arXiv subjects

Rui Li

Publications and source records attributed to Rui Li.

At least 19 recordsLinked to original sources

arXiv2026

Auto-RecSys: Harnessing Autonomous Research Agents for Industry-Scale Recommender System

Auto-research agents have shown the potential to automate hypothesis generation, experiment execution, and iterative refinement. However, scaling this paradigm to industry-scale recommendation models introduces two challenges: (1) long feedback loops, where model training can take days, making serial iteration prohibitively slow and requiring parallel exploration across multiple research directions; and (2) system complexity, where large configurations, fragile infrastructure dependencies, and multi-day GPU jobs require robust and recoverable execution. We present Auto-RecSys, an autonomous research system for long-horizon experimentation on industry-scale recommendation models. Auto-RecSys addresses these challenges through three harness designs: (1) distributed asynchronous execution for running multiple experiments in parallel across servers, (2) centralized cross-server memory for persistent and recoverable execution across sessions and failures, and (3) cognitive-procedural separation, where natural-language skill files guide LLM reasoning while deterministic scripts enforce operational correctness. Auto-RecSys further employs a dual-loop self-evolving architecture: an Execution Evolution Loop in which model-specific playbooks accumulate operational knowledge by recording failed attempts and crystallizing successful pipelines, and an Idea Evolution Loop in which experimental outcomes inform subsequent ideation. Evaluated on recommendation models, Auto-RecSys significantly reduces the human time required per experiment cycle and improves execution reliability as its playbooks mature.

cs.CL↗

arXiv2026

Full Inseparability and Genuine Multipartite Entanglement Coincide for Finite-Mode Gaussian States

For general mixed states, entanglement across every bipartition need not imply genuine multipartite entanglement (GME), because a biseparable decomposition may switch the separable cut from term to term. We prove that this convex ambiguity disappears for Gaussian states of finitely many bosonic modes. More generally, for any finite family of partitions, a Gaussian density operator in the trace-norm-closed convex class generated by states separable across those partitions is already separable across one fixed partition in the family. Only the target is Gaussian; a valid decomposition may be continuous and may contain arbitrary non-Gaussian states. Thus full inseparability and GME coincide, Gaussian k-separability and k-producibility reduce to fixed-partition tests, and party-wise tensor powers cannot activate GME from a biseparable Gaussian state. The proof combines a spectral selector with a holomorphic rigidity argument that converts one product vector in the square-root range of a Gaussian state into a block-local covariance certificate. The result shows that partition mixing, a generic mixed-state mechanism, adds no new exact finite-mode Gaussian states.

quant-ph↗

arXiv2026

Limiting Pointwise Decay for the compressible isentropic Navier-Stokes equations

We study the long-time pointwise behavior of small localized perturbations of a constant state for the one-dimensional compressible isentropic Navier-Stokes equations. After subtracting the two Burgers diffusion waves, the convergent sum of all higher-order diffusion waves, and the cross-family viscous corrections, we prove a cone-preserving pointwise estimate for the exact physical remainder and, in particular, \[ |R_i(x,t)|\leq C E_N\log(2+t)\Psi_i(x,t), \qquad \sup_{x\in\mathbb R}\Psi_i(x,t)\leq C(1+t)^{-1}. \] Here $E_N$ measures the size of the initial data and $\Psi_i$ is the cone-resolved weight; both are defined precisely in the main theorem below. Thus $\|R_i(t)\|_{L^\infty}\leq C E_N(1+t)^{-1}\log(2+t)$. The key new idea is to apply a familywise Cole--Hopf transformation to the spatial antiderivative of the remainder, which exactly eliminates the critical same-family first-order feedback. We further construct an approximate Green function adapted to the two characteristic families and combine it with Gaussian-mode extraction and a Kawashima-type energy argument. This yields a cone-preserving estimate at the limiting decay rate, up to a logarithmic loss.

