arXiv subjects

Wang

Publications and source records attributed to Wang.

At least 19 recordsLinked to original sources

arXiv2026

Chiral superconductivity on a closed Klein bottle: a chirality wall and its Majorana seam spectrum

We study a spinless $π$-flux lattice superconductor whose normal-state momentum-space fundamental domain is a Klein bottle. Within this model, a glide lift and a hidden antiunitary symmetry independently force the Bogoliubov--de Gennes (BdG) Chern number to vanish. A diagonal pairing breaks the antiunitary obstruction exactly when its real part is nonzero; this is necessary, not sufficient, for a chiral phase. We derive explicit Pfaffians determining Chern parity and identify three analytic families of gap closings in a real-pairing section. For a chiral state on a closed real-space Klein bottle, the prescribed pairing and gluing produce a $ΔC=2$ chirality wall. A translation-invariant unfolding resolves two co-propagating Majorana branches. Their finite-size levels depend on circumference and holonomy; the near-zero pair is lifted at first order by allowed local seam perturbations. A cut-cylinder control has the same low-energy channel count, so gluing reconnects existing gapless content. We also distinguish literal gluing from deck projection: particle--hole symmetry exchanges the deck eigenblocks, precluding their interpretation as independent BdG quotient Hamiltonians. The contribution is a model-specific account of pairing compatibility and closed-surface seam spectra. The energy cost is evaluated for a fixed pairing background; neither a protected zero mode nor a self-consistent wall solution is claimed.

cond-mat.supr-con↗

arXiv2026

An exactly solvable obstructed seam on a lattice Klein bottle: bulk-locked Majorana rings and register-dependent toric codes

We study a frustrated seam produced by literal reflection gluing of Wen's plaquette model on a closed lattice Klein bottle. The seam operators anticommute along two cycles for even circumference and one cycle of twice the length for odd circumference. Their algebra reduces to free Majorana rings, while the commuting bulk stabilizers constrain the allowed ring sectors. This bulk locking yields exact ground energies, excitation gaps and degeneracies, checked by phase-aware Pauli traces and finite-size diagonalization, including two 24-qubit energy predictions. The seam degeneracy is not topological: the single-site seam fields tested split it at second order. We contrast this obstructed gluing with consistent toric-code realizations. An edge register preserves the anyon labels; a Hadamard-dressed site register has colour-dependent seam transport, with ground-state degeneracy 2 or 4 for even circumference and a sign-frustrated exception at odd circumference. For the enumerated lattice isometries and sizes, we compute logical gates and distances, distinguishing abstract qubit relabelling from local hardware depth. Finally, on three 16- and 24-qubit lattices, chiral matter selects a holonomy doublet whose electric-field splitting follows two competing tunnelling routes. These matter results remain finite-size statements. The main result is the exact obstructed-seam solution with bulk sector locking; the register, gate and matter calculations delimit its relation to topological codes.

cond-mat.str-el↗

arXiv2026

A sharp lower bound for $h$-fold restricted sumsets of integers

Let $A\subseteq[0, L]$ be a set of $k\geqslant 9$ integers such that $0, L\in A$ and $\gcd(A)=1$. Define the $h$-fold restricted sumset $$h^{\wedge}A=\{a_1+\cdots+a_h: a_i\in A,\ 1\leqslant i\leqslant h, \ \ a_i\neq a_j\ {\rm for}\ i\neq j\}. $$ In this paper, we prove that, for every $1\leqslant h\leqslant k-1$, we have $$|h^{\wedge}A|\geqslant h(k-h)-k+2+\min\{L, h(k-h)-\mathbf{1}_h\}, $$ where $\mathbf{1}_h=1$ for $h\in\{2, k-2\}$ and $\mathbf{1}_h=0$ otherwise. The bound is sharp for every $1\leqslant h\leqslant k-1$ and the cardinality threshold $k\geqslant 9$ cannot be decreased. We also give a lower bound for the union of two consecutive restricted sumsets in a cyclic group and characterize equality.

math.NT↗

arXiv2026

The LSST Science Pipelines Software: Optical Survey Pipeline Reduction and Analysis Environment

