arXiv subjects

Chao Zhang

Publications and source records attributed to Chao Zhang.

At least 19 recordsLinked to original sources

arXiv2026

Think Globally, Generate Personally: Multi-View Thinking with Adaptive Generation for Generative Recommendation

Semantic-ID-based (SID) generative recommendation (GR) represents each item as a sequence of discrete tokens and predicts the next item through autoregressive SID generation. However, existing methods face two challenges. First, insufficient shared context modeling for user sequences: full-history and recent behaviors provide overlapping yet complementary evidence, but modeling them within a single sequence may dilute current cues, whereas processing them through separate streams may fail to coordinate them. Second, insufficient personalized adaptation in progressive SID decisions: although decoder states evolve with generated prefixes, existing models typically use a shared output-side transformation across recommendation instances, without explicitly selecting a transformation suited to the current user need. To address these challenges, we propose Multi-View Thinking with Adaptive Generation (MV-Thinker), a framework that Thinks Globally and Generates Personally. First, Multi-View Temporal Thinking uses view-specific thinking queries and readers over overlapping full-history and recent-history views, coordinating both sources into complementary user-conditioned contexts before SID generation. Second, Hierarchical Residual Adaptation organizes lightweight adapters into nested residual paths before a shared vocabulary projection, adaptively transforming context-bearing decoder states for SID decisions. Extensive experiments on four public datasets demonstrate that MV-Thinker consistently outperforms strong discriminative and generative recommendation baselines across multiple ranking metrics. The implementation code and datasets are available on: https://anonymous.4open.science/r/MV-Thinker

cs.AI↗

arXiv2026

Quasi-Normal Modes of Covariant Loop Quantum Black Holes

We investigate the quasinormal modes (QNMs) and time-domain response of a massless, minimally coupled test scalar field on static, spherically symmetric covariant loop quantum black hole (cLQBH) backgrounds. Focusing on a phenomenological, Gaussian-localized holonomy profile, we obtain a localized deformation of the scalar effective potential containing both positive and negative parts. This provides a geometric realization of the bump effect, with the potential deformation and tortoise-coordinate mapping determined consistently by the same background. We characterize the deformation by its location, amplitude, and width, and study its effects using frequency-domain shooting and time-domain evolution, supplemented by matrix-pencil mode extraction and waveform reconstruction. The QNM spectrum exhibits spiral trajectories as the bump location varies, enhanced sensitivity of the overtones, and mode overtaking, whereby the branch continued from the first Schwarzschild overtone becomes longer-lived than the continued fundamental branch. Despite substantial spectral changes, the early response can remain close to its Schwarzschild counterpart, with more pronounced differences emerging later. To quantify this behavior, we introduce a local waveform-deviation time and compare it with a relative-amplitude faintness time, thereby defining an operational observation window. The deviation time depends nonmonotonically on the bump location, and suitable parameter choices allow appreciable departures to emerge before the adopted faintness threshold is reached. These results connect localized holonomy corrections with spectral sensitivity and waveform evolution, providing a basis for future gravitational-perturbation studies and assessments of gravitational-wave detectability.

gr-qc↗

arXiv2026

Pricing for Information Revelation in Demand Response: A Strategic Communication Approach

Demand response aggregators require consumers' private flexibility information to dispatch loads efficiently. Yet self-interested consumers have incentives to misreport it. This paper investigates how much information heterogeneous consumers can credibly convey through nonbinding, unverifiable reports, and how a pre-announced retail tariff can improve this revelation. We model this interaction as a multi-sender strategic communication game with state-dependent bias, using the cheap-talk framework. The aggregator maximizes social welfare, and consumers have private valuations and quadratic payoffs. Closed-form conditions identify when the set of dispatched consumers is invariant to the reports. Under these conditions, reporting incentives decouple into independent single-sender subgames. We derive a closed-form uniform tariff that simultaneously minimizes the mean-squared strategic bias of all dispatched consumers. In this regime, the welfare loss relative to full communication is expressed as a weighted sum of residual posterior variances, showing that every equilibrium weakly improves expected social welfare over no communication. Numerical studies support the theoretical results and show that, when consumers coordinate on informative equilibria, strategic communication with the proposed bias-minimizing tariff recovers most of the full-communication welfare gain over no communication.

cs.GT↗

arXiv2026

Integral Harnack-type estimates and the rate of extinction for nonlocal doubly nonlinear parabolic equations