math.AP↗

arXiv2026

Dreaming in Flow: Generative Grounding Feedback for Self-Evolving Unified Multimodal Models

Unified multimodal models integrate visual understanding and generation within a single network, yet the two capabilities are commonly optimized as separate tasks. We introduce Generative Grounding Feedback(GGF), a self-evolving post-training framework that uses only text prompts and the model's own visual experience. Given a prompt, the model first generates a visual ``dream.'' Flow-level feedback compares text-, image-, and repair-conditioned predictions at the same noisy latent state, transferring image-grounded generation directions to the prompt condition. Dream replay grounding replays this dream through captioning and re-imagination, training claim-level evidence to remain consistent across the replay while separating unrelated visual experiences. Jointly optimized, these two directions let generation provide visual grounding for understanding and understanding refine subsequent generation without paired image--text supervision. Experiments across unified models with different understanding--generation integration designs show consistent improvements in text-to-image generation together with modest gains in visual understanding.

cs.CV↗

arXiv2026

When a high-mountain slope failure cascades downstream: physical footprint and evolving exposure of the 2026 Gyirong mixed rock-ice cascade

The 26 August 2026 Gyirong cascade connected a high-mountain failure to developed downstream valleys. Event-bracketing observations delineate a preferred changed/source surface of 1.01 km^2, within alternative interpreted envelopes of 0.49-1.84 km^2, and a representative 21.8 km source-to-port route descending 3.40 km. Across two precipitation products, five antecedent windows and six fixed spatial supports, all 60 matched-year ranks remain below the 90th-percentile wet threshold. The source-nearest 7-day temperature mean of 9.43 degrees C exceeds all 25 matched years from 2001-2025. Positive temperature anomalies extend to every tested support, but ranks vary from the 88th percentile to above all historical values. Within the subsequently affected 37.354 km^2 UNOSAT footprint, modelled built-up surface increases from 0.036 to 0.441 km^2 between 1975 and 2020; its fraction of the fixed area rises from 0.096% to 1.181%. Delivered rapid maps contain 695 building points and 15.671 km of roads graded destroyed. These measurements connect the physical footprint with environmental context and historical exposure development. They complement existing process reconstruction while leaving the initiation mechanism and the mechanical role of warming unresolved.

physics.ao-ph↗

arXiv2026

Stringent Constraints on Spin-Spin-Velocity-Dependent Exotic Interactions with a Levitated Magnet Force Sensor

Exotic spin-spin-velocity-dependent interactions, predicted in extensions of the Standard Model involving new bosonic fields, could resolve fundamental puzzles from dark matter to cosmic asymmetry. However, exploring these weak potential interactions at centimeter scales presents formidable challenges, primarily due to the overwhelming dominance of electromagnetic backgrounds that can easily obscure the weak exotic signals. Here, we utilize a levitated magnet force sensor with ultrahigh electron spin density to probe these interactions. We constrain two interactions individually through a designed spin source and a multi-layer magnetic shielding system that suppresses electromagnetic backgrounds. In this study, we constrain two types of interactions: the V_6 potential at force ranges from $10^{-3}$ m to $6 \times 10^{-2}$ m and the V_{14} potential at ranges greater than $10^{-3}$ m. Our measurements establish 95% confidence-level bounds of $|f_6| \leq 2.12 \times 10^{-13}$ and $|f_{14}| \leq 2.34 \times 10^{-23}$ at $\lambda = 1.6 \times 10^{-2}$ m, improving prior limits by up to 12 and 13 orders of magnitude, respectively. Our result demonstrates the levitated magnet as a highly sensitive probe for detecting new bosonic fields in extensions of the Standard Model.

physics.app-ph↗

arXiv2026

Large Scale Entanglement Structure Detection in 100-Qubit Systems via Local Joint Measurements