The NSF-DOE Vera C. Rubin Observatory is executing the Legacy Survey of Space and Time (LSST) as its prime mission, producing a series of data releases over the ten-year survey. The LSST Science Pipelines Software will be used to create these data releases and to perform the nightly prompt processing and alert production. This paper provides an overview of the LSST Science Pipelines Software, describing the components and their integration into pipelines that generate science-ready data products. We do not provide detailed descriptions of individual algorithms, but instead aim to show how the pipeline software is constructed and the breadth of the system. Similarly, we do not provide details of specific data products and the associated file formats.

astro-ph.IM↗

arXiv2026

Quantum walks and graph operations

Let $U_X(t)$ be the transition matrix of a quantum walk on a graph $X$ relative to its adjacency matrix $A$ or the Laplacian matrix $L$. This paper investigates the behavior of quantum walks under Cartesian products, joins, and graph complements. We have two main goals. First, we characterize the conditions such that peak state transfer and pretty good state transfer are preserved under these operations, allowing us to construct new families of graphs admitting these properties. Our second goal is to analyze the relationship between the quantum walks on a graph and its complement. We provide bounds for $f_{u,v}(t)=\big|U_{X^c}(t)_{u,v}-e^{itδ}U_X(-t)_{u,v}\big|$ and $g_{u,v}(t)=\big||U_X(t)_{u,v}|-|U_{X^c}(t)_{u,v}|\big|$, where $δ=-1$ when dealing with $A$ and $δ=n$ otherwise. Note that $f_{u,v}(t)$ and $g_{u,v}(t)$ both measure the difference between the behavior of quantum state transfer between vertices $u$ and $v$ in a graph and its complement. If $X$ is regular or $M=L$, then $f_{u,v}(t)$ is bounded above by $\frac{2}{|V(X)|}$. If $X$ is non-regular and $M=A$, then we utilize the main eigenvalues of a graph to obtain an upper bound for $f_{u,v}(t)$ which depends only on $A$. We also use the bounding matrix of the graph to give bounds for the Nordhaus-Gaddum type relations $|U_X(t)_{u,v}|+|U_{X^c}(t)_{u,v}|$ and $|U_X(t)_{u,v}|\cdot |U_{X^c}(t)_{u,v}|$. Finally, we demonstrate that most of our bounds are sharp for certain families of graphs.

math.CO↗

arXiv2026

UltraM2M: Leveraging Text Transcripts and Mixture Constraints for Weakly-Supervised Speech Enhancement

We propose UltraM2M, a weakly-supervised speech enhancement algorithm building upon the recent unsupervised mixture-to-mixture (M2M) algorithm. M2M realizes unsupervised speech enhancement by training deep neural networks on a set of real-recorded noisy-reverberant multi-channel mixture signals to estimate target speech and non-target signals. The two estimated signals are penalized by a so-called mixture-constraint (MC) loss, which constrains them to reconstruct the observed mixture signals. Although shown to be effective, the mixture constraint may be too weak to enable sufficient noise reduction. To deal with this, UltraM2M extends M2M by further leveraging text transcripts of real-recorded mixtures to design an automatic speech recognition (ASR) loss to penalize the estimated speech signal. The ASR loss can be viewed as a form of weak supervision that could help unsupervised enhancement. Evaluation results on the CHiME-4 dataset show the effectiveness of UltraM2M.

eess.AS↗

arXiv2026

A Vision-Language Foundation Model for Precise and Comprehensive Brain Tumor Diagnosis from Preoperative Multimodal Data

