arXiv subjects

Jing Ye

Publications and source records attributed to Jing Ye.

At least 19 recordsLinked to original sources

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

ARMOR: Accelerating RTL Simulation by Mitigating the Front-End Bottleneck Using Node Compression

RTL simulation is indispensable in chip design. High-performance simulators typically lower each node in the RTL graph into an instruction sequence. Although this per-node lowering enables aggressive compiler optimizations, it dramatically increases the code footprint, severely exceeding instruction cache capacity and causing front-end bottlenecks. Our profiling reveals that over 50% of pipeline stalls are caused by the CPU front-end, becoming a key performance bottleneck in state-of-the-art RTL simulators. However, reaping the optimization benefits of fully unrolling the RTL graph while simultaneously reducing the code footprint to mitigate front-end bottlenecks remains highly challenging. In this paper, we propose ARMOR, an efficient RTL simulator designed to alleviate the front-end bottleneck through node compression. The key idea is to exploit the data parallelism exposed by the unrolling RTL graph and the insufficient bit-space utilization revealed by per-node lowering, leveraging bit-level data parallelism to compress multiple nodes simultaneously, so that a single instruction sequence can serve multiple nodes instead of one per node. To achieve profitable node compression, we first propose a module-aware isomorphic subgraph identification method that leverages structural isomorphism across module instances to systematically identify compression opportunities at the subgraph level. We then propose an alignment-aware dense packing strategy that groups nodes into packs according to dataflow dependencies while preserving data reuse, complemented by greedy merging strategies to enhance bit-space utilization. Finally, we implement a unified bit-level parallelism scheme to support bit-level parallel execution of compressed nodes. Experimental results show that ARMOR achieves 1.6x speedup on CPU designs and 2.7x speedup on AI accelerators compared to state-of-the-art simulators.

cs.AR↗

arXiv2026

On the transcendence of twisted special $L$-values at non-negative integers in characteristic $p$

In this article, we study the transcendence of special values of certain Goss type $L$-series at non-negative integers, which takes values in a function field of characteristic $p$. We show that, for a Drinfeld module $\varphi$ over $K$ and an Artin representation $\rho:G_K\to \operatorname{GL}_n(\overline{\mathbb{F}}_q)$, the twisted special value $L(\varphi^\vee,\rho,k)$ is transcendental over $K$ for every non-negative integer $k$. The proof uses the theory of Artin twists of Drinfeld modules, Taelman's regulators of $t$-modules, and the algebraic independence theorem of Gezmis and Namoijam for tractable coordinates of logarithms. As a consequence, we deduce the transcendence of the special $L$-value $L(\rho,k)$ for every positive integers $k$.

math.NT↗

arXiv2026

An Efficient Fault-Tolerance Scheme for CKKS Computation on CPUs

Fully homomorphic encryption (FHE) enables computation on encrypted data, but its long ciphertext dataflow and high-dimensional modular arithmetic make it vulnerable to silent data corruption caused by transient hardware faults. Existing protection methods either target dedicated accelerators or impose substantial execution, modular-arithmetic, and memory-access overheads on CPUs. This work presents an efficient fault-tolerance scheme for CPU-based CKKS computation. It checks the input-output consistency of polynomial operators while reducing protection overhead at three levels. First, modulus-aware bucket checksum exploits wide CPU accumulators to reduce expensive modular reductions. Second, dataflow-fused in-operator checking embeds checksum accumulation into operator dataflows, avoiding separate scans of long ciphertext polynomials. Third, cross-operator check fusion eliminates redundant checksum computations between adjacent operators while preserving end-to-end checking invariants. We implement the scheme in OpenFHE and evaluate it on representative encrypted applications and ciphertext primitives under random single-bit transient faults. It achieves a 100 percent empirical detection rate across 150,000 non-crashing corrupted-result cases and maintains application accuracy close to the fault-free baseline over a wide range of fault rates. The scheme incurs only 6.0 percent to 8.4 percent runtime overhead, averaging 6.8 percent, and reduces average protection overhead by 4.9 times compared with direct checksum-based protection.

cs.AR↗

arXiv2026

Twisted-pair unilateral reconnection: A unifying driver for magnetically powered astrophysical bursts

