SwiftTrans addresses runtime efficiency gaps in LLM-based code translation by introducing Multi-Perspective Exploration and Difference-Aware Selection. The framework extends CodeNet, F2SBench, and introduces SwiftBench to evaluate runtime performance, showing consistent improvements in both correctness and efficiency across benchmarks.
SwiftTrans Improves LLM Code Translation Efficiency
JAMER: Project-Level Code Framework Dataset and Benchmark
JAMER introduces JamSet and JamBench, the first project-level game code dataset and benchmark on a professional game engine. Built from 8,133 verified Game Jam projects, it enables deterministic evaluation and reveals a capability cliff in AI models as project scale increases, with runtime pass rates dropping from 80.4% to 5.7%.
Zero-Shot Agentic LLMs Extract Lung Pathology from Narratives
A zero-shot agentic workflow using open-source LLMs extracts 13 College of American Pathologists synoptic fields from lung resection pathology reports. The best model (GPT-OSS-20B) achieved a Micro-F1 of 0.893, outperforming baseline recall and accurately capturing complex pathologic relations without task-specific training.
Best local models for reasoning in agentic AI
The creator of EverFern asks which local models work best for agentic workflows and browser/computer use. They note that model intelligence is rarely the bottleneck, with reliability and recovery systems being more critical than model choice.
Buddy System: Rust entropy monitor with NER-gated uncertainty for tiered LLM inference
The Buddy System uses a Rust entropy monitor to detect per-token uncertainty in local Gemma 3 4B inference, routing only uncertain tokens to Sonnet via NER-gated span extraction and semantic retrieval. Benchmarks show it achieves 71.4% accuracy at $0.21, outperforming the Anthropic Advisor pattern (62.9% at $0.44) across seven Hugging Face datasets, with a key improvement on SQuAD v2 by routing source passage chunks to the cloud model.
GLM 5.2 Achieves 98% Max Intelligence with Less Than Half Tokens
GLM 5.2 demonstrates 98% of maximum intelligence in coding tasks using less than half of its total token budget, according to a technical report by z_ai. The model's reasoning efficiency has improved significantly, with token usage increasing from 16.7k to 36.7k between GLM 5.1 and GLM 5.2, though high-level settings may strain local hardware performance.