arXiv subjects

Zhiyi Huang

Publications and source records attributed to Zhiyi Huang.

At least 19 recordsLinked to original sources

arXiv2026

Access Control as Verified Parse Constraints

Commercial security gateways repeatedly ship implementation bugs in the code path between the network and the policy decision: hand-written enforcement logic that diverges from the policy author's intent, and ad-hoc request parsers at the network boundary that introduce memory-safety flaws of their own. In both cases the bug is in the deployed enforcement code, not in the policy. Existing approaches either leave the enforcement runtime unverified or connect a formal model to a hand-written engine only by differential testing. Our contribution is a class result: a forward-only, backtrack-free EverParse validator is a verified recognizer for a bounded, finite-state class, and access-control decision functions with fixed-offset fields and bounded disjunction belong to it, so one machine-checked proof transfers to every policy in the class rather than being re-established per policy. Concretely, we encode a bounded policy language's decision function into a fixed-size byte buffer and verify the enforcement code once---covering all byte values---with an SMT solver, proving the validator accepts if and only if the decision function accepts, for every policy, request, and session. Editing rule content over a fixed endpoint set then needs no new proof; adding endpoints reruns the toolchain; extending the language needs new proofs. We establish faithful enforcement of a policy, not that a policy is itself secure. The verified gate is platform-independent, requiring only EverParse/Z3 and a C compiler, whose correctness we assume. We demonstrate a deployment on the seL4 microkernel, which ensures every request passes through the gate and that unverified components cannot corrupt the verified enforcement chain.

cs.CR↗

arXiv2026

Ad Insertion in LLM-Generated Responses

Sustainable monetization of large language models (LLMs) remains a critical open challenge. Traditional search advertising, which relies on static keywords, fails to capture the fleeting, context-dependent user intent---the specific information, goods, or services a user seeks---embedded in conversational flows. Beyond the standard goal of social welfare maximization effective LLM advertising requires contextual coherence (aligning ads semantically with transient user intent), computational efficiency (avoiding user-facing latency), and adherence to ethical and regulatory standards, including privacy preservation and explicit ad disclosure. Although recent solutions have explored bidding at the token and query levels, neither category holistically satisfies these constraints. We propose a framework that resolves these tensions through two decoupling strategies. First, we decouple ad insertion from response generation to facilitate pre-screening and explicit disclosure. Second, we decouple bidding from specific user queries by using ``genres'' (high-level semantic clusters) as a proxy. This allows advertisers to bid on stable categories rather than sensitive real-time responses, reducing computational burden and privacy risks. Applying the VCG auction mechanism to this genre-based framework provides approximate guarantees for dominant-strategy incentive compatibility (DSIC), individual rationality (IR), and social welfare. In synthetic experiments with $10^5$ advertisers and 100 candidate slots, VCG clears in approximately 1.25 seconds on a consumer-grade laptop. Finally, we introduce an ``LLM-as-a-Judge'' metric for estimating contextual coherence. Its predictions correlate with mean human ratings at Spearman's $ρ\approx 0.66$ and have a higher correlation with the leave-one-out group mean than 29 of 36 individual raters (80.6\%).

cs.GT↗

arXiv2026

Generalized Balls into Bins

Consider a set of bins and two-choice balls arriving by a Poisson process. We must allocate each incoming ball immediately to one of two incident bins. For a given function $f$ and every bin, we aim to bound the expectation of $f(L)$---where $L$ is the bin's final load---based on the arrival rate of balls incident to that bin. We call this problem Generalized Balls into Bins, capturing many problems as special cases including the original Balls into Bins by Azar et al. (1994) and Online Stochastic Matching by Feldman et al. (2009). We show that Greedy provides optimal amortized bounds for all convex and concave functions $f$. Further, we propose another algorithm that achieves non-trivial bounds without amortization. As an application, we design a competitive algorithm for a stochastic model of completion time minimization on unrelated machines.

cs.DS↗

arXiv2026

High-fidelity tabletop nanoscopy enabled by non-linear spectral preconditioning

Ptychography is a powerful lensless imaging technique that overcomes conventional numerical aperture limits to achieve diffraction-limited resolution. While routine at high-brilliance synchrotron facilities, its application to laboratory-scale sources is primarily limited by low photon flux. Under these conditions, the wide dynamic range of diffraction signals presents a critical bottleneck where detector bit-depth limitations hinder the simultaneous recording of low-frequency intensity and high-frequency details. Currently, most high-dynamic-range (HDR) imaging methods enforce strict radiometric linearity, assuming the fused intensity must be linearly proportional to the squared modulus of the wavefront to satisfy Poisson likelihood models. In this paper, we introduce a multi-scale non-linear fusion approach into the ptychographic pipeline, demonstrating that strict linearity is not a prerequisite for accurate reconstruction. This method mitigates the traditional trade-off between noise suppression and physical fidelity, enables robust imaging under strong dispersion, and significantly broadens the effective spectral bandwidth.

