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Yu Shi

Publications and source records attributed to Yu Shi.

At least 19 recordsLinked to original sources
arXiv2026

Tracing Gluon Saturation through Hadronization at EIC

Gluon saturation provides a window into the nonlinear nature of the strong interaction in nuclear matter. One direct consequence of saturation is the $\boldsymbol{k}_T$ broadening in the final state.We investigate how hadronization reshapes conventional signatures of gluon saturation at EIC within a complete event-generator framework. To this end, we implement in eHIJING an initial-state-radiation algorithm based on nonlinear small-$x$ evolution and complete the events with beam remnants, final-state radiation, and hadronization. In our simulations, the signal of parton-level nuclear $\boldsymbol{k}_T$ broadening is strongly diluted in both the nucleon energy correlator and leading-dihadron azimuthal decorrelation after hadronization. Global hadronic recoil, by contrast, remains sensitive to the underlying $\boldsymbol{k}_T$ broadening. We further demonstrate that Bayesian unfolding of the global hadronic recoil provides access to the underlying hard-scattering $\boldsymbol{k}_T$ distribution. These results establish the global hadronic recoil as a promising saturation observable at the EIC.

hep-ph↗
arXiv2026

Holographic subregion complexity in insulator/superconductor transition

We study holographic subregion complexity (HSC) across a fully backreacted insulator/superconductor transition in an AdS-soliton background and compare it with holographic entanglement entropy (HEE) and holographic complexity based on the complexity=volume (CV) proposal. Both HSC and HEE signal the second-order transition. For a strip subsystem, competing connected and disconnected Ryu-Takayanagi surfaces give rise to a confinement/deconfinement transition. At fixed chemical potential in the superconducting phase, HSC exhibits a finite jump at the critical width, whereas HEE remains continuous. Beyond this width, HSC grows linearly with the strip width, while HEE is constant. At fixed strip width, HSC first decreases and then increases with chemical potential for $\ell<\ell_c$, opposite to HEE, but increases monotonically for $\ell>\ell_c$. After consistent normalization and subtraction of the respective insulating references, the half-space HSC and CV complexity densities are analytically identical. These results show that HSC can diagnose the insulator/superconductor transition, but its qualitative response remains sensitive to the subsystem scale and entanglement-wedge topology.

hep-th↗
arXiv2026

Quantum Fisher Information Revealing Parameter Sensitivity in Long-Baseline Neutrino Experiments

Determination of the leptonic CP-violating phase $δ_{\rm CP}$, the atmospheric mixing angle $θ_{23}$, and the mass-squared difference $Δm_{31}^2$ constitutes a primary objective of current and next-generation long-baseline neutrino experiments. We employ Quantum Fisher Information (QFI) to quantify the maximum information that the neutrino quantum state contains about these oscillation parameters, treating the neutrino as an evolving pure quantum state. Computing the QFI as a function of the baseline-to-energy ratio $L/E$ for benchmark parameter sets from NuFit-6.0, we find distinct sensitivity hierarchies and $L/E$-dependent structures. Specifically, $δ_{\rm CP}$ and $θ_{23}$ exhibit bimodal QFI profiles with peaks around $L/E \sim 500$ and $1500~\mathrm{km/GeV}$, reaching $F_Q(δ_{\rm CP}) \sim 0.15$ and $F_Q(θ_{23}) \sim 15$, respectively. In contrast, $F_Q(Δm_{31}^2)$ increases rapidly with $L/E$ over the range considered, reaching values of order $10^7$. This hierarchy indicates that, within the considered single-parameter framework, the neutrino state carries substantially greater intrinsic quantum sensitivity to $Δm_{31}^2$ than to $θ_{23}$ or $δ_{\rm CP}$. We further compare the QFI with the classical Fisher information (CFI) obtained from flavor-transition probabilities, showing how much of the available information can be extracted through this specific measurement. For a fixed DUNE baseline, matter effects substantially enhance the QFI for $δ_{\rm CP}$ in the few-GeV region, while producing only minor modifications for $θ_{23}$ and $Δm_{31}^2$. These results show that the information available for estimating the three oscillation parameters differs significantly, and that the flavor-probability measurement does not always extract all the information available in the quantum state.

