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Xiang Liu

Publications and source records attributed to Xiang Liu.

At least 55 records · Page 3Linked to original sources
arXiv2026

Revisiting charmonium hybrid spectroscopy

Hadrons with explicit gluonic degrees of freedom, such as charmonium hybrids, are key to understanding nonperturbative behavior of strong interaction, yet they remain experimentally elusive. Within a constituent gluon model treating the hybrid as a $c\bar{c}g$ three-body system with a transverse electric gluon, we predict the masses of the lightest hybrid multiplet ($J^{PC}=1^{--}, 0^{-+}, 1^{-+}, 2^{-+}$) to lie in the range $4.19$--$4.32$ GeV. By analyzing their decay patterns, we provide specific, experimentally testable signatures to guide the search for these exotic states at current and future facilities.

hep-ph↗
arXiv2026

High second Chern number induced by long-range hopping in a four-dimensional Dirac model

Four-dimensional (4D) topological systems provide a promising platform for exploring topological phenomena beyond three dimensions. So far, extensive recent studies on 4D topological insulators have focused on the 4D Dirac model, while its second Chern number is restricted to a limited set of values. In this work, we demonstrate that introducing long-range hopping into the 4D Dirac model induces topological phases with high second Chern numbers. Furthermore, we show that the long-range hopping can transform a trivial insulator into a topological insulator with a nonzero second Chern number. Our work establishes long-range hopping as a powerful route for engineering 4D topological states and reveals new possibilities for realizing unconventional topological phases beyond minimal models.

cond-mat.mes-hall↗
arXiv2026

Numerical calculation of the $k$-space second Chern number in four dimensions

We propose an efficient numerical method to compute the $k$-space second Chern number in four-dimensional (4D) topological systems. Our approach employs an adaptive mesh refinement scheme to evaluate the Brillouin-zone integral, which automatically increases the grid density in regions where the Berry curvature is sharply peaked. We compare our method with the 4D lattice-gauge extension of the Fukui-Hatsugai-Suzuki method and a direct uniform grid integration scheme. Compared with these approaches, our method (i) achieves the same accuracy with substantially fewer diagonalizations, and thus runs faster; (ii) requires minimal memory to execute, enabling calculations for larger systems; and (iii) remains accurate even near topological phase transitions where conventional methods often face challenges. These results demonstrate that the adaptive subdivision strategy is a practical and powerful tool for calculating the $k$-space second Chern number.

cond-mat.mes-hall↗
arXiv2026

AuroraRL: Fast, Fault-Tolerant, and Cost-Efficient Reinforcement Learning over Decentralized Network

LLM reinforcement learning (RL) requires frequent synchronization of large model parameters between the trainer and distributed rollout actors. High-throughput RL post-training therefore relies on dedicated RDMA HPC/cloud clusters, an infrastructure cost most organizations cannot absorb. A natural alternative is to aggregate loosely-coupled GPUs over standard Ethernet and WAN links, but this commodity connectivity cannot sustain full-weight broadcasts: synchronizing an 8B model can take over 100~seconds on bandwidth-limited links, while rollout generation typically takes tens of seconds. Toward making RL practical in this regime, we observe that RL fine-tuning yields highly sparse per-step updates, with only around 1\% of parameter elements changing. On top of this insight, we present AuroraRL, a novel high-performance RL training system that preserves bit-exact updates without dropping or quantizing information, designed for commodity-networked, loosely-coupled GPU resources. AuroraRL represents each step as a sparse delta checkpoint, pipelines delta extraction with multi-stream transmission, overlaps transfer with rollout generation, and coordinates heterogeneous workers with throughput- and bandwidth-aware scheduling plus lease-based fault tolerance. Across Qwen3 4B--14B models deployed in up to four geographic regions, AuroraRL shrinks per-step weight transfer by 79$\times$ on Qwen3-8B, delivers 1.3--9.5$\times$ higher throughput than dense-broadcast baselines (PrimeRL-Full, async-tolerant, multi-stream variants), and brings end-to-end training within 8.91\% of an ideal RDMA single-datacenter baseline, while transparently tolerating common failures and preserving training accuracy. By leveraging on-demand, cross-cloud GPUs over commodity links, AuroraRL delivers 1.21--1.59$\times$ higher tokens per dollar than reserved RDMA clusters at comparable throughput.

