arXiv subjects

Yan Liu

Publications and source records attributed to Yan Liu.

At least 37 records · Page 2Linked to original sources

arXiv2026

Grounding SWE-Agent Decisions in Architecture-0 Design: Navigating Unknown Unknowns through Physical Mapping

Autonomous Software Engineering Agents (SWE-Agents) excel in deterministic coding tasks but struggle with Architecture 0, the nascent system design phase plagued by implicit engineering constraints, or Unknown Unknowns (UUs) that are rarely stated explicitly. To investigate how agents navigate UUs, we explore a progressive trajectory across pure-text self-play, tool-augmented feedback, and external physical mapping. Our empirical analysis reveals a cascading chain of failures. Pure-text reasoning inevitably devolves into polite consensus or plausible yet physically impossible fabrications. Attempting to bridge this gap via an early-stage execution sandbox unexpectedly triggers Specification Gaming: agents exploit their autonomy over validation scripts to bypass physical constraints, achieving superficial success without resolving core architectural flaws. To resolve this self-validation trap, we propose the Physical Mapping Guard (PMG). Grounded in the software engineering principle of Separation of Concerns, PMG revokes verification authority from the agent, forcing semantic intents to be evaluated by an external, deterministic Semantic-to-Physical (S2P) mapping engine. Extensive evaluations demonstrate that PMG completely eradicates physical-layer and validation-layer gaming. By precisely isolating residual failures to semantic reinterpretations and auditor overreach, PMG marks a critical step toward genuine affordance grounding in automated architectural design.

cs.SE↗

arXiv2026

Record-Breaking Elemental Superconductivity in Tetralayer Kagome Borophene

Superconductivity above the liquid-nitrogen temperature remains rare in two-dimensional elemental crystals, where strong covalent bonding often yields high phonon frequencies but insufficient electron-phonon coupling. Here, using first-principles calculations and fully anisotropic Migdal-Eliashberg theory, we predict tetralayer kagome borophene (TKB) stabilized by ABAB covalent stacking, as a liquid-nitrogen-temperature elemental superconductor. With a predicted critical temperature of 102 K, TKB sets a record-high value among previously reported elemental superconductors. Unlike known high-Tc boron-based superconductors dominated by in-plane sigma-bonding states and high-frequency in-plane B-B stretching modes, TKB realizes an out-of-plane s-pz-bonding-mediated pairing mechanism, in which interlayer s-pz bonding states at the Fermi level are strongly coupled to low-frequency out-of-plane vibrations of boron atoms. These results reveal a distinct out-of-plane pairing channel in multilayer borophene and establish covalent stacking engineering as a potential route for high-Tc superconductivity in two-dimensional materials.

cond-mat.supr-con↗

arXiv2026

Task-oriented Framework for Communication-Efficient Federated Learning: From Isolated Optimization to Holistic Synergy

Communication bottlenecks remain a primary obstacle to the large-scale deployment of federated learning (FL). This article proposes a comprehensive framework for building communication-efficient FL, founded on three fundamental pillars: model compression, client selection, and resource allocation. We first survey state-of-the-art techniques for each pillar, specifically elucidating how quantization, pruning, and low-rank approximation reduce payloads; how intelligent client schedulers exploit heterogeneity; and how emerging communication paradigms such as Integrated Sensing and Communication (ISAC) and Over-the-Air Computation (AirComp) redefine bandwidth and energy utilization. Subsequently, these insights are unified through a task-oriented design philosophy that couples strategy selection with cross-layer, multi-objective optimization. To validate the proposed framework, we present an autonomous driving case study with two complementary experiments: a task-oriented client scheduling strategy that improves object detection accuracy under the same communication time budget, and a joint quantization-bandwidth optimization that further reduces total training time under dynamic networks. Together, the experiments demonstrate the advantages of holistic task-oriented design for real-world FL deployment.

eess.SP↗

arXiv2026

Physically Plausible Video Generation via Visual-Semantic Chain-of-Events Conditioning

