arXiv subjects

Qian Wang

Publications and source records attributed to Qian Wang.

At least 19 recordsLinked to original sources

arXiv2026

Fully charmed tetraquarks on the Lattice with controlled errors

We introduce the lattice quark potential model (LQPM), which discretizes the nonrelativistic quark potential model on a periodic cubic lattice and diagonalizes the resulting many-body Hamiltonian exactly. Unlike variational approaches, which provide only upper bounds on the energy, LQPM removes this variational bias, and its remaining finite-volume uncertainty is removed by extrapolation. Formulated directly in coordinate space, the method is indifferent to the specific form of the potential and accommodates arbitrary interactions, such as including three-body forces and spin-orbit and tensor couplings, that are difficult to treat in conventional few-body approaches. As a benchmark, the charmonium and $Ω_{ccc}$ spectra are reproduced within a few MeV of experiment. For the fully charmed tetraquark $cc\bar{c}\bar{c}$, we obtain three resonances $J^P = 0^+, 1^+,$ and $2^+$ ground states above the $η_c η_c$, $η_c J/ψ$, $J/ψJ/ψ$ thresholds, respectively. Our results establish exact lattice diagonalization as a controlled, systematically improvable, and broadly applicable route to multiquark spectroscopy, providing a quantitative benchmark for the strong interaction and valuable guidance for future experimental searches for exotic hadrons.

hep-ph↗

arXiv2026

Clawdrain: Exploiting Tool-Calling Chains for Stealthy Token Exhaustion in OpenClaw Agents

Tool-calling LLM agents rely on third-party skills to extend their capabilities, but this openness exposes a supply-chain attack surface in which malicious skill instructions can manipulate agent execution across multiple turns. In this work, we present Clawdrain, a Trojanized OpenClaw skill that induces resource exhaustion through a Segmented Verification Process (SVP). SVP combines stateful progression, repair-driven repetition, and termination to create costly tool-calling chains while preserving a legitimate final payload. We evaluate Clawdrain in a production-like OpenClaw deployment with real API billing across Gemini 2.5 Pro, DeepSeek V4 Flash, and Claude Sonnet 4.6. Clawdrain achieves 2-16 times token amplification with plain SVP, while the three models exhibit distinct behaviors, including autoregressive compliance, proactive scripting, and extended trial-and-error reasoning. Our findings reveal that agent resource exhaustion arises not only from output generation, but also from repeated context re-ingestion, tool coordination, reasoning, and recovery, motivating system-level controls over cumulative skill behavior.

cs.CR↗

arXiv2026

High-fidelity simulation and reduced-order modeling of optical pumping in quantum devices

Spatially resolved modeling of optically pumped alkali-metal vapors requires the solution of a high-dimensional nonlinear diffusive quantum master equation (DQME) coupled to state-dependent light propagation. We develop a structure-preserving computational framework for this coupled three-dimensional system in the high-pressure limit. The high-fidelity solver combines second-order finite differences, explicit pseudo-time marching, and an exponential-integrator discretization of the light-propagation equation. The scheme preserves the density-matrix trace, while a derived global pseudo-time-step restriction guarantees nonnegative, bound-preserving population updates and frozen-coefficient $1$-norm stability. In the absence of a closed-form solution for the general coupled problem, the solver is verified against complementary analytical reductions, pumping-rate asymptotics, and a light-propagation invariant, all of which are accurately recovered. Tensorized GPU implementation reduces the per-iteration cost by more than two orders of magnitude relative to a single-core CPU. For repeated parameter queries, we further construct an intrusive POD--Galerkin reduced-order model. The quadratic spin-exchange term is treated by an exact nonlinear quadratic expansion, whereas the nonpolynomial and nonlocal optical-pumping term is hyper-reduced using the discrete empirical interpolation method. At out-of-sample parameter points, the reduced model closely reproduces the full-order population and polarization fields while achieving speedup of more than $7\times10^3$ relative to the single-core CPU full-order solver. The resulting hierarchy connects verified high-fidelity simulation with efficient reduced-order prediction for spatially resolved alkali-metal spin dynamics.

