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Xiang Liu

Publications and source records attributed to Xiang Liu.

At least 19 recordsLinked to original sources
arXiv2026

PIC: Revisiting INR for Image Coding with Fast Encoding and Sub-Millisecond Decoding

Implicit neural representation (INR) has achieved remarkable progress in novel view synthesis and image/video coding in recent years.Compared to conventional end-to-end image codecs, INR-based compressors demonstrate significant advantages in decoding complexity. However, their practical application has been hindered by the inferior encoding speed and underutilized decoding efficiency.In this work, we propose a feedforward INR image coding architecture, Practical INR Image Codec (PIC), that computes all the necessary information for INR network in a single forward pass, achieving an encoding speed of 20 FPS. Additionally, we implement a highly optimized decoder that reaches 2000 FPS decoding speed, significantly surpassing JPEG's performance at comparable rate-distortion (RD) performance. To the best of our knowledge, this work presents the first learning-based image codec that simultaneously outperforms or is comparable with JPEG in both RD performance and decoding speed while maintaining practical encoding speed. Code is available at https://github.com/actcwlf/PIC.

cs.CV↗
arXiv2026

GeoCo-SAVi: Geometry-Consistent Slot Attention for Explicitly Editable Object Representations

Object-centric video models represent scenes with slots, yet exposed geometry can vary in meaning with appearance. In Invariant Slot Attention (ISA), explicit position and scale can disagree with the decoded center and extent; edits can yield unexpected motion or resizing, and replacing appearance can shift geometry. GeoCo-SAVi promotes geometric authority and semantic alignment. Its spatially equivariant, object-wise decoder makes position and scale effective commands: changing them moves or resizes the rendered support. Factual position alignment ties position to the decoded center, and normalized attention overlap discourages duplicate allocation. Appearance transplantation aligns geometry semantics across objects, so recipient geometry governs layout while donor appearance supplies shape. A temporal initializer propagates calibrated slots across frames. On Obj3D, GeoCo-SAVi matches ISA reconstruction, reduces p-centroid error by over 80%, and cuts appearance-induced size variation by over 50% while producing the expected translation and scale responses. On 250 MOVi-C videos, it also improves reconstruction, instance grouping, and fixed-identity geometry control over two same-protocol references. GeoCo-SAVi transforms explicit geometry into compositional control, making both position and scale more readable and editable.

cs.CV↗
arXiv2026

Investigating three-body resonances in $\alpha\alpha\Omega/\Omega_{ccc}$ clusters within the $^{9}_{\Omega/\Omega_{ccc}}{\mathrm{Be}}$ nucleus

We investigate the bound and resonant states of the $\alpha+\alpha+\Omega$ and $\alpha+\alpha+\Omega_{ccc}$ three-body systems, corresponding to $^{9}_{\Omega}{\mathrm{Be}}$ and $^{9}_{\Omega_{ccc}}{\mathrm{Be}}$, within the Gaussian expansion method combined with the complex scaling method. The $\alpha\Omega$ and $\alpha\Omega_{ccc}$ interactions are constructed by folding the $N\Omega$ and $N\Omega_{ccc}$ potentials obtained from lattice QCD calculations by the HAL QCD Collaboration with the nucleon density distribution of the $\alpha$ particle. The uncertainty associated with the $\alpha$-particle matter radius is also examined. For the $\Omega$ sector, the strong $N\Omega$ attraction generates deeply bound $0_1^+$, $2_1^+$, and $4_1^+$ states in $^{9}_{\Omega}{\mathrm{Be}}$, accompanied by a pronounced contraction of the $\alpha\alpha$ core, demonstrating a strong gluelike effect of the $\Omega$ baryon. An unconventional inversion between the $0_1^+$ and $4_1^+$ levels is also predicted. In contrast, the $\Omega_{ccc}$ baryon produces considerably weaker attraction: the $0_1^+$ state of $^{9}_{\Omega_{ccc}}{\mathrm{Be}}$ is weakly bound, whereas the $2_1^+$ and $4_1^+$ states remain resonances. Their resonance energies and widths exhibit a clear dependence on the strength of the $\alpha\Omega_{ccc}$ interaction. These results reveal qualitatively different gluelike behaviors of the $\Omega$ and $\Omega_{ccc}$ baryons and provide predictions for the spectroscopy of exotic multistrange and triply charmed hypernuclei.

