Searcharxiv⌕ Search

arXiv subjects

Zhen Zhang

Publications and source records attributed to Zhen Zhang.

At least 19 recordsLinked to original sources
arXiv2026

Extreme Multiwavelength Variability in the Recurrent Intermediate-type Changing-look Seyfert Galaxy SDSS J234728.73+242745.7

We report the discovery of the Seyfert galaxy J234728.73+242745.7 as a recurrent intermediate-type CLAGN with extreme optical/UV variability. Optical spectra spanning nearly three decades reveal Type 1.0$\rightarrow$1.5$\rightarrow$1.2 transitions, accompanied by the disappearance and reappearance of broad He II $\lambda4686$ and a broad-line Balmer decrement evolving from 4.5 to 7.2 and then 2.4. Monitoring reveals coordinated variability from X-rays to the MIR. SED decomposition yields a host-corrected g-band high-to-low-state flux ratio of $5.4\pm0.4$, exceeding the Swift/XRT ratio of $2.7\pm0.8$ across the changing-look transition. This discrepancy may arise from reduced dust extinction, as correcting for the low-state continuum reddening ($A_V^{\rm cont}=0.66\pm0.23$ mag) brings the optical/UV ratios close to the X-ray value, although the correction is model-dependent and intrinsically weaker X-ray variability may also contribute. However, X-ray spectra show no compelling evidence for intrinsic gas absorption or its variability ($N_{\rm H}^{\rm intr}\lesssim5\times10^{20}$ cm$^{-2}$). In contrast, EP/WXT monitoring during the recent high state reveals order-of-magnitude X-ray fluctuations on monthly-to-sub-year timescales, exceeding contemporaneous optical variations. Meanwhile, the radio flux density remains stable at $\sim2$ mJy across all ASKAP observations since mid-2020. Together with optical/UV and MIR variability and the broad He II transition, these results favor intrinsic accretion-state changes as the primary driver, while allowing a secondary contribution from variable obscuration. The source displays a loop-like MIR color--magnitude evolution, likely driven by the delayed response of an extended torus. This recurrent CLAGN offers a laboratory for probing the dynamic response of the broad-line region and circumnuclear dust to accretion-state transitions.

astro-ph.HE↗
arXiv2026

Electroweak Constraints on Nuclear Neutron Distributions and the Low-Energy Weak Mixing Angle

Coherent elastic neutrino--nucleus scattering (CE$ν$NS) offers a low-energy probe of the weak mixing angle, $\sin^2θ_W$, and possible new physics in the neutrino sector. Its reliable interpretation, however, requires independent constraints on the neutron distributions of the target nuclei. We exploit correlations across nuclei within an extended Skyrme energy-density-functional framework to propagate constraints from the CREX and PREX-II parity-violating electron-scattering (PVES) measurements to CE$ν$NS targets. This yields neutron form factors and radii, with relative radius uncertainties below $1\%$. Using these inferred neutron form factors, we jointly analyze the COHERENT CsI, Ge, and Ar data and obtain $\sin^2θ_W=0.231^{+0.016}_{-0.015}$, consistent with the Standard Model prediction. These results provide the first electroweak-experimentally constrained benchmarks for nuclear-structure studies, neutrino-sector electroweak tests, and CE$ν$NS signal predictions in dark-matter detectors.

nucl-th↗
arXiv2026

Local or Escalate? Post-Training Expert Routing for Real-Time Duplex Speech Models

