arXiv subjects

Yao Tang

Publications and source records attributed to Yao Tang.

At least 19 recordsLinked to original sources

arXiv2026

Revisiting Sub-Neptune Population Synthesis: The Role of Core Luminosity in Boil-off, Photoevaporation, and the Radius Distribution

The observed bimodal radius distribution of close-in small planets provides important constraints on their formation and evolution. Two atmospheric escape mechanisms, photoevaporation and core-powered mass loss, have been widely invoked to explain this distribution. In this work, we revisit the physical assumptions underlying these models and investigate the role of core luminosity in driving outflows and shaping the radius distribution. We develop a population synthesis framework based on the SEAMIST evolution model, which self-consistently couples the two mass-loss mechanisms while accounting for envelope metallicity. We find that envelope metallicity strongly affects the resulting radius distribution, with 50-100$\times$ solar metallicity providing a good match to the observed population. Boil-off generally dominates atmospheric mass loss, while photoevaporation typically removes at most 3 wt\% of the envelope mass. We find that core luminosity contributes only $\sim$10-20\% of the thermal energy available for envelope re-equilibration during boil-off, producing an absolute mass-loss enhancement of only $\sim$0.4wt\%. Its effect is larger during photoevaporation, where the longer mass-loss timescale allows core heating to accumulate. Nevertheless, core luminosity has little impact on the long-term planetary radius distribution. These results suggest that core-powered mass loss is better understood as a modification to atmospheric escape through its lower-boundary conditions rather than as a distinct mass-loss mechanism. In our best-fit model, the radiative atmosphere contributes a significant $\sim$20\% of the total optical radius. Consequently, we highlight the need for improved radiative-atmosphere modeling in sub-Neptune evolution models to better understand the observed population distribution.

astro-ph.EP↗

arXiv2026

RoboReason: Boosting Robotics-Relevant VLM Reasoning Through Scalable Data Generation

Vision-Language Models (VLMs) have demonstrated impressive performance when integrated into robot manipulation pipelines, but real-world evaluation of VLM capabilities on downstream robotics tasks is both difficult and expensive to scale. We propose that spatial and temporal ordering tasks using images from stereo and wrist cameras attached to real-world robots are particularly well-suited for evaluating visual reasoning capabilities, as they assess skills directly applicable to robots. We evaluate VLM reasoning abilities on three questions: ordering shuffled frames conditioned on a language description of the task, ordering objects depthwise based on distance from the camera, and ordering objects from left-to-right in pixel coordinates. The complexity of these tasks can also be modulated by changing the size of the set to sort. Our experiments show that several state-of-the-art vision-language models struggle on ordering tasks. To fix this, we propose a scalable autonomous training data generation pipeline, and show that both supervised finetuning and reinforcement-learning on the generated data can significantly improve VLM reasoning performance both on spatiotemporal ordering tasks as well as downstream robotics and visual reasoning tasks.

cs.RO↗

arXiv2026

CoVA: Composing VAE Representations for Generative Learning

Representation alignment with pretrained visual encoders improves diffusion transformer training but requires auxiliary encoders and extra training overhead. Recent approaches instead exploit representations from the generative model's own variational autoencoder (VAE). However, reconstruction-oriented VAE features are not optimized for the semantic discrimination and spatial structure needed for generative learning. We introduce \textbf{CoVA} (\textbf{Co}mposed \textbf{VA}E Representations), a lightweight framework that composes VAE features online into stage-wise guidance for flow-matching training. A small latent composer is jointly trained with class supervision, masked latent reconstruction, and horizontal-flip consistency, yielding semantically discriminative, spatially structured dense features and sample-dependent class posteriors. CoVA aligns intermediate SiT features with the composed representations early in training, then removes the increasingly detrimental alignment and reuses detached composer posteriors for sample-dependent soft label conditioning. We further uncover a hidden confound in soft label conditioning: averaging class embeddings attenuates their magnitude, coupling semantic interpolation with conditioning strength. This attenuation alone can substantially improve FID while degrading Recall, revealing a non-semantic source of the apparent gain. CoVA corrects this confound by restoring the hard-label magnitude, isolating posterior-dependent directional semantics. CoVA adds only 8.5M training-only auxiliary parameters (1.3\% of SiT-XL/2) and introduces no inference-time overhead. On ImageNet 256$\times$256, CoVA reduces SiT-XL/2 FID from 17.2 to 7.4 (\textbf{57\%}) in 400K steps. With classifier-free guidance, it achieves an FID of \textbf{2.03} in only 400 epochs, versus 2.06 after 1,400 epochs for vanilla SiT-XL/2.

