arXiv:2607.08949v2 Announce Type: replace
Abstract: Directed fuzzing steers fuzzers toward user-defined sink functions to identify vulnerabilities, but it frequently fails to trigger crashes even after long campaigns. We identify two challenges that prevent directed fuzzers from exposing crashes: incomplete static analysis of indirect calls, which leaves reachable paths invisible to distance-based guidance, and lack of semantic guidance for crash preconditions, which blind mutation cannot satisfy within practical time budgets. A natural intervention point is the initial seed corpus: seeds that encode the right control-flow path and satisfy key crash preconditions shift fuzzing from blind exploration to local refinement. Existing seed generation approaches address neither: grammar-based and format-driven methods produce structurally valid inputs with no sink awareness, while LLM-based methods either lack sink targeting or inherit static analysis limitations through one-shot prompting. We present SeedSmith, an agentic LLM pipeline that replicates a security analyst's workflow: starting from a sink, it iteratively explores the codebase, resolves indirect calls, identifies crash preconditions, and synthesizes concrete inputs that satisfy them. Because SeedSmith operates as a seed generation front-end, its seeds are fuzzer-agnostic and improve any downstream mutation-based fuzzer without modification. On Magma, fuzzers using SeedSmith seeds achieve geometric mean crash-time speedups of 11.51 times (AFL++) to 14.66 times (AFLGo) over default seeds. On ARVO, SeedSmith enables fuzzers to trigger 16 previously unreachable bugs spanning 10 projects with diverse input formats.
Science Journals
arXiv:2607.13690v1 Announce Type: cross
Abstract: The $2p\to1s$ transition energy in muonic $^9$Be was measured using a metallic magnetic calorimeter, resulting in $E_{2p\to 1s}=33\,391.48(34)\,$eV. The result is 30 times more precise than the previous best measurement and enables the extraction of the corresponding nuclear charge radius $r_c($$^9$Be$)=2.5506(51)\,$fm. It is $2.4$ times more precise than the commonly used value based on electron scattering and differs from it by $2.3$ times the combined uncertainties. This measurement represents the first determination of a nuclear charge radius using muonic x-ray spectroscopy with microcalorimeters.
arXiv:2607.13710v1 Announce Type: cross
Abstract: Next-generation wireless networks must maintain reliable operation under abrupt and severe disruptions, particularly in ultra-reliable low-latency communication (URLLC) scenarios where strict time constraints dominate system design. This work addresses network resilience from a time-centric perspective by explicitly integrating finite blocklength (FBL) communication, thereby exposing transmission duration as a controllable resource for system recovery. To this end, we propose a unified cross-layer framework that jointly couples queue dynamics, rate adaptation, and blocklength optimization, enabling the system to actively absorb, adapt to, and recover from diverse resilience events.
To systematically evaluate these mechanisms, we introduce an interpretable resilience metric that decomposes disruption impact into absorption loss, adaptation efficiency, and recovery behavior, enabling a direct and intuitive assessment of system resilience. Building on this framework, we develop a three-stage alternating optimization approach that jointly optimizes PHY-layer parameters, including beamforming, reconfigurable intelligent surface (RIS) phase shifts, and blocklength, revealing the importance of time-aware resource allocation in the FBL regime. Numerical results demonstrate strong resilience performance under repeated channel disruptions and AI-driven traffic surges, highlighting the effectiveness of cross-layer resource adaptation. Finally, the proposed resilience metric enables an intuitive and consistent comparison of resilience performance across different approaches and disruption types, while revealing their respective strengths and limitations.
