arXiv subjects

Boyi Zhang

Publications and source records attributed to Boyi Zhang.

8 recordsLinked to original sources

arXiv2026

FaCTz: Fast Critical-Point and Topology-Aware GPU Compression for Scientific Vector Fields

Error-bounded lossy compression is essential for storing and transferring the vector-field data produced by large-scale scientific simulations. Although it enforces a user-specified error bound to limit numerical distortion, it does not preserve the field's topology: small admissible perturbations can create or eliminate critical points on which downstream feature analysis depends. Existing GPU compressors achieve high throughput but are topology-agnostic, whereas the only compressor with provable critical-point preservation (cpSZ) runs on the CPU at throughput far below the data-generation rates of modern GPU-based systems. We observe that, although preserving critical points is inherently a coupled and sequential constraint, it can be reformulated into independent parallel tasks, either on a per-block basis or, speculatively, on a per-point basis. We present FaCTz, the first GPU-based error-bounded lossy compressor that guarantees critical-point preservation. FaCTz provides a block-wise mode optimized for throughput and a speculative per-point mode optimized for compression ratio. Across three vector-field datasets, FaCTz preserves every critical point while achieving throughput of up to 60 GB/s, approximately two orders of magnitude (up to approximately 640x) faster than the multithreaded CPU implementation of cpSZ. Its speculative mode further improves the compression ratio by approximately a factor of two over the throughput-oriented mode.

cs.DC↗

arXiv2026

GIFT: LLM-Guided State-Reward Interface for Financial Reinforcement Learning

Financial portfolio trading is naturally formulated as a reinforcement learning problem, where an agent sequentially rebalances assets under changing market conditions to balance return, risk, and transaction costs. Yet in non-stationary markets, raw OHLCV states and short-horizon return rewards often provide an under-specified learning interface, motivating large language models as a way to inject financial knowledge into state and reward design while constraining open-ended generation. To this end, we propose GIFT, an LLM-guided framework for state-reward interface design in PPO-based financial reinforcement learning. Rather than using the LLM to make trading decisions, GIFT uses Factor-guided State Enhancement to generate state features from financial-factor primitives, Risk-rule-guided Reward Shaping to generate auxiliary rewards from portfolio-risk rules, and Diagnostic-guided Refinement to revise candidate interfaces using PPO rollout diagnostics. After refinement, GIFT fixes the selected state-reward interface before evaluation, with no further LLM queries or interface updates at test time. Comprehensive rolling-window experiments across diverse market regimes and portfolio scenarios demonstrate that GIFT improves learning-signal quality and out-of-sample risk-adjusted portfolio performance over baselines. Code and data are available at: https://github.com/KAG778/GIFT .

cs.AI↗

arXiv2026

PhysicsMind: Sim and Real Mechanics Benchmarking for Physical Reasoning and Prediction in Foundational VLMs and World Models

Modern foundational Multimodal Large Language Models (MLLMs) and video world models have advanced significantly in mathematical, common-sense, and visual reasoning, but their grasp of the underlying physics remains underexplored. Existing benchmarks attempting to measure this matter rely on synthetic, Visual Question Answer templates or focus on perceptual video quality that is tangential to measuring how well the video abides by physical laws. To address this fragmentation, we introduce PhysicsMind, a unified benchmark with both real and simulation environments that evaluates law-consistent reasoning and generation over three canonical principles: Center of Mass, Lever Equilibrium, and Newton's First Law. PhysicsMind comprises two main tasks: i) VQA tasks, testing whether models can reason and determine physical quantities and values from images or short videos, and ii) Video Generation(VG) tasks, evaluating if predicted motion trajectories obey the same center-of-mass, torque, and inertial constraints as the ground truth. A broad range of recent models and video generation models is evaluated on PhysicsMind and found to rely on appearance heuristics while often violating basic mechanics. These gaps indicate that current scaling and training are still insufficient for robust physical understanding, underscoring PhysicsMind as a focused testbed for physics-aware multimodal models. Our data will be released upon acceptance.

cs.CV↗

arXiv2025

TreeRare: Syntax Tree-Guided Retrieval and Reasoning for Knowledge-Intensive Question Answering

In real practice, questions are typically complex and knowledge-intensive, requiring Large Language Models (LLMs) to recognize the multifaceted nature of the question and reason across multiple information sources. Iterative and adaptive retrieval, where LLMs decide when and what to retrieve based on their reasoning, has been shown to be a promising approach to resolve complex, knowledge-intensive questions. However, the performance of such retrieval frameworks is limited by the accumulation of reasoning errors and misaligned retrieval results. To overcome these limitations, we propose TreeRare (Syntax Tree-Guided Retrieval and Reasoning), a framework that utilizes syntax trees to guide information retrieval and reasoning for question answering. Following the principle of compositionality, TreeRare traverses the syntax tree in a bottom-up fashion, and in each node, it generates subcomponent-based queries and retrieves relevant passages to resolve localized uncertainty. A subcomponent question answering module then synthesizes these passages into concise, context-aware evidence. Finally, TreeRare aggregates the evidence across the tree to form a final answer. Experiments across five question answering datasets involving ambiguous or multi-hop reasoning demonstrate that TreeRare achieves substantial improvements over existing state-of-the-art methods.

