Improving tool use in large reasoning models (LRMs) typically demands expensive test-time compute, training resources unavailable to API-only practitioners, or expensive iterative rollouts with execution feedback required by training-free context-engineering methods, all incompatible with resource-constrained deployment. We introduce **GEAR**, a training-free framework that extracts compositional rules from tool-use failures via single-pass meta-reasoning and distills them into inference prompts. We hypothesize that combining explicit rules with examples outperforms either alone. Across 78 API domains, **GEAR**+2-shot outperforms 3-shot by 24.9% on Qwen3-30B, validating our complementarity hypothesis. On Qwen3-4B, **GEAR** exceeds LoRA SFT by 24.3% and RAG-3-shot by 38.5%, while **GEAR**+2-shot beats the state-of-the-art context-engineering method ACE by 38.8% at 4.1× lower cost. Remarkably, Qwen3-30B with **GEAR**+2-shot nearly doubles zero-shot accuracy while remaining under 1 s latency, demonstrating rollout-free adaptation for resource-constrained deployment.

Large Language Models (LLMs) are widely deployed in downstream tasks, but keeping their knowledge up-to-date via retraining or fine-tuning is often computationally expensive. Model editing provides a more efficient alternative by updating a targeted subset of parameters, which often follows the locate-and-edit paradigm. Despite this efficiency, existing methods are limited: edits may fail to inject knowledge (UnderEdit) or unintentionally disrupt unrelated neighboring knowledge (OverEdit). To address these challenges, we propose two complementary methods: iterative model editing, which applies successive edits to mitigate UnderEdit, and neighbor-assisted model editing, which incorporates neighboring knowledge during editing to reduce OverEdit. Our extensive experiments show that these techniques improve editing performance across multiple LLMs, algorithms, and benchmarks, reducing UnderEdit by up to 38 percentage points and OverEdit by up to 6, while remaining broadly applicable to any locate-and-edit method.

This study examines the fairness of human- and AI-generated summaries of student reflections in university STEM classes, focusing on potential gender biases. Using topic modeling, we first identify topics that are more prevalent in reflections from female students and others that are more common among male students. We then analyze whether human and AI-generated summaries reflect the concerns of students of any particular gender over others. Our analysis reveals that though human-generated and extractive AI summarization techniques do not show a clear bias, abstractive AI-generated summaries exhibit a bias towards male students. Pedagogical themes are over-represented from male reflections in these summaries, while concept-specific topics are under-represented from female reflections. This research contributes to a deeper understanding of AI-generated bias in educational contexts, highlighting the need for future work on mitigating these biases.