A full-duplex model should answer easy questions immediately, but hard questions often need stronger reasoning, which may require waiting. The challenge is to choose between these paths at the moment the model begins to speak. Recent systems learn related mode decisions through special-token generation inside the dialogue model, coupling routing to turn-taking and temporally annotated full-duplex training. We study a post-training alternative. We freeze MiniCPM-o 4.5, using only its speech input and output, and fit a logistic failure readout on hidden states already available at its native listen-to-speak transition. The resulting gate requires no additional model generation and exposes thresholds that can be retuned without changing the duplex policy. A probe fitted on text-input states also reads the states of spoken questions, including unseen human recordings. On the internal benchmark, selective handoff improves answer accuracy by 27.1 points. Compared with always using the expert, it reduces mean server time to first answer audio by 31.5 percent. It also outperforms matched random routing across external speech question-answering pools, as well as a zero-shot GPT-5.4 mini router that rates the transcribed question. The training recipe also transfers to a second duplex model family. These results show that an existing duplex model can be equipped to answer easy turns immediately and wait for stronger reasoning or fresher knowledge on the turns that need it, without further duplex training.

cs.AI↗
arXiv2026

A Compute Center Anyone Can Join: A Peer-to-Peer System for AI Inference

Today, most people obtain AI inference by buying it from a small number of organizations that run large data centers, while many GPUs owned by individuals and small institutions sit idle for much of the day. Bitcoin showed that strangers can contribute resources under shared rules and receive checkable rewards, but it relies on an asymmetry: proof-of-work is expensive to produce and cheap to verify. Useful inference has no such asymmetry. We propose the Open Inference Market (OIM), which replaces the missing asymmetry with an economic mechanism. A worker delivers a result without knowing whether it will be audited; its unsettled earnings serve as collateral during a challenge window; and confirmed cheating forfeits all of it. We call this principle earnings as collateral. In a simplified model we show three things. The number of audits needed for deterrence is a constant per worker per day, independent of the worker's throughput, so the audited share falls as the network gets busier. A new worker can join with a short period of intensive auditing and a very small bond. And whether the mechanism works at all depends only on the ratio of the audit judge's false-flag rate to its detection rate. Under example parameters, about 0.3 replays per worker per day are enough to make cheating unprofitable for a rational worker. On this basis we lay out a path that hands four powers back to participants one at a time: owning compute, writing history, judging work, and releasing value. A compute center need not be a building; it can be a protocol that anyone can join.

cs.AI↗
arXiv2026

On the Value of Cross-Modal Misalignment in Multimodal Representation Learning

Multimodal representation learning, exemplified by multimodal contrastive learning (MMCL) using image-text pairs, aims to learn powerful representations by aligning cues across modalities. This approach relies on the core assumption that the exemplar image-text pairs constitute two representations of an identical concept. However, recent research has revealed that real-world datasets often exhibit \emph{cross-modal misalignment}. There are two distinct viewpoints on how to address this issue: one suggests mitigating the misalignment, and the other leveraging it. We seek here to reconcile these seemingly opposing perspectives and to provide a practical guide for practitioners. Using latent variable models, we thus formalize cross-modal misalignment by introducing two specific mechanisms: \emph{selection bias}, where some semantic variables are absent in the text, and \emph{perturbation bias}, where semantic variables are altered---both leading to misalignment in data pairs. Our theoretical analysis shows that, under suitable assumptions, population global minimizers of an alignment--entropy surrogate yield representations that depend only on the semantic variables unaffected by selection and perturbation biases and locally identify this subset almost surely. Extensive empirical studies on synthetic data and real image-text datasets further examine how cross-modal misalignment shapes semantic predictability and downstream performance, shedding light on its nuanced impact on multimodal representation learning.

cs.LG↗
arXiv2026

EP J062132.0+623837: A Multi-wavelength Study of a Changing-look Active Galactic Nucleus Discovered by Einstein Probe

