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Tao Jiang

Publications and source records attributed to Tao Jiang.

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

One- and two-dimensional cluster states for topological phase simulation and measurement-based quantum computation

Quantum entanglement is a fundamental resource for quantum information processing and serves as a critical benchmark for quantum hardware performance. Cluster states are a special class of entangled states that serve as universal resources for measurement-based quantum computation and possess an intrinsic symmetry-protected topological order, which confers robustness against symmetry-respecting noise. Here we report the scalable preparation and verification of genuine multipartite cluster states on the 105-qubit Zuchongzhi 3.1 superconducting processor. We achieve one-dimensional cluster states of up to 95 qubits and two-dimensional cluster states of up to 72 qubits. The symmetry-protected topological cluster states exhibit input-state-dependent robustness under symmetry-breaking perturbations due to an operational parity structure that enhances the performance of measurement-based quantum computation. Furthermore, we use our two-dimensional cluster states to implement the Deutsch-Jozsa algorithm within the measurement-based quantum computation framework, achieving higher output-state fidelity compared with traditional circuit-based models and a query efficiency advantage over classical approaches. Our work establishes a scalable platform that combines large-scale entanglement generation, symmetry-protected topological order and practical quantum algorithms to enable robust, fault-tolerant measurement-based quantum computation.

quant-ph↗
arXiv2026

DuoTok: Source-Aware Dual-Track Music Tokenization for Vocal-Accompaniment Generation

Multi-track music generation requires tokens that preserve acoustic fidelity, support sequence modeling, and maintain cross-track structure. Reconstruction-oriented codecs retain acoustic detail but are difficult to model, while semantic tokenizers may sacrifice fidelity or cross-track alignment. We present DuoTok, a source-aware dual-track music tokenizer for vocal-accompaniment generation based on staged disentanglement. DuoTok first learns a semantic audio representation through self-supervised pretraining, then shapes source-aware structure using feature replacement noise and multi-task supervision: spectral reconstruction, music source separation regularization, and an ASR head for lyric alignment. It freezes the encoder and learns hard-routed codebooks for vocals and accompaniment, while a diffusion decoder restores fine acoustic detail from discrete tokens. Across public benchmarks, DuoTok achieves a favorable predictability-fidelity trade-off at ultra-low bitrate. Under held-constant dual-track language modeling, it improves both unconditional vocal-accompaniment modeling and vocal-conditioned accompaniment prediction. Controlled diagnostics show larger predictability costs under cross-track corruption and greater gains from longer temporal context, supporting stronger use of cross-track and temporal structure rather than merely easier local prediction. DuoTok also maintains competitive reconstruction quality and preserves control-relevant musical attributes in its discrete space. These results suggest that tokenizer design is a core modeling problem for multi-track music generation, beyond compression alone.

cs.SD↗
arXiv2026

Dense Process Supervision for Search Agents via Fact Utility Estimation

Reinforcement learning (RL) for search agents typically relies on outcome rewards. However, it often fails to achieve effective credit assignment, due to the unclear value of intermediate steps. It is hard to separate their contributions from the final result. In this paper, we propose a dense process supervision method based on fact utility estimation, which models the reasoning process as the accumulation of discrete evidence facts. We first extract structured facts from raw observations and organize them into an explicit fact store. To support credit assignment, we then cluster semantically equivalent facts and infer the posterior utility of each fact cluster using Bayesian estimation over group rollouts. Finally, we convert the estimated fact utilities into dense step-level rewards to guide RL training. Experiments on seven single-hop and multi-hop QA benchmarks show that our method consistently outperforms existing baselines. Ablation studies validate clear relative improvements on multi-hop QA compared to outcome reward-only training.

cs.CL↗
arXiv2026

CDW Gap Collapse and Weyl State Restoration in Ta$_2$Se$_8$I via Coherent Phonons: A First-Principles Study

