arXiv subjects

Bin Chen

Publications and source records attributed to Bin Chen.

At least 55 records · Page 3Linked to original sources

arXiv2026

Shadows and Thin-Disk Images of Kerr-Newman Black Holes in a Bertotti-Robinson Magnetic Field

In this paper, we investigate the optical properties of Kerr-Newman-Bertotti-Robinson (KN-BR) black holes. We use the separability of null geodesics to analyze unstable spherical photon orbits and determine the radial extent of the photon shell. Because the spacetime is not asymptotically flat, we construct the critical curve on the screen of a finite-distance zero-angular-momentum observer. We then perform backward ray tracing for a geometrically thin and optically thin disk that extends from the outer region to the event horizon, and examine the resulting images, intensity profiles, critical-curve areas, and inner-shadow areas. We find that the genuine neutral Kerr-BR$_0$ and specially charged Kerr-BR$_s$ configurations have nearly identical optical appearances. It is remarkable that for the KN-BR black holes increasing the electric charge reduces the characteristic image size in the Kerr-Newman limit but enlarges it in the magnetized configurations considered here. We also find that the external magnetic field strongly increases the apparent image scale, while the observer inclination affects the inner-shadow area more significantly than the critical-curve area. These results may provide useful theoretical insight for future observations aimed at identifying such exotic magnetized black holes.

gr-qc↗

arXiv2026

The tetragonal-cubic transition of davemaoite: Implications for lower mantle seismic anomalies

Davemaoite (CaSiO3 perovskite) is the third most abundant mineral in Earth's lower mantle and a dominant phase in subducted oceanic crust. Its crystal structure is distorted (tetragonal or orthorhombic) at ambient conditions but is considered to transform to cubic at high temperatures. Previous experiments reported low, nearly pressure-independent transition temperatures (approximately 600 K), demonstrating a long-standing discrepancy with theoretical predictions that mostly exceed 1000 K. Here, we determine the phase stability and thermal equation of state of CaSiO3 davemaoite and its titanium-bearing solid solution [Ca(Si0.75,Ti0.25)O3] under simultaneous high-pressure and high-temperature conditions using a laser-heated diamond anvil cell combined with synchrotron X-ray diffraction. We find that the tetragonal-to-cubic transition occurs at substantially higher temperatures than previously reported, with the titanium substitution further stabilizing the tetragonal phase and shifting the transition boundary to even higher temperatures. These findings indicate that CaSiO3 davemaoite in subducted oceanic crust likely undergoes its ferroelastic transition in the mid-lower mantle, whereas Ti-rich davemaoite may remain tetragonal throughout most of the lower mantle, transforming to cubic near the core-mantle boundary. Our results demonstrate that the compositionally dependent phase behaviour of davemaoite can account for the seismic anomalies observed in both the mid-lower mantle and the lowermost mantle.

physics.geo-ph↗

arXiv2026

VoRTeC: Taming Foundation Flow for One-step Real time Video Compression

Ultra-low bitrate video compression still faces critical challenges: traditional neural video compression inevitably introduces blurring artifacts, while diffusion-based generative video compression suffers from excessive decoding latency and poor temporal consistency. To address these issues, we propose $\mathtt{VoRTeC}$, a Video Compression framework built upon a foundational flow model (Wan2.1). By compactly encoding latent video representations, predicting the positions of compressed representations along flow trajectories, and integrating multi-scale priors, $\mathtt{VoRTeC}$ enables the compressor to harness generative video flow priors effectively. Without accessing the parameters or gradients of flow matching networks, our framework achieves one-step decoding and reconstructions with high perceptual fidelity. Meanwhile, we maintain consistency across frame groups via tail-frame reuse and prior caching. Extensive experiments demonstrate that our method reduces bit consumption by 58\% compared to prior diffusion-based approaches, with decoding speed boosted by 3 to 197 times: $\mathtt{VoRTeC}$ achieves a decoding speed of 13 FPS at 720p and 32 FPS at 480p.

cs.CV↗

arXiv2026

Low electrical conductivity of dry CaSiO3 perovskite under lower mantle conditions

