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Researcher profile

Srinath Perera

Srinath Perera contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Artificial IntelligenceInformation RetrievalSoftware Engineering

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Published work

2 published item(s)

preprint2026arXiv

Robust Agent Compensation (RAC): Teaching AI Agents to Compensate

We present Robust Agent Compensation (RAC), a log-based recovery paradigm (providing a safety net) implemented through an architectural extension that can be applied to most Agent frameworks to support reliable executions (avoiding unintended side effects). Users can choose to enable RAC without changing their current agent code (e.g., LangGraph agents). The proposed approach can be implemented in most existing agent frameworks via their existing extension points. We present an implementation based on LangChain, demonstrate its viability through the $τ$-bench and REALM-Bench, and show that when solving complex problems, RAC is 1.5-8X or more better in both latency and token economy compared to state-of-the-art LLM-based recovery approaches.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

vue4logs -- Automatic Structuring of Heterogeneous Computer System Logs

Computer system log data is commonly used in system monitoring, performance characteristic investigation, workflow modeling and anomaly detection. Log data is inherently unstructured or semi-structured, which makes it harder to understand the event flow or other important information of a system by reading raw logs. The process of structuring log files first identifies the log message groups based on the system events that triggered them, and extracts an event template to represent the log messages of each event. This paper introduces a novel method to extract event templates from raw system log files, by using the vector space model commonly used in the field of Information Retrieval to vectorize log data and group log messages into event templates based on their vector similarity. Template extraction process is further enhanced with the use of character and length based filters. When evaluated on publicly available real-world log data benchmarks, this proposed method outperforms all the available state-of-the-art systems in terms of accuracy and robustness.

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