Large Language Models (LLMs) often produce emotionally flattened responses after alignment, limiting their effectiveness in affect-sensitive applications. In this paper, we propose EmoVec, a lightweight framework for controllable affective generation via latent vector steering. EmoVec extracts emotion-specific directions from paired neutral and emotion-conditioned responses using contrastive activation addition, and further purifies them through task-specific debiasing and principal subspace removal. During inference, these vectors are injected into the final residual stream with static or scenario-adaptive scaling, enabling continuous control over emotional intensity without updating model weights. Experiments across three LLMs and eight emotions show that EmoVec consistently improves emotional salience while largely preserving semantic content, fluency, and coherence. Ablation studies and human evaluation further confirm the effectiveness of vector purification and adaptive scaling, demonstrating EmoVec as a practical inference-time method for affective control in deployed LLMs.

Medical diagnostics is a high-stakes and complex domain that is critical to patient care. However, current evaluations of large language models (LLMs) remain limited in capturing key challenges of clinical diagnostic scenarios. Most rely on benchmarks derived from public exams, raising contamination bias that can inflate performance, and they overlook the confounded nature of real consultations beyond textbook cases. Recent dynamic evaluations offer a promising alternative, but often remain insufficient for diagnosis-oriented benchmarking, with limited coverage of clinically grounded confounders and trustworthiness beyond accuracy. To address these gaps, we propose DyReMe, a dynamic benchmark for medical diagnostics that provides a controlled and scalable stress test of diagnostic robustness. Unlike static exam-style questions, DyReMe generates fresh, consultation-style cases that incorporate clinically grounded confounders, such as differential diagnoses and common misdiagnosis factors. It also varies expression styles to capture heterogeneous patient-style descriptions. Beyond accuracy, DyReMe evaluates LLMs on three additional clinically relevant dimensions: veracity, helpfulness, and consistency. Our experiments show that this dynamic approach yields more challenging assessments and exposes substantial weaknesses of stateof-the-art LLMs under clinically confounded diagnostic settings. These findings highlight the urgent need for evaluation frameworks that better assess trustworthy medical diagnostics under clinically grounded confounders.

Large Language Models (LLMs) have made substantial strides in a broad array of natural language tasks. Recently, LLMs have demonstrated potential reasoning capabilities through prompt design, such as the Chain of Thought (CoT). Despite their superiority in question answering, LLMs still face challenges in answering questions that require multi-hop reasoning, often generating unreliable reasoning chains during answer generation. To improve LLMs’ performance in multi-hop reasoning, we introduce a novel reasoning approach, AnchorCoT, designed to assist LLMs in answering questions involving complex logical reasoning steps. AnchorCoT first predicts key entities which work as important “anchors” to guide the reasoning process and then employs a novel ranking algorithm to ensure the logical sequence of the predicted answers.We implement AnchorCoT on Qwen2.5-7B/14B and GPT-4o and evaluate our method on widely used multi-hop reasoning datasets, including HotpotQA, 2WikiMultiHopQA, and MuSiQue-Ans. The experimental results show that AnchorCoT outperforms existing methods in multi-hop question reasoning and provides more accurate reasoning results in multi-hop question answering tasks.

Large Language Models (LLMs) require continuous updates to maintain accurate and current knowledge as the world evolves. While existing knowledge editing approaches offer various solutions for knowledge updating, they often struggle with sequential editing scenarios and harm the general capabilities of the model, thereby significantly hampering their practical applicability.This paper proposes a two-stage framework combining robust supervised fine-tuning (R-SFT) with model merging for knowledge editing. Our method first fine-tunes the LLM to internalize new knowledge fully, then merges the fine-tuned model with the original foundation model to preserve newly acquired knowledge and general capabilities. Experimental results demonstrate that our approach significantly outperforms existing methods in sequential editing while better preserving the original performance of the model, all without requiring any architectural changes. Code is available at Applied-Machine-Learning-Lab/MM4KE.