arXiv subjects

Rui Sun

Publications and source records attributed to Rui Sun.

At least 37 records · Page 2Linked to original sources

arXiv2026

State-Robust Nash Predictions In Population Games

This paper introduces state-robust equilibrium (SRE), a local validity test for Nash predictions in finite-strategy population games when the payoff-relevant aggregate state may be misspecified. The reported prescription and payoff map are held fixed; only the state used to evaluate payoff comparisons varies. SRE is equivalent to local best-response invariance, absence of structural exposure, and validity along every vanishing interior aggregate-state error. In affine games, the tangent-cone, normal-cone, and linear-program tests characterize exposure and identify the exposing population, the pure strategy, and the aggregate-state direction. The main implication is a sharp negative result: robust mixing requires local payoff identity on the support; in generic affine games, SRE reduce to strict pure Nash equilibria, although weak boundary equilibria can survive through feasible-set protection. In affine games with polyhedral local uncertainty regions, the same inequalities yield a deterministic finite diagnostic for reported-state validity.

econ.TH↗

arXiv2026

On a system of two Diophantine inequalities with six prime variables

Suppose that $c,d,α,β$ are real numbers satisfying the inequalities $1<d<c<79/71$ and $1<α<β<6^{1-d/c}$. In this paper, it is proved that, for sufficiently large real numbers $N_1$ and $N_2$ subject to $α\leqslant N_2/N_1^{d/c}\leqslantβ$, the following Diophantine inequalities system \begin{align*} \begin{cases} |p_1^c+p_2^c+p_3^c+p_4^c+p_5^c+p_6^c-N_1|<\varepsilon_1 (N_1) \\ |p_1^d+p_2^d+p_3^d+p_4^d+p_5^d+p_6^d-N_2|<\varepsilon_2 (N_2) \end{cases} \end{align*} is solvable in prime variables $p_1, p_2, p_3, p_4, p_5, p_6$, where \begin{align*} \begin{cases} \varepsilon_1 (N_1)=N_1^{-(1/c)(79/71-c)} (\log N_1)^{201}, \\ \varepsilon_2 (N_2)=N_2^{-(1/d)(79/71-d)} (\log N_2)^{201} . \end{cases} \end{align*} This result constitutes an improvement upon the previous result of Han-Liu-Zhang [5].

math.NT↗

arXiv2026

TopoDIM: One-shot Topology Generation of Diverse Interaction Modes for Multi-Agent Systems

Optimizing communication topology in LLM-based multi-agent system is critical for enabling collective intelligence. Existing methods mainly rely on spatio-temporal interaction paradigms, where the sequential execution of multi-round dialogues incurs high latency and computation. Motivated by the recent insights that evaluation and debate mechanisms can improve problem-solving in multi-agent systems, we propose TopoDIM, a framework for one-shot Topology generation with Diverse Interaction Modes. Designed for decentralized execution to enhance adaptability and privacy, TopoDIM enables agents to autonomously construct heterogeneous communication without iterative coordination, achieving token efficiency and improved task performance. Experiments demonstrate that TopoDIM reduces total token consumption by 46.41% while improving average performance by 1.50% over state-of-the-art methods. Moreover, the framework exhibits strong adaptability in organizing communication among heterogeneous agents. Code is available at: https://github.com/Sundiasy/TopoDIM.

cs.MA↗

arXiv2026

The Screening Cost of Liquidity

A principal with cheap capital optimally forces her counterparty to borrow at above-market rates. The reason: the form of finance is a screening device. Advances provide liquidity but pool types; contingent transfers separate types, but, because they are not pledgeable, impose financing costs. The optimal contract preserves outside-finance exposure to maintain screening power. Two sufficient statistics pin down the optimal advance share. With complementary counterparties, a uniform subsidy that cheapens finance across every relationship can reduce the value of each. This explains the coexistence of early payment and contingent compensation in trade credit, venture capital, and internal capital markets.

