arXiv subjects

Zhen Guo

Publications and source records attributed to Zhen Guo.

At least 19 recordsLinked to original sources

arXiv2026

High Resolution Spectroscopic Follow-up Observation Results for 13 EMP Candidates Selected by Narrow-band Photometry

Extremely metal-poor (EMP; [Fe/H] < -3.0) stars preserve key information about the earliest stages of Galactic chemical evolution. Based on a training set of spectroscopic metallicities, we constructed catalogs of more than 120,000 EMP candidates from large-scale narrow-band photometric surveys. To validate this approach, we obtained high-resolution CFHT/ESPaDOnS spectra for 13 candidates selected from the SkyMapper-based catalog. The follow-up observation confirms that the narrow-band photometric selection is effective in identifying very metal-poor stars and retaining a substantial EMP fraction: four targets are confirmed as EMP stars, and all targets remain very metal-poor with [Fe/H] < -2.4. The photometric metallicities are systematically lower than the spectroscopic values by 0.44 dex, with a relatively small scatter of 0.19 dex, indicating a systematic offset in the photometric metallicity scale that could be reduced through improved calibration. Abundances for more than 20 chemical species are derived, leading to the identification of a new potassium-enhanced r-II star, J2001-1215. By combining chemical abundances with orbital properties, five stars are found to be dynamically consistent with known Galactic substructures, including Gaia--Sausage--Enceladus, Thamnos, and Sequoia. These stars provide high-resolution chemical measurements for the extremely metal-poor regime of these Galactic substructures. These results underscore the role of narrow-band photometry in efficiently selecting EMP candidates for targeted high-resolution spectroscopic follow-up, thereby enabling detailed chemical and dynamical studies of the early Milky Way.

astro-ph.GA↗

arXiv2026

MechAudit-40: White-Box Auditing across 40 LLM Attack Mechanisms

While LLM attacks span prompt optimization, multi-turn context manipulation, retrieval poisoning, and model backdoors, white-box defenses are typically evaluated on isolated attack families. Consequently, whether heterogeneous attacks leave internal representation shifts that generalize to unseen threat mechanisms remains unknown. We present MechAudit-40, a systematic evaluation of 40 attack mechanisms across five open-weight model architectures. Threat-specific success criteria, 100,000 matched clean-attack representation pairs, predefined categories, and grouped holdouts isolate genuine attack-induced displacement from target scale, corpus bias, and data-leakage shortcuts. Across this testbed, attacks induce structured multi-depth trajectories rather than isolated layer spikes. While raw peaks are non-portable across architectures, target-calibrated profiles preserve transferable geometric signatures: under complete mechanism holdout, hidden states alone recover the threat category of unseen attacks with 82.5% accuracy. Guided by this finding, we design MechAudit, a runtime auditor that operates under strict zero-oracle constraints without requiring clean baseline traces or attack metadata. MechAudit detects 81.1% of held-out attack executions at a 0.70% false-positive rate and maintains 78.1% recall when an entire functional category is withheld. In matched comparisons, MechAudit is the only detector that avoids mechanism-level coverage collapse, maintaining over 50% recall across all 40 mechanisms. Internal representations thus support cross-mechanism attack-exposure auditing against calibrated benign references, but decouple from downstream task compromise and parameter integrity.

cs.CR↗

arXiv2026

SCOPE: Subspace Clustering with Online Per-Head Top-K Estimation for Sparse Video Attention

Diffusion Transformers (DiTs) incur quadratic self-attention cost over spatiotemporal tokens. Existing training-free sparse attention methods often construct sparse masks from block-level or cluster-level proxy scores, which can obscure fine-grained differences among keys and miss high contribution keys under aggressive sparsity. Moreover, such proxy scores may yield overly concentrated softmax distributions, causing Top-$p$ to retain too few keys for some query clusters. Although a fixed Top-$k$ minimum alleviates this failure mode, a shared value cannot adapt to variations across heads and inputs. To address both limitations, we propose SCOPE, a training-free sparse attention framework that combines 3D-RoPE-aligned key subspace clustering with online per-head Top-$k$ estimation for efficient video-DiT inference. SCOPE partitions post-RoPE keys into temporal, height, and width subspaces, clusters them independently, and aggregates the corresponding centroid scores through lookup tables to obtain per key proxy scores for each query cluster. Building on existing hybrid Top-$p$/fixed Top-$k$ selection, SCOPE derives a head-specific Top-$k$ value online by averaging the initial retained key counts within each head, weighted by query cluster size, and selects additional keys only for query clusters whose initial retained key counts fall below this value. Sparse attention is then computed over the selected original keys and values. Across six model--task configurations, SCOPE consistently outperforms existing training-free baselines in both fidelity and latency, achieving up to a $1.99\times$ end-to-end speedup on 720p HunyuanVideo with $28.46$ dB PSNR relative to dense attention.