hep-ph↗
arXiv2026

$D$-dimensional aether charged black hole and aether waves in M-subclass of Einstein-aether theory

We obtain an exact $D$-dimensional aether charged black hole solution and gravitational wave polarizations in the M-subclass of the Einstein-aether theory with Lorentz invariance violated by an unit norm vector field---the aether field $u^μ$. This aether field can be timelike or spacelike and, the aether charge $Q_æ$ has a nonzero minimum value, which is different from the electric charge. The aether electric-like potential $u_t$ is regular, and the aether magnetic-like potential $u_r$ is singular at the horizons. Though the Lorentz symmetry is broken, the Smarr formula and the first law of black hole thermodynamics can be exactly constructed via the extended method of Killing potential. However, we find this conception of the aether charge doesn't exist in the $c_i$ subclass of the Einstein-aether theory. For the linearized M-subclass Einstein-aether theory with the timelike aether field, we find the speed of spin-2 modes (the usual gravitational wave) is still equal to 1. But there is only one polarization $γ_{12}$ that can propagate, while the other one, $γ_{11}$ cannot propagate for $D=4$ due to the effect of Lorentz symmetry breaking. The speed of spin-1 modes (the transverse aether wave) is also equal to 1. The third kind mode is the longitudinal aether-metric mode, which is linearly time dependent and not the spin-0 mode reported in the $c_i$ subclass Einstein-aether theory.

gr-qc↗
arXiv2026

Exploring $Z/γ$-mediated heavy FCNCs at the FCC-ee

The flavor structure of the Standard Model (SM) remains one of the most compelling questions in particle physics, with the third generation being particularly intriguing due to its significantly larger masses and comparatively less precisely measured properties. These features make third-generation flavor transitions particularly interesting in context of search for physics beyond the SM. In this work, we investigate flavor-violating transitions between the third and the first two generations, mediated by the neutral gauge bosons ($Z/γ$), within the framework of the SM Effective Field Theory (SMEFT), using dipole and Higgs-current operators. We determine the optimal sensitivities using the optimal observable technique (OOT) at different center-of-mass energies of the upcoming Future Circular Collider in the $e^+e^-$ mode (FCC-ee). We further derive complementary constraints on the relevant SMEFT operators from low-energy flavor-violating observables and heavy fermion decay channels. Our analysis also reveals characteristic interference patterns among the dipole contributions, which depend on the underlying flavor transition and exhibit distinct behavior between the $Z$ pole and higher-energy FCC-ee stages. The FCC-ee provides a complementary and direct probe of flavor-violating interactions at the electroweak scale, with the projections showing improved sensitivity for several interactions and comparable sensitivity to existing flavor constraints for several others. This highlights the importance of a systematic assessment across the different FCC-ee energy stages, which provides a comprehensive picture of its potential to explore flavor-violating phenomena and its complementarity with low-energy flavor experiments.

