Search arXiv⌕ Search

arXiv subjects

Lin Zhang

Publications and source records attributed to Lin Zhang.

At least 37 records · Page 2Linked to original sources
arXiv2026

Voice Agents under Acoustic Stress: From Signal Degradation to Interaction and Action

Voice agents must complete users' tasks despite noise, reverberation, and competing speech. Evaluating agents' robustness therefore requires following how acoustic conditions affect the conversation and the actions taken on the user's behalf. This overview examines what existing benchmarks reveal about agents' ability to complete tasks under acoustic stress and where further task-based evaluation is required. We then introduce TRACE, a practical workflow for designing, running, and interpreting evaluations of acoustic robustness in task-oriented human-agent interactions: the same agent attempts a specified task with an original recording and an acoustically stressed copy, and the resulting conversations are scored for task completion, wrong actions, recovery, and user effort. Finally, we explain how results from these evaluations can guide changes to an agent to prevent wrong actions and improve recovery.

eess.AS↗
arXiv2026

Asset price bubbles under model uncertainty and short-sale constraints: A discrete-time analysis

In this study, we investigate asset price bubbles in a discrete-time, discrete-state market under model uncertainty and short-sale constraints. Using a super-hedging valuation benchmark, we study the difference between the asset's market price and its fundamental value in this constrained market. We examine how assumptions on the liquidation time affect the conditional expectation bounds satisfied by the resulting bubble process. For assets with bounded maturity and no dividend payments, the G-supermartingale property of prices provides a necessary and sufficient condition for the existence of bubbles. In contrast, when maturity is unbounded, the infi-supermartingale property yields a necessary condition, while the G-supermartingale property remains sufficient. We also show that no dominance rules out bubbles when the liquidation time is bounded. For finite-horizon contingent claims, we distinguish upper-valuation relations from no-dominance benchmarks. Fundamental prices satisfy put-call bounds, whereas market prices satisfy put-call parity under no dominance. In the dividend-free setting, the same assumptions imply equality between American and European call fundamental values and, under the stated trading conditions, between their market prices.

q-fin.MF↗
arXiv2026

Quantum Probability Current Guided Reduction of Coupling Control Degrees of Freedom for Excitation Transport

Time-dependent coherent control can enhance excitation transport in open quantum networks, but independently controlling every inter-site coupling creates a control space of high dimension and leads to difficult optimization problems. We introduce an edge-ranking strategy based on the control-induced change in the gradient component of the time-integrated quantum probability current, which is obtained via a graph Hodge decomposition. When our strategy is applied to the seven-site Fenna-Matthews-Olson (FMO) model, the six-edge set retains $99.83\%$ of the enhancement achieved by full control, and the four-edge set retains $97.60\%$ while reducing the pulse fluence---used here as a proxy for control effort---by $41.55\%$ relative to full control. Dephasing scans and comparisons with random edge sets and random networks provide numerical support for the relevance and potential broader utility of the ranking. These results show that edge selection guided by the quantum probability current can substantially reduce the control space while preserving high transport performance with lower control effort.

quant-ph↗
arXiv2026

Bargmann Invariants Are Insufficient for Complete Local-Unitary Orbit Discrimination

Bargmann invariants constructed from a bipartite density operator and its two lifted marginals are polynomial invariants of local-unitary conjugation. We determine the precise information encoded in these invariants. Whenever one subsystem is a qubit, the ordinary marginal-word family determines the full spectrum of the partial transpose and hence decides whether the state has the positive-partial-transpose property. In $2\otimes2$ and $2\otimes3$ systems, this yields complete separability criteria. For the two-qubit system, a finite subfamily additionally separates local-unitary orbits, and a finite extension generates the polynomial invariant ring. These three tasks already diverge for qubit-qutrit states: we exhibit full-rank, locally maximally mixed states that agree on all ordinary marginal-word invariants yet have different operator-Schmidt ranks, together with a quartic correlation invariant that separates them. When both local dimensions are at least three, the analogous collapse on the locally maximally mixed sector produces isospectral pairs consisting of one separable state and one entangled state with negative partial transpose. The ordinary Bargmann algebra therefore coincides with the full local unitary invariant ring if and only if both subsystems are qubits. The missing data are geometric: they encode the placement of global eigenspaces relative to the tensor-product decomposition

quant-ph↗
arXiv2026

A Mean-Field Approach to the Dielectric Response of Bulk Superconductors for Light Dark Matter Direct Detection