arXiv:2603.28213v2 Announce Type: replace
Abstract: The design of Large Language Models (LLMs) and generative artificial intelligence (GenAI) has been shown to be "unfair" to less-spoken languages (Petrov et al., 2023) and to deepen the digital language divide (Bella et al., 2023). Critical sociolinguistic work has also argued that these technologies are not only made possible by prior sociohistorical processes of linguistic standardisation, often grounded in European nationalist and colonial projects (Migge and Schneider, 2025), but also exacerbate epistemologies of language as "monolithic, monolingual, syntactically standardized systems of meaning" (Schneider, 2024, p. 5). In our paper, we draw on earlier work on the intersections of technology and language policy (Kelly-Holmes, 2019) and bring our respective expertise in critical sociolinguistics and computational linguistics to bear on an interrogation of these arguments. We take two different complexes of non-standard linguistic varieties in our respective repertoires-South Tyrolean dialects, which are widely used in informal communication in South Tyrol, Italy (Alber et al., 2024), as well as varieties of Kurdish-as starting points to an interdisciplinary exploration of the intersections between GenAI and linguistic variation and standardisation. We discuss both how LLMs can be made to deal with non-standard language from a technical perspective, and whether, when or how this can contribute to "democratic and decolonial digital and machine learning strategies" (Migge and Schneider, 2025, p. 12), which has direct policy implications.
arXiv:2607.13211v1 Announce Type: cross
Abstract: Molybdenum disulfide (MoS$_2$) is a semiconductor whose vibrational and excitonic properties are highly sensitive to layer number and structural disorder. We demonstrate the growth of MoS$_2$ monolayers on inert, electronics-compatible SiO$_2$ substrates using room-temperature pulsed laser deposition (PLD). Control of the process parameters enables tuning from monolayer to multilayer films, which we investigate by multiwavelength Raman spectroscopy. The evolution of the Raman-shift difference between the $E_{2g}^{1}$ and $A_{1g}$ modes, combined with an assessment of defect density, tracks film growth as a function of the number of deposition laser pulses. Although excitonic effects strongly influence the optical response of two-dimensional transition-metal dichalcogenides, experimental reports of symmetry-selective exciton-phonon coupling remain limited. We provide experimental evidence of symmetry-dependent exciton-phonon coupling in PLD-grown monolayer MoS$_2$. Specifically, we observe modulation of the resonant behaviour of the out-of-plane $A_{1g}$ and in-plane $E_{2g}^{1}$ modes, related to their different coupling to A excitons, predominantly derived from Mo $d_{z^2}$ orbitals, and C excitons, characterized by mixed orbital contributions from Mo $d_{z^2}$ and S $p_x$ and $p_y$ states. Comparison with mechanically exfoliated monolayers reveals the role of growth-induced defects in modulating these interactions. These findings establish room-temperature PLD as a viable approach for growing two-dimensional MoS$_2$ on inert, electronics-compatible substrates and provide insight into the interplay between excitonic resonances and growth-induced disorder in two-dimensional MoS$_2$.
arXiv:2607.13856v1 Announce Type: new
Abstract: While $1/f$ noise is ubiquitous and has been found in various systems, its physics remains uncertain. From an analytical study of an ordinary diffusion equation, we find an additional example of the $1/f$ noise. The formula for this example, together with existing knowledge about scaling in fluid turbulence, implies a necessary and sufficient condition for the occurrence of any stationary $1/f$ noise. That is, the noise needs to be characterized by two constant frequencies of $f_{\rm low} \ll f_{\rm high}$. For a frequency range from $f = f_{\rm low}$ to $f_{\rm high}$, it is further needed that, except for the mean amplitude of the noise, there is no other constant parameter. Then, at $f_{\rm low} \ll f \ll f_{\rm high}$, the noise scales asymptotically as $1/f$. Being statistical and simple, our condition applies to any system and hence explains the ubiquity of the $1/f$ noise. It is also applicable to some systems with noise of $\alpha \ne 1.0$ for $1/f^{\alpha}$, via intermittency analogous to that of the turbulence.