cs.CL↗

arXiv2025

Pulse Compression by an Optical Push Broom On a Chip

In this study, we report a first experimental demonstration of pulse compression by a gradual refractive index front moving in a periodically modulated silicon waveguide, the so-called optical push broom effect. Optical push broom captures and confines the input signal pulse in a faster propagating refractive index front, driven by a pump pulse. This is a spatio-temporal analogue of light trapping in a tapered plasmonic waveguide where light is continuously changing its wavevector approaching zero group velocity and, thus, stopped without reflection. Here the signal is accelerated by the front until the signal velocity matches the front velocity, thus stopping the light in respect to the front. We employ the slowly varying envelope approximation to model this phenomenon. Notably, we well reproduced the experimental frequency shift at the output corresponding to the temporal delay at the input.

physics.optics↗

arXiv2024

Invisible Finger: Practical Electromagnetic Interference Attack on Touchscreen-based Electronic Devices

Touchscreen-based electronic devices such as smart phones and smart tablets are widely used in our daily life. While the security of electronic devices have been heavily investigated recently, the resilience of touchscreens against various attacks has yet to be thoroughly investigated. In this paper, for the first time, we show that touchscreen-based electronic devices are vulnerable to intentional electromagnetic interference (IEMI) attacks in a systematic way and how to conduct this attack in a practical way. Our contribution lies in not just demonstrating the attack, but also analyzing and quantifying the underlying mechanism allowing the novel IEMI attack on touchscreens in detail. We show how to calculate both the minimum amount of electric field and signal frequency required to induce touchscreen ghost touches. We further analyze our IEMI attack on real touchscreens with different magnitudes, frequencies, duration, and multitouch patterns. The mechanism of controlling the touchscreen-enabled electronic devices with IEMI signals is also elaborated. We design and evaluate an out-of-sight touchscreen locator and touch injection feedback mechanism to assist a practical IEMI attack. Our attack works directly on the touchscreen circuit regardless of the touchscreen scanning mechanism or operating system. Our attack can inject short-tap, long-press, and omni-directional gestures on touchscreens from a distance larger than the average thickness of common tabletops. Compared with the state-of-the-art touchscreen attack, ours can accurately inject different types of touch events without the need for sensing signal synchronization, which makes our attack more robust and practical. In addition, rather than showing a simple proof-of-concept attack, we present and demonstrate the first ready-to-use IEMI based touchscreen attack vector with end-to-end attack scenarios.

cs.CR↗

arXiv2023

Mastering Pair Trading with Risk-Aware Recurrent Reinforcement Learning

Although pair trading is the simplest hedging strategy for an investor to eliminate market risk, it is still a great challenge for reinforcement learning (RL) methods to perform pair trading as human expertise. It requires RL methods to make thousands of correct actions that nevertheless have no obvious relations to the overall trading profit, and to reason over infinite states of the time-varying market most of which have never appeared in history. However, existing RL methods ignore the temporal connections between asset price movements and the risk of the performed trading. These lead to frequent tradings with high transaction costs and potential losses, which barely reach the human expertise level of trading. Therefore, we introduce CREDIT, a risk-aware agent capable of learning to exploit long-term trading opportunities in pair trading similar to a human expert. CREDIT is the first to apply bidirectional GRU along with the temporal attention mechanism to fully consider the temporal correlations embedded in the states, which allows CREDIT to capture long-term patterns of the price movements of two assets to earn higher profit. We also design the risk-aware reward inspired by the economic theory, that models both the profit and risk of the tradings during the trading period. It helps our agent to master pair trading with a robust trading preference that avoids risky trading with possible high returns and losses. Experiments show that it outperforms existing reinforcement learning methods in pair trading and achieves a significant profit over five years of U.S. stock data.

q-fin.CP↗

arXiv2023

Select and Trade: Towards Unified Pair Trading with Hierarchical Reinforcement Learning

Pair trading is one of the most effective statistical arbitrage strategies which seeks a neutral profit by hedging a pair of selected assets. Existing methods generally decompose the task into two separate steps: pair selection and trading. However, the decoupling of two closely related subtasks can block information propagation and lead to limited overall performance. For pair selection, ignoring the trading performance results in the wrong assets being selected with irrelevant price movements, while the agent trained for trading can overfit to the selected assets without any historical information of other assets. To address it, in this paper, we propose a paradigm for automatic pair trading as a unified task rather than a two-step pipeline. We design a hierarchical reinforcement learning framework to jointly learn and optimize two subtasks. A high-level policy would select two assets from all possible combinations and a low-level policy would then perform a series of trading actions. Experimental results on real-world stock data demonstrate the effectiveness of our method on pair trading compared with both existing pair selection and trading methods.

q-fin.CP↗