We developed BrainVLM to classify all 12 World Health Organization (WHO) 2021 brain tumor types. BrainVLM integrates an uncertainty quantification strategy to indicate prediction reliability and a module for generating radiology reports to elucidate the clinical rationale. BrainVLM was trained on multi-modal data (MRI scans, demographics, and radiology reports) from 40,043 individuals. It was validated on 5,211 patients with pathologically confirmed brain tumors, including 3,877 held-out patients from the primary hospital and 1,334 patients from 11 independent hospitals. We further conducted two proof-of-concept studies to validate its clinical utility in AI-clinician workflows: 1) a blinded multireader study where 12 neuroradiologists across varying experience levels interpreted 248 retrospective cases with or without AI assistance, and 2) a real-world prospective study in which 1,009 patients were independently and blindly assessed by BrainVLM and radiologists before surgery. Additionally, we demonstrated BrainVLM's utility in preoperative molecular subgroup prediction for adult-type diffuse gliomas, using a multi-center cohort of 632 patients. In primary evaluation, BrainVLM achieved an area under the curve (macro-AUC) of 0.85 (95% CI: 0.84-0.86), and an F1 score of 0.82 (95% CI: 0.81-0.83), surpassing neuroradiologists (F1 = 0.80 (95% CI: 0.79-0.81)). In external validation across 11 centers, BrainVLM achieved an AUC = 0.80 (95% CI: 0.79-0.82) and F1 = 0.75 (95% CI: 0.73-0.78), compared with F1 = 0.71 (95% CI: 0.69-0.73) for neuroradiologists. In prospective real-world evaluation, BrainVLM maintained performance comparable to neuroradiologists. The BrainVLM project page is available at https://hku-healthai.github.io/brainvlm_project.github.io/.

cs.CV↗

arXiv2026

Forecasting Liquidity Withdraw with Machine Learning Models

Liquidity withdrawal is a critical indicator of market fragility. In this project, I test a framework for forecasting liquidity withdrawal at the individual-stock level, ranging from less liquid stocks to highly liquid large-cap tickers, and evaluate the relative performance of competing model classes in predicting short-horizon order book stress. We introduce the Liquidity Withdrawal Index (LWI) -- defined as the ratio of order cancellations to the sum of standing depth and new additions at the best quotes -- as a bounded, interpretable measure of transient liquidity removal. Using Nasdaq market-by-order (MBO) data, we compare a spectrum of approaches: linear benchmarks (AR, HAR), and non-linear tree ensembles (XGBoost), across horizons ranging from 250\,ms to 5\,s. Beyond predictive accuracy, our results provide insights into order placement and cancellation dynamics, identify regimes where linear versus non-linear signals dominate, and highlight how early-warning indicators of liquidity withdrawal can inform both market surveillance and execution.

q-fin.RM↗

arXiv2026

Mitra-v2 Technical Report

We introduce Mitra-v2, a tabular foundation model that delivers state-of-the-art performance on real-world classification and regression problems, from credit-risk scoring and clinical prediction to equipment-failure detection and house-price estimation. Mitra-v2 is trained only on synthetic data, with a pretraining distribution that is much larger and more diverse than Mitra-v1's. Built on a small 2D Transformer backbone, Mitra-v2 supports longer contexts and larger feature spaces. Improved optimization lets it learn from this larger task distribution. We evaluate Mitra-v2 on the TabArena and TALENT benchmarks, comprising more than 300 real-world datasets under two evaluation protocols. On the full TabArena benchmark, Mitra-v2 delivers state-of-the-art performance at the level of the industry-scale TabFM and EXAONE Tabular models, while surpassing TabPFN-3 by a wide margin in both classification and regression. Mitra-v2 matches the 1.6B-parameter TabFM with only 5% of its size (77M parameters), delivering frontier performance at a fraction of the cost. On TALENT, Mitra-v2 remains among the leading models, clearly outperforming TabPFN-3 and TabICLv2. It also ranks first on classification tasks with more than ten classes, even though it was pretrained only on tasks with at most ten classes. These results make Mitra-v2 one of the strongest and most broadly applicable open tabular foundation models released to date. We release the model weights, the inference and fine-tuning code, and our evaluation results under the Apache-2.0 license.

