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Cong Li

Publications and source records attributed to Cong Li.

At least 19 recordsLinked to original sources
arXiv2026

CLaW: Codec-Guided Adaptive Latent Watermarking for Traceable Diffusion Image Generation

With the rapid advancement of text-to-image diffusion models, increasingly realistic AI-generated content has raised serious concerns about misuse and copyright infringement. Digital watermarking offers a promising solution by enabling user-level traceability, yet existing methods remain difficult to deploy at scale, hindered by costly model retraining or detection, poor effectiveness-fidelity trade-offs, and limited robustness to image transformations. To address these challenges, we propose CLaW (Codec-guided Latent Watermarking), a robust and efficient watermarking framework for traceable diffusion image generation with frozen diffusion backbones and inversion-free detection. Specifically, CLaW first uses a pretrained watermark codec to map each watermark message to a latent residual, enabling low-cost sampling-time injection. Then, the residual is injected within a late denoising window, where image semantics are better preserved and the watermark signal is less perturbed by subsequent denoising updates, yielding a better balance between watermark effectiveness and image fidelity. Furthermore, we introduce a decoder-guided adaptive injection mechanism that uses decoding confidence as feedback to dynamically calibrate watermark strength, reinforcing weak watermark signals and improving robustness under image transformations. Extensive experiments show that CLaW preserves high visual fidelity and achieves state-of-the-art robustness under image transformations, enabling reliable user-level traceability.

cs.CV↗
arXiv2026

NNLO QCD corrections to direct $J/ψ$ production with open charm at B factories

We calculate the next-to-next-to-leading-order (NNLO) QCD corrections to direct colour-singlet $J/ψ$ production with open charm at B factories, using nested soft-collinear subtraction for massive partons. At $\sqrt{s}=10.6$ GeV, $m_c=1.5$ GeV and $μ_R=2m_c$, we obtain an approximate NNLO cross section of $0.324^{+0.041}_{-0.066}$ pb, with scale uncertainties from $m_c\leqμ_R\leq4m_c$. The total cross section is $18.4\%$ above NLO and exhibits good perturbative convergence through NNLO, with reduced scale dependence. An NLO-based mass extrapolation to $m_c=1.4$ GeV, combined with estimated $ψ(2S)$ feed-down, raises the rate to about $0.59$ pb. The NNLO correction brings the absolute momentum spectrum near its peak closer to Belle's prompt open-charm data, but shifts the angular shape further away from Belle's prompt charm-associated data. The difference in angular shape motivates more precise measurements at Belle II.

hep-ph↗
arXiv2026

Direct Access to Nuclear Photon TMDs with In Situ Probes in Ultraperipheral Heavy-Ion Collisions

Resolving local structure in photon distributions is an important goal in probing strong coherent electromagnetic fields. We propose using electron--positron pairs produced in situ in ultraperipheral heavy-ion collisions as dynamical probes to directly extract photon distributions. We define a theoretical reference cross section using the complete tree-level amplitude for two-photon-induced radiative lepton-pair production. When background effects factorize into a scalar Bose-enhancement factor, the ratio of the background-modified to reference differential cross sections, minus unity, yields the photon occupation number at a fixed momentum. Finite bins yield averages weighted by the reference cross section. We specify the conditions under which kinematic selections, acceptance, and radiative corrections preserve this local inversion.

hep-ph↗
arXiv2026

SpecMRL: Redesign Matryoshka Representation Learning within Parametric Spectrum

Matryoshka Representation Learning (MRL) is designed to support flexible representation sizes by jointly optimizing nested prefixes. While effective, this design lacks an explicit constraint to ensure that the leading portion provides a suitable compact encoding. To address this, we propose SpecMRL, which reorganizes the feature space to enable more effective prefix readout. Specifically, we extend MRL with a parameter-derived spectral basis organized with feature importance. SpecMRL otherwise follows standard MRL, with prefix readout performed in the learned spectral space. Experiments on MTEB show that SpecMRL outperforms MRL at all evaluated reduced dimensions, with larger gains under more aggressive compression. These results suggest that the learned spectral basis better prioritizes informative directions, enabling more effective representation compression. Notably, SpecMRL extends the performance frontier of MRL, surpassing full-dimensional MRL with only half the embedding dimensions.