arXiv:2607.13998v1 Announce Type: new
Abstract: The rapid proliferation of Agentic Artificial Intelligence fundamentally disrupts traditional customer loyalty paradigms. As AI evolves from passive recommendation algorithms to autonomous, goal-directed agents capable of executing purchasing decisions, the conventional understanding of consumer-brand relationships requires a structural reevaluation. By synthesizing extant literature across human-machine teaming, consumer decision-making, and algorithmic trust dynamics, we demonstrate that traditional loyalty models fail to account for algorithmic bounded rationality and constructed autonomy. To address this, we introduce the Dynamic Verifiable Multi-Agent Human Agentic Loyalty Loop (DVM-HALL) model. We formalize brand choice via a softmax probability formulation where human emotional equity, agentic machine-experience utility, calibrated trust, delegated authority, and verifiable execution jointly determine selection. The model features recursive updating mechanisms to dynamically calibrate trust and delegation after each interaction. Crucially, the framework integrates a verifiable execution layer for Decentralized Finance (DeFi) and tokenized loyalty settings, incorporating execution risks -- such as gas costs, slippage, MEV exposure, and smart-contract vulnerabilities -- as core predictors of agentic brand preference. Furthermore, we introduce the Net Human-Agent Score (NHAS), an auditable, risk-weighted metric designed to measure human-agent alignment using human feedback, execution logs, benchmark comparisons, and verifiable receipts. Finally, we propose a comprehensive three-stage empirical validation plan spanning controlled shopping experiments, multi-agent market simulations, and DeFi testbeds. This framework provides the foundational theory required for brands to navigate the impending transition toward machine customers.
arXiv:2607.14032v1 Announce Type: new
Abstract: Telephone broadcasting is a classical model for spreading information in a network. Given a connected graph $G(V,E)$ with source vertex $s$, each informed vertex may inform exactly one uninformed neighbor in every time step. The \textsc{Broadcasting} problem asks whether all vertices can be informed within $t$ steps; the minimum such value is the broadcast time $b(G,s)$. A related variant considers the worst-case source, $b(G)=\max_{u\in V} b(G,u)$. Both variants are NP-hard, and every $n$-vertex graph satisfies $b(G,s)\ge \log_2 n$. Fomin \textit{et al.}~\cite{fomin2023parameterized} recently gave FPT algorithms for this problem under several structural graph parameters. Instead of computing optimal broadcast schedules, we study faster approximation algorithms that produce valid schedules. We improve the $O^*(3^n)$ exact algorithm of Fomin \textit{et al.} to an $O^*((3-f(x))^n)$ algorithm with a $+x$ additive approximation, where $f(x)>0$ is a constant for every fixed $x$. We also give approximation algorithms on graphs of bounded vertex integrity, including a polynomial-time $+2k$ additive approximation algorithm. Complementing these positive results, we prove parameterized hardness for vertex cover above maximum matching ($\mathrm{VC}-\mathrm{MM}$), dominating set size, and graph diameter, indicating that FPT algorithms for these parameters are unlikely. Finally, we present a $+2$ additive approximation algorithm for distance-to-clique running in $O^*(2^{O(k\log k)})$ time, a $2$-factor approximation algorithm for distance-to-path running in XP time, and a polynomial-time algorithm for polar graphs.
arXiv:2607.13952v1 Announce Type: cross
Abstract: Kaonic atoms provide a unique experimental probe of strong interaction in the low-energy regime. In particular, the strong-interaction-induced shift ($\varepsilon_{1\text{s}}$) and width ($\Gamma_{1\text{s}}$) of kaonic hydrogen directly constrain the low-energy antikaon-nucleon ($\bar{K}N$) interaction at threshold and the theoretical description of the $\Lambda$(1405) resonance. We report a new high-precision measurement of kaonic hydrogen X-ray transitions performed by the SIDDHARTA-2 experiment at the DA$\Phi$NE collider (INFN-LNF), based on an integrated luminosity of 237 pb$^{-1}$. The extracted values, $\varepsilon_{1\text{s}}\,=\,-303.0\,\pm\,17.0\,(stat.)\,\pm\,2.5\,(syst.)$ eV and $\Gamma_{1\text{s}}\,=\,607\,\pm\,62\,(stat.)\,\pm\,6\,(syst.)$ eV, represent the most precise determination to date, improving the precision by approximately a factor-of-two with respect to the previous SIDDHARTA measurement. These results significantly tighten the experimental constraints on theoretical description of the low-energy $\bar{K}N$ interaction.
