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Yuan Li

Publications and source records attributed to Yuan Li.

At least 19 recordsLinked to original sources
arXiv2026

A Control Line Multiplexing Framework for Scalable Quantum Computing

Universal fault-tolerant superconducting quantum computers are expected to require millions of physical qubits; yet extending the prevailing dedicated-control-line paradigm to this scale would exceed the wiring density and cooling-power budget of dilution refrigerators. Microwave frequency-division multiplexing (FDM) revolutionized classical communications by enabling many independent channels to share common transmission infrastructure. Translating this principle to superconducting quantum control, however, requires an all-microwave architecture and experimental validation of frequency-multiplexed gate control. Recent work in quantum error correction has demonstrated microwave-based readout, single-qubit gates and leakage suppression, whereas two-qubit gates remain predominantly driven by unipolar pulses and thus constitute the missing element of an all-microwave architecture. Here, we propose a periodically extensible hybrid multiplexing framework in which all gate operations are frequency-division multiplexed using polychromatic microwave pulses and band-pass filters, thereby reducing the required control-line count by one to two orders of magnitude. In a 1:4 demonstration, we achieve average errors of $(6.4\pm0.2)\times10^{-3}$ for parallel FDM CZ gates and $(10.6\pm0.2)\times10^{-4}$ for parallel FDM $XY(π/2)$ gates, without statistically significant multiplexing-induced degradation. Together with on-chip DC sources, this FDM-based gate-control scheme completes the last missing module in a scalable multiplexed-control architecture toward universal fault-tolerant quantum computing.

quant-ph↗
arXiv2026

Initialization-Free LEO Doppler Positioning via Semidefinite Relaxation: A Cold-Start Approach

Low Earth Orbit (LEO) satellite Doppler measurements offer an alternative source of positioning information for Internet of Things devices when Global Navigation Satellite System signals are unavailable. Conventional local optimisation methods depend on an accurate initial position and can fail during a complete cold start. We propose an initialization-free LEO Doppler positioning framework based on semidefinite relaxation. The method reformulates the nonlinear positioning problem into a semidefinite programme to obtain a receiver position estimate without prior location knowledge. We analyse relaxation tightness under noiseless conditions and derive sufficient noise bounds under parameter disturbances. Simulations and field experiments using opportunistic Iridium-NEXT signals evaluate initialization sensitivity, positioning accuracy and computational cost. In the field experiment, the proposed method achieves a three-dimensional positioning error of 140 m without an initial position, whereas local solvers converge to poor solutions under large initial offsets. Using the semidefinite estimate to initialise Gauss-Newton or Dog-Leg reduces the positioning error to 130 m. The framework provides a cold-start initialization stage for subsequent local refinement in opportunistic LEO positioning. The implementation and datasets are publicly available to support reproducibility and further research.

cs.RO↗
arXiv2026

Lattice symmetry breaking and strain control of single-$\mathbf{q}$ magnetism in Na$_3$Co$_2$SbO$_6$

Distinguishing single-$\mathbf{q}$ from multi-$\mathbf{q}$ magnetism constrains microscopic spin Hamiltonians. Here we determine the ground state of the candidate Kitaev magnet Na$_3$Co$_2$SbO$_6$ through its coupling to the lattice. High-resolution neutron diffraction reveals a spontaneous monoclinic-to-triclinic distortion at the magnetic transition, in contrast to the absence of detectable lattice-symmetry breaking in triple-$\mathbf{q}$ Na$_2$Co$_2$TeO$_6$. Together with strain-selective magnetic responses, this identifies single-$\mathbf{q}$ order in Na$_3$Co$_2$SbO$_6$ over the proposed symmetry-preserving double-$\mathbf{q}$ state. Uniaxial compression along a low-symmetry in-plane direction achieves the domain selection that magnetic-field training could not, revealing the intrinsic field-induced phase boundaries of the dominant domain. These results establish strain as a symmetry-conjugate field for magnetic domains and highlight magnetoelastic coupling as a route to diagnose, manipulate, and potentially tune magnetic states.

cond-mat.str-el↗
arXiv2026

Non-radial solutions for the quasi-linear Hénon type $N$-Laplacian Liouville equation