Magnetic reconnection in twisted loops has long been invoked as an engine powering energetic transients from black hole accretion to neutron star mergers, yet never directly observed. Here we report the first direct observation of the complete reconnection of this type in a solar flare. We find a magnetic loop twisted to about 540 degrees, far exceeding the 180 degrees twist assumed in existing simulations. This extreme twist inherently enables efficient multiple X-line reconnection, akin to the role of turbulence in contemporary theory. Remarkably, the intertwined end breaks unilaterally after reconnection (unlike symmetric breaking in simulations), forming open field lines that release hot plasma -- providing a promising mechanism for coronal generation or heating. We first detect hard X-ray emission from the current sheet, directly proving it as a particle accelerator. Moreover, we discover a power-law relationship between quasi-periodic oscillation frequency and magnetic field strength across solar flares, black hole binaries, active galactic nuclei, magnetars, and gamma-ray bursts. This relation identifies twisted-pair unilateral reconnection as a common burst mechanism and provides a natural ruler for cosmic magnetic fields. These findings establish an observational foundation for future reconnection theory and simulations, offering a unified framework for magnetically powered bursts.

astro-ph.HE↗

arXiv2026

FT-Pilot: Automated Fault-Tolerant RTL Rewriting via Vulnerability-Guided LLMs

As integrated circuit technologies continue to scale toward advanced process nodes, the continual reduction in node capacitance and supply voltage has made digital systems increasingly vulnerable to soft errors. Although traditional full-chip hardening methods can improve reliability, they often incur unacceptable area and power overhead, making selective hardening a more practical engineering solution. However, existing approaches typically rely on time-consuming fault-injection simulation to determine hardening locations through vulnerability analysis, and still depend heavily on manual strategy selection and RTL modification during the hardening stage, making them ill-suited for efficient automated reliability optimization at early design stages. To address these challenges, this paper proposes FT-Pilot, a GNN-guided LLM framework for automatic RTL soft-error hardening. The framework first employs a GNN to identify critical vulnerable assets directly at the RTL level, and then introduces an LLM-driven rewriting engine composed of an analyzer and a rewriter, which performs RTL-level fault-tolerant code rewriting with the support of dual-knowledge-base retrieval-augmented generation and an automatic repair mechanism. Experimental results show that the proposed framework can automatically generate hardened RTL designs that are syntactically correct, functionally correct, and synthesizable across multiple benchmark circuits, while significantly reducing output error rates under soft-error scenarios. This work provides a practical automated path toward shift-left reliability optimization at the RTL level.

cs.AR↗

arXiv2026

CoverAssert: Iterative LLM Assertion Generation Driven by Functional Coverage via Syntax-Semantic Representations

LLMs can generate SystemVerilog assertions (SVAs) from natural language specs, but single-pass outputs often lack functional coverage due to limited IC design understanding. We propose CoverAssert, an iterative framework that clusters semantic and AST-based structural features of assertions, maps them to specifications, and uses functional coverage feedback to guide LLMs in prioritizing uncovered points. Experiments on four open-source designs show that integrating CoverAssert with AssertLLM and Spec2Assertion improves average improvements of 9.57 % in branch coverage, 9.64 % in statement coverage, and 15.69 % in toggle coverage.

cs.AR↗

arXiv2026

On the Vulnerability of FHE Computation to Silent Data Corruption

Fully Homomorphic Encryption (FHE) is rapidly emerging as a promising foundation for privacy-preserving cloud services, enabling computation directly on encrypted data. As FHE implementations mature and begin moving toward practical deployment in domains such as secure finance, biomedical analytics, and privacy-preserving AI, a critical question remains insufficiently explored: how reliable is FHE computation on real hardware? This question is especially important because, compared with plaintext computation, FHE incurs much higher computational overhead, making it more susceptible to transient hardware faults. Moreover, data corruptions are likely to remain silent: the FHE service has no access to the underlying plaintext, causing unawareness even though the corresponding decrypted result has already been corrupted. To this end, we conduct a comprehensive evaluation of SDCs in FHE ciphertext computation. Through large-scale fault-injection experiments, we characterize the vulnerability of FHE to transient faults, and through a theoretical analysis of error-propagation behaviors, we gain deeper algorithmic insight into the mechanisms underlying this vulnerability. We further assess the effectiveness of different fault-tolerance mechanisms for mitigating these faults.