cs.DC↗
arXiv2026

CogRec: Structure-Cognitive Fast-and-Slow Reasoning for Generative Recommendation

Semantic-ID-based generative recommendation represents each item as a hierarchical discrete token sequence and reformulates next-item prediction as constrained sequence generation. Existing methods, however, mainly use Semantic IDs as target sequences to be memorized, leaving the hierarchy, intra-layer relations, and item neighborhoods underused as an explicit reasoning space. Explicit reasoning-enhanced generative methods often produce a natural-language rationale before the item identifier, but this rationale is only weakly coupled with the discrete SID space in which the final prediction is made. We propose CogRec, a structure-cognitive fast-and-slow reasoning framework that grounds intermediate reasoning in the same SID topology used for target generation. CogRec augments the vertical SID hierarchy with intra-layer semantic graphs and item-level neighborhoods, and introduces SID Routing to represent recommendation reasoning through layer-wise Match, LateralJump, and Explore operations. Exact matching implements fast semantic localization, whereas lateral and exploratory operations instantiate slower structural navigation. A supervised multi-stage pipeline aligns the newly introduced SID tokens, establishes direct SID generation, and trains natural-language and SID-routing reasoning branches from a shared checkpoint under the same trie-constrained output space. Experiments on three public sequential-recommendation benchmarks show that SID Routing improves its corresponding direct-generation, indicate that structure-grounded reasoning is most useful when prefix matching is insufficient but learnable SID-space transitions remain available, whereas long or weakly supported routes introduce additional decoding cost and accumulated errors. Code is available at https://github.com/caskcsg/CogRec

cs.IR↗
arXiv2026

Mediator: Memory-efficient LLM Merging with Less Parameter Conflicts and Uncertainty Based Routing

Model merging aggregates Large Language Models (LLMs) finetuned on different tasks into a stronger one. However, parameter conflicts between models leads to performance degradation in averaging. While model routing addresses this issue by selecting individual models during inference, it imposes excessive storage and compute costs, and fails to leverage the common knowledge from different models. In this work, we observe that different layers exhibit varying levels of parameter conflicts. Building on this insight, we average layers with minimal parameter conflicts and use a novel task-level expert routing for layers with significant conflicts. To further reduce storage costs, inspired by task arithmetic sparsity, we decouple multiple fine-tuned experts into a dense expert and several sparse experts. Considering the out-of-distribution samples, we select and merge appropriate experts based on the task uncertainty of the input data. We conduct extensive experiments on both LLaMA and Qwen with varying parameter scales, and evaluate on real-world reasoning tasks. Results demonstrate that our method consistently achieves significant performance improvements while requiring less system cost.

cs.LG↗
arXiv2026

Predicting charmed-strange molecular tetraquarks with $K^{(*)}$ and $T$-doublet charmed or anticharmed meson

In this work, we first present a systematic investigation of the $T_{\bar{c}\bar{s}}$-type charmed-strange molecular tetraquark candidates composed of a $K^{(*)}$ meson and a $T$-doublet anticharmed meson using the one-boson-exchange model, which exhibit exotic flavor content $\bar{c}\bar{s} q q$. Our results suggest that the $K^* \bar D_1$ states with $I(J^P)=0(0^-,\,1^-)$ and the $K^* \bar D_2^*$ states with $I(J^P)=0(1^-,\,2^-)$ represent the most promising candidates of the $T_{\bar{c}\bar{s}}$-type charmed-strange molecular tetraquarks, while the coupled $K \bar D_1 / K^* \bar D_1 / K^* \bar D_2^*$ system with $I(J^P)=0(1^-)$ and the coupled $K \bar D_2^* / K^* \bar D_1 / K^* \bar D_2^*$ system with $I(J^P)=0(2^-)$ can only be regarded as the possible candidates of the $T_{\bar{c}\bar{s}}$-type charmed-strange molecular tetraquarks. We further extend our analysis to the $K^{(*)} {D}_1/K^{(*)} {D}_2^*$ systems, where our results suggest a series of $T_{c \bar s}$-type charmed-strange molecular tetraquark candidates. These findings provide a comprehensive picture of the molecular spectrum in the charmed-strange tetraquark sector composed of $S$-wave kaons and (anti-)charmed mesons in the $T$-doublet and can be tested in future experimental studies.