cs.LG↗

arXiv2026

$p1$: Better Prompt Optimization with Fewer Prompts

Prompt optimization improves language models without updating their weights by searching for a better system prompt, but its effectiveness varies widely across tasks. We study what makes a task amenable to prompt optimization. We show that the reward variance across different system prompts can be decomposed into two components: variance among responses, which captures generation stochasticity, and variance among system prompts, which captures differences in system prompt quality. Prompt optimization succeeds when variance among system prompts is sufficiently large, but fails when variance among responses dominates the variance of the system prompts. Surprisingly, we further show that scaling to more user prompts can hurt optimization by reducing variance among system prompts, especially on heterogeneous datasets where different user prompts favor different system prompts. Motivated by this insight, we propose $p1$, a simple user prompt filtering method that selects a small subset of user prompts with high variance across candidate system prompts. This subset of user prompts allows one to distinguish a good system prompt from a bad one, making system optimization easier. Experiments on reasoning benchmarks show that $p1$ substantially improves prompt optimization over training on the full dataset and outperforms strong baselines such as GEPA. Notably, training on only two prompts from AIME 24 yields a system prompt that generalizes well to other reasoning benchmarks.

cs.LG↗

arXiv2026

D-LiFT: Improving LLM-based Decompiler Backend via Code Quality-driven Fine-tuning

As one of the key tools in many security tasks, decompilers reconstruct human-readable source code from binaries. Yet, despite recent advances, their outputs often suffer from syntactic and semantic errors and remain difficult to read. Recently, with the advent of large language models (LLMs), researchers began to explore the potential of LLMs to refine decompiler output. Nevertheless, our study of these approaches reveals their problems, such as introducing new errors and relying on unreliable accuracy validation. In this paper, we present D-LIFT, an enhanced decompiler-LLM pipeline with a fine-tuned LLM using code quality-aware reinforcement learning. Unlike prior work that overlooks preserving accuracy, D-LIFT adheres to a key principle for enhancing the quality of decompiled code: preserving accuracy while improving readability. Central to D-LIFT, we propose D-Score, an integrated code quality assessment system to score the decompiled source code from multiple aspects, and use it to guide reinforcement learning fine-tuning and to select the best output during inference. In line with our principle, D-Score assigns low scores to any inaccurate output and only awards higher scores for readability to code that passes the accuracy check. Our implementation, based on Ghidra and a range of LLMs, demonstrates significant improvements for the accurate decompiled code from the coreutils and util-linux projects. Compared to baseline LLMs without D-Score-driven fine-tuning, our trained LLMs produce 55.3% more improved decompiled functions, as measured by D-Score. Overall, D-LIFT improves the quality of 68.2% of all the functions produced by the native decompiler.

cs.CR↗

arXiv2026

@skills: Attention is all you have

There are 56,804 public agent skills today, and teams write many more privately. The dominant delivery model is installation: once installed, a skill's description remains in the system prompt, competing for fewer than 100 reliable trigger slots. This leaves the long tail with no practical path to use and forces teams' own playbooks to compete for the same scarce space. We observe that installation bundles three separable functions: content, persistence, and automatic triggering. Only the last requires prompt residency. We therefore propose @skills, an open protocol that separates them. A path addresses any skill, subtree, or collection, and reading a skill is sufficient to use it, so nothing is installed or made resident. The operation vendors a copy at the same path into a project's Git-tracked tree for adaptation and ownership. The operation adds one .gitignore-style line, the only element that costs prompt residency. A directory is a menu, making bundles ordinary directories rather than all-or-nothing units. The protocol requires no manifest, lockfile, or registration, and SKILL.md remains unchanged. @skills is additive, ships as an installable package, and turns any agent that can read files and run commands into a client through a single instruction file. Its open specification is at https://github.com/SylphAI-Inc/atskills and it is implemented in the AdaL CLI at https://adalagent.ai . Because paths address skills well but cannot find them, the protocol is paired with a free hub at https://atskills.one for corpus-wide search and ranking, repository-free hosting, private and team collections, and one-screen authoring. The hub is optional: gh: and local paths resolve without it, and indexed GitHub skills retain their gh: identities. Install less, use more.