cs.CV↗

arXiv2026

Hot water emission during an outburst in a classical T Tauri star

In this paper, we present observations of an eruptive young star in the Rosette Nebula, identified by the Gaia Science Alerts system using Gaia time series data. We aim to investigate the evolution of the brightness and mass accretion rate of V557 Mon throughout its outburst and subsequent decline. In addition, we trace the evolution of the inner accretion disk during the outburst by monitoring molecular emission features. We compiled multi-band photometric time series from Gaia, ZTF, and several 1 m-class ground-based telescopes and obtained optical and near-infrared spectra at multiple epochs covering the outburst and fading phases. Stellar parameters were derived from quiescent colour/spectra and spectral energy distribution (SED) fitting. We also measured the mass accretion rate and fit models to molecular emission bands. Since late 2024, V557 Mon has undergone a year-long outburst consistent with EXor variability. Based on quiescent photometry, V557 Mon has a spectral type of M1 with an extinction of AV = 1.8+_0.3 mag, consistent with a 0.4-0.5 M_sol star at an age of 2 Myr. Our multi-epoch spectra and u-band photometry indicate a peak accretion rate of 6.3x10^(-7)M_sol/yr during the outburst, roughly 70 times higher than in quiescence. We report the detection of hot water vapour emission bands, together with TiO, VO, and CO emission features. Using ExoMol models, we measured the inner-disk temperature changed from 3000 K to 2000 K during the fading phase of the outburst. We report a recent EXor outburst in a low-mass Class II YSO. Our observations reveal the transient formation of a hot molecular inner disk, traced by variable water vapour emission during the EXor event. A positive correlation is found between the molecular excitation temperature and the overall stellar brightness.

astro-ph.SR↗

arXiv2026

A Bayesian Search for Planet Engulfment Signatures in Solar Analogs

We present a systematic Bayesian search for chemical fingerprints of planet engulfment in 113 solar twins and analogs with high-precision abundance measurements, 45 of which host known or candidate planets or brown-dwarf companions. We constructed a Bayesian framework with three sets of abundance models: random scatter, Galactic chemical evolution, and planet engulfment with bulk Earth or CM chondrite compositions. Through model comparisons, we identified three candidates whose abundance patterns strongly favor planet engulfment over the alternatives, with inferred engulfed masses of about 7.5-33 Earth masses. Our findings correspond to a nominal detection rate of 1-3% for planet-engulfment signatures among solar analogs. This work extends abundance-based engulfment searches beyond the binary-star context and provides a framework for probing star-planet co-evolution with solar analogs, which goes beyond the commonly used abundance-condensation-temperature correlation (Tc slope).

astro-ph.EP↗

arXiv2026

STEAM: Self-Supervised Temporal Ensemble Advantage Modeling for Real-World Robot Learning

Real-world robot learning increasingly relies on heterogeneous data, but demonstrations and rollouts often mix useful progress with stalls, corrections, and suboptimal behavior. Effective policy learning therefore requires frame-level advantages that distinguish reliable local progress from failures and regressions. We propose Self-supervised Temporal Ensemble Advantage Modeling (STEAM), a label-free method that learns such advantages from expert demonstrations. STEAM trains an ensemble of temporal-offset predictors on frame pairs within expert trajectories, using the normalized temporal offset between two frames as a self-supervised signal. Each predictor maps a frame pair to a distribution over temporal offsets, which is converted into a scalar advantage. STEAM then takes the minimum advantage across the ensemble to score mixed-quality rollout data conservatively. Across real-world bimanual towel folding, chip checkout, cola restocking, and single-arm pick-and-place tasks, STEAM identifies stalls, failures, and recoveries. When combined with CFGRL, STEAM further improves policy success rate by 59%, 54.3%, 23% and 16.2% over baselines, respectively.

