Recently, Multimodal Large Language Models (MLLMs) have demonstrated strong visual understanding and reasoning abilities across vision-language tasks. However, conventional Visual Question Answering (VQA) pipelines do not fully exploit the visual features and medical knowledge captured by MLLMs, leading to limited performance in zero-shot medical disease recognition. To address this issue, we propose LLaVA-RadZ, a simple yet effective framework for zero-shot radiology recognition. LLaVA-RadZ introduces Decoding-Side Feature Alignment Training (DFAT), which leverages decoder-side representations with modality-specific tokens for image-text alignment, and a Domain Knowledge Anchoring Module (DKAM), which incorporates disease-aware semantic anchors to mitigate the category semantic gap. Extensive experiments show that LLaVA-RadZ substantially improves the zero-shot disease recognition ability of MLLMs and achieves competitive performance compared with well-established CLIP-based radiology recognition methods.

Sales dialogues require multi-turn, goal-directed persuasion under asymmetric incentives, which makes them a challenging setting for large language models (LLMs). Yet existing dialogue benchmarks rarely measure deal progression and outcomes. We introduce SalesLLM benchmark, a bilingual (ZH/EN) benchmark derived from realistic applications covering Financial Services and Consumer Goods, built from 30,074 scripted configurations and 1,805 curated multi-turn scenarios with controllable difficulty and personas. We propose a fully automatic evaluation pipeline that combines (i) an LLM judge for sales-process progress, and (ii) fine-tuned BERT classifiers for end-of-dialogue buying intent. To improve simulation fidelity, we train a user model, CustomerLM, with SFT and DPO on 8,000+ crowdworker-involved sales conversations, reducing role inversion from 17.44% (GPT-4o) to 8.8%. SalesLLM benchmark scores correlate strongly with human ratings (average Pearson r = 0.86; inter-annotator agreement Krippendorff’s α = 0.86, 500 annotations per model). Across 15 mainstream LLMs, in Chinese the strongest models are competitive with typical (junior-to-intermediate) human salespeople — not with sales experts — while weaker ones fall below this baseline, and cross-lingual consistency remains poor. SalesLLM benchmark serves as a scalable benchmark for outcome-oriented sales agents. Our code and data are released at https://github.com/Bairong-Xdynamics/Benchmarking-LLM-Realistic-Selling-Skill

LLMs are increasingly used to arbitrate among multiple candidate outputs and compress them into a single final result. The final answer may not be incorrect, yet information compression can still make it suboptimal. We define this problem as source-resolution distortion (SRD), the irreversible loss or incorrect mapping of task-relevant source-side structure in the final output during arbitration.

We model pairwise arbitration as a compression process; we then present a source-bounded SRD measurement framework that aligns the reference structure fixed before arbitration with the final output and uses six metrics to measure its preservation and incorrect mapping. To reduce SRD, we further propose A²R (Auditable Arbitration for Resolution), a reference resolution method based on a decision ledger. Motivated by anti-aliasing in signal processing, A²R extracts query-relevant source units and records the local resolution relations among them in a decision ledger before using it to constrain final generation.

Across 520 pairwise cases, compared with Checklist Prompt, A²R-Decision lowers CER from 0.23 to 0.16 and raises Prov-H from 0.45 to 0.67. Its answer-preference scores against three structured baselines range from 51% to 53%, showing that similarly preferred answers can still differ markedly in source recoverability. A 200-case three-candidate extension preserves the system ordering.

Large language models (LLM) have emerged as a promising avenue for time series forecasting, offering the potential to integrate multimodal data. However, existing LLM-based approaches face notable limitations—such as marginalized role in model architectures, reliance on coarse statistical text prompts, and lack of interpretability. In this work, we introduce Augur, a fully LLM driven time series forecasting framework that exploits LLM causal reasoning to discover and use directed causal associations among covariates. Augur uses a two stage teacher student architecture where a powerful teacher LLM infers a directed causal graph from time series using heuristic search together with pairwise causality testing. A lightweight student agent then refines the graph and fine tune on high confidence causal associations that are encoded as rich textual prompts to perform forecasting. This design improves predictive accuracy while yielding transparent, traceable reasoning about variable interactions. Extensive experiments on real-world datasets with 25 baselines demonstrate that Augur achieves competitive performance and robust zero-shot generalization.

Text-based person search (TBPS) enables the retrieval of person images from large-scale databases using natural language descriptions, offering critical value in surveillance applications. However, a major challenge lies in the labor-intensive process of obtaining high-quality textual annotations, which limits scalability and practical deployment. To address this, we introduce two complementary modules: Multi-Turn Text Generation (MTG) and Multi-Turn Text Interaction (MTI). MTG generates rich pseudo-labels through simulated dialogues with MLLMs, producing fine-grained and diverse visual descriptions without manual supervision. MTI refines user queries at inference time through dynamic, dialogue-based reasoning, enabling the system to interpret and resolve vague, incomplete, or ambiguous descriptions—characteristics often seen in real-world search scenarios. Together, MTG and MTI form a unified and annotation-free framework that significantly improves retrieval accuracy, robustness, and usability. Extensive evaluations demonstrate that our method achieves competitive or superior results while eliminating the need for manual captions, paving the way for scalable and practical deployment of TBPS systems.