arXiv subjects

Yao Ma

Publications and source records attributed to Yao Ma.

At least 19 recordsLinked to original sources

arXiv2026

SR-Fraud: An Outcome-Supervised Reflective LLM Agent Framework for Non-Stationary Payment Fraud Detection

Real-time payment fraud detection is a non-stationary streaming prediction problem: adversaries adapt before supervised labels mature, and localized burst attacks can cause losses before retraining. Production systems typically rely on tabular classifiers and rules, which can struggle to capture these emerging sequential patterns before periodic retraining occurs. We present SR-Fraud, an outcome-supervised reflective LLM framework that decouples request-time decisions from offline adaptation. A frozen, stateless agent scores each transaction from a Hybrid Episodic Window to track behavioral shifts, while an offline reflection agent proposes boundary hypotheses from matured errors. A deterministic verifier then admits only supported hypotheses into an executable knowledge state. On a production payment-fraud benchmark, SR-Fraud improves all detection metrics over its frozen decision agent, obtains higher point estimates than static and periodically retrained CatBoost, and detects an emerging fraud burst.

cs.LG↗

arXiv2026

On the Construction of Trapdoor Claw-Free Functions with Certifiable Key

Trapdoor claw-free functions (TCFs) underpin much of classical-quantum cryptographic interaction, yet every TCF-based protocol states its guarantees relative to an honestly generated key. We give a family-agnostic abstraction of key certification for (noisy) TCF constructions, built on two notions: a certifiable key relation, an NP relation capturing a family's honest keys with witnesses recoverable from the trapdoor; and certified key generation, which emits with each key a certificate of membership satisfying completeness, certificate soundness with extractability, and key privacy. We instantiate certifiable key relations for different constructions, each met generically by a zero-knowledge argument of knowledge for the relation. As our main application, a generic compiler turns any TCF-based proof of quantumness into a zero-knowledge one, with each security property following from its counterpart in the certification scheme. Finally, we delimit the primitive's reach: for protocols resting on injective invariance, an accepting certificate is itself a family distinguisher, leaking exactly the bit such protocols must hide.

cs.CR↗

arXiv2026

Quantum Structural Renormalization and Anharmonic Stabilization of Superconductivity in P4/mmm YScH8

The recent synthesis of P4/mmm-YScH$_{8}$ at 191.7 GPa with a superconducting critical temperature Tc of 113 K has attracted considerable interest in the study of ternary superhydrides. Here we systematically evaluate the effects of quantum and anharmonic motion of ions in P4/mmm-YScH$_{8}$ using the stochastic self-consistent harmonic approximation (SSCHA). We find that these effects renormalize the crystal structure and lower the dynamical stability threshold pressure from $\sim$140 GPa to $\sim$84 GPa, a reduction of about 40%. Furthermore, neglecting these effects causes a significant overestimation of the $T_{c}$, for instance by $\sim$20 K at 190 GPa. Our prediction of $T_{c}$ at 190 GPa is 113 K ($μ^{*}$= 0.13), close to the experimental observation. Analysis of the phonon density of states and Eliashberg spectral function further reveals site-dependent contributions to superconductivity. These results establish that a consistent treatment of nuclear quantum motion and anharmonicity is essential for accurately predicting superconductivity in compressed ternary hydrides.

cond-mat.supr-con↗

arXiv2026

Investigation of S-wave tetraquark bound and resonant states with all Jacobi coordinates