We establish local boundedness and integral Harnack-type inequalities for locally nonnegative weak solutions to nonlocal doubly nonlinear parabolic equations in the fast diffusion range by combining refined energy estimates with a careful treatment of the nonlocal terms. As an application, quantitative decay estimates for both the local mass and the local supremum of weak solutions are derived near the extinction time. Furthermore, we study the extinction phenomenon for bounded nonnegative weak solutions to the Cauchy--Dirichlet problem and obtain quantitative upper bounds for the extinction time.

math.AP↗

arXiv2026

FARSim: a fast RF-chain-aware trigger-screening surrogate for radio detection of ultra-high-energy cosmic rays

Radio arrays provide a scalable route to detecting extensive air showers from ultra-high-energy cosmic rays, but trigger studies for candidate layouts are expensive when every energy, arrival direction, core position and trigger configuration is evaluated with full radio simulations. We present FARSim, a fast surrogate framework that reuses a reduced library of ZHAireS reference footprints to reconstruct ground-plane radio emission and to estimate trigger-relevant observables. The method combines vector geomagnetic and charge-excess field decomposition, geomagnetic-angle and energy scaling, geometrical projection, contour-based core sampling and event-rate integration. We evaluate an independent validation set of 1999 ZHAireS showers; 941 events satisfy the predefined radial-support and station-count requirements and yield a median event-level relative RMSE of 9.4% and a median Pearson coefficient $r_{\mathrm{P}}=0.965$ for the reconstructed geomagnetic lateral profiles. We further extend the peak-field surrogate to time-domain electric-field synthesis by combining the predicted three-component peak-field vector with geometry-dependent normalized pulse templates. Propagating these traces through an RF-chain response enables voltage-domain threshold and L1-trigger diagnostics. When evaluated at the true peak amplitude, the time-domain extension reaches a median vector-waveform $R^2$ of 0.986 over 2112 ZHAireS traces held out from pulse-shape model fitting. FARSim is therefore intended as a rapid, physics-informed screening layer for array-layout and trigger studies; absolute exposure predictions and detector commissioning remain the role of full end-to-end simulations.

astro-ph.IM↗

arXiv2026

Corrected detector-level reconstruction of inclined radio air showers from trigger-level ADC waveforms

Autonomous radio arrays record air-shower radio pulses as trigger-selected analogue-to-digital converter (ADC) voltage traces. We reconstruct the primary arrival direction and energy while accounting for noise, direction-dependent detector responses and atmospheric refraction in inclined showers. Waveform cross-correlation corrects relative trigger times; plane- and spherical-wavefront fits estimate the initial direction and effective emission point. A modified voltage-domain angular-distribution fit uses source-end tangents of refracted rays to refine the direction and distinguish the particle core from the radio-footprint centre, reducing the median opening-angle error by approximately 50% relative to straight source-to-station viewing angles. Antenna and radio-frequency-chain inversion recovers the electric field, and early-late corrections give its lateral peak-amplitude distribution. An event-to-reference peak-amplitude correction is calibrated on 2,500 simulated electric-field showers to account for 50-200 MHz coherence loss; a semi-analytic Fresnel parameterisation examines its finite-source interpretation. Proton and iron reference lateral-distribution fits give two energy estimates, averaged with fixed equal weights. Validation uses 6,000 independent showers. In the field-level test with simulation-input geometry, either correction reduces the relative energy root-mean-square error (RMSE) from 11.90% to 8.31%. Across three waveform treatments, 3,073 common detector-level events yield mean relative energy residuals of -2.30% to -1.86%, standard deviations of 8.46%-8.96%, and median opening-angle errors of 0.0315-0.0349 degrees. The method is intended for sparse radio arrays detecting highly inclined showers, such as the Giant Radio Array for Neutrino Detection (GRAND).

astro-ph.IM↗

arXiv2026

Gradient Hölder regularity for singular fractional $p$-Laplace equations

We prove interior $C^{1,α}$ regularity for globally bounded weak solutions to the fractional $p$-Laplace equation $(-Δ_p)^s u=0$ under the assumptions $n\ge2$, $1 p-1$, where $α=α(n,p,s)>0$. This resolves the open problem of gradient Hölder continuity for globally bounded solutions in the singular range $sp>p-1$. The proof is based on affine approximation and estimates independent of the normalized affine slope. When these slopes tend to infinity, we prove convergence to a solution of a linear nonlocal equation of order $sp-p+2$, together with convergence of the normalized energies to the corresponding quadratic energy. The regularity of the limiting equation yields an improved affine approximation. For bounded slopes, we prove that every globally Lipschitz weak solution of $(-Δ_p)^s u=0$ in $\mathbb R^n$ is affine and use this result in a compactness argument. The two estimates are combined in an iteration that preserves the required energy and tail bounds and gives Hölder continuity of the gradient.