physics.comp-ph↗

arXiv2026

SemantiScene-Edit: Expert-Synthesized Supervision and Dual-Path Source Conditioning for Instruction-Based Video Editing

Instruction-guided video editing requires executing a requested change while preserving the source video's appearance and temporal structure. We present SemantiScene-Edit, a video editor that couples a multimodal encoder with a pretrained video diffusion transformer. The encoder jointly processes source frames and the editing instruction, and learnable queries convey their semantic context to the generator through a trainable connector and cross-attention. A complementary visual pathway concatenates source-video latents with noisy target latents before patch embedding, supplying dense spatial and temporal information for content preservation. We train the model with flow matching and a three-stage image--video curriculum comprising semantic alignment, joint adaptation, and high-resolution refinement. To support training, we construct over 1.2 million instruction-conditioned video-editing records covering object addition, removal, replacement, and attribute modification. We also construct a 100-case benchmark for evaluating instruction execution, source preservation, temporal consistency, and visual quality. Comparative experiments show that SemantiScene-Edit effectively follows editing instructions while retaining source content. At inference, the model requires only a source video and a natural-language instruction.

cs.AI↗

arXiv2026

DICE: Tilting the Odds through Interface Attacks on System One Model

System One models offer typed judgments that applications can turn into decisions. Their answers, however, are conditional on the state and question supplied by surrounding components, and on how the application uses the response. We study this boundary in a Jev-based pipeline with bounded substitutions at three adapter outputs: state extraction, question generation, and output interpretation. Across three small synthetic studies, each adapter can change the final decision while Jev and its authentic API responses remain outside attacker control. The cases expose distinct failures of source fidelity, answer meaning, and response relevance. Checks that bind these relationships stopped the corresponding interventions we tested, although source verification also rejected some valid clean decisions. The pilot identifies where correct request-level judgments can lose their connection to the original task; it does not estimate the prevalence of these failures in deployed systems.

cs.CR↗

arXiv2026

PRET is a few-shot system for pan-cancer recognition without example training

Pathological examination stands as the cornerstone in cancer diagnosis, impacting millions worldwide annually. With the shortage of pathologists globally, artificial intelligence (AI) has emerged rapidly to automate the diagnostics process. However, conventional AI models require substantial labeled data for each disease, posing huge challenges in scalability and practicality. Therefore, we introduce PRET (pan-cancer recognition without examples training), a few-shot system to achieve flexible, scalable, and effective cancer recognition across diverse organs, hospitals and tasks without training. Evaluated on 23 international benchmarks comprising 4,484 whole-slide images, our method outperforms existing approaches across 20 tasks, achieving over 97% area under the curve on 15 benchmarks with a maximum improvement of 36.76%. Notably, PRET delivers clinical-grade diagnostic performance in lymph node metastasis detection using only eight slide examples, outperforming 11 pathologists. By offering a flexible and cost-effective solution for pan-cancer recognition, PRET paves the way for accessible and equitable AI-based pathology systems, particularly benefiting minority populations and underserved regions.

cs.CV↗

arXiv2026

Electron--phonon matrix elements from eigenvalue projection within the projector augmented-wave method

We present a finite-displacement approach to electron--phonon matrix elements that requires only eigenvalues and wave functions from the displaced calculations, for both norm-conserving pseudopotentials and the projector augmented-wave (PAW) method. Extending the eigenvalue-projectability approach, previously formulated for norm-conserving pseudopotentials, we reconstruct the displaced Hamiltonian from the displaced eigenvalues and from overlaps evaluated in the PAW metric of each displaced structure. Analytic terms constructed from the PAW dataset then yield the all-electron matrix element, without derivatives of the local potential or of the PAW strengths. The approach therefore applies in principle to any electronic-structure method available in PAW. To reduce the supercell cost, we combine it with nondiagonal supercells that sample the Brillouin zone equivalently to the primitive cell and recover the full matrix elements by crystal symmetry. Implemented in the ARES-EPC code, the method reproduces matrix elements from density-functional perturbation theory and from VASP+phelel for seven materials with mean absolute errors below 0.5 meV. Nondiagonal supercells reproduce the diagonal-supercell results to the same accuracy and reduce the cost of the displaced calculations by up to a factor of 23.