arXiv:2607.14048v1 Announce Type: new
Abstract: In this paper, we investigate a discrete-time SIS epidemic model and the epidemic thresholds on complex networks. We focus on proposing a community-level epidemic threshold set and establishing a comparative result between the local epidemic thresholds and the global epidemic threshold. To verify our theoretical findings and structural properties, we conduct numerical experiments on one synthetic network (Network1) and one real-world network (the Haslemere contact network). Our numerical simulations, along with the computation and statistical ranking of the epidemic threshold sets, align accurately with our theoretical results.
arXiv:2607.14049v1 Announce Type: new
Abstract: The emergence of Chain-of-Thought (CoT) reasoning has significantly enhanced the ability of large language models (LLMs) to tackle complex, multi-step tasks. However, when errors occur, current interaction approaches typically involve re-generating another response that may make mistakes again, or users laboriously flag the faulty step in follow-up turns that may get responses <You are right, I made a mistake here> followed by similar errors recurring. To address this issue, we propose an efficient human intervention mechanism for precisely correcting reasoning errors in LLMs, termed Deep Interaction. Our approach enables direct editing of the original response, allowing erroneous parts to be corrected while preserving accurate reasoning steps. We refine the edited CoT into a distilled prompt, which then steers the LLM along the corrected reasoning path. Experimental results show that our method achieves over a 25% improvement in correction success rate and reduces token usage by approximately 40% on STEM tasks reasoning compared to baseline approaches.
arXiv:2607.14051v1 Announce Type: new
Abstract: Forecasters are evaluated by backtesting, which replays resolved questions and grades the probability the system would have assigned before the outcome was known. For LLMs, two channels leak the answer into this test. A model that retrieves can surface reports written after the event, turning forecasting into a lookup, and each new model is trained on data closer to the event, so a question that lay in the future for last year's models sits inside this year's training data. Either way, the test grades recall while claiming to grade foresight. We introduce Hindcast, which closes both leaks by grading a model as if it stood at a chosen past date $t_0$, before the outcome existed in either channel. Hindcast replays resolved Polymarket prediction markets against a frozen snapshot of public Reddit, lets the model read only posts written before $t_0$, and scores each forecast against both what happened and the market's own price at $t_0$, itself a human forecast made from the same past information. Because the cutoff is set per market and the snapshot never changes, the evaluation re-runs on new markets as models improve, without going stale. Once the leak is closed, retrieval still helps most models, but only where Reddit discussed the event beforehand. Where the archive carried only speculation, retrieval hurts.
arXiv:2604.06130v2 Announce Type: replace
Abstract: The computational cost of concurrent multiscale finite element methods is dominated by the repeated solution of microscopic representative volume element (RVE) problems at macroscopic quadrature points. In this work, we introduce a quantum-classical framework for multiscale finite element analysis (QAFE$^2$) that leverages quantum parallelism to fundamentally alter the scaling of RVE-based homogenisation. At the single-RVE level, the proposed quantum solver attains polylogarithmic complexity with respect to the microscopic discretisation size, yielding an exponential asymptotic speedup over the best available classical solvers. More importantly, QAFE$^2$ exploits quantum superposition and entanglement to evaluate, in a single quantum execution, the entire ensemble of RVE problems associated with all macroscopic quadrature points. This capability is a form of intrinsic quantum concurrency with no classical analogue. Numerical experiments on one- and two-dimensional model problems with known analytical solutions confirm the accuracy of the proposed formulation and verify the theoretical computational scaling and parallel performance.