EP J062132.0+623837 is a new X-ray source identified in 14-day binned survey data from the Wide-field X-ray Telescope aboard the Einstein Probe (EP). Subsequent X-ray observations with the Follow-up X-ray Telescope (FXT) on board EP localized the source to the central region of the galaxy 2MASX J06213207+6238390 at $z=0.084$. Here, we present a comprehensive analysis of the multi-wavelength data for this object. During the FXT and Swift/XRT follow-up, the source remained X-ray active, with observed luminosities of $(3-9)\times10^{43}$ erg s$^{-1}$, and its X-ray spectra were well described by power-law models with photon indices of $Γ\sim1.5$. The optical spectrum of the host galaxy clearly reveals a broad-line active galactic nucleus (AGN) exhibiting significantly enhanced broad Balmer emission lines compared to an archival spectrum obtained four years earlier, thereby marking a changing-look transition from Type 1.8 to Type 1.5. Furthermore, long-term optical and mid-infrared light curves indicate that the nucleus underwent dramatic brightening around 2010 and has since shown substantial variability. Given the spatial coincidence and multi-wavelength behavior, we identify EP J062132.0+623837 as a changing-look AGN, initially detected by EP and subsequently confirmed to exhibit large-amplitude variability across multiple bands. This case study demonstrates the great potential of wide-field time-domain X-ray surveys for discovering changing-look AGNs, paving a new avenue for their systematic identification as a powerful complement to ongoing optical surveys.

astro-ph.GA↗
arXiv2026

Right On Q: Recovering Reasoning under Quantization via Self-Generated Trajectories

Quantization-aware training (QAT) aims to recover reasoning capabilities in low-bit large language models (LLMs). Existing recipes typically supervise the quantized student with the original full-precision model on fixed response sequences. However, the resulting low-bit quantized models still struggle on long-horizon reasoning tasks and often fail to terminate within a token budget that suffices for the full-precision model. We identify two challenges in closing this gap. First, fixed-response supervision leaves a mismatch between training prefixes and those encountered during student generation, and this trajectory divergence grows as responses lengthen. Second, on its own prefixes, the student underpredicts some next-token candidates and overpredicts others, and this distributional misalignment becomes more frequent at high-entropy prefixes. We present OnQ, a low-bit recovery pipeline that combines on-policy distillation and candidate-wise Dynamic KL. Starting from block-wise calibration, the quantized student generates responses and receives full-precision teacher supervision on its own prefixes. Dynamic KL weights forward and reverse KL by each candidate's teacher-student probability ratio to balance correction of under- and overprediction. Across Qwen3-1.7B, 4B, and 8B at 2- and 3-bit precision, OnQ achieves the highest nine-benchmark average, with the largest gains on long-horizon reasoning benchmarks. At 2 bits, it outperforms every baseline on every benchmark at every model size, improving the Qwen3-8B average by 10.26 points over the strongest baseline. It also produces shorter responses with fewer truncations and repetitive errors.

cs.CL↗
arXiv2026

DiscoveryRSI: Recursively Learning to Discover Valuable Hypotheses

Scientific discovery requires developing worthwhile hypotheses into practical investigations, yet learning from this evolving research process remains challenging. We introduce an operational definition of valuable and testable research hypotheses: evidence-grounded claims about consequential uncertainties, coupled with feasible investigations, explicit decision criteria, and alternative explanations. Our DiscoveryRSI framework develops hypothesis trees through multi-agent evolutionary search, compiles selected investigations into executable Harbor tasks, and recursively learns through three mechanisms: hypothesis credit optimization attributes scientific assessments to documented research contributions; harness replay identifies productive strategies and recurring failures to refine research workflows; and rediscovery applies accepted improvements to new investigations, generating further experience for subsequent learning. We also introduce DFM-Bench, which provides comprehensive assessment of hypothesis quality and cumulative research progress at the levels of hypotheses, revisions, development paths and research runs. Across four life-science domains, automated evaluation shows that hypothesis quality rises from 4.86 for the base model to 5.39 after two rounds of self-improvement, with the large gains in falsifiability and data resolvability. We further validate hypothesis quality through expert review, comparing our DiscoveryRSI-27B with other research agents. Preliminary human assessments show that DiscoveryRSI-27B scores higher than GPT-6 Luna on hypothesis importance and whether claims are plausible and evidence-bounded. DiscoveryRSI-27B achieved a 6.3% rate of hypothesis correspondence with research published after the model cutoff, largely outperform GPT-6 with 2.5% and Claude Opus 5 with 2.3%, while delivering greater human-rated improvement along research paths.

