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Wen Huang

Publications and source records attributed to Wen Huang.

At least 19 recordsLinked to original sources
arXiv2026

Decision-Centric Research: A Calibrated Decision Model and Benchmark for Scientific Agents

Scientific agents make consequential choices about evidence, next actions and research strategy, but choices embedded in generated text are difficult to evaluate independently. Decision-Centric Research (DCR) is introduced to make individual choices the common unit of supervision, prediction and evaluation through typed predictions over an explicit Research State. DCR defines 24 decision types, ten of which are instantiated in the Research Decision Benchmark (RD-Bench) of 2,000 rule-generated cases. The Decision-Centric Research Model (DCRM) shares a candidate-token readout across types to produce answers and option probabilities in one forward pass. Under a held-out research-domain split, a 0.6B DCRM (state-aware) achieves 89.4% Top-1 accuracy, compared with 27.8% for DCRM (question-only) trained with the same recipe and 36.4% for DeepSeek-V4.1-Flash. All 30 type-seed comparisons against DCRM (question-only) are significant after Holm correction. After held-out calibration, the resulting prediction sets contain a mean of 1.515 candidates at 97.1% empirical label coverage, reducing mean set size by 57.4% relative to DCRM (question-only) at similar coverage. These results establish a compact state-aware decision layer with accurate typed prediction and an inspectable uncertainty interface.

cs.AI↗
arXiv2026

Pair density waves via interband pairing --- a perspective from superfluid weight

Pair density waves (PDW) are superconducting states that exhibit a spatially modulated pairing order parameter, corresponding to Cooper pairs carrying finite center-of-mass momentum. While some recent studies have emphasized the role of quantum geometry in stabilizing PDW order in certain multiband systems, we here present a systematic investigation of an alternative, geometry-independent mechanism: PDW order driven purely by interband Cooper pairing. Using the superfluid weight (SW) as a diagnostic, we study two canonical models. The first is the classic Fulde-Ferrell model of an $s$-wave superconductor subject to a Zeeman splitting field, which realizes pure interband pairing between two rigidly shifted bands; the second is a spinless honeycomb-lattice model with nearest-neighbor $f$-wave pairing, a modified version of which was recently found to exhibit PDW instability in density-matrix renormalization-group simulations. In both models the SW receives no quantum geometric contribution at the mean-field level, yet it can be greatly suppressed or even turn negative, signaling a tendency toward pairing with spatial phase modulations. For the first model, we establish that the SW is increasingly suppressed as the Zeeman field grows, drastically turning negative upon the emergence of extensive gapless excitations, i.e., the Bogoliubov Fermi surfaces. For the honeycomb $f$-wave model, we demonstrate, both analytically and numerically, vanishing quantum-geometric SW and an invariably negative total SW. Our results thus establish the SW as a qualitative indicator of the tendency toward PDW order, and provide a microscopic rationale for the interband-pairing-driven PDW instability.

cond-mat.supr-con↗
arXiv2026

Topological Chiral Superconductivity from Doping Correlated Charge Transfer Insulator with Orbital Magnetism

Topological chiral superconductivity has been extensively studied from the perspective of electronic structures in momentum space, while its emergence from real-space processes in strongly interacting systems remains largely unexplored. Here, we study a doped charge-transfer insulator in a strongly repulsive spinless-fermion model with three sublattices and $2π$ flux per unit cell. Combining perturbative analysis, exact diagonalization, and density matrix renormalization group simulations, we establish a topological superconducting ground state with $p+ip$ symmetry. In the large charge-transfer gap limit, chiral charge-transfer excitations generate a correlated-hopping process that provides a microscopic mechanism for electron pairing and its chiral structure. The chiral superconductivity persists into the smaller-gap regime, where chiral excitonic two-particle bound state can emerge. Our results establish a concrete real-space route to topological chiral superconductivity upon doping strongly interacting charge-transfer insulators with orbital magnetism.

