LibEvoBench benchmark shows LLMs are version-oblivious on evolving APIs, with documentation helping but version specification not.
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Exploring and evaluating hallucinations in llm-powered code generation
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First empirical study shows crate hallucination in Rust LLMs has consistent rates across models insensitive to parameters and tests prompt-based mitigation.
Chain-of-Thought prompting balances high accuracy with low energy use in small language models for code generation, while multi-sampling strategies add high energy costs for small accuracy gains.
A study of seven LLMs finds that realistic prompt variations such as one-character misspellings trigger library hallucinations in up to 26% of cases, fabricated names in up to 99%, and time-based prompts in up to 85%, and introduces LibHalluBench for evaluation.
FASE approximates functional correctness via MST on structural and semantic dissimilarity graphs, reporting 25% better Spearman correlation and 19% better ROCAUC than LLM-based semantic entropy at 0.3% runtime cost on HumanEval and BigCodeBench.
Introduces functional equivalence methods and functional entropy to predict functional correctness of LLM-generated code via uncertainty quantification, outperforming NLI-based baselines in most tested settings.
RisCoSet applies multiple hypothesis testing to construct risk-controlling partial-program prediction sets for LLM code generation, achieving up to 24.5% less code removal than prior methods at equivalent risk levels.
SOMA estimates a local response manifold from early turns and adapts a small surrogate model via divergence-maximizing prompts and localized LoRA fine-tuning for efficient multi-turn serving.
A review of 114 studies creates taxonomies for code and data quality issues, formalizes 18 propagation mechanisms from training data defects to LLM-generated code defects, and synthesizes detection and mitigation techniques.
SOCIA-EVO generates statistically consistent simulators by separating structural refinement from parameter calibration via bi-level optimization and falsifying strategies through execution feedback in a Bayesian-weighted playbook.
ContextCov compiles agent instruction files into static, runtime, and architectural guardrails, raising constraint compliance to 88.3% on SWE-bench Lite tasks versus 67% and 50.3% for prompt and reflection baselines.
FELA deploys specialized LLM agents in an evolutionary framework to generate, validate, and refine explainable features from heterogeneous industrial event logs, improving downstream model performance.
PerfOrch is a four-agent multi-LLM system that uses offline profiling to build language-and-category rankings for routing tasks, achieving 97.19% and 95.83% pass@1 on HumanEval-X and EffiBench-X with generalization across benchmarks.
BashCoder-R1 applies CPT, L-CoT SFT, and R-GRPO to reach higher syntax, robustness, and functionality rates than baselines on the new BashBench benchmark of 952 tasks.
ABLE uses LLMs with sanitization and iterative refinement to generate bypass YARA rules from malware traces, achieving 79% success on 334 samples and 47% more family detections.
ClusterFusion++ fuses the entire Transformer block (LayerNorm to residual) via CUDA extensions and achieves 1.34x throughput on Pythia-2.8B with near-identical output fidelity.
LLMs achieve only 0-60% success when asked to contribute code to sizable open-source projects, often failing basic checks or simply repeating training data.
ICL4Decomp applies in-context learning to guide LLMs in generating re-executable decompiled code from binaries, reporting roughly 40% higher re-executability than prior methods across datasets and optimization levels.
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