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Jun Zhang

Publications and source records attributed to Jun Zhang.

At least 55 records · Page 3Linked to original sources

NP-Hardness of Bounded Distance Decoding for Reed-Solomon Codes

For an $[n,K]$ Reed--Solomon code, the covering radius is $n-K$. Gandikota, Ghazi, and Grigorescu proved deterministic NP-hardness of bounded-distance decoding when the decoding radius is $d$ below the covering radius for every $1\le d\le c\log n/\log\log n$, where $c>0$ is an absolute constant. We prove that, for every fixed rational $0<α<1/2$, bounded-distance decoding is NP-complete under deterministic polynomial-time many-one reductions over explicitly represented finite extension fields for the additive gap $d=\lfloor n^α\rfloor$ below the covering radius. The hard codes have odd block length~$n$, dimension $K=(n+1)/2-d$, decoding radius $(n-1)/2$, and rate tending to $1/2$. The alphabet size is subexponential in the evaluation set size: for a fixed $0<η<1$ depending only on $α$, it is $2^{Θ(n^η\log n)}=2^{o(n)}$. The proof passes through moments subset sum on $n-1$ nonzero field elements, with required subset size $(n-1)/2$ and $d$ prescribed moments. The arithmetic ingredient is a uniform positive-completion theorem over prime fields $\mathbb{F}_q$ with $q\ge d^{2+ρ}$, for any fixed $ρ>0$. A sharper form follows from a higher-dimensional point-count estimate based on Deligne's theorem; the weaker form used in our reduction is proved more elementarily using additive-character orthogonality, the one-variable Weil bound, a moment identity of order $2d$, and Newton identities. A universal completion pool, an extension-field quotient construction, and a deterministic linear-size simultaneous power condenser complete the reduction.

cs.CC↗

SciWalker: Synthesizing Scientific Coding Problems with Operator Graphs and Execution Feedback

Improving the scientific coding capabilities of large language models (LLMs) requires high-quality training data. However, such data remain scarce because manually authoring realistic problems is costly and time-consuming, while systematically covering diverse scientific domains and algorithmic combinations remains challenging. To address this, we introduce SciWalker, a framework for synthesizing scientific coding problems through operator-chain sampling and execution feedback. The framework combines scientific library interfaces with operation modes to instantiate operators, organizes them into operator graphs, and samples operator chains as computational workflow cues. Guided by these cues, we adopt LLMs to generate scientifically grounded problem statements, reference solutions, and tests, with failed generations iteratively repaired using execution feedback. By combining structured workflow composition with verification and quality review, SciWalker enables scalable task generation while promoting scientific grounding, computational diversity, and executability. Using this framework, we construct 8,178 high-quality problems spanning 5 scientific domains and 32 subdomains. To evaluate their training utility, we conduct reinforcement learning on Qwen3.5-9B using the GSPO algorithm. This training improves SciCode subproblem accuracy by 9.9 percentage points, from 29.3% to 39.2%, with gains across scientific code generation, code repair, and reasoning benchmarks. The code for SciWalker is available at https://github.com/lichenx1/SciWalker.

cs.AI↗

SARA: SLO-Aware Resource Allocation for Disaggregated Agentic LLM Services

Recent advances in large language models (LLMs) are driving the emergence of multi-modal and agentic services for mobile users through cloud and edge infrastructures, where long-context workloads pose daunting challenges for inference latency. Existing disaggregated LLM serving systems largely rely on hardware profiling, configuration enumeration, or heuristic scheduling, offering limited analytical guidance for cost-efficient resource allocation. In this paper, we propose SARA, a Service level objectives (SLOs)-Aware Resource Allocation framework for disaggregated agentic LLM serving systems, which maximizes goodput under a deployment cost constraint and a series of quantile-based SLO constraints. By capitalizing on queuing theory, we first model the prefill, KV cache transfer, and decode stages as an M/G/k queue, an M/G/1 queue, and a generalized birth-death process, respectively. The analysis reveals that the prefill and decode stages are dominantly limited by computational capacity and high-bandwidth memory (HBM) resources, respectively. With these mathematical models, we further derive tractable tail behaviors of different stage-wise service level metrics for both light- and heavy-tailed workloads. These characterizations explicitly map workload, model architecture, and hardware parameters to stage-wise SLO constraints and minimum resource requirements. Finally, we develop an effective resource allocation framework to maximize system goodput under limited cost budgets. Simulation and hardware results demonstrate that the proposed framework accurately predicts the stage-wise SLO with mean errors below 5%, and improves system goodput by 26.6% on average over state-of-the-art baseline methods under the same deployment cost.

