arXiv subjects

Ang Li

Publications and source records attributed to Ang Li.

At least 55 records · Page 3Linked to original sources

arXiv2026

Spectral-Sphere-Constrained Hyper-Connections

Hyper-Connections (HC) extend residual connections into multiple streams, employing residual matrices for cross-stream mixing to enrich model expressivity. However, unconstrained mixing disrupts the identity mapping property intrinsic to the residual connection, causing unstable training. To address this, Manifold-Constrained Hyper-Connections (mHC) and its variants restrict these matrices to be doubly stochastic via Sinkhorn-Knopp (SK) algorithm or permutation-based parameterizations. We reveal three limitations of this doubly stochastic constraint: (1) identity degeneration, where learned matrices collapse around the identity initialization and diminish cross-stream interactions, (2) a expressivity bottleneck, where the doubly stochastic constraint restricts the freedom of the subdominant spectrum of the residual matrices, preventing the model from selectively preserving or attenuating cross-stream variations, and (3) parameterization inefficiencies, manifesting as unstable SK iterations or the factorial-scaling overhead of permutation-based parameterizations. To overcome these flaws, we propose Spectral-Sphere-Constrained Hyper-Connections ($\mathrm{s}^{2}$HC). By confining residual matrices to a spectral norm sphere, $\mathrm{s}^{2}$HC restores free control over the subdominant spectrum, enabling the model to selectively preserve or attenuate cross-stream variations. This shift eliminates unstable SK iterations and factorial parameterization, enabling expressive, non-degenerate residual matrices while preserving training stability.

cs.LG↗

arXiv2026

Causeway: Restoring Task Accessibility for Instruction Switching in VLA Policies

Vision-language-action (VLA) policies can execute many tasks from standard initial states, yet a new instruction may fail after another task has altered the robot's physical state. We study instruction switching, where a new task is issued during or after the execution of a different one. We observe that a target task that is reliably completed from its standard initial states can become inaccessible from states produced by a preceding task. We call such states task islands. We propose Causeway, a training-free inference-time intervention. Given the current state and a re-entry pose for the target task, Causeway back-propagates through the frozen decoding computation and applies a state-directed write within the action-stream representation. The VLA decodes the return motion itself, without parameter updates, a new action head, or external action generation. Across 71 cross-object pairs, three switch timings, and three VLA architectures on LIBERO-Goal, Causeway raises bare-switch success from 3-26% to 47-65% and increases the rate of reaching the handoff neighborhood by 42-72 percentage points across models. Additional experiments on LIBERO-Object and a real xArm platform show that the recovery extends beyond the main LIBERO-Goal setting, both in simulation and on a robot.

cs.RO↗

arXiv2026

Decoder Model Compatibility Provides Information beyond the Logical Gap under Drifting and Correlated Quantum Noise

Quantum error-correcting decoders provide both corrections and confidence estimates for the logical-error risk of individual measurement records. Such confidence can guide postselection, but it depends on the accuracy of the decoder noise model. When the physical device drifts away from that model, confidence measures such as the logical gap can become miscalibrated. We show that this mismatch can itself provide useful information. On IBM quantum hardware, syndrome statistics drift across consecutive acquisition windows, and the resulting detector histories differ substantially from those predicted by the stationary detector error model used for decoding. We observe a similar hardware-model mismatch in independent Google surface-code memory data. We exploit this effect by assigning additional rejection weight to detector histories that are unlikely under the assumed model. For minimum-weight perfect matching (MWPM), the minimum weight $W$ provides an inexpensive proxy for syndrome surprisal. On held-out IBM repetition-code memories, likelihood-aware postselection reduces the retained logical-error probability (LEP) by $10.6\%$ at $d=7$ and $28.8\%$ at $d=9$, both at $15\%$ rejection. On Google ten-round $d=5$ XZZX surface-code memory data, it reduces retained LEP by $75.4\%$ at $90\%$ rejection. Applied to Google's magic-state cultivation experiment with the Tesseract decoder, it reduces tomography infidelity by $34.7\%$ with $30\%$ additional grafting-stage rejection, or increases accepted yield by $30.4\%$ at a target fidelity of $97.0\%$. Burst-noise simulations show that the advantage persists as code distance and syndrome-extraction volume increase. The method reuses quantities already produced during logical-gap decoding and requires only an additional fitting step.

quant-ph↗

arXiv2026

FTCircuitBench: A Benchmark Suite for Fault-Tolerant Quantum Compilation and Architecture