cs.CL↗
arXiv2026

When Should the Teacher Move? Temporal Coupling and Stability in Self On-Policy Distillation

Self on-policy distillation trains a student policy against a teacher derived from its own parameter history, yet the teacher's update schedule -- which governs the \emph{temporal coupling} between teacher and student -- has not been systematically studied as a stability variable. Through a controlled schedule sweep on Qwen3-8B, we establish that \emph{isolation periods}, defined as complete teacher freezing between updates, are the key structural property enabling stable learning, not teacher age. To characterize these underlying training dynamics, we introduce a diagnostic framework of temporal KL structure, refresh shock, and length-tail risk. This framework further uncovers \emph{state-oblivious collapse}: optimal short-horizon fixed schedules catastrophically fail under long-horizon training because a clock-driven refresh can copy a transiently drifting student into the teacher in a single, irreversible step. This failure mode is invisible under short-horizon evaluation and mechanistically distinct from EMA's chronic contamination. To address this, we propose \emph{Consolidation-Gated Teacher Refresh} (CGTR), which preserves isolation periods while gating each refresh on joint evidence of reward improvement and length-tail safety, ensuring every teacher movement responds to genuine student consolidation rather than a clock signal. With a single shared parameter set and no per-dataset retuning, CGTR achieves \textbf{zero collapse} and the best final score on all four tasks (Chemistry, Biology, Physics, ToolUse), self-regulating its refresh frequency to each task's learning dynamics.

cs.LG↗
arXiv2026

Quantitative Rules for Parallel Calibration and Drift-Resilient Maintenance of Large-Scale Superconducting Quantum Processors

As superconducting quantum processors scale toward hundreds of qubits, manual calibration becomes increasingly costly and difficult to sustain. Here we address two system-level factors governing automated calibration, namely, crosstalk induced by parallel operations and temporal drift of control parameters. On a 66-qubit superconducting processor, cross-entropy benchmarking shows that single-qubit calibration tolerates full-chip concurrency with minimal fidelity loss, whereas CZ calibration requires a minimum coupler-graph distance of four to avoid elevated control errors. Continuous monitoring of representative qubit and coupler parameters further yields quantitative refresh rules that assign refresh priority by error-budget consumption. These results turn two conventionally heuristic choices, the concurrency density of calibration tasks and the refresh priority of each control parameter, into measurable operating rules. Implemented within an automated calibration framework, the rules support a six-qubit Greenberger-Horne-Zeilinger state with a zero-noise-extrapolated fidelity of 89.64%, distance-dependent logical-error suppression in a single-cycle repetition-code benchmark, and a reduction of the mean single-qubit error from 0.023 to 0.011 on a 337-qubit processor, providing an experimentally grounded reference for the automated calibration and maintenance of current and future large-scale superconducting quantum systems.

quant-ph↗
arXiv2026

Tackling Sim-to-Real Mismatch Through Sampling-Based Disturbance Observers: From Analytical Models to Learned World Models

Robotic controllers increasingly rely on analytical models, simulators, cost-query interfaces, and learned world models. However, physical deployment can deviate from nominal assumptions, and additional disturbances may arise even when the model itself is accurate. In control systems, disturbance observers (DOB) are widely used to estimate such unmeasured effects from nominal models and measured feedback. Classical DOB formulations are generally built around explicit plant models. This paper develops the sampling-based disturbance observer (SDOB), extending the DOB principle to a broader range of models, including simulators and learned world models, through state-rollout or cost-query interfaces. SDOB separates two observable channels: state-effect disturbances, for which the feedback state differs from its prediction, and cost disturbances, for which the same query state receives different costs as the perceived environment changes. Diverse simulation and real-robot experiments across traditional and learned models demonstrate the effectiveness of SDOB in compensating for sim-to-real mismatch and improving control performance.