hep-ph↗
arXiv2026

EVOMAL: Self-Poisoning in Self-Evolving Coding Agents

Self-evolving LLM coding agents write their own tools by imitating retrieved skills from shared skill libraries. We identify a vulnerability in this loop: during authoring, a retrieved malicious skill can become the template for a new skill that preserves the payload. We call this self-poisoning: the agent authors, stores, and runs the resulting malicious skill. We exploit it through EvoMal, an attack that amplifies self-poisoning by wrapping an interchangeable payload in a banner, a set of benign-looking structural elements that induces an imitating agent to reproduce the enclosed code. The attacker plants malicious skills in the library without invoking them. The agent then authors and executes new skills carrying the harmful code. Each authored copy can re-enter the library and be imitated again, forming a self-propagating worm that persists after the planted skills are removed. We define the agent self-poisoning rate (ASPR) as the fraction of tasks that add a newly authored malicious skill to the library. Across six models on 153 tool-relevant SWE-bench Verified tasks, ASPR ranges from 20.3% to 41.8%, and the poisoned libraries hold 4.9 to 9.0 times as many malicious skills as were planted. The vulnerability also appears without a banner: DeepSeek-V4-Pro reaches 11.1% ASPR with the payload alone. Tailoring the planted skill descriptions to one task family raises ASPR to 86.7%. After the planted skills are removed, Qwen3 retains a round-5 ASPR of 68% because agent-authored copies remain. These copies evade existing defenses, which focus on attacker-submitted names, code, and signatures. We propose counter-prompt, a defense that discourages banner-style copying and reduces EvoMal's ASPR to at most 6.7% with no significant task-completion loss.

cs.CR↗
arXiv2026

SkillShield: Prompt-Space Security Skills for LLM Coding Agents

A coding agent edits files and executes shell commands with its developer's privileges, allowing malicious requests to translate directly into harmful actions or functional malware. Existing defenses have complementary limitations: weight-level alignment is unavailable to API-only deployers, whereas input filters and execution-boundary monitors require auxiliary classification or checking components along the agent's trajectory. We therefore introduce SkillShield, a system-prompt defense that synthesizes security skills offline from known attacks or recorded agent failures. These skills are injected into the system prompt at session start and remain active throughout the tool-use loop. Unlike a reference monitor, they protect the system by defining the security policies the model should follow during execution. Due to the limited system-prompt space, we examine three fixed-budget provisioning scopes: all-classes, with one skill covering all threat classes, per-bundle, with one skill targeting a related subset, and per-class, with one skill dedicated to a single known class and used as the upper-bound reference. None requires runtime request classification or routing. Across six large language models on RedCode, the default all-classes skill reduces malware-generation severity from 3.37 to 0.58 and achieves a 43.6% execution attack success rate, comparable to Llama Guard 3's 42.7% without its separate 8B classifier. The per-bundle and class-fixed per-class settings further reduce this rate to 36.2% and 14.5%, respectively. Under two non-adaptive jailbreak families, SkillShield continues to outperform all baselines on malware generation. Across 731 benign task descriptions, SkillShield yields a mean safety-refusal rate of 0.14%. These results demonstrate the potential of prompt-space security skills to prevent harmful actions and malware generation for LLM coding agents.

cs.CR↗
arXiv2026

Beyond Success and Failure: Length-Aware Contrastive Learning for GUI Agents

Graphical User Interface (GUI) agents powered by Multimodal Large Language Models (MLLMs) have shown strong potential for automating tasks across diverse digital environments, where reinforcement learning (RL) has become a dominant training paradigm. However, widely used methods such as Group Relative Policy Optimization (GRPO) suffer from reward-gradient misalignment, leading to inefficient and unstable optimization. Recent work addresses this issue by reformulating RL with verifiable rewards (RLVR) as contrastive or classification-based objectives, which improve stability by eliminating problematic gradient behaviors. Despite this progress, existing contrastive RLVR methods rely primarily on outcome-level supervision and fail to capture fine-grained differences in trajectory quality within the same outcome category. In this paper, we propose Length-Aware Contrastive Learning for GUI Agents (LACL-GUI), a contrastive RLVR framework that incorporates trajectory-level quality signals into policy optimization. LACL-GUI introduces structured preferences within both successful and failed trajectories, encouraging concise successful executions and differentiating failure quality based on divergence from successful trajectories, while preserving optimization stability. Experiments on GUI agent benchmarks show that LACL-GUI provides more effective learning signals and consistently improves agent performance over prior methods, highlighting the value of trajectory-level supervision in contrastive RLVR.

cs.AI↗
arXiv2026

Holographic subregion complexity in unbalanced Stückelberg holographic superconductors

