arXiv subjects

Di Wang

Publications and source records attributed to Di Wang.

At least 19 recordsLinked to original sources

arXiv2026

On the Limits of Sparse Linear Model with Shuffled Differential Privacy

Sparse linear regression is a fundamental problem in high-dimensional statistics, and its private counterparts leave a large gap between the central and the local model of differential privacy. Shuffled differential privacy (SDP) sits between the two, offering accuracy closer to the central model without the trust requirement of a central curator. This paper studies sparse linear regression under both non-interactive and interactive SDP protocols. In the non-interactive setting, any non-interactive $(ε,δ)$-SDP protocol is shown to incur an $\ell_2$-norm estimation error of $Ω( \frac{\sqrt{dk}}{nε} )$ with dimension $d$, sparsity $k$, and $n$ users, revealing an inherent dimensionality barrier. In addition, a non-interactive algorithm is proposed that achieves an $\ell_2$-norm upper bound of $\tilde{\mathcal{O}}( \sqrt{\frac{k}{n}} + \frac{d\sqrt{k}}{nε})$. To overcome this barrier, we turn to the interactive user-level setting, where each user holds $m$ samples. A two-round interactive algorithm is developed that first recovers the support through a heavy hitter step and then estimates the parameter, achieving an error of $\tilde{\mathcal{O}}( \sqrt{\frac{k}{nm}} + \frac{k}{n\sqrt{m}ε} )$ with no polynomial dependence on $d$. Furthermore, by extending the score attack framework to user-level data, a new lower bound $Ω( \sqrt{\frac{k\log(d/k)}{nm}} + \frac{k\log(d/k)}{n\sqrt{m}ε} )$ is established for any user-level $(ε,δ)$-DP, showing that the proposed algorithm is near-optimal with only a gap of the factor $\log(d/k)$.

cs.LG↗

arXiv2026

Efficient Exact Unlearning for Federated Zeroth-Order Optimization

Federated zeroth-order optimization (FedZOO) reduces the memory and communication costs of collaborative training for edge intelligence by avoiding backpropagation while communicating only random seeds and scalar coefficients. However, existing methods lack efficient mechanisms for exact unlearning to fully remove the influence of training data when users request deletion. A key insight is that total variation stability of the learning algorithm creates overlap between model distributions before and after data deletion, allowing exact unlearning to reuse computation through coupling. Based on this observation, we jointly design two stable zeroth-order learning algorithms and their corresponding exact-unlearning procedures: \textsc{FedZOO-S} obtains stability through client and mini-batch subsampling, enabling \textsc{FedZOUL-S} to perform efficient unlearning by preserving earlier updates and restarting from the first affected step. \textsc{FedZOO-NS} further combines subsampling with Gaussian noise to increase overlap between update distributions; \textsc{FedZOUL-NS} exploits this overlap through rejection--reflection coupling to reuse affected updates when the coupling is accepted. We prove exact unlearning for both algorithms: the model and saved state have the same joint distribution as retraining with the corresponding learner on retained data. We further establish stability and convergence guarantees for the learning algorithms and derive upper bounds on expected unlearning cost. Experiments demonstrate effective unlearning with competitive model utility and up to $56.1\%$ less cumulative unlearning time than re-optimization. Compared with first-order baselines, our methods reduce communication overhead by up to approximately four orders of magnitude.

cs.LG↗

arXiv2026

One-Shot Federated Model Editing for Device Dynamics at the Edge

Standard federated learning (FL) requires persistent device connectivity across multiple communication rounds, making it unsuitable for dynamic environments such as vehicular networks, where devices frequently join and leave. One-shot FL offers a promising alternative by completing training in a single communication round. However, one-shot FL suffers from model solidification and cannot readily adapt to subsequent client dynamics, including new client contributions and the withdrawal of previous contributions. We therefore consider exact model editing, which removes withdrawn contributions through unlearning or incorporates newly contributed ones through incremental learning without retraining from scratch. We identify that efficient editing fundamentally hinges on learning stability. To this end, we propose F-SOSA, the first stable one-shot FL algorithm that enables rapid and exact model editing. F-SOSA leverages sub-sampling of local models with carefully tuned rate for stable aggregation and performs curvature-aware model fusion to preserve utility. We prove that F-SOSA achieves vanishing training error and reasonably small generalization error bounds. Building on this stability, we further develop two single-round editing algorithms: F-SOSA-U for unlearning and F-SOSA-I for incremental learning. They ensure that the edited model, along with all intermediate algorithmic states, is statistically indistinguishable from retraining while incurring only an expected $O(ρ)$ fraction of its cost, where $ρ$ quantifies stability. Experiments demonstrate that our approach matches retraining performance while reducing cumulative time and communication overhead by at least 36% and 22%, respectively, under realistic urban mobility simulations.

