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cs.AI2026

SchemeArena: Factorized Stress Testing of Scheming in LLM Agents

Jie Ruan, Inderjeet Nair, Amy Liu +3

We study scheming in LLM agents, in which agents covertly pursue misaligned goals. Our focus is to understand how scheming arises from the interaction of key factors, such as instr…

cs.AI2026

Can LLM Agents Discover? Evaluating Creativity on ML Engineering Tasks

Shitanshu Bhushan, Yunxiang Zhang, Lu Wang

Recent AI systems promise autonomous scientific discovery, claiming to discover algorithms and produce research papers, yet understanding whether they exhibit creativity, the capac…

cs.AI2026

AdaMEM: Test-Time Adaptive Memory for Language Agents

Yunxiang Zhang, Yiheng Li, Ali Payani +1

A central challenge for language agents is utilizing past experience to adapt to dynamic test-time conditions. While recent work demonstrates the promise of agentic memory mechanis…

cs.AI2026

Value-Conflict Diagnostics Reveal Widespread Alignment Faking in Language Models

Inderjeet Nair, Jie Ruan, Lu Wang

Alignment faking, where a model behaves aligned with developer policy when monitored but reverts to its own preferences when unobserved, is a concerning yet poorly understood pheno…

cs.AI2025

LiveOIBench: Can Large Language Models Outperform Human Contestants in Informatics Olympiads?

Kaijian Zou, Aaron Xiong, Yunxiang Zhang +6

Competitive programming problems are increasingly used to evaluate the coding capabilities of large language models (LLMs) due to their complexity and ease of verification. Yet, cu…