arXiv subjects

Sen Yang

Publications and source records attributed to Sen Yang.

At least 19 recordsLinked to original sources

arXiv2026

Lifefin: Escaping Mempool Explosions in DAG-based BFT

Directed Acyclic Graph (DAG)-based Byzantine Fault-Tolerant (BFT) protocols have emerged as promising solutions for high-throughput blockchains. By decoupling data dissemination from transaction ordering and constructing a well-connected DAG in the mempool, these protocols enable zero-message ordering and implicit view changes. However, we identify a fundamental liveness vulnerability: an adversary can trigger mempool explosions to prevent transaction commitment, ultimately compromising the protocol's liveness. In response, this work presents Lifefin, a generic and self-stabilizing protocol designed to integrate seamlessly with existing DAG-based BFT protocols and circumvent such vulnerabilities. Lifefin leverages the Agreement on Common Subset (ACS) mechanism, allowing nodes to escape mempool explosions by committing transactions with bounded resource usage even in adverse conditions. As a result, Lifefin imposes (almost) zero overhead in typical cases while effectively eliminating liveness vulnerabilities. To demonstrate the effectiveness of Lifefin, we integrate it into two state-of-the-art DAG-based BFT protocols, Sailfish and Mysticeti, resulting in two enhanced variants: Sailfish-Lifefin and Mysticeti-Lifefin. We implement these variants and compare them with the original Sailfish and Mysticeti systems. Our evaluation demonstrates that Lifefin achieves comparable transaction throughput while introducing only minimal additional latency to resist similar attacks.

cs.CR↗

arXiv2026

Boost+: Equitable, Incentive-Compatible Block Building

Block space on the blockchain is scarce and must be allocated efficiently through block building. However, Ethereum's current block-building ecosystem, MEV-Boost, has become highly centralized due to integration, which distorts competition, reduces blockspace efficiency, and obscures MEV flow transparency. To improve equitability and economic efficiency in block building, we propose $\textsf{Boost+}$, a system that pools submitted order flows inside an attested execution environment and gives all participating block-building algorithms equal access to them. The core of $\textsf{Boost+}$ is the mechanism $M_{\textsf{Boost+}}$, built around a baseline block-building algorithm. $M_{\textsf{Boost+}}$ aligns incentives for both searchers (intermediaries that generate or route transactions) and builders: Truthful bidding is a dominant strategy for all builders. For searchers, truthful reporting is dominant whenever the baseline algorithm dominates competing builders, and it remains dominant for all conflict-free transactions, even when builders may win. We further show that even if a searcher can technically integrate with a builder, non-integration combined with truthful bidding still dominates any deviation for conflict-free transactions. We also implement a concrete baseline algorithm informed by empirical analysis of real-world transactions and evaluate its efficacy using historical data.

cs.CR↗

arXiv2026

WeSearch: A Unified Model-Centric Retrieval Framework for WeChat Search

We present WeSearch, a unified, model-centric retrieval framework for large-scale, continuously evolving content search. Prevailing methods assign documents fixed hierarchical Semantic IDs, implicitly assuming each document follows a single coherent semantic path-an assumption that often breaks down for multi-topic documents. WeSearch instead retrieves query-relevant clusters and then ranks atomic documents within the resulting query-specific candidate space, decoupling candidate pruning from fine-grained relevance modeling. Combined with relevance-aware training, compact document representations, and incremental index updates, this design enables weekly-or daily when needed-model refreshes. Deployed as the primary semantic retrieval path in WeChat Search alongside a lexical syntax tree, WeSearch shows consistent one-week A/B gains on corpora from hundreds of millions to billions of documents, and a one-month full-traffic A/B test confirms stable improvements of +0.68% CTR and +2.27% per-user article clicks, together with a 41% reduction in retrieval resource cost and a 19 ms reduction in graphic-text retrieval latency. WeSearch is now fully deployed in WeChat Search, serving 100% of production traffic.

cs.IR↗

arXiv2026

Constraining polymerized black holes with quasi-circular extreme mass-ratio inspirals

