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Xinyu Zhang

Publications and source records attributed to Xinyu Zhang.

At least 91 records · Page 5Linked to original sources
arXiv2026

AgroTools: A Benchmark for Tool-Augmented Multimodal Agents in Agriculture

Agricultural decision-making increasingly requires multimodal systems that can transform visual observations into reliable, executable actions. However, existing agricultural multimodal benchmarks mainly evaluate final-answer correctness and provide limited support for assessing whether models can use external tools to complete precision-sensitive workflows. In this paper, we introduce AgroTools, a benchmark for evaluating tool-augmented multimodal agents in agriculture. AgroTools contains 539 question-answer instances paired with 1,097 heterogeneous agricultural images, spanning five task families and an executable environment of 14 agricultural tools. Each query is annotated with structured tool-use traces, enabling a dual-view evaluation of both process-level execution quality and outcome-level task success. We benchmark 9 open-source and 4 closed-source multimodal large language models on AgroTools. Results show that current models remain far from reliable in agricultural tool-use settings, with clear bottlenecks in tool planning, argument generation, execution recovery, and final-answer synthesis. We hope AgroTools will support future research on multimodal agents for high-precision agricultural applications. The benchmark and evaluation are available at https://huggingface.co/datasets/AgroTools/AgroTools.

cs.CV↗
arXiv2026

Weyl's laws and Connes' Trace Theorem for operator-valued pseudo-differential operators

We investigate the spectral asymptotic behavior of operator-valued classical pseudo-differential operators ($Ψ$DOs) for negative order with symbols taking values in a semifinite von Neumann algebran $\mathcal{M}$ equipped with a normal semifinite faithful trace. Within the framework of Connes' noncommutative geometry, we extend Connes' trace theorem to this operator-valued (type II) setting. Our main results are as follows: (i) a symbolic characterization of complex powers for operator-valued elliptic $Ψ$DOs, extending Seeley's classical construction; (ii) a trace formula for localized Riemann $ζ$-functions that links the spectral residues of operator-valued elliptic operators to their principal symbols, thereby providing an operator-valued extension of the Connes--Wodzicki residue; (iii) Weyl's law for right-compactly supported operator-valued classical $Ψ$DOs of arbitrary negative order, which yields a direct spectral proof of the noncommutative integral that bypasses the use of Dixmier traces; (iv) Weyl's law for operator-valued commutators of certain Fourier multipliers with multiplication operators.

math.OA↗
arXiv2026

Kernelized Advantage Estimation: From Nonparametric Statistics to LLM Reasoning

Recent advances in large language models (LLMs) have increasingly relied on reinforcement learning (RL) to improve their reasoning capabilities. Three types of approaches have been widely adopted: The first relies on a deep neural network to estimate the value function of the learning policy in order to reduce the variance of the policy gradient. However, estimating and maintaining such a value network incurs substantial computational and memory overhead. The second avoids training a value network by approximating the value function using sample averages. However, it samples a large number of reasoning traces per prompt for accurate value function approximation, making it computationally expensive. The third samples only a single reasoning trajectory per prompt, which reduces computational cost but suffers from poor sample efficiency. This paper focuses on a practical, resource-constrained setting in which only a small number of reasoning traces can be sampled per prompt, while low-variance gradient estimation remains essential for high-quality policy learning. To address this challenge, we bring classical nonparametric statistical methods, which are both computationally and statistically efficient, to LLM reasoning. We employ kernel smoothing as a concrete example for value function estimation and the subsequent policy optimization. Numerical and theoretical results demonstrate that our proposal achieves accurate value and gradient estimation, leading to improved policy optimization.

cs.LG↗
arXiv2026

Swarm Skills: A Portable, Self-Evolving Multi-Agent System Specification for Coordination Engineering

As artificial intelligence engineering paradigms shift from single-agent Prompt and Context Engineering toward multi-agent \textbf{Coordination Engineering}, the ability to codify and systematically improve how multiple agents collaborate has emerged as a critical bottleneck. While single-agent skills can now be distributed as portable assets, multi-agent coordination protocols remain locked within framework-internal code or static configurations, preventing them from being shared across systems or autonomously improved over time. We propose \textbf{Swarm Skills}, a portable specification that extends the Anthropic Skills standard with multi-agent semantics. Swarm Skills turns multi-agent workflows into first-class, distributable assets that consist of roles, workflows, execution bounds, and a built-in semantic structure for self-evolution. To operationalize the specification's evolving nature, we present a companion self-evolution algorithm that automatically distills successful execution trajectories into new Swarm Skills and continuously patches existing ones based on multi-dimensional scoring (Effectiveness, Utilization, and Freshness), eliminating the need for human-in-the-loop oversight during the refinement process. Through an architectural compatibility analysis and a comprehensive qualitative case study using the open-source JiuwenSwarm reference implementation, we demonstrate how Swarm Skills achieves zero-adapter cross-agent portability via progressive disclosure, enabling agent teams to self-evolve their coordination strategies without framework lock-in.

