arXiv subjects

Ke Xu

Publications and source records attributed to Ke Xu.

At least 19 recordsLinked to original sources

arXiv2026

Self-Referential $K$-SAT and the Finite Analogue of Gödel's Incompleteness Theorem

We study self-reference and solution independence in Boolean $K$-SAT as a finite analogue of Gödel incompleteness. Independence is defined through factorial moments of the solution count. Constant-width random formulas fail this criterion due to correlations among overlapping assignments. At a covering threshold, logarithmic-width random CNFs have a Poisson solution count, with positive probabilities of unsatisfiability and unique satisfiability. Replacing a redundant clause by one falsified by the unique solution yields an unsatisfiable companion preserving the unsigned incidence graph. For every permitted local view of a good uniquely satisfiable formula, an unsatisfiable companion preserves that view. Irreducibility defeats deterministic and bounded-error evaluators using $N^c$ clause queries, for fixed $0 0$, transcripts of $N^{1-δ}$ queries leave $N-o(N)$ bits of witness entropy. On typical support graphs, expansion yields linear Resolution width, linear PC/PCR and SOS degree, and exponential proof size. Bounds hold for semantic Cutting Planes under a partition condition and for Positivstellensatz with bounded product width, uniformly over unsatisfiable support-preserving signings. Structural evidence and the limits of acceleration strategies motivate the self-referential $K$-SAT exhaustive-search hypothesis (SRK-ESH): no uniform deterministic algorithm deciding the promise family achieves a fixed proportional saving in the exponent. This dual-evidence criterion combines intrinsic hardness mechanisms with algorithmic evidence. We interpret this hypothesis as a quantitative projection of the Gödel-style local--global gap. The information obstruction constrains quantum local observation and statistical learning from bounded local information.

cs.CC↗

arXiv2026

Even central galaxies feel their environment: cosmic siphoning of cool gas accretion

Identifying the key processes regulating cool gas accretion in galaxies is essential for understanding the baryon cycle within galactic ecosystems. Recent studies indicate that the most fundamental internal process is likely the competition between halos and central black holes. This is revealed through a universal relation between increasing black hole masses and decreasing cool gas fraction ($μ_{\rm HI} \equiv M_{\rm HI}/M_{\star} \propto M_{\rm BH}^{-0.6}$). Here we further explore the primary external effects on cool gas accretion in central galaxies. We find that galaxies deviating significantly from the $μ_{\rm HI}-M_{\rm BH}$ relation are overwhelmingly isolated central galaxies without substantial satellites. These isolated, gas-poor centrals exhibit systematically higher black-hole-to-stellar-mass ratios and more prominent bulges. At larger scales, they are preferentially located near massive neighboring halos. We suggest that this may reflect a ``cosmic siphoning" scenario, replenished by their satellites and by gas flowing along the cosmic web. This model naturally explains the prevalence of gas-poor isolated central galaxies near massive halos. It complements internal regulation by supermassive black holes and suggests a cosmic 'rich get richer' scenario.

astro-ph.GA↗

arXiv2026

Detecting Insecure Compositions of Clean Agent Skills via Capability-Flow Graph Analysis

