Multimodal harmful meme detection is challenging because harmful intent is often expressed through implicit image-text associations, cultural references, rhetorical framing, and context-dependent targets. Existing methods mainly rely on supervised multimodal models, MLLM prompting, or fixed agent workflows. However, they still lack experience accumulation and rely on static skill design, making it difficult to reuse prior judgments or adapt to emerging meme templates and long-tail harm patterns. To address these limitations, we propose HarmEvoSkill (Harmful Meme Evolving Skill-Memory), a label-free self-evolving Skill-Memory framework for harmful meme detection. HarmEvoSkill converts unlabeled reference memes into Case Memory, Insight Memory, and Risk Memory. It then automatically discovers candidate skills, abstracts recurring reasoning patterns, and induces executable skill cards without predefined skill labels. During inference, a Skill Planner retrieves relevant memories, selects task-specific skills, and generates structured evidence packets. A graph-assisted judge organizes the evidence into a harmful–harmless claim graph, makes the final prediction, and updates memory through graph diagnosis. Extensive benchmark evaluations show that HarmEvoSkill achieves consistently strong performance among evaluated methods. On HarM, it improves Accuracy by 9.33% and Macro-F1 by 17.97% over the direct MLLM baseline.

Accurately forecasting large stock price movements after corporate earnings announcements is a longstanding challenge. Existing methods–sentiment lexicons, fine-tuned encoders, and standalone LLMs–often lack temporal-causal reasoning and are prone to narrative bias, echoing overly optimistic managerial tone. We introduce Context-Enriched Agentic RAG (CARAG), a retrieval-augmented framework that deploys a team of cooperative LLM agents, each specializing in a distinct analytical task: evaluating historical performance, assessing the credibility of guidance, or benchmarking against peers.Agents retrieve structured evidence from a Causal-Temporal Knowledge Graph (CTKG) built from financial statements and earnings calls, enabling grounded, context-rich reasoning. This design mitigates LLM hallucinations and produces more objective predictions.Without task-specific training, our system achieves state-of-the-art zero-shot performance across NASDAQ, NYSE, and MAEC datasets, outperforming both larger LLMs and fine-tuned models in macro-F1, MCC, and Sharpe, beating market benchmarks (S P 500 and Nasdaq) for the same forecasting horizon. Code, datasets, prompts, and implementation details are included in the supplementary material to ensure full reproducibility.

Multimodal Stance Detection (MSD) is crucial for understanding public discourse, yet effectively fusing text and image, especially with conflicting signals, remains challenging. Existing methods often face difficulties with contextual grounding, cross-modal interpretation ambiguity, and single-pass reasoning fragility. To address these, we propose Retrieval-Augmented Multi-modal Multi-agent Stance Detection (MM-StanceDet), a novel multi-agent framework integrating Retrieval Augmentation for contextual grounding, specialized Multimodal Analysis agents for nuanced interpretation, a Reasoning-Enhanced Debate stage for exploring perspectives, and Self-Reflection for robust adjudication. Extensive experiments on five datasets demonstrate MM-StanceDet significantly outperforms state-of-the-art baselines, validating the efficacy of its multi-agent architecture and structured reasoning stages in addressing complex multimodal stance challenges.