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Ru-Lin Shao

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#artificial intelligence Preprint Oct 2026

Stateless Language Agents: Scaling Long-Horizon Automated Research

Automated research systems increasingly run LLM agents over long horizons, but more inference does not by itself produce more progress: agents replay growing histories, duplicate one another's work, or stop experimenting while token consumption continues. Yet most evaluations use short budgets or benchmarks that satura...

Qizheng Zhang, Chang Ji, Isaac Sun et al. · 0 citations
#artificial intelligence Preprint Oct 2026

ScholarCatalyst: A Benchmark for Retrieving Papers That Inspire New Research

What makes great scientists great? Even as AI systems start to make progress on open problems, scientists remain far ahead of them at sensing which prior idea, buried in an ever-growing archive of research, a new problem needs. To study this skill, we draw on researchers who know firsthand which earlier work advanced t...

Sohyeon Kim, Yoonho Lee, Bo Liu et al. · 0 citations
#artificial intelligence Preprint Sep 2026

Context Language Models

Context Language Models are introduced, language models that natively manage their own context and can be steered with natural-language instructions evolved through a standard skill-optimization loop, improving held-out accuracy by up to 35.9 points on a context-management task while reducing compute.

Ru-Lin Shao, Shannon Zejiang Shen, J. Yin et al. · 0 citations

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