao Ma, Yan Xu, Junlong Zhou, Ming Hu, Yunming Liao Jan 2026

Dystop: Dynamic Staleness Control And Topology Construction For Asynchronous Decentralized Federated Learning, Yizhou Shi, Qianpiao Ma, Yan Xu, Junlong Zhou, Ming Hu, Yunming Liao

Research Collection School Of Computing and Information Systems

Federated Learning (FL) has emerged as a potential distributed learning paradigm that enables model training on edge devices (i.e., workers) while preserving data privacy. However, its reliance on a centralized server leads to limited scalability. Decentralized federated learning (DFL) eliminates the dependency on a centralized server by enabling peer-to-peer model exchange. Existing DFL mechanisms mainly employ synchronous communication, which may result in training inefficiencies under heterogeneous and dynamic edge environments. Although a few recent asynchronous DFL (ADFL) mechanisms have been proposed to address these issues, they typically yield stale model aggregation and frequent model transmission, leading to degraded training performance …

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Look, Compare And Draw: Differential Query Transformer For Automatic Oil Painting, Lingyu Liu, Yaxiong Wang, Li Zhu, Lizi Liao, Zhedong Zheng Jan 2026

Look, Compare And Draw: Differential Query Transformer For Automatic Oil Painting, Lingyu Liu, Yaxiong Wang, Li Zhu, Lizi Liao, Zhedong Zheng

Research Collection School Of Computing and Information Systems

This work introduces a new approach to automatic oil painting that emphasizes the creation of dynamic and expressive brushstrokes. A pivotal challenge lies in mitigating the duplicate and common-place strokes, which often lead to less aesthetic outcomes. Inspired by the human painting process, i.e., observing, comparing, and drawing, we incorporate differential image analysis into a neural oil painting model, allowing the model to effectively concentrate on the incremental impact of successive brushstrokes. To operationalize this concept, we propose the Differential Query Transformer (DQ-Transformer), a new architecture that leverages differentially derived image representations enriched with positional encoding to guide the stroke …

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Nondeterministic Polynomial-Time Problem Challenge: An Ever-Scaling Reasoning Benchmark For Llms, Chang Yang, Ruiyu Wang, Junzhe Jiang, Qi Jiang, Qinggang Zhang, Yanchen Deng, Shuxin Li, Shuyue Hu, Bo Li, Florian T. Pokorny, Xiao Huang, Xinrun Wang Jan 2026

Nondeterministic Polynomial-Time Problem Challenge: An Ever-Scaling Reasoning Benchmark For Llms, Chang Yang, Ruiyu Wang, Junzhe Jiang, Qi Jiang, Qinggang Zhang, Yanchen Deng, Shuxin Li, Shuyue Hu, Bo Li, Florian T. Pokorny, Xiao Huang, Xinrun Wang

Research Collection School Of Computing and Information Systems

Reasoning is the fundamental capability of large language models (LLMs). Due to the rapid progress of LLMs, there are two main issues of current benchmarks: i) these benchmarks can be crushed in a short time (less than 1 year), and ii) these benchmarks may be easily hacked. To handle these issues, we propose the ever-scalingness for building the benchmarks which are scaling over complexity against crushing, instance against hacking and exploitation, oversight for easy verification, and coverage for real-world relevance. This paper presents Nondeterministic Polynomial-time Problem Challenge (NPPC), an ever-scaling reasoning benchmark for LLMs. Specifically, the NPPC has three main …

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A Case Study Of Gender And Online Team Communication In Software Engineering Education, Rita Garcia, Christoph Treude Jan 2026

A Case Study Of Gender And Online Team Communication In Software Engineering Education, Rita Garcia, Christoph Treude

Research Collection School Of Computing and Information Systems

Collaboration is crucial in Software Engineering (SE), yet factors like gender bias can shape team dynamics and behaviours. This descriptive case study examines an eight-week project involving 39 SE students across eight teams contributing to GitHub projects. Focusing on gender, we used a mixed-methods approach to analyse Slack communications, identifying gender differences in how students respond to initiated communications and comparing how students’ communications influenced other aspects of students’ performance, including learning gains. We found higher help-seeking and leadership behaviours in the all-woman team involved in this case study, while men responded more slowly. Although communication did not directly affect …

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Generalization Bounds For Semi‑Supervised Matrix Completion With Distributional Side Information, Antoine Ledent, Mun Chong Soo, Minh Hieu Nong Jan 2026

Generalization Bounds For Semi‑Supervised Matrix Completion With Distributional Side Information, Antoine Ledent, Mun Chong Soo, Minh Hieu Nong

Research Collection School Of Computing and Information Systems

We study a matrix completion problem where both the ground truth R matrix and the unknown sampling distribution P over observed entries are low-rank matrices, and share a common subspace. We assume that a large amount M of unlabeled data drawn from the sampling distribution P is available, together with a small amount N of labeled data drawn from the same distribution and noisy estimates of the corresponding ground truth entries. This setting is inspired by recommender systems scenarios where the unlabeled data corresponds to ‘implicit feedback’ (consisting in interactions such as purchase, click, etc. ) and the labeled data …

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Hybrid Learning And Optimization Methods For Solving Capacitated Vehicle Routing Problem, Monit Sharma, Hoong Chuin Lau Jan 2026

Hybrid Learning And Optimization Methods For Solving Capacitated Vehicle Routing Problem, Monit Sharma, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