cs.PF↗

SalQ-VLM: Fine-Grained Saliency-Guided Quantization for Vision-Language Models

Large language models (LLMs) have demonstrated remarkable capabilities across diverse language tasks, motivating their extension to vision-language models (VLMs) for multimodal understanding. However, billion-parameter VLMs incur substantial memory and computational costs that hinder deployment in resource-constrained settings. Post-training quantization (PTQ) compresses models and accelerates inference without retraining, yet remains underexplored for VLMs. We identify two intrinsic VLM activation properties in PTQ: (1) visual over-representation, where vision tokens are excessive and often redundant, and (2) the modality gap separating text and vision tokens in the latent feature space. Prior methods largely overlook these properties, leading to quantization performance degradation. To address this mismatch, we propose SalQ-VLM, an importance-aware PTQ framework that prioritizes salient tokens and suppresses redundant vision tokens during calibration. We derive a gradient-driven importance factor that captures token-level importance variance and is theoretically grounded in the relationship among loss perturbation, activation errors, and output gradients. SalQ-VLM obtains this factor through a single lightweight block-wise gradient-caching pass and incorporates it into the layer-wise reconstruction objective. Because SalQ-VLM modifies only calibration, it adds no inference-time operations and remains compatible with existing high-performance kernels. Extensive evaluations across benchmarks and backbones show that SalQ-VLM consistently outperforms strong PTQ baselines, especially under ultra-low-bit quantization. Notably, it improves MME-RealWorld accuracy by 16.45% under INT2g128 quantization.

cs.CV↗

RiskChainBench: A Benchmark for Obfuscated Platform Message Restoration and Evidence-Grounded Web Investigation

Platform abuse campaigns conceal redirection instructions with emojis, homophones, character decomposition, and redundant symbols, then route users through disguised links to services associated with pornography, fraud, gambling, or illicit transactions. Existing benchmarks evaluate obfuscated text and risky webpages separately, obscuring how target recovery affects downstream evidence acquisition. We introduce RiskChainBench, pairing 3,600 synthetic token-text restoration inputs from 600 source sessions with 600 corresponding human-labeled local web environments. A model first restores the message, operational intent, and destination; the same underlying model then acts as a VLM-driven web agent that investigates the correctly associated website and produces a frozen, evidence-cited risk report without message-side semantics or domain-reputation cues. We score restoration and correct-routing web investigation separately and compose them offline by applying the frozen primary-entry prediction as a gate to the same Task 2 result. Human labels determine task correctness, while a fixed multimodal evidence judge assesses faithfulness, sufficiency, completeness, and consistency. Across ten models, Entry Top-1 ranges from 35.2% to 95.2% and web decision accuracy from 26.3% to 62.8%; the leading systems differ across entry recovery, full reconstruction, website decisions, and fine-grained typing. Execution failures account for 31.9% of web runs, whereas post-decision type errors account for only 0.9%, identifying stable exploration and risk judgment as the principal bottlenecks. We release the benchmark, protocol, and resettable local sandbox.

cs.CL↗

Hyper-derivative Algebraic Geometry Codes via Local Expansions

In this paper, we develop a systematic construction framework of hyper-derivative algebraic geometry codes via local expansions, extending hyper-derivative Reed-Solomon codes from the rational function field to general algebraic function fields. Using the residue theorem, we determine their Euclidean duals and illustrate that the duals naturally reverse. We further give criteria for reverse self orthogonality and reverse self duality in terms of two classes of bilinear forms. Finally, we provide an asymptotic bound on the rate and relative distance via function field towers.

cs.IT↗

PRISM: Parallel Residual Iterative Sequence Model

Generative sequence modeling faces a fundamental tension between the expressivity of Transformers and the efficiency of linear sequence models. Existing efficient architectures are theoretically bounded by shallow, single-step linear updates, while powerful iterative methods like Test-Time Training (TTT) break hardware parallelism due to two dimensions of serial dependency: token-level state reliance and step-level iteration loops. We propose PRISM (Parallel Residual Iterative Sequence Model) to resolve this tension. PRISM explicitly approximates the expressive gate-residual-direction iteration pattern of TTT in a parallelizable form. We employ a Write-Forget Decoupling strategy that isolates non-linearity within the injection operator. To bypass the serial dependency of explicit solvers, PRISM utilizes a two-stage proxy architecture: a short-convolution anchors the initial residual using local history energy, while a learned predictor estimates the refinement updates directly from the input. This design distills structural patterns associated with iterative correction into a parallelizable feedforward operator. Theoretically, we prove that this formulation achieves Rank-$L$ accumulation, structurally expanding the update scheme beyond the single-step Rank-$1$ bottleneck. Empirically, it achieves comparable performance to explicit optimization methods while achieving \textbf{174x higher throughput}. Codes are available in https://github.com/gpr-prism/prism/.