cs.CV↗

arXiv2026

Contributions of the breathing waveform and residual fluctuations to power and higher-order spectra in a Hall-thruster plume

Higher-frequency power in Hall-thruster plume-probe signals can arise from both harmonics of a repeatable breathing waveform and fluctuations about it, complicating spectral interpretation. We decompose 197 paired records from 20 sequentially sampled probe-pair geometries at one operating condition, estimating each phaseconditioned waveform from other records at the same geometry. The residual accounts for 87.1% and 99.7% of measured power in the 0.2-0.5 and 0.5-1 MHz bands, respectively, as medians across geometries. The repeatable waveform, however, reproduces the principal breathing-harmonic bicoherence structure. Residual power remains modulated by breathing phase after waveform removal, exceeding geometry-specific 95thpercentile circular-shift thresholds in both bands at all 20 geometries. Residual bicoherence is substantially weaker than that of the measured signal but exceeds record-specific random-phase thresholds in 195 of 197 records. An exact component expansion further shows that the all-waveform term dominates a specified signed third-order statistic over the fixed 50-80 kHz band near the second breathing harmonic. These results distinguish band-power allocation from harmonic organization within the evaluated frequency ranges. The attribution concerns uncalibrated probe-chain signals and does not identify distinct physical modes or measure energy-transfer rates. The findings motivate assessing repeatable-waveform contributions before interpreting breathing-harmonic bicoherence as evidence of interactions among residual fluctuations.

physics.plasm-ph↗

arXiv2026

CSF: Contextual Safety Filtering for Motion Generators

Text-conditioned motion generators produce trackable whole-body motion, but they have no notion of scene-dependent safety: the same action may target an object or a person. Existing safeguards either inspect the prompt, require labeled motion data, or enforce geometric constraints; therefore, they do not directly account for how scene context changes a motion's meaning. We introduce contextual safety filtering (CSF), a training-free filter that grounds natural-language safety rules in safe and unsafe reference trajectories produced by the generator. For each active rule, safe and unsafe reference trajectories define an affine safety value that a safe reference tracking CBF-QP enforces. Across four pretrained generators with different architectures, CSF activates the intended rules in all explicit and scene-triggered unsafe cases and reduces the danger-event rate by up to 90%, while preserving 88-100% of benign motions. We demonstrate the complete system on a real-world Unitree G1, where it successfully prevents unsafe motions in a variety of scenarios, including interactions with humans and objects.

cs.RO↗

arXiv2026

InteractionBench: A Real-Time Interaction Benchmark for Streaming Video Systems

A video assistant must speak when its instruction warrants a response and stay silent otherwise. We introduce a benchmark that evaluates this decision for the complete system of model, memory, and response controller. InteractionBench covers query responses, event triggers, and ongoing updates in 1,060 interactions over 812 videos, with 69 negative streams and 53 suites that pair counted events with look-alike near misses. It scores content accuracy, timing accuracy, and silence compliance on the video clock. Timely speech costs silence across systems. Polled Qwen3-VL-8B reaches 77.8 timing accuracy but 10.9 silence compliance. A native real-time interaction system reaches 29.2 silence compliance at 66.8 timing accuracy, yet emits on 89.9% of negative streams. No open-weight system clears a third of the near-miss suites. Fewer replies help only when chosen, as random deletion merely trades timing for silence. Offline scores miss these failures and mispredict online behavior. Adding restraint is costly, as the native system's controller adds little by itself and agentic systems add it only at about 30 s per poll.Project page: https://www.enxinsong.com/projects/interactionbench/ Code: https://github.com/Espere-1119-Song/InteractionBench Data: https://huggingface.co/datasets/InteractionBench/InteractionBench

cs.CV↗

arXiv2026

Hydrogen and Helium Dissolution, Outgassing, and Loss in Evolving Sub-Neptune Magma Oceans: Examining Demographic Features and Radius Evolution