Electrical conductivity (EC) provides important constraints on the composition and volatile distribution of Earth's deep mantle, yet the EC of davemaoite (CaSiO3 perovskite), a major lower-mantle phase, remains poorly constrained. We measured the EC of nominally dry CaSiO3 perovskite at pressures up to 89 GPa and temperatures up to 2200 K using impedance spectroscopy in a laser-heated diamond anvil cell. Conductivity increases with temperature but decreases systematically with pressure and it is substantially lower than previously reported, yet broadly consistent with recent theoretical predictions for oxygen-vacancy-mediated ionic transport. A distinct change in the temperature dependence coincides with the tetragonal-to-cubic phase boundary, revealing a modest enhancement of ionic transport across the structural transition. Along a normal lower-mantle geotherm, dry davemaoite is comparable in conductivity to bridgmanite near the top of the lower mantle but becomes progressively less conductive with depth. Under cold-slab conditions, dry davemaoite is substantially less conductive than dry subducted MORB and cannot account for the observed high-conductivity anomalies. Dry davemaoite therefore contributes little to bulk lower-mantle conductivity.

physics.geo-ph↗

arXiv2026

Generalizations of Koga's version of the Wiener-Ikehara theorem

We establish new versions of the Wiener-Ikehara theorem where only boundary assumptions on the real part of the Laplace transform are imposed. Our results generalize and improve a recent theorem of T. Koga [J. Fourier Anal. Appl. 27 (2021), Article No. 18]. As an application, we give a quick Tauberian proof of Blackwell's renewal theorem.

math.CV↗

arXiv2026

Weak Ties, Strong Signals: Efficient Training Data Detection in Diffusion LLMs via Independent Token Sampling

Diffusion large language models (dLLMs) offer a compelling alternative to autoregressive models, yet they may expose sensitive training data during denoising. Detecting such usage is challenging because dLLMs lack the efficient one-pass probability decomposition of causal architectures. Existing methods rely on random masking to obtain tractable token-wise detection signals under limited query budgets, but fail to control dependencies among masked tokens. We demonstrate that this token-wise approximation introduces a non-negative structural estimation error, which is theoretically characterized by the cumulative conditional mutual information (CMI) among masked tokens and can obscure subtle memorization signals. This insight suggests that reliable detection requires masked token sets with weak internal dependency. To avoid the prohibitive cost of directly estimating CMI over token combinations, we propose \textit{Independent Token Sampling} (ITS), a query-efficient framework that uses an attention-derived pairwise dependency proxy to approximate the CMI-aware selection criterion. ITS further incorporates a diversity-promoting strategy to improve token coverage across sampling rounds, yielding aggregated token-wise signals that are less affected by dependency-induced approximation error. Experiments on multiple datasets show that ITS consistently outperforms state-of-the-art baselines across different models and datasets, achieving an AUC improvement of 0.18 on the ArXiv dataset while maintaining strong performance under limited query budgets. The code is available at https://github.com/Chrisqcwx/DLLM-MIA .

cs.LG↗

arXiv2026

LAFR: Efficient Diffusion-based Blind Face Restoration via Latent Codebook Alignment Adapter

Blind face restoration from low-quality images is a challenging task that requires not only high-fidelity image reconstruction, but also preservation of facial identity. Although diffusion models like Stable Diffusion have shown promise in generating high-quality images, their VAE modules are typically trained on broad natural-image data dominated by HQ content; severely degraded LQ inputs therefore yield latents that fall in low-density regions of the diffusion prior, weakening the effectiveness of LQ conditions during the denoising process. Existing approaches often tackle this issue by retraining the VAE encoder, which is computationally expensive and memory intensive. To address this limitation efficiently, we propose LAFR, a codebook-based latent space adapter that aligns LQ latents to the in-distribution region the diffusion prior was trained to denoise, enabling sampling from a better matched latent condition without altering the original VAE. To further improve identity and structural consistency relative to OSEDiff-style baselines, we introduce a multilevel restoration loss combining constraints from semantic identity embeddings and facial structural priors. Furthermore, by leveraging the inherent structural regularity of facial images, we show that lightweight fine-tuning of a diffusion prior on just 0.9% of FFHQ can achieve competitive results while reducing training time by 70%. We also give a short analysis of why a codebook correction closes a mode-dependent latent gap that no global normalisation or affine map can. In summary, LAFR is a data and compute efficient one-step face-restoration method that improves perceptual quality, FID, landmark accuracy, and identity/structure consistency over OSEDiff-style baselines, without claiming uniform state of the art; it reaches this operating point while remaining competitive with far more expensive pipelines.