cs.LG↗

arXiv2026

One Teacher, Many Views: Reducing Reference Sensitivity in On-Policy Self-Distillation

On-policy self-distillation (OPSD) improves reasoning by training a model on its own trajectories with dense token-level supervision from a privileged copy conditioned on a reference solution. Existing methods typically use a single teacher distribution conditioned on the complete reference. We identify a limitation of this formulation: a reference is only one realization of the reasoning process. Although it provides evidence about the answer, it also encodes an ordering, phrasing, and solution path. Full-reference conditioning can therefore produce correct targets that depend excessively on information unavailable to the student at inference. We show that replacing one valid reference with another, while holding the problem and student state fixed, can substantially alter the teacher distribution, a phenomenon we call reference-induced target instability. To address this instability, we introduce Monte Carlo Privileged Distillation (MCPD), which models privileged context as a distribution over partial reference views and distills the marginal teacher distribution. MCPD evaluates the teacher under multiple views and averages the resulting next-token distributions. We show that this probability-space average is the exact target induced by minimizing the expected teacher-to-student KL divergence over privileged views. Across mathematical and scientific reasoning benchmarks, we show that MCPD produces more reference-consistent supervision and consistently improves over full-reference OPSD.

cs.LG↗

arXiv2026

Same molecule, Different Knowledge: Cross-Identifier Binding Failures in LLMs For chemistry

Chemical AI workflows routinely represent the same molecule through multiple co-referring identifiers, including trivial names, IUPAC names, and SMILES, implicitly assuming that these representations provide interchangeable access to an LLM's knowledge. To test this assumption, we first introduce a controlled benchmark and show that it does not hold. Across foundation models spanning 7B to 72B parameters, even chemistry-specialized models, co-referring molecular identifiers consistently expose different subsets of the model's knowledge. We then conduct a series of controlled behavioral, representational, and causal analyses to characterize this failure. We show that even when a model can identify a molecule from its SMILES and retrieve a fact through the corresponding name, the same fact can remain inaccessible directly from SMILES. We term this phenomenon \emph{cross-identifier binding failure}. Further analysis of internal representations, conflicting identifiers, and parameter editing shows that neither improved chemical representation competence nor modification of individual facts is sufficient to establish reliable access across identifiers. Together, these results indicate that the core failure lies in the binding between an identifier and knowledge already present in the model. Motivated by this diagnosis, we introduce Binding Self-Distillation, a simple mitigation that uses lexical identity during training to transfer the model's existing knowledge access to the structural route. This enables factual retrieval directly from SMILES at inference time, without requiring a name lookup or training the underlying model from scratch.

cs.LG↗

arXiv2026

Differentially Private Sparse Reward Estimation with Preference Feedback

Reinforcement Learning with Human Feedback (RLHF) has become a central paradigm for aligning Large Language Models with human values. However, the reliance on sensitive preference data necessitates rigorous differential privacy (DP) guarantees to prevent user information leakage. Existing private alignment methods typically assume dense reward parameterizations, resulting in estimation error bounds that scale polynomially with the ambient {model size $d$}. Such dependence is prohibitive in modern {overparameterized models} where $d$ is vast. In this work, we study \emph{Private Sparse RLHF} problem to address this limitation, assuming that human preferences (reward model) are governed by a subset of $s^* \ll d$ relevant features. We provide the first comprehensive theoretical framework for differentially private sparse reward estimation from pairwise preference feedback under the Bradley-Terry-Luce model, and introduce efficient algorithms for both Sample-DP and Label-DP settings. Theoretically, under the squared $\ell_2$ parameter estimation error, we establish upper bounds of $\widetilde{O}(s^*/n + {s^*}^2/n^2\varepsilon^2)$ for $(\varepsilon,δ)$-Sample-DP and $\widetilde{O}(s^*/n + s^*/n\varepsilon^2)$ for $\varepsilon$-Label-DP based on randomized response, where $n$ is the training data size. For the lower bounds, we show that the Sample-DP rate is nearly optimal, and that the guarantee for our Label-DP method cannot be further improved within the randomized-response framework. We also provide a general lower bound for the problem with Label-DP. Empirically, our framework demonstrates superior efficacy over dense baselines across sentiment generation and safety alignment benchmarks, offering a significantly more favorable privacy-utility trade-off.

