arXiv subjects

Yu Luo

Publications and source records attributed to Yu Luo.

At least 19 recordsLinked to original sources

arXiv2026

Self-Referenced Diagnosis of SPAD Dark-Count-Rate Switching for Quantum Communication Receivers

Single-photon avalanche diodes (SPADs) are essential components of quantum communication receivers. In practical systems, some SPADs exhibit intrinsic step-like changes in dark-count rate (DCR), commonly referred to as random telegraph signal (RTS) noise. Similar RTS-like variations, however, can also arise from stray light, temperature-dependent threshold crossings, cable disturbances, electrical interference, and other readout artifacts. Distinguishing intrinsic RTS from these artifacts is important because they originate from different mechanisms and require different corrective actions. We present a self-referenced diagnostic that primarily uses the low-to-high afterpulse-yield ratio R, together with complementary checks based on DCR-state reproducibility, cross-channel behavior, and event timing. Four commercial SPAD modules were monitored in darkness for more than 920 h. Three exhibited intrinsic DCR switching of 44.59%, 19.33%, and 2.77%, with corresponding R values of $0.9996\pm0.0027$, $1.0050\pm0.0023$, and $1.0027\pm0.0043$, all consistent with unity. Controlled experiments reproduced temperature-related threshold crossings, light leakage, cable-contact artifacts, and artificial electrical injection. These artifacts produced distinct correlation and timing signatures: temperature-related threshold crossings gave intermediate pair-survival responses, cable disturbances and electrical injection generated sub-dead-time events, while light leakage produced simultaneous cross-channel switching and nonreproducible DCR levels. Combining R with orthogonal timing and DCR-based checks provides a practical self-referenced diagnostic framework for distinguishing intrinsic RTS from non-intrinsic artifacts using primarily time-tagged dark-count data.

cs.ET↗

arXiv2026

E0.5: From Reconstructive to Future-Predictive 3D Foundations for Robotic World-Action Modeling

World-action models (WAMs) offer a promising framework for embodied intelligence, yet mainstream approaches remain centered on video foundation models or vision-language models. Even when geometric features extracted by 3D models such as VGGT are incorporated, they are typically passed into a video model or VLM that serves as the primary backbone, leaving 3D geometry as an auxiliary input or prediction target rather than the native foundation for world modeling. We present E0.5, a geometry-native world-action model built directly on 3D foundation models. E0.5 introduces two key innovations. First, it adopts a 3D foundation model as the primary backbone, making 3D geometry the native representation for both world modeling and action prediction. Second, it explicitly predicts future 3D states together with actions, enabling the model to capture geometric world dynamics beyond action-only modeling or future video prediction. Experiments on RoboTwin 2.0 first demonstrate the effectiveness of E0.5 without embodied-data pretraining, achieving success rates of 89.90\% and 50.09\% under clean and randomized settings, respectively. Building on these results, we further pretrain E0.5 on more than 160k robot trajectories from AgiBotWorld-Beta, jointly learning robot actions and future geometry within the pretrained 3D foundation. This embodied pretraining improves mean real-world manipulation success rates by approximately 15 and 7 percentage points on AgileX Piper and Zerith H1, respectively. Together, these results demonstrate the potential of geometry-native foundations to acquire robot interaction knowledge and transfer it to physical manipulation. E0.5 embodies this geometry-native approach as a member of the VWA family of world-action models.

cs.RO↗

arXiv2026

AutoFraming: Synthesizing Dynamic Frames of C Code with LLMs

Large language models (LLMs) have shown considerable promise in synthesizing formal specifications from source code. However, existing approaches primarily focus on functional contracts and loop invariants, while largely neglecting frame conditions, which specify the memory locations that may or may not be modified during execution and constitute an essential part of formal specifications. To address this limitation, we propose an LLM-based approach for synthesizing ACSL frame specifications alongside functional contracts from C code. Our approach analyzes code property graphs (CPGs) to extract frame-relevant information from the input code and leverages this information to guide the LLM in generating accurate frame conditions. The resulting specifications capture both region-separation constraints and data dependencies that govern variable modifications. Experimental results demonstrate the effectiveness of our approach. On a benchmark of 84 ACSL-compliant functions from non-trivial real-world systems, our method successfully synthesizes error-free frame specifications for 92.9% of the functions, representing a 179% relative improvement over state-of-the-art techniques. On a second benchmark consisting of 51 functions, the success rate increases from 80.4% to 96.1%. Furthermore, when integrated as an auxiliary component into existing LLM-based ACSL-synthesis frameworks, our approach improves frame-specification generation performance by up to 61.9%.