hep-ph↗
arXiv2026

Can We Steer the Black-Box? Towards Controllability-Centric Evaluation of Recommender Systems with Collaborative Agents

Recommender systems operate as Black-Boxes, leaving users and regulators unable to steer their outputs toward specific intentions or audit their behavior. This lack of controllability, defined as the system's ability to respond to explicit guidance, remains an unaddressed dimension in existing evaluation paradigms. To fill this gap, we propose CtrlBench-Rec, a collaborative multi-agent framework for systematic assessment of controllability. We formalize three fundamental tasks: target content discovery, interest profile shaping, and popularity bias mitigation, which together measure steerability from explicit commands to implicit representation steering and finally to overcoming algorithmic biases.Extensive experiments on real-world datasets and multiple recommendation models demonstrate that our framework effectively quantifies controllability and exposes critical system bottlenecks, most notably persistent resistance to guiding long tail content. CtrlBench-Rec provides the first standardized toolkit for controllable recommendation research, algorithmic auditing, and user empowerment. Our code is released on https://github.com/caskcsg/CtrlBenchRec.

cs.IR↗
arXiv2026

AlphaFunctor: Bridging The Gap Between Protein Function Annotation and Property Prediction

The fundamental relationship among protein sequence, structure, function, and physicochemical properties is a central principle in biology. While in principle protein function and properties should be able to be derived directly from protein sequence, in practice protein function and property prediction methods have been designed around specific datasets and specific property or function subsets, leading to an enormous gap between function annotation and property prediction. To address these challenges, we introduce AlphaFunctor, a category theory based foundation model-like platform to bridge the gap between protein function annotation and property prediction. Based on the hypothesis that protein function and properties can be directly derived from protein sequence, AlphaFunctor predicts protein functions as represented by Gene Ontology terms directly from sequence. Using these function predictions, AlphaFunctor further maps protein functions using topological spectral theory, path-complex neural networks, and protein domain analysis onto downstream property prediction. AlphaFunctor is (pre)trained in nearly 0.6 million protein function data points to deliver the state-of-the-art protein function annotation on three benchmark datasets. Without task-specific network redesign, AlphaFunctor maps qualitative protein function annotation to various qualitative and quantitative protein property predictions, outperforming other dataset-specific and task-specific competing predictors.

q-bio.BM↗
arXiv2026

Proposed mixing between $2P$ and $1F$ wave charmonia

We investigate $2P$-$1F$ mixing in charmonium, focusing on the close-in-mass $χ_{c2}(2P)$ and $χ_{c2}(1F)$ states. The conventional tensor force yields negligible mixing, motivating the inclusion of coupled-channel effects. Our unquenched calculation reveals sizable mixing angles of $7.5^\circ$ and $15.4^\circ$. We predict the corresponding two-photon and two-gluon decay widths as key observables for experimental verification. Additionally, we discuss the production of these two $2P$-$1F$ mixed states of charmonium via $γγ$ fusion. Current data are insufficient to determine the mixing, highlighting the need for precise future measurements to resolve this aspect of charmonium spectroscopy.

hep-ph↗
arXiv2026

Computing critical velocities in waveguides via multiparameter eigenvalue problems

In waveguide dynamics and moving-load problems (e.g., high-speed trains), critical velocities indicate the onset of strong vibration amplification. In systems that are invariant in the direction of motion, these velocities can be identified from dispersion relations as points where the phase and group velocities of a propagating mode coincide. Finding such points indirectly by tracing dispersion curves can be cumbersome and potentially unreliable for multimodal systems with complex branch interactions. We present a direct method for computing critical velocities in such scenarios, specifically in the context of semi-analytical methods. Starting from a polynomial parameter-dependent eigenvalue problem for the wavenumber-frequency relation, incorporating the additional condition of equal phase and group velocities yields a singular polynomial multiparameter eigenvalue problem that can be linearized and solved using established algorithms. The proposed approach enables the simultaneous computation of all critical points without requiring the tracing of dispersion curves. Its performance is demonstrated by several benchmark problems, confirming the accurate and robust identification of critical velocities.