econ.TH↗

arXiv2026

Coarse Screening

A seller investigates a buyer before setting prices, balancing the cost of acquiring information against the gain from tailoring the contract to the buyer's private type. The optimal signal is coarse: no matter how rich the type space, the seller never needs more than three outcomes per buyer. The bound equals the number of independent post-signal decisions plus one, a quantity we call the effective policy dimension. Screening involves two decisions, whether to allocate and what to charge, giving the ternary bound. Limited liability is the source: without it, the price is pinned by the envelope, only the allocation decision remains, and signals are binary as in monitoring. The Myerson exclusion rule is an artifact of not investigating. With investigation, every marginal buyer trades with positive probability, governed by a universal function that connects information design to rational inattention. The bound holds for any strictly convex information cost.

econ.TH↗

arXiv2026

Why Instruction-Based Unlearning Fails in Diffusion Models?

Instruction-based unlearning has proven effective for modifying the behavior of large language models at inference time, but whether this paradigm extends to other generative models remains unclear. In this work, we investigate instruction-based unlearning in diffusion-based image generation models and show, through controlled experiments across multiple concepts and prompt variants, that diffusion models systematically fail to suppress targeted concepts when guided solely by natural-language unlearning instructions. By analyzing both the CLIP text encoder and cross-attention dynamics during the denoising process, we find that unlearning instructions do not induce sustained reductions in attention to the targeted concept tokens, causing the targeted concept representations to persist throughout generation. These results reveal a fundamental limitation of prompt-level instruction in diffusion models and suggest that effective unlearning requires interventions beyond inference-time language control.

cs.CL↗

arXiv2026

Credible Uncertainty Quantification under Noise and System Model Mismatch

State estimators often provide self-assessed uncertainty metrics, such as covariance matrices, whose credibility is critical for downstream tasks. However, these self-assessments can be misleading due to underlying modeling violations like noise model mismatch (NMM) or system model misspecification (SMM). This letter addresses this problem by developing a unified, multi-metric framework that integrates noncredibility index (NCI), negative log-likelihood (NLL), and energy score (ES) metrics, featuring an empirical location test (ELT) to detect system model bias and a directional probing technique that uses the metrics' asymmetric sensitivities to distinguish NMM from SMM. Monte Carlo simulations reveal that the proposed method achieves excellent diagnosis accuracy (80-100%) and significantly outperforms single-metric diagnosis methods. The effectiveness of the proposed method is further validated on a real-world UWB positioning dataset. This framework provides a practical tool for turning patterns of credibility indicators into actionable diagnoses of model deficiencies.

eess.SP↗

arXiv2026

Agent Q-Mix: Selecting the Right Action for LLM Multi-Agent Systems through Reinforcement Learning

Large Language Models (LLMs) have shown remarkable performance in completing various tasks. However, solving complex problems often requires the coordination of multiple agents, raising a fundamental question: how to effectively select and interconnect these agents. In this paper, we propose \textbf{Agent Q-Mix}, a reinforcement learning framework that reformulates topology selection as a cooperative Multi-Agent Reinforcement Learning (MARL) problem. Our method learns decentralized communication decisions using QMIX value factorization, where each agent selects from a set of communication actions that jointly induce a round-wise communication graph. At its core, Agent Q-Mix combines a topology-aware GNN encoder, GRU memory, and per-agent Q-heads under a Centralized Training with Decentralized Execution (CTDE) paradigm. The framework optimizes a reward function that balances task accuracy with token cost. Across seven core benchmarks in coding, reasoning, and mathematics, Agent Q-Mix achieves the highest average accuracy compared to existing methods while demonstrating superior token efficiency and robustness against agent failure. Notably, on the challenging Humanity's Last Exam (HLE) using Gemini-3.1-Flash-Lite as a backbone, Agent Q-Mix achieves 20.8\% accuracy, outperforming Microsoft Agent Framework (19.2\%) and LangGraph (19.2\%), followed by AutoGen and Lobster by OpenClaw. These results underscore the effectiveness of learned, decentralized topology optimization in pushing the boundaries of multi-agent reasoning.