cs.AI↗

arXiv2026

An Infinite BART model

Bayesian additive regression trees (BART) is a popular Bayesian ensemble model used in regression and classification analysis. Under this modeling framework, the regression function is approximated by an ensemble of decision trees, interpreted as weak learners. In this work, we propose a generalization of the BART model that has two main features: first, it automatically selects the number of decision trees using the given data; second, the model allows clusters of observations to have different regression functions since each data point can only use a selection of weak learners, instead of all of them. This model generalization is accomplished by including a binary weight matrix in the conditional distribution of the response variable, which activates only a specific subset of decision trees for each observation. Such a matrix is endowed with an Indian Buffet process prior, and sampled within the MCMC sampler, together with the other BART parameters. We then compare the Infinite BART model with the classic one on simulated and real datasets. Specifically, we provide examples illustrating variable importance, partial dependence and causal estimation.

stat.CO↗

arXiv2026

A longitudinal Bayesian framework for estimating causal dose-response relationships

Existing causal methods for time-varying exposure and time-varying confounding focus on estimating the average causal effect of a time-varying binary treatment on an end-of-study outcome, offering limited tools for characterizing marginal causal dose-response relationships under continuous exposures. We propose a scalable, nonparametric Bayesian framework for estimating marginal longitudinal causal dose-response functions with repeated outcome measurements. Our approach targets the average potential outcome at any fixed dose level and accommodates time-varying confounding through the generalized propensity score. The proposed approach embeds a Dirichlet process specification within a generalized estimating equations structure, capturing temporal correlation while making minimal assumptions about the functional form of the continuous exposure. We apply the proposed methods to monthly metro ridership and COVID-19 case data from major international cities, identifying causal relationships and the dose-response patterns between higher ridership and increased case counts.

stat.ME↗

arXiv2026

StepAudio 3 Realtime Technical Report

Realtime spoken interaction demands deep reasoning, prompt responses, and fluid turn-taking. We present StepAudio 3 Realtime, an audio-language foundation model organized around a continuous listen-converse-think-act loop. Deep Perception captures rich acoustic cues to interpret user intent, while Seamless Duplex models synchronized audio streams to handle pauses, backchannels, and interruptions naturally. Crucially, we resolve the tension between deep deliberation and latency via Think-While-Speaking, executing private reasoning in parallel with spoken delivery. In reasoning mode, StepAudio 3 reaches a 73.0 macro average on StepAudioChat. With Think-While-Speaking, it achieves dialogue and reasoning performance comparable to dedicated reasoning models while speaking in real time. Furthermore, an integrated Voice Agent handles asynchronous tool execution without disrupting the dialogue flow. StepAudio 3 Realtime achieves top-tier performance across key dimensions: an exceptional 90.6 on the MMSU benchmark, 98.9 Overall on the Artificial Analysis Full-Duplex Bench, and a 56.0% macro task-success rate on $τ$-Voice.

cs.SD↗

arXiv2026

Environmental Dependence of Satellite Planarity and Orbital-Pole Coherence around Milky Way Analogues in a Hydrodynamical Cosmological Simulation

We examine whether the unusual spatial flattening and orbital-plane coherence of the Milky Way's classical satellites are connected to the Galaxy's location in the cosmic web. We compare the 11 classical satellites with the 11 most massive satellites of 11,922 Milky Way-mass hosts in the TNG300-1 hydrodynamical simulation. We measure flattening with both the simple inertia tensor (SIT), which retains radial information, and the reduced inertia tensor (RIT), which isolates angular anisotropy. Orbital-plane coherence is characterized by the minimum opening angle containing the axes of the eight most coplanar satellite orbits. For the Milky Way, we find $(c/a,b/a)=(0.183,0.558)$ for SIT and $(0.364,0.736)$ for RIT, together with an eight-orbit opening angle of $21.9^\circ$. The corresponding fractions of simulated systems at least as extreme are $4.94\%$, $12.28\%$, and $5.04\%$, respectively. We describe the environment using the velocity-shear tensor smoothed on $2\,h^{-1}\mathrm{Mpc}$ and compare the full sample with Local Group-like pairs, systems whose shear eigenvalues match those reconstructed for the Local Group, and hosts in knots, filaments, sheets, and voids. Among the well-sampled environmental subsets, the shape and opening-angle distributions differ only weakly. Although the cosmic web imprints preferred orientations on host spins and satellite orbital-plane axes, the Milky Way combines orientations that are uncommon in the simulation. We conclude that the present-day cosmic-web environment alone does not substantially increase the occurrence of Milky Way-like satellite configurations. This result does not rule out an origin in anisotropic accretion, since neither the present-day web class nor its shear eigenvalues fully encodes the assembly history of an individual satellite system.