cond-mat.str-el↗
arXiv2026

InferScale: Kubernetes-Native Predictive Autoscaling for SLO-Aware AI Inference

Kubernetes Horizontal Pod Autoscaler (HPA) is widely used for inference services, but reactive scaling is often too slow when pods take tens of seconds to become ready. The problem gets worse in shared clusters, where independent HPAs compete for the same resource budget and can hurt different tenants from run to run. We present InferScale, a Kubernetes-native autoscaler that combines inference-aware signals, short-horizon demand prediction, SLO-state control, fast-path spike handling, and tenant-priority headroom without replacing the default scheduler. We evaluate InferScale on Azure Kubernetes Service in two settings. On a real ResNet-50 ONNX workload with 21-second cold start, repeated ramp-burst runs improve SLO compliance from 47.8% +/- 1.5% under a stronger external-metric HPA to 65.3% +/- 9.0% under InferScale. In repeated controlled multi-tenant contention experiments, InferScale cuts Gold-tier SLO standard deviation from 16.1 to 1.2 percentage points and makes contention outcomes follow the priority policy more closely. Repeated single-service ablations show 6-8 percentage point degradation, with an exploratory priority ablation showing a 27-point drop under contention. The main takeaway is that, in startup-sensitive inference serving, the practical value of prediction lies less in precise forecasting than in moving the controller early enough to offset cold-start delay.

cs.DC↗
arXiv2026

Beta-shifts without typical periodic optimization

We show that beta-shifts whose lexicographically largest point is a Sturmian sequence do not have typical periodic optimization (TPO): on each such beta-shift there is a non-empty open set of Lipschitz functions whose unique maximizing measure is the Sturmian measure, which is not supported on a periodic orbit. These are the first known examples of beta-shifts without TPO, and, since they have the specification property, the first known examples of shift spaces with specification but without TPO. The corresponding beta-transformations do not have TPO in any space of Hölder functions on the interval. The main ingredient in the proofs is a rigidity property of Sturmian subshifts: modulo constants and Lipschitz coboundaries, the space of Lipschitz functions on such a subshift is one-dimensional. We also show that the set of $β>1$ for which the beta-shift or the beta-transformation does not have TPO is dense in $(1,\infty)$, but has zero packing dimension. The density is proved by a tuning construction, analogous to Douady--Hubbard tuning in the Mandelbrot set, which produces non-Sturmian subshifts with the same rigidity property. Finally, we show that every beta-shift has typical zero-entropy optimization: for an open dense set of Lipschitz functions, the maximizing measure is unique and its support has zero topological entropy.

math.DS↗
arXiv2026

Theory of the intrinsic optical anomalous Hall responses in time-reversal symmetry breaking superconductors

Determining the symmetry of the Cooper pairing in superconductors is a central challenge in condensed matter physics. Time-reversal symmetry breaking (TRSB) is among the most distinctive symmetry-breaking pattern of a superconductor. An important subset of TRSB pairing states, including the topological $p_x+ip_y$ pairing, is expected to exhibit the optical anomalous Hall effect. However, the microscopic ingredients that enable the Hall response and their underlying organizing principle in clean superconductors remain unelucidated. Here, we develop a unified theory for TRSB multiband superconductors possessing inversion symmetry, and establish their intrinsic optical Hall response as fundamentally a quantum-geometric phenomenon. We distinguish unitary and nonunitary TRSB states. Diagrammatically, we identify two classes of processes: single-electron tunneling and Cooper-pair tunneling. Both involve at least one interband tunneling event driven by the interband velocity-which is closely tied to the interband Berry connection. We rigorously prove that interband pairing is a prerequisite for unitary states to generate intrinsic Hall response, whereas it is not absolutely needed for nonunitary states. In nonunitary states with intraband pairing alone, the Hall effect can arise through single-electron tunneling, provided spin-orbit coupling is present. An effective theory obtained by integrating out the hole degrees of freedom reproduces these results and shows how pairing may endow electrons with effective Berry curvature. Finally, through symmetry analysis, we show that intrinsic resonances near the superconducting gap edge are allowed in nonunitary states but forbidden in unitary states. Our work thus establishes quantum geometry as a unifying principle for the intrinsic Hall response in superconductors and provides a guide for distinguishing different candidate TRSB pairings.