cs.CL↗
arXiv2026

LERA: LLM-Enhanced RAG for Ad Auction in Generative Chatbots

The integration of advertising auction mechanisms into large language model (LLM)-based chatbots presents a significant opportunity for commercialization, yet poses unique challenges in balancing relevance, efficiency, and user experience. Recently, Feizi et al.~\citep{feizi2023online} and Hajiaghayi et al.~\citep{hajiaghayi2024ad} outlined a retrieve-then-generate paradigm that decouples retrieval and generation, offering lightweight ad insertion and payment determination. However, current retrieval relies solely on text embedding similarity, which may lead to commercial misinterpretation and issues such as repetitive insertions. In this paper, we propose LERA, a two-stage retrieve-then-generate auction framework tailored for LLM chatbots. In the first stage, embedding-based coarse filtering pre-selects a small set of candidate advertisers. In the second stage, the LLM itself is queried with a carefully designed prompt to produce logits over candidates, which serve as refined organic relevance scores. These scores are combined with bids, and a critical-value payment rule accounts for both the coarse-filtering and fine-ranking thresholds, ensuring truthfulness for utility-maximizing advertisers. The framework naturally extends to multiple ad insertions within dynamic dialogue flows and long responses. Experiments on a synthetic advertiser-query benchmark show that LERA substantially improves ad selection accuracy and insertion diversity while incurring only controllable latency overhead.

cs.IR↗
arXiv2026

In-Context Multi-Objective Optimization

Balancing competing objectives is omnipresent across disciplines, from drug design to autonomous systems. Multi-objective Bayesian optimization is a promising solution for such expensive, black-box problems: it fits probabilistic surrogates and selects new designs via an acquisition function that balances exploration and exploitation. In practice, it requires tailored choices of surrogate and acquisition that rarely transfer to the next problem, is myopic when multi-step planning is often required, and adds refitting overhead, particularly in parallel or time-sensitive loops. We present TAMO, a fully amortized, universal policy for multi-objective black-box optimization. TAMO uses a transformer architecture that operates across varying input and objective dimensions, enabling pretraining on diverse corpora and transfer to new problems without retraining: at test time, the pretrained model proposes the next design with a single forward pass. We pretrain the policy with reinforcement learning to maximize cumulative hypervolume improvement over full trajectories, conditioning on the entire query history to approximate the Pareto frontier. Across synthetic benchmarks and real tasks, TAMO produces fast proposals, reducing proposal time by 50-1000x versus alternatives while matching or improving Pareto quality under tight evaluation budgets. These results show that transformers can perform multi-objective optimization entirely in-context, eliminating per-task surrogate fitting and acquisition engineering, and open a path to foundation-style, plug-and-play optimizers for scientific discovery workflows.

cs.LG↗
arXiv2026

GLM-5V-Turbo: Toward a Native Foundation Model for Multimodal Agents

We present GLM-5V-Turbo, a step toward native foundation models for multimodal agents. As foundation models are increasingly deployed in real environments, agentic capability depends not only on language reasoning, but also on the ability to perceive, interpret, and act over heterogeneous contexts such as images, videos, webpages, documents, GUIs. GLM-5V-Turbo is built around this objective: multimodal perception is integrated as a core component of reasoning, planning, tool use, and execution, rather than as an auxiliary interface to a language model. This report summarizes the main improvements behind GLM-5V-Turbo across model design, multimodal training, reinforcement learning, toolchain expansion, and integration with agent frameworks. These developments lead to strong performance in multimodal coding, visual tool use, and framework-based agentic tasks, while preserving competitive text-only coding capability. More importantly, our development process offers practical insights for building multimodal agents, highlighting the central role of multimodal perception, hierarchical optimization, and reliable end-to-end verification.