astro-ph.GA↗

arXiv2026

PRISM: Precision Forcing for Generalist Vision-Language-Action Policies

Coupling a pretrained vision-language model (VLM) with a diffusion or flow-matching action expert has become the mainstream vision-language-action (VLA) paradigm for embodied policies, yet they remain unreliable once a task demands precise manipulation, e.g., submillimeter cable insertion. We attribute this weakness to a training objective that averages flow-matching errors across contexts and flow times, allocating little supervision to the few decisive moments determining task success. Along the generative path, inference integrates a locally supervised velocity field, so equally penalized errors at different flow times can affect the executed action unequally. Consequently, precision-task performance deteriorates as physical tolerance tightens by orders of magnitude. We call this supervision imbalance across contexts and flow times Flow-Time Starvation. The remedy is to jointly align supervision allocation and representation learning with context-dependent precision demand, enabling the policy to recognize when greater precision is needed. We instantiate this precision-forcing strategy as Precision-aware Reweighting and Internalization for Skilled Manipulation (PRISM), which scores precision demand offline, strengthens flow supervision where errors are consequential and action signals are visible, and internalizes that demand in the VLM condition representation. Simulation and real-robot experiments show that PRISM preserves or improves general-task performance while raising mean end-to-end success from 38.3% to 86.7% across drive connector insertion, bimanual connector mating, and screw tightening, substantially extending the precision boundary of existing embodied policies without new sensors or observation modalities.

cs.RO↗

arXiv2026

Rethinking Tool Design for Agentic RCA: A Controlled Empirical Study

Large language model (LLM) agents are increasingly explored for root cause analysis (RCA) in microservice systems, yet empirical guidance on how to design and combine their tools remains limited. We conduct a controlled empirical study of tool abstraction and composition across models and microservice environments. We implement 24 structured tools for metric access (L1), evidence analysis (L2), and diagnosis (L3), alongside a Python-based reference setting (L0). Using 375 failure cases from three microservice systems, we evaluate eight configurations with Qwen3.7-Plus and compare four Qwen models on a shared subset of four configurations. Our results show that tool configurations affect root cause localization and failure type identification differently. With Qwen3.7-Plus, L3 achieves 82.8% top-1 localization accuracy compared with 85.4% for L0, while requiring less than half the time per case. Adding tool levels can improve type identification while reducing localization accuracy. Trajectory analysis reveals cases in which agents override correct diagnostic recommendations after consulting additional evidence. Model choice also changes tool benefits: adding L3 to L1+L2 improves diagnosis for three models but hurts Qwen3-8B, which rarely invokes L3. Tool configuration rankings further change across microservice systems. These findings provide an empirical foundation for designing and using RCA tools, guiding tool selection and composition according to the model, diagnostic objective, and target system.

cs.SE↗

arXiv2026

TimelyDAgger: Timing-Aware Expert Querying for VLA Policy Improvement

DAgger improves robot policies by aggregating expert supervision from states visited during policy execution. Robot-gated DAgger automates expert queries, allowing the robot to decide when to request expert takeover. While existing gates emphasize detecting the need for assistance, takeover timing also shapes the content of these demonstrations and their value for policy learning. We propose TimelyDAgger, combining Bridge-PCA monitoring of internal vision-language-action (VLA) features with Feedback-guided Threshold Adaptation based on expert behavior to improve takeover timing. We introduce an evaluation framework linking failure detection, takeover timing, and policy improvement, including Target-Aligned Supervision Ratio (TASR) for assessing supervision quality without retraining. Experiments show that takeover timing affects policy learning, with TimelyDAgger achieving competitive failure detection and higher post-training success in most evaluated settings under matched expert-action budgets. Project website: https://seen-e.github.io/TimelyDagger/.