cs.CV↗

arXiv2026

A Study of Bluetooth Access Control Based on NFT Soft Pairing

This paper proposes a Non-Fungible Token (NFT) soft pairing framework for Bluetooth service access control. Unlike conventional Bluetooth systems where pairing implicitly grants persistent service access, the proposed approach decouples native Bluetooth pairing from authorization without modifying the underlying protocol stack. The framework introduces a three-layer architecture consisting of a Bluetooth layer for connectivity, a blockchain layer for trusted execution and on-chain state verification, and an application layer where NFT soft pairing defines the authorization logic. In this design, Non-Fungible Bluetooth Tokens (NFBTs) represent user-side access credentials, while Non-Fungible Device Tokens (NFDTs) represent device identities. Their bidirectional on-chain binding forms a revocable and verifiable NFT soft pairing relationship. During access, users prove ownership of valid NFBTs through challenge-response signatures, and devices verify the corresponding on-chain state before granting service access. A prototype implemented with MetaMask and Ethereum demonstrates secure authentication, dynamic revocation, acceptable latency, and gas-efficient credential issuance based on ERC1155.

cs.CR↗

arXiv2026

Prescribed-order subdigraphs with large minimum out-degree

Alon introduced $d(s)$ as the largest integer $d$ such that every digraph on $2n$ vertices with minimum out-degree at least $s$ contains a subdigraph on $n$ vertices with minimum out-degree at least $d$. He proved that $s/2-d(s)=O(\sqrt{s\log s})$, and further asked whether this deficit can be bounded by an absolute constant. Steiner answered this question in the negative by constructing suitable tournaments, and showed that $s/2-d(s)=Ω(\log s)$. Using a different construction, we show that the deficit grows at least on the square-root scale, rather than merely logarithmically, improving the best known lower bound due to Steiner from $Ω(\log s)$ to $Ω(\sqrt{s})$ and leaving only a factor of $\sqrt{\log s}$ between the lower and upper bounds. This also completely settles a question raised by Steiner for tournament hosts. More generally, in the broader setting considered by Alon, our construction applies whenever the prescribed subdigraphs contain any fixed positive proportion of the vertices of the host digraph rather than specifically one half.

math.CO↗

arXiv2026

TEAMS: Text-prompted spatiotEmporal dual-heAd Mamba Snake

Deep snake is a promising family of instance segmentation methods that accurately predicts object-level contours, thereby overcoming common pixel-level misclassification issues such as mask cavities and jagged edges in semantic segmentation approaches. However, existing deep snake methods face challenges in handling complex morphological variations, accurately capturing fine-grained organ details, and correcting base detection errors. To mitigate these limitations, we propose a cohesive Text-prompted spatiotEmporal dual-heAd Mamba Snake (TEAMS), a novel vision-language Mamba snake framework with three key innovations: (1) A Spatiotemporal Snake Evolution Strategy (SSES) is introduced to tackle complex morphological variations by capturing bidirectional spatial dependencies along the snake contour and temporal dynamics across evolution steps in a state space model. (2) A Contour Morphology-Aware Mamba (CMAM) is proposed to quantify local contour morphologies to modulate the structured attention mask in the Mamba2 SSD dual form, which extends Mamba's capability to perceive the relative importance of its input sequence elements for better delineation of fine-grained organ details. (3) A Text-prompted Collaborative Dual-Head Snake (TCDHS) is designed to incorporate cues from textual prompts and transfer the evolved contour information to the base detection head, which enhances the deep snake workflow and mitigates wrong detections. Comprehensive evaluations on five datasets covering different organs and imaging modalities demonstrate that TEAMS outperforms existing semantic and deep snake segmentation methods (e.g., relative mDice/mBF improvements of 6.9%/9.1% in a spinal dataset), underscoring its potential as a reliable tool across diverse medical image segmentation scenarios.

cs.CV↗

arXiv2026

LEMUR: Latent Entropy-aware Multimodal Unlearning via Visual-anchored Reasoning Redirection

Reinforcement-learning (RL) post-training equips multimodal large reasoning models (MLRMs) with exploratory chains of thought (CoT), substantially improving visual reasoning. However, we find that this capability introduces a distinct privacy vulnerability: even when a sensitive fact is successfully unlearned from the final answer, the model may still reproduce it in its reasoning trace. This leakage is substantially more pronounced in natively RL-trained MLRMs than in their non -reasoning base models, revealing a privacy risk that existing unlearning methods are not designed to address. We show that RL-induced exploration leaves sensitive content with a distinctive token-level entropy signature that is largely absent from base models. Based on this observation, we propose LEMUR, a fully training-free, inference-time unlearning framework for natively RL-trained multimodal models. LEMUR uses entropy dynamics as a control signal to identify when sensitive reasoning begins and when sanitization should stop. During this interval, it redirects the reasoning trajectory through entropy-modulated visual-anchor latent injection, replacing committed tokens with sanitized, probability-weighted embeddings re-grounded in the input image. Across diverse MLRMs, LEMUR consistently outperforms existing unlearning met hods in suppressing both reasoning-trace and answer leakage, while better preserving non-sensitive utility and output fluency. These results demonstrate that RL-induced entropy dynamics provide a distinctive signal for privacy leakage and that exploiting this signal enables effective training-free unlearning for reasoning-capable multimodal models.