Identifying the entanglement structure of a many-body quantum state, namely how its constituents partition into unentangled blocks, is a central task in quantum information science, yet conventional tomography scales exponentially with system size. Here we introduce a scalable framework that recognizes large-scale entanglement structures directly from local correlation fingerprints. By choosing a representative local Pauli basis that satisfies a boundary-matching condition p_1 = p_R, the entire chain is read out in a single measurement configuration, keeping the measurement effort independent of system size. In noisy simulations, this single-basis protocol classifies GHZ-, W-, and cluster-type structures among 30 candidate partitions with a mean accuracy exceeding 95% for systems of up to 100 qubits. We further validate the protocol on a superconducting quantum processor, where it reliably classifies block structures for systems of up to 13 qubits before noise- and depth-induced degradation sets in at larger sizes. By mapping these failure modes explicitly, our results delineate the boundary of hardware-level scalability and point to a concrete strategy for characterizing entanglement structure on near-term quantum devices.

quant-ph↗

arXiv2026

The Radioactive Background of the JUNO Calibration System

The Jiangmen Underground Neutrino Observatory (JUNO) experiment is a reactor antineutrino detector employing 20 kton of ultra-pure liquid scintillator to determine the neutrino mass ordering and to precisely measure oscillation parameters. The total singles background rate from radioactivity is required to be below 10 Hz in the energy range of 0.7-12 MeV within the fiducial volume for reactor neutrino analysis. The calibration system is designed to characterize the detector energy and position responses, while several of its components are located close to the target and may contribute to the background budget. Therefore, extensive material screening and selection are required to construct a low-background calibration system and to ensure that its contribution remains within the design requirements. In this work, a comprehensive study of the radioactive background induced by the calibration system is presented, including material radioactivity measurements using high-purity germanium detectors and neutron activation analysis techniques, detailed Monte Carlo simulations to evaluate the background, and comparisons with in-situ detector data to validate the predictions. In this data analysis, dedicated spatial selection methods are developed to isolate calibration-related contributions and to suppress the liquid scintillator background. The total radioactivity contribution from the calibration system is estimated to be less than 76 mHz, which satisfies the requirement of 200 mHz (2% of the total background budget). The results from in-situ data are found to be consistent with the expectations based on material assay and simulation within uncertainties. These results demonstrate that the calibration-induced background is well understood, in agreement between data and simulation, and negligible for reactor antineutrino measurements in JUNO.

physics.ins-det↗

arXiv2026

Near-Unity Excitation and Radiative Efficiencies in Electroluminescence Without External Carrier Injection

Electroluminescence occurring without external charge injection is typically characterized by weak emission and excessive driving voltage, due to low excitation and radiative recombination efficiencies. Here, we demonstrate non-injecting electroluminescence (NI-EL) that challenges this conventional perception. To achieve this, we introduce an operational paradigm that leverages remote, state-abundant charge reservoirs, which elevates the excitation efficiency close to unity - a greater-than-20-fold improvement over the benchmark. This strategy is augmented by quantum dots (QDs) with nonmonotonically graded shells, raising the high-field radiative efficiency by approximately 7-8-fold. The resultant RGB NI-EL devices uniformly exhibit bright and efficient pulsed emissions, with key metrics including: a turn-on threshold of 3.7 Vrms for red; a luminance of 291,628 nits and a power efficiency of 302.6 lm/W for green, with light-outcoupling enhancement; and, for blue, the superior emitter stability of the first QD-based blue NI-EL over its light-emitting diode counterpart. The minimal dielectric loss, sub-100 ns response time, and external EL efficiency of up to 45.4% further reinforce the mechanism-performance causality. These results overcome the inherent mechanistic limitations of NI-EL and establish performance that rivals or surpasses injection-type EL, including AC- or DC-driven variants, positioning NI-EL as a promising platform for high-performance pulsed light sources.

physics.optics↗

arXiv2026

GeoUniPR: A Geometry-Consistent Unified Framework for Cross-Modal Place Recognition