We systematically explore the $S$-wave tetraquark systems $Qs\bar{n}\bar{n}$, $QQ\bar{n}\bar{n}$, $QQ\bar{Q}\bar{Q}$, and $ss\bar{s}\bar{s}$ ($Q=c,b$; $n=u,d$) within the constituent quark potential model. We incorporate all K-type Jacobi coordinates in addition to the conventional H-type configurations, optimize the basis expansion via a stochastic parameter generation strategy, and apply the complex scaling method to identify bound and resonant states. Our calculations demonstrate that while conventional H-type configurations suffice for low-lying states such as the $T_{cc}(3875)^+$ molecular candidate, the inclusion of K-type configurations becomes important for extracting highly excited resonances, allowing higher-energy resonances absent in H-only calculations to be identified. Furthermore, we identify resonance candidates for the $T_{cs0}(2900)$, $X(6900)$, and $X(7200)$, whereas the absence of fully-strange compact poles below 2.6 GeV challenges the interpretation of $ϕ(2170)$ and $X(2370)$ as $S$-wave compact $s s \bar{s} \bar{s}$ tetraquarks.

hep-ph↗

arXiv2026

Bound and Resonant Spectra of Few-Lepton Coulomb Systems

We present a unified calculation of bound and resonant states in purely leptonic Coulomb systems: $e^-e^-e^+$ ($\mathrm{Ps}^-$), $μ^+e^-e^-$ ($\mathrm{Mu}^-$), $μ^+μ^+e^-$ ($\mathrm{Mu}_2^+$), $e^+e^+e^-e^-$ ($\mathrm{Ps}_2$), and $μ^+μ^+e^-e^-$ ($\mathrm{Mu}_2$). Using an extended stochastic variational method combined with the complex scaling method, we resolve the natural-parity $S$- and $P$-wave spectra. Near their respective $n=2$ thresholds, all three trilepton systems exhibit Gailitis--Damburg sequences generated by $2S$ and $2P$ Stark mixing and the resulting inverse-square attraction. Although microscopically distinct from the Efimov effect, this mechanism produces the same inverse-square asymptotics and geometric scaling. The $\mathrm{Ps}^-$ and $\mathrm{Mu}^-$ systems exhibit resonance sequences of comparable density, whereas the $\mathrm{Mu}_2^+$ produces a much denser spectrum, with 20 resolved members in the $^3P^o$ channel. In $\mathrm{Mu}_2^+$, the deeper states follow molecular Born--Oppenheimer configurations, while the near-threshold spectrum is governed by the atomic $\mathrm{Mu}(2)+μ^+$ structure. In $\mathrm{Ps}_2$, coupling between threshold-degenerate configurations is essential for a near-threshold bound state. In $\mathrm{Mu}_2$, the Born--Oppenheimer organization of the resonance spectrum is channel dependent.

physics.atom-ph↗

arXiv2026

White dwarf-neutron star matter transition and the effect of light elements

White dwarfs and neutron stars are unique laboratories for dense nuclear matter physics. We develop a single relativistic mean-field framework that treats both classes of compact star, and the transition between them, on the same footing: the nuclei of white-dwarf matter are solved self-consistently as Wigner-Seitz cells with the full electromagnetic interaction, while the same Lagrangian yields the uniform nuclear matter of the neutron-star interior. Within this unified description we compute light-element white dwarfs seeded by $^4$He, $^{12}$C, and $^{16}$O, following each fixed-$A$ sequence along its neutronization path and connecting it to the neutron-star branch through exact Maxwell junctions, from which the corresponding mass-radius relations are derived. The helium- and carbon-seeded white-dwarf sequences attain maximum masses of ${\sim}1.4\,M_\odot$ and ${\sim}1.0\,M_\odot$, respectively. On the neutron-star branch, the retained light-element envelope changes the predicted radii only at the percent level---by approximately $0.2~$km at $1.4\,M_\odot$, within current observational uncertainties. Providing a consistent zero-temperature equation of state from white-dwarf to neutron-star densities, this unified framework offers a natural starting point for studies of white-dwarf--neutron-star binary mergers, progenitor-star evolution, decihertz gravitational-wave sources, and related multimessenger phenomena.

nucl-th↗

arXiv2026

Unified Multi-Dimensional Benchmark for Complex Graph Reasoning in Large Language Models