cs.GR↗

arXiv2026

Breaking optoelectronic SNR limitations via physics-consistent computational diffractive imaging

Ptychography is a powerful lensless imaging technique capable of approaching the diffraction limit, yet its performance is increasingly constrained by non-ideal detection hardware. In photon-limited measurements, weak high-frequency diffraction signals often overlap with spatially heterogeneous detector noise, whereas most reconstruction algorithms still treat the detector as an ideal measurement plane. Here, we introduce detector-informed measurement consistency into ptychographic reconstruction. By calibrating the pixelwise sensor response, the method construct a spatially resolved confidence map and embed it into the iterative amplitude constraint, allowing unreliable detector residuals to be down-weighted while preserving physically meaningful diffraction information. Experiments across transmission, reflection, and weak biological phase imaging show improved diffraction-data quality, an approximately twofold signal-to-noise ratio (SNR) enhancement, and reconstruction approaching the Rayleigh limit with a measured (k)-factor of about 0.65. Compared with previous advanced denoising methods, the proposed framework achieves a better balance between suppressing detector-induced background and preserving structural diffraction information. These results show that detector reliability can be used as an in-loop physical constraint to extend the performance of ptychographic imaging with imperfect sensors.

cs.GR↗

arXiv2026

Fractional Fully Online Matching

This paper studies fractional matching on general graphs in the fully online model of Huang et al. (JACM 2020), in which all vertices arrive online and remain available for only a limited time. The algorithm must make irrevocable fractional matching decisions while the relevant vertices are simultaneously available. We extend the classic Water-Filling algorithm, also known as Balance and originally introduced by Kalyanasundaram and Pruhs (TCS 2000), to the fully online setting. Using an online primal-dual framework, we prove that the generalized Water-Filling algorithm achieves a competitive ratio of $2-\sqrt{2}\approx 0.586$ in the fully online model, and that this analysis is tight. To surpass the $2-\sqrt{2}$ barrier, we incorporate the ideas of eager matching and history-based pricing into Water-Filling. We show that the resulting algorithm achieves an improved competitive ratio of $0.599$, thereby establishing that Water-Filling is not optimal in the fully online setting. On the hardness side, we further improve the known upper bound for fractional fully online matching, reducing the previous best bound of $0.6297$ due to Eckl et al. (ORL 2021) to $0.6132$.

cs.DS↗

arXiv2026

Pricing Pandora's Boxes: Revenue Maximization in Sequential Information Acquisition

We study a mechanism design problem in which a seller controls access to information about a set of stochastic alternatives, and a buyer sequentially acquires information in order to choose a single alternative with high value. The value distributions of the alternatives are known to both parties. The seller posts non adaptive prices for revealing each alternative's realized value, and the buyer responds optimally by following a Pandora's Box strategy: deciding which alternatives to inspect and when to stop by accepting the best inspected alternative. The seller's goal is to maximize his expected revenue, i.e. the total payment collected from all inspections. We study the revenue objective through the lens of simplicity versus optimality. Our main result is that a simple and efficiently computable pricing scheme obtains a 4 approximation in the worst case to the optimal revenue. This pricing rule equalizes the Weitzman indices across all alternatives. In contrast, we show that equalizing the prices themselves can be an unbounded factor worse than the optimum. Furthermore, for several natural special cases, including identically distributed alternatives and monotone hazard rate distributions, we fully characterize the optimal pricing. Finally, we also study a variant of our model under optional inspection, where the buyer may select an alternative without observing its realization. In this setting, we obtain an n/(n-1) approximation for the special case of n identically distributed alternatives, as well as a 2 approximation for the special case where each alternative's value distribution has support size two. Overall, our results highlight both the computational challenges and the power of simple pricing schemes in selling information to a sequential searcher.

cs.DS↗

arXiv2026

Mixture-of-Experts Serving

Mixture-of-Experts (MoE) models route each token to only a few expert networks, distributing the serving load across experts whose popularity shifts over time. A serving system must therefore dynamically decide how many GPUs to assign to each expert, trading off service latency against the cost of reconfiguring the assignment. We introduce a formal model of MoE Serving and initiate a principled study of online and offline algorithms for it. Our main result is a polynomial-time $O(\sqrt{\log k})$-competitive online algorithm, where $k$ is the number of GPUs beyond one per expert. We complement it with a matching $Ω(\sqrt{\log k})$ barrier for the online dual problem underlying our analysis. In the offline setting, we give a constant-factor approximation, show that MoE Serving is NP-hard, and rule out an FPTAS assuming ETH.

cs.DS↗

arXiv2026

Open-AoE: An Open Egocentric Manipulation Dataset and Toolchain for Embodied Learning

