The paper introduces FRESH, a Failure-aware Retrieval framework that uses Experience-Structured Heterogeneous graphs to transform past successes and failures into structured external memory for tool‑using agents. By explicitly modeling dependencies among tasks, actions, errors, repairs, and execution conditions, FRESH enables frozen language models to reuse reliable strategies, avoid recurring failures, and make safer decisions in stateful tool interactions. Experiments on τ‑Bench and AppWorld with multiple open‑source models demonstrate that FRESH consistently improves task success and tool‑use reliability compared to no‑memory agents and other memory‑based baselines.
By Jiaxing Li, Lei Song, Rui Dong, Youyong Kong
arXiv:2607. 21324v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) systems increasingly employ multiple LLM agents.
By Paolo Pedinotti, Enrico Santus
VikingRAG is a directory‑aware semantic data management system that reduces token usage in retrieval‑augmented generation by tightly integrating semantic and structural access. It employs multi‑round retrieval traces as reusable experience edges and an adaptive escalation strategy to avoid unnecessary multi‑round exploration. Experiments show that VikingRAG achieves comparable accuracy to state‑of‑the‑art methods while using only 11.6%–51.9% of their tokens, and further reductions to 5.1%–32.5% with trace reuse and escalation.
By Peiyuan Gao, Gaoyuan Zhang, Haojie Qin, Yahui Sun, Qianyi Zhang, Yunhao Zhang, Zeyu Wang, Wei Lu
The paper introduces RAG4Tickets, an AI system that uses Retrieval-Augmented Generation to improve ticket resolution on JIRA and GitHub. It embeds historical tickets, comments, and pull request data with Sentence-Transformers, indexes them with FAISS, and then uses a Large Language Model to generate grounded, explainable resolution suggestions based on retrieved evidence. Experiments show higher precision, recall, faster resolution times, and better developer acceptance compared to existing methods.
By Mohammad Baqar
AuditKIT is a source‑available evaluation library that unifies benchmark accuracy, judge‑based assessment, runtime performance, tool‑use behavior, and retrieval quality into a single result representation. It offers direction‑aware metric contracts, configuration fingerprints, and extensible metrics and backends, while providing turn‑aware tool‑call matching and distinguishing verified task outcomes from trajectory‑level diagnostics. Seven case studies demonstrate how answer extraction, runtime behavior, and metric selection can alter evaluation outcomes across model comparison, serving runtimes, retrieval, tool calling, and interoperability.
By Shreeyans Arora, Utsav Avaiya, Zera Lyngkhoi, Hem Gosalia, Ram Mohan Rao Kadiyala, Vinay Kumar Sankarapu, Pratinav Seth
arXiv:2608. 06346v1 Announce Type: new Abstract: LLM-based agentic systems have shown remarkable capabilities in complex domains, while suffering from cascading errors and difficulty in debugging.
By Yunjia Qi, Zehua Yin, Xintong Shi, Hao Peng, Songyuanyi Lu, Yixian Liu, Richeng Xuan, Yuhong Liu, Zhichao Hu, Xiaozhi Wang, Lei Hou, Bin Xu, Juanzi Li