In this paper, we investigate the following quasi-linear weighted $N$-Laplacian Liouville equation \begin{equation*}\label{0} -Δ_N u=|x|^{Nα}e^{u}, \qquad x\in \R^N, \end{equation*} where $N \geq 2$. For $\al>0$, by carefully studying the linearized problem and applying the approximation method and bifurcation theory, we prove that, when the parameter $α$ equals to the critical values $α(k):=\frac{\sqrt{k(N-1)(k+N-2)}}{N-1}-1$ for $k \geq 2$, there exist non-radial solutions $u$ (bifurcating from $U_{α(k)}$) to the above quasi-linear Hénon type Liouville equation such that $u\sim \ln|x|$, $|\nabla u|= O(|x|^{-1})$ at $\infty$ and $\int_{\R^N}|x|^{Nα}e^{u}\md x=N\left(\frac{N^2}{N-1}\right)^{N-1}(α+1)^{N-1}ω_N$. One should note that, $α(k)=k-1$ for $k\geq2$ when $N=2$. Our results successfully extend the existence result of J. Prajapat and G. Tarantello in \cite{PT} concerning the $2$-dimension and Laplacian case (i.e., $N=2$) to the more general $N$-dimension and $N$-Laplacian cases ($N\geq 2$), and extend the results of F. Gladiali, M. Grossi, and S. L. N. Neves in \cite{GGN} and the authors in \cite{DDGL} from $1<p<N$ to the much more complicated limiting case $p=N$. We introduced some new ideas and overcame a series of crucial difficulties, including the nonlinearity nature of the $N$-Laplacian $Δ_N$, the lack of Green integral representation formula and critical weighted Sobolev embedding inequality, the absence of Kelvin type transforms for linearized/difference equations, the invariance of the total mass under scalings of $u$, and the signs-changing and divergence (to $-\infty$) at $\infty$ of the solutions, which makes the suitable choices of the approximate problems, the (normalized) approximate function sequences and the working space to be quite difficult.

math.AP↗
arXiv2026

FRACTION: Fraud Explanation Generation via Agentic Graph Context Navigation

Graph fraud explanation generation emerges as an important task, which requires jointly leveraging graph structure and content information to produce human-readable explanations for a given risk label. An intuitive approach is to adapt existing graph-enhanced large language models (LLMs), which inject graph structure into LLMs via graph-conditioned instruction tuning or structure-aware embeddings. However, existing methods face two key challenges in graph fraud explanation generation: a lack of evaluation protocols tailored to explanation quality, and a mismatch between the granularity of graph information they provide and what explanation generation requires. In this paper, we propose FRaud Explanation Generation via Agentic Graph Context NavigaTION (FRACTION), a framework for graph fraud explanation generation. To evaluate explanation quality, we introduce a task-specific rubric that assesses faithfulness, graph grounding, helpfulness, consistency, and conciseness, and validate it against human judgments. To focus on fine-grained details of relevant neighbors without reading the entire neighborhood, we equip a language model with graph tools that progressively disclose neighbor records. To train our graph agent, we propose a rubric-guided trajectory distillation strategy through which the agent learns both tool-use behavior and structured explanation generation. Experimental results on three fraud datasets demonstrate consistent improvements over state-of-the-art baselines under a human-aligned rubric.

cs.LG↗
arXiv2026

Sensitivity of Rotation Symmetric Boolean Functions via Binary Necklaces

Rotation symmetric Boolean functions are Boolean functions invariant under cyclic permutation of their variables and can be represented by binary necklaces. We express local sensitivity as a weighted bichromatic degree in the necklace quotient and derive an exact formula for the multiplicities between neighboring necklaces. For an odd prime number of variables, we obtain explicit sensitivity formulas and show that multiplicity corrections occur only on an asymptotically negligible family of necklaces. For monomial rotation symmetric Boolean functions, we prove that every odd-degree function has maximum sensitivity, determine the sensitivity of every quadratic function, and determine the sensitivity of the consecutive quartic family. We also determine exactly the sensitivity of cyclic run-threshold functions for arbitrary length and threshold.

math.CO↗
arXiv2026

Block Sensitivity and Certificate Complexity of Rotation Symmetric Boolean Functions

