arXiv subjects

Jingyu Liu

Publications and source records attributed to Jingyu Liu.

At least 19 recordsLinked to original sources

arXiv2026

Sketch-and-Restart: Randomized Sketching in Quadrature-Based Restarting for Matrix Functions

We develop a sketch-and-restart framework for computing the action of a matrix function on a vector, $f(A) b$, where $A$ is large, sparse, and non-Hermitian. The framework combines quadrature-based restarting with Arnoldi-like decompositions generated by sketched or truncated Arnoldi processes. Within this framework, we develop two classes of restarted algorithms. The first uses a fixed Krylov subspace dimension and is based either on the sketched Arnoldi process or on a new sketched harmonic Arnoldi process proposed in this work. The second class chooses the Krylov subspace dimension adaptively by running the truncated Arnoldi process until the condition number of the generated basis, estimated from its sketch, exceeds a prescribed threshold. We also establish the convergence of the restarted sketched harmonic Arnoldi method for Stieltjes functions under the assumption that $A$ is positive real. Numerical experiments demonstrate the effectiveness of the proposed framework, including the computational savings achieved through sketching, the storage reduction enabled by adaptive truncation, and the acceleration obtained from thick restarting.

math.NA↗

arXiv2026

A Multi-Asset Dataset and Tail-Risk Framework for DeFi Yield Trading Derivatives

Yield trading has emerged as a pivotal innovation in Decentralized Finance (DeFi) by decoupling principal from yield streams to enhance capital efficiency. Despite its significance, the field lacks fine-grained datasets for systematic empirical analysis. We address this gap by constructing the first large-scale benchmark dataset for Pendle, the pioneering on-chain interest rate derivatives protocol, spanning 24 months across 340 active markets with complete lifecycle observations. Our analysis reveals a structural market transition toward stablecoin-dominated "flight-to-quality" dynamics. To tackle the heavy-tailed nature of yield data, we propose Tail-MSE, an asymmetric loss function that prioritizes tail risk awareness through dynamic penalty weighting. Experiments across six state-of-the-art time series models demonstrate consistent improvements in volatility forecasting robustness, enabling the Tail-MSE Sniper strategy to achieve superior risk-adjusted returns in over half of all markets.

cs.CE↗

arXiv2026

TrajCFG: Token-Wise Classifier-Free Guidance via Denoising Trajectory Feedback

Classifier-free guidance (CFG) typically applies a shared scale to all latent tokens, although their velocity predictions evolve differently during sampling. We introduce Trajectory-Adaptive Classifier-Free Guidance (TrajCFG), a training-free method for flow-matching models that adapts token-wise guidance using historical changes in guided velocity direction. TrajCFG maps this feedback to bounded scales, assigning stronger guidance to larger directional changes and weaker guidance to more consistent directions. It combines adjacent-step and interval-normalized longer-span feedback, reusing standard CFG predictions without additional model evaluations. Controlled diagnostics and ablations examine the roles of directional feedback, token-wise allocation, and history-span fusion. Experiments on text-to-video, image-to-video, text-to-audio-video, text-to-image, and image-to-3D generation show improvements over base samplers across multiple backbones. For text-to-video generation, TrajCFG improves VBench Total scores by 0.30-0.75 points over the strongest evaluated baseline on each of three backbones. These results support historical directional feedback as a broadly applicable signal for fine-grained guidance allocation. The project page is available at https://jyliu-98.github.io/trajcfg-project-page/.

cs.CV↗

arXiv2026

Diagnosing Task Insensitivity in Language Agents

Language agents select actions based on task descriptions, environmental observations, and interaction histories. We investigate whether task requirements reliably constrain these decisions when the surrounding context already suggests how to proceed. Across five models and three interactive environments, we find that models, including GPT-5.6-Sol, can continue to act when task descriptions are replaced with the meaningless placeholder xxxx, even when prompted to ask for clarification. Action generation is more frequent in states that suggest a concrete next operation, although these cues do not establish whether that operation serves the user's goal. These findings suggest a possible training-based explanation: contextual cues can make demonstrated responses predictable, allowing models to fit them with limited reliance on task requirements. Tracking seven models during fine-tuning, we observe an overall decrease in attention to task-description tokens and an increase in attention to current observations. To strengthen task dependence, we introduce Task-Perturbed Negative Log-Likelihood (TPNLL), which contrasts the prediction costs of the same response under different task descriptions while holding the non-task context fixed. TPNLL increases attention to task-description tokens and improves complete-episode success in out-of-distribution settings.