cond-mat.supr-con↗
arXiv2026

Globally Convergent Block Coordinate Descent Algorithms for Semi-Nonnegative Independent Component Analysis

We propose a subgradient-based block coordinate descent (BCD-SubG) framework for nonconvex optimization over products of probability simplices and a closed feasible set, motivated by semi-nonnegative independent component analysis (Semi-NNICA). Elementary column transformations are used to preserve simplex feasibility, while first-order information guides block and pair selection. We establish the global convergence of BCD-SubG using a constrained Lojasiewicz inequality; additional eventual-interiority conditions yield stationary limits and convergence rates. We then specialize BCD-SubG to the direct-fit and indirect-fit Semi-NNICA models and derive explicit updates that ensure global convergence without further assumptions. For both models, eventual interiority also yields stationary limits and convergence-rate estimates. Experiments on synthetic mixtures and simulated magnetic resonance spectroscopy data show generally lower recovery errors and shorter wall-clock times than the tested Semi-NNICA baselines. Joint diagonalization experiments further demonstrate lower final objective values.

math.OC↗
arXiv2026

3ViewSense: Spatial and Mental Perspective Reasoning from Orthographic Views in Vision-Language Models

Current Large Language Models have achieved Olympiad-level logic, yet Vision-Language Models paradoxically falter on elementary spatial tasks like block counting. This capability mismatch reveals a critical ``spatial intelligence gap,'' where models fail to construct coherent 3D mental representations from 2D observations. We uncover this gap via diagnostic analyses showing the bottleneck is a missing view-consistent spatial interface rather than insufficient visual features or weak reasoning. To bridge this, we introduce \textbf{3ViewSense}, a framework that grounds spatial reasoning in Orthographic Views. Drawing on engineering cognition, we propose a ``Simulate-and-Reason'' mechanism that decomposes complex scenes into canonical orthographic projections to resolve geometric ambiguities. By aligning egocentric perceptions with these allocentric references, our method facilitates explicit mental rotation and reconstruction. Empirical results on spatial reasoning benchmarks demonstrate that our method significantly outperforms existing baselines, with consistent gains on occlusion-heavy counting and view-consistent spatial reasoning. The framework also improves the stability and consistency of spatial descriptions, offering a scalable path toward stronger spatial intelligence in multimodal systems.~\footnote{https://github.com/Jasaxion/3ViewSense}

cs.CV↗
arXiv2026

Missing Old Logits in Asynchronous Agentic RL: Semantic Mismatch and Repair Methods for Off-Policy Correction

Asynchronous reinforcement learning improves rollout throughput for large language model agents by decoupling sample generation from policy optimization, but it also introduces a critical failure mode for PPO-style off-policy correction. In heterogeneous training systems, the total importance ratio should ideally be decomposed into two semantically distinct factors: a \emph{training--inference discrepancy term} that aligns inference-side and training-side distributions at the same behavior-policy version, and a \emph{policy-staleness term} that constrains the update from the historical policy to the current policy. We show that practical asynchronous pipelines with delayed updates and partial rollouts often lose the required historical training-side logits, or old logits. This missing-old-logit problem entangles discrepancy repair with staleness correction, breaks the intended semantics of decoupled correction, and makes clipping and masking thresholds interact undesirably. To address this issue, we study both exact and approximate correction routes. We propose three exact old-logit acquisition strategies: snapshot-based version tracking, a dedicated old-logit model, and synchronization via partial rollout interruption, and compare their system trade-offs. From the perspective of approximate correction, we focus on preserving the benefits of decoupled correction through a more appropriate approximate policy when exact old logits cannot be recovered at low cost, without incurring extra system overhead. Following this analysis, we adopt a revised PPO-EWMA method, which achieves significant gains in both training speed and optimization performance.