math.NA↗
arXiv2026

A Large-Scale Sparse Multiobjective Optimization Algorithm Based on Optimal Performance Scores

Large-scale sparse multiobjective optimization problems (LSSMOPs) involve a large number of decision variables and Pareto optimal solutions with only a few nonzero variables. However, as the number of decision variables grows, it becomes increasingly challenging to accurately identify the nonzero variables, and optimization performance is adversely affected. To address these issues, this paper proposes an evolutionary algorithm for LSSMOPs. Specifically, we propose a new initialization method capable of generating scores that accurately reflect the importance of variables, and an initial mask vector template that can locate nonzero variables. This leads to the generation of a high-quality initial population. Additionally, this paper introduces a new strategy to calculate the mutation probability for each variable and a novel optimization for real variables based on the Pareto-guided normal distribution, enabling the population to avoid being trapped in local optima and quickly converge to the global optimum. Experimental results from eight benchmark problems and three real-world applications demonstrate that the proposed algorithm achieves superior performance compared with state-of-the-art algorithms.

cs.NE↗
arXiv2026

ACE-Brain-0.5: A Unified Embodied Foundational Model for Physical Agentic AI

Embodied AI is moving from isolated perception or action modules toward physical agents that understand, plan under goals, act through robot bodies, monitor progress, and improve from experience. Existing systems address this loop only in parts: end-to-end policies generate actions but often lack spatial reasoning, planning, and execution assessment, while robot-agent systems orchestrate tools or specialists but do not learn a shared representation. This fragmentation limits general Physical Agentic AI. We present ACE-Brain-0.5, a unified embodied foundation model that organizes robot intelligence into five coupled functions: spatial perception, decision making, embodied interaction, self-monitoring, and self-improvement. Built on ACE-Brain-0, which established spatial intelligence as a shared scaffold across robot platforms, ACE-Brain-0.5 extends an understanding-centric model into a closed-loop foundation model. A single 8B backbone instantiates the first four functions: grounding objects and affordances, reasoning over 3D and egocentric spatial relations, decomposing instructions into subgoals, generating navigation and manipulation actions, and estimating progress for verification and recovery. To unify these capabilities without cross-task interference, we introduce SSR+, which extends Scaffold-Specialize-Reconcile with a Reactivate stage after task-vector merging. The fifth function, self-improvement, is realized by a companion framework that updates external execution state, including task schemas, spatial memory, and failure-recovery cases, from rollouts. Across fifteen benchmarks, ACE-Brain-0.5 improves over ACE-Brain-0 on 14 of 18 spatial perception and grounding benchmarks, achieves competitive navigation and manipulation performance, and provides strong progress estimation in ID and OOD settings. Together, these results mark an early step toward general Physical Agentic AI.

cs.RO↗
arXiv2026

Evaluating Agentic Harness Systems for Autonomous Computational Pathology

Autonomous computational pathology (ACP) converts high-level pathology analysis goals into executable, traceable and clinically bounded workflows. Realizing this capability requires adapting general agentic harness systems to pathology-specific tasks, tools, evidence standards and clinical claim boundaries. We contribute ACP-Bench, a framework that adapts existing harness systems from computational pathology support toward ACP workflow capability. ACP-Bench evaluates 41 pathology workflow tasks, including 24 biomarker, 7 morphology and 10 prognosis tasks spanning 6 body-system groups and 9 endpoint families. The benchmark evaluates 9 models and 3 harness groups (Claude Code, Codex and Open Code), yielding 369 complete trajectories. ACP-Bench evaluates each trajectory across workflow execution, diagnostic performance and clinical-boundary alignment, combining expert-adjudicated process audits, diagnostic assessment and pathologist-validated safety review. Across evaluated systems, workflow initiation, task interpretation and diagnostic reporting were more mature than tool-bound execution, result binding and reflective workflow revision, and formal end-to-end completion remained rare. ACP-Bench provides a reusable standard for auditing whether agentic systems can operationalize pathology workflows before claims of reliable clinical autonomy.

