arXiv subjects

Bin Chen

Publications and source records attributed to Bin Chen.

At least 19 recordsLinked to original sources

arXiv2026

The exact digirth and out-degree bounds for $C(k,k)$-subdivision and out-forest in digraphs

For each integer $k\ge2$, let $C(k,k)$ denote the oriented cycle formed by two internally vertex-disjoint directed paths of length $k$ with the same initial and terminal vertices. Picasarri-Arrieta and Rambaud ({\small European J. Combin., 2024}) asked for the smallest integer $g_k$ such that every digraph with minimum out-degree at least two and digirth at least $g_k$ contains a subdivision of $C(k,k)$. We completely resolve this problem by proving that $g_k=k+1$. Our proof relies on a refinement of Thomassen's theorem ({\small Discrete Math., 1985}) on two directed cycles intersecting in exactly one vertex: we prove that every digraph with minimum out-degree at least two contains two directed cycles intersecting in exactly one arc. We also establish an embedding theorem for out-forests with prescribed images of their roots, under minimum out-degree and digirth conditions determined by the number of leaves and the heights of vertices in antichains. As an application, for integers $k,\ell\ge2$, we show that every digraph with minimum out-degree at least $k$ and digirth at least $k(\ell-1)+1$ contains $k$ directed paths of length $\ell$ sharing only their initial vertex, which can be prescribed arbitrarily. This improves the previous digirth bound $(k^\ell-1)/(k-1)+1$ of Picasarri-Arrieta and Rambaud to a sharp bound.

math.CO↗

arXiv2026

Generalized Decoupled Learning for Enhancing Open-Vocabulary Dense Perception

Dense visual perception tasks have been constrained by their reliance on predefined categories, limiting their applicability in real-world scenarios where visual concepts are unbounded. While Vision-Language Models (VLMs) like CLIP have shown promise in open-vocabulary tasks, their direct application to dense perception often leads to suboptimal performance due to limitations in local feature representation. In this work, we present our observation that CLIP's image tokens struggle to effectively aggregate information from spatially or semantically related regions, resulting in features that lack local discriminability and spatial consistency. To address this issue, we propose DeCLIP, a novel framework that enhances CLIP by decoupling the self-attention module to obtain ``content'' and ``context'' features respectively. \revise{The context features are enhanced by jointly distilling semantic correlations from Vision Foundation Models (VFMs) and object integrity cues from diffusion models, thereby enhancing spatial consistency. In parallel, the content features are aligned with image crop representations and constrained by region correlations from VFMs to improve local discriminability. Extensive experiments demonstrate that DeCLIP establishes a solid foundation for open-vocabulary dense perception, consistently achieving state-of-the-art performance across a broad spectrum of tasks, including 2D detection and segmentation, 3D instance segmentation, video instance segmentation, and 6D object pose estimation.} Code is available at https://github.com/xiaomoguhz/DeCLIP

cs.CV↗

arXiv2026

A Density--Delay Law for Event-Driven State Progression in Open Distributed Systems

This paper studies delay-induced competition in an honest event-driven longest-chain network and distinguishes selected-tip divergence from lowest-common-ancestor (LCA) competing-branch depth. With density--delay load $x_N=λΔ_{\max}$, regenerative analysis gives the explicit network-size-uniform bound $μ_N(x_N)\le e^{2x_N}+e^{x_N}-2$ on proposal-clock mean LCA depth. Bounded $x_N$ suffices for this guarantee. At fixed delay, $g(N)=O(1/N)$ is one corresponding input-rate scaling. An explicit finite-horizon depth-exceedance bound is also established. Explicit-node simulations show strong load effects and comparatively small finite-size effects. The results concern honest contention, not adversarial security.

cs.DC↗

arXiv2026

Seeing Through the Chain: Understanding and Mitigating Hallucinations in Multimodal Large Reasoning Models

While multimodal reasoning models (MLRMs) have exhibited impressive capabilities, they remain prone to hallucinations, and effective solutions are still underexplored. In this paper, we experimentally analyze the hallucination cause and propose C3PO, a training-based mitigation framework comprising \textbf{C}hain-of-Thought \textbf{C}ompression and \textbf{C}ontrastive \textbf{P}reference \textbf{O}ptimization. Firstly, we identify that introducing reasoning mechanisms exacerbates models' reliance on language priors while overlooking visual inputs, which can produce CoTs with reduced visual cues but redundant text tokens. To this end, we propose to selectively filter redundant thinking tokens for a more compact and signal-efficient CoT representation that preserves task-relevant information while suppressing noise. In addition, we observe that the quality of the reasoning trace largely determines whether hallucination emerges in subsequent responses. To leverage this insight, we introduce a reasoning-enhanced preference tuning scheme that constructs training pairs using high-quality AI feedback. We further design a multimodal hallucination-inducing mechanism that elicits models' inherent hallucination patterns via carefully crafted inducers, yielding informative negative signals for contrastive correction. We provide theoretical justification for the effectiveness and demonstrate consistent hallucination reduction across diverse MLRMs and benchmarks.