cs.LG↗

FlexiSLM: A Spoken Language Model with Dynamic and Controllable Frame Rates

Spoken language models (SLMs) extend LLMs to speech input and output, but existing systems use fixed frame rates (e.g., 25 or 12.5 Hz), overlooking speech's time-varying information density and limiting inference-time quality-speed tradeoffs. Recent dynamic-frame-rate audio tokenizers enable very low average frame rates and controllability, yet had not been applied to SLMs. We introduce FlexiSLM, the first SLM with dynamic, controllable frame rates, using pretrained FlexiCodec for dynamic speech output tokens. It integrates this representation into a multi-task speech-to-speech SLM, extends it with input-side frame compression, and adds direct frame-rate conditioning for accurate control during inference. FlexiSLM outperforms fixed-frame-rate 7B models, including Qwen2.5-Omni and Kimi-Audio, at 12.5 and 6.25 Hz; it can be steered down to 4.0 Hz, and at 6.25 Hz roughly halves inference time relative to 12.5 Hz while retaining strong speech-to-speech quality. Audio samples: https://flexislm.github.io; code and data: https://github.com/AmphionTeam/FlexiSLM.

cs.SD↗

The Token Before the Value Is the Key: How Hybrid Architectures Organize Induction Circuits

Hybrid language models can improve capability as well as efficiency, raising the question of how architectural complementarity becomes learned computation. We examine the established induction roles of Carrying predecessor information, Matching a source by content, and Copying its value. How are these position-sensitive and content-based computations allocated across heterogeneous layers? We introduce layer-type-agnostic paired probes that track Carrying and Matching through a common block-update interface. In recurrent--global and local--global hybrids, Carrying concentrates in efficient layers and Matching in global receivers. The measured local contribution concentrates on lag one: the token immediately before the historical value. Changing predecessor support through lag-one masking, convolution removal, or early learning-rate reduction can relocate Carrying and Matching between stages. Source-key restoration and fixed-value selection trace the receiver's dependence on the prepared source. These interventions also change natural-text recall, with outcomes depending on configuration and target. Varying local windows and induction-enriched training text changes the early development of functional Carrying and Matching, connecting architectural priors and training evidence to formation timing. Together, the probes and interventions shift the explanatory focus upstream: the organization of Matching follows how Carrying is learned. The token before the value provides a concrete link between a hybrid's architecture, circuit development, and recall. Code is available in https://github.com/ckpassenger/bind-match-copy/tree/main.

cs.LG↗

Learning to Optimize UAV Path Planning for Data Sensing in Wireless Sensor Networks

UAVs have emerged as highly flexible platforms for data sensing in Wireless Sensor Networks (WSNs). Path planning for UAVs in such tasks plays a key role to assure remote sensing effectiveness and friendly energy consumption. However, existing approaches show two key limitations: i) they are primarily hand-crafted with certain design biases that harm adaptation on unseen tasks. ii) they predominantly assume idealized spatial complexities of actual environments through simplified simulation, causing them to underperform during real-world deployment. In this paper, we propose a novel learning-assisted planning framework, termed Landscape-Aware Meta Differential Evolution (LAMDE), to tackle the mentioned limitations. The major contributions come from the following aspects. We first re-formulate such UAV path planning problem to embrace challenging constraints. To efficiently navigate this highly constrained space, we propose a bi-level learning to optimize approach, where the meta-level is a trainable algorithm configuration policy that meta-learns an adaptable planning strategy for low-level planning algorithm. To address the potential training data scarcity and distribution shift in real-world environments, we introduce a landscape-aware automatic augmentation scheme that enriches training data. At the low-level, a Differential Evolution algorithm is deployed for solving the path planning tasks. To enhance the solving flexibility, we further design a variable-length encoding strategy that dynamically prunes redundant hover points and optimizes continuous flight parameters concurrently within a unified search space. Based on all proposed designs, we meta-train LAMDE and compare it with representative baselines. Comprehensive experiments demonstrate that LAMDE achieves state-of-the-art performance on the tested complex UAV path planning tasks in WSN data collection scenarios.