Coherent phonon excitation offers a nonthermal route to control quantum phases of condensed matter. In this work, we employ first-principles calculations to investigate the phonon landscape of (TaSe$_4$)$_2$I in its charge-density-wave (CDW) phase. We identify nine symmetry-preserving Raman-active modes that can suppress the $Γ$-Z direct gap to the meV scale and render the system globally gapless by generating Weyl nodes at generic k points. Among them, the 2.52 THz CDW amplitude mode A(18) directly weakens the Ta-chain tetramerization, approaching a transient restoration of the uniform-chain geometry, while its low frequency and relatively small, Ta-dominated displacement make it the most efficient mode. Other Raman modes, dominated by Se vibrations, require significantly larger displacements to reach the Weyl-semimetallic regime and are generally less effective than A(18) at reducing the Ta-chain tetramerization. Furthermore, among the IR-active modes considered, the low-frequency B$_3$(7) mode (1.15 THz) provides the most favorable channel for nonlinear modulation of the A(18) potential. Although the coupling favors displacement toward the CDW-suppressing direction, the estimated indirect response remains far below that required for strong gap suppression. Direct excitation of A(18) therefore remains the most effective pathway to the Weyl-semimetallic regime.

cond-mat.mtrl-sci↗
arXiv2026

Dual-Gradient Plasmonic qBIC Metasurface for Time-Resolved In Situ Optimization of Molecular Vibrational Sensing in Water

Metaphotonic platforms based on quasi-bound states in the continuum (qBICs), harnessing strong near-field enhancement and deeply subwavelength field confinement, provide a versatile framework for surface-enhanced infrared absorption (SEIRA) spectroscopy. However, real-time molecular sensing in water using qBIC metasurfaces remains challenging. First, strong water absorption in the mid-infrared region damps qBICs and obscures weak analyte signals. Second, conventional metasurfaces rely on discrete arrays targeting individual wavelengths and coupling conditions, increasing the device footprint and fabrication effort required to match the qBIC to the molecular vibrational resonance. Here, we present a dual-gradient plasmonic qBIC metasurface with diamond-shaped resonators for in situ molecular vibrational sensing in water, where spatially encoded gradients in the scaling factor and asymmetry parameter independently control the qBIC spectral positions and radiative rates, respectively, across a 1000 um x 700 um footprint. Building on this capability, we experimentally monitor lipid vesicle dynamics in real time and resolve the carbonyl vibrational signature despite the unavoidable water absorption. We then directly identify the optimal sensing condition and track its evolution across the spatially encoded parameter space. The results establish a compact single-chip strategy that combines adsorption-kinetics monitoring, vibrational fingerprint detection, and on-chip optimization, opening opportunities for investigating biological dynamics under aqueous conditions.

physics.optics↗
arXiv2026

CommerceVibe: Learning to Design E-Commerce Creatives as Executable Visual Code via Dual-Feedback Reinforcement Learning

High-quality e-commerce creatives are essential for presenting products and conveying marketing messages. Recent diffusion models enable scalable creative generation and produce visually compelling images, but their flattened raster outputs often contain distorted text and inconsistent product details, requiring refinement before deployment. Moreover, without explicit structure, the resulting creatives are difficult to edit and reuse, while complex design requirements remain challenging to encode as verifiable training signals. To address these challenges, we present CommerceVibe, which represents creatives as executable visual code and formulates generation as conditional HTML/CSS program synthesis. Given product images, design requirements, and product information, it produces renderable, editable, and reusable creatives. We further introduce dual-feedback reinforcement learning, in which rule-based feedback evaluates rendered programs for text readability, product visibility, and layout validity, while visual feedback from a vision-language model (VLM) assesses rendered creatives against input specifications across six perceptual and commercial dimensions. Together, these complementary feedback signals improve both constraint satisfaction and perception-dependent quality. We perform supervised fine-tuning (SFT) of Qwen3.5-9B on over 28,000 e-commerce examples, followed by dual-feedback reinforcement learning. On a 1,300-case benchmark, the optimized CommerceVibe model achieves a weighted score of 94.0/100, compared with 87.3 for the SFT-only variant, and outperforms strong external models. Blind evaluations by five e-commerce design experts further validate these improvements. CommerceVibe supports controllable, editable, and scalable e-commerce creative production.