Sub-Neptunes' molten interiors are expected to accommodate large quantities of volatiles, potentially altering their radius evolution. Previous studies have examined this effect in isolation with simplified evolution modeling, often assuming idealized interior and atmospheric conditions. To address this limitation, we introduce SEAMIST, a unified evolution model for sub-Neptunes and super-Earths that self-consistently combines, for the first time, interior structure, cooling, rock/iron solidification, boil-off, photoevaporation, H/He dissolution, and atmospheric composition. SEAMIST considers both a partially soluble case, in which hydrogen partitions into the magma ocean with a fraction set by mantle-envelope boundary conditions, and a fully miscible case, in which hydrogen may fully dissolve into the magma ocean at high temperatures. We identify a novel catastrophic boil-off mechanism, triggered by a positive feedback between hydrogen outgassing and mass loss that can operate billions of years after disk dispersal. In partially soluble models, the impact of H/He dissolution on radius evolution is modest. This contrasts with previous expectations, as we find that increasing hydrogen abundance from outgassing enhances mass-loss efficiency, counterbalancing volatile replenishment from the rock/iron interior. Fully miscible hydrogen, in contrast, significantly enhances envelope survival especially around low-mass stars. Overall, at intermediate to low masses, mass-radius curves from partially soluble models match observed distributions. The fully miscible case predicts a pronounced radius peak and excess planets around low stellar masses that appear inconsistent with current observations, although it reproduces the observed radius ``cliff" near 4$R_\oplus$ at higher masses. Our results suggest that high metallicity may explain the cliff, although alternatives cannot be entirely ruled out.

astro-ph.EP↗

arXiv2026

GOLF: Global Observation with Local Focus for Calibration-Aware Stereo Interaction Field Estimation

We present GOLF, the first-place solution to the SHOW3D Interaction Field Estimation Challenge at HANDS@ECCV 2026. Given synchronized egocentric stereo views, the task is to predict a 3D vector from each of 21 hand joints to the closest point on the manipulated object. GOLF combines dense global context, locally sampled hand/object evidence, and common-frame Plücker-ray geometry. We adapt DINOv3 ViT-H+/16 with LoRA and trainable LayerNorm parameters, then jointly decode both interaction fields. Our primary model achieves an official score of 27.61 and a mean ADE of 27.96 mm on the hidden test set. An equal-weight ensemble with a complementary directly fine-tuned variant improves these results to an official score of 27.47 and a mean ADE of 27.82 mm, securing first place.

cs.CV↗

arXiv2026

Inhomogeneous Cloud Coverage and Altitude-Dependent Heat Transport on the Hot-Jupiter NGTS-10 Ab from its Optical-to-Infrared Phase Curve

Hot-Jupiters, gas giant planets with equilibrium temperatures above 1,000 K, host large temperature gradients between their permanent day- and nightsides, resulting in circulation regimes that have no Solar System analogs. Past Kepler and Spitzer measurements show that the phase curves of these objects typically peak westward of the substellar point at optical wavelengths and eastward in the infrared, indicative of an interaction between circulation and cloud coverage. However, few hot-Jupiters have joint reflected light and thermal emission measurements, preventing a definitive statement as to the link between these phenomena. Here, we present the first phase curve of a hot-Jupiter that separates thermal emission and reflected light through JWST observations of NGTS-10 Ab with the NIRSpec PRISM instrument ($λ$=0.5-5.5 $μ$m). Using the spectrally-resolved phase curve, we jointly retrieve the planet's thermal and reflected light maps. We show that the temperature and reflectance distributions are anti-correlated, best explained by clouds evaporating from the eastern substellar region ($φ\approx$-17 to 48$^\circ$) where temperatures are highest. This is confirmed by comparisons of the phase-resolved spectra with three-dimensional circulation models, which show that NGTS-10 Ab's atmosphere hosts inhomogeneous cloud coverage, likely made-up of $μ$m-sized silicate particles, and weak atmospheric drag ($τ_\mathrm{drag}\gt 10^6$ s). Finally, by measuring the spectral variation of the thermal phase curve offsets, we infer a slope of 7.1$\pm$1.9 degrees per pressure dex, indicative of heat transport that becomes more efficient at depth. Future optical and infrared hot-Jupiter phase curve measurements over a wide range of equilibrium temperatures will enable a complete mapping of the interplay between heat transport and cloud formation in highly-irradiated exoplanet atmospheres.