cs.RO↗
arXiv2026

Floquet dressing of bulk metallic bands in 2H-NbSe2

Floquet dressing offers a route to controlling electronic states through periodic optical driving. Its realization in solids has centred primarily on topological-insulator surface states, semiconductors and two-dimensional Dirac semimetals. Extending this control to bulk metals would enable manipulation of the itinerant states underlying metallic transport and collective order. Yet strong screening and rapid scattering have long cast doubt on its feasibility in bulk metals, while photoelectron dressing complicates its identification. Here we identify Floquet dressing of the overlapping, bulk-derived metallic bands of 2H-NbSe2 using time- and angle-resolved photoemission spectroscopy. Photon-shifted replicas in a sparse unoccupied-state window retain the parent-band dispersions and appear only during pump--probe overlap. Rotating the pump's linear polarization at fixed probe polarization redistributes replica weight between momentum-space branches. With a common parameter set, a model accounting for the optical fields inside and outside the solid captures this redistribution through coherent Floquet--Volkov interference more closely than either pathway alone or their incoherent intensity sum. These findings extend Floquet dressing to the itinerant bulk bands of a multiband metal without relying on the observation of energy gaps opened by hybridization between parent bands and their photon-dressed replicas, providing a basis for exploring how coherent optical dressing couples to metallic transport and collective electronic phases.

cond-mat.str-el↗
arXiv2026

Codoku: Renewable Program-Reasoning Challenges for Frontier Coding Agents

Existing program-reasoning benchmarks ask large language models to predict a program's behavior on a given input. Coding agents break two assumptions on which these benchmarks rest: an agent can recover the answer by executing the program instead of reasoning about it, and fixed task sets drawn from existing programs are increasingly exposed to contamination, yet costly to renew. We introduce Codoku (code sudoku), a renewable benchmark in which a solver fills typed cells in a partial program to satisfy global static and dynamic constraints, such as a prescribed control-flow graph and execution path. Because a partial program cannot be executed and valid fillings are sparse in an exponentially large space of interdependent choices, neither tool use nor enumeration can substitute for program reasoning. Puzzles are synthesized from scratch via semantic reification, so fresh puzzles of controllable complexity can be generated on demand, each with a witness that guarantees solvability. We evaluate five frontier models on 300 puzzles through a coding agent free to use any tool within a fixed budget. Small puzzles already challenge open-weight models, whereas even proprietary models solve only about half of the large ones. Codoku thus offers a renewable testbed for program reasoning that can keep pace with rapidly improving coding agents. GitHub: https://github.com/connglli/Codoku.

cs.SE↗
arXiv2026

Semi-analytical two-loop QCD corrections to $e^+e^-\to J/ψ+χ_{cJ}$ at B factories

In this work, we compute the next-to-next-to-leading-order (NNLO) QCD corrections to the process $e^+e^-\to J/ψ+χ_{cJ}$ at B factories within the NRQCD factorization framework. The helicity amplitudes are obtained via asymptotic expansions around $r=0$ and $r=1$, with $r=16m_c^2/s$. Our asymptotic expressions reproduce the exact numerical results with high accuracy across the kinematic range $0\le r \le 1$, achieving a relative error below $10^{-5}$, which is sufficient for phenomenological applications. Notably, the large logarithmic terms are obtained analytically, and the explicit expressions established in this work provide a useful basis for further theoretical investigations. We compute the unpolarized cross sections. The $\mathcal{O}(α_s)$ correction is found to be large, while the $\mathcal{O}(α_s^2)$ correction for $χ_{c0}$ production amounts to $33\%$ of the leading-order (LO) cross section, significantly reducing the scale uncertainties. For $χ_{c1}$, the $\mathcal{O}(α_s)$ and $\mathcal{O}(α_s^2)$ corrections correspond to $35\%$ and $-15\%$, respectively. For $χ_{c2}$, the corresponding corrections are $24\%$ and $-38\%$. The large cancellation between the corrections for $χ_{c2}$ brings the NNLO cross section close to the LO prediction. Our prediction for $χ_{c0}$ is consistent with both the {\tt Belle} and {\tt BaBar} data within uncertainties. We also predict the angular distribution parameters $α^J_θ$, which are independent of nonperturbative inputs. A sharp discrepancy between the theory and the {\tt Belle} measurement is observed for $α^0_θ$, calling for further experimental and theoretical investigations. Moreover, future measurements of the angular distribution parameters for $χ_{c1}$ and $χ_{c2}$ will provide important tests of the theoretical framework.