Physically Plausible Video Generation (PPVG) seeks to synthesize videos consistent with physical principles, yet remains challenging due to underspecified natural language conditioning. Advanced chain-of-thought (CoT) frameworks augment prompts with physical knowledge. However, such prompts describe physical phenomena holistically, overlooking intermediate states and transition dynamics. In this paper, we reformulate PPVG as event-centric generation by representing physical evolution as a chain of causally connected and physically constrained events. Our framework comprises three key modules: (1) Physics-driven Event Chain Reasoning. This module decomposes physical phenomena into causally connected events represented by evolving scene graphs. Formula-derived physical quantities are bound to relevant objects and interactions, characterizing the direction and magnitude of each event transition. (2) Transition-aware Routed Keyframe Conditioning. This module routes each event to a specialized keyframe synthesis operator for appearance variation or object transformation. Consecutive keyframes are injected as residual guidance during denoising, enabling smooth visual transitions between event-boundary states. (3) Physics-injected Contrastive Semantic Guidance. This module constructs physics-informed positive and counterfactual negative prompts for classifier-free guidance, steering generation toward plausible dynamics and away from physics-violating counterparts. Experiments on PhyGenBench, VideoPhy, PhyWorldBench, and Physics-IQ demonstrate that our framework generates videos with superior physical plausibility across diverse domains.

cs.CV↗

arXiv2026

Phase calibration of quantum oscillations in the magnetostrictive coefficient using the topological antiferromagnet YbMnBi$_2$

The Berry phase accumulated along a cyclotron orbit encodes important information about electronic band topology and is commonly inferred from the phase of quantum oscillations. Measurements of the ac magnetostrictive coefficient have recently emerged as a sensitive thermodynamic probe of quantum oscillations, but the phase offset has not been experimentally calibrated. Here, using the topological antiferromagnet YbMnBi$_2$, we calibrate this offset by directly comparing quantum oscillations in magnetization with those in the ac magnetostrictive coefficient. Measurements of both responses on the same single crystal reveal a single fundamental frequency of approximately 160 T in fields up to 14 T, enabling a direct phase comparison free from ambiguities associated with multiple frequencies. We observe an approximately $π/2$ relative phase shift between the two oscillatory responses, consistent with the Maxwell relation linking the magnetostrictive coefficient to the stress derivative of magnetization. Our results establish the appropriate phase needed to extract cyclotron-orbit phase information from quantum oscillations in the ac magnetostrictive coefficient.

cond-mat.str-el↗

arXiv2026

Relaxation-Rectified Anti-Jaynes-Cummings Cascades for Autonomous Fock-State Stabilization

We propose an autonomous scheme for stabilizing prescribed Fock states in a Kerr cavity by rectifying anti-Jaynes--Cummings interactions with auxiliary-qubit relaxation. Full Lindblad simulations demonstrate steady states dominated by the Fock states $|1\rangle$ through $|4\rangle$, accompanied by pronounced Wigner negativity. An analytical birth--death model captures the steady-state populations and identifies the operating regime for stabilization. These results establish auxiliary-qubit relaxation as a resource for autonomous reservoir engineering and provide a route to nonclassical bosonic-state preparation in a single Kerr cavity and, through a boundary reservoir, in a Kerr-cavity chain.

quant-ph↗

arXiv2026

Causal inference via propensity scores for case-control studies

Propensity score methods for causal inference are increasingly being used in cohort and experimental designs, but their development and uptake in outcome-dependent sampling schemes, such as case-control studies, remains limited. Case-control studies involve the sampling of individuals with and without an outcome of interest with the goal of estimating the effects of past exposures. When the design is observational, statistical adjustment for confounding bias is necessary. In case-control studies, propensity score models can be fit using control data under the assumption that the controls are representative of the source population with respect to their exposure distribution conditional on covariates ("control exchangeability"). In this paper, we first demonstrate that relative effects, such as causal risk ratios, are estimable under three different case-control design variants using control-fitted propensity scores. We appropriate two existing estimators for these designs: inverse probability of treatment weighting and an efficient and doubly robust estimator. We also introduce a novel two-step propensity score caliper-matching procedure for case-control designs. We introduce novel diagnostic tools to verify two necessary types of overlap. We then contrast our estimators using simulated data and apply them to examine the association between regular aspirin use and ovarian cancer risk.