We study block sensitivity and certificate complexity of rotation symmetric Boolean functions, with particular emphasis on monomial rotation symmetric Boolean functions. We first show that every odd-degree monomial rotation symmetric Boolean function on $n$ variables satisfies $s=bs=C=n$. We then determine sensitivity, block sensitivity, and certificate complexity for every quadratic monomial rotation symmetric Boolean function. For the quadratic function $Q_{n,k}$ generated by $x_0x_k$, we prove, outside the short-orbit case, that \[ C(Q_{n,k})= \begin{cases} n,&4\mid n/\gcd(n,k),\\ n-\gcd(n,k),&4\nmid n/\gcd(n,k). \end{cases} \] Moreover, \(bs(Q_{n,k})=C(Q_{n,k})\) for every quadratic MRS function, and outside the short-orbit case, strict separation \(s<bs=C\) occurs exactly when \[ \frac{n}{\gcd(n,k)}\equiv2\pmod4. \] Finally, for the consecutive quartic family \[ R_n(x)=\bigoplus_{i\in\mathbb Z_n}x_ix_{i+1}x_{i+2}x_{i+3}, \] we determine all three measures in every residue class modulo $6$, using the sensitivity formulas and, in one residue class, finite-state results established in our companion preprint~\cite{LiZhangRSBFSensitivity}. In particular, for $r=2,3$ and $6q+r\ge5$ we obtain strict separation, \[ \boxed{s(R_{6q+r})=6q<bs(R_{6q+r})=C(R_{6q+r})=6q+1,\qquad r=2,3.} \] Thus this family gives an infinite collection of rotation symmetric Boolean functions for which sensitivity is strictly smaller than block sensitivity, while block sensitivity and certificate complexity coincide.

math.CO↗
arXiv2026

CoverSynth: Coverage-Guided Generation of Retrieval Evaluation Sets

Enterprise agents need retrieval evaluation sets that expose domain-specific failures and guide developers towards efficient deployment tradeoffs. Neither expert-human-authored sets nor current synthetic generation methods provide a methodology for achieving the necessary coverage, reliability, efficiency, and discriminability required for such decision-making. Simultaneously, the field does not have an assessment framework for determining when an evaluation set has satisfied these properties. We introduce an evaluation set assessment framework for these criteria and CoverSynth -- a generator specifically designed to satisfy these criteria without incurring the high cost of human expert labeling. Starting from an unannotated corpus, CoverSynth targets coverage across retrieval demands and system capabilities. Deterministic checks locate evidence quotes in their sources, while model-based checks assess semantic validity to guarantee reliable ground truth. Across five public enterprise-style corpora, our method provides informative variation in difficulty, revealing unseen failure modes such as an inability of agents to handle underspecified or ambiguous requests. On the RepLiQA corpus, 91\% of human-generated queries can be answered by all members on a panel of 12 retrieval systems, compared to only 12\% of CoverSynth-generated queries. Finally, in a repeated TechQA/RepLiQA study, 12 of 12 within-harness model contrasts have positive nominal 95\% intervals on generated sets, compared with 4 of 12 on human sets. CoverSynth makes coverage and evidence quality explicit construction objectives, producing corpus-grounded evaluations that reveal retrieval headroom and differences between agent configurations.

cs.AI↗
arXiv2026

Conditional Multi-Event Temporal Grounding in Long-Form Video

Multimodal large language models have made rapid progress in video temporal grounding, yet real-world applications routinely require localizing every event that satisfies compositional temporal and spatial conditions. Existing benchmarks fall short: they localize only a single moment per query, count without temporal conditions, or treat grounding and counting as disjoint tasks. We introduce CoMET-Bench for Conditional Multi-Event Temporal Grounding in long-form video, comprising 2789 queries over 600 videos averaging 33.8 minutes across five real-world domains, with each query composed from 4 temporal conditions, 3 spatial conditions, and a dedicated negative-query subset. We further propose a unified evaluation protocol jointly measuring counting, grounding, and negative-query recognition, including a new Rejection-F1 metric that prevents trivial gaming by lazy "always-empty" models. Benchmarking a broad suite of MLLMs, agent-based, and grounding-specialized methods reveals that existing approaches remain far from solving this task. Building on these findings, we propose CoMET-Agent, a training-free agentic framework that reformulates the task as structured search-and-aggregate, improving F1@0.5 by 6.1% over GPT-5 purely through structural reasoning. Failure analysis further surfaces three open directions: fine-grained entity tracking, position-uniform retrieval, and causal event pairing.

