Dynamic tool generation empowers Large Language Model (LLM) agents to synthesize tools on demand, yet a critical challenge remains: 32.4% of generated tools fail on first invocation.We present Causal Tool Diagnosis (CTD), a principled framework that moves beyond blackbox reliability prediction to interpretable failure attribution.CTD constructs a Structural Causal Model (SCM) capturing how specification quality, code characteristics, and execution environment jointly determine tool outcomes.Uniquely leveraging code's intervenability, we conduct controlled sandbox experiments to estimate causal effects-an advantage unavailable in pure text generation.CTD jointly predicts confidence (Spearman rank correlation coefficient ρ=0.90) and root cause attribution (78% accuracy), with attributions directly guiding targeted repairs (+9.6% success rate over error-type classification).Our ARCHI-TECT framework, integrating CTD throughout the tool lifecycle, achieves state-of-the-art on four benchmarks including StableToolBench (+3.8%),MINT (+4.6%),T-Eval (+3.7%), and SWE-bench Lite (+2.4%), with consistent improvements across all settings.

Recurrent Neural Network (RNN) is a powerful Deep Learning algorithm which has been widely used for speech recognition, handwriting recognition, context clustering, etc. Deep learning involves a large number of floating-point computations and requires a large amount of computing resource. Currently, software-based RNN implementations running on general-purpose processors like CPU and GPU take a long execution time and an excessive amount of energy. FPGA provides a low-power and highly parallel platform for accelerating RNN training and inference. With massive reconfigurable logics, FPGA can be customized to achieve dramatic speedup and energy efficiency for various deep learning applications. However, implementing RNN algorithms at the Register Transistor Level (RTL) is time-consuming, while existing tools for converting RNN code written in high-level languages to RTL designs are not efficient. In this paper, we present a design flow by which high-level RNN implementations (for example, in Python) can be converted to RTL designs efficiently and automatically. Experimental results show the generated RTL code that is deployed on an FPGA device is 7.87 times faster than the Python code run on CPU while achieving the same accuracy.

System logs provide invaluable resources for understanding system behavior and detecting anomalies on high performance computing (HPC) systems. As HPC systems continue to grow in both scale and complexity, the sheer volume of system logs and the complex interaction among system components make the traditional manual problem diagnosis and even automated line-by-line log analysis infeasible or ineffective. In this paper, we present a System Log Event Block Detection (SLEBD) framework that identifies groups of log messages that follow certain sequence but with variations, and explore these event blocks for event-based system behavior analysis and anomaly detection. Compared with the existing approaches that analyze system logs line by line, SLEBD is capable of characterizing system behavior and identifying intricate anomalies at a higher (i.e., event) level. We evaluate the performance of SLEBD by using syslogs collected from production supercomputers. Experimental results show that our framework and mechanisms can process streaming log messages, efficiently extract event blocks and effectively detect anomalies, which enables system administrators and monitoring tools to understand and process system events in real time. Additionally, we use the identified event blocks and explore deep learning algorithms to model and classify event sequences.

Monitoring high performance computing systems has become increasingly difficult as researchers and system analysts face the challenge of synthesizing a wide range of monitoring information in order to detect system problems on ever larger machines. We present a method for anomaly detection on syslog data, one of the most important data streams for determining system health. Syslog messages pose a difficult question for analysis because they include a mix of structured natural language text as well as numeric values. We present an anomaly detection framework that combines graph analysis, relational learning, and kernel density estimation to detect unusual syslog messages. We design an event block detector, which finds groups of related syslog messages, to retrieve the entire section of syslog messages associated with a single anomalous line. Our novel approach successfully retrieves anomalous behaviors inserted into syslog files from a virtual machine, including messages indicating serious system problems. We also test our approach on syslog messages from the Trinity supercomputer and find that our methods do not generate significant false positives.