arXiv subjects

Chao Chen

Publications and source records attributed to Chao Chen.

At least 127 records · Page 7Linked to original sources

arXiv2026

Study of the Magnetic Dipole Transition of $J/ψ\toγη_c$ via $η_c\to p\bar{p}$

Using $(10.087\pm0.044)\times10^9$ $J/ψ$ events collected with the BESIII detector at the $e^+e^-$ BEPCII collider, we present the first amplitude analysis of $J/ψ\toγp\bar{p}$ with the $p\bar p$ invariant mass in the $η_c$ mass region $[2.70,3.05]$~GeV/$c^2$. The product branching fraction $\mathcal{B}(J/ψ\toγη_c)\times\mathcal{B}(η_c\to p\bar{p})$ is precisely determined to be $(2.11\pm0.02_{\rm stat}\pm0.07_{\rm syst})\times10^{-5}$. Combining with the product branching fractions $\mathcal{B}(η_c\to p\bar{p})\times\mathcal{B}(η_c\to γγ)$ and $\mathcal{B}(J/ψ\toγη_c)\times\mathcal{B}(η_c\to γγ)$, the branching fractions of $\mathcal{B}(J/ψ\toγη_c)$ and $\mathcal{B}(η_c\toγγ)$ are calculated to be $(2.29\pm0.01_{\rm stat}\pm0.04_{\rm syst}\pm0.18_{\rm opbf})\%$ and $(2.28\pm0.01_{\rm stat}\pm0.04_{\rm syst}\pm0.18_{\rm opbf})\times10^{-4}$, respectively, which are consistent with the latest lattice quantum chromodynamics calculations. Here, opbf is the uncertainty from the other product branching fractions used in the calculation.

hep-ex↗

arXiv2026

Precise Measurement of Chromoelectric Dipole Moment of the Charm Quark

The combined symmetry of charge conjugation and parity ($C\!P$) is tested in the hadronic transition $ψ(3686)\toπ^+π^{-}J/ψ$, using a dataset of $(2712.4\pm14.3)\times10^6$ $ψ(3686)$ events collected by the BESIII detector at the BEPCII collider. The resulting asymmetry observable is $A_{cp} = (0.6\pm1.8\pm0.1)\times10^{-4}$, which is determined by combining the two channels $J/ψ\to e^+e^-$ and $J/ψ\toμ^+μ^-$. Additionally, by considering the relationship between the chromoelectric dipole moment (CEDM) and the $A_{cp}$ observable derived from the quantum chromodynamics multipole expansion theory based on the Chen-Kuang model and the Cornell potential model, we obtain the results of charm quark's CEDM with $d^{\prime}_{c} = (2.6\pm7.8\pm0.4\pm0.6)\times10^{-16}$ $e$cm, and $d^{\prime}_{c} = (3.5\pm10.5\pm0.6\pm0.5)\times10^{-16}$ $e$cm, respectively. The uncertainties are statistical, systematic, and theoretical, respectively. These results correspond to an upper limit of $|d^{\prime}_{c}|<2.1\times10^{-15}$ $e$ cm at 90\% confidence level, representing an order of magnitude improvement in sensitivity compared to the previous direct bound using the same decay process.

hep-ex↗

arXiv2026

MobilityBench: A Benchmark for Evaluating Route-Planning Agents in Real-World Mobility Scenarios

Route-planning agents powered by large language models (LLMs) have emerged as a promising paradigm for supporting everyday human mobility through natural language interaction and tool-mediated decision making. However, systematic evaluation in real-world mobility settings is hindered by diverse routing demands, non-deterministic mapping services, and limited reproducibility. In this study, we introduce MobilityBench, a scalable benchmark for evaluating LLM-based route-planning agents in real-world mobility scenarios. MobilityBench is constructed from large-scale, anonymized real user queries collected from Amap and covers a broad spectrum of route-planning intents across multiple cities worldwide. To enable reproducible, end-to-end evaluation, we design a deterministic API-replay sandbox that eliminates environmental variance from live services. We further propose a multi-dimensional evaluation protocol centered on outcome validity, complemented by assessments of instruction understanding, planning, tool use, and efficiency. Using MobilityBench, we evaluate multiple LLM-based route-planning agents across diverse real-world mobility scenarios and provide an in-depth analysis of their behaviors and performance. Our findings reveal that current models perform competently on Basic information retrieval and Route Planning tasks, yet struggle considerably with Preference-Constrained Route Planning, underscoring significant room for improvement in personalized mobility applications. We publicly release the benchmark data, evaluation toolkit, and documentation at https://github.com/AMAP-ML/MobilityBench.