cs.AI↗

arXiv2026

Real-Arithmetic Sketched Eigensolvers for Symmetric Matrices

In this work, we develop sketched variants of subspace iteration and TraceMIN, called sSI and sTraceMIN, for real symmetric eigenvalue problems by combining basis whitening with the sketched Rayleigh-Ritz (sRR) procedure. The nonsymmetric projected matrix in sRR may have nonreal eigenpairs, leading to complex arithmetic and additional computational costs. We overcome this difficulty using a real Schur formulation of sRR that keeps the iterations in real arithmetic. We further develop procedures for characteristic subspace selection and convergence monitoring within this formulation. On the theoretical side, we discuss suitable subspace embedding assumptions for sketched eigensolvers that use a fixed sketching matrix throughout the iterations. Under a mild embedding assumption, we establish local convergence results for sSI and sTraceMIN. Numerical experiments demonstrate runtime savings of up to 39% for sSI and 21% for sTraceMIN relative to their standard counterparts on the test problems.

math.NA↗

arXiv2026

SEIS: Self-Evolving Inference Systems

Inference systems determine how fast and how cheaply language models can be served, so making them faster has direct practical value. However, prior work focuses mostly on optimizing certain parts such as kernels or memory within the large system. In this work, we take a holistic approach and apply agentic self-evolution to optimize the whole system end-to-end. Our SEIS (Self-Evolving Inference Systems) autonomously optimizes the entire mini-sglang engine without human intervention through iterative sessions with inherited experiences and code changes. Serving Qwen3-0.6B on H100, the resulting engine reaches 3.27X the throughput of the original mini-sglang implementation and beats SOTA engines like vLLM, TensorRT-LLM, and SGLang in the single-request workload. The correctness of the optimized inference engine by SEIS is tested in terms of numerical difference and downstream accuracy on math and long-context retrieval tasks. The code and session histories show that the speedup comes from redesigning the whole engine and that building on earlier sessions beats independent attempts. These results suggest that agentic self-evolution can optimize a complex system end-to-end. The evaluation also has to evolve with the engine, and letting agents evolve it is a natural next step.

cs.AI↗

arXiv2026

When History Fails to Become Experience: Action Calibration in Language Agents

Language agents should draw on prior attempts and environmental feedback to improve subsequent decisions within the same task. However, providing additional interaction history can sometimes reduce task success, suggesting that agents do not consistently use this information effectively. To investigate this limitation, we examine how agents use history. We find that history improves task completion overall, yet much of this benefit persists even when past actions are shuffled. Disrupting the correspondence between actions and observations causes only a modest decline in task success. We therefore hypothesize that agents do not reliably connect past actions with their outcomes when deciding how to proceed. To test this hypothesis, we explicitly label each returned observation as the outcome of the preceding action. This simple annotation improves task success and reduces next-action repetition without introducing new environmental information. Building on this insight, we introduce a learned calibrator that explicitly reassesses past actions and selectively records experience to guide subsequent decisions, improving task success beyond outcome labeling alone.

cs.CL↗

arXiv2026

A Superfast Direct Solver for 2D Type-II Inverse Nonuniform Discrete Fourier Transform Based on Hierarchically Semiseparable Matrices

Reconstructing images on Cartesian grids from nonuniform Fourier samples is a basic computational task in Fourier reconstruction methods for computed tomography. This paper proposes a direct solver for the 2D type-II nonuniform discrete Fourier transform~(NUDFT) based on the face-splitting product of hierarchically semiseparable~(HSS) matrices. We define the face-splitting product of two HSS trees and prove that the face-splitting product of two HSS matrices admits an HSS representation on the resulting tree. The proof is constructive, providing explicit formulas for the HSS generators and bounds on the off-diagonal ranks at each level. For the NUDFT, a Fourier change of basis expresses the transformed 2D matrix as a face-splitting product of two transformed 1D NUDFT matrices. This identity yields an algebraic construction of a 2D HSS approximation from two 1D HSS approximations, for which we establish a Frobenius-norm approximation error bound. Combining this construction with recompression, URV factorization, and a two-dimensional inverse fast Fourier transform gives a direct solver for the associated least-squares problem. For fixed accuracy, square frequency grids, and balanced HSS trees with fixed leaf column sizes, the solver has an offline complexity of $O(M \log^{2} N + N^{3 / 2} \log^{3} N)$ and an online complexity of $O(M + N \log^{3} N)$ per right-hand side, where $M$ and $N$ are the numbers of sample points and unknown coefficients, respectively. Numerical experiments on random and polar grids illustrate the computational scaling and reconstruction accuracy of the method, and demonstrate a substantial reduction in lsqr iteration counts when the direct solver is used as a preconditioner.