cs.RO↗

arXiv2026

Spin-Orbit Geometry of AU Mic b and c from Back-to-Back Transits Observed Contemporaneously with Magellan PFS, LCOGT, and CHEOPS

Young planets offer a unique window into the early stages of planetary evolution. AU Mic is one of the nearest (9.8 pc) pre-main sequence stars (~20 Myr), hosting two transiting Neptune-sized planets and a debris disk. Previous studies have shown that the rotation of the central star, the debris disk, and the inner planet b are all aligned, suggesting that the system has not undergone violent evolution. Here we report new Rossiter-McLaughlin (RM) measurements for both AU Mic b and c, which happened to transit back-to-back on Aug 24 and 25, 2024, using the Magellan Planet Finder Spectrograph (PFS), accompanioned with contanporaneous photometry from LCOGT and CHEOPS. We confirm the aligned orbit of AU Mic b ($\lambda_b=1{\deg} \pm 12{\deg}$) and finding two possible solutions for AU Mic c: we slightly favor an aligned solution ($\lambda_c=-10{\deg} \pm 16{\deg}$) but cannot rule out a polar solution ($\lambda_c=87{\deg}\ ^{+36{\deg}}_{-29{\deg}}$). Broader considerations, including dynamical stability and transit possibility, also support the mutually aligned scenario. An unexpected stellar signal during ingress and the poor TTV predictions of AU Mic c prevent a precise constraint on its obliquity, and various attempts using chromatic spectral analyses fail to outperform simple data exclusion in mitigating the stellar contamination. Our observation highlights the importance of understanding stellar activity across multiple timescales and channels when characterizing young, active systems. A robust solution for the AU Mic architecture will require either a better understanding of stellar activity or future observations fortuitously free from strong stellar contamination.

astro-ph.EP↗

arXiv2026

CL-bench Life: Can Language Models Learn from Real-Life Context?

Today's AI assistants such as OpenClaw are designed to handle context effectively, making context learning an increasingly important capability for models. As these systems move beyond professional settings into everyday life, the nature of the contexts they must handle also shifts. Real-life contexts are often messy, fragmented, and deeply tied to personal and social experience, such as multi-party conversations, personal archives, and behavioral traces. Yet it remains unclear whether current frontier language models can reliably learn from such contexts and solve tasks grounded in them. To this end, we introduce CL-bench Life, a fully human-curated benchmark comprising 405 context-task pairs and 5,348 verification rubrics, covering common real-life scenarios. Solving tasks in CL-bench Life requires models to reason over complex, messy real-life contexts, calling for strong real-life context learning abilities that go far beyond those evaluated in existing benchmarks. We evaluate ten frontier LMs and find that real-life context learning remains highly challenging: even the best-performing model achieves only 19.3% task solving rate, while the average performance across models is only 13.8%. Models still struggle to reason over contexts such as messy group chat histories and fragmented behavioral records from everyday life. CL-bench Life provides a crucial testbed for advancing real-life context learning, and progress on it can enable more intelligent and reliable AI assistants in everyday life.

cs.CL↗

arXiv2026

The long-term outburst(s) of GPSV16: from an intermediate to a FUor classification

FU Ori outbursts are thought to play a key role in stellar mass assembly and in the chemistry of protoplanetary disks during the early formation of stars. However, uncertainties remain regarding the universality of these events and the physical mechanism driving the high-amplitude variability. In this work, we present an analysis of optical, near- and mid-IR photometry (ZTF, UKIDSS GPS, NEOWISE) and near-IR spectra (IRTF, Gemini) of the eruptive variable Class I YSO GPSV16. The YSO, associated with the HII region G71.52$-$00.38 ($d=3.61$~kpc), showed two outbursts, one with $\Delta K_{\rm s}=2.2$~mag (2005-2012) and a second starting in 2016 with $\Delta K_{\rm s}=5.6$~mag and accretion luminosity of $\sim$130 L$_{\odot}$. The outbursts displayed distinct spectroscopic characteristics: the first showed emission lines associated with a hot inner disk surface, whereas the second showed absorption lines arising from the cooler upper layers of a viscously heated disk. These features likely arose due to the different accretion rates reached during each outburst. The second outburst showed a two-stage mid-IR rise, requiring $\approx8.4$ years to reach peak brightness. The mid-IR rise also started 8 years before the onset of the optical outburst. The wavelength-dependent light curve points to an instability that is triggered at larger distances within the accretion disk and propagates inward. Assuming a propagation time of 8 years for the accretion wave, we estimate that the second outburst started at a distance of $r\sim0.4$~AU. These results show how long-term, multi-wavelength photometric monitoring can help identify the disk instabilities that trigger eruptions in YSOs.