cs.RO↗

On Twisted Roth-Lempel Codes

In 1989, Roth and Lempel constructed a well-known family of non-Reed-Solomon maximum distance separable (MDS) codes. For decades, this family of codes has attracted extensive research attention due to its algebraic structure, low-complexity decoding, and broad applications in cryptography and data storage. In this paper, we present a class of twisted Roth-Lempel codes. We investigate their minimum distance, MDS and NMDS properties. Specifically, we determine the necessary and sufficient conditions for the TRL codes to have minimum distance n-k or n-k+1. Furthermore, we determine the necessary and sufficient conditions for the TRL code to be an MDS or NMDS code. Moreover, we show that the dimension of the Schur square of the TRL code is at least 2k+1, and thus the TRL code is a non-RS code inequivalent to the corresponding RL code.

cs.IT↗

PACE: Progressive Angular-to-Norm Contrastive Embedding

Multimodal embedding models encode heterogeneous inputs into a shared embedding space, enabling efficient similarity computation across modalities and tasks. Most existing methods optimize cosine-based contrastive objectives, which promote stable training but restrict semantic compatibility to angular geometry, precluding embedding norms from serving as an additional semantic signal. However, directly optimizing the more expressive dot-product similarity, which leverages both angular and norm information, underperforms cosine-based training and exhibits unstable training dynamics. We attribute this discrepancy to premature optimization-space expansion, manifested as angular--norm entanglement and directional anisotropy in the representation space and further compounded by full-parameter fine-tuning. In this paper, we propose PACE, a two-stage framework that progressively expands both the representation and trainable parameter spaces. Stage I combines cosine-based objective with low-rank adaptation to establish a reliable angular geometry within constrained optimization spaces. Stage II switches to dot-product similarity and full-parameter fine-tuning, enabling embedding directions and norms to jointly encode semantic information. We further introduce Focal Embedding Loss, a confidence-adaptive objective that downweights queries with high positive retrieval confidence while emphasizing ambiguous queries with competitive negatives. Experiments across multiple backbone scales and diverse multimodal embedding tasks consistently validate the effectiveness of PACE.

cs.CV↗

Quantization of Preprojective Algebras and the Type A Rational Cherednik Algebra

For the Jordan quiver, we construct a radial quantum trace morphism from the quantum preprojective algebra to the algebra of invariant differential operators on the Cartan subalgebra. Its classical limit recovers the classical trace morphism associated with the commuting quotient, and we prove that both the quantum and classical trace morphisms are surjective. We further relate noncommutative quantizations of the Jordan preprojective algebra to the spherical rational Cherednik algebra of type A. Finally, we derive explicit formulas for the Harish--Chandra homomorphism and its radial part in power-sum coordinates.

math.RT↗

Two variants of Twisted Reed-Solomon Codes

Generalized Reed-Solomon codes and twisted generalized Reed-Solomon codes provide important sources of maximum distance separable codes. In this paper, we study two variants obtained by introducing column twists and simultaneous row-column twists into Reed-Solomon-type evaluation codes. For the column-twisted family, we provide necessary and sufficient conditions for the code to be MDS in terms of explicit subset product conditions. Under the stated parameter assumptions, the Schur square has dimension 2k+1, which leads to MDS codes that are not equivalent to Reed-Solomon codes. For the row-column twisted family, we establish necessary and sufficient conditions for the MDS property in terms of elementary symmetric functions. The larger Schur-square dimension provides a further distinction from both Reed-Solomon codes and known twisted families, thereby yielding new non-RS MDS codes. Finally, explicit parity-check matrices and dual descriptions are obtained for both code families. These results provide a foundation for subsequent studies of self-orthogonality, hull dimensions, and applications to quantum-code constructions.

cs.IT↗

Unified formalism and adaptive algorithms for optimal quantum state, detector and process tomography

Quantum tomography is a standard technique for characterizing, benchmarking and verifying quantum systems/devices and plays a vital role in advancing quantum technology and understanding the foundations of quantum mechanics. Achieving the highest possible tomography accuracy remains a central challenge. Here we unify the infidelity metrics for quantum state, detector and process tomography in a single index $1-F(\hat S,S)$, where $S$ represents the true density matrix, POVM element, or process matrix, and $\hat S$ is its estimator. We establish a sufficient and necessary condition for any tomography protocol to attain the optimal scaling $1-F= O(1/N) $ where $N$ is the number of state copies consumed, in contrast to the $O(1/\sqrt{N})$ worst-case scaling of static methods. Guided by this result, we propose adaptive algorithms with provably optimal infidelity scalings for state, detector, and process tomography. Numerical simulations and quantum optical experiments validate the proposed methods, with our experiments reaching, for the first time, the optimal infidelity scaling in ancilla-assisted process tomography.