cs.CV↗
arXiv2026

Synthetic Control Method with Mixed Frequency Data

Mixed-frequency data, where variables are observed at different temporal resolutions, commonly occur in economic and financial studies. Classical synthetic control methods (SCM) are ill-suited for such data, often necessitating aggregation or prefiltering that may discard valuable information. This paper proposes a novel Mixed-Frequency Synthetic Control Method (MF-SCM) to integrate mixed-frequency data into the synthetic control framework effectively. We develop a flexible estimation procedure to construct synthetic control weights under mixed-frequency settings and establish the theoretical properties of the MF-SCM estimator. Specifically, we first prove that the estimator achieves asymptotic optimality, in the sense that it achieves the lowest possible squared prediction error among all potential treatment effect estimators from averaging outcomes of control units. We then derive the asymptotic distribution of the average treatment effect (ATE) estimator using projection theory and construct confidence intervals for the ATE estimator. The method's effectiveness is demonstrated through numerical simulations and two empirical applications concerning the 2017 Tax Cuts and jobs Act in US and air pollution alerts.

stat.ME↗
arXiv2026

Asymptotic Properties of the Distributional Synthetic Controls

As an alternative to synthetic control, the distributional Synthetic Control (DSC) proposed by Gunsilius (2023) provides estimates for quantile treatment effect and thus enabling researchers to comprehensively understand the impact of interventions in causal inference. But the asymptotic properties of DSC have not been built. In this paper, we first establish the DSC estimator's asymptotic optimality in the essence that the treatment effect estimator given by DSC achieves the lowest possible squared prediction error among all potential estimators from averaging quantiles of control units. We then establish the convergence rate of the DSC weights. A significant aspect of our research is that we find the DSC synthesis forms an optimal weighted average, particularly in situations where it is impractical to perfectly fit the treated unit's quantiles through the weighted average of the control units' quantiles. Simulation results verify our theoretical insights.

econ.EM↗
arXiv2026

Generalized optimal parameter-transfer learning through Mallows-type model averaging

In many economic applications, multiple source datasets are available, but their effective combination is challenging due to heterogeneity across datasets. To address this problem, we study a parameter-transfer framework that shares only source-side estimates and propose a Mallows-type model averaging method for combining target and source models in the parametric setting. The weights are obtained from a Mallows-type criterion that is unbiased for the target prediction risk up to a weight-independent term, extending the classical Mallows criterion to the parameter-transfer framework. We establish that the proposed weights are asymptotically optimal when the target model is misspecified, and asymptotically allocate weights only to informative sources when the target model is correctly specified. These guarantees do not require any source model to be correctly specified. We also consider extensions of the framework to semiparametric and panel data settings. Simulation studies and house price application further demonstrate the effectiveness of our approach.

stat.ME↗
arXiv2026

Spectral Condition for $μ$P under Width-Depth Scaling

Generative foundation models are increasingly scaled in both width and depth, posing significant challenges for stable feature learning and reliable hyperparameter (HP) transfer across model sizes. While maximal update parameterization ($μ$P) has provided a principled solution to both problems for width scaling, existing extensions to the joint width-depth scaling regime remain fragmented, architecture- and optimizer-specific, and often rely on technically involved theories. In this work, we develop a simple and unified spectral framework for $μ$P under joint width-depth scaling. For deep residual networks whose residual blocks contain $k$ transformations, the framework specifies how the norms of weights and their per-step updates should scale with width and depth. It reveals a fundamental transition from $k=1$ to $k\geq 2$, unifying previously disparate $μ$P formulations and identifying the $k\geq 2$ case as more appropriate for practical architectures with multi-transformation branches such as Transformers. Building on this framework, we derive a general recipe for implementing $μ$P across a broad class of optimizers by mapping spectral constraints to concrete HP parameterizations, recovering existing results and extending them to additional optimizers. Finally, experiments on GPT-2 style language models show that the $μ$P formulation derived from the $k\geq 2$ case achieves stable feature learning and robust HP transfer under width-depth scaling, whereas standard parameterization and $μ$P in the $k=1$ case often fail to do so. These results support the practical effectiveness of the proposed spectral framework.