cs.CV↗

arXiv2026

GaussTrap: Stealthy Backdoor Attacks on 3D Gaussian Splatting for Targeted Scene Misperception

3D Gaussian Splatting (3DGS) has recently emerged as a powerful paradigm for real-time scene representation and novel view synthesis, gaining traction in safety-critical applications such as autonomous driving and AR/VR systems. However, the security vulnerabilities of 3DGS remain largely unexplored. In this paper, we conduct a comprehensive study of backdoor threats in 3DGS pipelines. We uncover an inherent security vulnerability in the adaptive density control mechanism of 3DGS, originally designed for detailed reconstruction, which enables attackers to induce localized gaussian proliferation, effectively embedding malicious backdoors into the scene geometry while remaining invisible from other viewpoints. To expose this threat, we propose GaussTrap, a novel stealthy poisoning framework tailored for 3DGS. GaussTrap operates in three stages (adversarial injection, geometric stabilization, and global fidelity restoration) to embed imperceptible yet adversarial Gaussian components into the reconstruction. It ensures consistent poisoning effects at specific viewpoints while preserving photorealism in benign views, thereby achieving high attack efficacy with low detectability. Extensive experiments on both synthetic and real-world benchmarks demonstrate that GaussTrap reliably induces targeted visual corruption under trigger conditions while maintaining high rendering fidelity elsewhere, highlighting a critical security blind spot in modern 3D rendering systems. The code is available at https://github.com/acang425/GaussTrap.

cs.CV↗

arXiv2026

Bitcoin: A Non-Continuous Time System

Bitcoin has no shared global clock. Each node sees the network from its own place and at its own time. The same block may arrive at different moments, and different blocks may briefly appear at the same historical position. Yet a common history still emerges. We describe this process as a non-continuous time system (NCTS), in which the protocol state $S_h=(C_h,U_h)$ is assigned the historical coordinate $τ(S_h)=h$. This coordinate does not measure how much physical time has passed. It marks where a state belongs in the history that later states can inherit. In this framework, history advances through accepted state transitions rather than through the passage of physical time. We examine three complementary aspects of this structure. At a fixed observation point, block arrivals occur at irregular physical intervals. Across monitors, the same block is observed at different physical times. Fork data further show that competing blocks may temporarily occupy the same historical height. Taken together, these observations support the distinction between physical observation time and the historical coordinate of the inherited state sequence. The inherited past becomes increasingly stable, while the next continuation remains open to permissionless competition. Bitcoin moves forward without a shared present by inheriting a shared past.

cs.CR↗

arXiv2026

Quantum Anomalies of Tensionless Bosonic Strings

We systematically investigate and compare the worldsheet actions, BRST structures and the quantum anomalies of four different formulations of tensionless ($T = 0$) bosonic string theory: the $(D+2)$-dimensional conformal string \cite{Gustafsson:1994kr}, the $D$-dimensional ILST null string \cite{Isberg:1993av}, the $D$-dimensional Carroll-Weyl gauged string \cite{Sheikh-Jabbari:2026vqh, Sheikh-Jabbari:2026tpf}, and the $D$-dimensional hybrid null string \cite{Chen:2026klv}. By expressing all fields and constraint generators strictly in terms of mode expansions and adopting a unified algebraic framework, we analyze their quantum anomalies under two distinct worldsheet vacua: the induced vacuum and the flipped vacuum. With the BRST-compatible vacuum definition and the symmetric $α=0$ zeta prescription, we show that no critical dimension is inferred from the vanishing of the quantum anomaly in the induced vacuum. In contrast, the flipped highest-weight vacuum leads to non-trivial constraints, reproducing the critical dimension $D=26$ for the ILST null strings, a $λ$-dependent critical dimension $D(λ)$ for the hybrid null string whose range covers every positive integer $D\geq 4$ (reproduces $D=26$ at $λ=1$), and more importantly showing that the conformal string and the Carroll-Weyl gauged string are structurally anomalous with no consistent critical dimension due to the discrepancy of their central charge parameters $\tilde d_i$. Furthermore, the ILST null string model has target-space conformal symmetry $SO(D,2)$, the ghost-completed $SO(D,2)$ charges are closed on the induced-vacuum BRST cohomology in the $α=0$ prescription, whereas the symmetry is quantum mechanically broken in the flipped vacuum.