cs.CV↗
arXiv2026

Grounded-Dreamer: Robot Learning from World Model Synthetic Data with Physical Grounding

Scaling robot manipulation policies requires large amounts of diverse, action-labeled data, yet collecting such data in the real world remains expensive and difficult to scale. Existing methods either depend on human teleoperation, rely on simulation with limited visual realism, or use human videos that lack embodiment-specific annotations. In this paper, we introduce Grounded-Dreamer, a framework for synthesizing physically grounded robot manipulation data with video world models while avoiding human teleoperation demonstrations. Grounded-Dreamer combines the visual realism of generative video models with simulator physics engines. Starting from a small set of real images depicting robot task initial states, our method automatically reconstructs task-relevant objects and camera geometry, aligns them in simulation, and collects physically valid trajectories. These trajectories are used to post-train both a video world model and an inverse dynamics model, enabling the generation of visually realistic robot manipulation videos and corresponding robot actions from real images. To filter physically implausible rollouts, Grounded-Dreamer replays generated trajectories in the reconstructed simulation and retains only those that successfully complete the tasks. The real-world robot policies are trained solely on the grounded synthetic trajectories without any human demonstrations. Empirical results in real-world tasks demonstrate that Grounded-Dreamer can synthesize physically grounded data and substantially facilitate policy learning without requiring human teleoperation effort.

cs.RO↗
arXiv2026

Diffusion Meta-Prompting and Steering for Generalizable Foundation Model Adaptation

Prompt learning is a popular method for adapting foundation models, but learned prompts are typically task-specific and fail to generalize to new classes, domains, or compositions of tasks. In this paper, we introduce a Diffusion Meta-Prompt (DMP) model , a framework that models the distribution of learned prompts using diffusion models. Given a repository of previously learned prompts, DMP is trained and sampled without access to the original task examples or task losses, and synthesizes new prompts conditioned on natural language task descriptions. To improve the sampling stability, we introduce a test-time steering strategy for DMP, which uses the best training-selected prompt in the repository as a latent anchor during diffusion sampling, without retraining the DMP or accessing test classes. DMP improves generalization across classification, retrieval and text-to-image generation tasks, supports concept composition and negative prompting without explicit training. It reduces storage and inference costs by over 90% compared to prompt retrieval methods. For composite classification, DMP achieves upto 2.0% average gain over prior meta-learning methods across 55 pairs of datasets with gains as high as 8.5% on specific pairs such as Eurosat and Flowers. DMP also enhances cross-task generalization with ~2-9% improvement for hierarchical classification task. We further provide a theoretical guarantee bounding the expected task loss of prompts sampled from a DMP. Code is available: https://github.com/DeepakSridhar/dmp

cs.CV↗
arXiv2026

Integrity and Credibility in Navigation: From Error Characterization to Operational Assuran

Navigation integrity and estimator credibility use error and uncertainty information to address different questions. This perspective distinguishes actual positioning error from reported statistical characterizations, formulates integrity as control of hazardous unwarned-use risk under the requirement of a specified operation, and describes credibility as the justification of an uncertainty or risk claim by its evidence usage and modeling process. Three hypothetical scenarios show how deteriorated accuracy, a contradicted uncertainty report, and a hazardous event can lead to different credibility and integrity judgments. The resulting assessment asks what happened, why the reported claim should be believed, and whether the information is sufficient for the intended operation.

eess.SP↗
arXiv2026

Gamma: Grounded Multi-Agent Memory with Mechanical Audit for Memory-Intensive Manipulation

