ClaMPAPP, a hybrid system, uses an LLM to extract structured clinical features from free-text notes and passes them to an XGBoost classifier for diagnosis. It outperformed end-to-end LLMs in both internal and external validation, with better stability and fewer missed appendicitis cases, demonstrating superior diagnostic performance and safety in pediatric triage.
LLM-as-Interface, ML-as-Predictor for Pediatric Appendicitis
P4IR Framework Improves LLM-Based Code Compliance Accuracy
P4IR, a two-stage framework, uses supervised fine-tuning and Group Relative Policy Optimization to enhance large language model-based automated code compliance systems. It reduces tree edit and token-level Levenshtein distances by up to 23.8% and 38.6% respectively, outperforming leading LLMs like Claude Opus, GPT-5.2, and GLM-4.7 in zero-shot settings with few-shot prompting, and reduces false positives by a statistically significant margin.
LLM-Generated GPU Kernels Face Correctness Illusion
Benchmarks using fixed-shape checks miss real bugs in LLM-generated GPU kernels. A controlled corpus of 24 kernels, including 9 buggy variants with transcription errors, reveals that an op-schema-aware oracle detects all failures and passes all correct controls, with identical results across five GPU architectures.
TxBench-PP: AI Agent Performance in Preclinical Pharmacology
TxBench-PP is a verifiable benchmark for small-molecule preclinical pharmacology, testing AI agents' ability to derive accurate conclusions from real-world assay data. Across 16 model-harness configurations, no system reliably made correct preclinical pharmacology decisions, with the best performance at 59.3% (Claude Opus 4.8 / Pi) and 55.3% (GPT-5.5 / Pi) of endpoint attempts.
TxBench-PP: AI Agent Benchmark in Preclinical Pharmacology
TxBench-PP is a verifiable benchmark for small-molecule preclinical pharmacology, testing AI agents' ability to derive accurate conclusions from real-world assay data. Across 16 model configurations, no system reliably passed all evaluations, with the best performing setup (Claude Opus 4.8 / Pi) achieving 59.3% success rate on 300 endpoint attempts.
Reverse-Engineering Transformer Attention with Executable Programs
A new method uses program synthesis to generate Python programs that reproduce attention patterns in transformer models. Fewer than 1,000 such programs achieve over 75% intersection-over-union similarity on TinyStories, and replacing 25% of attention heads with these programs increases perplexity by only 16% while preserving performance on question-answering tasks.