math.NA↗

arXiv2026

World-Action Models for Robot Learning and Control: A Survey

Robots operating in open environments act under partial observability, physical constraints, and dynamic task contexts. Beyond mapping observations and language instructions to actions, they must anticipate how candidate actions may affect future states and task-relevant outcomes. Recent advances in world models, video generation, and Vision-Language-Action (VLA) policies have motivated the development of World-Action Models (WAMs), which couple future world prediction with executable action generation. This survey provides a robotics-oriented review of WAMs. We clarify their scope relative to conventional world models, model-based reinforcement learning, action-conditioned video generation, and reactive VLA policies, and organize existing methods through a unified taxonomy covering representations, transition modeling, action interfaces, architectures, training pipelines, data modalities, and scaling strategies. We further review applications of WAMs in manipulation, navigation, and autonomous driving, and we summarize the datasets, benchmarks, metrics, and protocols used to evaluate WAM systems. Finally, we discuss key challenges in action alignment, world-action factorization, spatial and multi-view consistency, long-horizon memory, neural simulation for closed-loop policy learning, and efficient inference. Taken together, this survey aims to provide a concise technical foundation for integrating predictive world modeling with action generation, toward more reliable embodied robot intelligence. Project page: https://rcl-robotics.github.io/Awesome-World-Action-Models.

cs.RO↗

arXiv2026

A Gradient-based yet Spike-Timing-Dependent Solution to the Feedback Learning Problem in Neural Microcircuits

The brain uses discrete spikes for dynamic computation, yet, how neural microcircuits (NMCs) solve temporal credit assignment using local spike timing remains a fundamental open question. Dominant spiking neural network (SNN) approaches circumvent this by approximating backpropagation through surrogate gradients, decoupling learning from biological spike timing. Here, we reformulate temporal credit assignment as a state separation problem: extracting task-required components induced by historical perturbations directly from the current neural state. This enables an online feedback learning framework for NMCs through a gradient tunneling (GT) algorithm and the lead-lag expansion technique that derives credit assignment from local synaptic spike timing, while remaining compatible with ANN-SNN hybrid architectures. Experimentally, GT-trained NMCs excel at long-timescale evidence integration and noise-robust memory retention, and perform comparably to leading SNN online learning methods on real-world benchmarks with far fewer parameters. The proposed framework addresses the two-decade-old NMC feedback learning problem and suggests a computationally plausible explanation for the brain's learning mechanisms.

cs.NE↗

arXiv2026

Approximate Inversion of Discrete Fourier Integral Operators via Hierarchically Semiseparable Matrices

This paper introduces a novel method for approximating the inverse of discrete Fourier integral operators (FIOs). Given an $N \times N$ matrix representation $K$ of an FIO, the proposed algorithm consists of two stages. In the offline stage, we first construct a butterfly factorization (BF) $\tilde{K}$ of $K$, which enables fast forward matrix-vector multiplication. We then construct a hierarchically semiseparable (HSS) approximation $\tilde{G} \approx G$, where $G = K^{*} K$, using fast applications of $\tilde{K}$ and $\tilde{K}^{*}$ to random matrices. Finally, we apply the ULV factorization to the HSS matrix $\tilde{G}$ to obtain an approximation $\tilde{F} \approx G^{-1}$. Combining these approximations yields an approximate inverse $K^{-1} \approx \tilde{F} \tilde{K}^{*}$. The offline stage has complexity $O(N \log^{2} N)$ for 1D problems and $O(N^{1.5} \log N)$ for 2D problems. In the online stage, the proposed method approximates $K^{-1} u$ for a given input vector $u$ with complexity $O(N \log N)$ for both 1D and 2D problems. The proposed method can be used either as a direct solver or as a preconditioner for iterative methods. Numerical results for 1D and 2D FIOs demonstrate the effectiveness of the proposed method.

math.NA↗

arXiv2026

TxSum: User-Centered Ethereum Transaction Understanding with Micro-Level Semantic Grounding