Pretrained vision-language-action (VLA) policies fail on memory tasks, behaviour that depends on events observed long before the current frame, because their training data is overwhelmingly Markov and mapping episodic memory directly to action chunks is hard. Yet the same policy, finetuned once to follow a privileged oracle's grounded-subgoal text, nearly solves these tasks: the difficulty lies entirely in mapping memory to a grounded symbolic subgoal. Existing work entrusts this stage to a single VLM that selects keyframes and reasons over raw frames, and falls far short of the oracle: key identities and dynamics are buried in noisy pixels, the reasoning spans hundreds of frames in an unbounded context, and hallucination corrupts the memory itself. We instead present GAMMA (Grounded multi-Agent Memory with Mechanical Audit), which builds the stage as a collaborating two-agent memory system: a detector tool (SAM-3) for spatial grounding, a VLM writer that distils each replan window into one grounded text line, an append-only text memory bank, and a VLM reasoner that reads the bank and emits the next grounded subgoal at the oracle's cadence, wrapped in a propose-verify harness that admits, defers, corrects, or rejects every agent claim against mechanical visual evidence, at training-corpus construction and at deployment alike. On a sixteen-task memory-manipulation suite GAMMA recovers a large fraction of the oracle ceiling with the policy held fixed, reaching the ceiling on several tasks, and transfers with unchanged contracts to a second benchmark.

cs.RO↗
arXiv2026

COPC: Coupled Off-Policy Correction for Asynchronous LLM Reinforcement Learning

Asynchronous RL accelerates large language model post-training by decoupling rollout generation from optimization, but trains on stale trajectories. Existing methods primarily correct token-level policy mismatch through importance-ratio control in the actor objective. We show that this \emph{policy-side correction} alone is insufficient: advantage estimates also inherit mismatch from behavior-policy continuations, which we term \emph{advantage staleness}. We derive exact bias and variance decompositions for a general two-channel actor update, revealing nonseparable coupling between policy-weight and advantage-estimation errors: their interaction induces multiplicative bias terms, while squared policy weights amplify advantage uncertainty in gradient variance. This motivates the hypothesis that policy- and advantage-side correction should be coordinated. We introduce Coupled Off-Policy Correction (COPC), an actor--critic method combining token-level ratio masking with two-sided clipped-ratio weighting of TD residuals for return and advantage estimation. Joint parameter sweeps across staleness levels support this hypothesis: the effect of one correction parameter depends on, and can reverse with, the other. COPC achieves the highest reported performance on tool-integrated mathematical reasoning and search, outperforming the strongest reported asynchronous baseline in each setting. It also offers a broad high-performing parameter region and improved training stability. In search, COPC remains stable throughout training, while most evaluated asynchronous baselines collapse late in training. These gains persist at 64-step policy staleness. COPC adds minimal step-time overhead over asynchronous PPO and retains a $1.7\times$ step-time speedup over synchronous PPO.

cs.LG↗
arXiv2026

Exploring topology via momentum-selective tomography

Identifying and measuring topological invariants remains a fundamental challenge due to the non-local nature of momentum eigenstates in real space. Established approaches based on Bloch oscillations, quantum walks, charge pumping and quench dynamics, typically fail to provide direct momentum-dependent geometric information of each Bloch state. Here, we propose a novel framework for implementing direct band-topology tomography via momentum-selective measurements, and experimentally demonstrate it on a superconducting processor featuring a highly connected coupling architecture. Utilizing a ``star-like" geometry in which an auxiliary probe couples to a one-dimensional topological chain with spatially modulated coupling strengths, we selectively address the desired momentum states of the chain. This approach enables the direct and robust extraction of the winding phase that contributes to the associated topological invariant. Leveraging non-local couplers bridged by long tantalum airbridges, we realize an extended Su-Schrieffer-Heeger (SSH) model and map its full topological phase diagram. Through a nested Fourier analysis over time and space, we directly extract the winding phase across Brillouin zone and resolve quantized winding numbers of $ν=\{0,\pm1,2\}$ characterized by phase winding $\{0, \pm 2π,4π\}$. Our work establishes a versatile tool for probing topological characteristics and opens a new avenue for robust quantum computing architectures based on topological protection.

quant-ph↗
arXiv2026

Mapping Gas Accretion and Stellar Kinematics to Sub-kiloparsec Scales in NGC 4696 with JWST/NIRSpec