Cross-modal place recognition (CMPR) aims to identify the same location across heterogeneous sensing modalities, such as vision and LiDAR. Existing methods commonly bridge the modality gap using complex alignment modules, multi-stage training, or full fine-tuning of pretrained backbones. In this work, we revisit CMPR from the perspective of geometric consistency and propose GeoUniPR, a unified and concise geometry-consistent framework. GeoUniPR reduces cross-modal discrepancy at the representation level by projecting LiDAR point clouds into the camera perspective to construct Geometry-Consistent depth image views (DIV), which establish direct RGB-LiDAR correspondence. We further augment DIV with native LiDAR cues, including intensity and surface-normal information, yielding a multi-channel geometric representation that improves structural consistency. Based on this representation, GeoUniPR learns a unified embedding space using two modality-specific ViT-based encoders with identical architectures, trained through parameter-efficient adaptation without auxiliary alignment modules, multi-stage training, or full backbone fine-tuning. In addition, we introduce Spatially-Consistent InfoNCE (SC-InfoNCE), a CMPR-specific contrastive objective that suppresses distance-induced false negatives under spatial continuity. Extensive experiments on KITTI and KITTI-360 demonstrate that GeoUniPR achieves state-of-the-art (SOTA) performance in both same-modal and cross-modal place recognition, with strong cross-dataset generalization.

cs.CV↗

arXiv2026

AeroReformer2: Spoken-Query Referring Segmentation for Aerial Images

Spoken language offers a natural, hands-free interface for specifying an arbitrary target in dense remote-sensing imagery, yet existing referring remote-sensing image segmentation benchmarks accept only written expressions. To bridge this gap, we introduce \dataset, a spoken-query benchmark derived from RISBench that adds accent- and voice-diverse speech while preserving the original image, mask, and data splits. Its hard evaluation sets combine rotor, wind, and mixed interference with three signal-to-noise levels. We also propose \model, an efficient bilateral network that combines a boundary-preserving visual path with token-preserving speech encoding, kernel linear cross-modal attention, and a resolution refinement head. The design conditions visual features at two scales without materializing a dense speech--visual affinity matrix, then restores fine boundaries using high-resolution visual features. On the clean test split, \model with Swin-Base achieves 62.09\% mean intersection over union (mIoU) and 68.22\% overall intersection over union (oIoU), outperforming the strongest audio-adapted remote-sensing baseline by 5.38 and 2.08 percentage points, respectively. It retains the best hard-set mIoU at 54.09\%. To the best of our knowledge, this is the first benchmark and model study of full-sentence spoken-query referring segmentation for remote-sensing imagery. The code will be made publicly available.

cs.CV↗

arXiv2026

Levitated Milligram-scale Ferromagnetic Magnetometer at Room Temperature

Levitated mechanical oscillators are emerging ultrasensitive sensors with tremendous potential in both applied and fundamental physics. Levitated ferromagnets, with internal spin noises rapidly averaged, promise ultrahigh magnetic sensitivity. Here, we demonstrate a milligram-scale diamagnetically levitated ferromagnet system operating at room temperature. Through optimized geometry and multi-channel dissipation control, we achieve a magnetic sensitivity of 23~fT$/\sqrt{\text{Hz}}$ at frequency of 100-Hz level. We anticipate that a ferromagnetic magnetometer with subfemtotesla sensitivity is within reach, after modest technical improvements. This platform establishes a high-performance magnetometer for biomagnetic field detection and beyond-standard-model force searches.