cs.LG↗

arXiv2026

MBTI: A Multi-Branch Efficient Fine-Tuning Framework for Hyperspectral Image Classification with Foundation Models

Hyperspectral foundation models learn transferable spectral-spatial representations from large-scale unlabeled data. They provide an effective paradigm for adapting to downstream hyperspectral image (HSI) classification tasks with limited labeled samples. However, spectral band configurations vary substantially across sensors, which makes direct model transfer difficult. Existing adaptation strategies often compress, select, or reshape the original spectra to match model-specific input requirements. These operations may discard useful spectral information and weaken local spectral continuity. To address this problem, we propose MBTI, a Multi-Branch efficient fine-tuning framework for Hyperspectral Image classification. MBTI adapts hyperspectral foundation models to downstream classification tasks while preserving full-band spectral information. First, we introduce a spectral-continuity-preserving multi-branch preprocessing strategy. The original HSI is divided into multiple continuous spectral subsets, and a band reuse mechanism is used when the remaining bands cannot form a complete branch. This avoids invalid padding and unnecessary spectral loss. Second, independent Low-Rank Adaptation (LoRA) modules are inserted into each branch. They enable different spectral intervals to learn task-specific discriminative features while keeping most pre-trained parameters frozen. Finally, a multi-branch channel attention fusion module adaptively recalibrates and integrates features from all spectral branches. Experiments on three public hyperspectral datasets show that MBTI achieves competitive and superior performance compared with representative classification methods. Under the final rank-8 configuration, only about 2.33\%--2.36\% of the parameters are trainable. The code will be available at https://github.com/Azhenmiddleblock/MBTI/tree/main.

cs.CV↗

arXiv2026

Automated Multi-Agent Organization Design: From Architecture Search to Organizational Learning

Agentic system design is increasingly shifting from hand-crafted prompts and workflows toward automated search over agentic programs and interaction structures, with multi-agent systems receiving growing attention. Yet a central question remains unresolved: when does an automatically generated multi-agent structure exhibit a functionally meaningful division of responsibility? We argue that automated multi-agent design should move beyond searching for effective architectures toward identifying which responsibilities are functionally necessary, how they should be allocated, and when they should change. To explore this, we propose Automated Multi-Agent Organization Design (AMOD), which treats organization itself as a first-class design object, explicitly modeling how agent teams are composed, what functional responsibilities the agents assume, how they coordinate, and how collective decisions are made. We formulate AMOD as an execution-driven design loop. For each task, an automated designer instantiates an organization by assigning functional responsibilities and coordination rules to a set of agents. After execution, task outcomes, intermediate artifacts, and interaction traces are examined to identify which responsibilities may be missing, duplicated, or ineffective, and the organization is revised accordingly. We also propose testing discovered responsibilities by removing them, replacing them with generic roles, or changing their assignment under matched resource budgets, with complementary evaluations. AMOD provides a principled foundation for evidence-based and adaptive multi-agent organization design.

