4 citations · 4 across the 12 of their papers we have counts for
7 papers · 1 filter
Beyond State Capacity: Why Retrieval Breaks in Mamba
Nir Yarden, Amit Ben-Artzy, Roy Schwartz
Mamba models underperform Transformers on in-context retrieval. This gap is usually attributed to Mamba's fixed-size recurrent state, which loses information as the context grows.…
RLE-Bench: A Qualifying Exam for Coding Agents as Robot Learning Engineers
Haitong Ma, Chenxiao Gao, Rushi Qiang +2
Coding agents are beginning to move beyond purely digital tasks to tackle physical-world challenges, particularly in robotics. Existing robotics benchmarks, however, primarily focu…
Frontis-MA1: Training an AI4AI Model towards Recursive Self-Improvement in Machine Learning Engineering
Junlin Yang, Che Jiang, Yu Fu +21
Recursive self-improvement (RSI) requires AI systems that improve the process of building AI (i.e., AI4AI); machine learning engineering (MLE) offers a concrete, executable testbed…
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 with BPTT-Free Looped Refinement
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…