hep-ph↗
arXiv2026

Beyond Task-Only Matching: Personalized Skill Routing with Counterfactual Evaluation

The rapid expansion of reusable skill repositories makes skill routing a critical capability for large language model (LLM) agents. Existing methods treat routing as task-only semantic matching. However, when users with incompatible constraints issue an identical request, this assumption conflates task relevance with skill suitability: a task-only router can select a semantically plausible skill that is unsuitable for the requesting user. To expose this failure mode, we formulate \textit{personalized skill routing} as profile-conditioned retrieval, in which relevance depends jointly on the task and the user profile. We first introduce a profile-counterfactual benchmark, in which the task is held fixed while changes in the user profile induce changes in the reference skill. We further construct paired counterfactual supervision and propose SkillFeed, a progressive retrieve-and-rerank framework that first establishes task--skill alignment and then learns profile-conditioned discrimination. By retrieving body-level evidence and reranking semantically similar but profile-conflicting candidates, SkillFeed identifies skills that satisfy both task requirements and user constraints. On SkillFeed-Bench, SkillFeed attains 75.1\% top-1 retrieval accuracy, a 23.1-point improvement over the corresponding pretrained routing baseline. Adding profile conditioning yields a 35.1-point gain on queries where user profile changes the reference skill. This contrast shows that user profiles are most consequential precisely when they change skill suitability. Our website is publicly available at http://www.aiskillfeed.com .

cs.AI↗
arXiv2026

ConfAL-WM: Confidence-Guided Active Learning for Action-Conditioned World Models

Action-conditioned world models have become an important foundation for embodied prediction, planning, and synthetic data generation, but their errors under new task and scene distributions are often concentrated in localized spatiotemporal regions such as robot arms, manipulated objects, contact areas, and occluded objects. This paper presents ConfAL-WM, a confidence-guided active learning framework for post-training embodied world models. Built upon EVAC, we attach a lightweight confidence probe to UNet decoder features and predict dense confidence maps in the latent space. These maps are aggregated into task-, frame-, and patch-level scores, enabling both efficient data selection and localized training enhancement. Our pipeline first retrains the confidence probe and warms up EVAC with a small subset of target-domain data, then performs task-level prescreening to allocate sampling budgets, and finally applies selected-data retraining with optional frame or patch weighted data enhancement. Experiments on RoboTwin2.0 show that confidence-guided selection improves post-training efficiency, while dense frame and patch weighting further enhances prediction quality and embodied trajectory consistency compared with scalar reward, progress, and judge-based scoring baselines. A quick visual overview of this work is available at https://ConfAL-WM.github.io.

cs.RO↗
arXiv2026

MOSH-WM: Mask-Grounded Soft-Hamiltonian Dynamics for Object-Centric World Models

Object-centric world models forecast future videos by evolving a set of entity slots, but the variables receiving dynamics supervision are often unconstrained visual features. We introduce \method{}, a mask-grounded soft-Hamiltonian world model that makes its position-like state explicitly depend on slot-owned image support. A frozen video-slot encoder produces slots and masks; spatial moments of mask-owned support form a canonical state $Q$, temporal differences form $P$, and a learned energy supplies a soft directional bias to a bounded learned increment. Decoder-relevant appearance and identity are stored separately in a causal visual context. A gated composer and bounded residual then combine this context with the propagated phase state to reconstruct decoder-compatible slots. On OBJ3D, given six observed frames and evaluated over the following 30 frames, \method{} reduces LPIPS by 25.0\% and spatial MSE by 33.7\% relative to the strongest object-centric baseline. On CLEVRER, given six observed frames and evaluated over the following ten frames, the corresponding reductions are 14.5\% and 18.7\%. Horizon-resolved visual and object-state measurements show that the complete model accumulates error more slowly throughout the 30-frame closed-loop rollout. Project page:https://github.com/moshwm-anon/-moshwm-anon.github.io.