cs.AI↗

arXiv2026

Generalized Spin Groups and Applications to Magnons

Spin groups provide the natural symmetry framework for the isotropic Heisenberg model, in which spin and lattice degrees of freedom are separable. They have recently found broad applications in describing electronic band structure, magnon band topology, and transport phenomena. However, a typical spin Hamiltonian describing magnons contains not only isotropic Heisenberg exchange but also anisotropic interactions induced by spin-orbit coupling (SOC), including the Dzyaloshinskii-Moriya (DM) interaction, the Gamma exchange, and the single-ion anisotropy (SIA), which strongly influence magnon properties. More recently, some studies have also revealed relativistic magnon transport phenomena in altermagnets. Such systems typically exhibit partial spin-lattice locking, but are commonly assumed to be described by conventional magnetic groups. Here, we develop a systematic symmetry framework, generalized spin groups, for magnons. We first classify the symmetries of Heisenberg, DM, Gamma, and SIA interactions. The resulting classification contains 6 spin-only groups, together with a total of 161 nontrivial spin groups. The symmetries of magnetically ordered phases are then obtained through spontaneous symmetry breaking of the corresponding parent groups for spin Hamiltonians. We also identify 6 spin-only groups for ordered states, which generate 90, 264, 157, 264, 387, and 598 nontrivial spin groups, respectively. Finally, we apply generalized spin groups to the magnon thermal conductivity and spin conductivity tensors and derive all the symmetry-allowed forms. Our results provide a unified symmetry framework for analyzing magnon transport both with and without SOC, yielding more refined constraints on magnon responses.

cond-mat.other↗

arXiv2026

$U$-spin sum rules in charmed baryon decays beyond the $U$-spin limit

$U$-spin symmetry is an important flavor symmetry in studying charmed hadron weak decays. However, the sizeable $U$-spin breaking limits the predictive power of $U$-spin symmetry. In this work, we derive dozens of rate and decay parameter $U$-spin sum rules for charmed baryon decays beyond the exact $U$-spin symmetry limit. These sum rules provide tests of flavor symmetry up to the first-order $U$-spin breaking in the charmed baryon sector.

hep-ph↗

arXiv2026

Can Agents Keep Secrets They Were Never Told? Benchmarking Latent Privacy Risks Across Sessions with MemJoin

To support sustained interactions and personalized assistance, agents use long-term memory to retain and combine user information across sessions. Reusing such memories can lead an agent to state a sensitive relation while completing an ordinary task that does not require it. The relation to protect and the combination of clues that reveal it may remain hidden at memory write time, when later records and tasks are still unknown. In this paper, we introduce MemJoin, a privacy benchmark of 288 synthetic analysis cases in which the complete history entails a sensitive relation, but every individual session is insufficient. We evaluate six memory systems with the same answer-generating model, alongside full-history and no-history controls. Each system writes and updates memories in session order and later retrieves context for an ordinary task, while target annotations remain with the evaluator. We separately check which target attribute values appear in stored records and retrieved context, and whether the final answer states the complete, person-linked relation. In a separate paired test on a prespecified subset, removing shared identifiers and other connecting clues can change complete-relation output while all checked target attribute values remain visible. These findings show why checking attribute visibility alone is insufficient to evaluate what records from different sessions reveal together.

cs.CR↗

arXiv2026

Speculating LLMs' Chinese Training Data Pollution from Their Tokens

Tokens are basic elements in the datasets for LLM training. It is well-known that many tokens representing Chinese phrases in the vocabulary of GPT (4o/4o-mini/o1/o3/4.5/4.1/o4-mini) are indicating contents like pornography or online gambling. Based on this observation, our goal is to locate Polluted Chinese (PoC) tokens in LLMs and study the relationship between PoC tokens' existence and training data. (1) We give a formal definition and taxonomy of PoC tokens based on the GPT's vocabulary. (2) We build a PoC token detector via fine-tuning an LLM to label PoC tokens in vocabularies by considering each token's both semantics and related contents from the search engines. (3) We study the speculation on the training data pollution via PoC tokens' appearances (token ID). Experiments on GPT and other 23 LLMs indicate that tokens widely exist while GPT's vocabulary behaves the worst: more than 40% long Chinese tokens (i.e., a token with more than two Chinese characters) are either porn or online gambling. We validate the accuracy of our speculation method on famous pre-training datasets like C4 and Pile. Then, considering GPT-4o, we speculate that the ratio of "Yui Hatano" related webpages in GPT-4o's training data is around 0.5%.

cs.CL↗

arXiv2026

DIAL: Dynamic and Informed Adjustment of the Native Stochastic Law for Reliable Weather Ensembles

