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Yoonho Lee

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#artificial intelligence Preprint Oct 2026

Librarian Agents: Training LLMs to Curate Long-Context Workspaces that Anticipate Future Queries

Reasoning over long document contexts remains a challenge for large language models (LLMs). Standard retrieval methods are inherently reactive, bottlenecking agents with complex, cross-document reasoning at inference time, while precomputed structures like knowledge graphs often fall short because they rely on rigid he...

Anikait Singh, Teresa Zhang, Yoonho Lee et al. · 0 citations
Preprint Sep 2026

RRSI: Regularized Recursive Self-Improvement of Agent Harnesses

Regularized Recursive Self-Improvement of Agent Harnesses (RRSI) is introduced, which incorporates the principles of regularizations into harness self-improvement by constraining the evolution candidate proposal and selection.

Peng Xia, Ru-Jun Han, Zifeng Wang et al. · 28 citations · ⚡4
#artificial intelligence Preprint Oct 2026

Stateless Language Agents: Scaling Long-Horizon Automated Research

This work introduces Stateless Language Agents (SLAs), built on the principle of stateful search with stateless agents, and evaluates SLA against three recent frameworks on software engineering, kernel optimization, and algorithm design at budgets of up to one billion tokens.

Qizheng Zhang, Chang Ji, Isaac Sun et al. · 1 citation
#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

RRSI: Regularized Recursive Self-Improvement of Agent Harnesses

An LLM agent's capability is largely magnified by its harness, namely the prompts, control flow, tooling, memory, and context management surrounding the frozen backbone model. Recent methods increasingly automate this process by iteratively proposing and selecting component-wise edits of an agent harness, practically e...

Peng Xia, Ru-Jun Han, Zifeng Wang et al. · 3 citations · ⚡1
#artificial intelligence Preprint Aug 2026

WHALE: A Simple Recipe for Joint Harness-Weight Optimization

Weight-Harness Alternating LEarning (WHALE), a simple recipe that alternates two phases: updating the model under the current harness, then searching for a better harness under the updated model with online rejection-sampling fine-tuning and Meta-Harness, is proposed.

Haechan Kim, Yoonho Lee, Gisang Lee et al. · 11 citations · ⚡4

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