BACE reformulates LLM code synthesis as Bayesian co-evolution of code and test populations anchored on minimal public examples, achieving superior performance on LiveCodeBench v6.
Enabling programming thinking in large language models toward code generation
7 Pith papers cite this work, alongside 17 external citations. Polarity classification is still indexing.
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SpaceVLN proposes a stagewise closed-loop framework using Spatial Cognitive Memory and Spatial-CoT for zero-shot vision-and-language navigation and object-goal navigation, reporting SOTA results on R2R-CE, RxR-CE, GN-Bench, and HM3D-OVON plus real-robot tests.
DryRUN lets LLMs create their own test inputs and run internal simulations for self-correcting code generation, matching the performance of test-dependent methods like CodeSIM on LiveCodeBench without public tests or external signals.
LLM code generation lacks syntactic robustness on math-formula prompts, but formula-reduction pre-processing raises it from 54.05% to 74.42%.
A structured domain-knowledge translation guide injected into LLM prompts improves OS kernel specification generation from 55% to 97% Pass@1 across nine models.
Empirical study identifies patterns in how model classes respond to structured prompts, optimization, and other techniques across two Verilog benchmarks.
A systematic survey categorizes prompt engineering methods for LLMs and VLMs by application area, summarizing methodologies, applications, models, datasets, strengths, and limitations for each technique along with a taxonomy and summary table.
citing papers explorer
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BACE: LLM-based Code Generation through Bayesian Anchored Co-Evolution of Code and Test Populations
BACE reformulates LLM code synthesis as Bayesian co-evolution of code and test populations anchored on minimal public examples, achieving superior performance on LiveCodeBench v6.
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SpaceVLN: A Zero-Shot Vision-and-Language Navigation Agent with Online Spatial Cognitive Memory and Reasoning
SpaceVLN proposes a stagewise closed-loop framework using Spatial Cognitive Memory and Spatial-CoT for zero-shot vision-and-language navigation and object-goal navigation, reporting SOTA results on R2R-CE, RxR-CE, GN-Bench, and HM3D-OVON plus real-robot tests.
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You Don't Need Public Tests to Generate Correct Code
DryRUN lets LLMs create their own test inputs and run internal simulations for self-correcting code generation, matching the performance of test-dependent methods like CodeSIM on LiveCodeBench without public tests or external signals.
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Assessing, Exploiting, and Mitigating Syntactic Robustness Failures in LLM-Based Code Generation
LLM code generation lacks syntactic robustness on math-formula prompts, but formula-reduction pre-processing raises it from 54.05% to 74.42%.
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BODHI: Precise OS Kernel Specification Inference
A structured domain-knowledge translation guide injected into LLM prompts improves OS kernel specification generation from 55% to 97% Pass@1 across nine models.
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VeriInteresting: An Empirical Study of Model Prompt Interactions in Verilog Code Generation
Empirical study identifies patterns in how model classes respond to structured prompts, optimization, and other techniques across two Verilog benchmarks.
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A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications
A systematic survey categorizes prompt engineering methods for LLMs and VLMs by application area, summarizing methodologies, applications, models, datasets, strengths, and limitations for each technique along with a taxonomy and summary table.