cs.CL↗
arXiv2026

SPEM: Structure-Aware Procedural Memory for Efficient Skill Use in LLM Agents

LLM-based agents increasingly rely on reusable skills. However, scaling skill libraries makes efficient and accurate skill use difficult. Full skill loading incurs high token and runtime overhead. In contrast, flat semantic retrieval treats skills as independent text entries, ignoring dependencies, execution order, state transitions, and data flow constraints. We propose SPEM, a lightweight structure-aware procedural memory framework for efficient skill use in LLM-based agents. SPEM maintains Atomic Skill Memory for individual skills and Skill-DAG Memory for reusable compositions derived from successful executions. For a new task, SPEM reuses high-confidence Skill-DAG templates when available. Otherwise, it retrieves atomic skills and invokes Skill-GNN for state- and structure-aware skill selection. Successful executions are written back as new Skill-DAGs, allowing procedural memory to accumulate over time. Experiments on ALFWorld and SkillsBench show that SPEM achieves the best overall effectiveness--efficiency trade-off, reaching 87.1% ALFWorld success and 38.38% SkillsBench reward while reducing token and runtime costs against strong baselines.

cs.AI↗
arXiv2026

The Geometric Mechanics of Contrastive Representation Learning: Alignment Potentials, Entropic Dispersion, and Cross-modal Divergence

While InfoNCE underlies modern contrastive learning, its geometric mechanisms remain under-characterized beyond the canonical alignment--uniformity decomposition. We develop a measure-theoretic framework in which representation measures evolve on a fixed embedding manifold. In the large-batch limit, we prove value and gradient consistency, linking the stochastic objective to explicit deterministic energy landscapes and revealing a geometric bifurcation between unimodal and symmetric multimodal regimes. In the unimodal case, the intrinsic energy is strictly convex and admits a unique Gibbs equilibrium, showing that entropy acts as a tie-breaker within the aligned basin. In the multimodal case, the intrinsic geometry becomes cross-coupled and contains a persistent negative symmetric divergence term: each modality's marginal reshapes the effective landscape of the other, allowing strong pairwise alignment to coexist with a persistent modality gap. Controlled synthetic experiments and analyses of pretrained CLIP representations support these predictions. Overall, our results shift the analytical lens from pointwise discrimination to population geometry, showing that pairwise alignment alone is insufficient to control cross-modal marginal structure.

cs.LG↗
arXiv2026

Metric Poissonian Pair Correlation Property Part 2: The Case \(n\log n\)

We prove that $(αn\log n)_{n\ge1}$ has Poissonian pair correlation for almost every real $α$, solving a Problem posed by Aistleitner, El-Baz and Munsch [Open Problem 3, Geom. Funct. Anal. 2021]. Our main contribution is a new four-point counting estimate with a fixed power saving over $N^{7/3}$ at tolerance $N^{-1}$, valid on the ranges left after a first-moment and Borel--Cantelli argument. Its proof reduces the counting condition to the required proximity of an implicitly defined real root to an integer; a Fejér kernel majorizes this condition, and multidimensional van der Corput $A$- and $B$-processes supply the saving. A localized Mellin argument then yields the second-moment estimate needed for the metric conclusion.

math.NT↗
arXiv2026

Generalizable turbulence closures across bluff-body shapes by PINN-based solver-agnostic training