The dielectric function is central to describing many-body screening effects in dark matter (DM) direct detection with condensed matter targets. Current superconducting detector analyses employ the free-electron Lindhard dielectric function to model in-medium effects, an approximation whose validity in the superconducting state remains untested. We derive the electronic dielectric function for bulk superconductors within the Bardeen-Cooper-Schrieffer (BCS) framework, incorporating the full Bogoliubov quasiparticle coherence factors in the random-phase approximation. A systematic comparison with the Lindhard function for aluminum and tungsten silicide (WSi) reveals good agreement for energy depositions $ω\gtrsim5Δ$, establishing the Lindhard function as a robust approximation for superconducting DM detectors operating in this regime.

hep-ph↗
arXiv2026

DianShi-RxnDB: A Large-Scale, Fine-Grained Organic Reaction Data Platform Built via a Fully Automated Pipeline for Researchers and AI Agents

High-quality structured organic reaction data are essential for developing artificial intelligence for chemistry (AI4Chem), yet much of this knowledge remains dispersed across patent text, images, and reaction schemes. We present DianShi-RxnDB, a large-scale, fine-grained organic reaction data platform built via a fully automated extraction and normalization pipeline integrating patent text, images, and reaction schemes. Its corpus covers organic synthesis patents from the USPTO and EPO published between 1976 and 2025, yielding approximately 24 million reaction instances, of which approximately 14.8 million (61.7%) pass automated qualification checks. Each instance represents a specific single-step experiment recording participants, roles, quantities, temperatures, reaction times, yields, experimental procedures, and provenance links to source patents. In a manual evaluation of 1,300 sampled qualified instances, the micro-averaged field-level accuracy was 92.95%. A matched comparison with Pistachio further indicated advantages in deduplicated record counts, representation granularity, and field-level exact agreement. The platform provides a Web research workbench for searching, filtering, comparing, and source-verifying records, and a Model Context Protocol (MCP) service offering AI agents composable structured retrieval tools. DianShi-RxnDB is available at https://dianshi.opendatalab.org.cn/ .

cs.CL↗
arXiv2026

Sealing the Audit-Runtime Gap for LLM Skills

Large language model (LLM) ecosystems such as Claude Code and ChatGPT increasingly rely on skills: packages of natural-language instructions and executable tools. Once in the LLM's context, skill content cannot be reliably separated from trusted instructions, and a skill's executable side can invoke privileged actions, exposing the skill supply chain to injection, tampering, and rug-pull attacks. Existing defenses are stage-bound: centralized signing, audit reports unbound from the runtime artifact, or policy engines that cannot attest to what was approved. We present SIGIL, the first framework that seals the audit-runtime gap for LLM skills. SIGIL delivers verifiable hosting through a tamper-evident, decentralized on-chain registry from which LLMs fetch skills directly. The registry admits four publication types, Transparent, Licensed, Sealed, and Committed, spanning plaintext public distribution, monetized access, custodial use, and off-chain workflows; before admission, every skill is vetted by a Decentralized Autonomous Organization (DAO) audit committee that supports pluggable auditing methods under a stake-and-slash economic model. At load time, SIGIL delivers verified loading through a skill verification protocol executed by a Skill Verification Loader (SVL) embedded as the mandatory loading path: the SVL retrieves and decrypts the skill as its type requires, verifies its integrity against the on-chain record, and enforces its permission manifest before context injection. We evaluate SIGIL on a real-world deployment against 1,023 in-the-wild skills spanning six attack types. At load time, the SVL verifies each skill's integrity against its on-chain record and enforces its approved permission manifest, completing batched verification under 86 ms. Together, these results show that LLM skills can be cryptographically bound from publication through runtime at practical cost.

