arXiv subjects

Yi Li

Publications and source records attributed to Yi Li.

At least 19 recordsLinked to original sources

arXiv2026

PrivEscalate: Measuring and Augmenting the Threat of LLM-Automated Linux Privilege Escalation

As Large Language Model (LLM) agents increasingly automate offensive operations across the cyber kill chain, their efficacy in complex local post-exploitation tasks remains inadequately quantified. Among these, Linux privilege escalation is a key step between initial access and full system compromise. However, existing evaluations for this task are limited by small sample sizes (fewer than 15 scenarios), lacking the scale to compare model capabilities under executable verification. To address this, we present PrivEscalate, a large-scale benchmark for Linux privilege escalation, comprising 531 Dockerized scenarios spanning 14 sub-categories. We additionally derive 329 parameterized variants to measure sensitivity to environmental distractors. Evaluating six LLMs across three agent architectures reveals: (i) model capability is heterogeneous across vulnerability classes, with no single model dominating across the high-prevalence classes, motivating multi-dimensional risk assessments; (ii) LLM successes are sensitive to environmental perturbation, so configuration rotation can disrupt some exploit attempts but does not eliminate the measured risk; and (iii) agent architectures can materially change success rates and reorder model rankings, though the magnitude is model-dependent. Leveraging these insights, we develop PrivEscAgent, a domain-specialized wrapper that augments a generic ReAct agent with deterministic enumeration, category matching, and step planning. PrivEscAgent improves over prior Linux privilege-escalation agent baselines without underlying LLM modifications. We release PrivEscalate as an open-source, Dockerized measurement instrument supporting LLM agent evaluation, defensive tool validation, and red-team training.

cs.CR↗

arXiv2026

EventSpec: Defining and Detecting Event-Semantic Issues in Blockchain Ecosystems

In recent years, smart contracts have become the backbone of decentralized applications (DApps), and off-chain systems such as bridges, wallets, and indexers rely heavily on event logs to track contract execution and state changes. However, the Ethereum Virtual Machine (EVM) does not validate or enforce event semantics, so logs can diverge from on-chain state, misleading off-chain systems into accepting incorrect state transitions. Existing smart contract vulnerability detection tools focus on logic bugs, with limited support for detecting event-semantic defects. To address this gap, we collect audit reports and incident cases and apply open card sorting to define five classes of event-semantic defects: event collision, state-event mismatch, unauthorized event emission, event emission mismatch, and event parameter mismatch. We propose EventSpec, which infers event specifications from a contract corpus via behavior inference and semantic-constraint extraction and applies differential checking to identify event-semantic defects in target contracts. We run EventSpec on 6,617 real-world contracts and evaluate detection effectiveness based on manually labeled results; EventSpec achieves an overall comprehensive precision of 90.17%. We further provide an off-chain evaluation harness that reproduces two off-chain attack vectors on any EVM-compatible chain: event origin confusion caused by unintended emitters and event-state desynchronization where events lack matching state updates. Using this harness, we demonstrate the feasibility of these attacks on bridge relayers, blockchain explorers, and NFT marketplaces, and report six wallet issues, four of which were confirmed (including a $600 bounty), with two remaining pending.

cs.CR↗

arXiv2026

Resolving a conjecture on permutation polynomials over $\mathbb{F}_{2^n}$

Let $\delta\in\mathbb{F}_{2^n}$ satisfy $\operatorname{Tr}_{\mathbb{F}_{2^n}/\mathbb{F}_2}(\delta)=1$. We study the permutation behavior of $$ f(x) = \left(\frac{1}{x^2+x+\delta}\right)^{2^k}+x $$ over $\mathbb{F}_{2^n}$. Helleseth and Zinoviev proved that $f(x)$ is a permutation for $k=0,1$, and remarked that numerical evidence suggests that no other cases occur. In this paper, we confirm their assertion by proving that, for $0\leq k<n$, $f(x)$ is a permutation of $\mathbb{F}_{2^n}$ if and only if $k=0$ or $k=1$.

