Tsui-Wei Weng

Recent advances in long chain-of-thought (CoT) reasoning have largely prioritized answer accuracy and token efficiency, while overlooking aspects critical to trustworthiness. We argue that usable reasoning systems must be trustworthy, characterized by three properties: interpretability, faithfulness, and reliability. To this end, we propose ReFIne, a new training framework that integrates supervised fine-tuning with GRPO to encourage models to: (i) improve interpretability by producing structured, tag-based traces with high-level planning that are easier for humans to follow; (ii) enhance faithfulness by explicitly disclosing the decisive information guiding each solution, with consistent cross-section references; and (iii) promote reliability by providing self-assessments of both the derivation's soundness and the confidence of the final answer. We apply ReFIne to the Qwen3 models at multiple scales (1.7B/4B/8B) and evaluate across mathematical benchmarks of varying difficulty. Our experimental results show that ReFIne models generate clearer and better-structured reasoning traces (interpretability +44.0%), more faithfully expose their underlying decision process (faithfulness +18.8%), and offer informative confidence estimates (reliability +42.4%). These findings highlight an overlooked but important direction: reasoning models should be optimized not only for accuracy, but also for broader dimensions of trustworthiness.

Large reasoning models achieve strong performance on diverse tasks by producing extended chains of thought. **Self-reflection**, the ability to review and revise prior reasoning steps, is widely regarded as a key contributor to it. However, self-reflection also incurs substantial inference cost, and its governing mechanism remains underexplored. In this work, we study self-reflection through the lens of representation engineering. First, we identify a *reflection direction* in the model's latent space that separates reflection steps from non-reflection steps, and show that activation along this direction is strongly predictive of answer correctness, suggesting that self-reflection is regulated by the model's internal uncertainty. Next, building on this insight, we propose **ReflCtrl**, a framework that controls self-reflection via a *stepwise steering method*: interventions are applied only at the start of each new reasoning step, enabling fine-grained control over reflection frequency without degrading generation quality. Experiments across math and general reasoning benchmarks show that reflection is often redundant, especially in stronger models: ReflCtrl reduces total reasoning tokens by up to 33.6\% while preserving accuracy, and substantially outperforms the conventional approach which steers at every token at the same token budgets.

Learning effectively from limited data is critical in domains like security where labeled examples are scarce. Large language models (LLMs) have demonstrated some capabilities for data-efficient learning, especially through parameter-efficient adaptation methods, but continue to struggle when faced with few samples for difficult tasks. To meet this challenge, we propose Attention Head Reweighting (AHR), a data-efficient method that adapts LLMs to new text-classification tasks by learning only a single scalar per attention head. This drastically reduces the number of parameters which need to be learned by making use of the functional specialization of individual attention heads. Experiments on diverse open-source text classification datasets show that AHR can outperform standard baselines when learning from limited samples, while only modifying 0.0001\% of the model's parameters. In addition, our learned weights are easy to interpret and can be analyzed to better understand the mechanisms and attention heads responsible for in-context learning abilities in LLMs.