cs.CL↗

arXiv2026

The Inflation of Resetting Workers

The standard wage Phillips curve aggregates away from which workers reset wages when. I show this aggregation omits a first-order term: the covariance between workers' cost-push exposure and their reset frequency. I introduce two sufficient statistics and embed them in a multi-country HANK model calibrated to six euro-area economies. The omitted term generates 7 percent more cumulative core inflation in the baseline and 10--26 percent more when monetary policy is delayed. Two economies with identical openness can differ by 6.6 percentage-point-quarters solely from within-country composition. Targeted essentials subsidies reduce welfare loss by 32 percent relative to aggressive tightening. Out of sample, the model correctly predicts the persistence ranking across the UK, the US, and Japan.

econ.GN↗

arXiv2026

Risk Capacity and Optimal Monetary Policy

We characterize optimal monetary policy when policy endogenously moves risk premia through redistribution across agents who differ in their willingness to bear risk. The analytical core is Marginal Risk Capacity, the covariance of monetary policy exposures with marginal propensities to take risk. This sufficient statistic governs this channel as MPCs govern the consumption channel. MRC enters the Ramsey criterion as a risk premium wedge that breaks divine coincidence, vanishes if and only if macroprudential tools are available, and generates a new inflation bias under discretion. Solving the Ramsey problem globally reveals a risk capacity trap where transmission collapses, and optimal policy preemptively prevents it.

econ.TH↗

arXiv2026

Marine Heatwaves in the Arabian Sea: Drivers and Impacts on Atmospheric Circulation and Extreme Precipitation

Marine heatwaves (MHWs) threaten marine ecosystems and significantly impact weather patterns. In the Arabian Sea, summer MHWs are of particular concern due to their potential impacts on the Indian summer monsoon, a lifeline for nearly a billion people. However, the drivers of these MHWs and their influence on atmospheric circulation and monsoon rainfall remain poorly understood. Using satellite observations, reanalysis datasets, and numerical model experiments, we investigate the key drivers of MHW events and assess their impacts. When SST warming trends are retained, the eastern and northern Arabian Sea emerge as MHW hotspots, showing rapid increases during 1982-2023, largely due to anthropogenic warming. On detrending the SSTs to remove the influence of anthropogenic warming on individual MHWs, we find that most MHWs are short-lived (lasting <= 20 days) and are initiated by enhanced surface shortwave radiation and reduced latent heat loss associated with the suppressed convection phase of the Boreal Summer Intraseasonal Oscillations (BSISOs). Interannual SST anomalies, including ENSO and Indian Ocean Dipole (IOD), further modulate the year-to-year MHW variability. Conversely, the warm SSTs during MHWs exert strong atmospheric feedbacks. MHWs in the eastern Arabian Sea drive cyclonic winds, intensify moisture convergence and increase the risk of extreme precipitation along the southwest coast of India. In the northern Arabian Sea, MHW-induced cyclones trigger intense rainfall over northwestern India and Pakistan, contributing to extreme events like the 2022 Pakistan floods. These findings improve our capacity to predict Arabian Sea MHWs and assess their risks, offering significant socio-economic and ecological benefits.

physics.ao-ph↗

arXiv2026

Coupled Ferroelectricity and Phonon Chirality

The ability to control chirality and chiral phonons offers a route to manipulate the direction of spin and angular-momentum transport. In materials with rigid structural chirality, such as quartz, phonon chirality is fixed by the handedness and cannot be switched. By contrast, ferroelectric materials host a spontaneous polarization that can be reversibly switched by an external electric field. When chirality is coupled to this ferroelectric polarization, it enables electrical switching of crystal chirality and the associated phonon angular momentum, which is compatible with solid-state spintronic architectures, enabling control over chirality-dependent quantum states.1 Here, we report the experimental demonstration of the coupling between ferroelectricity and phonon chirality in the molecular ferroelectric triglycine sulfate. By electrically switching the crystal chirality, we achieve reversible and device-compatible control of phonon chirality, as revealed by in situ time-resolved magneto-optical Kerr effect measurements. The Kerr rotation reverses with electric-field switching, while phonon chirality vanishes in the paraelectric phase and is tunable in the racemic ferroelectric state. Furthermore, density functional theory calculations and circularly polarized Raman spectroscopy further corroborate the opposite circular phonon motions. These results establish an electrically addressable coupling pathway linking ferroelectricity, structural chirality, chiral phonons, and spin, opening a route toward chiral-phonon-enabled spin and phonon control technologies based on ferroelectric materials.