cs.CR↗

arXiv2026

Listening to the Echo: User-Reaction Aware Policy Optimization via Scalar-Verbal Hybrid Reinforcement Learning

While current emotional support dialogue systems typically rely on expert-defined scalar rewards for alignment, these signals suffer from severe information sparsity. They cannot explain why a response failed or how to adapt to dynamic user states, often diverging from the actual goal of facilitating positive emotional shifts. In practice, the most direct and reliable learning signal emerges from the user's continuous reactions during ongoing interaction. We therefore propose Reaction Aware Policy Optimization (RAPO), a framework that optimizes over interaction consequences rather than rubric scores. RAPO treats dialogue as a reaction-driven process and utilizes simulated user responses to generate dense natural-language feedback through three core components: Hindsight Dialogue Selection, which isolates pivotal turns that meaningfully alter user emotional trajectories; Generative Hindsight Feedback, which transforms user reactions into contrastive ranking signals and natural-language critiques; and Scalar-Verbal Hybrid Policy Optimization, which couples scalar reward optimization for global alignment with verbal feedback distillation for fine-grained semantic refinement. Extensive experiments on ESC and Sotopia demonstrate that RAPO significantly outperforms strong reinforcement learning baselines in driving positive interaction outcomes.

cs.AI↗

arXiv2026

Pecker: Bug Localization Framework for Sequential Designs via Causal Chain Reconstruction

Debugging represents a time-consuming and labor-intensive task in hardware design, with bug localization constituting a substantial portion of this process. While spectrum-based bug localization techniques have achieved remarkable success in software domains and shown promise for hardware description languages, their effectiveness severely degrades in sequential designs. Unlike software programs, hardware designs exhibit intrinsic temporal characteristics that create fundamental challenges: timing misalignment between bug activation and observation, and progressive error propagation through state elements that obscures the root cause. To address these limitations, we propose Pecker, a novel bug localization framework that reconstructs the broken causal chain in sequential designs. Our approach introduces two key innovations: temporal backtracking using Estimated Minimal Propagation Cycles to identify potential activation cycles, strategic trace pruning to eliminate state pollution effects. We evaluate Pecker on comprehensive benchmarks comprising both combinational and sequential circuits. Experimental results demonstrate that Pecker effectively localizes 51%/80%/85% bugs within Top-1/3/5 ranks respectively, significantly outperforming state-of-the-art techniques. Notably, Pecker maintains robust performance across circuit complexities while existing methods exhibit severe degradation on sequential designs.

cs.AR↗

arXiv2026

Iterative LLM-Based Assertion Generation Using Syntax-Semantic Representations for Functional Coverage-Guided Verification

While leveraging LLMs to automatically generate SystemVerilog assertions (SVAs) from natural language specifications holds great potential, existing techniques face a key challenge: LLMs often lack sufficient understanding of IC design, leading to poor assertion quality in a single pass. Therefore, verifying whether the generated assertions effectively cover the functional specifications and designing feedback mechanisms based on this coverage remain significant hurdles. To address these limitations, this paper introduces CoverAssert, a novel iterative framework for optimizing SVA generation with LLMs. The core contribution is a lightweight mechanism for matching generated assertions with specific functional descriptions in the specifications. CoverAssert achieves this by clustering the joint representations of semantic features of LLM-generated assertions and structural features extracted from abstract syntax trees (ASTs) about signals related to assertions, and then mapping them back to the specifications to analyze functional coverage quality. Leveraging this capability, CoverAssert constructs a feedback loop based on functional coverage to guide LLMs in prioritizing uncovered functional points, thereby iteratively improving assertion quality. Experimental evaluations on four open-source designs demonstrate that integrating CoverAssert with state-of-the-art generators, AssertLLM and Spec2Assertion, achieves average improvements of 9.57 % in branch coverage, 9.64 % in statement coverage, and 15.69 % in toggle coverage.

cs.AR↗

arXiv2026

ALIVE: Awakening LLM Reasoning via Adversarial Learning and Instructive Verbal Evaluation