cs.CV↗
arXiv2026

Binary Quantized Neural Network Training Is W[1]-Hard Parameterized by Input and Output Dimensions

Ganian et al. (ICLR 2026) proved that quantized neural network training is fixed-parameter tractable when parameterized jointly by architecture treewidth, input dimension $α$, and output dimension $ω$, and left open whether $α+ω$ alone yields fixed-parameter tractability. We prove that 2-QNNT is W[1]-hard parameterized by $α+ω$. The hardness already holds with zero error on $D_k=\{(ξ^{(r)},ξ^{(r)}):0\le r\le k\}$, where every input equals its target, $|D_k|=α=ω=k+1$, and the examples form a coordinatewise prefix chain. It also holds when every non-source bias is fixed to zero. Under the Exponential Time Hypothesis, no algorithm runs in $f(α+ω)|I|^{o(α+ω)}$ for any computable $f$. The reduction starts from DAG edge-disjoint paths, converts edge capacity to vertex capacity with a directed line graph, and normalizes the result into a valid layered architecture. The key structural step is a one-flip routing equivalence: on the prefix-chain inputs, nonnegative binary weights make every activation monotone, and each required output transition has a weight-one predecessor making the same transition. Iterating this relation backward extracts a path from the unique changing input, while different transitions yield vertex-disjoint paths. In particular, every neuron on these inputs has only $k+1$ possible activation profiles.

cs.LG↗
arXiv2026

Exact Minimax One-Bit Unbiased Compression: Heavy-Tail Necessity and Finite-Randomness Approximation

A pointwise-unbiased one-bit compressor reconstructs every real input in expectation while transmitting one bit. For a scalar source $P$ with CDF $F$, mean $m$, and $\mathcal J(P)=\int_{\mathbb R}\sqrt{F(r)(1-F(r))}\,dr$, we prove that the infimum of the source-averaged reconstruction second moment over all public-coin one-bit codes unbiased on $\mathbb R$ is $m^2+\mathcal J(P)^2$. For regular full-support sources, a distribution-centered random-threshold code attains this value; a converse over arbitrary randomized binary encoders and an equality analysis characterize every attaining code up to null sets, bit relabeling, and public-seed refinement. For the Gaussian location family $\mathcal N(μ,σ^2)$ with $|μ|\le cσ$, the equal prior on the endpoint means is least favorable and the minimax value is $σ^2Λ_c^2$. Exact Gaussian minimax optimality forces a critical heavy tail: at the endpoint means, absolute moments are finite exactly for $p<3$, and $\Pr(W>t)=Θ(t^{-3}/\sqrt{\log t})$. A Cauchy-mixture robustification inflates the second moment by at most $1/(1-η)$ while making every positive-order absolute moment finite. Finite-support public randomness with finite decoder means cannot achieve exact unbiasedness on $\mathbb R$, but a bounded-output approximation using exactly $R$ shared random bits has explicit bias and second-moment bounds converging to the minimax constant. Finally, coordinate allocation communicates exactly $B$ bits per Gaussian-gradient query. On Kim's continuous quadratic hard family, the expected optimization guarantee matches the lower bound in its dependence on $(σ,d,B,\varepsilon)$, and a finite-variance high-probability bound incurs only a logarithmic confidence factor.

cs.LG↗
arXiv2026

Depth Enables Local Entropy: Quadratic Depth Dependence in Deep Variation-Norm ReLU Regression