cs.AI↗

arXiv2026

Measurement of the branching fraction of $D_{s}^{*+}\to e^{+}e^{-}D_{s}^{+}$

The branching fraction of the electromagnetic Dalitz decay $D^{*+}_{s}\to e^{+}e^{-}D^{+}_{s}$ is measured with an $e^{+}e^{-}$ collision data sample collected by the BESIII experiment at center-of-mass energies between 4.128 and 4.226 $\mathrm{GeV}$, corresponding to a total integrated luminosity of 7.33 $\mathrm{fb}^{-1}$. The measurement yields the branching fraction ${\mathcal{B}(D^{*+}_{s}\to e^{+}e^{-}D^{+}_{s})=(7.28\pm0.61_{\mathrm{stat}}\pm0.31_{\mathrm{syst}})\times10^{-3}}$. The result is consistent with the previous one, with a 2.5-fold improvement in precision. This provides an important input for constraining the parameters of theoretical models and for determining the absolute branching fractions of $D^{*+}_{s}\to π^{0}D^{+}_{s}$ and $D^{*+}_{s}\to γD^{+}_{s}$, measured with a relative method.

hep-ex↗

arXiv2026

Cross Paraphrastic Invariance Learning for Hallucination Detection

Large language models (LLMs) frequently generate hallucinations, which are unsupported by a source document. To avoid costly LLM-as-evaluator pipelines and the heavy annotation demands of existing classifiers, we propose CPIL (Cross Paraphrastic Invariance Learning), a two-stage Siamese framework that maximizes the utility of existing labeled data. Concretely, CPIL constructs informative training pairs by: (i) generating paraphrastic views of each document-claim example as positives, and explicitly aligning their representations to enforce invariance to surface form; and (ii) mining same-document, opposite-label pairs as hard negatives to sharpen document-sensitive decision boundaries. Then CPIL conduct a two-stage model training: Stage 1 performs contrastive pretraining to learn a paraphrase-invariant, grounding-aware embedding space; and Stage 2 attaches a lightweight classifier for binary groundedness. On the LLM-AggreFact benchmark (11 tasks), CPIL surpasses strong baselines concerning F1 scores with only ~1% labeled data, showing its prediction superiority and label efficiency.

cs.CL↗

arXiv2026

Constrained Paraphrase Consistency for LLM Hallucination Detection

Large language models (LLMs) can generate factually inconsistent claims, motivating accurate and scalable hallucination detectors. Prior work largely enlarges training sets via synthesis or new annotations, introducing increasing cost and potential bias while underusing the consistency implied by semantically equivalent paraphrases. We propose Consistency-Constrained Hallucination Detector (CCHD), which formulates training as a constrained optimization problem. The standard cross-entropy on original document-claim pairs is complemented by (i) paraphrase-consistency constraints bounding divergence across paraphrased views, and (ii) label-preservation constraints tying paraphrases to ground truth. We solve the problem by gradient descent-ascent over model parameters and per-view Lagrange multipliers, adding only a few scalar dual variables and no inference-time overhead. With DeBERTa and Flan-T5 backbones, CCHD consistently outperforms strong baselines (FactCG, MiniCheck, and AlignScore) on standard factuality benchmarks, demonstrating its superiority on hallucination detection.

cs.CL↗

arXiv2026

Search for the isospin-violating decays $\boldsymbol{χ_{cJ}\toΛ\barΣ^{0}+c.c.}$ and $\boldsymbol{η_{c}\toΛ\barΣ^{0}+c.c.}$

Using a sample of $(2712.4\pm14.3)\times10^{6}$~$ψ(3686)$ events collected with the BESIII detector, we perform a search for the isospin-violating decays $χ_{cJ}\toΛ\barΣ^{0}+c.c.~(J=0,1,2)$ and $η_{c}\toΛ\barΣ^{0}+c.c.$ No significant signal for $χ_{cJ}$ or $η_{c}$ is observed in the $Λ\barΣ^{0}$ invariant mass distribution. The upper limits on the branching fractions at the 90\% confidence level are set to be $\mathcal{B}$($χ_{c0}\toΛ\barΣ^{0}+c.c.$)$<1.5\times 10^{-6}$, $\mathcal{B}$($χ_{c1}\toΛ\barΣ^{0}+c.c.$)$<1.6\times 10^{-6}$, $\mathcal{B}$($χ_{c2}\toΛ\barΣ^{0}+c.c.$)$<1.7\times 10^{-6}$ and $\mathcal{B}$($η_{c}\toΛ\barΣ^{0}+c.c.$)$<6.2\times 10^{-5}$ for the first time.