The quest for expert-level reasoning in Large Language Models (LLMs) has been hampered by a persistent \textit{reward bottleneck}: traditional reinforcement learning (RL) relies on scalar rewards that are \textbf{costly} to scale, \textbf{brittle} across domains, and \textbf{blind} to the underlying logic of a solution. This reliance on external, impoverished signals prevents models from developing a deep, self-contained understanding of reasoning principles. We introduce \textbf{ALIVE} (\emph{Adversarial Learning with Instructive Verbal Evaluation}), a hands-free alignment framework that moves beyond scalar reward optimization toward intrinsic reasoning acquisition. Grounded in the principle of \emph{Cognitive Synergy}, ALIVE unifies problem posing, solving, and judging within a single policy model to internalize the logic of correctness. By coupling adversarial learning with instructive verbal feedback, ALIVE enables models to internalize evaluative criteria directly from raw corpora, effectively transforming external critiques into an endogenous reasoning faculty. Empirical evaluations across mathematical reasoning, code generation, and general logical inference benchmarks demonstrate that ALIVE consistently mitigates reward signal limitations. With identical data and compute, it achieves accuracy gains, markedly improved cross-domain generalization, and higher self-correction rates. These results indicate that the reasoning trinity fosters a self-sustaining trajectory of capability growth, positioning ALIVE as a scalable foundation for general-purpose reasoning alignment without human-in-the-loop supervision.

cs.AI↗

arXiv2026

Artin twists of Drinfeld modules and Goss L-series

Twisted $L$-functions by Dirichlet characters offer deep insights into arithmetic geometry, especially in the study of elliptic curves and abelian varieties over number fields. In the function field setting, Drinfeld modules and Anderson modules serve as analogues of elliptic curves and abelian varieties, and Goss $L$-series play the role of Hasse-Weil $L$-functions. This paper introduces a motivic framework for studying twisted Goss $L$-series via Anderson motives associated to Drinfeld modules and Artin representations. For a Drinfeld module and an Artin representation on the absolute Galois group, we present a construction of Anderson motives associated to them and we show that it comes from a uniformizable abelian Anderson module. We also study their associated $L$-series, which recover the norm of the twisted Goss $L$-values. These results provide an interpretation of twisted Goss $L$-values in terms of regulators of Anderson modules with the help of Taelman's class number formula.

math.NT↗

arXiv2026

Beyond Text-to-SQL: Can LLMs Really Debug Enterprise ETL SQL?

SQL is central to enterprise data engineering, yet generating fully correct SQL code in a single attempt remains difficult, even for experienced developers and advanced text-to-SQL LLMs, often requiring multiple debugging iterations. We introduce OurBench, the first benchmark for enterprise-level SQL reasoning and debugging. Our benchmark is built on two key innovations: (1) an automated construction workflow that uses reverse engineering to systematically inject realistic bugs into large-scale SQL code, enabling scalable and diverse benchmark generation; and (2) an execution-free evaluation framework tailored to enterprise settings, providing fast, accurate, and resource-efficient assessment. OurBench comprises 469 OurBenchSyn queries featuring syntax errors with explicit error messages, and 516 OurBenchSem queries targeting semantic errors in which the code fails to meet user intent. The queries are highly complex, averaging over 140 lines and featuring deep and wide abstract syntax trees. Evaluation of nearly 30 LLMs reveals a substantial performance gap: the best-performing model, Claude-4-Sonnet, achieves only 36.46 percent accuracy on OurBenchSyn and 32.17 percent on OurBenchSem, while most models score below 20 percent. We further explore four solution strategies, identify key challenges, and outline promising directions for enterprise SQL debugging with LLMs.

cs.AI↗

arXiv2026

Lifecycle Cost-Effectiveness Modeling for Redundancy-Enhanced Multi-Chiplet Architectures