quant-ph↗

arXiv2026

Sample-Adaptive Latent Rewards for Uncertainty-Guided Diffusion Post-Training

Latent reward models can supervise visual diffusion models without decoding intermediate states into pixel space. This makes alignment with human preferences more efficient. However, existing latent reward models output only scalar scores. They do not estimate the uncertainty of each prediction. The generator therefore cannot determine which feedback is reliable. This can drive optimization in the wrong direction and lead to reward hacking. We propose \textsc{SURE}, a unified latent-space framework for image and video diffusion models. It learns reward distributions and directly uses their reliability to guide dense post-training. First, we propose sample-adaptive latent reward model (\textsc{SURE-LRM}). It predicts a Gaussian utility for each noisy latent. Its mean predicts the reward score. Its variance reflect the uncertainty of prediction without human annotation. The learned distribution then guides post-training through uncertainty-guided reward feedback learning (\textsc{SURE-REFL}). This method provides uncertainty-guided dense feedback along the denoising trajectory. At selected transitions, \textsc{SURE-REFL} queries the frozen \textsc{SURE-LRM}. It converts detached variance into reliability weights for samples at the same transition. Each weighted reward is backpropagated only through its local transition. The entire process remains in latent space and requires neither pixel-space decoding nor the full denoising graph. Experiments show that \textsc{SURE-LRM} improves preference prediction over strong baselines. \textsc{SURE-REFL} achieves the sota performance among various metrics and further improves optimization stability. It also achieves the highest VBench quality, semantic, and total scores among the evaluated methods.

cs.CV↗

arXiv2026

LabRobFail: A Benchmark for Robotic Failure Analysis in Chemical Self-driving Laboratory

The deployment of embodied agents in self-driving laboratories could accelerate scientific discovery, yet their reliability is constrained by the irreversible and safety-critical nature of chemical experiments. Progress is further hindered by scarce failure data and the lack of fine-grained evaluation protocols. To address these challenges, we introduce LabRobFail, a failure-centric framework for learning and evaluating robotic failure analysis in chemical laboratories. LabRobFail-Sim injects controllable failures at the control, physics, and semantic levels, enabling the construction of LabRobFail-Data, which contains over 20,000 trajectories across 70+ task scenarios, five failure categories, and 11 fine-grained failure types. LabRobFail-Bench evaluates six capabilities spanning task understanding, failure detection, temporal localization, severity assessment, failure classification, and actionable correction. We further develop LabRobFail-VLM, a domain-specialized vision-language model that generates structured failure diagnoses and recovery instructions. On seen environments, it achieves 90.83% failure-detection accuracy and 77.21% temporal-localization accuracy, substantially outperforming general-purpose VLMs. When integrated as a real-time supervisor, it improves downstream task success rates by 4-16 percentage points, demonstrating the value of fine-grained failure understanding for closed-loop recovery and reliable laboratory autonomy. Our code and data are available at https://github.com/Su-ISE-2001/SciRobo

cs.RO↗

arXiv2026

High-accuracy ultrasonic positioning of calibration sources in the Jiangmen Underground Neutrino Observatory

Precise source positioning is essential for detector calibration in large liquid scintillator detectors such as JUNO, particularly in regions where purely mechanical control is insufficient. An ultrasonic positioning system has been developed to reconstruct the three-dimensional coordinates of a calibration source without interfering with photon collection or contaminating the liquid scintillator. The method combines a sound-speed modeling based on dedicated laboratory measurements and in-detector temperature profiles, waveform-based arrival-time reconstruction, and an in-situ calibration of the effective receiver geometry using central-axis deployments. With six active receivers, central-axis positioning yields a mean error of 1.23 cm relative to the known deployment reference. For off-axis operation in the Cable Loop System calibration plane, a detector-realistic simulation that includes timing resolution, sound-speed variation, and receiver-coordinate smearing predicts a positioning uncertainty of 2.40 cm. These results demonstrate that ultrasonic positioning can provide centimetre-level source accuracy for large liquid scintillator detectors and can support off-axis calibration in JUNO-like experiments.

physics.ins-det↗

arXiv2026

Point Ladder Tuning: Parameter-Efficient Hierarchical Adaptation for 3D Point Cloud Understanding

