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cs.AI2026
Agent in a Bottle: Can LLM Agents Turn Their Capabilities Into Cheap, Scalable Artifacts?
Ankit Sonthalia, Haritz Puerto, Alexander Rubinstein +2
Large language models (LLMs) can solve many narrow tasks, but querying them separately for millions of related instances can be prohibitively expensive. Can LLM agents autonomously…
cs.LG2026
MEME: Multi-entity & Evolving Memory Evaluation
Seokwon Jung, Alexander Rubinstein, Arnas Uselis +2
LLM-based agents increasingly operate in persistent environments where they must store, update, and reason over information across many sessions. While prior benchmarks evaluate on…
cs.AI2026
MASEval: Extending Multi-Agent Evaluation from Models to Systems
Cornelius Emde, Alexander Rubinstein, Anmol Goel +4
The rapid adoption of LLM-based agentic systems has produced a rich ecosystem of frameworks (smolagents, LangGraph, AutoGen, CAMEL, LlamaIndex, i.a.). Yet existing benchmarks are m…