Realizing large-scale quantum advantage is expected to require quantum error correction (QEC), making the compilation and optimization of logical operations a critical area of research. Logical computation imposes distinct constraints and operational paradigms that differ from those of the Noisy Intermediate-Scale Quantum (NISQ) regime, motivating the continued evolution of compilation tools. Given the complexity of this emerging stack, where factors such as gate decomposition precision and computational models must be co-designed, standardized benchmarks and toolkits are valuable for evaluating progress. To support this need, we introduce FTCircuitBench, which serves as: (1) a benchmark suite of impactful quantum algorithms, featuring pre-compiled instances in both Clifford+T and Pauli Based Computation models; (2) a modular end-to-end pipeline allowing users to compile and decompose algorithms for various fault-tolerant architectures, supporting both prebuilt and custom optimization passes; and (3) a toolkit for evaluating the impact of algorithms and optimization across the full compilation stack, providing detailed numerical analysis at each stage. FTCircuitBench is fully open-sourced and maintained on Github.

quant-ph↗

arXiv2026

Not All Memories Are Equal: Hierarchical Collaborative Memory for Validity-Aware Retrieval in LLM Agents

In team collaboration scenarios, memory is heterogeneous and continually evolving. Team memories capture collective decisions, protocols, and current consensus, while individual memories preserve member-specific observations, execution traces, and intermediate progress. Existing memory-augmented systems typically retrieve from all stored memories as a flat pool, ranking them by semantic relevance, importance, or recency without modeling hierarchical structure or evolving validity. As a result, they often surface semantically relevant but outdated or conflicting memories, especially individual memories that no longer align with current team consensus, instead of prioritizing currently valid memories. This is particularly problematic when collaborative LLM agents answer user questions, since their responses should be grounded in valid memories. We propose HiCoMER, a framework for hierarchical collaborative memory management and validity-aware retrieval in LLM agents. HiCoMER first maintains the validity of team and individual memories and then retrieves memories that remain valid, rather than retrieving directly from all stored memories. It consists of three components: a Hierarchical Memory Conflict Updater, a Validity-Aware Memory Retriever, and a Memory-Grounded Answer Generator. To evaluate HiCoMER, we construct two new datasets for memory-grounded question answering in collaborative settings. Experiments on both datasets show that HiCoMER consistently outperforms strong baselines by reducing outdated retrieval, preserving current team consensus, and improving downstream QA quality.

cs.CL↗

arXiv2026

PTIR-GS: Path-Traced Inverse Rendering with Global Illumination in 3D Gaussian Fields

Ray tracing enables 3D Gaussian fields to serve as a representation for physically based light transport. Faithful inverse rendering requires forward rendering and backward optimization to be defined within a consistent light-transport pipeline. Existing Gaussian inverse-rendering methods typically rely on splatting-derived G-buffers and screen-space optimization. The local affine approximation of perspective projection can introduce inconsistencies with path tracing, while simplified rendering formulations often neglect or approximate indirect illumination and visibility. Therefore, we propose a splatting-free path-traced inverse-rendering framework for 3D Gaussian fields that unifies forward rendering and backward optimization within the same recursive multi-bounce light-transport pipeline. We formulate light transport over overlapping Gaussian primitives directly in path space, enabling Monte Carlo path tracing and pathwise gradient replay over the same recursively sampled transport paths. The framework jointly optimizes materials and environment light under the full rendering equation, with ray-traced visibility and global illumination explicitly evaluated. Extensive experiments demonstrate competitive material estimation and improved path-traced rendering quality, producing more plausible shadows, reflections, and relighting under global illumination.

cs.GR↗

arXiv2026

Assessing Adversarial Robustness of Latent Reasoning Models

Large language models increasingly rely on long chain-of-thought (CoT) trajectories for complex reasoning, but autoregressive generation brings substantial memory and inference costs. Latent reasoning models (LRMs) offer a more efficient alternative by compressing intermediate reasoning into a small number of continuous latent vectors. Despite their efficiency, however, the adversarial robustness of LRMs remains largely underexplored. In this work, we systematically evaluate the robustness of latent reasoning across textual and multimodal settings, covering eight models and six benchmarks. We find that, across our evaluated settings, LRMs are generally less robust than explicit CoT baselines under adversarial perturbations, with particularly severe degradation under white-box attacks. Further analysis reveals distinct failure modes across modalities: textual latent states exhibit brittle dynamics and high sensitivity to specific input patterns, while latent states in multimodal models can remain largely invariant to input perturbations and have limited influence on final predictions. These findings expose robustness limitations of current latent reasoning approaches and highlight the need to jointly consider efficiency and robustness when designing implicit reasoning systems. We have open-sourced our code to facilitate reproduction of our research https://github.com/PKU-ML/latent-reasoning-model-assessment.