AI agents increasingly extend their capabilities by invoking third-party skills, where each skill encapsulates instructions, scripts, and resources for agents to execute on demand. This paper reveals that deploying many benign skills together introduces compositional security risks: multiple individually clean skills can compose into safety-violation chains, because the agent passes an upstream skill's output to a downstream skill's input across invocations. The risk arises not from any single skill, but from the agent's runtime orchestration, and thus cannot be captured by existing malicious skill detection methods. To this end, we develop KnotScryer to detect insecure skill compositions by treating skills as typed capability flows. In particular, we abstract each skill into a capability summary, and construct the Skill Capability-Flow Graph (SCFG), where edges denote cross-skill information flow. Moreover, we design a search policy that tracks protected or untrusted data along handoff edges to locate paths reaching insecure operations. Finally, a two-layer verification method replays the insecure information flow for reachability analysis and applies a task-anchored LLM chain audit to confirm the semantic plausibility of the identified violation chains. We deploy KnotScryer to analyze 13,598 clean skills from ClawHub, and validate the detected chains across three agent frameworks and three LLMs. KnotScryer identifies 612 insecure compositions, which are consistently exploited across agent frameworks. Meanwhile, under GLM-5.1, it outperforms tag-match and graph-only baselines by improving the exploitation rate from 16.5%-18.2% to 85.6%, while reducing the search space by 85.3%. Furthermore, 13.7% of the detected chains consist entirely of skills labeled safe by a single-skill scanner, showing that KnotScryer effectively identifies insecure compositions that per-skill auditing cannot.

cs.CR↗

arXiv2026

Coupling perturbations of the conformal Einstein-Dirac system on round spheres

We study the conformal Einstein-Dirac system on the round sphere $S^m$, $m\geq3$, with coupling coefficient $1+\eps k$, where $k\in C^2(S^m,\R)$ and $\eps$ is small. Morse and degree conditions yield solutions with positive scalar component, and a generic nondegeneracy condition gives multiplicity modulo spinorial symmetries. For a suitable fixed coefficient, we obtain a noncompact solution sequence with infinitely many distinct energy values. A Kazdan-Warner identity gives nonexistence for a smooth Morse potential and its small $C^2$-perturbations, even with positive coupling. The existence proofs use a local reduction in the interior of the positive cone, accommodating the negative powers occurring when $m\geq5$.

math.AP↗

arXiv2026

SPECMEM: Accelerating Agentic Tool Calling via Live Memory Management

Tool-calling agents are mostly optimized for accuracy; in deployment, however, their growing cost is latency: every tool call is decoded autoregressively. Speculative decoding accelerates this without changing the output, and retrieved past calls make strong drafts under real use, where each user's behavior tends to repeat. Yet existing systems keep no cross-session memory, or build the draft store once and never update it. We propose SpecMem, a tool-call memory kept alive and managed throughout deployment: verified calls are written back online into bounded per-user stores with LRU eviction, and a side-effect gate defers irreversible calls until verification confirms. On multi-session, multi-user query streams served by three open-weight models, a frozen store steadily loses acceptance while SpecMem holds it: a 27% gap in mean accepted tokens (+11% to +34% across benchmarks, tracking how much novel work arrives after the store is built) and up to 1.74x wall-clock speedup. Ablations attribute the gain almost entirely to keeping the store live; local per-user scoping adds no acceptance but closes a measured cross-user leakage channel, and gating on declared side effects, rather than on retrieval confidence, is what eliminates irreversible mistakes.

cs.CL↗

arXiv2026

Cognitively Decoupled Meta-Induction for Out-of-Distribution Generalization

In-context learning (ICL) tackles downstream tasks conditioned on demonstrations. This paradigm relies heavily on neighbor-based matching between queries and demonstrations, limiting out-of-distribution (OOD) performance. For better generalization, rule-based inductive reasoning derives task-level regularities, which we term task-level inductive representations (TIR), before directly answering. However, existing methods lack generalizable inductive ability across diverse tasks. Inspired by meta-learning, we propose Cognitive-Decoupling meta-Induction (CoDI) for TIR-based inductive reasoning on general tasks. CoDI comprises a Cognitive-Decoupling Framework for query-blind TIR induction, and a Dual-Loop Meta-Inductive Learning method for cross-task inducer optimization. Empirically, CoDI achieves substantial performance gains on five ID and OOD benchmarks, and preserves OOD gains without explicit induction at inference. Code is available at https://github.com/Yuqian-Wang2277/codi-meta-induction.

cs.LG↗

arXiv2026

SAPO: Self-Supervised Attention Policy Optimization for Improving Temporal Video Grounding in Vision-Language Models