cs.LG↗
arXiv2026

OmniVChat: Synthesizing, Benchmarking, and Training for Native Audio-Visual Dialogue

We define OmniVChat (Omni Video Chat) as the task of native audio-visual dialogue between a user and an omni model. In OmniVChat, omni models directly and simultaneously receive audio and video from a user and return text. The user's query is embedded in the audio and video, without a separate text question, external captioning, or speech recognition. Direct audio-visual input reduces external latency and computation while preserving perceptual cues. However, research on OmniVChat faces two constraints: data availability and evaluation. Recordings of people using their own devices are scarce. Furthermore, good replies often depend on multimodal context and can be phrased in many ways, making keyword matching unreliable for evaluation. Recent progress in agent systems and video generation makes generation for comprehension viable, which means using synthesized dialogues for training and evaluation. Therefore, we present OmniVChat-Studio, a multi-agent data engine for synthesizing single- and multi-turn audio-visual dialogues. We use synthesized dialogues to build OmniVChat-Bench, an evaluation benchmark that evaluates omni models' basic dialogue abilities across five ability categories. Replies are judged by a large language model based on explicit scoring criteria. We also present OmniVChat-RL, a reinforcement learning reward design that jointly targets reply correctness, efficiency, and style in OmniVChat. Training Qwen3-Omni-Instruct with OmniVChat-RL on synthesized dialogues improves its performance on both OmniVChat-Bench and the human-recorded OmniVChat-Bench-Human. These gains validate the reward design and show transfer to real-world dialogues in training and evaluation.

eess.AS↗
arXiv2026

W2Rep: Learning Visual Representations by Watching the World Change

Images capture the world at one moment, whereas video reveals how it changes. Image self-supervision learns spatial structure from a single moment, while video methods commonly learn temporal relationships inside a representation computed jointly from several frames. We ask whether watching a scene change can instead improve features available from one image without sacrificing the ability to represent video. We introduce W2Rep, a masked feature-prediction framework in which an independently encoded source image participates in prediction at the same or another moment. The predictor is conditioned on visible video context, the queried location, and the signed time interval between source and target. This gives the cross-frame objective two complementary roles: the image path learns features that remain useful across time, while the video path must gather evidence that is missing from the source image. Across model scales and downstream tasks, W2Rep improves frozen and fine-tuned recognition under our comparison protocol, while joint video encoding provides further gains over frame-wise aggregation. Controlled experiments show that these gains depend on directly updating the source-image features and on using both video context and temporal displacement. Overall, change across a video can supervise a visual encoder whose representations remain useful at either image or video granularity. Code is available at~\href{https://wenooi.github.io/W2Rep}{https://wenooi.github.io/W2Rep}.

cs.CV↗
arXiv2026

Omni Demand Understanding: A Benchmark for Contextual User-Intent Inference in Multimodal Interaction

Natural audio-visual interaction is emerging as an important interface for AI assistants, allowing users to communicate through speech and vision rather than carefully composed text prompts. However, existing benchmarks of interactive capabilities still focus primarily on response quality, leaving a more fundamental question underexplored: can a model correctly infer the user's underlying demand from complex multimodal interaction? Real-world user demands are often underspecified in speech and must be inferred from multimodal cues and dialogue history. This inference is further complicated by ambiguous or disfluent expression and noisy acoustic environments. Conversely, request-like speech may not constitute a demand to the assistant, leading to false triggers. We establish Omni Demand Understanding (ODU) as a distinct multimodal contextual inference problem: given an interaction stream, a model must detect whether a user demand is present and infer intent from multimodal and conversational context. ODU evaluates this capability along five dimensions, covering both single-turn and multi-turn interactions. We construct ODU-Bench using a challenge-driven taxonomy, taxonomy-guided agentic video generation, and human-recorded interactions, followed by media-grounded annotation and human verification. We evaluate 14 native MLLMs. Even the strongest, Gemini 3.1 Pro, recovers only 44.7% of key information that must be inferred from visual, acoustic, or conversational context. Moreover, 11 of the 14 models exhibit false-trigger rates above 50% on non-demand scenarios. These results reveal a systematic capability gap in current MLLMs' ability to infer contextual user demands. We hope ODU can establish the evaluation of a previously underexplored yet essential capability in multimodal interaction: correctly understanding user demands before generating an appropriate response.

