Jupyter Agents: training LLMs to reason with notebooks

Learning to reason with LLMs

MemSyco-Bench: Benchmarking Sycophancy in Agent Memory

Memory has emerged as a cornerstone of modern LLM-based agents, supporting their evolution from single-turn assistants to long-term collaborators. However, memory is not always beneficial: retrieved memories often induce a critical issue of sycophancy, causing agents to over-align with the user at the cost of factual accuracy or objective reasoning.

AgentBrew: Lifelong Knowledge Brewing from Strong Teachers to Weak LLM Agents

arXiv:2607. 16851v1 Announce Type: new Abstract: Deploying LLM agents typically requires a compact test-time student, even if a stronger teacher is available during training.

MemSyco-Bench: Benchmarking Sycophancy in Agent Memory

arXiv:2607. 01071v1 Announce Type: cross Abstract: Memory has emerged as a cornerstone of modern LLM-based agents, supporting their evolution from single-turn assistants to long-term collaborators.

MAR:Multi-Agent Reflexion Improves Reasoning Abilities in LLMs

arXiv:2512. 20845v2 Announce Type: replace Abstract: LLMs have shown the capacity to improve their performance on reasoning tasks through reflecting on their mistakes, and acting with these reflections in mind.

AGI Maze as a Benchmark Framework for World-Modeling Agents

arXiv:2607. 00627v1 Announce Type: new Abstract: Large language models (LLMs) are powerful pattern-completion systems, but their default operating mode - predicting the next token from a static context - does not reliably produce persistent, manipulable representations of an external world.