cs.CV↗
arXiv2026

CoT-Core: Accelerating LLM Evaluation via CoT-Aware Coreset Selection

Evaluating Large Language Models (LLMs) incurs prohibitive computational overhead during continuous development processes. While coreset selection accelerates evaluation, existing methods either suffer from a severe ``cold start'' bottleneck requiring massive historical logs (e.g., Item Response Theory) or exhibit a surface lexical bias that misses the underlying reasoning manifold of tasks. We propose CoT-Core, a novel training-free core question selection framework. Recognizing that lexically disparate questions can share equivalent underlying logic, CoT-Core prompts LLMs to unroll zero-shot Chain-of-Thought (CoT) reasoning trajectories. Projecting these paths into a latent space effectively clusters questions by intrinsic logical equivalence rather than superficial text similarity. Extensive experiments on GSM8K, MMLU, MMLU-Pro, and GPQA demonstrate that CoT-Core drastically reduces evaluation costs while maintaining high-fidelity score estimation, and delineate the boundary conditions of reasoning-aware pruning, revealing that its efficacy is intrinsically gated by task complexity.

cs.AI↗
arXiv2026

Production of high-orbital kaon excited states in the $K^{-}p$ reaction

In this work, a systematic investigation of the production of high-orbital-excitation kaons in $K^{-}p$ reactions is carried out within an effective Lagrangian approach. The relevant $t$-channel processes are constructed, and the model is calibrated using a single adjustable parameter determined from existing experimental data. With this parameter, the measured production cross sections for the $K_3^*(1780)$, $K_2(1820)$, $K_2(1770)$ and $K_4^*(2045)$ states are successfully reproduced. Employing the same framework, the production cross sections for other high-orbital kaons are predicted. The results indicate that these states possess sizable cross sections and exhibit characteristically forward-peaked angular distributions, which is a typical feature of $t$-channel exchange, highlighting their great potential for observation in future experiments.

hep-ph↗
arXiv2026

HoloRec: Holistic Encoding and Interleaved Reasoning for Generative Recommendation

Generative recommendation models that formulate the task as sequence generation overcome the objective fragmentation problem of traditional cascade architectures, yet existing approaches still suffer from flat semantic representations lacking hierarchical structure for multi-step reasoning and an externally constructed chain-of-thought (CoT) that requires expensive annotations and remains disconnected from the generation objective. We propose HoloRec, an endogenous chain-of-thought recommendation mechanism that unifies representation, reasoning, and generation by constructing a hierarchical semantic encoding matrix via multi-granularity nested residual quantization optimized by a holistic reconstruction loss. HoloRec supports two inference modes: a non-thinking mode that uses lightweight multi-granularity supervised alignment for fast prediction, and a thinking mode that employs an interleaved reasoning scheme to generate CoT steps on the fly, directly embedding reasoning into the generation process without external data. Experiments on multiple public recommendation datasets demonstrate that HoloRec consistently outperforms baselines, with especially significant gains in sparse scenarios, and the thinking mode achieves better accuracy than the non-thinking mode with only modest inference overhead.

cs.IR↗
arXiv2026

The dynamically generated $h_1$ state by the $K^*\bar{K}^*$ interaction and its $K_1(1270)\bar{K}$ and $b_1(1235)π$ decays

We investigate the dynamically generated $h_1$ state with spin-parity $J^P = 1^+$ and a mass around 1790~MeV, arising from the $K^* \bar{K}^*$ interaction within the chiral unitary approach. The partial decay widths into the $K_1(1270)\bar{K}$ and $b_1(1235)π$ channels are calculated via a triangular loop mechanism. In this mechanism, the $h_1$ state couples to $K^* \bar{K}^*$, and final-state interactions between $K^*$ and $\bar{K}^*$ proceed through pseudoscalar-meson exchange, leading to the final states $\bar{K}$ (or $π$) and $K_1(1270)$ [or $b_1(1235)$]. We also present the invariant mass distributions of a vector meson and a pseudoscalar meson originating from the decays of $K_1(1270)$ or $b_1(1235)$, along with the corresponding decay widths. Our results show that these decay widths are all of the order of a few MeV. We hope that future experiments can test the predictions presented here, thereby helping to identify this $h_1$ state.

hep-ph↗