cs.CL↗
arXiv2026

LongAudioSpan: Spanning the Duration and Depth of Audio Comprehension

General audio comprehension now covers speech, sound, and music over durations from seconds to hours, driven by large audio-language models (LALMs) that are increasingly omni-modal. Yet the benchmarks that test them still rely on clips of seconds, where scores saturate and models converge; recent long-form efforts extend duration but evaluate long audio much as short clips are. We introduce LongAudioSpan, a benchmark that spans both duration and depth: it pairs audio from 10 minutes to over 2 hours with 3,240 questions across three cognitive levels, namely perception, understanding, and reasoning. Two paths supply the questions, differing in how question content is sourced and how ground truth is obtained. Native QA extracts questions from the audio's content, posing each as a multiple-choice item and an open-ended one graded by detailed rubrics. Anchor QA instead injects ground truth, planting acoustic anchors into the audio and building a perception-to-reasoning chain scored only to the first error. A fully automated pipeline constructs every item through structured captioning, QA generation, and adversarial critic feedback. Evaluating 12 LALMs on LongAudioSpan, we find the hard part comes before reasoning: distilling a few relevant facts from a long, redundant signal. This difficulty grows with audio length and falls hardest on perception, especially temporal grounding. LongAudioSpan is available at https://huggingface.co/datasets/holvan/LongAudioSpan.

cs.SD↗
arXiv2026

CLIMATEAGENT: Multi-Agent Orchestration for Complex Climate Data Science Workflows

Climate science demands automated workflows to transform comprehensive questions into data-driven statements across massive, heterogeneous datasets. However, generic LLM agents and static scripting pipelines lack climate-specific context and flexibility and thus perform poorly in practice. We present ClimateAgent, an autonomous multi-agent framework that orchestrates end-to-end climate data analytic workflows. ClimateAgent decomposes user questions into executable subtasks coordinated by an Orchestrate-Agent and a Plan-Agent; acquires data via specialized Data-Agents that dynamically introspect APIs to synthesize robust download scripts; and completes analysis and reporting with a Coding-Agent that generates Python code, visualizations, and a final report with a built-in self-correction loop. To enable systematic evaluation, we introduce Climate-Agent-Bench-85, a benchmark of 85 real-world tasks spanning atmospheric rivers, drought, extreme precipitation, heat waves, sea surface temperature, and tropical cyclones. On Climate-Agent-Bench-85, ClimateAgent achieves 100% task completion and a report quality score of 8.32, outperforming GitHub Copilot (6.27) and a GPT-5 baseline (3.26). These results demonstrate that our multi-agent orchestration with dynamic API awareness and self-correcting execution substantially advances reliable, end-to-end automation for climate science analytic tasks. The source code of ClimateAgent is available at https://github.com/Relaxed-System-Lab/ClimateAgent.

cs.LG↗
arXiv2026

An Inexact Riemannian Gradient Descent Algorithm on the Stiefel Manifold with One Newton-Schulz Iteration

In this paper, we propose an inexact Riemannian gradient descent algorithm on the Stiefel manifold (IRGS-StieONS) using an adaptive step size, where the ``inexact'' refers to the inexactness of retraction. It is proven that one single Newton-Schulz iteration for the retraction is sufficient for global convergence and local linear convergence. Compared to the landing and augmented Lagrangian-based algorithms, the proposed algorithm is the first infeasible algorithm that permits adaptive step sizes with a practical initial step size and guarantees global convergence and local linear convergence under mild assumptions. Moreover, we show that the local convergence rate depends on the condition number of the Riemannian Hessian, which matches the Riemannian steepest descent algorithm. This result implies that the infeasibility in the proposed algorithm does not influence the local convergence rate. Furthermore, a stochastic gradient version of IRGD-StieONS is proposed and is shown to achieve the same convergence rate as Riemannian stochastic gradient descent with decreasing step size. Numerical experiments demonstrate that both IRGD-StieONS and its stochastic counterpart exhibit superior performance and robustness.