We study Gaussian regression over the explicit vector-valued Parhi--Nowak deep-RBV^2 architecture with depth L, width w, layer-sum variation budget A, and output bound B. For this O(L w^2)-parameterized architecture, the known lower and upper bounds differ by one factor of depth. We construct a local packing showing that the quadratic depth dependence is intrinsic under an explicit sample-size-dependent radius condition. The packing has log-cardinality Omega(L^2 w^2 log w); its codewords lie in an O(lambda) L^2 ball and are pairwise Omega(lambda)-separated. The main ingredients are a bias-corrected bounded-coefficient approximation theorem and balanced amplification: multiplying a depth-D ReLU network by q can be implemented using one constant channel so that every coefficient grows by only q^(1/D). Translation to vector-valued RBV^2 blocks then has layer-sum cost O(D w^2 q^(1/D)). Gaussian Fano yields a radius-explicit lower bound governed by the output, testing, and representation scales. Under A=B=R, sigma proportional to R, and the stated radius condition, this gives minimax risk at least of order L^2 w^2 log(w) R^2/n. A pseudodimension-based finite-net upper bound gives O-tilde(L^2 w^2 R^2/n) for unbounded Gaussian responses. Thus the minimax risk has quadratic polynomial dependence on depth, up to logarithmic factors, and exhibits a transition to representation-limited behavior at smaller radius.

cs.AI↗
arXiv2026

Predict Before Replay: Joint FEC and Flight Control for Reliable Scale-Up Links

Scale-up accelerator fabrics send latency-sensitive flits over serial links at hundreds of gigabits per second. Their reliability pipeline first relies on FEC, then detects residual failures and replays unacknowledged data. At these line rates, delayed feedback lets later flits enter the replay window before a residual failure is reported, so standard replay can amplify one corrupted flit into a suffix retransmission. This paper presents PREFACE, a pre-FEC controller for temporally correlated burst errors. A two-state Bayesian filter converts corrected-symbol observations into a next-flit burst posterior and jointly selects FEC strength with an outstanding-flit cap. We implement PREFACE in ns-3 with publicly verifiable UALink 200G 1.0 replay semantics. PREFACE improves goodput by 10.52%, lowers P99 latency by 50.75%, cuts replay by 47.52%, and improves modeled ring AllReduce by 13.1--27.0%.

cs.NI↗
arXiv2026

Completion-Path Credits: Multi-Resource Control for Scale-Up Fabrics

Scale-up fabrics connecting GPUs and AI accelerators carry tensor transfers together with remote reads, writes, atomics, and notifications over shared target-side receiver resources. Byte-denominated credits protect link buffers and streaming HBM traffic, but poorly represent small operations dominated by Atomic execution or response injection. This paper presents SemaCredit, a receiver controller that admits each remote-memory operation against a vector of target-resource demands and returns each component when its corresponding HBM, Atomic, or response stage completes. In a deterministic event simulator with multipath queues, eight HBM partitions, a serialized Atomic engine, and a response engine, SemaCredit matches a strong per-resource byte baseline on HBM-hotspot traffic while reducing small-operation P99 latency by 52.4% under Atomic contention and 10.2% under response incast. Application-shaped mixes show 57.7% and 14.5% P99 latency improvements for AllReduce-shaped and remote-read-shaped traffic while matching byte credits on HBM-dominated MoE traffic.

cs.NI↗
arXiv2026

Range of the displacement operator of PDHG with applications to quadratic and conic programming

Primal-dual hybrid gradient (PDHG) is a first-order method for saddle-point problems and convex programming introduced by Chambolle and Pock. Recently, Applegate et al.\ analyzed the behavior of PDHG when applied to an infeasible or unbounded instance of linear programming, and in particular, showed that PDHG is able to diagnose these conditions. Their analysis hinges on the notion of the infimal displacement vector in the closure of the range of the displacement mapping of the splitting operator that encodes the PDHG algorithm. In this paper, we develop a novel formula for this range using monotone operator theory. The analysis is then specialized to conic programming and further to quadratic programming (QP) and second-order cone programming (SOCP). A consequence of our analysis is that PDHG is able to diagnose infeasible or unbounded instances of QP and of the ellipsoid-separation problem, a subclass of SOCP.

math.OC↗
arXiv2026

Electronic excitation of ultrafast collective amorphous-amorphous transitions in glassy phase-change material