We present JWST/NIRSpec IFU spectroscopy of the central $618\times618$ pc$^2$ ($\sim3''\times3''$) of NGC 4696, the BCG in the Centaurus cluster. Leveraging the $\sim0.1''$ ($20.6$ pc) pixel size of JWST, we resolve a compact circumnuclear rotating disk (radius of $\sim120$ pc) traced by Pa$α$ and H$_2$ 1$-$0 S(1) emission, which allows a reassessment of the AGN position based on the kinematic centre of this disk. A central Pa$α$ velocity dispersion reaching $σ\sim449$ km s$^{-1}$ implies a SMBH mass of $\sim10^9$ M$_\odot$, corresponding to a sphere of influence of $r_\mathrm{inf}\sim60$ pc, resolved by our observations. Position-velocity diagrams reveal an increase from $\sim-200$ to $\sim600$ km s$^{-1}$ on scales of $\sim150$ pc (a gradient of $4.7$ km s$^{-1}$ pc$^{-1}$) and an accretion rate of $\sim18$ M$_\odot$ yr$^{-1}$ feeding the CND. The Pa$α$ emission shows a double-component in the core, with a high-dispersion redshifted component reaching $σ\sim600$ km s$^{-1}$. In contrast, MUSE H$α$ observations covering the $\sim10$ kpc-scale filamentary structure recover only weak velocity gradients ($\lesssim150$ km s$^{-1}$) within $\sim300\times300$ pc$^2$ and do not resolve the disk due to larger PSFs and pixel sizes. ALMA CO(2-1) data reveal only compact molecular clumps within a $\sim410\times410$ pc$^2$ region, with no extended counterpart to the structures traced by Pa$α$ and H$_2$ 1$-$0 S(1). Stellar kinematics show a smooth velocity field and broad dispersion profile, clearly decoupled from both the multiphase gas and the hot ICM probed by XRISM. These results provide a direct, spatially resolved view of gas dynamics within the inner few hundred parsecs, demonstrating the power of JWST/NIRSpec to probe SMBH feeding.

astro-ph.GA↗
arXiv2026

Polymorph-Tunable Spin Texture and Excitonic Structures in Monolayer WSi2P4

Two-dimensional semiconductors that simultaneously possess a direct band gap and strong spin-orbit coupling (SOC) are highly attractive for quantum optoelectronics. Using first-principles GW plus Bethe-Salpeter equation (GW-BSE) calculations, we show that monolayer WSi2P4-an experimentally accessible member of the MSi2X4 family-hosts a direct K-valley gap together with strong SOC. Its three competing polymorphs (alpha, beta and gamma) are all direct-gap semiconductors and are kinetically locked behind 1.3 eV migration barriers. The sigma_h mirror plane of the D3h of alpha and gamma phases enforces a persistent spin texture across the Brillouin zone, weakly modulated near gamma by interband SOC mixing, whereas the polar beta phase (C3v) exhibits Rashba spin splitting. SOC splits the doubly-degenerate lowest bright exciton into dark states and splits the original absorption peak into two peaks (A and B), whose relative brightness is governed by the K-valley conduction-band splitting. alpha-WSi2P4 displays the brightest Peak A because a tiny band crossing of opposite-spin branches can open a spin-allowed radiative channel. These results establish the MSi2P4 family as a phase-tunable platform for the cooperative engineering of spin texture, band splitting, and excitonic brightness within a single material system.

cond-mat.mtrl-sci↗
arXiv2026

DatalogBench: Evaluating Large Language Models on Text-to-Datalog Synthesis

Datalog underpins reasoning tasks such as program analysis, but its programs are hard to write. Existing synthesizers automate this task but require users to state their intent as input-output examples. Large language models (LLMs) suggest a more natural route, text-to-Datalog synthesis from a natural-language question, yet how well they do so has not been systematically evaluated. We present DatalogBench, a benchmark of 136 text-to-Datalog synthesis tasks curated from existing Datalog-based artifacts. Synthesized programs are graded by execution on held-out inputs against an oracle validated by mutation analysis. Across six LLMs and four prompting configurations, exact match peaks at 68.4%, and relation descriptions or an input-output example have only modest, model-dependent effects. Under direct prompting, most failures occur at compile time, typically because a model invents auxiliary predicates that it never declares or types consistently. Two coding agents reach up to 83.8% and eliminate nearly all such failures, leaving mostly semantic errors concentrated in recursive tasks. DatalogBench thus identifies recursive reasoning and decomposition as open challenges for current LLMs and agents, and offers a reliable, execution-grounded measure of both.

cs.PL↗