Lost in Execution: On the Multilingual Robustness of Tool Calling in Large Language Models (2026 · 3 citations)
Tool-calling benchmarks for LLM agents are almost entirely in English, so it is unclear how well models produce executable function calls when users write in other languages.
Are LLMs Reliable Rankers? Rank Manipulation via Two-Stage Token Optimization (2026 · 2 citations)
LLMs are now widely used to rerank candidate items in retrieval and recommendation systems. This paper asks whether an attacker can push one chosen item to the top of a listwise LLM ranking by adding a short, natural-looking phrase to that item's own description. Earlier attacks tended to trade stealth for strength…
Topology Matters: Measuring Memory Leakage in Multi-Agent LLMs (2026)
LLM agents increasingly work in teams that exchange messages over a communication graph. This paper asks how the shape of that graph affects how easily private data stored in one agent's memory can be extracted by a malicious teammate. The authors build MAMA (Multi-Agent Memory Attack), a controlled benchmark.

Co-authors here

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Where their works here were published, the most works first, with the editions of those that have a page.

Papers of our library cite their works 5 times (of the 149,439 papers whose bibliographies we read); 1 citing paper with a page here →.

Updated 1 November 2026