cs.LG↗
arXiv2026

Towards Cross-lingual Values Judgment: A Consensus-Pluralism Perspective

As large language models (LLMs) are employed worldwide, existing evaluation paradigms for their multilingual capabilities primarily focus on factual task performance, neglecting the ability to judge content's deep-level values across multiple languages. To bridge this gap, we first reveal two primary challenges in constructing values judgment benchmarks, cultural diversity and disciplinary complexity, and propose a novel two-stage human-AI collaborative annotation framework to alleviate them. This framework identifies the issue scope and nature, establishes specific annotation criteria, and utilizes multiple LLMs for final review. Building upon this framework, we introduce \textbf{X-Value}, the first \textit{Cross-lingual Values Judgment Benchmark} designed to evaluate the capability of LLMs in judging deep-level values of content. X-Value comprises 4,750 Question-Answer pairs across 14 languages, covering 7 major global issue categories, and provides 12 granular annotation metadata to facilitate a rigorous evaluation of model performance. Systematic evaluations of X-Value are conducted across 17 LLMs using distinct prompting strategies. Multi-dimensional analysis of accuracy and F1-scores reveals their limitations in cross-lingual values judgment and indicates performance disparities across categories and languages. This work highlights the urgent need to improve the underlying, values-aware content judgment capability of LLMs.\footnote{Samples of X-Value are available at https://huggingface.co/datasets/Whitolf/X-Value.}

cs.CL↗
arXiv2026

Beyond Position Bias: Shifting Context Compression from Position-Driven to Semantic-Driven

Large Language Models (LLMs) have demonstrated exceptional performance across diverse tasks. However, their deployment in long-context scenarios faces high computational overhead and information redundancy. While soft prompt compression has emerged as a promising way to mitigate these costs by compressing sequences into compact embeddings, existing paradigms remain fundamentally constrained by position bias: they primarily rely on learnable tokens insertion at fixed positions or group tokens according to their physical token layout, thereby inducing performance instability and semantic fragmentation. To overcome this bottleneck, we propose Semantic Consistency Context Compression (SeCo), a method that shifts context compression from position-driven to semantic-driven. Rather than constraint by physical token layout, SeCo dynamically anchors compression directly in the semantic space by selecting query-relevant tokens as semantic centers and aggregating remaining tokens via consistency-weighted merging. This design inherently preserves semantic consistency while eliminating position bias. Extensive experiments on 14 benchmarks across two backbone models demonstrate that SeCo consistently shows superiority in downstream tasks, inference latency, and out-of-domain robustness. The code is available at https://anonymous.4open.science/r/seco-EE5E.

cs.CL↗
arXiv2026

"Training robust watermarking model may hurt authentication!'' Exploring and Mitigating the Identity Leakage in Robust Watermarking

The rapid advancement of generative AI has underscored the critical need for identifying image ownership and protecting copyrights. This makes post-processing image watermarking an essential tool -- it involves embedding a specific watermark message into an image, with successful verification if a similar message can be decoded from the watermarked image. However, this method is susceptible to both adversarial attacks that manipulate the watermarked image to yield an unverified message upon decoding, and the proposed identity leakage-related attacks (e.g., forging watermarked images). The threat of identity leakage is particularly exacerbated in both empirical and certified robust watermarking methods. To defend against the aforementioned attacks, we propose W-IR, the first image watermarking framework that simultaneously incorporates identity protection and robustness. To enhance model robustness, we introduce a novel randomized smoothing technique as part of a robust watermarking, that offers certified robustness against perturbations across two distinct transformation spaces: pixel-level and coordinate-level. Moreover, to further mitigate identity leakage, we propose a new strategy based on residual information loss, aimed at minimizing the mutual information between the residual and watermarked images. Our work strikes a superior balance between robustness and identity leakage mitigation. Extensive experiments demonstrate that our W-IR framework achieves high certified accuracy for authenticity while effectively reducing identity leakage. \footnote{The code is available at https://github.com/holdrain/W-I-R.}

cs.CR↗
arXiv2026

Saliency-Aware Regularized Quantization Calibration for Large Language Models

Post-training quantization (PTQ) is an effective approach for deploying large language models (LLMs) under memory and latency constraints. Most existing PTQ methods determine quantization parameters by minimizing a layer-wise reconstruction error on a predetermined calibration dataset, typically optimized via either scale search or Gram-based methods. However, from the perspective of generalization risk, existing PTQ calibration objectives based solely on empirical reconstruction error over limited or unrepresentative calibration data may move the quantized weights away from the original floating-point weights, potentially degrading downstream performance. To address this issue, we propose \emph{Regularized Quantization Calibration} (RQC), a unified framework that augments standard PTQ objectives with a regularizer that explicitly controls weight deviation from the original weights. We further generalize this framework to incorporate a saliency-aware regularizer, resulting in \emph{Saliency-Aware Regularized Quantization Calibration} (SARQC). The proposed regularization encourages quantized weights to remain close to the original weights during calibration, leading to improved generalization at inference time. SARQC integrates seamlessly into existing PTQ pipelines and enhances both scale-search-based and Gram-based methods under a unified formulation. Extensive experiments on dense and Mixture-of-Experts LLMs demonstrate consistent improvements in perplexity and zero-shot accuracy, without introducing additional inference overhead.