cs.CR↗
arXiv2026

Marginal spectral distributions on regular bipartite unitary orbits

Fix the spectrum of a bipartite density matrix and randomize its eigenbasis according to Haar measure. We study the probability distributions induced on the spectra of the two marginal states. For arbitrary subsystem dimensions $m$ and $n$, the joint characteristic function of the reduced density matrices is expressed as a Harish-Chandra-Itzykson-Zuber integral whose external eigenvalues are the pairwise sums $x_i + y_j$. Repeated external eigenvalues are handled by confluent determinant limits. In the two-qubit case, we derive an explicit alternating-spline formula for the joint density of the two marginal Bloch radii. Its support is the Bravyi-Klyachko compatibility region. We also obtain a compact truncated-power formula for the Bloch-radius density of either individual qubit marginal. In the qubit-qutrit case, we derive a truncated-power formula for the qubit Bloch-radius density and a bivariate spline formula for the joint density of the largest and smallest eigenvalues of the qutrit marginal. The latter two variables determine the full qutrit spectrum because the trace is fixed. The derivations combine confluent HCIZ integrals, distributional Fourier inversion, orbital measures, and the SU(2) and SU(3) derivative principles. The resulting densities are piece-wise polynomial on chambers determined by subset sums of the fixed global eigenvalues, in agreement with the Duistermaat-Heckman description of projected coadjoint-orbit measures.

quant-ph↗
arXiv2026

From Multi-Modal Paths to Executable Trajectories: A Trajectory Planning Framework for 4WIS Robots

Four-wheel independent steering (4WIS) mobile robots support multiple motion modes, offering high maneuverability in narrow and complex environments. However, existing planning methods often fail to fully exploit these capabilities, leading to suboptimal trajectory quality. To address this limitation, this paper proposes a multi-modal global trajectory planning framework that couples mode-augmented front-end search with mode-consistent segment-wise trajectory optimization. In the front-end stage, Hybrid A* is extended to a four-dimensional state space incorporating motion modes, while mode-switching-aware cost and heuristic functions embed mode decisions into the global search process. Multi-modal Reeds-Shepp curves and an intelligent terminal connection strategy are further designed to improve search efficiency. In the back-end stage, a segment-wise trajectory optimization framework based on an improved iterative safe corridor scheme is developed to convert discrete multi-modal paths into smooth, kinematically feasible trajectories with stationary mode transitions. Experimental results show that the proposed method achieves the best overall performance in safety, arrival time, terminal accuracy and computation time. Real-world experiments on a physical 4WIS robot further validate the practical effectiveness and executability of the generated trajectories, providing a flexible and high-performance solution for multi-modal mobile robot trajectory planning.

cs.RO↗
arXiv2026

Bound-state-mediated remote charging of a quantum battery

Remote charging of a quantum battery (QB) is hindered by radiative leakage of the excitation into the photonic environment that acts as a mediator for energy transfer. We consider a charger-battery model consisting of two two-level systems (TLSs) that are locally coupled to two sites of a one-dimensional coupled cavity array. When their transition frequency lies outside the propagation band, the system forms atom-photon bound states with localized photonic components, and the overlap of these components lifts the degeneracy of the even- and odd-parity bound states, yielding an energy splitting that drives coherent energy transfer from the charger to the QB. In this way, the band gap suppresses resonant emission and the localized bound states mediate remote charging. From the parity-resolved spectrum, we relate the charging time to the energy splitting and the charged ergotropy to the fraction of TLS population on the bound states. Bound states closer to the band edge extend the interaction range of the TLSs but contain a large photonic fraction and are consequently more susceptible to photon loss.

quant-ph↗
arXiv2026

SABER: Benchmarking Operational Safety of LLM Coding Agents in Stateful Project Workspaces

Large language models are increasingly deployed as coding agents, shifting safety from individual responses to action sequences. Existing benchmarks, however, primarily assess whether models refuse unsafe prompts, leaving impacts on stateful workspaces largely unexamined. We present SABER, a benchmark for environment-aware operational safety that places models in realistic agent-style projects and evaluates safety from the final environment state after a sequence of actions. Beyond binary safety-violation reports, SABER categorizes violations by cause, enabling analysis of model-specific safety profiles. Our evaluations show that even the best-performing model has more than a 54% harmful safety-violation rate (HSR), suggesting that current alignment remains insufficient for realistic project environments. SABER further reveals distinct safety profiles across models. Our benchmark is publicly available at https://github.com/sssr-lab/saber.

cs.SE↗
arXiv2026

Dielectric Response for Light Dark Matter Direct Detection Beyond the Longitudinal Approximation

