3 citations · 11 across the 17 of their papers we have counts for
6 papers · 1 filter
Despite Instructions: Frontier Agents Improvise Covert Channels at Test Time
Jacob Dineen, Silei Ren, Muhao Chen +2
In security-sensitive applications, language-model agents are often required to coordinate without disclosing confidential information. Yet repeated interactions may also let ordin…
Hindcast: Replaying Prediction Markets to Evaluate LLM Forecasters
Xiao Ye, Jacob Dineen, Evan Zhu +3
Forecasters are evaluated by backtesting, which replays resolved questions and grades the probability the system would have assigned before the outcome was known. For LLMs, two cha…
VisAnalog: A Diagnostic Suite for Visual Concept Transfer on Natural Images
Zhaonan Li, Kyle R. Chickering, Bangzheng Li +13
A useful test of visual concept learning is not just whether a model can recognize a concept in a single image, but whether it can preserve and manipulate concept-level properties…
Mid-Training with Self-Generated Data Improves Reinforcement Learning in Language Models
Aswin RRV, Jacob Dineen, Divij Handa +4
The effectiveness of Reinforcement Learning (RL) in Large Language Models (LLMs) depends on the nature and diversity of the data used before and during RL. In particular, reasoning…
Skill Reuse as Compression in Agentic RL
Zhikun Xu, Yu Feng, Jacob Dineen +3
Large language model agents trained with reinforcement learning (RL) often learn brittle, task-specific shortcuts. We hypothesize that agents generalize better when their successfu…
Vocabulary Dropout for Curriculum Diversity in LLM Co-Evolution
Jacob Dineen, Aswin RRV, Zhikun Xu +1
Co-evolutionary self-play, where one language model generates problems and another solves them, promises curriculum learning without human supervision. The promise breaks down earl…