quant-ph↗

Superconducting Hydride Mg2RhH6 Experimentally Achieved at Lower Pressure

Although tremendous progress has been made in recent years in the field of polyhydride superconductors, the realization of high critical temperature superconductivity still relies on formidable high pressures. Searching for superconducting hydrides at lower pressures is of particular importance. Here we report the first experimental synthesis of the Mg2RhH6, which achieves superconductivity under a significantly reduced pressure of 30 GPa. The synthesis of Mg2RhH6 proceeds via a two step process (1) preparation of the Mg2RhH5 precursor containing hydrogen atoms stabilized by covalent bonds, followed by (2) hydrogen supplementation resulting in the filling of electrons into anti bonding orbitals above 30 GPa, which was accompanied by the structural transition from RhH5 square pyramid to RhH6 octahedron. Superconductivity is achieved at 30 GPa with a Tc of 24 K, which is further enhanced to 29 K at 53 GPa, evidenced by a sharp drop of resistivity to zero and characteristic suppression of Tc under applied magnetic fields. Our experiments prove the Mg2RhH6 superconductor to be thermodynamically stable above 30 GPa, making it the first case exhibiting a Tc of approximately 30 K at a readily accessible pressure. This study pioneers a highly promising pathway for the rational design and discovery of high temperature superconductors within phonon mediated BCS framework.

cond-mat.supr-con↗

MSSP: Multi-Scale Spatially-Constrained Partition for Unsupervised Semantic Segmentation of 3D Point Clouds

3D point cloud semantic segmentation is essential for real-world spatial understanding, yet the prohibitive cost of human annotations motivates unsupervised approaches that require no labels. Existing superpoint-based methods typically rely on spectral analysis at a fixed granularity, failing to capture the hierarchical semantic structures inherent in complex indoor scenes. To bridge this gap, we present a Multi-Scale Spatially-Constrained Partition (MSSP) framework that combines multi-scale spectral analysis with spatially-constrained clustering. Multi-scale spectral analysis constructs enriched superpoint descriptors across multiple clustering granularities; however, the resulting high-dimensional feature space calls for a structural prior to translate into cleaner segmentation. Spatially-constrained clustering supplies this prior by restricting superpoint merging to physically adjacent regions, imposing the spatial coherence needed for multi-scale features to be effective. Extensive experiments on S3DIS and ScanNet show that MSSP achieves the best mIoU among unsupervised methods on the main benchmarks, with particularly significant gains on S3DIS. Notably, our ablation reveals a regularize-then-enrich interaction: multi-scale features alone do not improve final segmentation, yet become highly effective when coupled with spatial regularization, underscoring that spatial coherence is aprerequisite for multi-scale representations in superpoint clustering.

cs.CV↗

One Merger, Three Bands: Multiband Detection Rates and Parameter Estimation of Stellar-mass Binary Black Hole Coalescences from Millihertz to Kilohertz

Stellar-mass binary black holes sweep through the millihertz, decihertz, and kilohertz gravitational-wave bands before merger, making them prime targets for multiband observations. We forecast their detection rates and parameter-estimation precision for networks combining the millihertz observatories LISA, Taiji, and TianQin; the decihertz concepts LGWA, AMIGO, and AMIGO-5; and the ground-based LVK and Cosmic Explorer. Using the binary-black-hole mass and redshift distributions inferred from GWTC-4, we first estimate the single-band yields and then account explicitly for detector lifetimes and relative mission start times when calculating multiband rates. For a signal-to-noise-ratio threshold of 8, the LISA--Taiji--TianQin network is expected to detect $53.6^{+19.7}_{-14.1}$ systems in the millihertz band, while the largest three-band yield is $23.5^{+8.7}_{-6.2}$ events for LISA--Taiji--TianQin combined with LGWA. The yield is maximized when the decihertz observation begins approximately $0$--$3\,{\rm yr}$ after the millihertz mission. Ground-informed subthreshold searches with a threshold of 5 increase the multiband yields by a factor of approximately $4$--$5$, reaching $125.1^{+46.1}_{-32.9}$ events for the same network. Fisher-matrix forecasts for a GW150914-like source show that decihertz observations provide the dominant improvement in most multiband parameter constraints, whereas millihertz data primarily sharpen the detector-frame chirp mass and sky localization. Combining the three bands improves the chirp-mass precision by orders of magnitude relative to ground-based observations alone and can reduce the localization area and coalescence-time uncertainty substantially. These results demonstrate that coordinated mission scheduling and targeted subthreshold searches are essential for realizing the scientific potential of stellar-mass multiband gravitational-wave astronomy.

astro-ph.IM↗