cs.RO↗

arXiv2026

SVOM/C-GFT: Instrumentation and Performances on the SVOM Alerts

The Chinese Ground Follow-up Telescope (C-GFT) is an optical facility upgraded to support the Space Variable Objects Monitor mission (\textit{SVOM}). Located at the Jilin Observation Station, it is capable of rapidly identifying and monitoring the optical counterparts of Gamma-Ray Bursts (GRBs). The 1.2-m telescope is equipped with two switchable focal-plane instruments: the prime-focus wide-field LATIOS camera and the Cassegrain-focus three-channel CATCH camera. In this paper, we present a system overview, including the observatory, the telescope, the instrumentation, the automated operational framework managed by the Operations Center, and the data processing pipelines. We also report the performance results obtained during over one year of \textit{SVOM}'s post-launch operations. The results demonstrate that the system meets its design specifications and delivers robust observational and operational performance.

astro-ph.IM↗

arXiv2026

Beyond Memory: Harnessing Long-Horizon Agents with Explicit Belief States

Large language model (LLM) agents can now undertake increasingly complex tasks, but the way they organize interaction history into memory does not ensure a coherent understanding of the current world. We introduce PoS, an inference-time framework that constructs and continually maintains explicit belief states as the agent's decision context. Each belief combines an estimate of the current world state with unresolved task requirements, making explicit what the agent still needs to learn and accomplish. To keep this belief reliable and actionable, PoS validates its consistency and monitors task progress to detect Belief Trapping, where the agent continues to act without making meaningful progress toward the goal. Recovery is then tailored to both the trapping pattern and the type of unresolved task requirement. Experiments on four benchmarks spanning execution and diagnosis show that PoS achieves the highest overall performance on every benchmark with all three LLM backbones. Ablations demonstrate the importance of consistency validation and recovery, while context-scaling experiments show resilience to context growth. Together, these results support belief construction and continual maintenance as a foundation for long-horizon context management beyond history retention and compression.

cs.AI↗

arXiv2026

Pappas's conjecture on ramified unitary local models

We prove that the wedge local model for a ramified unitary group of signature $(r,s)$ ($r>s$) at self-dual level is flat when $p\geq 2s-1$, confirming Pappas's conjecture in this case. The proof first reduces the problem to determining the defining equations of a nilpotent orbit closure of self-adjoint matrices, and then to computing the global sections of the corresponding Kempf resolution. The computation is carried out by reducing to the type $A$ case using the Koszul complex.

math.AG↗

arXiv2026

Beyond State-as-Action: Exploiting Command-State Discrepancy for Robot Imitation Learning

Constructing action targets from measured robot motion is an established approach in imitation learning. Under interaction constraints, however, command-state discrepancy may reflect control demands that motion alone does not capture. We investigate when this information matters and how to exploit it. Across three real-robot tasks, task and phase analyses reveal larger supervision gaps under constrained interaction, while selective command retention provides evidence of locally useful command information. Building on these findings, we propose Command-State Discrepancy Weighting (CSDW), which accounts for robot response times and combines subsequent progress, persistent unmet demand, and demand changes into continuous weights for command supervision. The method requires no task-phase annotations or changes to policy architecture or inference. CSDW improves over uniform command supervision on constrained tasks, while methods perform similarly in the less constrained task. Project page: https://seen-e.github.io/CSDW/.

cs.RO↗

arXiv2026

StepAudio 3 Gen Technical Report

We introduce StepAudio 3 Gen, a general-purpose audio generation model that supports zero-shot text-to-speech (TTS), voice design, vocal generation, sound effects, music, vibe speech, and mixtures of multiple audio types within a unified framework. At its core, StepAudio 3 Gen is a discrete autoregressive generator that models audio directly over residual vector quantization (RVQ) tokens, departing from the diffusion Transformer-based continuous generation paradigm prevalent in recent general audio models. Its StepAudio Tokenizer represents general audio at 12.5 Hz in a shared $16 \times 2048$ residual code space, jointly quantizing semantic and waveform-level acoustic features so that each code layer preserves both types of information. For generation, the backbone predicts the first codebook along the time axis using autoregressive modeling, while a lightweight causal Transformer completes the remaining fifteen codebooks along the codebook axis. Our study further identifies three key design principles: (1) interference-aware progressive pretraining for acquiring audio capabilities while preserving the textual abilities of the large language model, (2) RVQ Adaptor for effectively incorporating multi-codebook acoustic representations, and (3) discrete autoregressive modeling over a shared representation across general audio domains. With progressive pretraining, multi-task instruction training, and supervised fine-tuning, StepAudio 3 Gen achieves state-of-the-art performance on both TTS and voice design, while retaining strong generation capabilities across speech, vocals, sound effects, and music. Audio samples are available at https://stepaudiollm.github.io/step-audio-3-gen/.