Within the subregion complexity-volume conjecture, we numerically compare holographic subregion complexity (HSC) and holographic entanglement entropy (HEE) for a strip in unbalanced Stückelberg holographic superconductors. Varying the Stückelberg parameter $γ$ yields both second- and first-order transitions. Both observables signal these transitions, but with markedly different robustness. The qualitative HEE signatures persist across strip widths, and the finite part of HEE remains smaller in the superconducting phase than in the normal phase. The HSC is instead strongly width dependent: its temperature trend is opposite to that of HEE at small $\ell$ and agrees with it at large $\ell$. Consequently, the superconducting and normal HSC branches reverse their relative ordering, creating a crossover region where they nearly coincide. There, HSC alone cannot reliably determine the occurrence or order of the transition, and the physical branch must be selected from the grand potential. Thus, HEE provides a more robust diagnostic, whereas HSC is a scale-dependent probe whose interpretation depends explicitly on the subsystem size.

hep-th↗
arXiv2026

Refusing Intent, Not Form: Wrapper-Based Intent-Group Supervision for LLM Safety

Safety tuning can improve harmful refusal, but models may learn surface-form shortcuts: wrapped harmful prompts bypass safety, while similarly wrapped benign prompts are over-refused. We propose Wrapper-Based Intent-Form Augmentation (WIFA), an automatic intent-group augmentation method that pairs wrapped harmful examples with structurally matched wrapped benign counterexamples, requiring no external teacher or manual per-wrapper intent labels. We use WIFA as a common data layer for two complementary fine-tuning routes: WIFA-Boost, a two-stage high-safety recipe, and Anchored Group-Consistent Refusal Training (A-GCRT), which regularizes refusal/compliance decision scores across same-intent wrappers and anchors harmful and benign groups on opposite sides of a margin. In the Qwen setting, WIFA-Boost reaches the strongest transformed-harmful refusal, while A-GCRT reduces OR-Bench over-refusal from 25.7\% for the base model to 17.4\%; reproduced baselines do not match these operating points. Llama results and ablations over data structure, two-stage order, and A-GCRT components support this intent-group interpretation without claiming universal below-base over-refusal.

cs.CL↗
arXiv2026

Quintessence model in the late Universe

Scalar fields have shown great potential in inducing tailored modifications compared to cosmic evolution in the $Λ$CDM model. We investigate a quintessence model with five physically motivated scalar field potentials: the general power-law, quadratic, fifth-power, hyperbolic sinh, and axion-like potentials. The model parameters are constrained by using the late-time cosmological observations, including Cosmic Chronometers (CC), Pantheon$^{+}$, BAO, and DESI DR2 data. To assess the influence of the local distance-ladder calibration, we additionally perform a separate analysis using the Pantheon$^{+}$\& SH0ES compilation. Our analysis shows that the inclusion of the SH0ES calibration systematically shifts the inferred Hubble constant toward higher values, whereas the addition of BAO and DESI DR2 data prefers comparatively lower values and significantly reduces parameter degeneracies. We center the action of these models in the late Universe which leaves early $Λ$CDM cosmology unchanged. Across all five potentials, an inverse correlation between $H_0$ and $Ω_{m0}$ is consistently observed, indicating that higher values of the Hubble constant are associated with a lower present-day matter density and a stronger contribution from dark energy to the current accelerated expansion.

gr-qc↗
arXiv2026

Gluonic nucleon energy correlators and fracture functions for Color Glass Condensate