hep-ex↗

arXiv2026

Kunlun: Establishing Scaling Laws for Massive-Scale Recommendation Systems through Unified Architecture Design

Deriving predictable scaling laws that govern the relationship between model performance and computational investment is crucial for designing and allocating resources in massive-scale recommendation systems. While such laws are established for large language models, they remain challenging for recommendation systems, especially those processing both user history and context features. We identify poor scaling efficiency as the main barrier to predictable power-law scaling, stemming from inefficient modules with low Model FLOPs Utilization (MFU) and suboptimal resource allocation. We introduce Kunlun, a scalable architecture that systematically improves model efficiency and resource allocation. Our low-level optimizations include Generalized Dot-Product Attention (GDPA), Hierarchical Seed Pooling (HSP), and Sliding Window Attention. Our high-level innovations feature Computation Skip (CompSkip) and Event-level Personalization. These advances increase MFU from 17% to 37% on NVIDIA B200 GPUs and double scaling efficiency over state-of-the-art methods. Kunlun is now deployed in major Meta Ads models, delivering significant production impact.

cs.IR↗

arXiv2026

Generating Graph-Like Logical Rules for Knowledge Graph Reasoning via Diffusion Models

Logical rules constitute a cornerstone of knowledge graph (KG) reasoning, valued for their interpretability and ability to model relational patterns. However, existing rule mining methods predominantly focus on simple chain-like rules and therefore neglect the richer relational information encoded in graph-like structures, such as cycles and branches. This limitation is further exacerbated by computational bottlenecks caused by the combinatorial explosion of the search space, which is especially challenging for graph-like rules. Meanwhile, generative approaches such as diffusion models, despite their success in other domains, cannot be directly applied to rule mining because their training objectives are not aligned with the goal of learning high-quality rules, and non-differentiable KG rule quality metrics cannot directly guide model optimization. To address these limitations, we propose GRiD, a framework that reformulates graph-like rule discovery as a discrete generative process conditioned on the target relation. GRiD employs a two-phase training strategy. First, supervised pre-training enables GRiD to capture structural priors from subgraphs sampled from the KG meta-graph. Subsequently, reinforcement learning is applied to fine-tune GRiD through policy gradient optimization guided directly by non-differentiable rule-quality metrics. Experiments on six benchmark datasets show that GRiD achieves competitive performance on KG completion tasks. Ablation studies confirm the efficiency and robustness of GRiD and further show that graph-like rules complement chain-like rules in KG completion. Our code and datasets are available in https://github.com/Haoxiang-Cheng/GRiD.

cs.AI↗

arXiv2026

MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation

In semi-supervised segmentation, capturing meaningful semantic structures from unlabeled data is essential. This is particularly challenging in histopathology image analysis, where objects are densely distributed. To address this issue, we propose a semi-supervised segmentation framework designed to robustly identify and preserve relevant topological features. Our method leverages multiple perturbed predictions obtained through stochastic dropouts and temporal training snapshots, enforcing topological consistency across these varied outputs. This consistency mechanism helps distinguish biologically meaningful structures from transient and noisy artifacts. A key challenge in this process is to accurately match the corresponding topological features across the predictions in the absence of ground truth. To overcome this, we introduce a novel matching strategy that integrates spatial overlap with global structural alignment, minimizing discrepancies among predictions. Extensive experiments demonstrate that our approach effectively reduces topological errors, resulting in more robust and accurate segmentations essential for reliable downstream analysis. Code is available at https://github.com/Melon-Xu/MATCH.