hep-th↗

arXiv2026

Strip Convolution and Direction-Aware Exclusion Loss for Oriented Ship Detection

Oriented ship detection in very high resolution (VHR) remote sensing imagery remains challenging due to elongated hull geometry and dense target distributions in complex port scenes. Existing methods typically address geometric representation and duplicate suppression separately. To jointly tackle these issues, we propose an oriented ship detector with two complementary components. The C3k2_Strip module employs orthogonal strip convolutions to better capture elongated hull structures, while the Class-Aware Direction-Aware Exclusion Loss (CA-DAEL) suppresses redundant predictions using class, direction, and confidence cues. Experiments on HRSC2016 and DIOR-R achieve 78.45% and 53.71% mAP50:95, respectively, with only 2.91M parameters. On HRSC2016, the proposed method improves mAP50:95 by 6.32 percentage points over the YOLOv11-OBB baseline, demonstrating its effectiveness for accurate oriented ship detection.

cs.CV↗

arXiv2026

Cubic Nonlinear Schrödinger Systems and Orthonormal Fermionic Ground States in $\mathbb R$

We study the one-dimensional nonlinear Schrödinger system \[ -u_i''-2\Big(\sum_{k=1}^N u_k^2\Big)^{p-1}u_i=μ_i u_i \quad \mbox{in } \ \mathbb R,\ \ i=1,2,\cdots,N, \tag{S} \] where $u=(u_1,\cdots,u_N)\in H^1(\mathbb{R})^N$, $μ_1\leqμ_2\leq\cdots\leqμ_N<0$, $1 0$ such that $J_N(p)$ is attained for any $p\in(2-δ_N,2)$, whereas $J_N(p)$ is not attained for any $p\in[2,2+δ_N)$. These settle some conjectures of [Gontier--Lewin--Nazar, ARMA, 2021] and [Frank--Gontier--Lewin, CMP, 2021].

math.AP↗

arXiv2026

Racket Kinematics and Ball Performance During the Forehand Reverse Flick: A Pilot Comparison With the Backhand Flick in Table Tennis

Background Following changes in table tennis ball materials, offensive returns have become more important for initiating sustained topspin offense. However, using the backhand flick (BF) to return forehand short balls often increases the difficulty of recovery and continuity, revealing a technical gap. This study preliminarily verified a novel forehand short return technique, the forehand reverse flick (FRF), and analyzed its similarities and differences with the BF. Methods Four elite athletes completed seven consecutive days of FRF specific training. Infrared motion capture and ultra-high-speed cameras were used to collect data on racket kinematics, movement duration, and ball performance. Results The success rate of the FRF increased steadily, reaching 86%. Racket trajectories of the two techniques were highly similar along the X (r = 1) and Y (r = 0.99) axes but differed along the Z (r = -0.04) axis. Racket and ball velocities were comparable between techniques, whereas the FRF showed lower resultant acceleration (approximately 265.57 m/s) and required about 0.03 s more for movement duration. Ball velocity was comparable between techniques, for the ball spin, the FRF generated lower spin (approximately 76.61 r/s) about 64% of the BF value (approximately 120.13 r/s). The highest participant mean spin rate reached 93 r/s, about 77% of the BF mean. Conclusion Overall, the FRF was found to have favorable learnability and training value, with potential for further optimization and competitive application.

physics.med-ph↗

arXiv2026

When History Helps and Hurts: Selective History Use across Multimodal Turns

Reliable multimodal interaction depends on selective use of conversational history: an earlier question may remain relevant while its previous answer is outdated, whereas a current request may depend on historical evidence despite conflicting new observations. Existing multi-turn evaluations rarely separate these history-use demands from underlying question difficulty. To address this gap, we introduce ReTurn, a benchmark of 7,000 base tasks spanning visual and audio evidence for evaluating selective history use. For task-carrying history, Reconfirm/Reground require applying a historical question to current media while varying historical agreement; for evidence-carrying history, Retrieve/Rebind require answering a current question using historical media while varying current-media competition. Each pair preserves the target question, media, and answer. Tasks support open-ended and multiple-choice evaluation, with matched single-turn counterparts serving as answerability references. Across 13 omni-modal, vision-language, and audio-language models, median model-level open-ended accuracy falls from 93.7% with direct input to 72.3% in conversation. Behavioral probes show that high question recall can coexist with weaker task application, while competing media can redirect answers away from historical targets. Supervised adaptation yields only partial gains. ReTurn provides a controlled framework for assessing whether multimodal models select and use the historical information required by each request.