cs.LG↗
arXiv2026

LongWoF-Bench: Evaluating EvoMap Genes for Verifiable Long-Workflow Tasks

Large language models are increasingly expected to execute complex workflows whose success depends on maintaining interdependent constraints and producing artifacts that satisfy strict end-to-end verification. Yet successful execution experience is typically lost after a single run, forcing subsequent models to rediscover strategies and failure modes from scratch. We study whether such experience can instead be externalized and reused through EvoMap, where verifier-confirmed execution trajectories are consolidated into structured Gene. To evaluate this setting, we introduce the Long-Workflow Benchmark (LongWoF-Bench), comprising 778 machine-verifiable tasks across code generation, agent-environment synthesis, mathematical reasoning, and rule following. On the 252 tasks with verifier-confirmed Opus trajectories, evolved EvoMap Gene outperform Skill across all seven evaluated models by 8.7-15.5 percentage points, with the gains extending to consumer models from different model families. In contrast, reference-distilled Gene do not exhibit the same advantage, indicating that compact representation alone is insufficient and that Gene utility is closely associated with verified experience provenance. For Claude Opus, Gene reuse also completes 39 more tasks than Skill while reducing solve-time token consumption by 9.9%. Together, these results show that verified execution experience can be retained and shared as a reusable external resource, enabling models to improve long-workflow completion without repeatedly paying the full cost of experience discovery.

cs.CL↗
arXiv2026

Bound-state spectra of $\chi_{cJ}$ in finite nuclei and the universal pattern of mass levels

In this work, we investigate possible $\chi_{cJ}$--nuclear bound states with $J=0,1,2$ using in-medium mass shifts generated by virtual $D^{(*)}\bar{D}^{(*)}$ loops within an unquenched framework. The resulting $\chi_{cJ}$--nucleus potentials are constructed in the local density approximation, and the bound state spectra are calculated for $^{12}{\rm C}$, $^{16}{\rm O}$, $^{40}{\rm Ca}$, $^{90}{\rm Zr}$, $^{197}{\rm Au}$, and $^{208}{\rm Pb}$. Bound states are obtained for all systems considered. The $\chi_{c0}(1P)$ and $\chi_{c1}(1P)$ spectra are nearly degenerate, whereas the larger in-medium mass shift of $\chi_{c2}(1P)$ leads to deeper binding. Although the absolute bound state energies depend appreciably on the cutoff parameter, the energy differences relative to the $1s$ level are considerably less sensitive to it and exhibit a regular pattern that decreases approximately as $A^{-2/3}$ with increasing nuclear mass number. A cosh-type potential with a common nuclear geometry provides a compact description of these spectra. The predicted bound-state structures and level-spacing systematics could be investigated in future high-statistics near-threshold photoproduction experiments at the upgraded JLab facility.

hep-ph↗
arXiv2026

AutoResearch: Insight In, Hallucination Out

Autonomous research systems are increasingly capable of executing long research workflows, yet automation alone does not ensure that the resulting process remains scientifically grounded. We introduce AutoResearch, a two-stage system that connects Idea Generation with Idea Execution to address both how research ideas are formed and how they are reliably established through experimentation. In Idea Generation, AutoResearch continuously integrates emerging research signals with accumulated domain knowledge, identifies transferable mechanistic insights, and uses multi-model generation and cross-review to produce grounded, testable research plans. In Idea Execution, coordinated agents decompose these plans into experiments, iteratively implement and diagnose them, and employ independent evidence-based review before accepting research conclusions. Across representative settings in cross-modal retrieval, systems optimization, and benchmark-driven machine learning, AutoResearch turns generated ideas into measurable progress, detects and corrects unreliable experimental results, and makes evidence-conditioned decisions to continue, revise, or terminate research directions. For example, on RSICD benchmark, an AutoResearch-generated idea improves mean Recall from 32.84 to 34.69, while recording only 5 audit-confirmed issue events compared with 11-27 for other autonomous research systems. These results demonstrate a research process in which meaningful insight is grounded before experimentation and conclusions are grounded before acceptance: Insight In, Hallucination Out.