Graph reasoning provides a promising testbed for evaluating the reasoning ability of large language models (LLMs), as graph instances can be programmatically generated, structurally controlled, and naturally scaled to long-input settings. However, existing graph reasoning benchmarks have limited coverage of data complexity, rely heavily on manual construction, and lack unified evaluation across text-based and code-based reasoning modes. To address these limitations, we propose {\dataset}, a five-stage \textit{semi-automatic} framework for constructing complex graph reasoning benchmarks. It expands benchmark coverage along five dimensions: \textit{Graph Size}, \textit{Task Complexity}, \textit{Task Description}, \textit{Graph Loading}, and \textit{Task Source}. The framework uses an LLM-based data generator to automatically produce task descriptions, graph data, reference solutions, graph-loading scripts, question forms, and evaluation scripts, while retaining human validation at key quality-control stages. Based on it, we construct a benchmark with $202$ tasks and evaluate LLMs under text-based, code-based, and augmented reasoning settings. Experiments show that the complexity dimensions reveal model limitations that are less visible in existing benchmarks; existing fine-tuned models struggle to generalize to GraphGym, whereas retrieval-augmented methods show scenario-dependent adaptability, improving textual reasoning but not consistently improving coding reasoning. These findings suggest that ours serves as a challenging and diagnostic benchmark for graph reasoning and provides empirical guidance for future enhancement methods. Code and dataset will be published soon.

cs.CL↗

arXiv2026

PerfAgent: Profiler-Guided Iterative Refinement for Repository-Level Code Optimization

Large language model (LLM) agents now perform well on correctness-oriented repository-level tasks, including SWE-Bench issue resolution and feature implementation in real codebases. However, they still struggle with repository-level code optimization, which requires preserving behavior while improving runtime performance. Passing tests is not enough in this setting; a patch must preserve behavior, implement code optimization, and approach expert speedups. Current agents often miss bottlenecks hidden behind abstraction layers and native extensions, stop after shallow speedups, or insufficiently test the code patches that thus may silently break edge cases. We present PerfAgent, a profiler-guided, verifier-in-the-loop workflow that gives an off-the-shelf coding agent the feedback needed to find real hotspots, improve beyond the first passing patch, and use profiler evidence rather than timing alone to decide what to optimize next. On two challenging optimization benchmarks, GSO and SWE-fficiency-Lite, PerfAgent more than doubles the rate of expert-matching patches over OpenHands with GPT-5.1, improving from 19.6% to 39.2% on GSO and from 26% to 74% on SWE-fficiency-Lite. It also surpasses an oracle best-of-five baseline at substantially lower cost, showing that the gains come from better feedback rather than additional test-time sampling.

cs.SE↗

arXiv2026

Structural and Vibrational Properties of D$_3$Se from First Principles: Anharmonic Quantum and Isotope Effects

Hydrogen-rich superconductors have garnered considerable interest following the discovery of hot superconductivity in high pressure H$_3$S, reviving prospects for room temperature superconductors under high-pressures. Using H$_3$Se as a reference system, we investigate the vibrational and superconducting properties of D$_3$Se in the Im\bar{3}m phase across 60-200 GPa by combining first-principles calculations with the stochastic self-consistent harmonic approximation to treat ionic quantum and anharmonic effects. These effects introduce significant renormalization to the phonon spectra and stabilize the lattice down to at least 70 GPa, well below the harmonic prediction of >110 GPa. Ultimately, the phonon renormalizations alter the electron-phonon coupling, introducing a decrease in the superconducting critical temperature by about 3-16 K across the studied pressure range with respect to standard calculations. Including anharmonic phonons within the Migdal-Eliashberg theory yields $T_c \approx 154$ K at 75 GPa (with $μ^* = 0.1$, $λ = 3.0$), highlighting D3Se as a promising high-Tc superconductor at moderate pressures. Examining the role of anharmonicity in the isotope effect, we find that at 200 GPa it suppresses the isotope coefficient $α$ to 0.29 one third below the harmonic value (0.44) which approaches the BCS limit of 0.5. This dramatic reduction demonstrates that anharmonicity fundamentally governs the isotope effect on this system. The stark discrepancy between anharmonic and harmonic descriptions underscores the need for targeted experimental efforts to resolve the origin of the persistent theory-experiment discrepancy in compressed hydrides.