hep-ph↗
arXiv2026

Mind or Message? Auditing Theory of Mind in Multi-Agent Social Simulation

Language model agents are increasingly used to simulate social interaction, and the resulting transcripts read as though the agents understand one another. We ask whether that appearance rests on a model of the partner's mind or on the surface record of what the partner said. We build a social simulation in which both questions have exact answers: 40 multi-issue negotiations whose hidden preference weights and whose full Pareto frontier are known by construction. Two model families negotiate across 160 dyads, every transcript is frozen before any measurement, and 2880 counterfactual probes then hold the evidence byte identical while moving one factor at a time: the reader's own stake, the partner's tone, an identity label, and the order of recursion. The agents are socially fluent and economically poor. They reach agreement in 96.2% of dyads with 0 protocol failures, yet only 0.7% of deals land on the Pareto frontier, they leave 20.5% of the available joint value unclaimed, and they miss the one issue on which their interests are perfectly aligned in 76.6% of deals; on the frontier and on that aligned issue, a package drawn at random from the set both sides would accept does as well. The probes locate the failure. Swapping only the reader's own payoff sheet, while the partner's words and offers stay identical, moves the inferred top priority by 15.0 percentage points, which is egocentric projection rather than inference, while a tone rewrite moves it by 5.3 percentage points and an identity label by 0.0. Most tellingly, an agent predicts what its partner believes about it 72.5% of the time while that partner's belief is itself correct only 51.2% of the time: the agents track the conversation far better than they track the mind behind it.

cs.LG↗
arXiv2026

UBA-ORL: Unlearning-Activated Backdoor Attacks on Offline Reinforcement Learning

Offline reinforcement learning (offline RL) enables policy learning from pre-collected static datasets without online exploration, and is increasingly deployed not only in safety-critical domains such as autonomous driving and robotic control but also in data-mining applications such as recommendation and behavior analysis. While compliance-driven data removal enhances privacy, it also opens a previously unrecognized attack surface. We introduce UBA-ORL (Unlearning-activated Backdoor Attack on Offline Reinforcement Learning), the first unlearning-activated backdoor attack for offline RL: in the evaluated settings, the attack is substantially suppressed after normal training and becomes pronounced after a compliance-driven deletion (unlearning) request. UBA-ORL employs a dual-sample mechanism: alongside backdoor trajectories (BD) that link a trigger to malicious actions under inflated rewards, the attacker injects camouflage trajectories (CM) sharing the same trigger pattern but preserving benign actions with equally high rewards. During training, BD and CM provide competing supervisory signals; upon a legitimate deletion request on the CM subset, the residual BD signal can re-dominate, reactivating the backdoor on demand. Empirical results show that UBA-ORL achieves controllable activation under the evaluated offline-RL configurations, while no-trigger return changes vary by configuration, exposing a previously overlooked security risk in compliance-driven offline RL platforms. We urge the community to develop joint pre-/post-unlearning auditing mechanisms for compliant unlearning services.