astro-ph.SR↗

arXiv2026

InnoAds-Composer: Efficient Condition Composition for E-Commerce Poster Generation

E-commerce product poster generation aims to automatically synthesize a single image that effectively conveys product information by presenting a subject, text, and a designed style. Recent diffusion models with fine-grained and efficient controllability have advanced product poster synthesis, yet they typically rely on multi-stage pipelines, and simultaneous control over subject, text, and style remains underexplored. Such naive multi-stage pipelines also show three issues: poor subject fidelity, inaccurate text, and inconsistent style. To address these issues, we propose InnoAds-Composer, a single-stage framework that enables efficient tri-conditional control tokens over subject, glyph, and style. To alleviate the quadratic overhead introduced by naive tri-conditional token concatenation, we perform importance analysis over layers and timesteps and route each condition only to the most responsive positions, thereby shortening the active token sequence. Besides, to improve the accuracy of Chinese text rendering, we design a Text Feature Enhancement Module (TFEM) that integrates features from both glyph images and glyph crops. To support training and evaluation, we also construct a high-quality e-commerce product poster dataset and benchmark, which is the first dataset that jointly contains subject, text, and style conditions. Extensive experiments demonstrate that InnoAds-Composer significantly outperforms existing product poster methods without obviously increasing inference latency.

cs.CV↗

arXiv2026

TraceGuard: Process-Guided Firewall against Reasoning Backdoors in Large Language Models

Large Reasoning Models (LRMs) introduce a reasoning-level attack surface: adversaries can corrupt intermediate inferences while preserving a plausible trace and an apparently benign output. Existing output guardrails cannot reliably identify where such a trace first becomes unsupported. We present TraceGuard, a compact, locally deployable reasoning firewall that treats model-generated reasoning as untrusted input. Its design combines grounded generation of verifiable audit traces, Step-Aware Supervised Fine-Tuning (SSFT) for process-level supervision, and Verifier-Guided Reinforcement Learning (VGRL) for hardening against difficult reasoning traces. TraceGuard audits intermediate steps, localizes the initial Point of Fracture, and grounds its final decision in the complete audit evidence. We evaluate TraceGuard across heterogeneous open-weight architectures, reasoning domains, and reasoning-integrity attack families. A compact Qwen3-4B-Guard substantially outperforms an unaligned 20B model under strict end-to-end detection. Its auditing behavior transfers to attack families excluded from training, resists in-scope black-box probing, and remains robust in an additional white-box stress test. Overall, 210,456 step-level audit decisions support compact, process-aligned verification as an effective, deployable defense boundary for reasoning systems.

cs.CR↗

arXiv2026

Searching for White Dwarf Candidates Formed Through Binary evolution in Star Clusters

White dwarfs (WDs), the evolutionary endpoints of most stars, can form through both single-star and binary channels. While single-star evolutionary models enable reliable WD age estimates, binary evolution introduces interactions that can accelerate WD formation and result in a variety of exotic WDs, which may exhibit strong magnetic fields, rapid rotation, or even serve as potential gravitational wave sources. Such systems offer valuable insights into magnetic field generation, angular momentum evolution, and compact object physics. Star clusters, with their approximately coeval populations, allow precise age determination of member WDs. If a WD's total age derived from single-star evolution exceeds that of its host cluster, it likely indicates a binary origin. In this study, we use \textit{Gaia} 5D astrometry to identify 439 WD candidates in 117 open clusters, with 244 likely formed via binary evolution. We discuss the possibility of dynamical ejection for WDs meeting only 2D (proper motion space) membership criteria. Spectroscopic observations further reveal a subset with strong magnetic fields and rapid rotation, supporting their binary evolutionary origin.