cs.CL↗

arXiv2026

Structured Edge-Aware Graph Attention Network for Transmitter-Resolved Pointwise Radio Map Estimation

Transmitter-resolved radio map estimation (RME) from sparse measurements is essential for obtaining source-specific received-power information in wireless networks. This paper proposes SeaGAT, a Structured Edge-Aware Graph Attention Network for transmitter-resolved pointwise RME. For each target--transmitter query, SeaGAT constructs a target-centered graph from a bounded transmitter-specific reference set. Reference representations generate messages and provide measurement context to attention scoring, whereas structured edge representations encode explicit query--reference relations and enter only the scoring procedure. Within a fixed sampled support, we derive a differential identity that decomposes local variation of the evidence aggregate into attention-weighted message changes and score-induced weight redistribution; edge-relation perturbations act only through the latter, whereas a reference-RSS perturbation can affect both pathways. The bounded star graph yields graph-side computation linear in the selected reference count $K$, while support-truncation analysis bounds deviation from the same-parameter full-support aggregate by the product of omitted attention mass and cross-support message diameter. Extensive computer simulations evaluated with ray-tracing dataset show that SeaGAT achieves the lowest mean RMSE compared with baselines; replacing learned attention with uniform weights increases RMSE by $1.58$--$2.14$~dB. It also maintains stable transfer across the tested urban-layout and carrier-frequency shifts.

eess.SP↗

arXiv2026

World Model-Guided Reinforcement Learning via Counterfactual User Engagement Simulation

Reinforcement learning for user-centric agents is limited by the cost, latency, and risk of collecting online feedback, as well as by the lack of counterfactual comparisons under the same user state. In this paper, we propose World Model-Guided Reinforcement Learning via counterfactual user engagement simulation (WMG-RL), a framework in which a frozen user simulator provides reward supervision before real user exposure. Motivated by language world models, we instantiate the simulator as a User Engagement World Model (UEWM), which treats a recommended item as the agent action and the user's heterogeneous feedback as the environment observation. Rather than learning one fixed environment transition, UEWM learns to infer user-specific dynamics from engagement history and apply them to candidate items. In WMG-RL, a downstream policy proposes multiple candidate items for the same history; UEWM predicts the corresponding engagement feedback in parallel; and the simulated feedback is converted into dense rewards for policy optimization. Experiments show that UEWM provides reliable and transferable reward signals across domains, and that WMG-RL enables a compact 1.7B student policy to match or surpass much larger LLMs on downstream recommendation tasks.

cs.IR↗

arXiv2026

Unsteady airfoil aerodynamics in attached flow: From unsteady thin airfoil theory to wind turbine application

Attached-flow unsteady aerodynamics underpin the dynamic stall models used in wind turbine aerodynamic and aeroelastic codes, particularly over the outboard blade region that dominates power production and loading. Here, angle of attack and relative velocity vary while the flow remains predominantly attached. A complete engineering model combines airfoil polar lookup, shed-wake memory, non-circulatory loads, consistent force definitions, and separated-flow dynamics. Although the underlying theory is classical, existing descriptions do not provide a complete and internally consistent implementation route for these attached-flow contributions in wind turbine solvers. This work formulates the attached-flow contributions as lift, drag, and moment coefficients for blade-element momentum (BEM), lifting-line (LL), and actuator-line (AL) methods, for use with 2-D airfoil polars from measurements or CFD. Starting from classical unsteady thin-airfoil theory, circulatory and non-circulatory loads are derived in coefficient form and key modeling choices are clarified. Shed-wake memory is formulated using downwash velocity rather than angle of attack as the aerodynamic state variable. Rotor-level implementation is verified using two cases. A coned straight-blade case provides a cross-method benchmark and quantifies thrust and power errors caused by omitting three required contributions. Lift-direction projection has the largest influence on power, while omitting the mid-chord heaving-acceleration term removes the non-circulatory normal-force cancellation and produces a thrust error. A zero-onset-flow vertical-axis wind turbine (VAWT) case verifies that, in the ideal thin-airfoil limit, all circulatory and non-circulatory contributions cancel, yielding zero total rotor torque.