cs.AI↗
arXiv2026

Truncation of the Radial Ladder in Heavy Quarkonia

A fundamental open question in hadron spectroscopy is whether the radial excitation ladder of quarkonia truncates at a finite level---a possibility that would challenge the conventional quark-antiquark bound-state picture and offer decisive clues to the nonperturbative strong interaction. Taking advantage of the newly observed high-mass hadronic states, we address this issue by solving a screened Godfrey--Isgur Hamiltonian for charmonium and bottomonium using the Gaussian expansion method. The calculated spectra saturate at $4.74\,\mathrm{GeV}$ ($c\bar{c}$) and $11.67\,\mathrm{GeV}$ ($b\bar{b}$), while root-mean-square radii grow to $\sim10\,\mathrm{fm}$---an order of magnitude above the confinement scale---where adjacent mass gaps drop below $10\,\mathrm{MeV}$. Combining a threshold-based mass-gap criterion, defined by the onset of mass--radius decoupling, with additional diagnostics, we locate the operational upper radial limits at $n\approx8$--$10$ for charmonium and $n\approx12$--$13$ for bottomonium. Beyond these limits, the conventional $q\bar{q}$ description ceases to apply. This work provides the first quantitative determination of these upper limits, and the proposed criterion is directly testable with forthcoming high-statistics data from BESIII, Belle II, and LHCb.

hep-ph↗
arXiv2026

Exploring charmonia $\eta_c(6S,7S)$ in the $\Lambda_c^+\bar{\Lambda}_c^-$ invariant mass spectrum of $B^0 \to K_S^0\Lambda_c^+\bar{\Lambda}_c^-$

We analyze the $B^0 \to K_S^0 \Lambda_c^+ \bar{\Lambda}_c^-$ decay recently observed by the LHCb Collaboration. The $\Lambda_c^+\bar{\Lambda}_c^-$ invariant-mass spectrum exhibits an accumulation near 4.63~GeV, which we investigate together with the accompanying $\Lambda_c^+ K_S^0$ spectrum constrained by the known $\Xi_c(2923)$ and $\Xi_c(2939)$ states. Our amplitude model includes a nonresonant term, the two established $\Xi_c$ contributions, and high-lying charmonium states in the $\Lambda_c^+\bar{\Lambda}_c^-$ channel. Guided by screened Godfrey--Isgur and quark-pair-creation calculations, we test the 4.63~GeV enhancement with a fixed $\eta_c(6S)$ amplitude at $(4629.00, 24.20)$~MeV, {which improves the description of the 4.63~GeV region}, and examine a fixed $\eta_c(7S)$ contribution at $(4718.84, 22.84)$~MeV, which improves the higher-mass region of the spectrum. We also evaluate the $\eta_c(nS) \to \Lambda_c^+ \bar{\Lambda}_c^-$ baryonic decays via a hadron-loop mechanism and estimate the $B^0 \to K_S^0 \eta_c(nS)$ production in naive factorization. Within the narrow width approximation, the fit yields a $\mathcal B(B^0\to K_S^0\eta_c(6S)) \mathcal B(\eta_c(6S)\to\Lambda_c^+\bar{\Lambda}_c^-)$ of about $6.2 \times 10^{-6}$, while the factorization and hadron-loop calculations give rates roughly one to three orders of magnitude lower than the above fitted ratio. These results highlight the need for more precise data and a full amplitude analysis to determine the line shapes and clarify the nature of the higher-mass contribution.

hep-ph↗
arXiv2026

Macaron-V1: Towards Open Continual Learning with Self-Improvement and Mixture-of-LoRA

Macaron-V1 is an open agent-model family for experiential intelligence: learning from experience in real environments and continuing to learn after deployment. It is organized around two system goals. Adaptation is pursued through recursive improvement of versioned model-harness pairs, where experience from one configuration is evaluated under an external contract and used to construct its successor. Collaboration is pursued via the Mixture-of-LoRA (MoL) architecture that freezes a base model, composes specialist LoRA adapters, and selects one LoRA per user turn. The flagship Macaron-V1-Venti (748B) combines a 744B GLM-5.2 base with four LoRAs for chat, agent, coding, and GenUI; the Qwen3.6-35B-based Macaron-V1-Tall (50B) uses the same design for local deployment. This report presents Macaron-V1 as a co-designed system spanning architecture, algorithms, and infrastructure. The MoL architecture supports continual learning through extensible LoRA specialists. The algorithm combines Model-Harness Co-design and recursive self-improvement loop, including the UI4A component-native GenUI harness, a stateful action substrate, versioned Harness Context Protocol contract, and the agentic RL framework MindForge. The supporting infrastructure includes the post-training platform MinT, the long-context RL method LongStraw, and stability techniques for sparse MoE and DSA base models. We evaluate Macaron-V1 on Personal Intelligence, GenUI, and general capability benchmarks against frontier baselines. Our results validate the current system, while compounding gains from continual learning and collective intelligence remain open questions.