The dielectric formalism for light dark matter--electron scattering in semiconductors has, to date, employed only the longitudinal dielectric function $ε_L$, with the transverse response $ε_T$ universally neglected on qualitative grounds. A complete derivation and quantitative evaluation of $ε_T$ in this context has been lacking. We provide this derivation within the random phase approximation for a homogeneous electron gas. For silicon, we find that the transverse energy loss function is $4--6$ orders of magnitude below the longitudinal one in the bulk plasmon regime, providing the first rigorous justification for the conventional longitudinal approximation. The sizable transverse corrections appear for deposited energies $ω\lesssim1\,\mathrm{eV}$, which lies below the energy required to reliably produce one electron-hole pair in silicon detectors. Our results provide a quantitative error assessment for existing longitudinal calculations and identify kinematic regimes where transverse corrections ought to be included for relativistic dark-matter interpretations.

hep-ph↗
arXiv2026

Advancing MLLM-based UAV Image Understanding and Reasoning: A Benchmark and a Training-Free Multi-Agent System

Multimodal Large Language Model (MLLM)-based UAV aerial image understanding and reasoning is essential for aerial intelligence yet poses distinct challenges arising from extreme scale variation, arbitrary camera orientations, and high object density. Despite growing interest, existing evaluations remain fragmented across individual datasets and narrow tasks, leaving a critical gap in unified assessment of UAV understanding and reasoning capabilities. To fill this gap, we construct UAVQA-Bench, a benchmark of 1,500 human-annotated QA pairs drawn from 13 public UAV datasets, covering 6 capability dimensions and 16 tasks in both multiple-choice and visual grounding formats. Systematic evaluation of a broad range of open-source and closed-source MLLMs as well as agent-based systems on UAVQA-Bench identifies three key failure modes: domain-toolset mismatch, unchecked error propagation, and static reasoning. Motivated by these findings, we propose UAV-MAS, a training-free multi-agent system for MLLM-based UAV aerial image understanding and reasoning, comprising a Domain-Specific Perception Engine (DSPE) that routes queries to task-appropriate visual tools, a Context-Aware Iterative Refinement module (CAIR) that validates intermediate reasoning to curb error accumulation, and a Difficulty-Aware Adaptive Search mechanism (DAAS) that adjusts search depth to question difficulty. UAV-MAS with a 32B open-source MLLM achieves 77.0% overall accuracy on UAVQA-Bench, surpassing Gemini 3 Pro by 4.0\%, while the 8B variant improves 8.7\% over its base model.

cs.CV↗
arXiv2026

AeroGround: A Comprehensive Benchmark for Aerial-Ground Collaborative Reasoning

Vision-language models (VLMs) have been widely employed in understanding and reasoning tasks for unmanned aerial vehicles (UAVs). Existing UAV benchmarks primarily focus on aerial-view scenarios. However, whether current VLMs can perform well on understanding and reasoning tasks in aerial-ground collaborative scenarios which are practical in real-world applications like rescue and infrastructure inspection remains underexplored. To address this gap, we introduce AeroGround, a comprehensive benchmark for evaluating VLMs in aerial-ground collaborative reasoning. AeroGround is built upon a simulated aerial-ground dataset containing approximately 29,000 multimodal observation groups from diverse open environments, and provides 2,250 high-quality question-answering instances covering cross-view correspondence, spatial understanding, and reasoning. Experiments on 16 pretrained VLMs, together with two domain-adapted variants, reveal a substantial gap between current models and human performance: the best model achieves an average accuracy of 54.4%, whereas humans reach 93.3%. By systematically revealing the strengths and limitations of existing models in aerial-ground collaborative reasoning, AeroGround provides a foundation for developing more capable aerial-ground collaborative embodied intelligence systems.

cs.CV↗
arXiv2026

Extreme Value Alpha and Crash Risk: Separating Structural Tails from Lottery Tails with LLM-Extracted Disclosure Networks

A heavy upper tail in a stock's returns is ambiguous: it can be a lottery tail, transient jump risk that investors overpay for (the MAX discount), or a structural tail, the statistical shadow of an economic reconfiguration that precedes extreme winners. Returns alone cannot separate them, so tail heat alone is not an alpha signal. Our discriminator is the firm's disclosure-measured network: a directed, span-grounded graph from 10-K filings via an auditable LLM pipeline, whose rewiring decomposes into edge birth, death, and drift. The central sign pattern: tail heat with network death is the crash side; tail heat with an intact or forming network is where structural tails and historical winners live. Pilot evidence from 24 technology firms (2014-2025) supports the crash side: upper-tail heat interacted with death mass predicts negative forward abnormal returns (monthly t = -2.9; firm-vintage t = -3.9; wild-cluster p = 0.04; robust to two-way clustering and controls), and the same configuration preceded NVIDIA's 2018 and 2022 drawdowns. This death-side signal is immediately useful as a risk-monitoring danger flag. The alpha side is directionally positive but not significant in the pilot and awaits the confirmatory test. A pre-registered replication on 50 random S&P 500 firms failed, defining the boundary: outside coherent ecosystems the disclosure graph nearly vanishes (83% of firm-vintages have zero death mass), so the discriminator exists only where firms densely document counterparties. The confirmatory design is fully pre-specified, with a gatekept primary pair, archived power simulations, positive-only winner labels, selection-corrected benchmarks, and a frozen ecosystem-coherent universe with a density gate; it activates only if the gate passes. If confirmed, tail heat becomes a conditional signal separating crash risk from structural winners.