Video vision-language models (VLMs) have strong semantic understanding, yet remain unreliable at precise temporal grounding. Existing gains from explicitly aligning video frames with absolute time suggest that VLMs may already encode accurate temporal locations internally but lose precision when expressing them as timestamps. Our analysis across four VLMs and two grounding datasets confirms this hypothesis: attention intervals outperform predicted intervals on approximately 66% of valid examples. We term this discrepancy the temporal generation gap. Because precise temporal annotations are expensive, this finding motivates closing the gap to improve temporal grounding using only unlabeled video-query pairs. We propose Self-Supervised Attention Policy Optimization (SAPO), which uses internal temporal attention as self-supervision for Group Relative Policy Optimization and applies a ground-truth-free criterion to select high-value samples. SAPO consistently improves temporal grounding across multiple datasets and models spanning weak to strong initial grounding ability. Relative mIoU gains reach 33.7% on Qwen2.5-VL-3B and 5.4% on the strong Time-R1-7B. The improvements transfer across datasets while preserving general video understanding.

cs.CV↗

arXiv2026

OmniGround: Strengthening Omni-Modal Temporal Grounding via Attention-Consistency Training

Omni-modal large language models can jointly understand video, audio, and text, yet they remain weak at temporal grounding, a capability essential for real-world audio-visual applications. To understand this limitation, we look beyond final predictions and examine the model's internal temporal evidence. We identify a temporal generation gap: the internal attention of the model localizes the target moment more accurately than the timestamps it generates. To close this gap, we propose TACG (Temporal Attention-Consistency Grounding), a post-training method that treats internal attention as a reliable temporal pivot and aligns it with both the model prediction and the ground-truth annotation through attention-consistency training. We further build a 580K-sample training dataset by reconstructing temporal QA from dense audio-visual annotations and organizing general omni-modal instruction data. Using TACG and this training dataset, we train OmniGround, a general-purpose omni-modal model with stronger temporal grounding capability, without injecting any additional temporal cues. OmniGround reaches state-of-the-art omni-modal temporal grounding, including 37.5 mIoU on LongVALE and 35.7 mIoU on UnAV-100, while staying competitive on both general audio-visual understanding and vision-only grounding.

cs.CV↗

arXiv2026

Wan-Alpha++: Animating a Single RGBA Asset into Controllable Transparent Video Effects

RGBA video generation is essential for creating compositable visual assets in filmmaking, game development, and digital content production. However, generating high-quality RGB appearance alongside temporally coherent transparency remains a significant challenge. Existing text-to-RGBA video methods often entangle RGB and alpha distributions and offer limited control over the precise appearance of a target asset. In addition, incorporating image conditioning typically suffers from the emergence-disappearance effects, which fail existing first-frame or first-and-last-frame conditions due to their fully transparent boundary frames. To address these issues, we present Wan-Alpha++, a unified framework that brings controllable RGBA video generation to both text- and image-to-video diffusion backbones through a shared RGBA latent space, extending our conference version Wan-Alpha from the text-conditioned setting to image-conditioned generation with an interior reference frame. Our method first learns shiftable RGB-alpha distributions while preserving the pretrained RGB prior. Specifically, we introduce a transparency-aware bidirectional diffusion objective to produce distinguishable RGBA latent representations, and a transparency-guided mean sampler that shifts the noise distribution to improve transparency generation and enable spatial controllability. Building upon this RGBA generation prior, we introduce temporal phase reparameterization for image-conditioned effect generation. By cyclically transforming a zero-positive-zero alpha pattern into a positive-zero-positive pattern, it relocates visible content to both temporal boundaries, facilitating effective appearance conditioning with limited RGBA data.

cs.CV↗

arXiv2026

NEP-kappa 2.0: A unified framework for phonon transport and nonequilibrium thermalization with machine-learning interatomic potentials

