From the 1 of 11 linked papers with an AI index.
11 papers
Frontis-MA1: Training an AI4AI Model towards Recursive Self-Improvement in Machine Learning Engineering
Junlin Yang, Che Jiang, Yu Fu +21
The paper presents Frontis-MA1, a 35‑billion‑parameter model trained as a meta‑evolution agent for machine learning engineering, using a new OpenMLE stack that combines operator le…
Matryoshka Agent: Unfolding Sub-Agents for Long-Horizon Machine Learning Engineering
Rushi Qiang, Changhao Li, Haotian Sun +3
Machine learning engineering (MLE) tasks require long-horizon decision making over iterative solution debugging and refinement, under expensive and feedback-driven environment inte…
Forward-Free Diffusion Language Models
Haotian Sun, Rushi Qiang, Yuqian Zheng +1
Diffusion language models generate text through iterative denoising, offering a powerful alternative to autoregressive generation. However, discrete language spaces lack a natural…
Revisiting DAgger in the Era of LLM-Agents
Changhao Li, Rushi Qiang, Jiawei Huang +4
Long-horizon LM agents learn from multi-turn interaction, where a single early mistake can alter the subsequent state distribution and derail the whole trajectory. Existing recipes…
Exploration-Driven Optimization for Test-Time Large Language Model Reasoning
Changhao Li, Yuchen Zhuang, Chenxiao Gao +4
Post-training techniques combined with inference-time scaling significantly enhance the reasoning and alignment capabilities of large language models (LLMs). However, a fundamental…
Matryoshka Pilot: Learning to Drive Black-Box LLMs with LLMs
Changhao Li, Yuchen Zhuang, Rushi Qiang +4
Despite the impressive generative abilities of black-box large language models (LLMs), their inherent opacity hinders further advancements in capabilities such as reasoning, planni…