cs.LG↗

arXiv2026

MADBench: A Benchmark for Modality-Aware Audio Deepfake Detection

Recent advances in speech synthesis and audio generation have made high-fidelity acoustic forgery low-cost and difficult to attribute, enabling a realistic attack scenario in which speech and background audio are independently manipulated over otherwise authentic video. Yet existing research either focuses on visual manipulation, addresses speech detection in isolation, or conflates speech and non-speech audio as a single undifferentiated audio stream, overlooking the distinct forensic challenges posed by background audio. This conflation is consequential: the two acoustic components arise from fundamentally different generative mechanisms, exhibit distinct artifact profiles, and pose different challenges to detection systems. We introduce MADBench, the first benchmark that treats speech and environmental audio as distinct acoustic components, enabling component-aware evaluation of audio deepfake detection across independently manipulated forgery sources. We benchmark representative state-of-the-art detectors and multimodal large language models under a unified protocol. Our experiments reveal that environmental audio manipulation is more detectable than synthetic speech across general-purpose encoders, while existing pretrained detectors fail on both acoustic components, and manipulated environmental audio asymmetrically degrades speech deepfake detection, findings entirely invisible under the single-label paradigm of prior benchmarks. MADBench establishes a rigorous foundation for future research into robust, component-aware audio deepfake detection.

cs.SD↗

arXiv2026

Completing the Penrose Process without a Horizon

In its standard black-hole realization, the Penrose process uses an event horizon to remove a negative-energy fragment. We show that, at the kinematic level, horizon absorption can instead be replaced by confinement within a compact ergoregion, without imposing an absorbing or reflecting inner boundary. For a broad class of regular, stationary, axisymmetric, asymptotically flat horizonless spacetimes, we prove that every smooth connected component of the spatial ergosurface is a compact torus, independently of the field equations and matter content. Future-directed geodesics with negative Killing energy encounter a forbidden neighborhood of each such boundary component and are therefore confined. We derive an exterior no-barrier condition and identify an open set of on-shell, future-directed, four-momentum-conserving splittings producing both a confined negative-energy fragment and an amplified partner. Under equatorial reflection symmetry, once the latter enters the exterior channel on an outward branch, it necessarily reaches infinity. A rotating boson star explicitly realizes the complete process without a horizon.

gr-qc↗

arXiv2026

Holography in flipped AdS/$\mathbb{Z}$: Another approach to dS holography

Motivated by the subtleties in the conventional dS/CFT correspondence, we explore analytic continuation as a constructive route to de Sitter holography. We show that quantum field theories in de Sitter space and in a spacetime that we call flipped $\mathrm{AdS}/\mathbb{Z}$ (fAdS) are related by analytic continuation. We then develop a holographic description of fAdS in terms of a boundary theory referred to as flipped CFT (fCFT). In particular, we construct the extrapolate dictionary for general asymptotically fAdS spacetimes and compute the holographic two-point functions, finding agreement with an independent derivation based on the conformal symmetry of fCFT. The analytic continuation relation between fAdS and dS further suggests that fCFT may provide a starting point for an alternative holographic description of de Sitter physics. As a nontrivial check of this picture, we show that the Cardy formula of fCFT$_2$ reproduces the Bekenstein--Hawking entropies of the cosmological horizons in both pure dS$_3$ and Kerr-dS$_3$.

hep-th↗

arXiv2026

FlowErase-OPD: Multi-Concept Erasure via Anchored On-Policy Distillation in Flow Matching Models

Recent advances in flow matching models have substantially improved the quality of text-to-image generation, but have also raised increasing safety concerns due to their potential to generate harmful or undesirable content. Existing concept erasure methods for flow matching models predominantly focus on removing individual concepts, while effectively erasing multiple concepts simultaneously remains challenging. We propose FlowErase-OPD, a framework for multi-concept erasure based on on-policy distillation (OPD). Our approach first distills multiple single-concept erased models into a unified LoRA module and introduces Anchored Multi-Teacher Distillation (AMTD), which incorporates a retention teacher to mitigate the trade-off between concept erasure and preservation of generative capabilities. To further improve the coordination of multiple erasure objectives, we develop Adaptive Retention Control (ARC), which dynamically adjusts the sampling frequency and loss weight of each erasure teacher, together with the relative contribution of erasure and retention teachers throughout training. Extensive experiments on nudity, object, and artistic-style erasure demonstrate that FlowErase-OPD consistently improves the trade-off between erasure effectiveness, image quality, and semantic alignment, achieving state-of-the-art performance across diverse multi-concept erasure settings. Furthermore, the resulting models exhibit strong robustness against adversarial attacks. These results highlight the potential of on-policy distillation as a principled framework for safe and controllable generation in flow matching models.