arXiv:2605.15886v2 Announce Type: replace
Abstract: This paper introduces a dataset of interlinked multimodal political communications from the Russian government, addressing persistent deficiencies in the availability of social text- and image-based data for authoritarian politics contexts. The dataset comprises two large corpora of official speeches delivered by senior actors within the Kremlin and the Russian Ministry of Foreign Affairs over multiple decades. For each speech, we provide Russian- and English-language texts, associated images and captions where available, and harmonized metadata including (e.g.) dates, speakers, (geo)locations, and official government content tags. Unique identifiers link images to speeches and align Russian and English versions of the same communication texts. We further augment these linked datasets with validated topical annotations for both speech texts and speech images, which are generated via transformer-based multimodal topic modeling and refined by a Russian politics expert. The resulting data resources support multimodal, multilingual, temporal, and/or spatial analyses of (authoritarian) political communication and offer a valuable testbed for social science research and large language model (LLM) applications in political domains.
arXiv:2607.13417v1 Announce Type: cross
Abstract: This paper proposes a rigorous framework for sensing of environmental objects using diffraction mechanisms prevalent at wireless communication frequencies. Specifically, we develop a physics-consistent parameterized diffraction channel model, derive maximum likelihood (ML) approaches for estimating the blockage shape, range, and source directions of arrival (DoAs), and quantify fundamental performance limits via the Cram\'er--Rao bound (CRB). In our physics-based modeling, we integrate various approximations for the wave propagation (far-field, paraxial Fresnel, and exact near-field regimes), enabling a wide range of applicability. The underlying model is frequency-agnostic, and we derive Fresnel-number scaling laws that map the diffraction pattern, and hence the estimation problem, across carrier frequency, object size, and range. We quantify the maximum likelihood estimation performance and its relationship to the CRB, and we study the impact of the modeling approximations developed in this work. Numerical results demonstrate that ML estimators closely approach the CRB at moderate to high signal-to-noise ratio (SNR), and highlight the utility of diffraction-based modeling for high-fidelity blockage characterization.
arXiv:2607.13424v1 Announce Type: cross
Abstract: The domination number $\gamma(G)$ of a graph $G$ is the smallest possible size of a vertex set that intersects every radius-$1$ ball of $G$, and the packing number $\rho(G)$ is the maximum number of pairwise vertex-disjoint radius-$1$ balls. We prove that $\frac{\gamma(G)}{\rho(G)}\le 5$ for every planar graph and $\frac{\gamma(G)}{\rho(G)} \le \frac{18\sqrt3}{\pi}\approx 9.924$ for every unit disk graph, thus yielding Erd\H{o}s-P\'osa-type bounds for the hypergraph of radius-$1$ balls in the two graph classes. This improves upon results of Guti\'errez and Paul, and D\'ucz and Gujgiczer, who in turn lowered bounds of Bonamy, Csik\'os, Gujgiczer and Yuditsky, and B\"ohme and Mohar. For both graph classes, the best known lower bound on the optimal constant remains $3$.
arXiv:2607.13995v1 Announce Type: cross
Abstract: Classes of graphs excluding a path and a biclique as induced subgraphs are extensively studied in the literature. One of the key structural results for such graphs is a Ramsey-type result due to Galvin, Rival, and Sands (1982), establishing the existence of a function $f$ bounding the maximum length of a path in terms of clique number $\omega$. We improve the best known bound on $f$ to a function that is a singly exponential in $\omega^c$, for some constant $c$, which we show is best possible, up to optimizing $c$.