cond-mat.supr-con↗

arXiv2026

Spacelike-Collinear Scattering by the Method of Regions

We study the spacelike-collinear limit of gauge-theory scattering amplitudes using the Method of Regions. The corresponding splitting amplitude violates strict collinear factorisation through its dependence on the non-collinear partons. While the associated colour dependence has long been known, starting at two loops the splitting amplitude also acquires dependence on their kinematics. We show that this kinematic dependence originates from a unique hidden region present in the asymptotic expansion of the five-point amplitude in the spacelike-collinear limit, but absent in the timelike limit. More generally, we propose that hidden regions provide the mechanism by which crossing-related asymptotic limits cease to be analytically connected. We develop a general algorithm for the systematic identification of hidden regions. Applying it to the five-point amplitude in super Yang-Mills theory, we compute the hidden-region contributions to the complete set of basis integrals and recover the exact kinematically dependent factorisation-violating splitting amplitude. In momentum space, the hidden region is characterised by soft and Glauber loop momenta. This explains why the Wilson-line calculation captures the complete kinematic dependence, thereby accounting for the observed universality across gauge theories.

hep-ph↗

arXiv2026

NNStar: An end-to-end AI agent for nuclear matter and neutron star physics

Constraining the equation of state of dense matter requires confronting effective models with massive data that spans many orders of magnitude in scale, from sub-saturation nuclear matter properties to the masses, radii, and tidal deformabilities of neutron stars. Exploring the high-dimensional coupling space of such a model and fine tuning it against all of these constraints is a labor- and time-intensive task. We present \textsc{NNStar}, an end-to-end artificial-intelligence agent that automates this workflow. Rather than a bespoke application, \textsc{NNStar} is delivered as a portable \emph{skill} for an open large-language-model (LLM) agent platform -- a self-describing module that pairs worked usage conventions with symbolic and numerical physics engines that (i) build a relativistic mean-field model directly from a Lagrangian, (ii) solve the mean-field equations of motion and evaluate the saturation properties, (iii) construct the $β$-equilibrium equation of state, splice it to a crust, and integrate the Tolman--Oppenheimer--Volkoff equations, and (iv) score the resulting predictions through a Bayesian joint analysis against nuclear matter and astrophysical observations. The agent can read a model, fit its parameters, and report the full set of nuclear matter and neutron star observables without human intervention. \textsc{NNStar} therefore provides a new, AI-driven framework for analyzing nuclear matter and neutron-star observations.

nucl-th↗

arXiv2026

Compact star and compact star matter properties from a baryonic extended linear sigma model with explicit chiral symmetry breaking

Based on a baryonic extended linear sigma model including explicit chiral symmetry breaking effect, the structure of neutron stars with the emergence of hyperons is investigated using the relativistic mean field approximation. It is found that, except for the lightest scalar meson $σ$ whose structure is not well understood so far, the vacuum mass spectra of relevant hadrons and nuclear matter properties around saturation density can be well reproduced. Nevertheless, based on the present model and the applied relativistic mean field approach, we found that, to have a realistic mass-radius relation of neutron stars, the $πN$ sigma term $σ_{πN}$ that denotes the contribution of explicit symmetry breaking should deviate from its empirical values at vacuum. Specifically, $σ_{πN}\sim -600$ MeV, rather than $(32\text{--}89) \rm \ MeV$ at vacuum. With an appropriate choice of $σ_{πN}$ and $K(n_0)$, our framework can give a more observationally favored mass-radius relation of neutron stars with the emergence of hyperons, suggesting a possible density dependence of the low energy constants, at least within the present leading order framework with the relativistic mean field approach. The present result provides a new perspective on the relation between microscopic explicit chiral symmetry breaking in dense matter and macroscopic structure of compact stars and calls for more systematic treatments beyond leading order relativistic mean field calculation.