astro-ph.EP↗

arXiv2026

EpaCache: Error-Propagation-Aware Caching for Accelerating Diffusion-Based Visual Generation

Diffusion-based visual generative models deliver strong image and video synthesis quality but incur high inference costs because sequential samplers repeatedly evaluate large networks. Caching-based methods reduce inference latency by reusing intermediate computations across adjacent timesteps. However, existing cache controllers rely primarily on local temporal variation and overlook the trajectory-level consequences of cache reuse. We introduce Error-Propagation-Aware Cache (EpaCache), a training-free caching policy that adaptively allocates the reuse budget on timesteps with lower downstream impact. Experiments on image and video synthesis models demonstrate that EpaCache consistently improves the latency--fidelity trade-off over existing caching methods. On FLUX.1-dev, EpaCache outperforms the prior state-of-the-art caching method in both latency and fidelity, reducing inference time from $11.7$ s to $11.3$ s while improving PSNR from $21.4$ to $22.8$. On HunyuanVideo, EpaCache achieves a $2.63\times$ speedup over uncached inference and improves SSIM from $0.891$ to $0.905$ over the prior state-of-the-art method at matched latency.

cs.AI↗

arXiv2026

An Evolving Cosmic Shoreline and Sandbar Bounding the Rocky Airless Valley

Recent JWST observations challenge the traditional 'cosmic shoreline' from both sides, revealing thick volatile atmospheres on the hottest close-in 'lava worlds,' where irradiation should drive the most extreme escape, and bare rocky surfaces on cooler terrestrial planets around M dwarfs, where atmospheres would be expected to survive. Using a coupled atmosphere-interior evolution model, we show that atmosphere retention is governed not by a single escape boundary but by two: a hot, outgassing-regulated 'cosmic sandbar' and a cooler, escape-regulated 'cosmic shoreline,' separated by an 'airless valley' that may mark a graveyard of stripped sub-Neptune cores. The sandbar arises because long-lived magma oceans, sustained further by tidal heating from secular eccentricity excitation in multi-planet systems, keep most volatiles dissolved and expose only a small atmospheric reservoir to escape, whereas cooler planets solidify, sequestering volatiles in the deep solid mantle while overexposing the rest to loss. This two-regime structure recasts the single cosmic shoreline as two boundaries set by distinct physics: outgassing and escape. We provide time-evolving fits for both boundaries across G, K, and M stellar types as a function of volatile inventory, planetary mass, age, and tidal heating. Lava worlds with thick atmospheres are unlikely around stars cooler than K-type unless sustained by extreme tidal and/or other interior heating. This framework links atmosphere survival from USP lava worlds to habitable zone planets, informing target selection and interpretation for TRAPPIST-1 and JWST DDT characterization.

astro-ph.EP↗

arXiv2026

SPARE: Structural Parameter-Free Affinity Regularization for Flow Matching

Denoising diffusion transformers achieve strong generation quality but converge slowly during training. Regularizing their internal representations has emerged as an effective accelerator, yet existing methods split into two families with complementary costs. Target-based methods strengthen representations by aligning them to external features, which requires an external encoder and a learnable projection head to bridge feature spaces. Target-free methods hold no reference at all, and can only repel the model's own features across samples or layers, discarding whatever structure the data contains. Prior work suggests that spatial structure, rather than global semantics, drives the gains of alignment. We therefore ask whether such structure can serve as a target directly, and whether it exists not only within an image but across images. Our key insight is that the clean data latent already carries this structure in the relations among its tokens, where a relation is the similarity between two tokens, a single scalar comparable across feature spaces without a projection head. We propose Structural Parameter-free Affinity Regularization (SPARE), a regularizer that matches the pairwise affinities of intermediate tokens to those of the clean latents. To exploit this structure fully, SPARE extends the matching to token pairs across images, precisely the pairs that prior target-free methods repel by default, and calibrates both relation types with a single learning objective. On ImageNet $256 \times 256$ with SiT backbones under matched 400K-iteration budgets, SPARE adds no encoder, head, or parameters and only 0.08 GB of training memory, yet attains the lowest FID among parameter-free regularizers in every tested setting, recovers 37 to 54\% of REPA's FID reduction, and improves over REPA when combined with it, reaching FID 1.90 under classifier-free guidance at 1M iterations.