stat.ME↗

arXiv2026

LabDex: A Hierarchical Benchmark for Dexterous Manipulation in Laboratories

Autonomous laboratories hold great promise for accelerating scientific discovery. To achieve this vision, robots are supposed to dexterously manipulate diverse labware and instruments and execute long-horizon, state-dependent experimental procedures. Yet existing benchmarks do not jointly capture dexterous hand use, real-world laboratory interactions, and multi-stage experimental procedures, limiting systematic training and evaluation. To bridge this gap, we introduce LabDex, a large-scale real-world dataset and benchmark for dexterous manipulation in chemistry laboratories, organized around a hierarchical task taxonomy spanning atomic skills, compositional tasks, and long-horizon experiments. First, LabDex is cross-platform and, for the first time, unifies real-world and simulation platforms under a common framework, providing standardized task definitions, demonstrations, and evaluation protocols. Second, LabDex is large-scale and systematically organizes chemistry laboratory operations into three interconnected levels: Atomic Skills, which characterize fundamental dexterous manipulation capabilities; Compositional Skills; and Long-Horizon Laboratory Workflows. This hierarchical design not only supports the evaluation of end-task performance, but also enables the analysis of how fundamental dexterous skills compose and influence more complex laboratory operations. We conduct cross-level evaluations of representative robot learning methods in both real-world and simulation environments. The experimental results validate the effectiveness of the LabDex task design and demonstration data, and show that the benchmark supports the training and systematic evaluation of existing robotic policies across laboratory dexterous manipulation tasks at different levels, providing a foundation for further research and development of autonomous laboratory robots.

cs.RO↗

arXiv2026

Learning latent progression states from spatial heterogeneity in uterine histopathology

Tumor progression is accompanied by changes in architecture, morphology and microenvironmental organization, yet progression-associated heterogeneity is usually compressed into static diagnostic categories in histopathology. Here we present SpaTIE, a uterus-specific computational pathology framework that learns morphology-aware representations and organizes spatial histopathological heterogeneity into progression-associated tumor states. SpaTIE was developed using 10,426 uterine hematoxylin and eosin whole-slide images and evaluated in TCGA-UCEC and TCGA-UCS cohorts. The learned representations formed morphology manifolds, supported diagnostic, molecular and survival-related prediction tasks, and localized attention to informative tumor regions. Beyond supervised prediction, SpaTIE inferred tumor-state axes from cross-sectional morphology without temporal or molecular supervision. These morphology-derived states were spatially coherent and showed associations with clinicopathological variables and survival outcomes, while not simply recapitulating staging or diagnostic labels. Integrative multi-omics analyses linked the inferred states to DNA methylation, somatic copy-number variation, mutation, RNA-seq and RPPA profiles, highlighting molecular programs related to chromatin regulation, copy-number-associated structural variation, receptor tyrosine kinase signaling, cell adhesion, extracellular-matrix remodeling and metabolic adaptation. Progression-guided virtual perturbation further prioritized molecular features coupled to the morphology-derived state organization. Together, these findings suggest that uterine histopathology contains recoverable progression-associated tumor-state information and establish SpaTIE as a framework for connecting spatial morphology with multi-omics-informed tumor-state discovery.

cs.CV↗

arXiv2026

zenDot: An LLM-integrated quantum TCAD platform for semiconductor quantum-device design and optimization automation

Semiconductor quantum-device design still lacks an integrated Technology Computer-Aided Design (TCAD)-like environment that connects material geometry, quantum many-body simulation, and automated design. Here we introduce zenDot, a large-language model (LLM)-integrated quantum TCAD platform that links a material-labelled device state to a unified condensed-matter physics toolbox. The device and calculation components are integrated into a desktop workbench, Python API, and an embedded LLM agent, allowing electrostatics, charge and transport characterization, correlated-state calculations, and qubit modelling to be executed within one reproducible environment. We demonstrate zenDot on a Si/SiO2 double quantum dot, where a single device state reproduces the characterization workflow and supports hybrid, tunnel-charge, and singlet-triplet qubit analyses. A platform-level universal-control scan revises the singlet-triplet operating point and reduces the predicted worst-gate infidelity by nearly 30-fold. Beyond analysis, the LLM agent directly operates the same physics environment as human users, proposing design changes, executing registered simulations, and iterating on solver-returned metrics under physics-aware validation. Across three demonstration tasks it completes 18 validated design iterations, including geometry modification followed by a full re-solve from the material stack. zenDot establishes a machine-operable quantum TCAD workflow that connects device physics with LLM-driven design exploration.

quant-ph↗

arXiv2026

Cat-DPO: Category-Adaptive Safety Alignment

Aligning large language models with human preferences must balance two competing goals: responding helpfully to legitimate requests and reliably refusing harmful ones. Most preference-based safety alignment methods collapse safety into a single scalar that is applied uniformly to every preference pair. The result is a model that looks safe on average but stays relatively unsafe on a minority of harm categories. We cast safety alignment as a per-category constrained optimization problem and derive Cat-DPO, a direct-preference-optimization algorithm with a separate adaptive safety margin for each harm category. The margin tightens when the model still produces unsafe responses on a category and relaxes once the model catches up, so the training signal tracks each category's current difficulty rather than averaging under one global rate. Across two LLM backbones and six preference-learning baselines, Cat-DPO improves aggregate helpfulness and harmlessness and compresses per-category safety variance and the best-to-worst gap, offering a drop-in per-category refinement of direct preference safety alignment.