astro-ph.SR↗

arXiv2026

Gaia24ccy: An outburst followed the footsteps of its predecessor

Accretion-driven outbursts in young stellar objects remain poorly understood, largely limited by a statistically small sample of closely followed-up events. This underscores the importance of a thorough exploration of each outbursting object. We studied a peculiar outbursting system, Gaia24ccy, which exhibited two $\Delta g \sim$ 3.8 mag outbursts in 2019 and 2024. The system consists of two unresolved, nearly identical, and rapidly rotating young stars: Gaia24ccy A (1.1419 days) and Gaia24ccy B (1.7898 days). Periodogram analyses just before the onset of the outbursts suggest Gaia24ccy B to be the outbursting component. Unlike any previously known EXor sources, the two outburst profiles show a very similar evolution: both rose at the same rate for the first 15 days, followed by many 'sub-bursts' on the timescale of 10-20 days. The 2019 outburst lasted 145-255 days, while the 2024 outburst persisted for 367 days. We infer the unstable region to lie at $r_{\rm trigger} \simeq 0.019-0.047$ au ($\sim5-12.3 R_\star$). The accreted mass per event $M_{\rm acc}\sim10^{-5} M_\odot$ can be provided by a compact inner-disk reservoir. The photometric rise/decay timescales and the mid-infrared color evolution favor a thermal-viscous trigger in a hot inner disk, while the appearance of rich emission-line spectra indicates concurrent magnetospheric compression - together best described by a hybrid picture. Finally, we explain the reddening of the mid-infrared color observed during the outburst as a consequence of the competing emission from the viscous disk and the photosphere.

astro-ph.SR↗

arXiv2026

WoVR: World Models as Reliable Simulators for Post-Training VLA Policies with RL

Reinforcement learning (RL) promises to unlock capabilities beyond imitation learning for Vision--Language--Action (VLA) models, but its requirement for massive real-world interaction prevents direct deployment on physical robots. Recent work attempts to use learned world models as simulators for policy optimization, yet closed-loop imagined rollouts inevitably suffer from hallucination and long-horizon error accumulation. Such errors not only degrade visual fidelity, but also mislead policy optimization by providing unreliable learning signals. We propose WoVR, a reliable world-model-based RL framework for post-training VLA policies. Instead of assuming a faithful world model, WoVR explicitly regulates how RL interacts with imperfect imagined dynamics. It improves rollout stability through a controllable action-conditioned video world model, reshapes imagined interaction to reduce effective error depth via Keyframe-Initialized Rollouts, and maintains policy--simulator alignment through World Model-Policy co-evolution. Extensive experiments demonstrate that WoVR enables stable long-horizon imagined rollouts and effective policy optimization, achieving superior LIBERO performance and consistent real-world gains across multiple robotic platforms. These results show that world models can serve as practical simulators for RL when hallucination is explicitly controlled. Additional visualization results are available at https://wovr-corl.github.io.

cs.RO↗

arXiv2026

RLinf-USER: A Unified and Extensible System for Real-World Online Policy Learning in Embodied AI

Online policy learning directly in the physical world is a promising yet challenging direction for embodied intelligence. Unlike simulation, real-world systems cannot be arbitrarily accelerated, cheaply reset, or massively replicated, suggesting that real-world policy learning is not merely an algorithmic problem, but inherently a systems problem. We present USER, a \underline{U}nified and extensible \underline{S}yst\underline{E}m for real-world online policy lea\underline{R}ning. On the systems side, USER introduces a hardware abstraction layer for unified robot management and an adaptive communication plane that enables efficient cloud-edge training. On the learning side, USER adopts a fully asynchronous training framework, designs a persistent and cache-aware replay buffer, and provides extensible abstractions for rewards, algorithms, and policies. Experiments in both simulation and the real world demonstrate that USER supports multi-robot coordination, heterogeneous manipulators, cloud-edge training with large models, and long-running asynchronous training. Together, these capabilities establish USER as a unified and extensible systems foundation for real-world online policy learning.