We present NEP-kappa 2.0, a unified framework that integrates density-functional theory, material-specific machine-learning interatomic potentials, and pretrained force models for advanced phonon calculations. A common workflow enables harmonic lattice dynamics, quasi-harmonic and stochastic self-consistent phonon treatments, three- and four-phonon transport, Wigner population--coherence transport, and time-dependent phonon dynamics. Applications to 3C-SiC, BAs, and Cs$_2$AgInCl$_6$ demonstrate consistent force-model comparisons, finite-temperature renormalization, higher-order scattering, and intermode coherence. Beyond steady-state transport, mode-resolved simulations of selectively excited 3C-SiC reveal that half of the injected optical-phonon energy reaches acoustic modes within approximately 3.75 ps, primarily through one-optical--two-acoustic scattering. Acoustic branches exhibit response times differing by a factor of five, with competing scattering channels producing a transient energy overshoot. Channel-resolved entropy production and persistent nonthermal occupations distinguish interbranch energy redistribution from intrabranch thermalization. The framework connects equilibrium phonon transport with microscopic nonequilibrium energy dynamics.

cond-mat.mtrl-sci↗

arXiv2026

Speculating LLMs' Chinese Training Data Pollution from Their Tokens

Tokens are basic elements in the datasets for LLM training. It is well-known that many tokens representing Chinese phrases in the vocabulary of GPT (4o/4o-mini/o1/o3/4.5/4.1/o4-mini) are indicating contents like pornography or online gambling. Based on this observation, our goal is to locate Polluted Chinese (PoC) tokens in LLMs and study the relationship between PoC tokens' existence and training data. (1) We give a formal definition and taxonomy of PoC tokens based on the GPT's vocabulary. (2) We build a PoC token detector via fine-tuning an LLM to label PoC tokens in vocabularies by considering each token's both semantics and related contents from the search engines. (3) We study the speculation on the training data pollution via PoC tokens' appearances (token ID). Experiments on GPT and other 23 LLMs indicate that tokens widely exist while GPT's vocabulary behaves the worst: more than 40% long Chinese tokens (i.e., a token with more than two Chinese characters) are either porn or online gambling. We validate the accuracy of our speculation method on famous pre-training datasets like C4 and Pile. Then, considering GPT-4o, we speculate that the ratio of "Yui Hatano" related webpages in GPT-4o's training data is around 0.5%.

cs.CL↗

arXiv2026

Efficient Memory Crystallization for Graph Learning under Non-Stationary Distribution Shifts

Deep graph learning models deployed in real-world systems often need to cope with non-stationary environments, where the underlying graph distribution drifts continually over time. Prevailing solutions rely on training auxiliary generative modules to synthesize memory graphs for cross-domain adaptation, which incurs substantial computational overhead and scales poorly under prolonged distribution shifts. We argue that a more economical path exists: rather than generating memory, one can crystallize it. To this end, we propose Efficient Memory Crystallization (EMC), a training-free test-time framework that distills each incoming graph domain into a compact, semantically faithful memory through a closed-form solution to a memory-oriented distribution-matching objective, thereby eliminating redundant domain information under continual covariate shifts. To preserve both generalizability and adaptability as the model traverses a long sequence of target domains, EMC further models inter-domain dependencies through state-evolving memories and admits a theoretically grounded, tighter generalization error bound than direct adaptation. Extensive experiments demonstrate the superior performance of EMC over state-of-the-art baselines on graphs under non-stationary distribution shifts, while reducing average runtime by 87.4% and GPU memory consumption by 92.4% relative to the recent competitor, making continual graph adaptation practical at scale.

cs.LG↗

arXiv2026

ANT: A Multi-Granularity Network Traffic Dataset and Benchmark for Agents Behavior Auditing