math.OC↗
arXiv2026

Multiple superconducting phases and order-parameter evolution in pressurized UTe$_2$

The recently discovered heavy-fermion spin-triplet superconductor candidate UTe$_2$ provides a rich platform for unconventional pairing and topological phenomena. However, limited has been known about its superconducting order parameters and their evolution with control parameters, largely due to the lack of appropriate symmetry-sensitive detections. Here, we report comprehensive point-contact spectroscopy measurements of pressurized UTe$_2$ on the (0~0~1) surface. The observation of Andreev bound states strongly suggests the presence of a $p_z$ component in the superconducting order parameters. Quantitative analysis based on an extended Blonder-Tinkham-Klapwijk model unveils the superconducting order parameters with a finite odd-$k_z$ component (e.g. $B_{2u}$ or $B_{3u}$) for both ambient and pressurized UTe$_2$. Remarkably, the multiple superconducting phases can be distinguished by a single parameter $\langle Δ_{z}\rangle/\langleΔ_{x(y)}\rangle$, the relative weight between the $p_z$-wave and $p_{x(y)}$-wave pairings. These findings place stringent constraints on the pairing symmetry and provide essential spectroscopic signatures for distinguishing pressure-induced multiple superconducting phases in UTe$_2$.

cond-mat.str-el↗
arXiv2026

From Manifold Identification to Newton Acceleration on Intersections: Sparse Stiefel Optimization

We study a Newton acceleration for sparse composite optimization on the Stiefel manifold. The main difficulty is geometric: the active manifold identified by the nonsmooth regularizer may fail to intersect the Stiefel manifold transversely, which obstructs a Riemannian Newton step on the identified manifold. In the transverse case, we prove local identification of the ManPG tangent proximal mapping. For nontransverse cases, we introduce an off-diagonally perturbed Stiefel family that generically restores the identification geometry while yielding an \(O(\|Δ\|_F)\)-KKT guarantee for the original problem. We also derive verifiable support-level conditions for clean intersection, which cover nontransverse sparse patterns and yield the smooth moving local models used by the Newton correction. Based on these results, we propose MIX, a safeguarded ManPG/Newton-CG method on moving identified intersections. In the general clean-intersection setting, we prove global descent and KKT-residual guarantees for MIX. In the transverse or generically perturbed cases, if the sequence has an accumulation point satisfying certain regularity assumptions and the second-order sufficient condition (SOSC), then the full sequence converges to that point, with finite active-manifold identification and a local Q-superlinear rate. Numerical experiments on compressed modes and sparse PCA show that MIX substantially improves efficiency while preserving solution quality. Beyond the Stiefel manifold, we also outline how the safeguarded global-convergence mechanism of MIX extends to general smooth equality-constrained manifolds.

math.OC↗
arXiv2026

A globally and superlinearly convergent QO-free method for nonlinear optimization on Riemannian manifolds

The quadratic optimization-free (QO-free) method is a class of powerful and effective algorithms for solving nonlinearly constrained optimization problems in Euclidean spaces. The aim of the present work is to extend this method to solve optimization problems on manifolds with additional equality and inequality constraints. We first present a specific algorithm in the manifold setting. At each iteration, three linear systems sharing a common linear operator are solved to determine the master search direction. In addition, a higher-order correction direction is obtained by solving a reduced linear least squares subproblem to circumvent the Maratos effect which is assumed not to arise in existing related literature. A Riemannian arc search is then performed within the tangent space of the current iterate to generate the new iterate. Under appropriate assumptions, we establish the global and strong convergence of the proposed method. Moreover, we prove that the unit step size will eventually be accepted by the arc search, upon which the superlinear convergence of the algorithm is established. Finally, numerical results demonstrate that the proposed method is very competitive compared with other existing approaches.

math.OC↗