math.NT↗

arXiv2026

Photonic magnetism and altermagnetism without magnetic materials

Magnetism, a fundamental property of solids arising from the collective alignment of electronic spins of magnetic atoms, has recently been expanded by the discovery of altermagnetism, a symmetry-driven phase distinct from both ferromagnetism and antiferromagnetism. Initial efforts to emulate altermagnetism in photonic systems are also being pursued, primarily through the magneto-optic response of materials; however, their intrinsic frequency limitations pose challenges for extending photonic altermagnetism into the optical regime, thereby restricting their material choices and optical applications. Here, we establish a microscopic framework of photonic magnetism through group theory symmetry analysis, defining photonic counterparts of electron spin and magnetic atoms. This approach enables the realization of photonic ferromagnetic, antiferromagnetic, and altermagnetic phases within structured optical lattices constructed by purely dielectric, nonmagnetic materials, which not only broadens the photonic magnetism concept, but also lifts the material and frequency limitations. Analytical models, numerical simulations, and experimental measurements reveal spin-momentum locking directly corresponding to their electronic analogs, showing d-, g-, and i-wave circular dichroism (CD) splitting bands at telecom wavelengths (around 1550 nm). These results demonstrate that altermagnetic order, and magnetism more broadly, can be reconstructed in photonic systems through symmetry and mode coupling alone, opening a new route towards spin-dependent light control and bosonic analogs of quantum magnetic phenomena in dielectric photonic crystals.

physics.optics↗

arXiv2026

A Universal Context-Reuse Layer for Cross-Model KV Sharing

Modern large language model (LLM) serving systems increasingly operate over repeated or shared context, yet each model typically performs its own prefill computation even when another model has already processed the same input. Existing KV-cache reuse mechanisms substantially reduce redundant computation within a single model, but generally assume that the producer and consumer of a cache are identical. We study \emph{cross-model KV sharing}, which translates the KV state produced by a source model into a representation that can be consumed by a different target model, including models that differ in scale, architecture, attention configuration, tokenizer, and model family. We evaluate the approach in both within-family and cross-family settings. For Qwen2.5-7B $\rightarrow$ Qwen2.5-1.5B, translated KV states improve LongBench2 accuracy from 27.59\% to 34.48\%, a gain of 6.89 percentage points over the native 1.5B baseline, while reducing handoff cost relative to native target prefill. For the cross-family Qwen2.5-1.5B $\rightarrow$ Gemma-2-2B setting, KV handoff reduces target-side prefill cost by up to 67.05\% at 4K context length while maintaining decoding perplexity close to native-model baselines. In a more heterogeneous Llama3.1-70B $\rightarrow$ Qwen2.5-7B setting, cross-family handoff achieves 44.0\% accuracy compared with 45.7\% for native Qwen2.5-7B inference, while reducing measured latency from 899ms to 138ms. These results provide initial evidence that KV states can serve as transferable computational representations rather than strictly model-local caches, and motivate \emph{context mobility} as a systems abstraction for reducing redundant prefill across heterogeneous LLM and multi-agent inference workflows.

cs.LG↗

arXiv2026

Learning to Allocate Incentives for Incentivized Advertising via Offline Model-Based Reinforcement Learning