In this paper, we focus on the gravitational waves emitted by a stellar-mass object in a quasi-circular inspiral orbit around a central supermassive polymerized black hole in loop quantum gravity. Treating the stellar-mass object as a massive test particle, we derive its equations of motion and the corresponding radial effective potential. We find that the peak of the radial effective potential decreases with the quantum parameter $\hat{k}$. We also examine the impact of quantum corrections on the properties of stable circular orbits around the polymerized black hole. We model the smaller object's trajectory as an adiabatic evolution along stable circular orbits using a semi-relativistic approach. In this method, the motion of the object is described by relativistic geodesics, and changes in the object's energy and orbital angular momentum due to gravitational radiation are calculated using the mass quadrupole formula. The corresponding gravitational waveforms are generated using the numerical kludge method, revealing that quantum corrections cause phase advances in the gravitational waveforms. We further analyze the potential constraints on the quantum parameter $\hat{k}$ from future space-based gravitational wave observations, concluding that these observations will likely impose stronger constraints on $\hat{k}$ than those obtained from black hole shadow measurements.

gr-qc↗

arXiv2026

Constraints on quantum Oppenheimer-Snyder black holes with eccentric extreme mass-ratio inspirals

We investigate the potential of extreme mass-ratio inspirals to constrain quantum Oppenheimer-Snyder black holes within the framework of loop quantum gravity. We consider a stellar-mass object orbiting a supermassive Oppenheimer-Snyder black hole in an equatorial eccentric trajectory. To explore the dynamical behavior of the system, we analyze its orbital evolution under gravitational radiation within the adiabatic approximation and the mass-quadrupole formula for different initial orbital configurations. Our results show that the quantum correction parameter $\hatα$ slows down the evolution of the orbital semi-latus rectum and eccentricity. We then employ the numerical kludge method to generate the corresponding time-domain gravitational waveforms. To assess detectability, we include Doppler modulation due to the motion of space-based detectors and compute the frequency-domain characteristic strain. By evaluating mismatches between response signals for different values of $\hatα$, we show that even small corrections $(\hatα \sim 10^{-5})$ produce distinguishable effects. Our analysis suggests that future space-based detectors such as LISA can probe quantum gravitational corrections in the strong-field regime and place constraints significantly stronger than those from black hole shadow observations.

gr-qc↗

arXiv2026

Diffusion-based Evolutionary Optimization for 3D Multi-Objective Molecular Generation

De novo 3D molecular design with multiple properties and fragment-retention constraints is formulated as a constrained multi-objective optimization problem. Existing 3D diffusion models provide strong capabilities for generating novel molecules, but adapting them to new conditions and multi-objective optimization tasks remains difficult. We present Diffusion-based Evolutionary Molecular Optimization (DEMO), a framework that draws solely on the unconditional molecular prior of a frozen pretrained diffusion model to generate chemically valid 3D candidates, while evolutionary search handles conditional generation and constrained multi-objective optimization, without retraining or fine-tuning. DEMO performs crossover and mutation on intermediate diffusion states through an Evolutionary-Guided Diffusion (EGD) operator, combines 2D topology and 3D geometry in Structure-Aware Environmental Selection (SAES) to preserve structural diversity, and uses three cooperating populations to coordinate structural exploration, fragment-constraint satisfaction, and property optimization. Experimental results demonstrate that EGD outperforms task-specific conditional generators in property targeting; SAES preserves structural diversity while retaining competitive hypervolume in multi-objective optimization; and DEMO improves objective convergence and generates diverse feasible molecules under fragment constraints. These results show how evolutionary search can reuse existing diffusion models for diverse molecular design requirements.

cs.NE↗

arXiv2026

Large-bulk-viscosity limit for two-dimensional compressible micropolar fluids with vacuum

We consider the Cauchy problem for the two-dimensional compressible micropolar fluid system on the torus $\mathbb T^2$. The initial density is bounded and nonnegative and may contain vacuum, while the initial velocity and microrotation belong to $H^1(\mathbb T^2)$. For sufficiently large bulk viscosity $ν$, we prove the global-in-time existence of solutions without a smallness assumption on the $H^1$ size of the data, provided that the initial compressible component is well prepared in the sense that $\sqrtν\|\operatorname{div}u_0\|_{L^2}$ is uniformly bounded. The proof combines the effective viscous flux with a coupled energy scheme that exploits the vorticity--microrotation dissipation at both the basic and material-derivative levels. For the linear pressure law, we also prove uniqueness in the stated solution class by means of a coupled backward parabolic problem, logarithmic interpolation, and Osgood's lemma. For fixed initial data with $\operatorname{div}u_0=0$, we also justify the limit $ν\to\infty$: every sequence of the corresponding solutions admits a subsequence converging to a solution of the two-dimensional inhomogeneous incompressible micropolar system.