cond-mat.mtrl-sci↗

arXiv2026

Blame Errant Words, Not the Canvas: Defect-Aware Spatial Credit Assignment for Visual Text Generation

Recent reinforcement learning approaches have improved visual text generation, yet generated images still suffer from incorrect wording and malformed glyphs. We identify two coupled limitations: recognition-based rewards often overlook visible glyph defects, while whole-image scalar feedback obscures the uneven quality of individual text regions. These limitations motivate our central principle: $\textit{Blame errant words, not the canvas}$, to effectively preserve local quality distinctions. First, we introduce $\mathbf{TextDART}$, a defect-aware region-targeted reward model that localizes individual text regions and independently evaluates their semantic correctness and glyph quality, providing fine-grained, region-precise feedback. Building on TextDART, we propose $\mathbf{SCANNER}$, which turns these regional judgments into spatially differentiated policy feedback. It normalizes rewards for each text target across sampled images and casts the resulting regional advantages onto the corresponding latent tokens. Extensive experiments on existing benchmarks and our newly established $\textbf{PolyText-Bench}$ demonstrate that our approach achieves state-of-the-art performance in text accuracy and glyph fidelity. Project page: https://sujiehu.github.io/textdart-scanner

cs.CV↗

arXiv2026

EgoJEPA: Beyond Retargeting for Dexterous Manipulation from Egocentric Videos

Egocentric human videos offer a scalable alternative to costly robot demonstra- tions, yet transferring their manipulation priors to robot control remains chal- lenging. The commonly adopted retargeting-based approaches convert human wrist and finger poses into embodiment-specific action commands through hand- crafted geometric mappings. However, such mappings require meticulous recal- ibration across different embodiments and risk propagating hand-tracking errors into downstream robot control pipelines. To this end, we propose EgoJEPA, a three-stage framework that learns embodiment-agnostic representations from ego- centric human videos without rule-based human-to-robot retargeting: 1) Human- action pre-training learns effect-aware latent actions through JEPA-style visual prediction conditioned on hand-motion differences; 2) Latent-action-based world modeling jointly predicts future visual dynamics and latent actions, which decou- ples human prior pre-training from robot-specific control parameterizations; 3) Embodiment-specific tuning adapts the pretrained model to diverse robots through lightweight action adapters. Experiments on five real-world manipulation tasks across three robot embodiments demonstrate the effectiveness and efficiency of EgoJEPA. It achieves a 74% average success rate under the out-of-domain evalu- ation, compared with 65% for the retargeting baseline, while reducing cumulative cross-embodiment wall-clock cost by 47.2%, from 743.2 to 392.7 hours.

cs.RO↗

arXiv2026

Fully-heavy multiquarks in neural-network quantum states

Exotic hadrons beyond the conventional quark model provide a direct window into the dynamics of strong interaction. However, extracting the multiquark spectroscopy has to face the quantum many-body problem, which is still a theoretical challenge. In this case, diquark-antidiquark model is proposed as an approximation. Although this model can describe the spectroscopy roughly, it cannot describe the detailed dynamics. Furthermore, the methods aiming at dealing with many-body problem, e.g. the Gaussian expansion method and Diffusion Monte Carlo, are proposed, but face severe computational bottlenecks. In this work, we introduce the neural-network quantum state (NNQS) approach to investigate the spectra of fully-heavy multiquark states within the non-relativistic potential quark model. By employing deep neural networks to represent the complex many-body spatial wave function, and constructing the color-spin part exactly from group theory to enforce fermionic antisymmetry, our approach effectively overcomes the dimensionality obstacles inherent in traditional methods. The results are compared with various model calculations, demonstrating that NNQS offers superior accuracy and flexibility, particularly in treating high-dimensional correlations. This work establishes NNQS as a promising tool for exploring the spectroscopy of exotic hadrons.