cs.RO↗

arXiv2026

CL-bench: A Benchmark for Context Learning

Current language models (LMs) excel at reasoning over prompts using pre-trained knowledge. However, real-world tasks are far more complex and context-dependent: models must learn from task-specific context and leverage new knowledge beyond what is learned during pre-training to reason and resolve tasks. We term this capability context learning, a crucial ability that humans naturally possess but has been largely overlooked. To this end, we introduce CL-bench, a real-world benchmark consisting of 500 complex contexts, 1,899 tasks, and 31,607 verification rubrics, all crafted by experienced domain experts. Each task is designed such that the new content required to resolve it is contained within the corresponding context. Resolving tasks in CL-bench requires models to learn from the context, ranging from new domain-specific knowledge, rule systems, and complex procedures to laws derived from empirical data, all of which are absent from pre-training. This goes far beyond long-context tasks that primarily test retrieval or reading comprehension, and in-context learning tasks, where models learn simple task patterns via instructions and demonstrations. Our evaluations of ten frontier LMs find that models solve only 17.2% of tasks on average. Even the best-performing model, GPT-5.1, solves only 23.7%, revealing that LMs have yet to achieve effective context learning, which poses a critical bottleneck for tackling real-world, complex context-dependent tasks. CL-bench represents a step towards building LMs with this fundamental capability, making them more intelligent and advancing their deployment in real-world scenarios.

cs.CL↗

arXiv2026

A near-infrared stellar atlas of the Galactic plane from the VVVX survey

The VISTA Variables in the Via Lactea eXtended (VVVX) ESO public survey observed the Galactic plane and the outer Galactic bulge in the near-infrared to mitigate the effects of extinction that severely limit optical observations of these regions. By significantly expanding the area covered by the original VVV survey, VVVX enables a deeper and broader exploration of the most obscured and crowded regions of the Milky Way. We aim to extend and complete our photometric catalogs of the entire Galactic plane region accessible from the southern hemisphere, focusing on the areas newly covered by the VVVX survey. Building on previous work, we applied point-spread function fitting techniques to detect point sources and extract their deep J, H, and Ks photometry across the VVVX footprint. The resulting catalogs were calibrated using astrometric and photometric reference data. Cross-matching between filters and epochs was used to ensure a high level of reliability and completeness. We produce a deep, highly complete near-infrared catalog of more than 700 million sources in the Galactic plane and outer Galactic bulge. When combined with our previous VVV atlas, the full catalog includes over 1.5 billion sources. The derived density maps and color-magnitude diagrams enable detailed studies of Galactic structure, extinction, and stellar populations, and highlight features such as the Carina arm tangency, the Sagittarius stream, and numerous star clusters. This extended atlas provides an unprecedented view of the innermost regions of the Milky Way. It is now publicly available through the VISTA Science Archive, offering a valuable resource for the astronomical community to investigate the structure and evolution of the Galactic disk and bulge.

astro-ph.GA↗

arXiv2026

Observational Signatures of Planetary Tidal Disruption Events Around Solar-Mass Stars

The tidal disruption of planets by their host stars represents a growing area of interest in transient astronomy, offering insights into the final stages of planetary system evolution. We model the hydrodynamic evolution and predict the multi-wavelength observational signatures of planetary TDEs around a solar-mass host, focusing on Jupiter-like and Neptune-like progenitors and examining how different eccentricities of the planet's pre-disruption orbit shape the morphology and emission of the tidal debris. We perform 2D hydrodynamic simulations using the FARGO3D code to model the formation and viscous evolution of the resulting debris disk. We employ a viscous alpha-disk prescription and include a time-dependent energy equation to compute the disk's effective temperature and subsequently derive the bolometric and multi-band photometric light curves. Our simulations show that planetary TDEs produce a diverse range of luminous transients. A Jupiter-like planet disrupted from a circular orbit at the Roche limit generates a transient peaking at $L_{bol} \approx 10^{38}$ erg s$^{-1}$ after a 12-day rise. In contrast, the same planet on an eccentric orbit (e=0.5) produces a transient of comparable peak luminosity but on a much shorter timescale, peaking in only 1 day and followed by a highly volatile light curve. We find that the effect of eccentricity is not universal, as it accelerates the event for Jupiter but delays it for Neptune. A robust "bluer-when-brighter" colour evolution is a common feature as the disk cools over its multi-year lifetime. The strong dependence of light curve morphology on the initial orbit and progenitor mass makes these events powerful diagnostics. This framework is crucial for identifying planetary TDEs in time-domain surveys.

astro-ph.EP↗