Complete your ad view and grab a 5-cent bonus! In incentivized advertising, a platform promises users a bonus before observing downstream ad revenue, encouraging them to click and complete ads. It must balance the incentive promised in advance against the revenue realized afterward: insufficient incentives forfeit monetization opportunities, whereas excessive incentives reduce net profit. Because current incentives may also shape user expectations and future engagement, incentive allocation is a sequential decision problem with delayed revenue, cost sensitivity, and carryover effects. Existing work has not studied decision-making algorithms for this setting. Auto-bidding assumes available ad opportunities, while targeted promotion optimizes incentives outside the ad monetization pipeline. We formulate the problem as an MDP and develop an offline model-based RL framework for cost-controllable sequential incentive allocation. It learns a world model of user feedback and ad revenue, then performs conservative policy optimization. An independent counterfactual scorer evaluates each learned policy on held-out logs, enabling pre-launch selection without costly online exposure. Experiments on large-scale industrial data and online A/B tests show that the scorer provides a stable offline signal. The deployment path from causal inference to offline RL and then Offline-MBRL further validates the framework: MB-IQL improves per-user net profit by 7.96\% over TD3+BC, whereas reverting to plain IQL reduces it by 6.56\% (both $p<0.0001$).

cs.AI↗

arXiv2026

D3ER: Supporting Multi-Modal Recommendation via Disentangle and Distillation-based Dynamic Ensemble

Incorporating items' information shared among multiple modalities into a fused representation, multi-modal recommendation (MR) has demonstrated documented success than canonical unimodal recommendation. Although several attempts have been made to extract the discriminative information unique in each modality, existing methods suffer from a core limitation: the joint learning of modal-homogeneity discriminative information (HOI) and modal-heterogeneity discriminative information (HEI) tends to weaken their individual effectiveness. To remedy this deficiency, we propose a novel method, dubbed Disentangle and Distillation-based Dynamic Ensemble for multi-modal Recommendation (D3ER). We introduce gradient boosting into MR for the first time to formalize the optimization objective for alternately learning HOI and HEI. This design enables models dedicated to each type of information to focus on their proficient samples, thereby promoting specialized optimization. Furthermore, to mitigate the inherent high storage cost and risk of local optima in gradient boosting, we enhance our framework with knowledge distillation and a global correction regularization. Experiments on prevalent real-world datasets confirm the superiority of our proposed method on MR.

cs.IR↗

arXiv2026

Ab initio anatomy of quadrupole correlations in $^{16}$O and $^{20}$Ne

Azimuthal hadronic flow measured in ultra-relativistic ion--ion collisions provides a new means of imaging multipole correlations in the ground state of atomic nuclei. Early interpretations largely relied on a classical-rotor picture, in which the measured mean-square elliptic flow is directly related to an intrinsic quadrupole deformation. Atomic nuclei, however, contain additional many-body correlations generated by the Pauli exclusion principle, collective shape fluctuations and non-collective dynamical processes, whose impact on this correspondence has not yet been elucidated. Here, we resolve this issue through an ab initio analysis of $^{16}$O and $^{20}$Ne based on chiral nuclear interactions, combining the in-medium similarity renormalization group with the quantum-number-projected generator coordinate method. By successively isolating antisymmetrization, collective rotational and vibrational, and non-collective dynamical correlations, we determine, for the first time, how each component contributes to the mean-square quadrupole eccentricity. We uncover an unexpected compensation among these distinct correlation mechanisms: despite sizable individual contributions, the squared effective quadrupole deformation inferred from the elliptic flow remains close to the square intrinsic deformation of the nucleus. This result provides a microscopic explanation for the surprising success of the classical-rotor approximation and establishes a quantitative foundation for interpreting $^{16}$O+$^{16}$O and $^{20}$Ne+$^{20}$Ne collision data recently collected at the Large Hadron Collider.

nucl-th↗

arXiv2026

Fujiki Class $\mathcal C$ Varieties and a K\"ahler Criterion

In this article, we show that flips and divisorial contractions preserve the K\"ahler condition (for strongly $\mathbb{Q}$-factorial compact K\"ahler generalized klt pairs with $B+\beta_X$ big), and we give a criterion for varieties in Fujiki's class $\mathcal C$ to be K\"ahler. We also prove the existence of small $\mathbb Q$-factorializations for generalized klt pairs and of dlt modifications for generalized pairs.

