A new adaptive LLM tutoring system uses subject-aware prompting to enhance student engagement. It outperforms static models in simulation and shows real-world effectiveness, reducing interactions by 3 turns and increasing exercise conversion rates to 28.1% with a stochastic strategy.
Adaptive LLM Tutoring Improves Engagement and Efficiency
Sakana AI Launches Sakana Fugu: Multi-Agent Orchestration Model
Sakana AI has launched Sakana Fugu, an orchestration model that routes tasks across a swappable pool of frontier LLMs via a single OpenAI-compatible API. Fugu Ultra outperforms individual models on key benchmarks like SWE Bench Pro and GPQA-D, and the system demonstrates superior performance on complex, multi-step tasks such as auto-research, Rubik's Cube solving, and blindfold chess.
Local AI for Local Office Files
A Reddit user asks which AI agent is best for handling local office files like Excel, PDF, Word, and JSON. The post seeks user experiences and implemented workflows for such tasks.
Probe-and-Refine Tuning Improves Coding Agent Performance
A new method called probe-and-refine tuning uses synthetic bug-fix probes to iteratively improve repository guidance files with single-shot LLM calls, without agent loops or tool use. On SWE-bench Verified, it achieves a 33.0% mean resolve rate—14.5 percentage points higher than the initial static knowledge base—showing improved coverage rather than patch precision. The method enables agents to use larger step budgets effectively, and performance remains stable across models when diagnostic output is sufficient.
SoftSkill: Behavioral Compression for Contextual Adaptation
SoftSkill proposes a method to compress natural-language skills into compact latent priors, improving task performance on SearchQA, LiveMath, and DocVQA. It outperforms SkillOpt by 5.2 to 12.5 points on key benchmarks while replacing hundreds to thousands of Markdown tokens with a few virtual tokens.
AutoPass: Evidence-Guided LLM Agents for Compiler Performance Tuning
AutoPass uses runtime and compiler evidence to guide LLM-generated optimization decisions, outperforming expert heuristics and classical autotuning methods. It achieves geometric-mean speedups of 1.043x on x86-64 and 1.117x on ARM64 systems without prior training or fine-tuning.