math.AP↗

arXiv2026

A Pretrained Model for Bayesian Filtering in Heterogeneous Dynamical Systems

Filtering has not yet had its foundation-model moment: state estimation is still dominated either by online Bayesian filtering algorithms that require known dy- namics as inputs, or by learning-based neural filters trained from scratch for a single system or parametric family. The core challenge is structural heterogene- ity: dynamical systems differ in their governing dynamics, physical scales, and noise levels, precluding a shared representation space for a reusable filter. To bridge this gap, we introduce FilterFM, the first pretrained model for Bayesian filtering that estimates latent states across heterogeneous dynamical systems with a single checkpoint. FilterFM employs a hierarchical two-level observation se- quence encoder and a prior-to-posterior state-reconstruction module that together handle varying observation and state dimensions across heterogeneous systems, while modeling state uncertainty via quantile prediction. Trained on a mixture of heterogeneous filtering tasks, FilterFM exhibits superior in-domain performance, delivering significant gains in estimation accuracy, runtime efficiency, and noise robustness compared to existing baselines. Beyond in-domain performance, it marks the first model to successfully enable zero-shot filtering on unseen dynamics, bridging the generalization gap in state estimation. Furthermore, we uncover clear scaling behaviors in foundation-model-based Bayesian filtering, which combined with comprehensive component ablation studies, validate the structural optimality of our design and broaden the generalization scope for this research field.

cs.LG↗

arXiv2026

EPSBench: Benchmarking LLM Agents for Earnings Forecasting

Quarterly earnings forecasting requires an agent to connect financial evidence to the income-statement line items that produce a company's reported earnings. We introduce EPSBench, a benchmark for studying this ability across 149 firm-quarters from 86 US companies. Agents read a fixed collection of filings, earnings-call transcripts, and news, then estimate company-specific income-statement drivers with cited evidence. A shared accounting calculator converts these estimates into GAAP diluted earnings per share (EPS), providing a common basis for comparing forecast assumptions. We evaluate six models under a common harness, assessing point forecast error relative to date-matched analyst consensus and checking whether their prediction intervals achieve the stated coverage. Three models show no detectable paired difference in point forecast error from consensus, and three have higher error. Across all six models, nominal 80% intervals contain reported EPS in only 37--70% of runs that return an interval, and most misses fall above the upper bound because the forecasts run low. The driver records trace most of the error gap between models to operating-income estimates.

cs.CL↗

arXiv2026

Forecast-Dojo: Replayable Environments for Benchmarking and Training LLM Forecasting Agents

Forecasting requires large language model (LLM) agents to gather evolving evidence, revise their beliefs, and make calibrated predictions over time. Yet evaluating and improving these capabilities is challenging because real-world events unfold continuously and outcomes may take months to resolve. We introduce Forecast-Dojo, a replayable environment that reconstructs resolved prediction-market events as sequences of dated forecasting steps, enabling reproducible evaluation, longitudinal analysis, and training from agent interactions. It comprises 1,568 Polymarket events split temporally into training and evaluation sets, alongside 18.8 million dated news articles. Across 12 models, research tools consistently improve forecasting performance, and greater capture of new evidence is associated with larger improvements over time. Nevertheless, all models trail historical market forecasts, while carrying a belief notebook across dates reduces research costs without consistently improving forecast quality. We supervise fine-tuned Qwen3-30B-A3B-Thinking-2507 on collected trajectories from Qwen3-235B-A22B-Thinking-2507 to demonstrate the environment's training utility. The student's accuracy is improved from 34.8% to 42.8% on the evaluation set.

cs.AI↗

arXiv2026

The point-particle-limit effective-source approach for computing gravitational self-force in the Lorenz gauge

The traditional effective-source method is hampered by complex analytical expressions and the inherent smoothness limit, which incur high computational costs and complicate implementation. To overcome these limitations, we introduce the point-particle-limit effective source method, which analytically takes the size of the effective source to zero, thereby transforming the problem into a well-defined jump condition of retarded metric field at the particle position governed by the local singular field. This formulation naturally pairs with a discontinuous Galerkin scheme, whose inherent capacity for accommodating solution discontinuities enables highly accurate enforcement of the jump conditions. We apply both the traditional and point-particle-limit effective source method to calculate the time-domain gravitational metric perturbation and gravitational self-force in the Lorenz gauge on a point particle in a circular orbit around a Schwarzschild black hole. The comparison of numerical results shows the excellent advantage of the point-particle-limit effective source method, which validates the correctness and efficiency of the point-particle-limit effective source method and thereby establishes a numerical foundation for computing generic geodesic orbits or long-time self-consistent orbital evolution.

