arXiv subjects

Yuhang Gu

Publications and source records attributed to Yuhang Gu.

5 recordsLinked to original sources

arXiv2026

The Scaling Laws of Skills in LLM Agent Systems

As agent systems scale, skills accumulate into large reusable libraries, yet their scaling laws remain poorly understood. Across 15 frontier LLMs, 1,141 real-world skills, and over 3M routing or execution decisions, we identify two coupled laws. Routing law: single-step routing accuracy decays logarithmically with library size ($R^2{>}0.97$ for all models), with errors progressing from local skill competition to cross-family drift and capture by overly general "black-hole skills". Execution law: before state realization, joint routing is approximately multiplicative, whereas correct execution can improve difficult downstream decisions by about $4{\times}$. A single parameter, the routing logarithmic decay slope $b$, couples the two laws: routing-side fits predict execution-side rescue across models, showing that the same library property controls both pre-execution collapse and downstream recoverability. The laws are actionable: law-guided optimization raises held-out routing accuracy from 71.3% to 91.7%, reduces hijack from 22.4% to 4.1%, and transfers directionally to downstream ClawBench and ClawMark execution settings, improving mean pass rate from 49.3% to 61.6% on ClawBench and from 28.4% to 34.5% on ClawMark. These results show that agent performance depends not only on model capability, but also on the structure, granularity, and exposure policy of the skill library.

cs.CL↗

arXiv2026

Sim-FA: A GPGPU Simulator Framework for Fine-Grained Asynchronous Pipeline Analysis

To efficiently support Large Language Models (LLMs), modern GPGPU architectures have introduced new features and programming paradigms, such as warp specialization. These features enable temporal overlap between the producer and consumer, as well as between matrix multiplication and activation function operations, substantially improving performance. To conduct effective AI infrastructure and computer architecture research, cycle-accurate simulators that support these new features, together with analytical models that faithfully capture workload characteristics, are essential. However, existing academic tools provide limited support for these emerging requirements. Existing cycle-accurate simulators do not incorporate new NVIDIA GPU features, such as the Tensor Memory Accelerator (TMA), in a timely manner. Moreover, existing analytical models can misestimate DRAM traffic under certain configurations. In this paper, we build Sim-FA, a cycle-accurate simulation framework for Hopper TMA/WGMMA pipelines. We first develop an operator-agnostic trace frontend that instruments kernels at the Triton TTGIR level and validates it on 23 GEMM shapes, achieving 5.49\% MAPE against H800, confirming that the simulator core is not tied to any single operator. Because FlashAttention-3 introduces additional complexity beyond standard TMA/WGMMA kernels (asymmetric producer-consumer pipelines, softmax, ping-pong synchronization), we further build an FA3-specialized frontend that achieves 5.7\% MAPE with a maximum error of 12.7\%. Within the same framework, SimFA-python serves as an analytical fast path for large-scale design-space exploration where cycle-accurate simulation is prohibitively slow; validated against cuTile kernels on Blackwell (GB10), it explains why existing analytical models can produce inaccurate traffic estimates.

cs.AR↗

arXiv2025

DCO: Dynamic Cache Orchestration for LLM Accelerators through Predictive Management

The rapid adoption of large language models (LLMs) is pushing AI accelerators toward increasingly powerful and specialized designs. Instead of further complicating software development with deeply hierarchical scratchpad memories (SPMs) and their asynchronous management, we investigate the opposite point of the design spectrum: a multi-core AI accelerator equipped with a shared system-level cache and application-aware management policies, which keeps the programming effort modest. Our approach exploits dataflow information available in the software stack to guide cache replacement (including dead-block prediction), in concert with bypass decisions and mechanisms that alleviate cache thrashing. We assess the proposal using a cycle-accurate simulator and observe substantial performance gains (up to 1.80x speedup) compared with conventional cache architectures. In addition, we build and validate an analytical model that takes into account the actual overlapping behaviors to extend the measurement results of our policies to real-world larger-scale workloads. Experiment results show that when functioning together, our bypassing and thrashing mitigation strategies can handle scenarios both with and without inter-core data sharing and achieve remarkable speedups. Finally, we implement the design in RTL and the area of our design is $\mathbf{0.064mm^2}$ with 15nm process, which can run at 2 GHz clock frequency. Our findings explore the potential of the shared cache design to assist the development of future AI accelerator systems.

cs.AR↗

arXiv2025

SURE-Med: Systematic Uncertainty Reduction for Enhanced Reliability in Medical Report Generation

Automated medical report generation (MRG) holds great promise for reducing the heavy workload of radiologists. However, its clinical deployment is hindered by three major sources of uncertainty. First, visual uncertainty, caused by noisy or incorrect view annotations, compromises feature extraction. Second, label distribution uncertainty, stemming from long-tailed disease prevalence, biases models against rare but clinically critical conditions. Third, contextual uncertainty, introduced by unverified historical reports, often leads to factual hallucinations. These challenges collectively limit the reliability and clinical trustworthiness of MRG systems. To address these issues, we propose SURE-Med, a unified framework that systematically reduces uncertainty across three critical dimensions: visual, distributional, and contextual. To mitigate visual uncertainty, a Frontal-Aware View Repair Resampling module corrects view annotation errors and adaptively selects informative features from supplementary views. To tackle label distribution uncertainty, we introduce a Token Sensitive Learning objective that enhances the modeling of critical diagnostic sentences while reweighting underrepresented diagnostic terms, thereby improving sensitivity to infrequent conditions. To reduce contextual uncertainty, our Contextual Evidence Filter validates and selectively incorporates prior information that aligns with the current image, effectively suppressing hallucinations. Extensive experiments on the MIMIC-CXR and IU-Xray benchmarks demonstrate that SURE-Med achieves state-of-the-art performance. By holistically reducing uncertainty across multiple input modalities, SURE-Med sets a new benchmark for reliability in medical report generation and offers a robust step toward trustworthy clinical decision support.

cs.AI↗

arXiv2024

Gravitational waves from equatorially eccentric extreme mass ratio inspirals around swirling Kerr black holes

The swirling-Kerr black hole is a novel solution of vacuum general relativity and has an extra swirling parameter characterizing the rotation of spacetime background. We have studied the gravitational waves generated by extreme mass ratio inspirals (EMRIs) along eccentric orbits on equatorial plane in this novel swirling spacetime. Our findings indicate that this swirling parameter leads to a delayed phase shift in the gravitational waveforms. Furthermore, we have investigated effects of the swirling parameter on the potential issue of waveform confusion caused by the orbital eccentricity and semi-latus rectum parameters. As the swirling parameter increases, the relative variations in the eccentricity increase, while the variations in the semi-latus rectum decrease rapidly. These trends of the changes related to the orbital eccentricity and the semi-latus rectum with the swirling parameter resemble those observed with the MOG parameter in the Scalar-Tensor-Vector-Gravity (STVG) theory, but with different rates of change. Furthermore, our results also reveal that effects of the background swirling parameter on the relative variations in the eccentricity and the semi-latus rectum are distinctly different from those of the black hole spin parameter. These results provide deeper insights into the properties of EMRI gravitational waves and the background's swirling.

gr-qc↗