The intrinsic nature of glass states and glass transitions remain a fundamental open question in condensed-matter physics and materials science. The key to solving the glass transition problem lies in achieving a complete understanding of the physics governing the structural relaxation. Nonetheless, directly probing dynamic atomic-scale structural changes in order to identify the precise local structural motifs and establish quantitative structure-property relationships remains an outstanding challenge. By combining femtosecond electron diffraction with time-dependent density-functional theory molecular dynamics simulations, we directly capture ultrafast amorphous-amorphous transitions indicated by collective bond stretching (0.2 ps) and angle bending (0.5-2 ps) in glassy phase-change material GeTe. The ultrafast bond stretching is accompanied by localized oscillation modes with the frequency of 3.10 THz, unambiguously signaling the local Peierls-like bonding structure and the flexibility of these polarized bonds. These ultrafast collective atomic motions, captured across timescales ranging from femtoseconds to picoseconds, directly reveals the structural origin of the boson peak and provide compelling evidence for many-body interactions in amorphous materials. Furthermore, the ultrafast amorphous-amorphous transitions induce a drastic insulator-metal transition, directly revealing both the underlying switching mechanism and the fundamental speed limit of the ovonic threshold switch. These insights establish a fundamental framework for rationally engineering relaxation pathways and phase-change/threshold switch in amorphous materials. Femtosecond electron diffraction provides a powerful novel approach to deciphering the structural complexity and functional mechanisms of amorphous materials by resolving collective atomic motions from random diffusion dynamics in the time domain.

cond-mat.mtrl-sci↗
arXiv2026

Sub-angstrom many-body localization driven by phononic flat bands in real quantum materials

Defects, fluctuations, degenerate states and correlated interactions facilitate the emergence of exotic properties in condensed matter systems while also inducing atomic-scale local correlated structures that deviate from the average long-range order. Establishing the structure-property relationship from the perspective of these atomic-scale local correlated structures remains ambiguous and controversial due to the lack of direct methods for identifying such local correlated structures. In this work, based on the photoexcited ultrafast structural response, we propose a Bragg scattering phase breaking regime to identify sub-angstrom local correlated structures in quantum materials. With this regime, we unambiguously identify the many-body-interaction driven local correlated structures in the low temperature ground state of AgCrSe2, characterized by static off-center displacements of Ag atoms ranging from 0 to 0.5 angstrom. The competition between Ag-Ag Coulomb correlations and potential wells induced by CrSe2 layers, leading to phononic flat bands and driving the system into a many body localization (MBL) regime. As temperature rising, these static local correlated structures transform to a dynamic state where the thermal fluctuations overwhelm the multiple localized states. These distinctive local correlated structures constitute the first experimental observation of MBL with vortex-like topological characteristic in a real material system. Emergent vibrational modes arising from MBL have been confirmed and show excellent agreement with inelastic neutron scattering experiments. Our work not only offers a universal approach to characterize sub-angstrom local correlated structures across a wide range of quantum materials but also deepens our understanding of the fundamental mechanism behind exotic properties from the perspective of atomic-scale local correlated structures.

cond-mat.mtrl-sci↗
arXiv2026

G0.5: One Autoregressive Stream for Robot Reasoning and Action

The prevailing recipe for Vision-Language-Action (VLA) models couples a pretrained VLM with a separately trained flow-matching action expert. This makes the VLM a context encoder rather than a decision-maker. We introduce G0.5, a pretrained autoregressive VLA in which a single transformer decoder emits reasoning and action tokens under a single objective. Three components make this tractable at foundation-model scale: a learnable cross-embodiment action tokenizer that maps heterogeneous robot actions into a shared vocabulary; a native chain-of-thought stream interleaving task decomposition, object grounding, and action hints with action tokens; and a visual memory module that injects multi-second history through the vision encoder. Because reasoning and action share a single set of weights, the pretrained VLM's capabilities carry over to physical behavior: the model follows instructions closely, and prompts directly steer action granularity, task horizon, and out-of-distribution scene handling without further training. Pretrained on a large collection of robot datasets together with VQA samples, G0.5 surpasses state-of-the-art models across 7 independent regimes: real-world fine-tuning on R1lite and R1pro robots (76.7\% vs.\ 53.3\% for $π_{0.5}$ and 24.4\% for GR00T-N1.7), the 2025 BEHAVIOR Challenge on 50 long-horizon household mobile manipulation tasks using a generalist policy (31.4\% vs.\ 26.3\% for $π_{0.5}$ and 26.1\% for the challenge winner), DROID post-training followed by zero-shot transfer to an unseen environment and objects (82.5\%), a language-following Pick-and-Place benchmark, LIBERO (98.9\%), RoboTwin 2.0 (93.3\%), and SimplerEnv-Bridge (87.3\%).