cs.AI↗
arXiv2026

Plug-and-play Class-aware Knowledge Injection for Prompt Learning with Visual-Language Model

Prompt learning has become an effective and widely used technique in enhancing vision-language models (VLMs) such as CLIP for various downstream tasks, particularly in zero-shot classification within specific domains. Existing methods typically focus on either learning class-shared prompts for a given domain or generating instance-specific prompts through conditional prompt learning. While these methods have achieved promising performance, they often overlook class-specific knowledge in prompt design, leading to suboptimal outcomes. The underlying reasons are: 1) class-specific prompts offer more fine-grained supervision compared to coarse class-shared prompts, which helps prevent misclassification of data from different classes into a single class; 2) compared to class-specific prompts, instance-specific prompts neglect the richer class-level information across multiple instances, potentially causing data from the same class to be divided into multiple classes. To effectively supplement the class-specific knowledge into existing methods, we propose a plug-and-play Class-Aware Knowledge Injection (CAKI) framework. CAKI comprises two key components, i.e., class-specific prompt generation and query-key prompt matching. The former encodes class-specific knowledge into prompts from few-shot samples that belong to the same class and stores the learned prompts in a class-level knowledge bank. The latter provides a plug-and-play mechanism for each test instance to retrieve relevant class-level knowledge from the knowledge bank and inject such knowledge to refine model predictions. Extensive experiments demonstrate that our CAKI effectively improves the performance of existing methods on base and novel classes. Code is publicly available at \href{https://github.com/yjh576/CAKI}{this https URL}.

cs.CV↗
arXiv2026

Ordering Power is Sanctioning Power: Sanction Evasion-MEV and the Limits of On-Chain Enforcement

Centralized stablecoins such as USDT and USDC enforce sanctions through contract-layer blacklist functions. Yet on public blockchains, a freeze is still an ordinary transaction competing with the sanctioned party's transfer for priority. It exposes a gap between contract-layer authority and ordering-layer enforcement: when both race for the same block, the outcome is set not by legal mandate, but by block producers' choices. Because both sides can pay for priority, sanction races create rents for block producers, which we call Sanction-Evasion MEV (SE-MEV). To measure this gap, we build the first longitudinal dataset of on-chain sanctions enforcement and evasion for Ethereum-based USDT and USDC from November 2017 to August 2025, covering more than $1.5 billion in frozen value. At least 7.3% of sanctioned USDT addresses and 18.7% of sanctioned USDC addresses had already been drained to zero before the freeze took effect. We also trace an escalation from issuer-side out-of-gas failures, to public gas auctions, private order flow, and direct payments to block producers, showing that block producers extract MEV from sanction enforcement. We then develop a game-theoretic model of stablecoin sanctions with MEV. It shows that compliant issuers cannot rationally stay outside the ordering market; fixed participation costs concentrate evasion among specialized MEV-aware adversaries; and the implicit MEV tax rises with regulatory penalties, creating incentives for vertical integration into block-building infrastructure. The problem extends beyond stablecoins. Any privileged on-chain action executed as an ordinary transaction -- emergency pauses, governance interventions, or judicial freezes -- faces the same conflict. Where ordering power follows economic incentives, ordering power is sanctioning power; contract-layer authority alone cannot guarantee enforcement.

cs.CR↗
arXiv2026

OptiVerse: A Comprehensive Benchmark towards Optimization Problem Solving

While Large Language Models (LLMs) demonstrate remarkable reasoning, complex optimization tasks remain challenging, requiring domain knowledge and robust implementation. However, existing benchmarks focus narrowly on Mathematical Programming and Combinatorial Optimization, hindering comprehensive evaluation. To address this, we introduce OptiVerse, a comprehensive benchmark of 1,000 curated problems spanning neglected domains, including Stochastic Optimization, Dynamic Optimization, Game Optimization, and Optimal Control, across three difficulty levels: Easy, Medium, and Hard. The experiments with 22 LLMs of different sizes reveal sharp performance degradation on hard problems, where even advanced models like GPT-5.2 and Gemini-3 struggle to exceed 27% accuracy. Through error analysis, we identify that modeling & logic errors remain the primary bottleneck. Consequently, we propose a Dual-View Auditor Agent that improves the accuracy of the LLM modeling process without introducing significant time overhead. OptiVerse will serve as a foundational platform for advancing LLMs in solving complex optimization challenges.

cs.CL↗