hep-ph↗

arXiv2026

X2Real: an eXtensive simulation benchmark for real-world generalist policies

Generalist robot manipulation policies have developed rapidly, yet their reliable evaluation remains challenging due to fundamental flaws in existing simulation benchmarks: prominent sim-to-real gaps, narrow task coverage, and unfair evaluation caused by ambiguous training-test pipelines. Prior works only partially resolve these issues and lack simultaneous faithfulness, diversity, and fairness, while static benchmark designs fail to sustain long-term policy development. We present X2Real, an evolvable simulation benchmark for faithfully evaluating the real-world performance of robotic manipulation policies based on Nvidia Isaac Lab-Arena. Following three core principles (faithfulness, diversity, and fairness), X2Real calibrates simulation visual and physical properties to align with real hardware, achieving a 0.84 linear correlation between simulated and real-robot evaluation results. It features a comprehensive taxonomy with 10 capability dimensions and 44 hierarchical long-horizon tasks, covering basic manipulation skills and advanced capacities such as visual grounding, language understanding, and bimanual control. We further adopt multi-axis domain randomization and strictly disjoint training-evaluation pipelines to mitigate benchmark exploitation and ensure credible evaluation. Powered by a custom physical domain-specific language, the Mana simulation ecosystem supports modular task design and iterative performance analysis, alongside a nearly 300-hour annotated simulation trajectory dataset. X2Real offers a faithful, diverse, and fair evolving evaluation infrastructure, effectively bridging the sim-to-real evaluation gap and supporting the advancement of generalist robotic manipulation policies.

cs.RO↗

arXiv2026

ARES: a plane-wave density-functional-theory code in Rust

ARES (Ab initio atomic mateRial modEling Software at JLU) is a plane-wave density-functional-theory (DFT) code written entirely in Rust as a layered 22-crate workspace. It supports norm-conserving pseudopotentials and the projector augmented-wave (PAW) method with the local-density approximation (LDA) and Perdew-Burke-Ernzerhof (PBE) functionals, Γ-only and explicit k-point sampling, smearing and mixing, forces, stress, relaxation, band structures, densities of states (DOS) and dispersion corrections; execution is serial or MPI-distributed over k-point pools, band groups and plane-wave rows. The code's distinguishing engineering choice is its validation apparatus. Every numerical component is checked against Quantum ESPRESSO driven from the same physical and numerical inputs, and the comparison is organized as numbered fixtures kept in the repository whose reference data is regenerated by a script instead of being captured once. On six materials spanning a semiconductor, three metals and two two-dimensional systems, band energies within one electronvolt of the Fermi level agree with the reference to a median below 0.4 meV. The pseudopotentials the code reads are validated against all-electron reference data as well: the median delta factor is 0.696 meV/atom over all 65 paired elements. Restrictions are stated with the capabilities: two functionals, no spin-polarized or spin-orbit path, a stress implementation with an explicit scope, and no accelerator execution.

cond-mat.mtrl-sci↗

arXiv2026

Artificial Foveated Perception for Mitigating Shortcut Learning in Robotic Foundation Models

Robotic foundation models still need task-specific fine-tuning before deployment, and the fine-tuned policies often break under modest changes in scene layout, lighting, or nearby distractors. We trace this brittleness to \textit{shortcut learning}: fine-tuning supervises actions but not the visual evidence the policy uses, so the policy can settle on scene-level correlations that predict the demonstrations without causing success. We propose Artificial Foveated Perception (AFP), a lightweight, policy-agnostic module that takes the same vision and language inputs as existing Vision-Language-Action and World Action Model pipelines and predicts task-conditioned masks over the relevant objects, the robot, and other action-critical regions. During fine-tuning the masks serve as an auxiliary grounding signal that aligns the policy's visual attention with task-relevant regions; the policy architecture is unchanged, and at inference the policy runs on the original observation stream with no AFP call in the control loop. In simulation with four robotic foundation models and on a real robot with $π_{0.5}$, AFP improves generalization under environmental perturbations, reduces overfitting, and shortens fine-tuning. Ablations over mask quality and grounding-loss design show that these gains come from directing policy learning toward task-relevant visual evidence. Code, data, and videos are available at https://apollo-lab-yale.github.io/26-CoRL-AFP-website/.