cs.AI↗

arXiv2026

Optical Counterparts to X-ray sources in LSST DP1

We present a crossmatch between a combined catalog of X-ray sources and the Vera C. Rubin Observatory Data Preview 1 (DP1) to identify optical counterparts. The six fields targeted as part of DP1 include the Extended Chandra Deep Field South (E-CDF-S), the Euclid Deep Field South (EDF-S), the Fornax Dwarf Spheroidal Galaxy (Fornax dSph), 47 Tucanae (47 Tuc) and science validation fields with low galactic and ecliptic latitude (SV\_95\_-25 and SV\_38\_7, respectively). We find matches to 2314 of 3830 X-ray sources. We also compare our crossmatch to DP1 in the E-CDF-S field to previous efforts to identify optical counterparts. The probability of a chance coincidence match varies across each DP1 field, with overall high reliability in the E-CDF-S field, and lower proportion of high-reliability matches in the other fields. The majority of previously known sources that we detect are, unsurprisingly, active galaxies. We plot the X-ray-to-optical flux ratio against optical magnitude and color in an effort to identify Galactic accreting compact objects using a {\em Gaia} color threshold transformed to LSST $g$--$i$, but do not find any strong candidates in these primarily extragalactic counterparts. The DP1 dataset contains high-cadence photometry collected over a number of nights. We calculate the Stetson $ J $ variability index for each object under the hypothesis that X-ray counterparts tend to exhibit higher optical variability; however, the evidence is inconclusive whether our sample is more variable over DP1 timescales when compared to field objects.

astro-ph.HE↗

arXiv2026

An AI system to help scientists write expert-level empirical software

The cycle of scientific discovery is frequently bottlenecked by the slow, manual creation of software to support computational experiments\cite{hannay2009how}. To address this, we present Empirical Research Assistance (ERA), an AI system that creates expert-level scientific software whose goal is to maximize a quality metric. The system uses a Large Language Model (LLM) and Tree Search (TS)\cite{silver2016mastering} to systematically improve the quality metric and intelligently navigate the large space of possible solutions. ERA achieves expert-level results when it explores and integrates complex research ideas from external sources. The effectiveness of tree search is demonstrated across a diverse range of tasks. In bioinformatics, ERA discovered 40 novel methods for single-cell data analysis that outperformed the top human-developed methods on a public leaderboard. In epidemiology, ERA generated 14 models that outperformed the CDC ensemble and all other individual models for forecasting COVID-19 hospitalizations. ERA also produced expert-level software for geospatial analysis, neural activity prediction in zebrafish, and numerical solution of integrals, and a novel rule-based construction for time series forecasting. By devising and implementing novel solutions to diverse tasks, ERA represents a significant step towards accelerating scientific progress.

cs.AI↗

arXiv2026

Muse Spark Safety & Preparedness Report

Muse Spark is the latest large language model developed by Meta. In this report, we first present evaluations for catastrophic risk domains under Meta's Advanced AI Scaling Framework, along with the evidence that informed our launch decision. We then discuss additional considerations, such as Muse Spark's broader content safety and behavioral profile, that are relevant to overall safety but fall outside the catastrophic risk domains governed by the Framework. Our preparedness results covering Chemical and Biological, Cybersecurity, and Loss of Control risks assess Muse Spark's deployment within Meta AI as presenting acceptable levels of residual risks under our Advanced AI Scaling Framework. We conducted a broad set of evaluations targeting dual-use and high-risk capabilities across these catastrophic risk domains. Those evaluations identified elevated risks prior to mitigations, with Chemical and Biological capabilities assessed as likely reaching the "high risk" category under the Advanced AI Scaling Framework before safeguards were applied. We have implemented a multi-layered set of mitigations that address the identified risks, and Muse Spark demonstrates state-of-the-art refusal across a range of benchmarks related to hazardous workflows in chemistry and biology. We therefore release Muse Spark as the underlying model of Meta AI.

cs.CY↗

arXiv2026

Can LLM Agents Simulate Multi-Turn Human Behavior? Evidence from Real Online Customer Behavior Data