cs.CV↗

arXiv2026

Act Like a Pathologist: Tissue-Aware Whole Slide Image Reasoning

Computational pathology has advanced rapidly in recent years, driven by domain-specific image encoders and growing interest in using vision-language models to answer natural-language questions about diseases. Yet, the core problem behind pathology question-answering remains unsolved, considering that a gigapixel slide contains far more information than necessary for a given question. Pathologists naturally navigate tissue and morphology complexity by scanning broadly, and zooming in selectively according to the clinical questions. Current models, in contrast, rely on uniform patch sampling or broad attention maps, often attending equally to irrelevant regions while overlooking key visual evidence. In this work, we try to bring models closer to how humans actually examine slides. We propose a question-guided, tissue-aware, and coarse-to-fine retrieval framework, HistoSelect, that consists of two key components: a group sampler that identifies question-relevant tissue regions, followed by a patch selector that retrieves the most informative patches within those regions. By selecting only the most informative patches, our method becomes significantly more efficient: reducing visual token usage by 70% on average, while improving accuracy across three pathology QA tasks. Evaluated on 356,000 question-answer pairs, our approach outperforms existing methods and produces answers grounded in interpretable, pathologist-consistent regions. Our results suggest that bringing human-like search and attention patterns into WSI reasoning is a promising direction for building practical and reliable pathology VLMs. Code is available at https://github.com/winston52/HistoSelect.

cs.CV↗

arXiv2026

Precision Measurement of $D_{s}^{*+}$-$D_{s}^{+}$ Mass Difference

We measure the mass difference between $D_{s}^{*+}$ and $D_{s}^{+}$, $Δm_s$, using the decay chain $D_{s}^{*+} \to D_{s}^{+}(\to K^{+} K^{-} π^{+})π^{0}$, utilizing $e^+e^-$ annihilation data corresponding to an integrated luminosity of 3.19 fb$^{-1}$ collected at a center-of-mass energy of 4.178 GeV with the BESIII detector. The measured value of $Δm_s = [144\,201.9 \pm 44.2({\rm stat.}) \pm 29.9({\rm syst.}) \pm 15.0({\rm PDG})]$ keV/$c^2$ is about seven times more precise than the current Particle Data Group average, where the last uncertainty is from the Particle Data Group average of the $D^{*+} - D^{+}$ mass difference.

hep-ex↗

arXiv2026

Embedded underwater front-end electronics for the 3-inch photomultipliers in the JUNO experiment

The Jiangmen Underground Neutrino Observatory (JUNO) is a 20-kton liquid scintillator-based, low-radioactivity, multi-purpose neutrino detector located 693 meters (1800 m.w.e.) underground in the Guangdong province, China. To detect scintillation light produced in the target, the detector is equipped with 17,612 20-inch photomultipliers (PMTs), forming the Large PMT system (LPMT). In addition, 25,600 3-inch photomultipliers (the Small Photomultiplier System or SPMT) are deployed in the gaps between the LPMTs. This paper presents the design and performance of the underwater front-end electronics developed for the SPMT system. It details the individual electronics boards and their key components, the inter-board interfaces, the system-level design, and the firmware architecture that supports data acquisition and control. It also outlines mechanical and thermal integration, board validation procedures, and system performance metrics. The readout chain includes digitization of 128 PMT channels per unit, synchronized time-stamping, charge measurement, event packaging, and bandwidth management. Comprehensive validation confirms the system's readiness to meet JUNO's stringent physics goals. The underwater electronics achieve noise levels as low as 0.04 photoelectrons with minimal crosstalk (below 0.4%) and a bandwidth of 57 MB/s, ensuring reliable single photo-electron detection and operation under high-rate conditions. The SPMT system has now been fully integrated and installed in JUNO. Its commissioning and physics performance will be reported in a future publication.

physics.ins-det↗

arXiv2026

GSAM: A Generalizable and Safe Robotic Framework for Articulated Object Manipulation

Articulated object manipulation is a unique challenge for service robots. Existing methods employ end-to-end policy learning, visionmotion planning, and large-language/visual-language model (LLM/VLM), but often overlook the diversity of articulated objects and the complexity of interactions between end-effector and handle, leading to limited generalization and destructive collisions. To address this, we propose GSAM, a generalizable and safe robotic framework for articulated object manipulation. Specifically, a vision-based perceiver generates the kinematic parameters. Considering that pre-trained markers in perceiver yield raw estimations that may deviate from commonsense, we present a f ine-tuned VLM-based refiner, using chain-of-thought (COT) commonsense reasoning to refine perception. To prevent destructive collisions, we design an interaction constraint function generator, integrating articulated object, interaction pose, and obstacle avoidance knowledge into a base. LLM then functionalize these constraints and apply them to trajectory and posture planning. A kinematic-aware manipulation planner verifies reachability for trajectory and posture. Experiments on 50 hinge tasks across 5 object categories and 50 randomly initialized end-effectorhandle configurations show that GSAM reduces standard deviation by 3.1% and improves manipulation success rate by 36.0% compared to the best baseline, respectively demonstrating the superior object generalization and interaction safety of GSAM in practical scenarios.