cs.SD↗

arXiv2026

Scalable PII Discovery in Mobile App Databases via Hypothesis-Driven Search

Discovering personally identifiable information (PII) in mobile forensic databases is difficult because the relevant table-column regions are unknown, distributed across heterogeneous SQLite schemas, and may contain values embedded in free-text or semi-structured fields. We present a hypothesis-driven framework that treats PII localization as bounded, adaptive search under uncertainty. An agent ranks candidate table-column regions, probes sampled values, and maintains a memory of prior evidence, confidence scores, and decisions to refine subsequent hypotheses. The framework separates lightweight PII exploration from targeted extraction, normalization, and deduplication over validated regions, thereby limiting exhaustive inspection to regions supported by sampled evidence. We evaluate the framework on 25 SQLite databases from 10 Android and iOS applications in the Cellebrite CTF corpus, targeting email addresses, phone numbers, domain names, person names, and postal addresses. Against a corpus-level distinct ground-truth set of 3,751 entities, Gemini 2.5 Pro achieves 94.5% F1 while reducing the effective extraction search space by 79.9% on average. Results across 12 model backends show strong performance among several frontier models, but substantial sensitivity to model capability.

cs.CR↗

arXiv2026

The Steep-spectrum Radio-loud AGN Luminosity Function and Its Implications for Black Hole Growth and Star Formation

We study the cosmic evolution of radio-loud active galactic nuclei (AGNs) using a beaming-minimized sample of 4{,}555 steep-spectrum sources over $0<z\lesssim4$, compiled from the XXL survey, VLA-COSMOS, and other wide-field data sets. We model the rest-frame 1.4 GHz radio luminosity function (RLF) with a luminosity-and-density evolution (LADE; DE+LE) framework coupled to a flexible local LF family. Among the tested parameterizations, Model~C is statistically preferred and provides a globally consistent description of the binned RLFs while remaining compatible with local RLF measurements and Euclidean-normalized source counts. In the fiducial solution, the LE term rises toward cosmic noon ($z\sim2$--3) and then flattens or mildly declines, whereas the DE term decreases monotonically with redshift. This combined evolution naturally reproduces the observed luminosity-dependent turnover redshift $z_{\rm peak}(L)$ (often termed ``cosmic downsizing'') without imposing \emph{a priori} distinct evolutionary laws for low- and high-power sources. We further show that the same LADE functional family calibrated for star-forming galaxies also describes radio-loud AGNs when fitted independently, enabling a unified two-component (SFG+AGN) model consistent with both the local RLF and source-count statistics. Finally, converting the AGN RLF to a kinetic luminosity function yields a radio-mode black hole accretion rate density (BHAD) whose redshift dependence closely tracks the radio-based cosmic star formation rate density (after a conventional rescaling), with both histories peaking near $z\sim2$.

astro-ph.GA↗

arXiv2026

Recurrent Residual Quantization: A Progressive Multi-Precision Representation for LLMs

Serving large language models (LLMs) under diverse deployment constraints requires flexible trade-offs between accuracy, memory footprint, and throughput. However, conventional quantization methods typically require a separate checkpoint for each target bit-width. We introduce Recurrent Residual Quantization (RRQ), a post-training quantization (PTQ) framework that represents weights as a low-bit quantized base together with a sequence of quantized residual corrections, enabling multiple effective precisions from a single checkpoint. Starting from a 2-bit model obtained via post-training quantization (PTQ) or round-to-nearest (RTN), RRQ progressively adds lightweight 2-bit residuals generated via RTN to construct 4-, 6-, and 8-bit representations. The method is calibration-free and avoids joint multi-bit optimization. In our Qwen3-8B setup, the full all-RTN 2-/4-/6-/8-bit package is constructed in 1,293 seconds, 3.3 times faster than the measured MatGPTQ construction. Experiments on six recent LLMs show competitive accuracy at 6 and 8 bits, with model-dependent behavior at 4 bits. The code will be made publicly available upon publication.

cs.LG↗