cs.RO↗
arXiv2026

Polynomial-Time Singular Witnesses for Non-SNS Sign Patterns

Sign-nonsingularity asks whether every real matrix with prescribed entry signs is nonsingular. Polynomial-time algorithms recognize square sign-nonsingular patterns through their connection with even directed cycles, but recognition does not itself produce an exact numerical witness in the negative case. We give a deterministic polynomial-time algorithm that, for any square sign pattern $A$, either reports that $A$ is sign-nonsingular or outputs $B\in\mathbb{Z}^{n\times n}$ and $z\in\mathbb{Z}^n\setminus\{0\}$ such that $\operatorname{sgn}(B)=A$ and $Bz=0$. After normalizing a perfect matching, an even directed cycle yields two determinant terms of opposite signs. Making either term dominant produces endpoint realizations with opposite determinant signs. Changing their magnitudes one coordinate at a time exposes an affine sign-changing step, whose zero is rational; clearing its denominator gives the integer witness. Entries of $B$ have $O(n^2\log n)$ bits, and entries of $z$ have $O(n^3\log n)$ bits. The result settles Conjecture 14.12.4 in the Handbook of Satisfiability.

cs.DS↗
arXiv2026

Kimi K2.5: Visual Agentic Intelligence

We introduce Kimi K2.5, an open-source multimodal agentic model designed to advance general agentic intelligence. K2.5 emphasizes the joint optimization of text and vision so that two modalities enhance each other. This includes a series of techniques such as joint text-vision pre-training, zero-vision SFT, and joint text-vision reinforcement learning. Building on this multimodal foundation, K2.5 introduces Agent Swarm, a self-directed parallel agent orchestration framework that dynamically decomposes complex tasks into heterogeneous sub-problems and executes them concurrently. Extensive evaluations show that Kimi K2.5 achieves state-of-the-art results across various domains including coding, vision, reasoning, and agentic tasks. Agent Swarm also reduces latency by up to $4.5\times$ over single-agent baselines. We release the post-trained Kimi K2.5 model checkpoint to facilitate future research and real-world applications of agentic intelligence.

cs.CL↗
arXiv2026

Generalized Mermin Inequalities for Benchmarking Large-Scale GHZ States

Multipartite Bell tests provide a correlation-only route to benchmarking quantum processors, but their application at large scales is hindered by the rapid decay of many-body correlators under noise and exponentially many terms in conventional Bell expressions. Here we address these scalability obstacles by introducing a finite-setting generalized Mermin family of state-tailored Bell inequalities with analytic certification bounds, in which the measurement-setting number $m$ provides an additional certification dimension complementary to the system size $n$. We show that, for the powers-of-two setting choices considered here, increasing $m$ leaves the ideal normalized multipartite quantum value unchanged while lowering the relevant classical bounds, thereby strengthening the Bell-violation ratios and yielding an improved noise-robustness scaling compared to the standard Mermin inequality. We test this construction experimentally on a programmable superconducting processor by preparing Greenberger-Horne-Zeilinger (GHZ) states of up to 80 qubits. Using randomized sampling for direct Bell-operator estimation, we observe Bell ratios that grow exponentially with system size, certify a nonlocality depth of 14, and show that increasing $m$ strengthens both the Bell ratio and depth certification. All results are obtained solely from measured correlators and analytical bounds, without readout correction, tomography, or model-based mitigation. Generalized Mermin inequalities therefore provide a sharper Bell benchmark for noisy large-scale GHZ states.

quant-ph↗