stat.AP↗
arXiv2026

A Survey of Bargmann Invariants: Geometric Foundations and Applications

Bargmann invariants, a class of unitary-invariant quantities arising from the overlaps of quantum state vectors, provide a profound and unifying framework for understanding the relative geometry of the projective Hilbert space. This survey offers a comprehensive overview of their theoretical characterization and practical applications, with particular emphasis on recent progress in determining the full structure of their admissible set. The core of this review demonstrates how these invariants serve as a powerful tool for characterizing the intrinsic geometry of the space of quantum states, leading to applications in determining local unitary equivalence and constructing a complete set of polynomial invariants for mixed states. On the operational side, we review the cycle-test quantum circuit for the direct estimation of Bargmann invariants without full state tomography, and demonstrate their utility in witnessing quantum imaginary, discriminating local unitary equivalence, and detecting entanglement via partial-transpose moments---with explicit complete invariant sets provided for two-qubit systems. By connecting fundamental geometric classification with experimentally feasible estimation protocols,this survey establishes Bargmann invariants as indispensable probes of the relational, noncommutative, and geometric structure of quantum states, and identifies key open problems for multipartite high-dimensional systems and for quantum resource theories.

quant-ph↗
arXiv2026

Zellige: Moldable Sequence Placement for Mixed Image-Video DiT Training

High-quality video generation requires training Diffusion Transformers (DiTs) jointly on image and video data, posing a mixed-length sequence training problem across GPUs. Existing systems rely on data parallelism (DP), context parallelism (CP), or their combination; we model these designs as disjoint-group placement and prove that they face a fundamental tradeoff between inter-group load imbalance and intra-group communication redundancy. We present Zellige, a moldable sequence placement system that jointly selects each sequence's parallelism configuration and participating ranks. Zellige consists of three components: a hardware profiler that estimates the execution time and memory consumption of candidate placements, a two-stage planner that balances compute-heavy anchor sequences and packs lighter filler sequences into the remaining capacity, and a coalesced attention engine that efficiently executes whole sequences alongside distributed-attention shards. Across 21 plans, the hardware profile predicts step makespan and peak allocated memory with mean absolute percentage errors of $3.4%$ and $1.5%$, respectively. The two-stage planner solves each batch in 33--119 ms, significantly faster than a joint-placement reference that optimizes all sequences together, while their modeled makespans differ by at most $0.32%$. In end-to-end evaluations, Zellige outperforms KnapFormer by $1.12$--$1.48\times$ on 16 A800 GPUs and $1.27$--$1.54\times$ on 32 A6000 GPUs.

cs.DC↗
arXiv2026

ISAC-Enabled On-Demand UAV Charging for Wireless Rechargeable Sensor Networks

Unmanned aerial vehicles (UAVs) equipped with wireless power transfer (WPT) extend the lifetime of wireless rechargeable sensor networks (WRSNs) by delivering energy on demand. This article presents an integrated sensing and communication (ISAC)-enabled on-demand UAV charging framework coordinated by a central base station. A prioritized charging queue captures node urgency and service cost through residual energy, traffic load, estimated UAV travel time, and flight-direction alignment. This bidirectional coupling ensures that scheduling decisions shape the UAV trajectory, while updated mobility estimates from ISAC dynamically reorder the queue. ISAC-assisted estimation of UAV distance, speed, and position updates travel-time predictions under mobility uncertainty. A time-allocated partial charging policy distributes limited hover time across queued nodes according to criticality. Simulations show gains in energy usage efficiency, travel distance, and charging delay compared with representative baselines. We discuss deployment considerations, including computational overhead, scalability, and parameter selection, to aid practitioners evaluating the framework for IoT scenarios.

cs.NI↗