math.AP↗

arXiv2026

Tracking Is Not Sensing: Artifact-Aware Thermal Video Respiration Estimation in Head-Free Marmosets

Continuous physiological monitoring is essential for linking behavior, internal state, and neural activity in neuroscience and translational research. In common marmosets, a key nonhuman primate model for social neuroscience, respiratory rate provides a basic readout of arousal, stress, and physiological regulation. Contact sensors can provide accurate measurements, but they add preparation burden and may interfere with head-free behavioral protocols. Thermal video offers a noncontact alternative by capturing breathing-related temperature changes around the nose and mouth. However, in head-free marmosets, these cues are weak, localized, and easily corrupted by head motion, pose changes, and fur. Here we show that this setting exposes a broader challenge in video-based physiological sensing: visually reliable tracking does not necessarily imply physiologically valid measurement. Using synchronized thermal video and contact respiration recordings, we show that marmoset respiration is recoverable from thermal video but strongly ROI-sensitive. Under head-free motion, visually plausible nasal tracking can still produce traces inconsistent with contact respiration, demonstrating that tracking is not sensing. We therefore estimate respiratory rate from learned temporal signals extracted from thermal clips and introduce Selective Temporal Attenuation Regularization (STAR). STAR selectively attenuates high-motion thermal discontinuities through local temporal imputation during training, regularizing models toward respiration-consistent dynamics while leaving inference unchanged on raw video. Across multiple deep-learning backbones, STAR improves head-free estimation, reaching 2.23 bpm MAE compared with 20.76 bpm for tracked ROI analysis.

cs.CV↗

arXiv2026

EviLink: Multi-Path Schema Linking with Uncertainty-Guided Evidence Acquisition for Large-Scale Text-to-SQL

Schema linking is a difficult and important step in large-scale Text-to-SQL, where systems must identify a compact yet sufficient schema context from large and ambiguous databases. Existing methods often treat schema linking as deterministic selection around a single SQL path, but complex questions may admit multiple valid realizations with different schema needs. We reframe schema linking as uncertainty-aware schema-need inference over multiple plausible SQL paths, where the system distinguishes required schema items from path-dependent uncertain ones and acquires evidence only where needed. We instantiate this reframing with EviLink, which combines multi-hypothesis schema grounding with uncertainty-guided evidence acquisition. Experiments on BIRD-Dev and Spider2-Snow show that this perspective improves the balance among schema completeness, schema relevance, and token cost. On Spider2-Snow, EviLink achieves 93.04% field-level strict recall rate, uses 116.55K average tokens, and improves downstream SQL generation under a fixed generator.

cs.CL↗

arXiv2026

TreeRef-BFN: Equivariance-Free De Novo Molecule Generation based on 2D Topology and Internal 3D Geometry

De novo 3D molecular generation jointly models molecular size, topology, and geometry. Most methods pre-sample molecular size and generate Cartesian coordinates, limiting variable-size conditional tasks such as fragment completion and scaffold decoration while often relying on equivariant architectures. Internal-coordinate methods avoid rigid-body redundancy but typically require a known molecular graph or autoregressive construction, which may accumulate errors. We propose TreeRef, a tree-based molecular representation that assigns molecular topology and topology-dependent local 3D geometry to a naturally variable-size tree. RingRef nodes encode ring closures while preserving the tree structure, while Null nodes allow molecular size to emerge directly from node occupancy. Based on TreeRef, we develop TreeRef-BFN, a Bayesian Flow Network with a standard Transformer backbone that globally couples these locally defined variables and jointly generates discrete molecular variables and continuous local geometry. A single pretrained TreeRef-BFN supports unconditional generation and variable-size structure-conditioned 3D generation through masking alone, without retraining. Empirical studies demonstrate strong chemical validity, molecular stability, and diversity, accurate local geometric distributions, fast sampling, and competitive property-conditioned generation, establishing TreeRef-BFN as an efficient and flexible framework for 3D molecular generation.