gr-qc↗

arXiv2026

Primal-Dual Representation Learning for Low-Rank Constrained MDPs

Representation learning has substantially improved the efficiency of unconstrained continuous-state Markov Decision Processes (MDPs), but its extension to Constrained MDPs (CMDPs) is difficult because the transition representation must be learned while constraint violation is controlled. In this paper, we study representation learning for low-rank CMDPs with continuous state spaces, and propose REP-PD-TEG for the soft cumulative-violation setting. In the feature learning period, the algorithm interacts with the environment using a decaying-$ε$-greedy mixture execution policy, and uses the collected samples to learn transition features by maximum likelihood. Based on the learned features, it constructs uncertainty bonuses and plans with an optimistic Lagrangian objective to obtain the return policy. We prove regret and soft-violation guarantees for the return policies, and quantify the influence of the decaying-$ε$-greedy exploration. In particular, REP-PD-TEG achieves the optimal $\widetilde O(\sqrt K)$ return policy bound, and gives a $\widetilde O(K^{3/4})$ overall sample bound when exploratory actions are charged. For the stricter hard cumulative violation, where feasibility must be verified episode by episode, we propose REP-PD-H-TEG. It plans under a bonus-corrected lower-confidence utility certificate with the learned representation, and performs a stabilized dual search to obtain the return policy, yielding regret and hard-violation bounds. Together, these results give the first theoretically guaranteed representation-learning method for low-rank continuous-state CMDPs with both soft cumulative guarantees and certified hard-violation control.

cs.LG↗

arXiv2026

Agentic Knowledge-Model Co-Evolution via Consistency Regularization

LLM agents can evolve by learning from accumulated experience. Existing approaches extract reusable external knowledge, such as skills and memories, from experience to guide subsequent task solving, with some further internalizing this knowledge through model training. However, different ways of analyzing and transforming the same experience can yield knowledge with substantially different utility, even when the resulting guidance appears reasonable. Inspired by consistency regularization, we identify consistent utility gains across different ways of analyzing the same experience, rather than relying on any single way of analyzing it. We introduce SPLICE, a framework for consistency-regularized knowledge-model co-evolution. It constructs evolution views by perturbing how the same experience is analyzed, producing different knowledge candidates. Knowledge updates are then guided by common gain, which accounts for utility gains and their consistency across views. During model evolution, self-distillation internalizes the evolved knowledge alongside reinforcement learning, using knowledge associated with consistent utility degradation as a contrastive reference. The two processes interleave, with model updates driving further knowledge evolution. Experiments across diverse domains show that SPLICE outperforms knowledge evolution and model training baselines by up to 7.1 and 4.6 points in average accuracy, while its evolved knowledge reduces output tokens per correct solution by up to 44% and transfers across model sizes and families.

cs.AI↗

arXiv2026

The ALMA-QUARKS Survey: Discovery of Superfine Dusty Fibrils inside Massive Star-forming Clumps

We report the discovery of more than 320 superfine dusty filamentary structures (fibrils) inside 121 massive star forming clumps that are located in widely different Galactic environments (Galactocentric distances of ~1.2 - 12.9 kpc). These fibrils are identified from the 1.3-mm continuum emission in the ALMA-QUARKS survey, which has a linear resolution of ~900 AU for a source at ~3 kpc. The typical width of these fibrils identified in clumps is found to be ~0.01 pc, which is about ten times narrower than that of dusty filaments in nearby clouds identified by the Herschel Space Observatory. The mass (M) versus length (l) relation for these fibrils follows M ~ l^2, similar to that of Galactic filaments identified in space (e.g., Herschel) and ground-based single-dish (e.g., APEX) surveys. However, these fibrils in the ALMA-QUARKS survey are significantly denser (N_{H_2} = 10^{23} - 10^{24} cm^{-2}) than the filaments found in previous Herschel surveys (N_{H_2} = 10^{20} - 10^{23} cm^{-2}). This work contributes a large sample of newly found superfine fibrils in massive clumps, following the identification of large 0.1 pc-wide filaments and associated internal velocity coherent molecular fibers in nearby molecular clouds, further emphasizing the crucial role played by filamentary structures in star formation at various physical scales.