The growing demand for compute-intensive applications has made multi-chiplet architectures a promising alternative to monolithic designs, offering improved scalability and manufacturing flexibility. However, effectively managing the economic effectiveness remains challenging. Existing cost models either overlook the amortization of compute value over a chip's operational lifetime or fail to evaluate how redundancy strategies, which are widely adopted to enhance yield and fault tolerance, impact long-term cost efficiency. This paper presents a comprehensive cost-effectiveness framework for multi-chiplet architectures, introducing a novel Lifecycle Cost Effectiveness (LCE) metric that evaluates amortized compute costs by jointly optimizing manufacturing expenses and operational lifetime. Our approach uniquely integrates: (1) redundancy-aware cost modeling spanning both intra- and inter-chiplet levels, (2) reliability-driven lifetime estimation, and (3) quantitative analysis of how redundancy configurations on overall economic effectiveness. Extensive trade-off and multi-objective optimization studies demonstrate the effectiveness of the model and reveal essential co-optimization strategies between module and chiplet-level redundancy to achieve cost-efficient multi-chiplet architecture designs.

cs.AR↗

arXiv2026

EmoHarbor: Evaluating Personalized Emotional Support by Simulating the User's Internal World

Current evaluation paradigms for emotional support conversations tend to reward generic empathetic responses, yet they fail to assess whether the support is genuinely personalized to users' unique psychological profiles and contextual needs. We introduce EmoHarbor, an automated evaluation framework that adopts a User-as-a-Judge paradigm by simulating the user's inner world. EmoHarbor employs a Chain-of-Agent architecture that decomposes users' internal processes into three specialized roles, enabling agents to interact with supporters and complete assessments in a manner similar to human users. We instantiate this benchmark using 100 real-world user profiles that cover a diverse range of personality traits and situations, and define 10 evaluation dimensions of personalized support quality. Comprehensive evaluation of 20 advanced LLMs on EmoHarbor reveals a critical insight: while these models excel at generating empathetic responses, they consistently fail to tailor support to individual user contexts. This finding reframes the central challenge, shifting research focus from merely enhancing generic empathy to developing truly user-aware emotional support. EmoHarbor provides a reproducible and scalable framework to guide the development and evaluation of more nuanced and user-aware emotional support systems.

cs.CL↗

arXiv2025

ParaGate: Parasitic-Driven Domain Adaptation Transfer Learning for Netlist Performance Prediction

In traditional EDA flows, layout-level performance metrics are only obtainable after placement and routing, hindering global optimization at earlier stages. Although some neural-network-based solutions predict layout-level performance directly from netlists, they often face generalization challenges due to the black-box heuristics of commercial placement-and-routing tools, which create disparate data across designs. To this end, we propose ParaGate, a three-step cross-stage prediction framework that infers layout-level timing and power from netlists. First, we propose a two-phase transfer-learning approach to predict parasitic parameters, pre-training on mid-scale circuits and fine-tuning on larger ones to capture extreme conditions. Next, we rely on EDA tools for timing analysis, offloading the long-path numerical reasoning. Finally, ParaGate performs global calibration using subgraph features. Experiments show that ParaGate achieves strong generalization with minimal fine-tuning data: on openE906, its arrival-time R2 from 0.119 to 0.897. These results demonstrate that ParaGate could provide guidance for global optimization in the synthesis and placement stages.

cs.LG↗

arXiv2025

The Solar Close Observations and Proximity Experiments (SCOPE) mission

The Solar Close Observations and Proximity Experiments (SCOPE) mission will send a spacecraft into the solar atmosphere at a low altitude of just 5 R_sun from the solar center. It aims to elucidate the mechanisms behind solar eruptions and coronal heating, and to directly measure the coronal magnetic field. The mission will perform in situ measurements of the current sheet between coronal mass ejections and their associated solar flares, and energetic particles produced by either reconnection or fast-mode shocks driven by coronal mass ejections. This will help to resolve the nature of reconnections in current sheets, and energetic particle acceleration regions. To investigate coronal heating, the mission will observe nano-flares on scales smaller than 70 km in the solar corona and regions smaller than 40 km in the photosphere, where magnetohydrodynamic waves originate. To study solar wind acceleration mechanisms, the mission will also track the process of ion charge-state freezing in the solar wind. A key achievement will be the observation of the coronal magnetic field at unprecedented proximity to the solar photosphere. The polar regions will also be observed at close range, and the inner edge of the solar system dust disk may be identified for the first time. This work presents the detailed background, science, and mission concept of SCOPE and discusses how we aim to address the questions mentioned above.

astro-ph.SR↗