cs.CL↗

arXiv2026

Autocorrelation in black hole flare movies: Departures from Kerr

Autocorrelation in black hole movies encodes the time delays and angular separations between gravitationally lensed images, offering a means of testing the spacetime geometry. Using an orbiting hot spot in the parametrized Johannsen-Psaltis metric with a single deformation parameter, we investigate the ability of lensing-induced correlations to distinguish departures from Kerr. From ray-traced movies, we extract the slope (k) and intercept (b) of an approximately linear secondary ridge in the time-angle autocorrelation map as potential future observables. Variations in the spin and the metric deformation parameter produce distinctive trajectories in the (k,b) plane, allowing the two observables to jointly constrain both parameters when the orbital radius, observer inclination, and orbital angular velocity are specified. By varying the orbital angular velocity, we construct a Kerr reference region in the (k,b) plane, and we find that, for some low-spin configurations with negative deformation parameters, the measured slope-intercept pairs of the secondary peaks lie outside this reference region. This feature appears for sub-Keplerian, Keplerian, and super-Keplerian motion, indicating that secondary-ridge measurements from black hole flare movies have the potential to distinguish non-Kerr signatures.

astro-ph.HE↗

arXiv2026

IronMan: Information-Constrained Video-Action Learning for Robot Manipulation

Video Action Models (VAMs) couple visual dynamics modeling with action generation for robot manipulation. However, video representations are not naturally suited to action generation, as exposing the action policy to excessive visual detail can impair its generalization ability. Therefore, we introduce IronMan (Information-constRained videO-actioN learning for robot MANipulation), a robust video-action learning framework built on the information bottleneck principle. The core principle of this framework is to impose information constraints that suppress irrelevant visual information while preserving action-relevant dynamics cues. IronMan employs a dynamics-aware bottleneck that distills noisy, entangled one-step video features into compact world representations. Extensive simulation and real-world experiments demonstrate strong in-distribution (ID) performance and out-of-distribution (OOD) robustness while maintaining efficient inference. IronMan achieves success rates of 99.0% on LIBERO and 79.4% on RoboTwin clean2clean, outperforming all the evaluated baselines. Under OOD shifts, IronMan achieves a success rate of 79.1% on LIBERO-Plus, exceeding the strongest baseline by 10.4 percentage points. Project page: https://youngsoul0731.github.io/ironman-project-page/

cs.RO↗

arXiv2026

Self-lensing of gravitational waves from binary extreme-mass-ratio inspirals: Extended caustics and paired flashes

We investigate strong gravitational lensing of gravitational waves from a compact source orbiting a Kerr black hole, motivated by self-lensing in binary extreme-mass-ratio inspirals. In the geometric-optics limit, we construct a point-to-point lens map from null-geodesic integrals and use the Jacobi map to compute the strain magnification of individual images. While lensed signals are generally demagnified, we show that they can be strongly amplified near the extended caustics of the Kerr lens map. For suitable source--observer geometries, the orbit crosses a caustic twice per revolution, producing two amplification peaks. Their arrival-time separation depends on the black-hole spin, observer inclination, and orbital radius through both the source motion and propagation delays. These signatures might offer a means of constraining the source--observer geometry and probing the spacetime of the central black hole.

gr-qc↗

arXiv2026

CHILLGuard: Towards Fine-Grained Chinese LLM Safety Guardrail with Scalable Data Construction and Model-aware Preference Alignment

Malicious content generated from large language models (LLMs) could pose severe safety risks and ethical concerns. While existing LLM safety guardrails excel in English or multilingual settings, they lack adaptation to Chinese-specific regulatory policies, cultural context, and linguistic nuances, failing to support fine-grained risk classification for diverse deployment needs. In this paper, we introduce a 5-macro, 31-micro category fine-grained risk taxonomy for Chinese scenarios, and build CHILLGuard: a dedicated Chinese LLM content safety guardrail. To address the critical scarcity of high-quality annotated Chinese safety data, we propose a scalable multi-stage data construction pipeline: we expand multi-source corpus via retrieval-augmented generation, generate implicit harmful samples through prompt engineering rewriting, and refine high-quality data via multi-model voting-based label calibration. Based on this, we build CHILLGuardTrain, a large-scale training set with 405,007 samples, and CHILLGuardTest, a rigorously curated annotated test set with 51,745 samples. We then train CHILLGuard on CHILLGuardTrain under a generator-classifier collaborative framework via Model-aware Direct Preference Optimization. Extensive experiments under multiple settings demonstrate the state-of-the-art performance of CHILLGuard, e.g., a 15.92% relative improvement of F1 score over Qwen3Guard-8B-Strict on our benchmark. We release our resources at https://github.com/cswbyu/CHILLGuard.