Ensemble weather forecasts can look reliable in global averages while hiding too little spread in some cases and too much in others. These errors vary across variables, spatial scales, lead times, and weather states. Adapting randomness during forecasting can correct these errors, but training fixes stochastic behavior beforehand and output calibration acts afterward. Inference guidance also acts during forecasting, but typically scores ensemble members separately rather than the ensemble as a whole. Reliability feedback arrives at the end, and tracing it backward through many chaotic forecast steps is expensive and unstable. We therefore introduce Dynamic and Informed Adjustment of the Native Stochastic Law (DIAL), which controls a frozen model's native stochasticity. DIAL learns from past forecasts to predict whether each variable and scale needs more or less spread. As a forecast develops, it estimates how possible noise changes will affect later forecast accuracy and reliability. It combines these predictions to make small noise updates at each step. This loop avoids retraining the weather model and full backpropagation through the forecast. We evaluate DIAL on FourCastNet 3 (FCN3) using later ERA5 years unseen during training. DIAL provides better scale-resolved reliability than learned postprocessing and reward-based control baselines. Relative to the unmodified Native FCN3 reference, DIAL achieves an 8.27% improvement in the primary multiscale continuous ranked probability score (CRPS).

cs.CV↗

arXiv2026

Topological Diagrams and Hadronic Rescattering: A Framework for CP Violation in Charmed Baryon Decays

Establishing CP violation in the charmed baryon sector remains a central challenge in particle physics. In this work, we develop a framework for CP violation in charmed baryon decays by combining quark-level topological amplitudes with the hadron-level rescattering mechanism. The two descriptions are connected through (1,1)-rank amplitudes, which are linear combinations of topological amplitudes and can be matched directly to the chiral Lagrangian. We verify that the rescattering amplitudes derived from topological amplitudes are consistent with those obtained directly from the chiral Lagrangian and show explicitly that the isospin sum rules are preserved. The $u$-, $t$-, and $s$-channel rescattering amplitudes contributing to each (1,1)-rank amplitude in the $SU(3)_F$ limit are derived. It is found that the long-distance rescattering contributions to quark-loop amplitudes can be related to those of tree amplitudes without introducing additional nonperturbative parameters. We perform global fits to available branching-fraction and decay-parameter data and predict the CP asymmetries in charmed baryon decays. The $Λ^+_c\to pη^\prime$ mode, with a predicted CP asymmetry of order $10^{-3}$, is identified as a promising channel for CP violation searches in charmed baryon sector. We also suggest measuring more observables in $Ξ^0_c$ decay modes to reduce the uncertainties in the CP violation predictions. In addition, the Körner-Pati-Woo theorem is found to be in tension with rescattering dynamics, casts doubt on the validity of its conventional proof. A precise measurement of the branching fraction of the $Λ^+_c\to Σ^+K^0_S$ mode would provide a stringent test the Körner-Pati-Woo theorem.

hep-ph↗

arXiv2026

Magnetic Structures Database from Symmetry-aided High-Throughput Calculations

Magnetic structures-the spatial arrangement of magnetic moments-underpin the diverse functionalities of magnetic materials, yet they have been experimentally determined for only a small fraction of known compounds. Conventional first-principles prediction requires candidate magnetic configurations as input, but the number of possible configurations is in principle infinite. Although the classic Landau-theory based symmetry analysis is widely used to classify magnetic ordering, their direct application to generate input magnetic configurations remains a major challenge due to unavoidable residual degrees of freedom. Here we turn this classification language into a practical predictive framework by resolving these freedoms through a two-step symmetry-aided strategy: an irrep-compatible maximal-magnetic-subgroup selection, followed by the physically motivated high-symmetry-direction construction. This highly efficient scheme is justified within the newly developed oriented spin space group framework. Benchmarked against 1,043 Landau-compatible experimental structures in MAGNDATA, our candidate sets reproduce 90% of the experimentally reported magnetic structures in the four high-symmetry crystal systems with only ~51 candidates per material on average, and subsequent first-principles calculations with material-specific Hubbard U parameters recover the experimental ground state with 83% accuracy. Applying the validated workflow to 19,808 transition-metal compounds, we construct a database of more than 5,000 magnetic materials-nearly twice the size of the experimental MAGNDATA. The database reveals statistical regularities accessible only at scale, including the occurrence rate of magnetism, the prevalence of competing magnetic states, and enables the identification of hundreds of magnetic topological materials, altermagnets, and other emerging magnetic phases and functionalities.