nucl-th↗

arXiv2026

Identifying and Resolving Pitfalls of Knowledge-Based VQA Benchmarks: Auditing, Repairing, and Augmenting

Knowledge-Based Visual Question Answering (KB-VQA) aims to evaluate whether Visual Language Models (VLMs) can retrieve, ground, and reason over external structured knowledge beyond visual evidence. In practice, answer accuracy is widely adopted as the primary evaluation metric, implicitly treating correctness as a proxy for knowledge-grounded reasoning. However, for existing KB-VQA benchmarks, this proxy relies on critical assumptions that are often overlooked and rendered unreliable by benchmark issues: annotated answer must be derivable from the associated knowledge base, question must be well-posed with sufficient constraints, and visual setting must meaningfully require grounded disambiguation. In this work, we show that these assumptions are systematically violated in existing KB-VQA benchmarks. Our audit reveals substantial instances with missing or contradicted answers and underspecified questions that render accuracy a misleading metric. Furthermore, we find that existing datasets rely on visually trivial, single-entity scenes that bypass the need for sophisticated visual-to-knowledge mapping. We demonstrate that even with controlled architectures, these flaws lead to distorted model rankings and overestimations of reasoning capabilities. To address this, we introduce (1) a principled audit-and-repair protocol that restores answer derivability and question clarity, and (2) a controlled multi-entity augmentation protocol that introduces visual ambiguity to challenge initial retrieval and grounded reasoning. Re-evaluation under corrected and augmented settings yields markedly different performance trends. Our findings call for rethinking evaluation protocols and designing more interaction-aware KB-VQA benchmarks that prioritize verifiable reasoning over simple matching.

cs.CL↗

arXiv2026

Ground Then Rank: Revisiting Knowledge-Based VQA with Training-Free Entity Identification

Knowledge-Based Visual Question Answering (KB-VQA) requires grounding visual queries to external knowledge beyond directly observable content in images. While recent multi modal large language models (MLLMs) show strong perceptual abilities, they struggle on KB-VQA tasks requiring groundings from both fine-grained entity and evidence levels. Most existing multi-modal retrieval augmented generation (MM-RAG) methods tightly couple entity discrimination and section-level evidence ranking into a single re-ranking stage, leading to high cost and limited generalization. In this work, we revisit existing MM-RAG solutions from a workflow perspective and argue both entity-level and fact-level groundings are key bottlenecks. We observe that although MLLMs often fail under open-ended entity naming, they can better identify the correct entity when selecting from a small set of candidate names. Based on this insight, we propose a simple and training-free identify-before-answer IBA framework that decouples entity identification from section-level re-ranking. Our approach prompts an MLLM to select high-confidence entities using only candidate names, followed by an off-the-shelf textual re-ranker for evidence selection. Experiments on Encyclopedic-VQA and InfoSeek show that our method consistently outperforms fine-tuned multi-modal re-ranking baselines while reducing training and inference complexity. Additional analyses reveal that the improvements arise not only from better entity identification, but also from selecting more informative evidence once correct entity is fixed. Our implementation is made public to ease reproducibility.