Understanding the economic intent of Ethereum transactions is critical for user safety, yet current tools expose only raw on-chain data or surface-level intent, leading to widespread ``blind signing'' (approving transactions without understanding them). Through interviews with 16 Web3 users, we find that effective explanations should be structured, risk-aware, and grounded at the token-flow level. Motivated by these findings, we formulate TxSum, a new domain-grounded NLP task for DeFi transaction explanation, and construct a dataset of 187 complex Ethereum transactions with 2,375 token-flow annotations and transaction-level summaries. We further introduce MATEX, a grounded multi-agent framework for high-stakes transaction explanation. It selectively retrieves external knowledge under uncertainty and audits explanations against raw traces to improve token-flow-level factual consistency. MATEX achieves the strongest overall explanation quality, especially on micro-level factuality and intent quality. It improves user comprehension on complex transactions from 52.9% to 76.5% over the strongest baseline and raises malicious-transaction rejection from 36.0% to 88.0%, while maintaining a low false-rejection rate on benign transactions.

cs.CE↗

arXiv2026

MSR-IVA: Masked Structural Residual Independent Vector Analysis for State-Aware Fusion of Structural MRI and Dynamic Functional Network Connectivity

Multimodal fusion of structural MRI (sMRI) and dynamic functional network connectivity (dFNC) can reveal how brain structure relates to changing functional states. When the same structural latent representation is coupled with multiple states, applying independent vector analysis (IVA) separately to each state can produce unrelated structural decompositions, while forcing identical decompositions may suppress state-specific relationships. In addition, not every subject expresses every dynamic state. We propose masked structural residual IVA (MSR-IVA), a state-aware framework that combines a shared structural representation with state-specific residual adaptations and masks for incomplete state expression. On an Alzheimer's Disease Neuroimaging Initiative cohort, MSR-IVA improved matched source coupling by 6.5% and reduced unmatched dependence by 15.7% relative to the independent pairwise IVA baseline. Among subjects expressing both states, mean absolute cross-state structural source correlation was 0.9177 for MSR-IVA versus 0.2978 for no sharing, demonstrating controlled structural sharing that preserves source correspondence while allowing state-specific adaptation.

cs.LG↗

arXiv2026

Long-range coupling enabled multiband group-velocity control of topological edge states from slow light to light stopping

Topological edge states provide robust optical transport immune to disorder, yet their propagation velocity is usually constrained by the intrinsic band dispersion, limiting dynamic control of topological light transport. We introduce long-range next-nearest-neighbor (NNN) couplings into a Harper--Hofstadter photonic lattice and establish a versatile platform for group-velocity engineering. We demonstrate that the NNN couplings play two distinct roles: the vertical coupling opens a previously closed topological band gap by lifting the degeneracy of bulk bands, while the horizontal coupling reshapes the edge-state dispersion through momentum-dependent corrections, enabling controllable topological slow-light transport. Furthermore, the band-gap Chern numbers associated with different gaps exhibit opposite signs, giving rise to topological edge states with opposite chiralities. Propagation simulations reveal robust unidirectional transport of these counter-chiral edge states with reduced group velocities. By continuously tuning the NNN coupling strength, the group velocity of topological edge modes can be reduced toward zero at specific momenta, resulting in topological light-stopping effects. These results demonstrate that long-range NNN couplings provide an effective mechanism for engineering momentum-dependent topological group velocities and offer new possibilities for robust slow-light devices, optical delay lines, and multiband integrated photonic systems.

physics.optics↗

arXiv2026

Time-resolved role of coherence and delocalization in photosynthetic energy transfer from an extended exciton model

Photosynthetic antenna complexes achieve high quantum efficiency through exciton transport in coupled pigment networks. Conventional Frenkel-exciton models treat each chromophore as a structureless site and neglect internal electronic degrees of freedom that can influence coherence and delocalization. Here we develop an extended excitonic network model that preserves the pigment-pigment coupling topology while introducing tunable intrachromophoric electronic mixing within the single-excitation manifold. Using a Lindblad open-quantum-system framework, we quantify coherence, delocalization, and trapping efficiency across parameter space. We show that intrachromophoric mixing plays a time-dependent role: enhanced mixing on the antenna side promotes short-time coherent delocalization and improves excitation injection, whereas excessive mixing near the trapping site induces persistent delocalization and suppresses transfer efficiency. Simulated two-dimensional electronic spectra reveal enhanced cross peaks and systematic blue shifts, providing spectroscopic signatures of coherence-modulated transport. These results establish a microscopic connection between internal electronic structure and quantum transport performance in excitonic networks.