cond-mat.mtrl-sci↗

arXiv2026

Hallucination-aware Faithful Reasoning in Remote Sensing

Multimodal large language models (MLLMs) have demonstrated promising capabilities for remote sensing reasoning, yet they frequently generate visually unsupported reasoning that contradicts the visual evidence. Existing post-training methods optimize primarily for final-answer correctness, providing limited supervision for identifying and correcting hallucinations that arise during the reasoning process. To address this limitation, we propose \textbf{FaithRS}, a hallucination-aware self-reflection framework for faithful reasoning in remote sensing. We first construct \textbf{SFHD-43K}, a dataset of 43K structured, fine-grained hallucination annotations, and train a detector to identify hallucination types, underlying causes, and supporting visual evidence. FaithRS then incorporates this diagnostic feedback into a Safe Detector-Guided Corrective Rollout framework, where a safety-aware gate selectively determines whether and how to revise the response. Training is driven by a composite reward that jointly optimizes answer accuracy, reasoning faithfulness, hallucination reduction, format compliance, and revision safety. Experiments with Qwen3.5 backbones (0.8B, 4B, and 9B) show consistent improvements across four remote sensing benchmarks, with gains of up to 11.64 points.

cs.CV↗

arXiv2026

ReDiS: Reasoning-Diagnosis Self-Evolution for Multimodal Reasoning in Remote Sensing

Multimodal large language models (MLLMs) have shown strong potential for remote sensing understanding, yet reliable reasoning remains challenging when tasks require fine-grained visual grounding and multi-step inference. Existing approaches typically rely on either costly pre-constructed reasoning trajectories or self-generated signals such as response consistency, which can reinforce shared errors and provide unreliable supervision. We introduce \textbf{Reasoning--Diagnosis Self-Evolution (ReDiS)}, a framework that separates reasoning exploration from candidate evaluation while allowing the two processes to evolve jointly. ReDiS instantiates two role-specific experts from a shared multimodal backbone: a \textbf{Reasoner} that explores diverse visual evidence--answer candidates and a \textbf{Diagnoser} that jointly evaluates the complete candidate set and provides credibility-aware feedback. As the Reasoner evolves, it exposes new candidate distributions and failure modes; the Diagnoser adapts to these changes and, in turn, reshapes the learning signal for subsequent exploration. To handle groups in which all sampled answers are incorrect and group-relative advantages vanish, we further introduce \textbf{Failure-Triggered Recovery (FTR)}, which provides minimal outcome-level correction without prescribing intermediate reasoning trajectories. Experiments on LHRS-Bench, MME-RS, and CHOICE show consistent improvements over Qwen3.5 backbones at 0.8B, 2B, and 4B scales. ReDiS improves category-averaged accuracy by 6.47 percentage points at 0.8B, while ReDiS-4B achieves the highest average performance among the compared remote sensing MLLMs.

cs.CV↗

arXiv2026

OmniRSI: Taming Latent Diffusion for Unified Multimodal Remote Sensing Image Generation

Multimodal remote sensing image generation emerges as a promising paradigm for recovering complete and complementary observations from heterogeneous and partially observed remote sensing data. However, existing methods predominantly depend on sensor-specific fine-tuning of natural image generation models, limiting unified multimodal generation capacity due to the absence of shared representations and robust conditioning mechanisms. To address these challenges, we propose OmniRSI, a unified multimodal remote sensing image generation model. Specifically, we develop a remote sensing-specific variational autoencoder that captures multimodal priors and promotes a locally smooth latent space for unified representation and robust reconstruction across diverse observed data. Building upon that, we fully adapt Stable Diffusion 3.5 with a ControlNet branch to support unified multimodal generation and translation conditioned on textual and visual cues. Besides, we devise a training-free cyclic latent alignment mechanism that iteratively enriches incomplete visual conditions with generated multimodal information, progressively improving guidance reliability while preserving observed measurements. We also construct RSI-MM, a large-scale dataset comprising 11.5M multi-resolution images with text annotations and partially paired data for training and evaluation. Extensive experiments verify that OmniRSI achieves superior performance in multimodal reconstruction, generation and translation.

cs.CV↗