math.AG↗

arXiv2026

Targeted Deep Survival Contrasts: Valid Inference for Treatment-Specific Survival Benefit with Neural Networks

Neural survival models are increasingly asked to support counterfactual claims---how much a treatment would change survival in a population---rather than only prognostic risk scores. Answering such questions from observational data requires valid inference for treatment-specific survival contrasts under confounding and covariate-dependent censoring, targets for which standard deep survival estimators are biased and provide no honest uncertainty. We propose Targeted Deep Survival Contrasts (TDSC), which extends Targeted Deep Architectures (TDA)---targeted maximum likelihood estimation embedded in a network's weight space---to the full vector of treatment-specific survival curves over a time grid, and hence to the benefit curve and the restricted mean survival time (RMST) difference. A single universal targeting path, one ridge projection of the stacked efficient influence functions onto closed-form last-layer gradients per iteration, simultaneously solves the projected estimating equations for all coordinates; a one-step residual top-up converts the plug-in into a doubly robust estimator of the unrestricted target; and a multiplier bootstrap yields simultaneous confidence bands for the benefit curve. We prove joint asymptotic linearity, band validity, and double robustness of the top-up for a cross-fitted variant requiring no Donsker conditions. Across seven Monte Carlo banks with confounded treatment, sign-varying effect heterogeneity, and dependent censoring, the TDSC plug-in attains nominal pointwise and simultaneous coverage with 35% lower MSE than a per-timepoint one-step (AIPCW) built from the same nuisance fits. Under a badly wrong outcome model the plug-in tracks its working parameter and its intervals fail (44% coverage), while the top-up restores nominal inference for the unrestricted causal target (94-95%)---and in-sample diagnostics separate the two regimes.

stat.ME↗

arXiv2026

CACSurv: Concordance-Aligned Comparative Learning with Large Language Models for Cancer Survival Prediction

Cancer survival prediction supports treatment planning, risk stratification, and follow-up management. Existing methods use structured clinical variables, whole-slide images, genomic profiles, or multimodal inputs, while patient reports remain underexplored. We study report-centric survival prediction using reports that organize pathological, clinical, and molecular evidence. Large language models (LLMs) can reason over such reports, but case-wise time regression introduces two mismatches. First, a formulation mismatch arises because survival evaluation depends on ordering comparable patients, whereas independent time predictions do not enforce ranking consistency. Second, a supervision mismatch arises because a censored patient's observed time indicates survival beyond that point and cannot serve as an exact regression target, although it still implies orderings relative to patients who died earlier. To address these mismatches, we propose CACSurv, a Concordance-Aligned Comparative framework for report-centric survival prediction. CACSurv reformulates survival modeling as mini-cohort comparative reasoning, where an LLM predicts relative prognostic orderings. We introduce concordance-aligned rewards derived from comparable relations under right censoring, enabling censored outcomes to provide ranking supervision without exact event-time targets. At inference, Monte Carlo Reference Aggregation compares each patient with sampled references and aggregates positions into a cohort-level ranking. We establish TCGA-SurvReport, a benchmark covering six TCGA cancer cohorts. CACSurv achieves the highest C-index on all six cohorts and an average C-index of 0.722, outperforming the strongest published survival model by 6.5 percentage points and the strongest LLM time-regression baseline by 4.2 percentage points. Our code, models, and dataset will be available at https://github.com/xmed-lab/CACSurv.