cond-mat.mtrl-sci↗

arXiv2026

A Disguise-and-Squeeze PIR Scheme for the MDS-TPIR Setting and Beyond

We consider the problem of private information retrieval (PIR) from MDS coded databases with colluding servers, i.e., MDS-TPIR. In the MDS-TPIR setting, $M$ files are stored across $N$ servers, where each file is stored independently using an $(N,K)$-MDS code. A user wants to retrieve one file without disclosing the index of the desired file to any set of up to $T$ colluding servers. The general problem in studying PIR schemes is to maximize the PIR rate, defined as the ratio of the size of the desired file to the size of the total download. Freij-Hollanti et al. proposed a conjecture of the MDS-TPIR capacity (the maximum achievable PIR rate), which was later disproved by Sun and Jafar by a counterexample with $(M,N,T,K)=(2,4,2,2)$. In this paper, we propose a new MDS-TPIR scheme based on a disguise-and-squeeze approach. The features of our scheme include the following. Our scheme generalizes the Sun-Jafar counterexample to $(M,N,T,K)=(2,N,2,K)$ with $N\geq K+2$ for an arbitrary $(N,K)$-MDS coded system, providing more counterexamples to the conjecture by Freij-Hollanti et al. For $(M,N,T,K)=(2,N,2,K)$ and a GRS (generalized Reed-Solomon codes) coded system, our scheme has rate $\frac{N^2-N}{N^2+KN-2K}$, beating the state-of-the-art results. We further show that this rate achieves the linear MDS-TPIR capacity when $K=2$. Our scheme features a significantly smaller field size for implementation and the adaptiveness to generalized PIR models such as multi-file MDS-TPIR and MDS-PIR against cyclically adjacent colluding servers. Lastly, we provide an $ε$-error MDS-TPIR scheme for $T\geq 3$ based on the disguise-and-squeeze framework.

cs.IT↗

arXiv2026

Modulating Surface Acoustic Wave Generation through Superconductivity

Surface acoustic waves (SAWs), with their five orders-of-magnitude slower propagation velocity, allow for considerably shorter wavelengths at the same frequency compared to electromagnetic waves. The short wavelengths allow for device miniaturization and on-chip integration. The generic design of these devices involve piezoelectric substrates with comblike arrays of Al or Au electrodes known as interdigitated transducers deposited on the surface. However, Al and Au both have shortcomings at the cryogenic temperatures required for quantum applications, namely the formation of two-level systems and the lack of superconductivity perpetuating Ohmic losses, respectively. In this work, SAWs are generated in the high-MHz to low-GHz range using niobium nitride (NbN) interdigitated transducers (IDTs) and Bragg reflectors. We demonstrate the fabrication of acoustic devices through photolithography and reactive ion etching (RIE). The sharp transition between superconducting and normal states and the corresponding change in SAW transmission allows for fine control of the 'on' (superconducting) and 'off' (normal) states of NbN, with a Δ_T = K separating the transmission minimum and maximum. We demonstrate a 16x difference in transmission between the 'on' and 'off' states of the device. The SAW transmission behavior mirrors the change in resistance of NbN at its Tc. These findings open up new possibilities for the integration of NbN SAW resonators into existing quantum architectures based on NbN and a method for adjusting transmission properties independent of applied voltage.

cond-mat.mes-hall↗

arXiv2026

Adaptive Augmentation-Aware Latent Learning for Robust LiDAR Semantic Segmentation

