Personalized value alignment has become increasingly important as large language models (LLMs) are expected to accommodate diverse user preferences. However, existing methods typically align model outputs with a static value profile across prompts, overlooking that the salience of value dimensions varies substantially across contexts. Inspired by Lewin’s Field Theory, which views human behavior as jointly shaped by personal dispositions and situational constraints, we model personal values as priors and context-dependent preferences as posteriors. We propose BaCVA, an inference-time Bayesian Context-aware personalized Value Alignment method that approximates posterior personalized preferences by integrating static personal values with scenario-specific value salience. BaCVA first estimates contextual value salience from generally normative responses, and then employs a dual-view personalization module to infer posterior preferences from complementary personal-value and scenario-driven perspectives. This Bayesian formulation enables more accurate and adaptive personalized value alignment while improving data efficiency via prior values. Extensive experiments on benchmarks demonstrate its superiority over strong baselines.

As LLMs evolve from standalone assistants into autonomous agents, they increasingly operate in populations where communication, coordination, and competition form community-like interaction networks. In such settings, individually value-steered behaviors can accumulate into group-level dynamics, raising the question of how human values shape collective outcomes in interacting LLM-agent populations. We introduce CIVA, a community-level value analysis framework that varies value direction and prevalence, then measures how these interventions propagate through repeated interactions into macro-level dynamics and micro-level behaviors. We apply it in a resource-constrained multi-agent community where LLM agents autonomously communicate, cooperate, and compete. The value scan reveals distinct structural response patterns: activation-supporting values such as benevolence supports persistence and resilience, an activation-constraining value such as tradition regulates growth and cohesion, and a bidirectionally disruptive value such as power perturbs collective regimes in both directions. At the micro level, representative interventions induce emergent behaviors including deception, betrayal, self-preservation, and power-seeking. These results show that human values can act as population-level structural variables in agent communities and motivate evaluation for multi-agent value alignment.

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.

Generative retrieval offers a unified paradigm for personalized search by autoregressively decoding items as Semantic IDs. However, personalized search hinges on reasoning about query-history relevance, in which explicit textual reasoning incurs heavy cost and high latency, whereas purely implicit reasoning does not provide a structured intermediate latent interface for retrieval. To resolve this tension, we propose LaPR, an adaptive latent retrieval reasoning framework that turns explicit retrieval reasoning into compact latent signals. Specifically, LaPR constructs verified step-wise retrieval traces, induces step-conditioned semantic slots via vector quantization, and distills textual reasoning into these slots through a text-latent to pure-latent curriculum. At inference, LaPR adaptively predicts the reasoning depth and corresponding slots from the query and user history, providing latent reasoning signals for efficient Semantic-ID generation. Extensive experiments show that LaPR consistently surpasses all baselines, achieving SOTA personal retrieval performance while substantially reducing reasoning-token cost and inference latency.

Current medical retrieval benchmarks primarily emphasize lexical or shallow semantic similarity, overlooking the reasoning-intensive demands that are central to clinical decision-making. In practice, physicians often retrieve authoritative medical evidence to support diagnostic hypotheses. Such evidence typically aligns with an inferred diagnosis rather than the surface form of a patient’s symptoms, leading to low lexical or semantic overlap between queries and relevant documents. To address this gap, we introduce R2MED, the first benchmark explicitly designed for reasoning-driven medical retrieval. It comprises 876 queries spanning three tasks: Q A reference retrieval, clinical evidence retrieval, and clinical case retrieval. These tasks are drawn from five representative medical scenarios and twelve body systems, capturing the complexity and diversity of real-world medical information needs. We evaluate 15 widely-used retrieval systems on R2MED and find that even the best model achieves only 31.4 nDCG@10, demonstrating the benchmark’s difficulty. Classical re-ranking and generation-augmented retrieval methods offer only modest improvements. Although large reasoning models improve performance via intermediate inference generation, the best results still peak at 41.4 nDCG@10. These findings underscore a substantial gap between current retrieval techniques and the reasoning demands of real clinical tasks. We release R2MED as a challenging benchmark to foster the development of next-generation medical retrieval systems with enhanced reasoning capabilities.

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.

Medical information retrieval (MIR) is vital for accessing knowledge from electronic health records, scientific literature, and medical databases, supporting applications such as medical education, patient queries, and clinical diagnosis. However, effective zero-shot dense retrieval in the medical domain remains difficult due to the scarcity of relevance-labeled data. To address this challenge, we propose Self-Learning Hypothetical Document Embeddings (SL-HyDE), a framework that leverages large language models (LLMs) to generate hypothetical documents conditioned on a query. These documents encapsulate essential medical context, guiding dense retrievers toward the most relevant results. SL-HyDE further employs a self-learning mechanism that iteratively improves pseudo-document generation and retrieval using unlabeled corpora, eliminating the need for labeled data. In addition, we introduce the Chinese Medical Information Retrieval Benchmark (CMIRB), a comprehensive evaluation suite reflecting real-world medical scenarios, comprising five tasks and ten datasets. By benchmarking ten models on CMIRB, we provide a rigorous standard for evaluating MIR systems. Experimental results demonstrate that SL-HyDE significantly outperforms HyDE in retrieval accuracy, while exhibiting strong generalization and scalability across diverse LLM and retriever configurations. Our code and data are publicly available at: https://github.com/ll0ruc/AutoMIR.

Large language models (LLMs) have been widely adopted as the core of agent frameworks in various scenarios, such as social simulations and AI companions. However, the extent to which they can replicate human-like motivations remains an underexplored question. Existing benchmarks are constrained by simplistic scenarios and the absence of character identities, resulting in an information asymmetry with real-world situations. To address this gap, we propose MotiveBench, which consists of 200 rich contextual scenarios and 600 reasoning tasks covering multiple levels of motivation. Using MotiveBench, we conduct extensive experiments on seven popular model families, comparing different scales and versions within each family. The results show that even the most advanced LLMs still fall short in achieving human-like motivational reasoning. Our analysis reveals key findings, including the difficulty LLMs face in reasoning about “love & belonging” motivations and their tendency toward excessive rationality and idealism. These insights highlight a promising direction for future research on the humanization of LLMs.