cs.RO↗

arXiv2026

VAttnPO: Visual Attention-Guided Policy Optimization

Multimodal reinforcement learning with verifiable rewards commonly assigns one sequence advantage to an entire response. This outcome-level signal distinguishes overall response quality but leaves changes in visual-evidence use across generated tokens implicit. We introduce VAttnPO (Visual Attention-Guided Policy Optimization), which uses answer-conditioned visual attention to refine token-level credit while preserving the verifier-derived trajectory-mean advantage. A frozen old policy constructs a visual reference by teacher forcing the known correct answer and produces a visual distribution for each rollout token. VAttnPO measures their agreement in visual-attention space using Jensen--Shannon divergence, rather than reading guidance from teacher output-token distributions or introducing an attention-matching objective. Trajectory centering, relative-position weighting, and credit balancing turn this agreement into a zero-mean, position-aware correction to the GRPO sequence advantage. Token intervention, visual-index permutation, and trajectory analysis provide diagnostic evidence linking this signal to model behavior, visual correspondence, and generation position. On Qwen3-VL-8B-Instruct, VAttnPO raises six-task image AVE from 70.97 with GRPO to 73.50 and scores higher on every reported structured, video, and MME metric. Gains over GRPO also hold on Qwen2.5-VL-7B-Instruct and InternVL3-8B-Instruct.

cs.CV↗

arXiv2026

Does $ψ(4660)$ exist?

The nature of the vector charmoniumlike states $ψ(4360)$ and $ψ(4660)$ is controversial, and the existence of the $ψ(4660)$ has been questioned: the BESIII measurement of $e^+e^-\to Λ_c^+\barΛ_c^-$ reveals a nearly flat line shape around 4.66 GeV with a nonzero value at threshold, in contrast to the near-threshold enhancement reported earlier by Belle. We address this puzzle in an S-wave coupled-channel framework for $e^+e^-$ annihilation in $\sqrt{s}\in [4.0,5.0]~\mathrm{GeV}$. The formalism includes the open-charm channels $Λ_c^+\barΛ_c^-$, $Ξ_c^+\barΞ_c^-$, $Ξ_c^0\barΞ_c^0$ (via SU(3) symmetry), the inelastic $ψ(2S)π^+π^-$ channel mediated by $σ(500)$ and $f_0(980)$, and the bare $ψ(4360)$ and $ψ(4660)$ as the 4S and 5S excitations. Short-range contact potentials in the heavy-quark limit are used, and the amplitudes follow from solving the Lippmann-Schwinger equation. A combined fit to Belle and BESIII data on $e^+e^-\to Λ_c^+\barΛ_c^-$ and $e^+e^-\to ψ(2S)π^+π^-$ yields two poles, at $(4401.93 \pm 16.96i)~\mathrm{MeV}$ and $(4631.39\pm 62.96i)~\mathrm{MeV}$, for the $ψ(4360)$ and $ψ(4660)$. Tracing the pole trajectories as the coupling to the bare states is switched off, we show that both poles originate from the bare $4S$ and $5S$ states strongly renormalized by coupled-channel dynamics. In particular, the $ψ(4660)$ emerges as a broad resonance about 58~MeV above the $Λ_c^+\barΛ_c^-$ threshold; its flat line shape follows from this threshold interplay, providing evidence for its existence. Results are cutoff-robust.

hep-ph↗

arXiv2026

Why Ammoniated Lithium Borohydrides Liquefy and Resolidify?

