arXiv subjects

Jiaming Qian

Publications and source records attributed to Jiaming Qian.

5 recordsLinked to original sources

arXiv2026

COMPASS: Cognitive MCTS-Guided Process Alignment for Safe Search Agents

LLM-powered search agents enable multi-step reasoning and tool use. However, these capabilities introduce retrieval-induced safety degradation, as harmful intents may decompose into seemingly innocuous sub-queries that lead to unsafe outcomes. Existing alignment methods struggle to capture sparse safety signals and fail to supervise diverse violations across multi-step interactions. We propose COMPASS, a Cognitive MCTS-Guided Process Alignment framework designed to achieve robust safety alignment throughout the agent workflow while preserving general utility. COMPASS integrates cognitive tree exploration (CTE) to efficiently synthesize stealthy attack trajectories, and introspective step-wise alignment (ISA) to isolate risky intermediate actions for fine-grained process supervision. Empirical results show that COMPASS achieves a favorable safety-utility trade-off while requiring substantially less training data.

cs.AI↗

arXiv2026

Simultaneous super-resolution and optical sectioning with four-beam interference structured illumination microscopy (4I-SIM)

Structured illumination microscopy (SIM) has emerged as a widely adopted super-resolution fluorescence imaging modality, offering high speed, low phototoxicity, large field-of-view, and compatibility with conventional probes. However, when applied to thick or scattering specimens, conventional two-dimensional SIM (2D-SIM) suffers from the missing cone problem in its optical transfer function, resulting in prominent out-of-focus background and severe reconstruction artifacts that compromise image fidelity. Here, we present four-beam interference structured illumination microscopy (4I-SIM), which introduces additional interference orders to expand lateral frequency support and compensate the axial missing cone simultaneously. This strategy achieves artifact-free super-resolution with intrinsic optical sectioning, effectively overcoming the fundamental limitation of 2D-SIM without additional acquisition overhead. Experimental validation across diverse thick fixed and live specimens demonstrates that 4I-SIM delivers nearly twofold lateral resolution enhancement and substantially improved sectioning compared with its 2D counterpart, achieving lateral and axial resolutions of 103 nm and 336 nm, respectively. In particular, 4I-SIM reveals mitochondrial remodeling and apoptosis under high-glucose stress with millisecond temporal resolution -- features that remain obscured with conventional SIM. With minimal hardware modification, low phototoxicity, and open-source reconstruction tools, 4I-SIM establishes a practical and reproducible platform for simultaneous super-resolution and optical sectioning imaging in complex biological environments.

eess.IV↗

arXiv2026

PADER: Paillier-based Secure Decentralized Social Recommendation

The prevalence of recommendation systems also brings privacy concerns to both the users and the sellers, as centralized platforms collect as much data as possible from them. To keep the data private, we propose PADER: a Paillier-based secure decentralized social recommendation system. In this system, the users and the sellers are nodes in a decentralized network. The training and inference of the recommendation model are carried out securely in a decentralized manner, without the involvement of a centralized platform. To this end, we apply the Paillier cryptosystem to the SoReg (Social Regularization) model, which exploits both user's ratings and social relations. We view the SoReg model as a two-party secure polynomial evaluation problem and observe that the simple bipartite computation may result in poor efficiency. To improve efficiency, we design secure addition and multiplication protocols to support secure computation on any arithmetic circuit, along with an optimal data packing scheme that is suitable for the polynomial computations of real values. Experiment results show that our method only takes about one second to iterate through one user with hundreds of ratings, and training with ~500K ratings for one epoch only takes <3 hours, which shows that the method is practical in real applications. The code is available at https://github.com/GarminQ/PADER.

cs.CR↗

arXiv2025

FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models

Federated prompt learning (FPL) for vision-language models is a powerful approach to collaboratively adapt models across distributed clients while preserving data privacy. However, existing FPL approaches suffer from a trade-off between performance and robustness, particularly in out-of-distribution (OOD) shifts, limiting their reliability in real-world scenarios. The inherent in-distribution (ID) data heterogeneity among different clients makes it more challenging to maintain this trade-off. To fill this gap, we introduce a Federated OOD-aware Context Optimization (FOCoOp) framework, which captures diverse distributions among clients using ID global prompts, local prompts, and OOD prompts. Specifically, FOCoOp leverages three sets of prompts to create both class-level and distribution-level separations, which adapt to OOD shifts through bi-level distributionally robust optimization. Additionally, FOCoOp improves the discrimination consistency among clients, i.e., calibrating global prompts, seemingly OOD prompts, and OOD prompts by semi-unbalanced optimal transport. The extensive experiments on real-world datasets demonstrate that FOCoOp effectively captures decentralized heterogeneous distributions and enhances robustness of different OOD shifts. The project is available at GitHub.

cs.CV↗

arXiv2020

Deep-learning-enabled geometric constraints and phase unwrapping for single-shot absolute 3D shape measurement

Fringe projection profilometry (FPP) is one of the most popular three-dimensional (3D) shape measurement techniques, and has becoming more prevalently adopted in intelligent manufacturing, defect detection and some other important applications. In FPP, how to efficiently recover the absolute phase has always been a great challenge. The stereo phase unwrapping (SPU) technologies based on geometric constraints can eliminate phase ambiguity without projecting any additional fringe patterns, which maximizes the efficiency of the retrieval of absolute phase. Inspired by the recent success of deep learning technologies for phase analysis, we demonstrate that deep learning can be an effective tool that organically unifies the phase retrieval, geometric constraints, and phase unwrapping steps into a comprehensive framework. Driven by extensive training dataset, the neutral network can gradually "learn" how to transfer one high-frequency fringe pattern into the "physically meaningful", and "most likely" absolute phase, instead of "step by step" as in convention approaches. Based on the properly trained framework, high-quality phase retrieval and robust phase ambiguity removal can be achieved based on only single-frame projection. Experimental results demonstrate that compared with traditional SPU, our method can more efficiently and stably unwrap the phase of dense fringe images in a larger measurement volume with fewer camera views. Limitations about the proposed approach are also discussed. We believe the proposed approach represents an important step forward in high-speed, high-accuracy, motion-artifacts-free absolute 3D shape measurement for complicated object from a single fringe pattern.

eess.IV↗