Nucleon energy correlators (NECs) and fracture functions provide novel tools for probing nucleon and nuclear structure at small $x$ through measurements in the target fragmentation region (TFR) of deep inelastic scattering (DIS). We investigate gluonic NECs and fracture functions using the Color Glass Condensate effective theory at eikonal accuracy. We find that only the unpolarized and linearly polarized gluon components in an unpolarized target are nonvanishing at this order, and that both are determined by the adjoint dipole $S$-matrix. Furthermore, we show that the linearly polarized gluonic NEC $h_{1}^{t,g}$ generates a characteristic $\cos 2ϕ$ azimuthal asymmetry in the DIS energy pattern in the TFR. Unlike analogous observables in the current fragmentation region, this asymmetry is governed by the ratio of the linearly polarized gluon NEC $h_{1}^{t,g}$ to the unpolarized quark NEC $f_{1}^{q}$, making it particularly sensitive to the saturation scale. Our numerical analysis shows that this asymmetry exhibits substantial nuclear suppression, providing a novel window into the onset of gluon saturation at the future Electron-Ion Collider.

hep-ph↗
arXiv2026

When Latents Forget Pixels: Restoring Fidelity in Diffusion Transformer Super-Resolution

Image super-resolution (SR) with large generative models has recently achieved remarkable perceptual quality, yet maintaining fidelity to the LR observation remains challenging. In particular, we observe that diffusion transformers (DiTs) built on latent representations suffer from a critical limitation: the compression bottleneck of the VAE weakens fine-grained spatial information, leading to hallucinated details that are weakly grounded in the input image. In this work, we revisit generative SR from a representation perspective and propose a pixel-grounded super-resolution (PGSR) framework that preserves LR-observed pixel evidence before VAE compression and reuses it throughout restoration. Instead of relying solely on the compressed latent condition, PGSR extracts pre-VAE pixel evidence from the upsampled LR image and reuses it at two stages. First, Condition-Side Trajectory Guidance fuses LR-derived pixel evidence with the latent LR condition to guide the latent restoration trajectory. Second, Decoder-Side Pixel Grounding injects multi-scale pixel features into the frozen VAE decoder to ground the final rendering with LR-observed cues. To efficiently adapt large pretrained DiT models, we keep the latent autoencoder and main flow-matching backbone frozen, and train only lightweight restoration modules. We further study an efficient local-window attention variant for improved high-resolution efficiency and scalability. Extensive experiments demonstrate that PGSR improves the realism--fidelity trade-off and produces more faithful, visually convincing results than existing latent generative SR approaches.

cs.CV↗
arXiv2026

WHALE: A Scalable Unified Model for Recommendation with Wukong-HSTU Architecture

As scalability becomes increasingly important in recommendation modeling, recent architectures have advanced the modeling of two broad sources of ranking signals along separate paths: non-sequence features, including user, item, context, and cross features; and sequence features from user behavior histories. Wukong and HSTU have emerged as representative scalable backbones for these paths: Wukong scales high-order non-sequence feature-interaction modeling, while HSTU scales long user-behavior sequence modeling. Despite their complementary strengths, practical architectures that combine these two types of feature modeling remain underexplored. We present WHALE, a scalable unified recommendation architecture that jointly models non-sequence and sequence features on top of Wukong and HSTU. Each WHALE layer contains a Wukong module, an HSTU module, and an attention-based fusion module in which Wukong-derived interaction representations query HSTU-derived behavior representations. This design keeps both backbones active throughout the network and enables progressive Wukong-HSTU exchange, allowing high-order feature crosses to repeatedly retrieve fine-grained evidence from long user histories. To make WHALE practical for industrial deployment, we introduce customized Triton kernels and other model-systems co-design techniques to improve training and inference efficiency. On large-scale industrial recommendation data, WHALE achieves consistent gains in offline experiments. Additionally, it delivers positive online gains with a modest serving-throughput trade-off. The method has been deployed in production systems. Overall, WHALE provides a practical example of how these two sources of information can be scalably unified in an industrial recommendation model.

cs.IR↗
arXiv2026

Realizing Scaling Laws in Recommender Systems: A Foundation-Expert Paradigm for Hyperscale Model Deployment