physics.flu-dyn↗

arXiv2026

Training-Free VLM Personalization via Calibrated Residual Decoding

Vision-language models can be personalized in a training-free manner by directly providing user profiles, preferences, or visual references at inference time, without updating model parameters. However, direct personalized prompting does not guarantee that the model will reliably exploit such evidence. The predictive distribution under the positive user profile often mixes two sources: personalized signals genuinely supported by the current profile, and the model's generic visual or linguistic priors. As a result, from the positive-profile response alone, it is difficult to determine whether a high-confidence answer is supported by the user profile or merely reflects the model's default preference. To address this problem, we propose a training-free calibrated residual decoding framework. Given the same image and question, we construct three evidence conditions: a positive profile , a counterfactual profile , and an empty profile . Our method keeps the prediction under as the anchored base, and explicitly estimates the marginal contribution of personalization from score differences across the three conditions. We further introduce normalized-entropy-based uncertainty calibration, allowing the strength of personalized enhancement to adapt to the reliability of the residual signal. Experiments on MMPB, YoLLaVA, and MyVLM show that the proposed method improves personalized multimodal understanding without fine-tuning, with consistent gains on identity-sensitive visual personalization tasks. Additional analysis shows that entropy calibration stabilizes residual decoding when the contrastive personalization signal is uncertain.

cs.CV↗

arXiv2026

Gate-Level Quantum Simulation of Nonunitary Linear Dynamics with Hybrid Oscillator-Qubit Architecture

We translate the one-mode unitary-dilation framework for nonunitary linear dynamics into a gate-level hybrid oscillator--qubit architecture. An ancillary oscillator encodes the kernel through state preparation and postselection, while a qubit register simulates the discretized system operator. The construction applies to time-independent dynamics $\dot u=-(L+iH)u$, including discretized partial differential equations, and removes the $\mathcal{O}(\log M_a)$ ancilla-qubit overhead of a discrete-variable (DV) $M_a$-term quadrature register. We bound the ideal state's squeezed-Fock coefficient-projection error, which decays superalgebraically in cutoff $N$ for Schwartz-class kernels and at a stretched-exponential rate under stronger joint decay and smoothness assumptions. The finite squeezed-Fock state generically has stellar rank $N-1$, making $N$ a measure of the oracle's non-Gaussian resource. For oscillator dimension $N_{\mathrm{Fock}}$, a $p$th-order product formula reaches error $ε_t$ in $\mathcal{O}(t^{1+1/p}N_{\mathrm{Fock}}^{(p+1)/(2p)}ε_t^{-1/p})$ Trotter steps, up to generator-dependent commutator factors. A perturbation bound separates total scaled-map error from postselection probability. We benchmark Law--Eberly synthesis and assess variational SNAP+$\mathcal D$ state preparation on heat and advection--diffusion equations. At the prescribed DV sizing, the hybrid CV--DV route has smaller fixed-scale error in all ten instances, while the DV route accepts with fewer repetitions in every instance. These results quantify when a continuous qumode can replace a discretized ancilla register.

quant-ph↗

arXiv2026

Discovery of Three Glitches in the previously quiet pulsar PSR J1637$-$4642

We present the discovery and analysis of three rotational glitches in the young pulsar PSR J1637$-$4642. The timing observations span from 19 February 2009 to 6 October 2024 (MJD 54881$-$60589) from the Murriyang radio telescope of the Parkes Observatory. The first and strongest glitch occurred around MJD 58352 with a fractional frequency change of $Δν/ν\sim 2.7 \times 10^{-6}$, while two additional smaller glitches were detected at MJD 59443 and MJD 60445 with fractional changes of $2.2 \times 10^{-9}$ and $2.8 \times 10^{-8}$, respectively. Prior to this, the pulsar had shown no glitch activity since its discovery in the Parkes Multibeam survey. Only the first glitch exhibits detectable exponential recovery, with a decay timescale of $\sim$100 days and a small recovery fraction $\approx 0.015$, accompanied by a permanent increase in the magnitude of the spin-down rate. Modeling the post-glitch evolution of $\dotν$ within the vortex-creep framework using Bayesian inference gives a superfluid moment-of-inertia fraction $\approx 0.0187$, consistent with the inner-crust superfluid. These results reinforce the standard superfluid glitch paradigm and demonstrate that even ``quiet'' pulsars can still host substantial glitch activity.

astro-ph.HE↗

arXiv2026

AMPLIFAI: A Multiphase CT Dataset for Benchmarking Clinical Reasoning in LI-RADS Assessment of Liver Lesions

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related mortality worldwide, with early detection improving survival from <20% to >70%. The standardized Liver Imaging Reporting and Data System (LI-RADS) criteria provide an imaging-based diagnostic framework to evaluate liver lesions for HCC, serving as a foundation for automating HCC detection with artificial intelligence (AI). However, the lack of large, publicly available datasets with high-quality annotations has limited the development and evaluation of AI models for automated LI-RADS assessment. We introduce AMPLIFAI dataset, the first public dataset of 590 multiphase abdominal CT studies annotated with LI-RADS categories, lesion size, and voxel-level segmentations for three major LI-RADS features: arterial phase hyperenhancement, washout, and enhancing capsule. The dataset was curated and harmonized from four public datasets and augmented with expert annotations from five board-certified radiologists and one resident. Following the Datasheets for Datasets format, this paper details the dataset's composition, curation and harmonization process, and annotation workflow to support transparent, reproducible research in medical imaging AI.