cs.CR↗
arXiv2026

CellRFT: Reinforcement Fine-Tuning for Single-Cell Perturbation Modeling

Predicting cellular responses to perturbations supports the study of gene function, disease mechanisms, and therapeutic strategies. Despite advances in single-cell perturbation modeling, existing models typically optimize surrogate losses that do not directly reflect the biological criteria used for evaluation, so better data fitting need not yield better biological predictions. To address this mismatch, we introduce \textbf{CellRFT}, a reinforcement fine-tuning framework that uses biological evaluation as direct training feedback. CellRFT uses policy-gradient optimization to learn from non-differentiable evaluations of generated cell populations and integrates multiple biological rewards through hierarchical reward aggregation. Comprehensive experiments demonstrate CellRFT's applicability across different pretrained models and effectiveness in improving perturbation prediction, reveal that optimizing one biological criterion can help or hinder others, and show that complementary rewards can improve criteria beyond those directly optimized, offering a way to probe how biological metrics shape model behavior, with the potential to inform evaluation design. Code will be made available.

cs.LG↗
arXiv2026

NSFlow: End-to-End Differentiable Neuro-Symbolic Optical Flow for Visual Odometry

Sparse optical flow provides stable inter-frame correspondence, playing a key role in Visual Odometry (VO) and Visual-Inertial Odometry (VIO). Classical optimization-based methods, such as Lucas-Kanade (LK), perform well under small displacements but are sensitive to large motions and illumination changes. Modern regression-based learning methods, while more robust in complex scenes, are often computationally heavy and lack explicit geometric consistency, making them less suitable for efficient VO/VIO front-ends. To bridge this gap, we propose a hybrid neuro-symbolic framework that combines the strengths of both paradigms. Our method uses a Convolutional Neural Network (CNN) to extract robust feature representations, which is fed into a differentiable LK optimizer to estimate optical flow in an end-to-end trainable manner. Through implicit differentiation, gradients are propagated across the iterative solver, enabling joint optimization of feature extraction and flow estimation. The resulting system integrates seamlessly into existing VO/VIO pipelines and runs in real-time on embedded platforms. Experiments show that our method outperforms conventional optimization-based flow in challenging conditions such as dynamic lighting and low texture, while also achieving higher accuracy and lower latency than purely regression-based alternatives. When deployed in a VIO system, our method demonstrates significant performance improvement, achieving an average error reduction of 42\% on challenging datasets while enhancing tracking stability. The code is publicly available.

cs.CV↗
arXiv2026

Semi-analytical results for $e^+e^-\to J/ψ+ X_{{\rm non\,}c\bar{c}}$ up to $\mathcal{O}(α_s v^2)$ at B factories

Within the NRQCD factorization framework, we investigate the color-singlet contribution to $e^+e^- \to J/ψ+ X_{{\rm non\,}c\bar{c}}$ at B factories, computing the $\mathcal{O}(α_s)$, $\mathcal{O}(v^2)$, and $\mathcal{O}(α_s v^2)$ corrections to both the unpolarized cross section and the $J/ψ$ angular distribution. The $\mathcal{O}(α_s v^2)$ correction is obtained for the first time, and the validity of NRQCD factorization at this order is explicitly verified. Using the differential equation method, the short-distance coefficients are obtained as asymptotic expansions in $r = m_c/\sqrt{s}$ up to $r^{40}$, which reproduce exact results with high precision at B factory energies, achieving relative errors around $10^{-14}$ for the cross section and around $10^{-7}$ for the angular distribution. Notably, with the same input parameters, our $\mathcal{O}(α_s)$ and $\mathcal{O}(v^2)$ corrections are consistent with those reported in the literature. Phenomenologically, the $\mathcal{O}(α_s)$ correction (with $μ_R=\sqrt{s}/2$) reaches about $50\%$ of the leading-order cross section, while the $\mathcal{O}(v^2)$ and $\mathcal{O}(α_s v^2)$ corrections are accidentally small. After including feeddown contributions from $ψ(2S)$, the predicted cross section $0.523_{-0.197}^{+0.285}$ pb agrees with the {\tt Belle} measurement within uncertainties. However, the predicted angular distribution parameter $0.120_{-0.036}^{+0.041}$ deviates from the experimental value $5.71\pm 2.51$ by more than $2σ$, calling for further experimental and theoretical investigations.

