arXiv AI August 12, 2026 By Touseef Hasan, Laila Cure, Souvika Sarkar

TRACE: Trustworthy Retrieval-Augmented Conversational Engine

arXiv:2608. 10176v1 Announce Type: new Abstract: Public service chatbots are expected to deliver recommendations from an underlying public service directory, while also making sure that the recommendations respect explicit user constraints.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Jul 2

From "Strings" to "Things" for Personal Knowledge Graphs: Evaluating LLM Triple Extraction for Recommendation Systems

arXiv:2607. 00003v1 Announce Type: cross Abstract: Personal Knowledge Graphs (PKGs) offer a privacy-preserving framework for modeling user preferences, yet constructing them from unstructured, decentralized conversational data remains a challenge.

By Abhirup Dasgupta, Fernando Spadea, Oshani Seneviratne

llms safety

arXiv AI

Aug 18

LlamaRec-LKG-RAG: A Single-Pass, Learnable Knowledge Graph-RAG Framework for LLM-Based Ranking

arXiv:2506. 07449v2 Announce Type: replace-cross Abstract: Recent advances in Large Language Models (LLMs) have driven their adoption in recommender systems through Retrieval-Augmented Generation (RAG) frameworks.

By Vahid Azizi, Fatemeh Koochaki

llms rag fine-tuning

arXiv AI

Sep 11

PRAGMA: Evaluating Personalized Guidance with Memory Alignment in Lifelong Conversations

PRAGMA is a benchmark designed to evaluate personalized guidance in long‑term conversations. It includes curated longitudinal conversation histories, evidence annotations, and guidance scenarios that reflect evolving user contexts and incorrect assumptions. Experiments show that current retrieval, memory, and long‑context models struggle to recover relevant conversational evidence and to use it effectively for personalized guidance.

By Hyojeong Yu, Hyukhun Koh, Minsung Kim, Yunah Jang, Kyomin Jung

llms benchmarks safety

arXiv AI

Aug 7

Benchmarking the Benchmarks: Evaluating Benchmarks for Conversational Agents

arXiv:2608. 06329v1 Announce Type: cross Abstract: Task-oriented conversational agents are evaluated using curated or automatically generated benchmarks, yet benchmark quality is rarely assessed.

By Noam Koren, Roy Bar-Haim, Abigail Goldsteen

llms agents benchmarks

arXiv AI

Jul 29

SAFAARI: Schema-Aware Framework for Accelerated Advertiser Response Intelligence

arXiv:2607. 25042v1 Announce Type: new Abstract: The evolution of customer support systems is rapidly advancing with agentic chatbots, yet these systems face significant limitations when accessing enterprise data without predefined API endpoints.

By Bhanu Teja Rangaraju, Chandan Kumar

agents data-engineering

arXiv AI

Aug 25

HERO: Human-profile Enhanced Retrieval Optimization Framework for Long-term Agent Memory

arXiv:2608.22310v1 Announce Type: new Abstract: Long-term memory is crucial for personalized responses and long-horizon agent interactions. Existing methods often rely on LLMs to compress or rewrite...

By Yuanhua Lin, Yile Li, Zhiyuan Zhao, Jing Shang, Jian Sun

llms agents benchmarks

One email a morning, machine-written

Related stories

From "Strings" to "Things" for Personal Knowledge Graphs: Evaluating LLM Triple Extraction for Recommendation Systems

LlamaRec-LKG-RAG: A Single-Pass, Learnable Knowledge Graph-RAG Framework for LLM-Based Ranking

PRAGMA: Evaluating Personalized Guidance with Memory Alignment in Lifelong Conversations

Benchmarking the Benchmarks: Evaluating Benchmarks for Conversational Agents

SAFAARI: Schema-Aware Framework for Accelerated Advertiser Response Intelligence

HERO: Human-profile Enhanced Retrieval Optimization Framework for Long-term Agent Memory