cs.CV↗

arXiv2026

Towards Precision Therapy in Hepatocellular Carcinoma: A Clinical-Reasoning LLM for Risk Stratification and Treatment Guidance

Hepatocellular carcinoma (HCC) is a common malignancy and a leading cause of cancer-related mortality. Current guidelines and staging systems provide coarse categories, but often miss within-stage heterogeneity and the clinical context in electronic medical records (EMRs). We present HCC-STAR (Hepatocellular Carcinoma Staging, Treatment And pRognosis), a clinically aligned large language model that reads routine EMR narratives and jointly outputs risk score-based staging, ranked guideline-consistent treatments with evidence-based rationales, and individualized survival estimates. We curated about 30,000 HCC cases from SEER and expanded them into EMR-style narrative training data using a clinician-validated, prompt-based augmentation workflow. On this corpus, we developed a knowledge-aligned reasoning framework optimized with a step-verifiable composite reward, moving beyond text-level memorization of clinical guidelines. In a multi-center cohort of 6,668 patients from 12 hospitals in China, HCC-STAR achieved state-of-the-art performance in treatment recommendation and risk stratification compared with clinical guidelines and competitive models, including GPT-5 and Gemini-2.5 Pro. Hypothetical overall-survival analysis showed a median survival of 51 months under adherence to HCC-STAR recommendations, compared with 29 and 32 months under BCLC and CNLC. In clinician-centric evaluations, blinded hepatobiliary specialists rated HCC-STAR's reasoning and evidence-based justifications as trustworthy. The model surpassed resident and attending physicians in treatment accuracy and helped physicians make more accurate decisions faster when used as an assistant. These findings support HCC-STAR as a reliable and verifiable decision-support system for risk stratification and precision therapy in HCC.

cs.AI↗

arXiv2026

Generative Site-Specific Beamforming for UPAs via Decoupled Channel Sensing

A cross-fused generative site-specific beamforming (GenSSBF) framework is proposed for low-overhead beam alignment in uniform planar array (UPA) systems. A decoupled channel sensing strategy is developed, where the azimuth and elevation domains of the UPA are probed independently, and the online sweeping overhead is reduced from multiplicative to linear complexity compared to exhaustive two-dimensional codebook sweeping. However, the resulting reference signal received power (RSRP) observations only contain marginal angular power information. The explicit azimuth-elevation coupling of the UPA channel is therefore lost. Beam generation from these separate observations becomes highly ambiguous. To address this issue, a bidirectional cross-attention encoder is designed to extract and fuse the latent dependency between the azimuth and elevation sensing branches. Conditioned on the fused feature, a conditional normalizing flow generator is proposed to generate a compact set of high-fidelity beam candidates. These candidates are further verified through lightweight pilot measurements for final beam selection. A task-oriented training objective is also introduced to encourage the generated candidate set to contain at least one high-gain beam, rather than fitting the full conditional beam distribution. Simulation results based on DeepMIMO scenarios show that the proposed framework consistently outperforms deterministic beam prediction and conventional discrete Fourier transform (DFT) codebook search. Compared with the full 1024-beam two-dimensional DFT search, normalized beamforming gain improvements of 83.6%, 74.6%, and 38.1% are achieved in the I2_28, O1B_28, and Boston5G_28 scenarios, respectively, while the sweeping overhead is reduced by 93.8%.

eess.SP↗

arXiv2026

OneBar: An End-to-End Content-Grounded Generative Query Recommendation Framework for E-Commerce Video Feeds

Short-video platforms now expose clickable search entries beneath the video player, enabling users to easily express content-induced search intent. However, conventional query recommendation systems on short-video platforms suffer from latency constraints and objective misalignment, while recent generative approaches struggle with noisy content-side metadata and preference drift. To address these issues, we propose OneBar, an end-to-end generative framework for real-time query recommendation for E-Commerce video feeds. OneBar features three key innovations: (1) a collaborative-multimodal intent grounding module that fuses multimodal video understanding and behavior-derived collaborative anchors; (2) a Unified End-to-End architecture equipped with a prompt-compression mechanism for efficient online serving; and (3) a progressive preference learning strategy for efficient preference-internalization, which internalizes hierarchical behavior preferences into the generative policy, eliminating the need for a separately trained reward model. Compared with online base, OneBar increases Query Exposure by 16.91\% and Query Click by 18.68\%, while maintaining a slight Query CTR gain of 0.19\%. The additional search traffic further contributes to 20.36\% more guided orders and 21.67\% higher GMV.

