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Junlan Feng

13 papers hereh-index 334 citations28 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author11
  • last author2

Across the 13 of 13 papers where every author was matched, so the position is known.

fields
  • cs.CL10
  • cs.AI3
same name
  • Junlan Feng — 27 papers, h 17
  • Junlan Feng — 17 papers, h 15
  • Junlan Feng — 16 papers, h 7
  • Junlan Feng — 12 papers, h 3
  • Junlan Feng — 10 papers, h 1
  • Junlan Feng — 8 papers, h 2

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20232026
collaborators
Showing 2026Show all

4 papers · 1 filter

cs.CL2026

Relay, Don't Route: Adaptive Population Handoff for Cost-Efficient LLM-Driven Evolution

Sichun Luo, Yi Huang, Guanzhi Deng +6

Large language model (LLM)-driven evolution has shown promise for program search and algorithm discovery, but relying on strong models throughout long evolutionary runs is costly.…

cs.AI2026

Harness-Aware Self-Evolving: Co-Evolving Model Weights, Harness, and Task Solutions

Haochen Luo, Yi Huang, Sichun Luo +5

Self-evolving frameworks usually optimize task solutions while treating the surrounding harness as fixed. We introduce Harness-Aware Self-Evolving (HASE), an agentic reinforcement-…

cs.CL2026

SeaEvo: Advancing Algorithm Discovery with Strategy Space Evolution

Sichun Luo, Yi Huang, Haochen Luo +7

Large Language Model (LLM)-guided evolutionary search is increasingly used for automated algorithm discovery, yet most current methods track search progress primarily through execu…

cs.CL2026

CCR-Bench: A Comprehensive Benchmark for Evaluating LLMs on Complex Constraints, Control Flows, and Real-World Cases

Xiaona Xue, Yiqiao Huang, Jiacheng Li +9

Enhancing the ability of large language models (LLMs) to follow complex instructions is critical for their deployment in real-world applications. However, existing evaluation metho…

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