CoDe-R refines LLM decompiler output via rationale-guided semantic injection and dynamic fallback inference, making a 1.3B model the first to exceed 50% average re-executability on HumanEval-Decompile.
Lost in the middle: How language models use long contexts
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Introduces Efficiency Frontier framework for deployment-aware cost-performance optimization of LLM context strategies, reporting ~25% token reduction at F1≈0.78 on 5,000 HotpotQA instances.
citing papers explorer
-
CoDe-R: Refining Decompiler Output with LLMs via Rationale Guidance and Adaptive Inference
CoDe-R refines LLM decompiler output via rationale-guided semantic injection and dynamic fallback inference, making a 1.3B model the first to exceed 50% average re-executability on HumanEval-Decompile.
-
The Efficiency Frontier: A Unified Framework for Cost-Performance Optimization in LLM Context Management
Introduces Efficiency Frontier framework for deployment-aware cost-performance optimization of LLM context strategies, reporting ~25% token reduction at F1≈0.78 on 5,000 HotpotQA instances.