cs.CV↗

arXiv2026

An algebra for covariant observers in de Sitter space

In $d$-dimensional de Sitter spacetime, consistency of perturbative expansion requires imposing all second-order gravitational constraints associated with the $SO(1,d)$ isometry group, rather than restricting to the $\R\times SO(d-1)$ subgroup, to address linearization instability. Since generic de Sitter isometries do not preserve a fixed static patch, these constraints cannot be implemented within a fixed local algebra. We develop a framework that imposes all $SO(1,d)$ constraints while incorporating multiple observers on arbitrary timelike geodesics. We introduce the covariant observer whose geodesic transforms covariantly under the isometry group. Upon quantization, the observer is described by a superposition of geodesics, with associated static patches fluctuating, providing a quantum reference frame $L^2(SO(1,d))$. We realize this structure in an action model where a particle carries conserved charges corresponding to generators of de Sitter isometries, which parametrize its geodesic and upon quantization lead to a fluctuating geodesic. Inspired by the timelike tube theorem, we propose that the observable algebra accessible to a covariant observer is generated by all degrees of freedom within its fluctuating static patch, including quantum fields and other observers treated as part of the matter system. Imposing the $SO(1,d)$ constraints yields a gauge-invariant algebra given by an averaged modular crossed product algebra over static patches and configurations of other geodesics, generalizing a local algebra associated with a fixed region to that of a fluctuating region. We show this algebra is type II by constructing a faithful normal trace, leading to an observer-dependent notion of von Neumann entropy. For semiclassical states, imposing a UV cutoff in QFT and proposing a quantum generalization of the first law, we show agreement between algebraic and generalized entropies.

hep-th↗

arXiv2026

Balancing Efficiency and Efficacy: Training-Free Attention-Guided Switching Between Explicit and Latent Thoughts for MLLMs

Reasoning in Multimodal Large Language Models (MLLMs) requires both fine-grained visual perception and rigorous logical deduction. Explicit text-based Chain-of-Thought (CoT) is computationally expensive and prone to visual hallucinations, while existing latent reasoning methods typically require costly training. Furthermore, directly adapting training-free LLM reasoning mechanisms to the multimodal setting yields unstable performance. We identify that this failure stems from their reliance on token-level entropy, which fundamentally conflates perceptual ambiguity (e.g., unclear visual details) with logical uncertainty (e.g., complex reasoning steps). To overcome this bottleneck, we present a novel training-free inference strategy for MLLMs that explicitly decouples perception and reasoning. We propose a novel metric, the vision-to-text attention ratio, to dynamically gauge the model's cognitive focus. Guided by this metric, our proposed framework, Attention-Guided Switching (AGS), adaptively triggers latent reasoning for perceptual tokens to preserve high-fidelity visual information in the continuous space, while enforcing explicit text generation for logical tokens to maintain structural anchoring. Extensive experiments demonstrate that our method achieves state-of-the-art performance, significantly improving both accuracy and inference efficiency by reducing autoregressive steps and latency. Code is released at https://github.com/swordAndSnow/MM26-AGS.

cs.MM↗

arXiv2026

MeV-peaked electrons in a coronal source of a solar flare

Analysis of γ-rays in solar flares has suggested a distinct continuum component dominating at MeV energies, which differs from the well-studied X-ray continuum produced by flare-accelerated electrons with spectra steeply falling with energy. The origin, precise spatial location, and extent of this mysterious MeV component have been unknown up to now. If it is produced by bremsstrahlung, such a γ-ray component requires an unusual population of electrons peaked at a few MeV. Here we report a joint study of this MeV-peaked electron population in the 2017-Sep-10 solar flare with Fermi MeV γ-ray data and EOVSA spatially resolved microwave imaging spectroscopy data. We demonstrate that the microwave spectrum from the MeV-peaked distribution has a distinctly different shape from that produced by the electrons with falling energy spectrum. We inspected microwave maps of the flare and identified an evolving area where the measured microwave spectra matched the theoretically expected one for the MeV-peaked population, thus pinpointing the site where this MeV component resides. The locations are in a coronal volume adjacent to the region where prominent release of magnetic energy and bulk electron acceleration were detected, which implies that transport effects play a key role in forming this population.

astro-ph.SR↗