Adverse weather conditions significantly degrade the performance of LiDAR point cloud semantic segmentation networks by introducing large distribution shifts. Existing augmentation-based methods attempt to enhance robustness by simulating weather interference during training. However, they struggle to fully exploit the potential of augmentations due to the trade-off between minor and aggressive augmentations. To address this, we propose A3Point, an adaptive augmentation-aware latent learning framework that effectively utilizes a diverse range of augmentations while mitigating the semantic shift, which refers to the change in the semantic meaning caused by augmentations. A3Point consists of two key components: semantic confusion prior (SCP) latent learning, which captures the model's inherent semantic confusion information, and semantic shift region (SSR) localization, which decouples semantic confusion and semantic shift, enabling adaptive optimization strategies for different disturbance levels. Extensive experiments on multiple standard generalized LiDAR segmentation benchmarks under adverse weather demonstrate the effectiveness of our method, setting new state-of-the-art results.

cs.CV↗

arXiv2026

LLM4AD: A Platform for Algorithm Design with Large Language Model

We introduce LLM4AD, a unified Python platform for algorithm design (AD) with large language models (LLMs). LLM4AD is a generic framework with modularized blocks for search methods, algorithm design tasks, and LLM interface. The platform integrates numerous key methods and supports a wide range of algorithm design tasks across various domains including optimization, machine learning, and scientific discovery. We have also designed a unified evaluation sandbox to ensure a secure and robust assessment of algorithms. Additionally, we have compiled a comprehensive suite of support resources, including tutorials, examples, a user manual, online resources, and a dedicated graphical user interface (GUI) to enhance the usage of LLM4AD. We believe this platform will serve as a valuable tool for fostering future development in the merging research direction of LLM-assisted algorithm design.

cs.AI↗

arXiv2026

How Do Latent Reasoning Methods Perform Under Weak and Strong Supervision?

Latent reasoning has been recently proposed as a reasoning paradigm and performs multi-step reasoning through generating steps in the latent space instead of the textual space. This paradigm enables reasoning beyond discrete language tokens by performing multi-step computation in continuous latent spaces. Although there have been numerous studies focusing on improving the performance of latent reasoning, its internal mechanisms remain not fully investigated. In this work, we conduct a comprehensive analysis of latent reasoning methods to better understand the role and behavior of latent representation in the process. We identify two key issues across latent reasoning methods with different levels of supervision. First, we observe pervasive shortcut behavior, where they achieve high accuracy without relying on latent reasoning. Second, we examine the hypothesis that latent reasoning supports BFS-like exploration in latent space, and find that while latent representations can encode multiple possibilities, the reasoning process does not faithfully implement structured search, but instead exhibits implicit pruning and compression. Finally, our findings reveal a trade-off associated with supervision strength: stronger supervision mitigates shortcut behavior but restricts the ability of latent representations to maintain diverse hypotheses, whereas weaker supervision allows richer latent representations at the cost of increased shortcut behavior.

cs.AI↗

arXiv2026

SoK: Blockchain-Based Decentralized AI (DeAI)

Centralization enhances the efficiency of Artificial Intelligence (AI) but also introduces critical challenges, including single points of failure, inherent biases, data privacy risks, and scalability limitations. To address these issues, blockchain-based Decentralized Artificial Intelligence (DeAI) has emerged as a promising paradigm that leverages decentralization and transparency to improve the trustworthiness of AI systems. Despite rapid adoption in industry, the academic community lacks a systematic analysis of DeAI's technical foundations, opportunities, and challenges. This work presents the first Systematization of Knowledge (SoK) on DeAI, offering a formal definition, a taxonomy of existing solutions based on the AI lifecycle, and an in-depth investigation of the roles of blockchain in enabling secure and incentive-compatible collaboration. We further review security risks across the DeAI lifecycle and empirically evaluate representative mitigation techniques. Finally, we highlight open research challenges and future directions for advancing blockchain-based DeAI.

cs.LG↗