Egocentric videos of human manipulation provide scalable supervision for embodied intelligence, yet existing resources rarely combine low-cost continuous capture, manipulation-level structured annotations, and reusable tools for robot learning. We present Open-AoE, an open, community-oriented egocentric manipulation dataset and toolchain spanning the full pipeline from smartphone capture to model training. Its first release contains approximately 2,000 hours of manipulation video collected in natural environments by 500+ contributors using 400+ smartphones. The dataset provides text annotations, MANO-based hand poses, camera trajectories, and temporally localized atomic actions. Open-AoE further includes a data processing pipeline that transforms raw recordings into structured samples through temporal action segmentation, semantic annotation, hand reconstruction, and camera trajectory reconstruction. Meanwhile, we provide a separate downstream toolchain supports visualization, cross-embodiment retargeting, model-specific data conversion, and training recipes for VLA policies, WAMs, and World Models. By integrating scalable capture, structured processing, and downstream adaptation, Open-AoE reduces the barriers to both data contribution and reuse, providing practical open infrastructure for embodied model training, human-to-robot transfer, and world modeling.

cs.RO↗

arXiv2026

NewsTorch: A PyTorch-based Toolkit for Learner-oriented News Recommendation

News recommender systems are devised to alleviate the information overload, attracting more and more researchers' attention in recent years. The lack of a dedicated learner-oriented news recommendation toolkit hinders the advancement of research in news recommendation. We propose a PyTorch-based news recommendation toolkit called NewsTorch, developed to support learners in acquiring both conceptual understanding and practical experience. This toolkit provides a modular, decoupled, and extensible framework with a learner-friendly GUI platform that supports dataset downloading and preprocessing. It also enables training, validation, and testing of state-of-the-art neural news recommendation models with standardized evaluation metrics, ensuring fair comparison and reproducible experiments. Our open-source toolkit is released on Github: https://github.com/whonor/NewsTorch.

cs.IR↗

arXiv2026

Calibeating Made Simple

We study calibeating, the problem of post-processing external forecasts online to minimize cumulative losses and match an informativeness-based benchmark. Unlike prior work, which analyzed calibeating for specific losses with specific arguments, we reduce calibeating to existing online learning techniques and obtain results for general proper losses. More concretely, we first show that calibeating is minimax-equivalent to regret minimization. This recovers the $O(\log T)$ calibeating rate of Foster and Hart [FH23] for the Brier and log losses and its optimality, and yields new optimal calibeating rates for mixable losses and general bounded losses. Second, we prove that multi-calibeating is minimax-equivalent to the combination of calibeating and the classical expert problem. This yields new optimal multi-calibeating rates for mixable losses, including Brier and log losses, and general bounded losses. Finally, we obtain new bounds for achieving calibeating and calibration simultaneously for the Brier loss. For binary predictions, our result gives the first calibrated algorithm that at the same time also achieves the optimal $O(\log T)$ calibeating rate.

cs.LG↗

arXiv2026

Optimal Stopping with a Predicted Prior

There are two major models of value uncertainty in the optimal stopping literature: the secretary model, which assumes no prior knowledge, and the prophet inequality model, which assumes full information about value distributions. In practice, decision makers often rely on machine-learned priors that may be erroneous. Motivated by this gap, we formulate the model of optimal stopping with a predicted prior to design algorithms that are both consistent, exploiting the prediction when accurate, and robust, retaining worst-case guarantees when it is not. Existing secretary and prophet inequality algorithms are either pessimistic in consistency or not robust to misprediction. A randomized combination only interpolates their guarantees linearly. We show that a family of bi-criteria algorithms achieves improved consistency-robustness trade-offs, both for maximizing the expected accepted value and for maximizing the probability of accepting the maximum value. We further prove that for the latter objective, no algorithm can simultaneously match the best prophet inequality algorithm in consistency, and the best secretary algorithm in robustness.

cs.DS↗

arXiv2025

Agentar-DeepFinance-100K: A Large-Scale Financial Dataset via Systematic Chain-of-Thought Synthesis Optimization

Recent advancements in large language models (LLMs) have demonstrated remarkable general reasoning capabilities, holding significant potential for applications in the financial domain, a field that requires robust and reliable reasoning. It has been demonstrated that distilling high-quality chain-of-thought (CoT) rationales from advanced general reasoning models offers a promising and efficient path to the financial reasoning model. However, existing CoT synthesis methods suffer from shallow CoT sampling, leaving the question of how to construct a well-designed knowledge space for finance reasoning unexplored. In this paper, we present Agentar-DeepFinance-100K, a large-scale financial reasoning dataset characterized by its systematic CoT synthesis optimization. We first introduce a comprehensive CoT synthesis pipeline featuring Multi-perspective Knowledge Extraction (MKE) and Self-Corrective Rewriting (SCR) to generate exhaustive and deep financial reasoning trajectories. Furthermore, a systematic investigation, termed CoT Cube, is conducted to analyze critical factors that influence CoT effectiveness, such as necessity, length and synthesizer, yielding valuable insights for high-quality financial CoT construction. Experiments demonstrate that models trained on our Agentar-DeepFinance-100K achieve significant improvements on financial benchmarks. We publicly release Agentar-DeepFinance-100K , hoping to advance the research in financial reasoning models.