cs.CL↗

arXiv2026

ibUMAP: Coherent and Scalable Field Evaluation for UMAP Optimization

UMAP achieves scalable layout optimization through stochastic negative sampling. However, this stochasticity can lead to unstable embeddings across reruns and downstream reuse, as the estimated repulsive forces depend on the ordering of sampling events. We present ibUMAP, a coherent field-based alternative that evaluates attraction and repulsion from a shared embedding snapshot and applies them synchronously. Its degree-weighted repulsive field is motivated by the conditional expectation of negative sampling for a fixed embedding and represented by three scalar moments, which are evaluated efficiently on CPUs and GPUs using an interpolation-based FFT scheme. This formulation avoids explicit all-pairs computations while inducing optimization dynamics that differ from those of standard online UMAP. Controlled experiments show that synchrony and kernel capping alter the local-global fidelity trade-off, whereas FFT evaluation produces small average changes in final quality. End-to-end benchmarks show median speedups of 3.29x unseeded and 5.79x seeded over umap-learn on CPU, and 1.44x over cuML on million-scale datasets under unseeded GPU execution. These gains accompany greater run-to-run stability and measurable fidelity trade-offs.

cs.LG↗

arXiv2026

PrecogUI: Proactive GUI Agents via Pre-cognitive Simulation and Experience Retrieval

Existing reactive Graphical User Interface (GUI) agents often fail in long-horizon, dynamic scenarios, where unexpected disturbances trigger attention-diverting and cascading failures. To address this, we propose PrecogUI, a pre-cognitive architecture that shifts the paradigm from reactive execution to proactive decision-making. Specifically, we design a Proactive Experience Pool (PEP), which caches recurring anomaly and success patterns as "state-action-result" tuples in a dual-memory repository. Furthermore, we introduce a Proactive Simulation Executor (PSE) that learns to forecast the next symbolic UI layout given a candidate action, enabling early anomaly avoidance and ranking candidate actions by predicted reliability. Finally, a Pre-cognitive Execution Controller (PEC) fuses these priors and predictions, prioritizes handling of foreseen anomalies, and ensures execution robustness through a closed-loop error correction mechanism. For robust evaluation, we develop AutoTraj, an automatic data-generation engine, to construct InterfereBench, a benchmark for long-horizon tasks with strong disturbances. Experiments demonstrate that PrecogUI surpasses state-of-the-art methods on InterfereBench while maintaining competitive performance on public benchmarks. The code will be publicly available.

cs.AI↗

arXiv2026

MoTIF-X: A Multimodal Tokenized Framework for Interpretable and Extensible Molecular Representation Learning

Molecular representation learning is central to computer-aided drug discovery. Molecular graphs, SMILES strings, and 3D conformations provide complementary structural information, yet many multimodal approaches encode these views independently and align them only at a later stage, limiting fine-grained cross-modal interaction and substructure-level interpretability. To address these limitations, we introduce MoTIF-X, a motif-centered framework that uses graph-grounded chemical motifs as shared anchors for multimodal integration and interpretation. Its first pretraining stage learns motif representations through hierarchical contrastive learning across atomic, motif, and molecular scales. The second stage contextualizes these representations with SMILES and torsion-angle tokens through multimodal masked token modeling. After pretraining on drug-like molecules with multiple conformers, MoTIF-X achieved the lowest mean absolute error on all nine OpenADMET ExpansionRx endpoints and the best overall performance among the evaluated methods. Significance analyses supported its advantage in the vast majority of endpoint-baseline comparisons after multiple-testing correction. Ablation studies supported the complementary contributions of motif-token contextualization, multimodal integration, and two-stage pretraining. Beyond molecular properties, the framework extended to drug-target interaction prediction, achieving the best average classification performance across the evaluated benchmarks and generalizing to an external drug-cold-start dataset without additional fine-tuning. Its motif-centered design also enabled substructure-level interpretation: higher motif attribution scores were associated with larger experimentally measured activity shifts. Together, these findings support MoTIF-X as a transferable and interpretable framework for molecular modeling.

cs.LG↗