Ammonia (NH$_3$) absorption drives LiBH$_4\cdot x$NH$_3$ through a re-entrant ``solid--liquid--solid'' transition: LiBH$_4\cdot$NH$_3$ is a well-defined solid ammoniate, compositions near LiBH$_4\cdot 2$NH$_3$ are liquid-like or partially liquefied, whereas LiBH$_4\cdot 3$NH$_3$ returns to a more rigid non-liquid ammoniate state. However, the microscopic origin of this unintuitive response remains a long-lasting mystery. Here, we uncover its mechanism. Cross-database analysis identifies borohydrides as a particularly state-diverse and composition-responsive material family. Structure prediction and ab initio molecular simulations reveal that increasing NH$_3$ loading increases the direct Li--N coordination number while progressively decreasing BH$_4^-$-associated contacts in the local Li environment. Near $x \approx 2$, these contributions are most balanced among the simulated compositions, and the sampled Li--N/N$\cdots$B coordination landscape is broadest. Further ammoniation produces Li--N-dominant coordination and slower BH$_4^-$/NH$_3$ contact renewal, accompanying recovery of a more rigid ammoniate state. Pressure--composition isotherm, $^1$H and $^{11}$B nuclear magnetic resonance, and Raman measurements support this non-monotonic state evolution and associated BH$_4^-$/NH$_3$ reorganization. These findings transform ammonia-induced liquefaction from an empirical phase anomaly into a competition between native-network disruption, mixed-coordination frustration, and ligand-built network reconstruction, providing a framework for chemically switching between transport-favouring fluidity and stability-favouring rigidity in hydrogen-rich materials.

cond-mat.mtrl-sci↗

arXiv2026

Trading Strategy Optimization via Textual Gradient

Quantitative trading strategy design aims to discover trading programs from historical data that remain effective in future markets, which can be viewed as a black-box program optimization problem. LLM-based textual gradients offer a promising approach by providing explicit optimization directions for iterative strategy refinement. However, directly applying textual gradients faces two challenges: (1) optimization is myopic, underutilizing experience from previous evaluations; and (2) aggregate backtest feedback overlooks temporal robustness, potentially favoring strategies that perform well only in specific market periods. To address these challenges, we propose TradeGrad, an experience-guided textual-gradient framework for robust trading strategy optimization. TradeGrad leverages accumulated optimization experience to estimate textual gradients and employs multi-scale revisions for both strategy exploration and refinement. It further introduces the Cross-Period Robust Objective (CPRO), which emphasizes performance in unfavorable historical periods to promote temporal robustness. Experiments on cross-sectional and time-series strategy design in Chinese A-share and U.S. equity markets show that TradeGrad achieves the best in-sample and out-of-sample performance across all four settings. Notably, its Chinese cross-sectional strategy achieves 27.99% annualized return, 12.19% maximum drawdown, and a Sharpe ratio of 1.63, approximately 68% higher than the CSI 300 benchmark. Further analyses validate the proposed components and show consistent improvements in both in-sample and out-of-sample performance throughout optimization. The code is available at https://github.com/transcend-0/TradeGrad.

cs.AI↗

arXiv2026

I2CD: Direct Image-to-Convex Decomposition for Simulation-Ready Collision Geometry

Physics simulators and motion planners require convex collision geometry, yet image-to-3D generative models output dense, frequently non-manifold visual meshes. Bridging the two today takes a slow, brittle reconstruct-then-decompose pipeline of repair, decimation, and approximate convex decomposition. We present I2CD, which predicts a convex decomposition directly from a single RGB image. Rather than train a new image-to-3D model, I2CD freezes the pretrained Hunyuan3D-2 image-conditioned diffusion transformer and shape decoder and trains only a lightweight cross-attention head (38M parameters, under ten GPU-hours) whose learned "convex-slot" tokens emit the halfplane parameters of $K$ convex polytopes. The output is compact, convex by construction, and loads into physics engines without any post-processing, in ${\sim}0.5$s per image. On $227$ held-out OmniObject3D and Google Scanned Objects instances, I2CD attains the highest volumetric IoU among eight reconstruct-then-decompose pipelines while running $6$-$37\times$ faster end-to-end. In a cross-simulator study in MuJoCo, PyBullet, Genesis, and Isaac Sim, every engine uses I2CD geometry as delivered, whereas raw generated meshes "load" everywhere but are silently replaced by a different collision shape in most cases or need seconds to minutes of per-object preprocessing. On a physical xArm7, I2CD produces planner-ready geometry for a $20$-object cluttered scene in $11$s versus $328$s for the strongest baseline, at comparable pick-and-place execution success ($85$ vs. $90$ of $100$ trials).

cs.RO↗