cs.CE↗

arXiv2025

Identification of Causal Direction under an Arbitrary Number of Latent Confounders

Recovering causal structure in the presence of latent variables is an important but challenging task. While many methods have been proposed to handle it, most of them require strict and/or untestable assumptions on the causal structure. In real-world scenarios, observed variables may be affected by multiple latent variables simultaneously, which, generally speaking, cannot be handled by these methods. In this paper, we consider the linear, non-Gaussian case, and make use of the joint higher-order cumulant matrix of the observed variables constructed in a specific way. We show that, surprisingly, causal asymmetry between two observed variables can be directly seen from the rank deficiency properties of such higher-order cumulant matrices, even in the presence of an arbitrary number of latent confounders. Identifiability results are established, and the corresponding identification methods do not even involve iterative procedures. Experimental results demonstrate the effectiveness and asymptotic correctness of our proposed method.

cs.LG↗

arXiv2025

Optimal 4-Approximation for the Correlated Pandora's Problem

The Correlated Pandora's Problem posed by Chawla et al. (2020) generalizes the classical Pandora's Problem by allowing the numbers inside the Pandora's boxes to be correlated. It also generalizes the Min Sum Set Cover problem, and is related to the Uniform Decision Tree problem. This paper gives an optimal 4-approximation for the Correlated Pandora's Problem, matching the lower bound of 4 from Min Sum Set Cover.

cs.DS↗

arXiv2025

Edge-weighted Matching in the Dark

We present a $0.659$-competitive Quadratic Ranking algorithm for the Oblivious Bipartite Matching problem, a distribution-free version of Query-Commit Matching. This result breaks the $1-\frac{1}{e}$ barrier, addressing an open question raised by Tang, Wu, and Zhang (JACM 2023). Moreover, the competitive ratio of this distribution-free algorithm improves the best existing $0.641$ ratio for Query-Commit Matching achieved by the distribution-dependent algorithm of Chen, Huang, Li, and Tang (SODA 2025). Quadratic Ranking is a novel variant of the classic Ranking algorithm. We parameterize the algorithm with two functions, and let two key expressions in the definition and analysis of the algorithm be quadratic forms of the two functions. We show that the quadratic forms are the unique choices that satisfy a set of natural properties. Further, they allow us to optimize the choice of the two functions using powerful quadratic programming solvers.

cs.DS↗

arXiv2025

A Learning Framework For Cooperative Collision Avoidance of UAV Swarms Leveraging Domain Knowledge

This paper presents a multi-agent reinforcement learning (MARL) framework for cooperative collision avoidance of UAV swarms leveraging domain knowledge-driven reward. The reward is derived from knowledge in the domain of image processing, approximating contours on a two-dimensional field. By modeling obstacles as maxima on the field, collisions are inherently avoided as contours never go through peaks or intersect. Additionally, counters are smooth and energy-efficient. Our framework enables training with large swarm sizes as the agent interaction is minimized and the need for complex credit assignment schemes or observation sharing mechanisms in state-of-the-art MARL approaches are eliminated. Moreover, UAVs obtain the ability to adapt to complex environments where contours may be non-viable or non-existent through intensive training. Extensive experiments are conducted to evaluate the performances of our framework against state-of-the-art MARL algorithms.

cs.MA↗

arXiv2025

CoDe: A Cooperative and Decentralized Collision Avoidance Algorithm for Small-Scale UAV Swarms Considering Energy Efficiency

This paper introduces a cooperative and decentralized collision avoidance algorithm (CoDe) for small-scale UAV swarms consisting of up to three UAVs. CoDe improves energy efficiency of UAVs by achieving effective cooperation among UAVs. Moreover, CoDe is specifically tailored for UAV's operations by addressing the challenges faced by existing schemes, such as ineffectiveness in selecting actions from continuous action spaces and high computational complexity. CoDe is based on Multi-Agent Reinforcement Learning (MARL), and finds cooperative policies by incorporating a novel credit assignment scheme. The novel credit assignment scheme estimates the contribution of an individual by subtracting a baseline from the joint action value for the swarm. The credit assignment scheme in CoDe outperforms other benchmarks as the baseline takes into account not only the importance of a UAV's action but also the interrelation between UAVs. Furthermore, extensive experiments are conducted against existing MARL-based and conventional heuristic-based algorithms to demonstrate the advantages of the proposed algorithm.

cs.RO↗