cs.AI↗

arXiv2026

Online Video Agent Harness for Long Video Understanding

Long video understanding often behaves like a visual needle-in-a-haystack problem: query-relevant evidence is sparsely distributed across long temporal spans, while packing dense frames into a single VLM context incurs \textit{context rot} and high cost. Existing video agents often rely on query-agnostic offline preprocessing or ad hoc tool sets, which can miss query-specific details and waste computation. In this work, we present VideoXAgent, a purely online video-agent harness for long video understanding that starts from the given video file and user query, plans and decomposes the task, invokes specialized expert tools on demand, and aggregates multimodal evidence to produce a final answer while resolving conflicts among observations. To support this on-demand invocation, we design a suite of heterogeneous expert tools guided by a data-driven taxonomy of atomic capabilities, spanning scripts, VLMs, and domain models (e.g., detection, OCR, ASR, face recognition). The harness further enforces objective evidence prompting and budget-aware control to curb hallucination and non-termination. Across Video-MME-Long, LongVideoBench-Long, LVBench, and MINERVA, VideoXAgent is competitive with frontier LMMs and video agents under a smaller context footprint---about 50k tokens of agent context per sample, even on hour-long videos. In particular, on complex video-reasoning benchmarks such as MINERVA, it matches this level while using only about 15\% of the context of a 1,024-frame dense-packing baseline. Notably, the harness remains effective with a visually weak or even text-only orchestrator, suggesting that strong long-video understanding can emerge from progressive agentic evidence seeking rather than from packing the full video into a single context. Project page: https://go-agent-x.github.io/video_agent_harness/

cs.CV↗

arXiv2026

ClinConsensus: A Physician-Calibrated Benchmark for Evaluating Clinical Rubric Coverage in Chinese Medical LLMs

Open-ended medical LLM evaluation remains weakly grounded in physician-calibrated coverage of clinically relevant response criteria, especially in localized clinical settings. We introduce \textsc{ClinConsensus}, a Chinese medical benchmark of 2{,}500 expert-curated cases spanning 36 specialties, 12 task themes, multiple difficulty levels, and lay-facing versus professional-facing settings. Each case is paired with 30 case-specific binary rubric criteria. To evaluate whether responses satisfy enough physician-authored criteria, we propose \emph{Clinician-Anchored Coverage Score} (CACS), a physician-calibrated threshold metric instantiated at $k=10$, and develop a dual-judge framework combining a GPT-5.1 grader with a physician-supervised Qwen3-8B judge. Evaluating 11 frontier LLMs, we find a persistent coverage gap: Rubric Accuracy ranges from 39.6\% to 52.1\%, whereas CACS@10 ranges from 17.8\% to 32.9\%, leaving a 19.2--21.9 point gap across models. Stratified analyses further reveal substantial variation across reasoning, evidence use, structured extraction, medication instructions, follow-up, and dialogue register. These results suggest that medical LLM evaluation should measure thresholded, rubric-grounded clinical coverage rather than average partial correctness.

cs.CL↗

arXiv2026

GRRM: Group Relative Reward Modeling for Machine Translation

While Group Relative Policy Optimization (GRPO) offers a powerful framework for LLM post-training, its effectiveness in open-ended domains like Machine Translation hinges on accurate intra-group ranking. We identify that standard Pointwise Quality Metrics (PQM) fall short in this context: candidates are evaluated in isolation, so the comparative context is missing for distinguishing fine-grained linguistic nuances. To address this, we introduce the Group Quality Metric (GQM) paradigm and its instantiation, the Group Relative Reward Model (GRRM). Unlike traditional independent scorers, GRRM jointly processes the entire candidate group, leveraging comparative analysis to rigorously resolve relative quality and adaptive granularity. Empirical evaluations confirm that GRRM achieves competitive ranking accuracy among all baselines; integrating GRRM into the GRPO training not only improves general translation quality but also unlocks reasoning capabilities comparable to state-of-the-art reasoning models. We release codes, datasets, and model checkpoints at https://github.com/NJUNLP/GRRM.

cs.CL↗

arXiv2026

Global well-posedness of radially symmetric strong solutions to two-dimensional compressible liquid crystal flows with large data and vacuum

We study the initial boundary value problem of the two-dimensional compressible nematic liquid crystal flow with the shear viscosity $μ$ being a positive constant and bulk viscosity $λ$ being a power function of density with the power exponent $β$. Under the condition $β>1$, we establish the global existence and large time behavior of the radially symmetric strong solutions to this Vaigant--Kazhikhov type model of simplified compressible Ericksen-Leslie system of Dirichlet boundary conditions for the velocity and Neumann boundary ones for the director with arbitrary large data and vacuum. This work improves the results of Zhong and Zhou (\textit{Math. Ann.} \textbf{390}, 2024; \textit{J. Math. Pures Appl.}, \textbf{212}, 2026) for general 2D domains by removing the geometric angel condition on the director and relaxing the constrain on $β$ from $β>4/3$ to $β>1$. The key ingredient is that the rigidity mechanism arising from the radial symmetry of director prevents concentration phenomena in the transported harmonic heat flow.