physics.optics↗

arXiv2026

AdaptICA: Data-Adaptive Transformation Learning for Independent Component Analysis

Independent component analysis (ICA) is widely used to recover latent structure from signal and imaging data, but standard ICA assumes that the observed measurement scale preserves a linear mixing structure. This assumption may fail for features produced through nonlinear preprocessing, such as band-specific power in motor-imagery EEG. We propose AdaptICA, an adaptive transformation-based framework that jointly learns grouped componentwise transformations and the demixing structure using a profiled mutual-information criterion. Because the transformation and demixing parameters may compensate for one another, their joint estimation introduces new identifiability and asymptotic challenges. We establish identifiability, consistency, and asymptotic normality of the transformation estimator, together with joint strong consistency of the transformation and demixing estimators. AdaptICA selects the transformation structure data-adaptively and includes the identity transformation as a candidate, thereby reducing to standard ICA when no scale adjustment is needed. Extensive simulations support the theoretical results. Applications demonstrate that AdaptICA can recover more independent and interpretable sources when transformation is beneficial while retaining standard ICA when the original measurement scale is adequate.

stat.ME↗

arXiv2026

From PBS to ePBS: the Microstructure of Block Building

Ethereum's Glamsterdam upgrade introduces enshrined proposer-builder separation (ePBS), replacing relay-centric PBS with direct builder bids to proposers. We study how this shift changes the block-building microstructure through a general imperfect-information two-stage auction with verifiable messages, where an early bid serves as both a price offer and a signal. PBS and ePBS are modeled as restrictions of the same block-building game: PBS fixes stopping and disclosure exogenously, while ePBS lets the proposer choose stopping and disclosure ex post. Latency heterogeneity is captured by asymmetric information updates: fast builders observe disclosed early information before rebidding, while slow builders do not. We combine exact perfect Bayesian equilibrium characterizations in tractable cases with calibrated no-regret learning in finite games. For PBS, we show that separating equilibria preserve the standard first-price-auction payoff benchmark and provide conditions for their existence. For ePBS, we demonstrate a ratchet effect: because the proposer can defer block proposal and use early bid information in the second stage, builders anticipate ex-post extraction and shade or pool early bids, generating allocation inefficiency and revenue-efficiency valleys. We interpret this ratchet distortion as a commitment failure. Under full commitment, the optimal policy collapses to the static Myerson auction and removes the ratchet channel. To realize part of this commitment advantage in a feasible mechanism, we propose a Trusted Execution Environment (TEE) sidecar that enforces limited commitment. We formulate the revenue-maximizing TEE mechanism as a bilinear optimization problem. In conservative finite benchmarks, the TEE design increases the proposer revenue relative to the first-price benchmark by approximately $25\%$.

cs.GT↗

arXiv2026

Nemotron-Labs-Diffusion: A Tri-Mode Language Model Unifying Autoregressive, Diffusion, and Self-Speculation Decoding

We introduce Nemotron-Labs-Diffusion, a tri-mode language model (LM) that unifies AR, diffusion, and self-speculation decoding within a single architecture. Trained with a joint AR-diffusion objective, Nemotron-Labs-Diffusion can switch modes to sustain high throughput across deployment settings and concurrency levels. Our study shows that (1) AR and diffusion objectives are complementary: diffusion improves lookahead planning, while AR provides left-to-right linguistic priors. (2) In self-speculation mode, diffusion drafts while AR verifies, outperforming multi-token prediction (MTP) methods in both acceptance rate and real-device efficiency. (3) A speed-of-light analysis further demonstrates diffusion's long-term potential, with up to 76.5% more tokens per forward pass than self-speculation under an optimal sampler. Scaling to 3B, 8B, and 14B parameters, our Nemotron-Labs-Diffusion family, including base, instruct, and vision-language models, consistently outperforms state-of-the-art open-source AR and diffusion LMs in both accuracy and speed. For example, Nemotron-Labs-Diffusion-8B decodes 6x more tokens per forward than Qwen3-8B with comparable accuracy, translating to 4x higher throughput on SPEED-Bench with SGLang on a GB200 GPU.

cs.CL↗