Our approach also has consequences for treedepth. In particular, we show that, for graphs excluding a path and a biclique as induced subgraphs, treedepth is bounded by a polynomial function of clique number. In turn, this result implies that every hereditary graph class that admits a function bounding treedepth of graphs in the class in terms of clique number, admits a polynomial such function. This gives a treedepth analogue of a recent result on pathwidth due to Hajebi (2025).
arXiv:2607.13571v1 Announce Type: cross
Abstract: Sound event detection relies on frame-level strong labels whose annotation is expensive. Active learning addresses this problem by selecting the audio segments whose labels help the classifier most. One of the prevailing acquisition strategies for this task, mismatch-first farthest-traversal (MFFT), combines the disagreement between two classifiers and the diversity of the selected segments through hard sequential decisions. It selects whole groups of high-disagreement segments first and spreads only the remaining budget by farthest traversal. On two multi-label datasets we show that this design is blind to the similarity among the selected segments and fails under low budgets, with every mismatch-first variant ending below the plain geometric strategy it builds on. We propose mismatch-weighted facility location (MW-FL), which spends the entire budget through a disagreement-weighted coverage objective that penalizes similarity among the selected segments. The disagreement signal from MFFT is used to obtain the nonnegative weights of this facility-location objective, without introducing hyperparameters. Experiments across two geometric mechanisms with three ways of using disagreement show that coverage of the selected segments is the dominant factor, hard disagreement gating of selection is harmful on both mechanisms, and soft disagreement weighting helps on top of coverage. MW-FL attains the best area under the learning curve on both datasets.
arXiv:2607.13916v1 Announce Type: cross
Abstract: Artificial transaction generation remains an important source of potential market manipulation on cryptocurrency exchanges, as it may distort reported liquidity and reduce market transparency. This study proposes a diagnostic framework for detecting unusual trading patterns based on complexity and statistical-structure measures derived from high-frequency trade-level data. The analysis considers log-returns, trading volume, and transaction counts, using tail distributions, autocorrelation functions, multifractal characteristics, approximate entropy, and detrended cross-correlations. The methodology is applied to BTC, ETH, and XRP traded on Binance, Bitget, KuCoin, and Kraken over the period from April 1 to June 30, 2025. The results reveal a pronounced anomaly on Bitget for BTC and ETH after mid-May 2025. The number of transactions increases sharply, but there is no proportional increase in traded volume or return fluctuations. This regime is characterised by numerous low-volume trades, weaker autocorrelations, reduced multifractal organisation, higher short-pattern irregularity, and weaker cross-correlations involving the transaction-count series. These features are consistent with a noise-like component in trading activity and may indicate artificially increased transaction counts, although they do not provide direct proof of wash trading. The findings show that complexity-based indicators can be useful for detecting exchange-specific trading anomalies that remain hidden in price-based measures.
arXiv:2607.13601v1 Announce Type: cross
Abstract: Microscopic urinalysis is a routine diagnostic test at hospitals. Recent studies have demonstrated the effectiveness of deep learning methods to automate microscopic urinalysis. These methods rely on high-quality images of the urine samples in which each cell is clearly identifiable. However, in practice, the urine sample on a glass slide has a multi-layer structure; hence, all the cells are not clearly visible within the depth of field of a lens focused at a particular focal plane. It demands acquiring multiple images at different focal planes to correctly identify each cell in a given urine sample, which is a time-consuming task.
In this paper, we propose to simplify the task by recording a video, in place of acquiring multiple images, while gradually changing the focus of the lens manually by hand. A typical length of the video is from 2 to 14 seconds. We reconstruct an all-in-focus image from the recorded video frames and apply a deep learning model to detect and classify urine sediments. As a proof of concept, we conduct experiments on 14 videos acquired by a trained lab technician in a usual diagnostic lab environment and show the effectiveness of the proposed automated urinalysis pipeline with our novel reconstruction algorithm.
arXiv:2607.13812v1 Announce Type: cross
Abstract: We introduce TCAM-Diff, a novel 3D medical image generation model that reduces the memory requirements to encode and generate high-resolution 3D data. This model utilizes a decoder-only autoencoder method to learn triplane representation from dense volume and leverages generalization operations to prevent overfitting. Subsequently, it uses a triplane-aware cross-attention diffusion model to learn and integrate these features effectively. Furthermore, the features generated by the diffusion model can be rapidly transformed into 3D volumes using a pre-trained decoder module. Our experiments on three different scales of medical datasets, BrainTumour 128 x 128 x 128, Pancreas 256 x 256 x 256, and Colon 512 x 512 x 512, demonstrate outstanding results. We utilized MSE and SSIM to assess reconstruction quality and leveraged the Wasserstein Generative Adversarial Network (W-GAN) critic to assess generative quality. Comparisons with existing approaches show that our method gives better reconstruction and generation results than other encoder-decoder methods with similar-sized latent spaces.
