Loom is a generative consensus framework designed for real‑world root‑cause analysis (RCA) that combines open‑form hypotheses from modular heuristics with a lightweight large language model (LLM) synthesis step. It projects hypotheses into a continuous embedding space and uses an iterative centroid‑based reweighting algorithm to resolve conflicts, producing a single consensus that is then synthesized by one LLM call. On the OpenRCA benchmark Loom matches state‑of‑the‑art autonomous agents on some datasets while achieving significantly higher efficiency—about 26× faster and 33× faster with an 8B‑parameter synthesizer.
whyItMatters":"Loom demonstrates how embedding‑space reweighting can bridge the gap between statistical rigor and expressive LLMs, enabling efficient, trustworthy RCA in industrial settings."
By Ron Begleiter, Katya Egert Berg, Gilad Saban, Gil Shabat
arXiv:2604. 02923v4 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have demonstrated advanced capabilities but often suffer from factual inaccuracies (hallucinations) and systematic biases.
By Shuai Wu, Xue Li, Yanna Feng, Yufang Li, Zhijun Wang, Ran Wang
arXiv:2606. 29604v1 Announce Type: cross Abstract: We aim to discover diverse, generalizable perturbations of LLM internals that can surface hidden behavioral modes.
By Andrew Mack, Nina Panickssery, Alexander Matt Turner
MABPD (Multi‑Agent Bias Probing & Detection) is a training‑free pipeline that uses three specialized large language model agents to analyze news articles from complementary perspectives and resolve disagreements via a Structured Argument Debate (SAD) protocol. SAD imposes an asymmetric burden of proof—biased claims lacking grounded textual evidence receive zero weight—along with role‑weighted voting and post‑consensus verification, replacing task‑specific supervised decision boundaries. Ablation studies show that the debate module alone accounts for up to a 10.6‑point F1 gain, and on the BABE benchmark MABPD attains 83.4% macro F1, within 0.7 percentage points of the supervised state‑of‑the‑art, while achieving 75.0% zero‑shot accuracy on the SemEval 2019 HyperPartisan corpus.
By Garvit Joshi (Graphic Era University, Dehradun, India), Stavya Dhyani (Graphic Era University, Dehradun, India), Jasmine (Graphic Era University, Dehradun, India), Arun Chauhan (Graphic Era University, Dehradun, India)
arXiv:2607. 29087v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in enterprise settings, yet individual models remain bounded by model-specific capability limitations.
By Yanbin Fang, Xuan Wei, Wei Chen
arXiv:2608. 15389v1 Announce Type: new Abstract: LLM-based Text-to-SQL progress is reported across heterogeneous benchmarks, backbones, and inference protocols, making cross-system comparison fragile.
By Changruo Zhao, Zujun Peng, Yu Tian, Yuting Liu, Yiyun Su, Huiying Zhu, Luyan Zhang, Heming Zeng