Recent research shows that LLM Agents can generate ``believable'' human behaviors via prompt-only methods, and such agents have been increasingly adopted in downstream applications. However, existing evaluation of these agents only focuses on qualitative believability (whether human raters think they are accurate), leaving open questions of whether LLM agents can accurately generate step-by-step actions mimicking a particular human's behavior in a multi-turn interaction task. In this work, we take shopping as a case study and present the first large-scale quantitative evaluation of state-of-the-art LLMs' ability to accurately simulate human behavior. Using real-world data from 31,865 online shopping sessions containing 230,965 user actions, our evaluation reveals that prompt-based LLMs (DeepSeek-R1, Llama, Claude) achieve only 11.86% accuracy in generating human actions, highlighting a substantial gap in actual behavioral accuracy. Through experiments, we also showcase that strategies as simple as fine-tuning LLMs on real human click-through data augmented with synthesized reasoning traces can greatly enhance models' performance. The fine-tuned Qwen2.5-7B achieves 17.26% action generation accuracy and 33.86% F1 score on final purchase prediction, representing substantial improvements of 5.4% and 13.85% over prompt-only baselines. This work establishes the first rigorous benchmark for human behavior simulation and provides actionable insights for developing more accurate LLM agents for future downstream applications.

cs.CL↗

arXiv2026

EDRP: Enhanced Dynamic Relay Point Protocol for Data Dissemination in Multi-hop Wireless IoT Networks

Emerging IoT applications are transitioning from battery-powered to grid-powered nodes. DRP, a contention-based data dissemination protocol, was developed for these applications. Traditional contention-based protocols resolve collisions through control packet exchanges, significantly reducing goodput. DRP mitigates this issue by employing a distributed delay timer mechanism that assigns transmission-start delays based on the average link quality between a sender and its children, prioritizing highly connected nodes for early transmission. However, our in-field experiments reveal that DRP is unable to accommodate real-world link quality fluctuations, leading to overlapping transmissions from multiple senders. This overlap triggers CSMA's random back-off delays, ultimately degrading the goodput performance. To address these shortcomings, we first conduct a theoretical analysis that characterizes the design requirements induced by real-world link quality fluctuations and DRP's passive acknowledgments. Guided by this analysis, we design EDRP, which integrates two novel components: (i) Link-Quality Aware CSMA (LQ-CSMA) and (ii) a Machine Learning-based Block Size Selection (ML-BSS) algorithm for rateless codes. LQ-CSMA dynamically restricts the back-off delay range based on real-time link quality estimates, ensuring that nodes with stronger connectivity experience shorter delays. ML-BSS algorithm predicts future link quality conditions and optimally adjusts the block size for rateless coding, reducing overhead and enhancing goodput. In-field evaluations of EDRP demonstrate an average goodput improvement of 39.43\% than the competing protocols.

cs.NI↗

arXiv2026

Architectural Insights for Post-Tornado Damage Recognition

Rapid and accurate building damage assessment in the immediate aftermath of tornadoes is critical for coordinating life-saving search and rescue operations, optimizing emergency resource allocation, and accelerating community recovery. However, current automated methods struggle with the unique visual complexity of tornado-induced wreckage, primarily due to severe domain shift from standard pre-training datasets and extreme class imbalance in real-world disaster data. To address these challenges, we introduce a systematic experimental framework evaluating 79 open-source deep learning models, encompassing both Convolutional Neural Networks (CNNs) and Vision Transformers, across over 2,300 controlled experiments on our newly curated Quad-State Tornado Damage (QSTD) benchmark dataset. Our findings reveal that achieving operational-grade performance hinges on a complex interaction between architecture and optimization, rather than architectural selection alone. Most strikingly, we demonstrate that optimizer choice can be more consequential than architecture: switching from Adam to SGD provided dramatic F1 gains of +25 to +38 points for Vision Transformer and Swin Transformer families, fundamentally reversing their ranking from bottom-tier to competitive with top-performing CNNs. Furthermore, a low learning rate of 1x10^(-4) proved universally critical, boosting average F1 performance by +10.2 points across all architectures. Our champion model, ConvNeXt-Base trained with these optimized settings, demonstrated strong cross-event generalization on the held-out Tuscaloosa-Moore Tornado Damage (TMTD) dataset, achieving 46.4% Macro F1 (+34.6 points over baseline) and retaining 85.5% Ordinal Top-1 Accuracy despite temporal and sensor domain shifts.

cs.CV↗