cs.AI↗

arXiv2026

A Near-Optimal Lower Bound for $\ell_p$-Subspace Embeddings, $1\leq p<2$

For $d \geq 2$, $p \geq 1$ and $\epsilon > 0$, let $N_p(d,\epsilon)$ be the smallest integer $N$ such that for every integer $n$ and every $A\in\mathbb{R}^{n\times d}$, there exists a matrix $\Phi\in\mathbb{R}^{N\times n}$ satisfying $(1-\epsilon)\lVert Ax\rVert_p\leq \lVert\Phi A x\rVert_p\leq (1+\epsilon)\lVert Ax\rVert_p$ for all $x\in\mathbb{R}^d$. For every constant $p\geq 1$ with $p\not\in 2\mathbb{Z}$, when $d\gtrsim_p \log(1/\epsilon)$, the bound \[ N_p(d,\epsilon) \gtrsim_{p} \frac{d}{\epsilon^2 \operatorname{polylog}(d/\epsilon)} \] is established. This improves the previous lower bound $\Omega(1/(\epsilon^2\operatorname{polylog}(1/\epsilon)))$ due to Li et al. (SICOMP 2021) and is optimal up to logarithmic factors for $1\leq p<2$. The central technical idea originated from ChatGPT 5.6 Sol.

cs.DS↗

arXiv2026

KHiM-Mamba: Injecting Pathology Knowledge into Mamba via Hidden-State Modulation for Whole Slide Image Analysis

Whole slide image analysis is commonly formulated as multiple instance learning (MIL), where instance features are contextually updated and aggregated into a slide representation, a process we term slide encoding dynamics. Recently, selective state-space models (SSM) have emerged as promising MIL architectures due to their long-sequence modeling capability and linear complexity. However, existing SSM-based MIL methods rely solely on visual features during MIL. Meanwhile, in large-scale WSIs, where sparse diagnostically decisive regions are surrounded by abundant irrelevant information, such purely vision-driven selective dynamics can misallocate state updates and readouts, causing the evolving SSM state to accumulate task-irrelevant evidence and dilute critical diagnostic cues over long scan trajectories. In this work, we propose the Knowledge-Aware Hidden-State Modulation architecture (KHiM-Mamba), which innovatively regulates Mamba's core selective state-space mechanism with explicit knowledge priors, steering slide encoding dynamics toward diagnostically meaningful evidence accumulation. Specifically, we redesign the original SSM layer to perform knowledge modulation operations during the evolution of hidden states, thereby guiding what visual evidence is accumulated and retrieved from the hidden state at each encoding step. Furthermore, we additionally introduce a local-adaptive vocabulary retrieval module that uses large language models to assign each patch fine-grained, tissue-specific semantic descriptions, enabling precise modulation across diverse tasks. Experiments on 11 public benchmarks across 4 tasks show that KHiM-Mamba consistently achieves state-of-the-art performance.

eess.IV↗

arXiv2026

Dark sector radiation corrections to invisible dark photon production: beyond fixed order

In this work we study invisible dark photon production at electron-positron colliders in a dark Abelian Higgs model at NLO (next-to-leading-order), and the physical distribution of squared missing mass $M_X^2$. We show that the fixed order correction to the total cross section for the process $e^+e^-\to \gamma A'$, with $A'$ the dark photon, is infrared-safe, but the corresponding differential distribution of $M_X^2$ reveals a quasi-collinear $1/M_X^2$ divergence when the masses of dark sector particles are much smaller than the hard scale. By using the Sudakov resummation method, we obtain an integrable, normalized distribution of $M_X^2$, which is actually the ``jet mass'' of the dark photon branch. We also discuss how these dark sector corrections impact the invisible dark photon search at electron-positron colliders.

hep-ph↗

arXiv2026

Degraded Infrared Small Object Detection via Degradation-Adapted Physics-Guided Restoration

Infrared small object detection has made significant progress in recent years. However, degradations such as fog and nonuniformity can suppress target-background contrast, substantially increasing detection difficulty. Existing methods mainly rely on image restoration as preprocessing, but they are typically designed for specific degradation types and fail to generalize to varying degradations. To alleviate this, we propose DAISOD, a degradation-adapted infrared small object detection framework for robust detection under different degradations. DAISOD first identifies the type and severity of degradations, then adapts the processing via dedicated branches, and finally fuses the results for subsequent detection. Moreover, a physics-guided restoration mechanism is incorporated to explicitly estimate degradation parameters and remove degradation effects through physical models, avoiding excessive restoration that may erase small targets. Moreover, we construct a degraded infrared small object detection dataset covering diverse degradation types and levels. Extensive experiments show that DAISOD outperforms state-of-the-art methods under various degradation conditions.