Data-driven turbulence closures are commonly calibrated by embedding a CFD solver within an inverse procedure that requires a converged flow solution at every optimization iteration, which is fragile and costly. We instead formulate closure identification as a PDE-constrained, data-driven inverse problem within a physics-informed neural network (PINN), and subsequently stabilize the inferred closure for deployment in a numerical solver. This separation eliminates the training cost of repeated CFD solves and associated adjoint or ensemble evaluations, providing substantial flexibility to investigate alternative closure forms. Using this flexibility, we develop four closures: three model the Reynolds stress on a realizable tensor basis---a local map, a non-local model transporting the turbulent kinetic energy and recovering the out-of-plane normal stress, and the same with a learned length scale $\ell$---and a fourth models the Reynolds force $\mathbf{F}=-\nabla\cdot\boldsymbolτ$ directly, free of the realizability constraint. All four are trained across six two-dimensional bluff-body wakes at $Re=10^4$ and deployed frozen in a standard finite-element solver, stabilized primarily by input-gradient smoothing. Under a strict leave-one-shape-out (LOSO) protocol, all four reduce the field errors of a steady SST $k$-$ω$ baseline, most strongly on the mean velocity. The learned-length-scale closure is most accurate on the stress fields, while the force model generalizes best on the mean velocity and drag (LOSO drag error $\sim 7\%$). The closures also train efficiently on Particle Image Velocimetry data, enabling geometries intractable for DNS.

physics.flu-dyn↗
arXiv2026

Ablation-Driven Selective Laser Direct Mass Transfer for Micro-LEDs with High Speed, Enhanced Gap Tolerance, and High Precision

Micro light-emitting diodes (micro-LEDs) offer superior performance for next-generation displays, yet their scalable manufacturing is severely bottlenecked by the mass transfer process from donor to receiver substrates. The existing laser-induced forward transfer (LIFT) methods are restricted by low throughput, limited selectivity, and long reaction times. In this study, we introduce a novel defocused direct laser ablation transfer (D-DLAT) method utilizing polyimide as a dynamic release layer. Whereas most indirect laser transfer methods trigger chip release followed by passive descent toward the receiver, D-DLAT directly ablates the release layer to actively propel the small-sized chips towards the receiver via ablation shock. By optimizing the ablation profiles and scanning paths, this method achieves high-speed transfer with significantly enhanced precision and robustness. We demonstrate the successful transfer of micro-LEDs ranging from 600 $\unicode{x03BC}$m down to 35 $\unicode{x03BC}$m in size, with a minimum reported placement error of less than 1 $\unicode{x03BC}$m, and a critical transfer gap of up to 50 $\unicode{x03BC}$m. Furthermore, the D-DLAT approach enables full selectivity, facilitating complex patterned transfers and array repitching operations that are essential for full-color display integration. Our findings indicate that D-DLAT effectively overcomes the fundamental limitations of current transfer technologies, demonstrating substantial potential for the scalable and high-yield manufacturing of advanced micro-LED displays. Ultimately, D-DLAT establishes a robust paradigm for the high-speed manipulation and heterogeneous integration of micro-optoelectronic devices, extending its profound implications beyond displays to the broader landscape of advanced micro-assembly and flexible electronics.

physics.optics↗
arXiv2026

Computed, not Memorized: Bit-Factorized Identifiers for Efficient Generative Retrieval

Generative retrieval is known for its efficient end-to-end indexing and retrieval. In particular, numeric Document Identifiers (DocIDs) are generally more efficient because they are shorter and generally fixed-length sequences over a small vocabulary, requiring fewer decoding steps during beam search. However, they tend to generalize poorly to unseen documents and must be learned during indexing-stage training. In this paper, we propose a numeric DocID design for generative retrieval that enables better adaptation to unseen documents without retraining. Our DocID design inherits the language model's knowledge: each DocID is a sequence of tokens factorized into bits, and each token is scored as the sum of its bits. Unlike existing numeric DocIDs, whose tokens carry meaning only through embeddings memorized during indexing training, each bit here derives its meaning from the model's representation. Therefore, the DocIDs of unseen documents remain scorable without retraining. Experiments on three benchmarks show that our DocID design substantially improves unseen-document retrieval while maintaining comparable performance on seen documents. It achieves over 20 percentage points higher Hit@10 on unseen documents and up to 7 times lower inference latency than existing numeric DocID designs on NQ320K and TriviaQA, while performing only 3 percentage points below weight-update methods on MS MARCO without additional training. These results show that an appropriate DocID design can heavily reduce retraining costs when adapting generative retrievers to new documents.