Scaling laws have been established for recommender systems, yet efficiently deploying foundation model (FM) across multiple recommendation surfaces remains a major unsolved challenge. Existing methods for transfer learning face fundamental limitations in this setting: knowledge distillation suffers from transfer fidelity degradation in the large-data regime, and static user or item embeddings lack the expressiveness to capture contextualized user-item interactions. We propose the Foundation-Expert paradigm, where a central FM generates target-aware embeddings which are ingested by lightweight surface-specific expert models. Target-aware embeddings are representations that dynamically capture a user's interest in a specific item conditioned on their full interaction history. Unlike knowledge distillation, which transfers FM knowledge as soft labels, our approach provides these embeddings as input features to each expert model, enabling direct interaction with surface-specific representations. This paradigm achieves transfer ratios of 0.64--1.0 from FM to experts, substantially exceeding existing methods. Fully deployed at Meta serving tens of billions of daily requests since 2025, it delivers 0.050% statistically significant online topline metric improvement and 0.359% cumulative gains across multiple surfaces.

cs.IR↗
arXiv2026

Regular Black Hole Formation and Gamma-Ray Burst from Matter Conversion

During the gravitational collapse of a massive star into a regular black hole, a new form of matter must be produced in order to prevent the formation of a central singularity. Since such matter is not present in the initial stellar configuration, it must emerge dynamically during the collapse. This formation process is expected to be accompanied by a strong release of energy in the form of electromagnetic radiation, which may be observable. Here we investigate the gravitational collapse of baryonic matter into Dymnikova-Hayward-Bardeen regular black holes. We estimate the radiation density and the corresponding bolometric luminosity generated by the formation of the matter sector responsible for singularity avoidance. We show that such processes provide a possible mechanism for gamma-ray bursts. Moreover, compatibility with gamma-ray burst requires small regularization effects. As a result, the corresponding regular black holes differ weakly from the Schwarzschild black hole.

gr-qc↗
arXiv2026

How a minimal length scale modifies thermodynamics of RN AdS Black Holes?

We investigate the thermodynamic modifications of the Reissner-Nordstroem anti-de Sitter (RN AdS) black hole induced by a minimal length scale,which naturally emerges in $κ$-deformed space-time. By constructing the modified metric via phase-space commutation relations,we derive the deformed Hawking temperature, entropy, and enthalpy. We analyze the thermal stability through the heat capacity and study the P-V criticality, revealing that the black hole undergoes a small-to-large phase transition analogous to the Van der Waals system, albeit with a critical ratio slightly lowered by non-commutativity. Furthermore, we examine the Joule-Thomson expansion and find that the non-commutative (NC) parameter expands the cooling region in the temperature-pressure plane. Our results demonstrate that while the overall thermodynamic analogy with the Van der Waals fluid persists, the minimal length scale systematically deform the coexistence region and inversion curves, offering potential observational signatures for quantum gravity.

gr-qc↗
arXiv2026

Holographic Timelike Entanglement and Subregion Complexity in Localized AdS3*S3*T4 Black Holes

We study timelike entanglement entropy and timelike subregion complexity in localized black holes with asymptotic AdS3*S3*T4 geometry, focusing on the black-pole solution. Unlike the BTZ solution, the black pole exhibits a nontrivial dependence on the internal sphere through the functions $K_y(r,θ)$ and $G(r,θ)$. Both observables are constructed from spacelike and timelike Lorentzian branches, but they probe the geometry in different ways: timelike entanglement yields a complex lifted area, while timelike complexity gives a real, finite renormalized volume. We employ a localized timelike prescription in which the branch profile is built at an angular label $θ_0$ and subsequently lifted over the physical internal angle $θ$. In the large-$r$ regime, the leading angular dependence drops out, recovering the expected short-interval behaviour. In the exact black-pole geometry, the temporal families become non-monotonic, making a fixed-boundary-interval selection essential. As the boundary interval increases, the selected branches move inward and become sensitive to the localized cap-horizon transition region. These results demonstrate that timelike Lorentzian observables probe localized-geometry effects that are absent in BTZ and in the leading large-$r$ description.

hep-th↗