arXiv:2607.13993v1 Announce Type: cross
Abstract: In the context of dynamical mean-field theory (DMFT) calculations for strongly correlated electron systems, quantum impurity solvers play a central computational role in treating correlated lattice models and realistic materials. Consequently, developing efficient and robust quantum impurity solvers remains a key challenge. In this paper, we present an open-source quantum impurity solver package based on the natural orbitals renormalization group (NORG) method, dubbed $\texttt{iNORG}$. This software delivers high accuracy with reduced computational cost by optimizing the bath representation using natural orbitals and incorporating advanced features such as efficient Hilbert space selection and efficient algorithms for computing Green's functions. We first introduce the basic principle of the NORG method and then discuss the implementation details. The software framework, major features, and installation procedure for $\texttt{iNORG}$ are explained as well. Finally, several simple examples are presented to demonstrate the usage of $\texttt{iNORG}$.
arXiv:2607.13981v1 Announce Type: new
Abstract: We study model checking for an epistemic metric temporal logic with past, interpreted over finite B\"uchi automata under synchronous perfect recall. The logic is motivated by observation-based verification problems such as diagnosis and opacity, where an observer sees only a projection of an execution and reasons about events that may have occurred earlier. These requirements use no alternation between different agents' knowledge. We therefore consider the agent-alternation-free fragment, in which nested knowledge operators must refer to the same agent. We show that model checking for this fragment is EXPSPACE-complete. The lower bound already holds with one agent, one occurrence of the knowledge operator, and no non-trivial metric bounds. For the upper bound, we combine temporal test automata with perfect-recall observers. Because past formulas may have different truth values on indistinguishable histories ending in the same system state, the observer must track temporal automaton states in addition to system states.
arXiv:2607.13701v1 Announce Type: cross
Abstract: Quadratic Sum-Of-Squares (QSOS) optimization problems appear in system identification and machine learning, but standard Schur-complement and second-order cone liftings enlarge conic dimensions and create computational bottlenecks for interior-point methods. This paper introduces a lifting-free regularization that preserves the original conic structure by adding a norm penalty to SOS variables, yielding closed-form primal updates and an unconstrained, concave dual with Lipschitz-continuous gradient. Accelerated first-order methods efficiently maximize this dual, and convergence analysis shows non-asymptotic recovery of the solution. Numerical experiments on constrained regression problems show the proposed method can be 40\% faster than existing solvers such as SCS and handle larger problems than MOSEK, with memory scaling only in the number of equality constraints.
arXiv:2502.13467v2 Announce Type: replace
Abstract: The $K$-Max combinatorial multi-armed bandit problem arises in applications such as recommendation and distributed decision making, where the reward is determined by the maximum outcome among $K$ selected arms. When outcomes are continuous and only the maximum value together with the winner's index is observed, this problem introduces unprecedented difficulties including discretization errors, non-deterministic tie-breaking, and severe estimation biases. To overcome these barriers, we introduce DCK-UCB, an efficient algorithm combining adaptive discretization with bias-corrected confidence bounds. We prove that DCK-UCB achieves a $\widetilde{O}(T^{3/4})$ regret bound, the first sublinear guarantee in this setting. Numerical experiments show strong performance over baseline methods. Furthermore, for the specific case of exponential distributions under full-bandit feedback, we propose the MLE-Exp algorithm that attains a near-optimal $\widetilde{O}(\sqrt{T})$ regret bound. This work establishes fundamental theoretical guarantees and provides a powerful algorithmic solution for continuous combinatorial bandits.