hep-ph↗
arXiv2026

HDA-MoE: Hybrid Parallelism and Dynamic, Adaptive Scheduling for Mixture-of-Experts with 3D Near-Memory Processing

Mixture-of-Experts (MoE) architectures have become a key technique for scaling Large Language Models (LLMs), enabling high model capacity with reduced computational cost. However, this efficiency comes at the expense of increased memory capacity and bandwidth demands. Recent 3D Near-Memory Processing (NMP) architectures, which vertically integrate memory and compute through hybrid bonding, provide high internal bandwidth and energy efficiency, making them attractive for accelerating MoE inference. Nevertheless, the distributed memory and compute organization of NMP systems introduces new challenges for mapping MoE workloads. Existing parallelization strategies, such as Tensor Parallelism (TP) and Expert Parallelism (EP), suffer from either high communication costs or unbalanced computation utilization, leading to inferior efficiency. In addition, the dynamic routing behavior of MoE models further complicates efficient deployment. To address these challenges, we present HDA-MoE, a framework that optimizes MoE execution on NMP architectures through hybrid parallel deployment and runtime scheduling. HDA-MoE integrates an offline hybrid parallel mapping algorithm with an online dynamic and adaptive scheduling mechanism to reduce communication overhead while improving computation utilization. Experimental results show that HDA-MoE achieves a speedup of 1.1x--3.4x over TP, 1.1x--1.5x over EP, 1.1x--3.7x over the Hybrid TP-EP compute-balanced baseline, and 1.1x--1.3x over HD-MoE. Source code is available at https://github.com/PKU-SEC-Lab/HDA-MoE-TCAD26.

cs.AR↗
arXiv2026

Distributed Secure Learning Control for Large-scale Multirobots under Stealthy Actuator Attacks

Distributed learning control for multirobot systems (MRS) offers significant flexibility in presence of uncertainties but lacks provable performance guarantees. A promising direction involves integrating reinforcement learning (RL) into distributed model predictive control (DMPC), leveraging the strengths of RL in nonlinear policy design and the receding-horizon replanning capabilities of DMPC. However, ensuring secure control within such a learning framework under malicious cyber attacks, particularly stealthy ones, remains a critical challenge, because the distributed policies generation depends on information exchange among neighbors, where compromised agents can rapidly influence the behavior of others through the communication network. This article proposes a distributed secure learning control (DSLC) framework for large-scale MRS under malicious, stealthy actuator attacks. Our framework offers two key features: (i) a unified approach that enables secure learning control across various coordination scenarios and (ii) a game-theoretic distributed learning-based predictive control strategy that learns how to balance the attacker and defender through a differential-game based DMPC framework. Specifically, DSLC employs a distributed attacker-actor-critic architecture to learn the optimal defense and attack policies online within each prediction interval. Unlike numerical optimization-based controllers that calculate open-loop control sequences, our method simultaneously generates adversarial attack policies and corresponding defense policies in analytical closed-loop form. The defense policies could be directly generalized to MRS with varying scales and diverse actuator attack probabilities. The effectiveness and scalability of DSLC are validated through comprehensive simulations and real-world experiments in multiple wheeled robots via various control tasks.

cs.RO↗
arXiv2026

Resolving Local Structure of Distribution Functions in Collider Measurements

Collider measurements may blur narrow features in underlying distributions before fitting, since a data point samples a finite region. We show that observables linear in a selected target distribution, after externally constrained inputs are absorbed into a nonnegative finite response, have a calculable distribution-space locality set by hard dynamics and kinematics. The baseline-normalized $F_{\rm dir}(X)$ yields the local value at zero width and a controlled average otherwise, with covariance setting the leading bias. This guides the choice of processes and observables that retain unexpected fine structure. An EIC photon-radiation observable realizes the exact zero-width limit.

hep-ph↗