The growing adoption of large language model (LLM) agents creates a need for network administrators and security teams to audit agent behavior within organizational networks without inspecting private user content. Network traffic offers an observable source of evidence, but how much it reveals about agent tasks and operations remains unclear. Existing traffic datasets lack the joint task and stage annotations needed to evaluate this question. We introduce ANT (Agent Network Traffic), a dataset providing agent behavior information at risk, scenario, and behavior primitive granularities alongside network traffic. ANT contains 3,114 execution episodes across 20 tasks and five scenarios, comprising 276,417 bidirectional flows and 40,049 behavior primitive segments organized into 47 macro groups. We establish a benchmark for agent risk identification, scenario recognition, and behavior primitive classification using 13 representative traffic analysis baselines. The results show that existing methods recover useful but uneven behavioral signals. They struggle to identify risk when malicious workflows resemble benign tasks and to distinguish scenarios with similar traffic patterns. Primitive classification is more reliable for frequent macro groups and those with distinctive traffic patterns than for rare or semantically similar groups. ANT provides a common basis for developing more precise auditing and forensic analysis of agent behavior from network traffic. Our data and code are available at https://anonymous.4open.science/r/ant-main-suite-7BC0/.

cs.AI↗

arXiv2026

Exploring Heterogeneous Model Merging Approach for Complex Knowledge Transfer

Specialized models encode task-oriented behavior, but transferring that behavior to a general language model usually requires training, distillation, or representation alignment. We study whether such ability can instead be transferred directly at the parameter level. We apply two existing training-free heterogeneous merging methods, previously shown to transfer knowledge between general language models, to specialist-to-general transfer, projecting a specialist donor into the recipient's shape and interpolating backbone parameters without gradient updates or semantic alignment. Intersection-Merge (IM) injects a prefix-aligned donor slice matching the recipient shape, while Activate-Prune-Merge (APM) uses forward-pass activation statistics to select which donor dimensions to retain before injection. Across embedding, reranking, reward modeling, and MoE code-specialist transfer, both methods improve the general recipient, showing that simple heterogeneous merging can move capabilities across diverse specialist roles.

cs.CL↗

arXiv2026

HyTopo A High-Performance Hierarchical Algorithm for Mapping the Structural Evolution and Transition Networks of Gas Hydrates

Resolving the formation and dissociation mechanisms of hydrate requires identifying not only hydrate-like water, but also the specific cage topologies by which disordered liquid water reorganizes into, or escapes from, crystalline hydrate networks. Here we introduce HyTopo, a cage-topology-centered analysis framework that combines a hydrate order parameter, hierarchical ring and cage recognition, and cage-transition network analysis. Based on local orientational correlations, HyTopo classifies water molecules into liquid-like, precursor-like, hydrate-like, and ice-like environments, and uses this information to guide efficient topological searches for crystalline hydrate cages and defective intermediates. Applications to methane hydrate growth, homogeneous nucleation, and depressurization-induced dissociation reveal that hydrate evolution is governed by heterogeneous and pathway-dependent cage dynamics. During hydrate growth, EA-WPU suppresses the overall proliferation of cage-like structures while altering the balance between defective cages and crystalline sI cages. During nucleation, incomplete cage motifs dominate the early pathway and provide a structural reservoir from which a small fraction of stable 512 and 51262 cages. Hydrate dissociation proceeds through interfacial collapse, bulk decomposition, and dissolved-gas exsolution, with cage lifetimes and transition pathways strongly dependent on cage topology. These results show that HyTopo provides a physically interpretable and topology-resolved route to connect local water ordering, cage formation, structural defects, and hydrate phase transitions in complex molecular simulations.

cond-mat.mtrl-sci↗

arXiv2026

NetLexicon: Learning Discrete Behavioral Representations for Encrypted Web Traffic Analysis