cs.RO↗

arXiv2026

Learning Bijective Surface Parameterization for Inferring Signed Distance Functions from Sparse Point Clouds with Grid Deformation

Inferring signed distance functions (SDFs) from sparse point clouds remains a challenge in surface reconstruction. The key lies in the lack of detailed geometric information in sparse point clouds, which is essential for learning a continuous field. To resolve this issue, we present a novel approach that learns a dynamic deformation network to predict SDFs in an end-to-end manner. To parameterize a continuous surface from sparse points, we propose a bijective surface parameterization (BSP) that learns the global shape from local patches. Specifically, we construct a bijective mapping for sparse points from the parametric domain to 3D local patches, integrating patches into the global surface. Meanwhile, we introduce grid deformation optimization (GDO) into the surface approximation to optimize the deformation of grid points and further refine the parametric surfaces. Experimental results on synthetic and real scanned datasets demonstrate that our method significantly outperforms the current state-of-the-art methods. Project page: https://takeshie.github.io/Bijective-SDF

cs.CV↗

arXiv2026

Rethinking Federated Unlearning via the Lens of Memorization

Federated learning (FL) increasingly needs machine unlearning to comply with privacy regulations. However, existing federated unlearning approaches may overlook the overlapping information between the unlearning and remaining data, leading to ineffective unlearning and unfairness between clients. In this work, we revisit federated unlearning through the lens of memorization. We argue that unlearning should mainly remove the unique memorized information attributable to the data to be forgotten, while preserving overlapping patterns that are also supported by the remaining data. Specifically, we propose Grouped Memorization Evaluation, an example-level metric that separates memorized knowledge from overlapping knowledge. Building on this metric, we introduce Federated Memorization Pruning (FedMemPrune), a pruning-based unlearning approach that resets redundant parameters responsible for memorization. Extensive experiments show that FedMemPrune closely matches retraining-based unlearning baselines while more effectively eliminating memorization than existing federated unlearning algorithms, yielding strong unlearning performance without sacrificing the utility of retained knowledge.

cs.LG↗

arXiv2026

Study of the reactions $\bar{n} p \to 2π^{+}π^{-}$, $2π^{+}π^{-}π^{0}$, and $2π^{+}π^{-}2π^{0}$ using $J/ψ\to p π^{-}\bar{n}$

We report an experimental investigation of the reactions $\bar{n} p \to 2π^{+}π^{-}$, $\bar{n} p \to 2π^{+}π^{-}π^{0}$, and $\bar{n} p \to 2π^{+}π^{-}2π^{0}$ using $(10.087 \pm 0.044) \times 10^{9}$ $J/ψ$ events collected with the BESIII detector at the BEPCII storage ring. The antineutron ($\bar{n}$) is produced in the decay $J/ψ\to p π^{-} \bar{n}$ with studied momentum from 200~MeV/$c$ to 1174~MeV/$c$, while the target proton originates from the hydrogen nuclei in the cooling oil of the beam pipe. This novel method pioneers the study of $\bar{n}$-nucleon interactions at an $e^{+}e^{-}$ collider, providing the first experimental data for $\bar{n}$ momenta exceeding 800~MeV/$c$.

hep-ex↗

arXiv2026

TransitLM: A Large-Scale Dataset and Benchmark for Map-Free Transit Route Generation

Public transit route planning traditionally depends on structured map infrastructure and complex routing engines, and no existing dataset supports training models to bypass this dependency. We present TransitLM, a large-scale dataset of over 13 million transit route planning records from four Chinese cities covering 120,845 stations and 13,666 lines, released as a continual pre-training corpus and benchmark data for three evaluation tasks with complementary metrics. Experiments show that an LLM trained on TransitLM produces structurally valid routes at high accuracy and implicitly grounds arbitrary GPS coordinates to appropriate stations without any explicit mapping. These results demonstrate that transit route planning can be learned entirely from data, enabling end-to-end, map-free route generation directly from origin-destination information. The dataset and benchmark are available at https://huggingface.co/datasets/GD-ML/TransitLM, with evaluation code at https://github.com/HotTricker/TransitLM.

cs.CL↗