math.AP↗

arXiv2026

NoteFlow: Leveraging Charts as Sight Glasses for Consistent and Continuous Data Flow Tracing

Computational notebooks offer a flexible environment for exploratory data analysis (EDA), but this flexibility often leads to disorganized and iterative execution of notebook cells, making it difficult to track how data states evolve. Consequently, data scientists must devote extra mental effort to staying aware of data states, which is both tedious and prone to overlooking anomalies. To address this challenge, we developed NoteFlow, a notebook extension that leverages charts as ``sight glasses'' to provide a consistent and continuous tracing of data flow. NoteFlow allows users to (1) validate various facets of the current data state using recommended charts provided immediately after each cell execution, and (2) trace the global evolution of selected charts to continuously observe how particular data attributes evolve throughout the EDA process. We evaluated NoteFlow's effectiveness through a controlled study with 12 participants and a one-month field study with 2 data scientists on real-world workflows.

cs.HC↗

arXiv2026

Crypto x AI, AI x Crypto: A Survey

The intersection of crypto x AI is spawning papers, products, online posts, and companies. All the surrounding buzz, though, obscures what exactly has been done, what the opportunities and challenges are, and what open questions deserve attention. This survey paper asks what AI can do for blockchain-based technologies (broadly construed as "crypto") (crypto x AI), and vice versa (AI x crypto). We systematize existing work, summarize key takeaways, highlight open research questions, and offer a perspective on pervasive industry misconceptions, concluding that AI and crypto are still in the very early stages of meaningful integration.

cs.CR↗

arXiv2026

A Systematic Gaia--ZTF Search for Short-Period Blue Compact-Binary Candidates

We present a catalog of 147 short-period (10.34--106.46~min) blue compact-binary candidates, identified by combining Gaia DR3 astrometry and photometry with ZTF DR23 light curves via a Gaia selection, period searches, and machine-learning morphology ranking. Of these, 111 lack prior compact-binary classifications. Multiwavelength data (DESI DR1, GALEX, AllWISE) reveal a heterogeneous sample: on the Gaia colour--magnitude diagram, 52 sources lie on the white-dwarf locus, 69 in the hot-subdwarf region, and 26 are intermediate. Among 26 sources with DESI spectra, only about one third follow the white-dwarf cooling sequence; the rest are more luminous blue stars with white-dwarf-like low-resolution spectra. We highlight a prioritized subset of new white-dwarf-locus candidates for follow-up, including ten with periods below 40~min and none with existing radial-velocity data. Under fiducial binary assumptions, 17 of these newly identified white-dwarf-locus candidates would exceed the adopted LISA signal-to-noise threshold (led by a 37~pc white dwarf), with the count depending on chirp mass (9 for $0.15\,M_\odot$, 17 for $0.3\,M_\odot$, 21 for $0.6\,M_\odot$), assuming orbital modulation. However, for most of the white-dwarf-locus sample, observed modulation amplitudes exceed any plausible ellipsoidal signal by three to five orders of magnitude, implying that rotating magnetic or chemically inhomogeneous single white dwarfs offer a viable alternative that ZTF photometry alone cannot rule out---the catalog includes at least one confirmed case. We release the full 147-source catalog, including periods, Gaia/spectroscopic classifications, harmonic/ellipsoidal diagnostics, and supplementary tables of fiducial GW estimates and UV--IR photometry.

astro-ph.SR↗

arXiv2026

FISSION: Label Augmentation for Bot Detection

Bot accounts and coordinated influence operations are often discovered via heuristic methods, leaving a dearth of reliable ground-truth labels for training detection systems. To address this challenge, we study a natural question: can we generate labels to assist in learning embeddings in which bots and accounts from the same coordinated operation are close? We present FISSION, a method to generate labels by splitting each account's activity into positively labeled sub-accounts. Given this label source, we train detection models which preserve behavioral regularities recurring across positive sub-accounts. We evaluate FISSION and show it outperforms prior methods in detecting Wikipedia sockpuppets and Twitter/X bots.

cs.AI↗