cs.CL↗

arXiv2026

Emergent Strategic Reasoning Risks in AI: A Taxonomy-Driven Evaluation Framework

As reasoning capacity and deployment scope grow in tandem, large language models (LLMs) gain the capacity to engage in behaviors that serve their own objectives, a class of risks we term Emergent Strategic Reasoning Risks (ESRRs). These include, but are not limited to, deception (intentionally misleading users or evaluators), evaluation gaming (strategically manipulating performance during safety testing), and reward hacking (exploiting misspecified objectives). Systematically understanding and benchmarking these risks remains an open challenge. To address this gap, we introduce ESRRSim, a taxonomy-driven agentic framework for automated behavioral risk evaluation. We construct an extensible risk taxonomy of 7 categories, which is decomposed into 20 subcategories. ESRRSim generates evaluation scenarios designed to elicit faithful reasoning, paired with dual rubrics assessing both model responses and reasoning traces, in a judge-agnostic and scalable architecture. Evaluation across 11 reasoning LLMs reveals substantial variation in risk profiles (detection rates ranging 14.45%-72.72%), with dramatic generational improvements suggesting models may increasingly recognize and adapt to evaluation contexts.

cs.AI↗

arXiv2026

A Wall Function for Turbulent Boundary Layers under Rotation via Symbolic Regression

This study employs symbolic regression to derive physically interpretable, white-box wall-function expressions for turbulent boundary layers under system rotation. Flows in a rotating frame are subject to Coriolis forces, which deflect the boundary layer profile from static case. The classical law of the wall, formulated under non-rotating conditions, is ill-suited to describing the effects of rotation. To obtain the wall function under rotation, we examine the deflection behavior of the turbulent boundary layers on the leading and trailing sides, and construct wall functions that are valid over a wide range of rotation numbers. The analytical expressions show that, as the rotation effect intensifies, the boundary layer on the leading side contracts whereas that on the trailing side expands, and the leading side exhibits a tendency towards relaminarization, consistent with high-fidelity numerical results. The resulting symbolic expressions are compact and interpretable. The wall functions obtained in this study complement conventional wall functions, and provide a new avenue for turbulence model closure subject to system rotation.

physics.flu-dyn↗

arXiv2026

Unifying and Optimizing Data Values for Selection via Sequential Decision-Making

Data selection has emerged as a crucial downstream application of data valuation, yet the theoretical foundations for using data values in selection remain underexplored. We reformulate data selection as a sequential decision-making problem where the optimal selection sequence arises from dynamic programming, and data values can be understood as encodings of this optimal sequence. This framework unifies and reinterprets existing methods like Data Shapley through the lens of approximate dynamic programming, revealing them as myopic linear approximations to the sequential problem. We further analyze how selection optimality degrades with utility curvature under submodularity, explaining when and why these approximations fail. To bridge theory and practice, we propose an efficient bipartite graph-based surrogate that preserves submodular structure while enabling scalable greedy selection with provable guarantees. Experiments on classical ML benchmarks and large-scale LLM fine-tuning data selection demonstrate substantial improvements over existing methods. Code is publicly available at https://github.com/frankhlchi/SeqDataVal

cs.AI↗

arXiv2026

ARES: Adaptive Red-Teaming and End-to-End Repair of Policy-Reward System

Reinforcement Learning from Human Feedback (RLHF) is central to aligning Large Language Models (LLMs), yet it introduces a critical vulnerability: an imperfect Reward Model (RM) can become a single point of failure when it fails to penalize unsafe behaviors. While existing red-teaming approaches primarily target policy-level weaknesses, they overlook what we term systemic weaknesses cases where both the core LLM and the RM fail in tandem. We present ARES, a framework that systematically discovers and mitigates such dual vulnerabilities. ARES employs a ``Safety Mentor'' that dynamically composes semantically coherent adversarial prompts by combining structured component types (topics, personas, tactics, goals) and generates corresponding malicious and safe responses. This dual-targeting approach exposes weaknesses in both the core LLM and the RM simultaneously. Using the vulnerabilities gained, ARES implements a two-stage repair process: first fine-tuning the RM to better detect harmful content, then leveraging the improved RM to optimize the core model. Experiments across multiple adversarial safety benchmarks demonstrate that ARES substantially enhances safety robustness while preserving model capabilities, establishing a new paradigm for comprehensive RLHF safety alignment.

cs.AI↗