Hierarchical Lexical Graph for Enhanced Multi-Hop Retrieval

Retrieval-Augmented Generation (RAG) grounds large language models in external evidence, yet it still falters when answers must be pieced together across semantically distant documents. We close this gap with the Hierarchical Lexical Graph (HLG), a three-tier index that (i) traces every atomic proposition to its source, (ii) clusters propositions into latent topics, and (iii) links entities and relations to expose cross-document paths. On top of HLG we build two complementary, plug-and-play retrievers: StatementGraphRAG, which performs fine-grained entity-aware beam search over propositions for high-precision factoid questions, and TopicGraphRAG, which selects coarse topics before expanding along entity links to supply broad yet relevant context for exploratory queries. Additionally, existing benchmarks lack the complexity required to rigorously evaluate multi-hop summarization systems, often focusing on single-document queries or limited datasets. To address this, we introduce a synthetic dataset generation pipeline that curates realistic, multi-document question-answer pairs, enabling robust evaluation of multi-hop retrieval systems. Extensive experiments across five datasets demonstrate that our methods outperform naive chunk-based RAG achieving an average relative improvement of 23.1% in retrieval recall and correctness. Open-source Python library is available at https://github.com/awslabs/graphrag-toolkit.

Towards Robust Dialogue Breakdown Detection: Addressing Disruptors in Large Language Models with Self-Guided Reasoning

Large language models (LLMs) are rapidly changing various domains. However, their capabilities in handling conversational breakdowns still require an in-depth exploration. This paper addresses the challenge of detecting and mitigating dialogue breakdowns within LLM-driven conversational systems. While powerful models from OpenAI and Anthropic excel in many dialogue tasks, they can still produce incoherent or contradictory responses, commonly referred to as breakdowns, which undermine user trust. To tackle this, we propose an approach that combines specialized fine-tuning with advanced prompting strategies, including few-shot learning, chain-of-thought reasoning, and analogical prompting. In particular, we fine-tune a small 8B model and demonstrate its robust classification and calibration capabilities in English and Japanese dialogue. We also validate its generalization on the BETOLD dataset, achieving a 7\% accuracy improvement over its base model. Furthermore, we introduce a real-time deployment architecture that selectively escalates suspicious responses to more resource-intensive frontier models only when breakdowns are detected, significantly cutting operational expenses and energy consumption. Experimental results show our method surpasses prior state-of-the-art specialized classifiers while also narrowing performance gaps between smaller open-source models and large proprietary ones. Our approach offers a scalable solution for robust conversational AI in high-impact domains by combining efficiency, interpretability, and reliability.