astro-ph.GA↗

arXiv2026

Finite Classification of Transversal Diagonal Gates Beyond Stabilizer Codes

We establish that transversal diagonal gates on exact qubit codes of distance at least two can be exhaustively classified within a finite search space. Although code-preserving physical gates can have continuous phases, any projective logical action induced by a transversal diagonal gate can always be realized by a physical gate of finite order. Crucially, we prove that this physical order has an upper bound determined solely by the code parameters. This order bound enables the subset-sum-linear-programming (SSLP) framework introduced in arXiv:2504.20847 to exhaustively enumerate all candidate discrete symmetries, after which the Knill-Laflamme equations determine the remaining continuous code parameters. Applying this method to the nontrivial diagonal sector of exact $((6,2,3))$ codes, SSLP yields $66$ candidate symmetry classes, of which exactly $15$ are realizable, supporting full projective diagonal logical cyclic groups of orders $2$, $3$, and $5$. In particular, for order $5$, the code is unique up to local unitaries and qubit permutations, equivalent to the code constructed in arXiv:2504.20847. This code also attains the minimum length $n=6$ among exact $((n, 2, \ge 3))$ codes whose full projective transversal group cannot be conjugated into the projective Clifford group.

quant-ph↗

arXiv2026

Same Evidence, Different Answers: Canonical-Context On-Policy Distillation for Multi-Turn Language Models

Large language models (LLMs) often solve a task when all instructions are given in a single prompt, but fail when the same information is revealed gradually across turns. When a clean FULL prompt and a RAW-SHARDED conversation contain the same complete user evidence, the model should still arrive at the same answer. We argue that a key reason for this gap is self-anchored drift: responses produced under partial information introduce unsupported assumptions, and those assumptions later distort the final answer. To reduce this effect, we propose Canonical-Context On-Policy Distillation (CCOPD). During training, the same base model is used in two roles: a frozen teacher conditioned on the clean FULL prompt and a trainable student that receives the same evidence incrementally through a multi-turn conversation; CCOPD aligns the student's behavior on its own trajectories with the teacher's canonical full-context behavior. Trained only on math problem conversations, CCOPD yields a 32\% average relative improvement in RAW-SHARDED performance over the original base model across math and five zero-shot out-of-domain task families, while largely preserving full-context performance. Further analyses suggest that CCOPD strengthens grounding in user evidence and reduces sensitivity to contamination from earlier assistant turns.

cs.CL↗

arXiv2026

MMTA: Benchmarking Multimodal Temporal Analysis with Time Series, Text, and Vision

Time-series analysis is increasingly studied in multimodal settings, yet most benchmarks pair temporal signals with text or with visualizations derived from the same signal. This leaves open whether multimodal large language models (MLLMs) can integrate time series with real-world visual evidence. We introduce MMTA (multimodal temporal analysis), a benchmark of tri-modal tasks with diverse cross-modal complementarity patterns among time series, text, and vision. MMTA spans nine domains -- robotics, autonomous driving, human action, manufacturing, AR assistance, healthcare, recommendation, finance, and climate -- under video+TS and image+TS layouts, with a 3,524-sample test split covering classification and forecasting. The benchmark standardizes heterogeneous real-world sources into a shared schema with domain-specific prompts, provenance documentation, and success-weighted metrics that penalize both wrong answers and failed generations. Evaluating eight open-source and proprietary MLLMs reveals a shared bottleneck: representing time series as text creates very long token sequences and weakens fine-grained alignment with visual evidence, especially in long-video settings. We then introduce TimeOmni-v, a multimodal LLM extension with a dedicated time-series encoder, temporal positional alignment, modality-aware token layout, and a forecasting head. TimeOmni-v improves long-video classification and time-series inference efficiency, showing that MMTA not only exposes representation and grounding failures but also motivates targeted model design.

cs.LG↗

arXiv2026

AGR: Agentic Generative Retrieval with Tool-Augmented Search

Large-scale search and recommendation systems derive much of their retrieval quality from rich, heterogeneous features, yet existing generative retrieval (GR) methods struggle to exploit such feature ecosystems efficiently. Static feature injection either encodes all available signals or places them according to predetermined schemes, increasing serving cost and introducing irrelevant or conflicting evidence. More fundamentally, existing methods cannot adaptively determine when a feature is needed, which feature to select, or where it should influence hierarchical Semantic ID (SID) generation. We propose Agentic Generative Retrieval (AGR), a tool-augmented paradigm that interleaves SID generation with on-demand feature acquisition. Conditioned on the request and decoded SID prefix, AGR decides whether to invoke a tool, which feature to acquire, and where its observation should influence subsequent decoding, thereby steering generation toward feature-consistent SID branches. To supervise this behavior, we construct tool-augmented trajectories based on feature applicability and information gain along the SID hierarchy, and train the model through SID pretraining followed by tool-augmented fine-tuning. Experiments on an industrial dataset and three public benchmarks, together with an online A/B test, consistently demonstrate the effectiveness of AGR.

cs.IR↗