cs.CL↗

arXiv2026

Critical and multicritical Kasner scaling in holographic phase transitions

We study how holographic phase transitions imprint their scaling laws on the local Kasner geometry inside Einstein-scalar black holes. At fixed double-trace coupling, we derive the near-critical scaling of the deviation of the first-epoch Kasner exponent from the Schwarzschild value: $p_t^{(1)}+\frac{1}{3} \propto O^2 \propto (T_c-T)^{1/r}$, where $O$ denotes the boundary condensate and $r$ labels ordinary criticality ($r=1$), tricriticality ($r=2$), and higher multicriticality ($r\geq3$). A super-exponential scalar potential generates a sequence of Kasner epochs separated by scalar-field bounces. We further find that any later epoch that can be tracked continuously across the transition inherits the same temperature exponent, while its coefficient depends on the epoch. Numerical solutions confirm these predictions for the ordinary and tricritical cases. Finally, we show analytically that the leading irrelevant exponent of the infrared fixed point governs the low-temperature Kasner scaling, and verify the resulting scaling numerically for the first Kasner exponent.

hep-th↗

arXiv2026

Coupling Does Not Reduce the Auxiliary-Mode Count for $1/|ω|$ Spectra in Passive Lindblad Networks

Representing continuous environments by finitely many Markovian auxiliary modes is fundamental in non-Markovian open quantum systems, yet a critical question remains: at a fixed mode budget, can coherent intermode coupling reduce the spectral approximation error? We prove that intermode coupling offers no advantage when passive, number-conserving Gaussian Lindblad auxiliary networks approximate a $1/|ω|$ spectrum over a finite two-sided frequency band. For any mode budget $N$, the general coupled class and its uncoupled diagonal subclass share the same optimal error, which is exactly the degree-$2N$ Zolotarev error for sign approximation. This optimum is attainable by $N$ independent damped auxiliary modes at zero detuning. The result holds when the auxiliary network is in a stationary vacuum state, the system couples to it via a single Hermitian bath operator, and no white-noise feedthrough term is present. Consequently, although a general coupled network has $O(N^{2})$ real parameters, coherent intermode coupling, collective dissipation, and nonnormal structure cannot reduce the number of auxiliary modes required to reach a prescribed tolerance. This exact relation yields both the minimum mode count for a prescribed positive-frequency dynamic range and tolerance, and the maximum dynamic range attainable for a prescribed mode budget and tolerance.

quant-ph↗

arXiv2026

Cognitive World Model for Progressive BDI/E Trajectory Evaluation of Conversational Agents

As LLM-based conversational agents advance toward increasingly open-ended and interaction-intensive scenarios, task completion alone provides an incomplete assessment of their effectiveness. The evolution of users' internal states, including beliefs, desires, intentions, and emotions (BDI/E), serves as an intermediate signal connecting agent behaviors with interaction outcomes and reflects how conversational strategies shape users during multi-turn interactions. However, existing evaluation paradigms primarily focus on surface-level responses or final outcomes, providing limited insight into the underlying cognitive processes. This limitation makes it difficult to diagnose why agents succeed or fail and to optimize their interaction strategies. To address this challenge, we propose Cognitive World Model (CogWM), an LLM-based cognitive user model that jointly models users' BDI/E states and corresponding responses, enabling explicit cognitive trajectory tracking. Trained on 150K user-turn samples with Qwen3-14B, CogWM achieves superior performance over existing user simulation baselines in both response fidelity and cognitive state understanding. Interactions with six state-of-the-art LLMs demonstrate that CogWM enables progressive comparison of agents through cognitive trajectories, revealing distinct agent patterns and complementary relationships between cognitive evolution and behavioral outcomes.

cs.AI↗

arXiv2026

The holographic dual of the GHZ state

Current holographic research mostly focuses on a subset of quantum systems with a classical gravity dual. Although the precise boundary of this subset remains unknown, certain constraints are recognized; for instance, holographic entropies obey inequalities that are violated by general quantum states such as the GHZ states. This paper, however, proposes a gravity dual for the GHZ states -- a non-manifold geometry termed the booklet wormhole. We demonstrate an exact match for all entropy properties with the GHZ state, as well as the identity of the Euclidean partition functions for both systems. The booklet wormhole circumvents the conventional holographic entropy inequalities because different topologies are inevitably included in the gravitational path integral, even in the large-$N$ limit. This provides the first explicit holographic duality with a non-perturbative quantum effect. Remarkably, the construction is simple, and the dual state is maximally entangled, making it ideal for gedanken experiments.