cs.IR↗
arXiv2026

Emergent medium-range order in amorphous silicon

Medium-range order (MRO) is an important feature of amorphous materials, but its nature in terms of atomic arrangement has remained elusive. Here, using large-scale machine-learning-driven atomistic simulations of amorphous silicon, we probe the MRO of this archetypal network glass. By characterizing the structure in three dimensions, we reveal that the symmetry of the atomic arrangement around a central atom shows a crossover as a function of distance. At short distances, the structure is determined by local bonding constraints, which evolve from predominantly tetrahedral environments in low-density amorphous silicon to highly coordinated and strongly disordered local environments in very-high-density amorphous silicon. At larger distances, however, distinct cubic- and octahedral-symmetry patterns emerge in the three-dimensional atomic-density distribution, despite the absence of these symmetries at shorter length scales. We find that this crossover is accompanied by a quasi-periodic spatial organization of the topological rings in the structure. The characteristic spacing between ring centers matches the length scale of the first sharp diffraction peak (FSDP) in the structure factor, establishing a direct connection between this reciprocal-space signature of MRO and its real-space organization. These results demonstrate that the FSDP, the spatial correlation of the rings, and emergent orientational symmetries are not independent manifestations of MRO, but reflect different aspects of a common structural organization. Our findings provide a unified real-space picture of MRO in amorphous silicon and suggest that intermediate-range structural order in network glasses emerges from the interplay between local bonding constraints and extended atomic arrangements driven by efficient packing.

cond-mat.soft↗
arXiv2026

Poissonian Pair Correlation for Bernoulli Orbits

Let \(α, β\in \mathbb{T} = \mathbb{R}/\mathbb{Z}\), and let \((ξ_n)_{n \ge 1}\) be independent random variables taking the values \(α\) and \(β\) with equal probability. We study the random walk \[X_n = ξ_1 + \cdots + ξ_n \pmod 1\] and present a deterministic Diophantine condition on \((α, β)\) that implies almost sure Poisson pair correlation. To the best of our knowledge, this is the first result providing explicit examples in this framework where the increment measure is purely discrete. The classical Fourier contraction method fails in this setting; a different approach is required. Our proof uses the parabolic geometry of returns to \(1\) and an exact covariance formula for overlapping blocks.

math.NT↗
arXiv2026

Memory-Augmented Phase-Field Modeling: Onsager Formulation, Internal-Variable Lifting, and Structure-Preserving Computation

In this work, we develop a general memory-augmented phase-field (MAPF) framework based on Onsager's variational principle, in which memory effects are incorporated through a general relaxation kernel while preserving the thermodynamic consistency of the underlying system. Rather than treating fractional models as fundamental formulations, we show that they can be interpreted as reduced descriptions associated with specific power-law memory kernels. The proposed MAPF equations provide a unified modeling framework for non-Markovian phase-field dynamics. To facilitate analysis and computation, we further introduce an equivalent internal-variable representation that transforms the original nonlocal-in-time dynamics into an extended Markovian gradient system. This formulation preserves key structural properties, including energy dissipation and mass conservation, and enables the development of efficient structure-preserving numerical schemes. In particular, we construct an energy-stable scalar auxiliary variable approach combined with a fast rank-one implementation that exploits the relaxation-spectrum-induced coupling structure, resulting in a computationally efficient algorithm with reduced storage requirements. The proposed framework establishes a systematic connection between memory-dependent phase-field modeling, internal-variable thermodynamics, and structure-preserving numerical computation. Numerical experiments demonstrate the accuracy, stability, and efficiency of the proposed methods for phase-field dynamics with long-time memory effects.

math.NA↗