We propose a hybrid quantum–classical framework for the Capacitated Vehicle Routing Problem (CVRP) that integrates the Augmented Lagrangian Method (ALM) with deep reinforcement learning (RL). Directly solving CVRP via Variational Quantum Eigensolver (VQE) requires a slack-based QUBO formulation, where converting inequalities to equalities greatly increases the qubit count. To circumvent this, we employ an ALM-based reformulation that enforces constraints through Lagrange terms instead of slack variables, drastically reducing quantum resource demands. An RL agent, trained with Soft Actor–Critic, adaptively tunes the Lagrange penalties to improve convergence and feasibility. Experiments show that RL-Q-ALM outperforms static-penalty and plain VQE baselines in both …

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Towards Secure Program Partitioning For Smart Contracts With Llm’S In-Context Learning, Ye Liu, Yuqing Niu, Chengyan Ma, Ruidong Han, Wei Ma, Yi Li, Debin Gao, David Lo Jan 2026

Towards Secure Program Partitioning For Smart Contracts With Llm’S In-Context Learning, Ye Liu, Yuqing Niu, Chengyan Ma, Ruidong Han, Wei Ma, Yi Li, Debin Gao, David Lo

Research Collection School Of Computing and Information Systems

Smart contracts are highly susceptible to manipulation attacks due to the leakage of sensitive information. Addressing manipulation vulnerabilities is particularly challenging because they stem from inherent data confidentiality issues rather than straightforward implementation bugs. To tackle this by preventing sensitive information leakage, we present PARTITIONGPT, the first LLM-driven approach that combines static analysis with the in-context learning capabilities of large language models (LLMs) to partition smart contracts into critical (privileged) and normal codebases, guided by a few annotated sensitive data variables. We evaluated PARTITIONGPT on 18 annotated smart contracts containing 99 sensitive functions. The results demonstrate that PARTITIONGPT successfully generates …

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Scaling Up Cooperative Multi-Agent Reinforcement Learning Through Hierarchical Heterogeneous Modular Architectures, Minghong Geng Jan 2026

Scaling Up Cooperative Multi-Agent Reinforcement Learning Through Hierarchical Heterogeneous Modular Architectures, Minghong Geng

Research Collection School Of Computing and Information Systems

Multi-agent reinforcement learning enables sophisticated collaborative behaviors in autonomous systems, yet fundamental scalability barriers persist: existing methods struggle to coordinate large agent populations and face challenges with extended decision-making horizons. This research develops hierarchical approaches to scale up multi-agent learning systems through two complementary directions: structural scaling for coordinating increasing numbers of agents and temporal scaling for extending decision-making horizons. This paper presents four integrated contributions: a taxonomic survey establishing hierarchical architectures as the theoretical foundation for scalable multi-agent learning systems, a benchmark for long-horizon multi-objective multi-agent reinforcement learning, a framework integrating self-organizing neural networks with multiple reinforcement learning agents …

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Realign: Text-To-Motion Generation Via Step-Aware Reward-Guided Alignment, Wanjiang Weng, Xiaofeng Tan, Junbo Wang, Guo-Sen Xie, Pan Zhou, Hongsong Wang Jan 2026

Realign: Text-To-Motion Generation Via Step-Aware Reward-Guided Alignment, Wanjiang Weng, Xiaofeng Tan, Junbo Wang, Guo-Sen Xie, Pan Zhou, Hongsong Wang

Research Collection School Of Computing and Information Systems

Text-to-motion generation, which synthesizes 3D human motions from text inputs, holds immense potential for applications in gaming, film, and robotics. Recently, diffusion-based methods have been shown to generate more diversity and realistic motion. However, there exists a misalignment between text and motion distributions in diffusion models, which leads to semantically inconsistent or low-quality motions. To address this limitation, we propose Reward-guided sampling Alignment (ReAlign), comprising a step-aware reward model to assess alignment quality during the denoising sampling and a reward-guided strategy that directs the diffusion process toward an optimally aligned distribution. This reward model integrates step-aware tokens and combines a …

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Artificial Intelligence, Fundamental Motives, And Evolutionary Mismatch, Amy J. Lim, Jose. C. Yong, Edison Sora Tan Jan 2026

Artificial Intelligence, Fundamental Motives, And Evolutionary Mismatch, Amy J. Lim, Jose. C. Yong, Edison Sora Tan

Research Collection School of Social Sciences

In recent years, the intersection of artificial intelligence (AI) and psychology has garnered unprecedented attention, particularly following the advent of generative AI tools in 2022. These tools, capable of producing human-like text, images, and even deepening our understanding of cognitive processes, have not only captured the public imagination but also sparked new concerns and debates within the psychological community. While AI has been a subject of research for decades, the emergence of its generative capabilities has truly thrust AI into the spotlight. This article explores how these advancements are reshaping our understanding of human cognition and behavior, as well as …

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Evaluating Defi Vulnerabilities: The Role Of Bug Bounty Programs On Defi Software Supply Chain, Ping Fan Ke, Yi Meng Lau, Lingxiao Jiang Dec 2025

Evaluating Defi Vulnerabilities: The Role Of Bug Bounty Programs On Defi Software Supply Chain, Ping Fan Ke, Yi Meng Lau, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Decentralized finance (DeFi), powered by blockchain technology, enables peer-to-peer financial transactions without intermediaries. Despite rapid adoption, DeFi attracts malicious actors exploiting vulnerabilities. To mitigate risks, we propose a framework assessing entry points in the DeFi software supply chain: smart contracts, oracles/third-party feeds, user interfaces, off-chain storage, and crypto wallets. Applying this framework, we evaluate whether industry solutions—particularly bug bounty programs—adequately address these gaps. Our preliminary analysis indicates that most programs cover smart contract vulnerabilities (85.7%), followed by user interface issues (21.3%) and crypto wallet loopholes (11.9%). However, third-party risks, such as oracle feeds, are frequently deemed out of scope. This …

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