cs.LG↗
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

Quantum Arago-Fresnel interference of displaced spin states of photons

The four laws by Arago and Fresnel distinguish the coplanarity of two light beams to determine their capacity of interference, laying the historic milestone for conceptualizing the polarization of light. Equipped with modern descriptions of non-classical states, we re-investigate the macroscopic Arago-Fresnel interference producible by photon helicities. To this end, we compute the Stokes parameter of a polarized beam combined from a regular coherent state (displaced from the vacuum) and a displaced single-photon spin state (displaced from either a left- or right-spin state of photon). The spin orientation, together with its relative asymmetry with respect to the polarizing orientation of the displacing coherent state, produces distinguishing parameter dependences and thus distict interference fringes. Conversely, this quantum interferometry establishes a purely optical method to determine the spin of an unknown incident photon.

quant-ph↗
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

The $K(1690)$ signal from COMPASS as a strange hybrid state

A pseudoscalar resonance structure, denoted as the $K(1690)$, was recently discovered by the COMPASS Collaboration in the scattering reaction $K^-+p\to K^-\pi^-\pi^++p$. If the $K(1690)$ is a genuine state, there are three observed pseudoscalar strange mesons, namely the $K(1460)$, $K(1690)$, and $K(1830)$, in the 1.0$-$2.0 GeV region. However, within the conventional quark model, only the $2^1S_0$ and $3^1S_0$ strange meson states are expected in this energy region. Therefore, at least one of these states should be interpreted as an exotic candidate. In this work, we study the spectrum of strange mesons systematically, and find that the $K(1460)$ and $K(1830)$ are good candidates of $2^1S_0$ and $3^1S_0$ strange mesons, respectively. A further investigation of their strong decays also supports this assignment. Furthermore, we note that the production mechanism of the $K(1690)$ is similar to that of the $\pi_1(1600)$, which has been widely regarded as a hybrid meson candidate. We investigate the strong decays of the $K(1690)$ within a constituent gluon model by treating it as a $0^-$ strange hybrid meson and find that the results support the $K(1690)$ as a hybrid state. Finally, we identify several important decay modes of the $K(1690)$ that may be used in future experiments to test whether it contains the $n^1S_0$ ($n=$2 and 3) $s\bar{q}$ component. We also suggest BESIII to search for the $K(1690)$ state through the $J/\psi\to K K(1690) \to KK^{\ast}_0(1430)\pi\to KK\pi\pi$ process.

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

EVOQUANT: Self-Evolving Verifier-Guided Strategy Optimization for Robust Quantitative Trading

Quantitative strategy optimization remains largely manual, requiring domain experts to identify weak signals, tune risk-control rules, and repeatedly validate iterative revisions. Large language models can accelerate this process, but directly relying on them to rewrite trading strategies often introduces hallucinated edits, strategy drift, and backtest overfitting. We propose EVOQUANT, a self-Evolving Verifier-guided framework for strategy Optimization in Quantitative trading. Our method utilizes LLMs to deeply diagnose performance bottlenecks, generates semantically controlled candidate edits, selects the best strategy through a multi-stage verification pipeline, and distills optimization experience into reusable knowledge for continual self-improvement. We evaluate our method using seven representative strategies: four from the A-share market and three from the Crypto market. Experimental results show that our method significantly improves the Sharpe ratio across all tested strategies: the average test Sharpe increases from -0.298 to 0.538, and the best-performing strategy achieves a 199% relative improvement. Ablation studies and stress tests under stricter conditions further validate the effectiveness and robustness of the framework. Overall, this work transforms quantitative strategy optimization from costly manual trial and error into an automated and verifiable iterative paradigm, offering a new path for applying large language models to financial strategy research.

cs.AI↗