cs.CV↗

arXiv2026

Autonomous Optimization of Complex Oxides for Thermochemical Fuel Production

Two-step thermochemical fuel production, including H2O and CO2 splitting, offers a promising route to sustainable fuel manufacturing, with performance governed by redox-active oxides that enable cyclic reduction-oxidation reactions. Maximizing thermal-to-fuel conversion efficiency demands materials that simultaneously satisfy multiple stringent thermodynamic and kinetic targets. Addressing these requirements has increasingly driven materials design toward complex, multi-cation oxides, such as mixed-cation fluorites, perovskites, and high-entropy oxides, wherein composition, defect chemistry, phase stability, and morphology should be co-optimized. This creates a challenging materials optimization problem that is poorly suited to traditional trial-and-error approaches. In this review, we argue that thermochemical fuel production provides a compelling frontier for autonomous materials design and optimization. We first examine why redox-active complex oxides are difficult to develop, owing to multidimensional phase spaces, harsh operating conditions, and competing functional targets. We then discuss how high-throughput computation, automated synthesis, characterization and testing, and machine learning can be integrated into closed-loop workflows to address these challenges. Building on broader oxide materials research, we organize recent progress into a capability roadmap for complex-oxide optimization, spanning compositionally diverse synthesis, operando characterization, robotic testing, operation-condition computation, and multi-objective optimization. Finally, we outline key experimental, computational, and data challenges for building self-improving materials development platforms for materials development in thermochemical fuel production.

cond-mat.mtrl-sci↗

arXiv2026

Combating Knowledge Corruption in Agent Systems: A Byzantine-Tolerant Secure Collaborative RAG Framework

While retrieval-augmented generation systems partially address the hallucination issues in large language models, it also introduces new vulnerabilities to knowledge corruption attacks. Adversaries exploit these vulnerabilities by poisoning documents provided by RAG system to manipulate LLM outputs. To counter this threat, we propose SecureCollaRAG, a Byzantine-tolerant collaborative RAG framework leveraging Multi-source Knowledge Validation Mechanism. Our approach enables agent system to securely verify document provenance through dynamic GNN-based credibility scoring, effectively preventing stealthy knowledge corruption attacks while preserving essential domain knowledge integrity. Through extensive evaluations and formal analysis, we demonstrate that SecureCollaRAG maintains robustness against attackers under non-IID data distributions.

cs.CR↗

arXiv2026

Recent Sharp Rise in Inhomogeneous Hydrological Extremes Stress Vegetation Growth in China

The intensifying spatial inhomogeneity of rainfall under greenhouse warming implies that more inhomogeneous hydrological extremes (IHEs), i.e., coexistence of extreme rainfall or drought, may be triggered. While vegetation growth in China is sensitive to hydrological hazards, the variability of IHEs and their ecological impacts remain underexplored. Here, we find a significant increase in IHEs during China's growing season since 2000 (+2.1 events or +14.52 days per decade), with a rapid sharp rise to an annual average of 6.4 events or 42.0 days in the past five years. The primary driver is the enhanced inhomogeneity of moisture-dynamic coupled weather conditions, overlapping with a northward shift of climatological precipitation distribution. This results in a "Wet-North and Dry-South" pattern of IHE impacts, which poses severe and asymmetric threats to vegetation growth in China, with the expansion of drought areas exerts stronger stress on vegetation than the compensatory effects of rainfall. Our findings suggest that the sharp rises of IHEs tend to yield net negative impacts on vegetation growth, highlighting the need for stronger hydrological management to reduce future risks.

physics.ao-ph↗