Detecting Alzheimer’s disease (AD) from narrative transcripts challenges large language models (LLMs): pre-training rarely covers this out-of-distribution task, and all transcript demos describe the same scene, producing highly homogeneous contexts. These factors cripple both the model’s built-in task knowledge (task cognition) and its ability to surface subtle, class-discriminative cues (contextual perception). Because cognition is fixed after pre-training, improving in-context learning (ICL) for AD detection hinges on enriching perception through better demonstration (demo) sets. We demonstrate that standard ICL quickly saturates, its demos lack diversity (context width) and fail to convey fine-grained signals (context depth), and that recent task vector (TV) approaches improve broad task adaptation by injecting TV into the LLMs' hidden states (HSs), they are ill-suited for AD detection due to the mismatch of injection granularity, strength and position. To address these bottlenecks, we introduce DA4ICL, a demo-centric anchoring framework that jointly expands context width via Diverse and Contrastive Retrieval (DCR) and deepens each demo's signal via Projected Vector Anchoring (PVA) at every Transformer layer. Across three AD benchmarks, DA4ICL achieves large, stable gains over both ICL and TV baselines, charting a new paradigm for fine-grained, OOD and low-resource LLM adaptation.

Visual Question Answering (VQA) has increasingly shifted from direct prediction to step-by-step reasoning, driven by advances in Multi-modal Large Language Models (MLLMs) and Multi-modal Chain-of-Thought (MCoT). However, small MLLMs (S-MLLMs) often suffer from a "Faithfulness Gap", where they arrive at correct answers with inconsistent or hallucinatory intermediate reasoning processes. In this work, we first conduct a systematic diagnostic of MCoT consistency and identify two prevalent patterns that undermine conventional trajectory-level optimization: Intermediate Error and Inconsistent Reasoning. We categorize intermediate errors into Caption Errors—Hallucination, Contradiction, and Irrelevance, and Reasoning Errors—Divergence and Spuriousness. Driven by these findings, we curate MCoT-SP, a Stage-wise Preference dataset with severity-aware annotations constructed through an automated pipeline that detects and corrects intermediate errors using entailment-based checks and LLM-based correction, followed by post-verification to ensure the reliability of the preference pairs. Leveraging this resource, we propose Intermediate Process Direct Preference Optimization (IP-DPO), which performs stage-wise preference optimization by treating each intermediate stage as independent preference units, while jointly integrating preference learning with supervised fine-tuning on preferred complete MCoT responses to preserve global coherence. Extensive evaluations on LLaVA-Next and Qwen2.5-VL demonstrate that IP-DPO outperforms standard DPO and SFT baselines across two benchmarks spanning six VQA datasets. Our analysis indicates that improvements in intermediate consistency substantially exceed the corresponding improvements in final answer accuracy, revealing that IP-DPO primarily improves the underlying reasoning process rather than merely overfitting to target labels.

Detecting Alzheimer's Disease (AD) from narrative transcripts remains a challenging task for large language models (LLMs), particularly under out-of-distribution (OOD) and datascarce conditions. While in-context learning (ICL) provides a parameter-efficient alternative to fine-tuning, existing ICL approaches often suffer from task recognition failure, suboptimal demonstration selection, and misalignment between label words and task objectives, issues that are amplified in clinical domains like AD detection. We propose Explicit Knowledge In-Context Learners (EK-ICL), a novel framework that integrates structured explicit knowledge to enhance reasoning stability and task alignment in ICL. EK-ICL incorporates three knowledge components: confidence scores derived from small language models (SLMs) to ground predictions in task-relevant patterns, parsing feature scores to capture structural differences and improve demo selection, and label word replacement to resolve semantic misalignment with LLM priors. In addition, EK-ICL employs a parsing-based retrieval strategy and ensemble prediction to mitigate the effects of semantic homogeneity in AD transcripts. Extensive experiments across three AD datasets demonstrate that EK-ICL significantly outperforms state-of-the-art fine-tuning and ICL baselines. Further analysis reveals that ICL performance in AD detection is highly sensitive to the alignment of label semantics and task-specific context, underscoring the importance of explicit knowledge in clinical reasoning under low-resource conditions.

Alzheimer's Disease (AD), characterized by significant cognitive and functional impairment, necessitates the development of early detection techniques.Traditional diagnostic practices, such as cognitive assessments and biomarker analysis, are often invasive and costly.Deep learning-based approaches for non-invasive AD detection have been explored in recent studies, but the lack of accessible data hinders further improvements in detection performance.To address these challenges, we propose a novel semantic perturbation-based data augmentation method that essentially differs from existing techniques, which primarily rely on explicit data engineering.Our approach generates controlled semantic perturbations to enhance textual representations, aiding the model in identifying AD-specific linguistic patterns, particularly in scenarios with limited data availability.It learns contextual information and dynamically adjusts the perturbation degree for different linguistic features.This enhances the model's sensitivity to AD-specific linguistic features and its robustness against natural language noise.Experimental results on the ADReSS challenge dataset demonstrate that our approach outperforms other strong and competitive deep learning methods.