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From the 3 of 20 linked papers with an AI index.

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cs.CL2026

When Your Agent Opens the Chat App: Agent-Controlled Search over Raw Chat Logs Rivals Structured Memory

Ruizhe Li, Licheng Zhang, Benfeng Xu +3

Agent-memory systems increasingly buy retrieval quality with structure, transforming raw conversation histories into summaries, embeddings, trees, or knowledge graphs before any qu…

cs.CL2026

Beyond Borrowed Histories: Person-Aligned User Simulation for Interactive Role-Playing Evaluation

Yuhang Zhu, Mingxuan Du, Benfeng Xu +3

The paper presents PALATE, a benchmark that uses per‑user simulated agents to evaluate role‑playing language models through free‑form, multi‑turn conversations and personalized sat…

cs.CL2026

BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms

Pengyu Wang, Benfeng Xu, Shaohan Wang +5

The paper conducts a controlled scaling study of various retrieval-augmented generation methods and finds that BM25 becomes the most accurate and cost‑effective approach once the c…

cs.CL2026

Keep It InMind: Benchmarking the Implicit-Association Blind Spot in Agent Memory

Ruizhe Li, Mingxuan Du, Benfeng Xu +1

Long-term memory systems store what a user says in an external store and retrieve it when a related query arrives. This interface rests on an assumption so natural that it is rarel…

cs.CL2026

FS-Researcher: Test-Time Scaling for Long-Horizon Research Tasks with File-System-Based Agents

Chiwei Zhu, Benfeng Xu, Mingxuan Du +4

Deep research is emerging as a representative long-horizon task for large language model (LLM) agents. However, long trajectories in deep research often exceed model context limits…

cs.CL2026

SearchAttack: Red-Teaming LLMs against Knowledge-to-Action Threats under Online Web Search

Yu Yan, Sheng Sun, Mingfeng Li +6

Recently, people have suffered from LLM hallucination and have become increasingly aware of the reliability gap of LLMs in open and knowledge-intensive tasks. As a result, they hav…