cs.IR↗

arXiv2026

Latent Reasoning with Normalizing Flows

Large language models often improve reasoning by generating explicit chain-of-thought (CoT), demonstrating the importance of intermediate computation. However, textual CoT forces this computation through a discrete, serial, and communication-oriented token stream: each reasoning step must be verbalized before the model can proceed, even when the underlying update is semantic, uncertain, or only partially formed. Latent reasoning offers a higher-bandwidth alternative by performing intermediate computation in compact continuous states before committing to text. Yet existing latent-reasoning methods often sacrifice key advantages that make CoT effective in autoregressive language models, including native left-to-right generation, probabilistic sampling, compatibility with KV-cache decoding, and tractable likelihood estimation. We propose NF-CoT, a latent reasoning framework that preserves these advantages by modeling continuous thoughts with normalizing flows. NF-CoT instantiates a TARFlow-style normalizing flow inside the LLM backbone, defining a tractable probability model over compact continuous thoughts distilled from explicit CoT. Continuous-thought positions are generated by an NF head, while text positions are generated by the standard LM head within the same causal stream. This design provides exact likelihoods for latent thoughts, enables probabilistic left-to-right decoding with the original KV cache, and supports direct policy-gradient optimization in the latent reasoning space. On code-generation benchmarks, NF-CoT improves pass rates over explicit-CoT and prior latent-reasoning baselines while substantially reducing intermediate-reasoning cost.

cs.CL↗

arXiv2026

The Velocity Deficit: Initial Energy Injection for Flow Matching

While Flow Matching theoretically guarantees constant-velocity trajectories, we identify a critical breakdown in high-dimensional practice: the Velocity Deficit. We show that the MSE objective systematically underestimates velocity magnitude, causing generated samples to fail to reach the data manifold-a phenomenon we term Integration Lag. To rectify this, we propose Initial Energy Injection, instantiated via two complementary methods: the training-based Magnitude-Aware Flow Matching (MAFM) and the training-free Scale Schedule Corrector (SSC). Both are grounded in our discovery of a crucial asymmetry: velocity contraction causes harmful kinetic stagnation at the trajectory's start, yet acts as a beneficial denoising mechanism at its end. Empirically, SSC yields significant efficiency gains with zero retraining and just one line of code. On ImageNet-1k (256x256), it improves FID by 44.6% (from 13.68 to 7.58) and achieves a 5x speedup, enabling a 50-step generator (FID 7.58) to beat a 250-step baseline (FID 8.65). Furthermore, our methods generalize to Text-to-Image tasks and high-resolution generation, improving FID on MS-COCO by ~22%.

cs.CV↗

arXiv2026

Horizontal transport as a source of disequilibrium chemistry on the nightside of a hot exoplanet

Hot Jupiters have temperature gradients of several hundreds of degrees between their permanent day and nightsides. In equilibrium, the primary carbon reservoir is expected to transition from CO on the dayside to CH4 on the nightside. Theory predicts that the atmospheric circulation, characterised by km/s winds, can advect chemical species from the dayside to the nightside faster than the time needed for the CO-to-CH4 chemical reaction to reach equilibrium. However direct evidence of this process has, so far, remained elusive, partly because it is often degenerate with other processes, such as vertical mixing or non-stellar elemental abundances. Here, we present observational evidence for such day-to-night transport of chemical species by observing both the dayside and the nightside of the hot Jupiter NGTS-10A b with the JWST/NIRSpec instrument. We constrain the presence of H2O and CO with similar abundances on both the dayside and nightside. Our observations are compatible with a solar-composition atmosphere at chemical equilibrium on the dayside, but indicative of disequilibrium chemistry for the nightside as it is significantly depleted in CH4 compared to equilibrium chemistry predictions. We further show that the lack of CH4 on the planet's nightside cannot be attributed to non-solar elemental abundances or to vertical mixing mechanisms and must therefore be due to horizontal chemical quenching. Our study shows the fundamental role atmospheric transport plays in shaping the distribution of chemical species on exoplanet atmospheres.

astro-ph.EP↗