hep-th↗

arXiv2026

Trustworthy Protein-Ligand Binding Affinity Prediction via Reliability-Aware Multi-Engine Fusion

Accurate protein-ligand binding affinity prediction is central to computational drug discovery, yet modern docking engines frequently disagree without indicating which prediction to trust. Consensus scoring and ensemble methods improve mean accuracy but treat all predictions identically without interpretable confidence measures or uncertainty decomposition, ignoring the chemical context of each protein-ligand pair. To address this limitation, we introduce RELIABLE-BA (RELIABiLity-aware Evidential fusion for Binding Affinity), an evidential framework for multi-engine binding affinity prediction. Our model comprises three steps: (1) modeling each engine as an evidential expert via Normal-Inverse-Gamma distributions, (2) scaling epistemic uncertainty through learned reliability from molecular context while preserving each expert's predictive mean, and (3) fusing experts through closed-form aggregation that captures both individual uncertainty and inter-engine disagreement. Experiments on the PDBBind and BDB2020+ benchmarks demonstrate competitive point prediction with substantially improved uncertainty calibration, and additional validation on the SARS-CoV-2 Mpro dataset and 5HT2A receptor demonstrates applicability to clinically relevant drug targets. Crucially, these uncertainty estimates enable reliable filtering of protein-ligand pairs, reducing prediction error by up to 25% when retaining only high-confidence pairs. To our knowledge, RELIABLE-BA is the first multi-engine binding affinity prediction framework to combine evidential fusion with context-dependent reliability, offering a principled path toward trustworthy AI-guided drug discovery. Our code is publicly available at https://github.com/yongchand/RELIABLE-BA.

cs.LG↗

arXiv2026

Phase-Selected Efficient Single-Photon Frequency Conversion via Local Fano Resonance in a Two-Giant-Atom Waveguide-QED System

Efficient single-photon frequency conversion is investigated in a two-giant-atom waveguide-QED system, where a two-level giant atom and a $Λ$-type three-level giant atom couple to a common one-dimensional waveguide. While the $Λ$-type atom provides the inelastic channel, the two-level atom induces secondary coherent coupling, creating multi-path interference for the converted photon. Using the real-space approach and within the Markovian approximation, we derive analytical four-channel scattering amplitudes and reveal that the inelastic transmission spectrum, governed by three complex resonance poles, exhibits a multi-peak interference pattern. By introducing a local single-pole approximation, we reduce this complex spectrum to a local Fano lineshape, decomposing it into a coherent superposition of a local background term and a single-pole resonant term. The interplay between these two terms-controlled by the photon propagation phase between the giant atoms' coupling points-determines the conversion efficiency, with the background suppression condition leading to a Lorentzian reduction. Based on the single-pole resonance weight, we formulate a phase-selection criterion for highly efficient conversion. Compared with both the small-atom and single $Λ$-type giant-atom models, the two-giant-atom scheme achieves substantially enhanced inelastic transmission over a broader frequency-conversion range. This work reveals how phase-controlled local Fano resonance enables high-efficiency frequency conversion, establishing a general paradigm for engineering resonant light-matter interactions in structured quantum systems.

quant-ph↗

arXiv2026

A Unified Electrostatic-to-Spin Framework for Asymmetric Multi-Gate CMOS Quantum Devices

In advanced complementary metal-oxide-semiconductor (CMOS) quantum chips, compact gate stacks make it difficult to connect lithographic geometry, electrostatic confinement and many-electron spin filling in one transparent model. This connection is central to design-technology co-optimization (DTCO). Here we develop a reduced-order analytical framework for asymmetric multigate silicon quantum-dot devices. Its electrostatic core, the Poisson-kernel coupled-interface Green-function (PK-GF) model, agrees with an independent finite-volume solution at the millivolt scale for the matched two-dimensional problem, without fitting to that solution. We then pass the gate-derived confinement, rather than a harmonic or fitted potential, to a spin-valley many-body calculation for a jellybean quantum dot with N = 2-17 electrons at B = 5 T. The unrestricted Hartree-Fock (UHF) solution supports occupation-dependent, Wigner-molecule-like charge localization but likely overestimates spin polarization. Complete active-space configuration interaction (CASCI) supports a low-spin branch within the tested active spaces, which aligns with the experiments. The workflow therefore connects CMOS layout, device electrostatics, and potential-determined quantum observables, providing an auditable modelling layer for CMOS-based qubit design and DTCO.

cond-mat.mes-hall↗