Encrypted Web traffic analysis requires effective representations of observable communication behavior. Existing pretraining methods often adapt NLP/CV objectives and sequence architectures, motivating learning objectives that capture traffic-specific interaction patterns. We present NetLexicon, a discrete pretraining framework that learns reusable behavioral states from unlabeled traffic. It converts contextual traffic windows into discrete states through vector quantization, constructing a compact traffic lexicon. We design two complementary pretraining objectives. State Transition Prediction (STP) forecasts subsequent sequence structure and packet features from observed history, while Statistical Feature Alignment (SFA) grounds learned states in window-level traffic statistics. Together, they guide the lexicon to capture recurring communication behaviors and their evolution. We evaluate NetLexicon on four benchmarks covering Web application identification, service type identification, and malware detection. NetLexicon improves Macro-F1 by up to 25.5 percentage points over the strongest baseline on each benchmark and reduces fine-tuning time per epoch by up to 23.6 times relative to the evaluated baselines. Further analysis shows that the learned discrete states capture recognizable patterns in packet size, timing, and data transfer. These results demonstrate that incorporating observable behavioral structure into pretraining supports effective, efficient, and interpretable representations for encrypted traffic analysis.

cs.NI↗

arXiv2026

Privacy Leakage on DNNs: A Survey of Model Inversion Attacks and Defenses

Deep Neural Networks (DNNs) have revolutionized various domains with their exceptional performance across numerous applications. However, Model Inversion (MI) attacks, which disclose private information about the training dataset by abusing access to the trained models, have emerged as a formidable privacy threat. Given a trained network, these attacks enable adversaries to reconstruct high-fidelity data that closely aligns with the private training samples, posing significant privacy concerns. Despite the rapid advances in the field, we lack a comprehensive and systematic overview of existing MI attacks and defenses. To fill this gap, this paper thoroughly investigates this realm and presents a holistic survey. Firstly, our work briefly reviews early MI studies on traditional machine learning scenarios. We then elaborately analyze and compare numerous recent attacks and defenses on Deep Neural Networks (DNNs) across multiple modalities and learning tasks. By meticulously analyzing their distinctive features, we summarize and classify these methods into different categories and provide a novel taxonomy. Finally, this paper discusses promising research directions and presents potential solutions to open issues. To facilitate further study on MI attacks and defenses, we have implemented an open-source model inversion toolbox on GitHub (https://github.com/ffhibnese/Model-Inversion-Attack-ToolBox).

cs.CV↗

arXiv2026

PhonoMC: Occupation-based deviational Monte Carlo for phonon transport with temperature-dependent scattering

Nanoscale self-heating involves phonon transport across confined geometries, material interfaces, and temperature fields over which the scattering rates can vary substantially. We develop PhonoMC, an occupation-based deviational Monte Carlo method for solving the phonon Boltzmann transport equation within the relaxation-time approximation. A fixed equilibrium state is retained as the deviational reference, while the local temperature reconstructed from the represented energy is used to evaluate mode-dependent scattering rates. Collisions are updated with a separately determined relaxation temperature to conserve energy over each time step, and prescribed lattice heating is introduced by changing carrier occupations rather than continuously adding computational particles. For cross-plane transport through a 100-nm Si film, the deviational formulation reproduces the full-population heat flux while reducing its standard deviation by a factor of approximately 3.3 at $ΔT=100$~K with $10^5$ carriers. In contrast, keeping the scattering rates fixed at 300~K overestimates the heat flux by 23.7\% at $ΔT=250$~K. Calculations of Si thin films distinguish finite-length effects from surface-scattering suppression, and Si/3C-SiC bilayers are used to examine interfacial thermal resistance. The method is further applied to localized heating in FinFET-like structures, where replacing the lower Si substrate with higher-conductivity 3C-SiC leads to a higher hotspot temperature because of the additional resistance associated with the confined Si region and the Si/SiC interface. These results show that a fixed deviational reference can be combined with local temperature-dependent scattering and sustained heat deposition in a mode-resolved Monte Carlo description of nanoscale thermal transport.

cond-mat.mtrl-sci↗