Fine-tuning pre-trained point-cloud backbones typically updates all parameters, resulting in substantial computation and memory overhead. More importantly, modern point backbones rely on aggressive tokenization and downsampling, which yields compact global tokens but irreversibly discards fine-grained local geometry, an inherent bottleneck for parameter-efficient adaptation. Consequently, existing PEFT methods that operate only on these coarsened tokens can modulate global semantics but struggle to recover the missing multi-scale locality. We present Point Ladder Tuning (PLT), a locality-aware PEFT framework that performs hierarchical, instance-conditioned adaptation while keeping the backbone frozen. PLT forms a lightweight closed loop: (i) a Hierarchical Ladder Network (HLN) constructs a multi-resolution local feature pyramid directly from raw points; (ii) a Local-Global Fusion (LGF) aligns and fuses local pyramids with intermediate backbone semantics; and (iii) a Dynamic Prompt Generator produces instance-aware multi-scale prompts to modulate the frozen backbone effectively. For dense prediction, we further introduce a lightweight segmentation head that progressively upsamples fused features and leverages backbone priors to refine fine structures. Extensive experiments on classification and dense prediction show that PLT consistently surpasses prior PEFT baselines with minimal tunable parameters. PLT achieves state-of-the-art performance using only 2.71% trainable parameters for classification and 7.69% for dense prediction, and scales favorably to larger backbones, requiring merely 0.36% parameters on PointGPT-L. The code is released at https://github.com/JunLinChang/ECCV2026-PLT.

cs.CV↗

arXiv2026

WHALE: A Scalable Unified Model for Recommendation with Wukong-HSTU Architecture

As scalability becomes increasingly important in recommendation modeling, recent architectures have advanced the modeling of two broad sources of ranking signals along separate paths: non-sequence features, including user, item, context, and cross features; and sequence features from user behavior histories. Wukong and HSTU have emerged as representative scalable backbones for these paths: Wukong scales high-order non-sequence feature-interaction modeling, while HSTU scales long user-behavior sequence modeling. Despite their complementary strengths, practical architectures that combine these two types of feature modeling remain underexplored. We present WHALE, a scalable unified recommendation architecture that jointly models non-sequence and sequence features on top of Wukong and HSTU. Each WHALE layer contains a Wukong module, an HSTU module, and an attention-based fusion module in which Wukong-derived interaction representations query HSTU-derived behavior representations. This design keeps both backbones active throughout the network and enables progressive Wukong-HSTU exchange, allowing high-order feature crosses to repeatedly retrieve fine-grained evidence from long user histories. To make WHALE practical for industrial deployment, we introduce customized Triton kernels and other model-systems co-design techniques to improve training and inference efficiency. On large-scale industrial recommendation data, WHALE achieves consistent gains in offline experiments. Additionally, it delivers positive online gains with a modest serving-throughput trade-off. The method has been deployed in production systems. Overall, WHALE provides a practical example of how these two sources of information can be scalably unified in an industrial recommendation model.

cs.IR↗

arXiv2026

SalientGS: Unified SfM-to-3DGS with Importance-Guided MCMC Gaussian Allocation

Reconstructing 3D scenes from unordered images remains bottlenecked by expensive Structure-from-Motion (SfM) preprocessing and frozen pose interfaces. We present SalientGS, a unified SfM-to-3D Gaussian Splatting (3DGS) pipeline. Its central contribution is importance-guided Markov Chain Monte Carlo (MCMC) Gaussian allocation, which aggregates multi-view residuals into per-Gaussian underfit and redundancy signals. These signals define a smooth importance-weighted sampling distribution that biases both birth and relocation toward underfit regions. This reallocates capacity from well-fit areas without altering the underlying stochastic gradient Langevin dynamics (SGLD). SalientGS achieves end-to-end reconstruction in 15 minutes with state-of-the-art perceptual quality. The supplementary material provides dedicated sections for Per-Scene Qualitative Comparisons and Per-Image Learned Perceptual Image Patch Similarity (LPIPS) Analysis, including failure cases. Code and evaluation scripts are available at https://github.com/Six-Bit-TX/SalientGS.

cs.CV↗