cs.CV↗

arXiv2026

A Microphysical Probe of Neutron Star Interiors: Constraining the Equation of State with Glitch Dynamics

Glitches in neutron stars originate from the sudden transfer of angular momentum between superfluid components and the observable crust. By modeling this glitch dynamics--including vortex motion, mutual friction, and angular momentum exchange--one may hope to probe the dense matter equation of state. In this work, we explore, within a highly idealized three-component framework, whether the glitch rise could in principle carry information about microphysical inputs such as entrainment and mutual friction. We compute the glitch rise in response to self-consistently calculated microphysical parameters (pinning and mutual friction) based on unified equations of state, and compare theoretical predictions of the overshoot patterns and timing residuals to the 2016 Vela glitch. Within this specific framework, the models favor crustal superfluid coupling on timescales of order $\sim100$ s, overshoot behavior in the core associated with relatively strong central mutual friction, and rise times consistent with the observed upper limit of 12.6 s. Using a Markov Chain Monte Carlo analysis of the timing residuals, we then map the regions of parameter space that are compatible with the data under our adopted assumptions. Our analysis favors comparatively weak entrainment in the inner crust and an overall core mutual friction that is weaker than that in the inner crust. These exploratory results demonstrate that, under such restrictive assumptions, glitch-rise morphology is sensitive to microphysical inputs and that future high-cadence timing observations, interpreted with more realistic dynamical models, could potentially help constrain the internal dynamics and composition of neutron stars.

astro-ph.HE↗

arXiv2026

Toward a Measurement of the Higgs Boson Mass with Natural-Width Precision at FCC-ee

Higgs boson mass measurements with sub-10 MeV precision enable sub-percent determinations of Higgs boson couplings and prevent the Higgs boson mass from becoming a limiting input to electroweak fits. Probing the electron Yukawa coupling through resonant Higgs boson production requires a precision comparable to the Higgs boson natural width of approximately 4 MeV. Using the leptonic ZH recoil channels, we show that FCC-ee can reach a Higgs boson mass precision of 4 MeV, including statistical and systematic uncertainties, thereby enabling this unique measurement. We identify the detector and accelerator performance required to reach this precision.

hep-ex↗

arXiv2026

RoboSynChallenge: Mastering Real-World Dexterity via Generalizing Synthesized Manipulation Skills

Achieving generalizable robotic manipulation remains a central challenge in embodied intelligence. Despite rapid advances in model architectures and learning algorithms, progress is often limited by the scarcity and narrow diversity of real-world data. The RoboSynChallenge competition introduces a unified benchmark to evaluate and advance the generalizability of manipulation policies across a spectrum of tasks, environments, and difficulty levels. To alleviate the shortage of realistic data, the challenge integrates large-scale synthetic data generation with standardized real-world robotic evaluation. Participants are encouraged to leverage synthesized state-action trials to improve general-purpose policy learning, while final assessments are conducted exclusively on unseen real-world manipulation environments. Baseline implementations, including Transformer-, Diffusion-, Vision-Language-Action, and World-Action-Model-based policies, are provided to ensure reproducibility and comparability. By coupling scalable simulation-based training with rigorous real-world validation, RoboSynChallenge aims to foster the development of broadly capable, data-efficient, and adaptable manipulation systems, thereby paving the way toward truly general robotic intelligence.

cs.RO↗

arXiv2026

General Probabilities of Causation with Causal Knowledge

Probabilities of causation (PoCs) characterize individual causal responses that cannot be directly observed and therefore generally require partial identification. Tian and Pearl first derived theoretically sharp bounds for binary PoCs, including the probability of necessity (PN), the probability of sufficiency (PS), and the probability of necessity and sufficiency (PNS). Mueller et al. subsequently tightened the bounds for binary PNS by incorporating causal information encoded in covariates and mediators. More recently, Li and Pearl, as well as Shu et al., extended PoCs to multivalued settings and derived corresponding theoretical bounds. These developments naturally raise the question of whether additional causal knowledge can further tighten the bounds in multivalued settings. This paper addresses this question by deriving tighter bounds for multivalued PoCs through the incorporation of causal information encoded in covariates and mediators. We illustrate the theoretical results with toy examples, while simulation studies further demonstrate that the proposed bounds are tighter than existing nonbinary bounds.

cs.AI↗