ACCD detects coordinated online harm campaigns by combining adaptive causal temporal scoring, semi-supervised account classification, and stability validation. On a filtered Twitter IRA task, it improves account-level coordination screening over correlation and fixed-parameter CCM baselines, while reducing manual labeling and runtime.

Large language model (LLM) agents increasingly rely on function calling to act across multi-turn interactions, but compact models remain brittle when tool availability, required parameters, and dialogue constraints change over time. Existing synthetic tool-use data often broadens coverage without targeting the recurring failures of a specific model. We present FUSE, a failure-guided refinement approach that uses the model’s own function-calling errors to synthesize harder training scenarios and then emphasizes difficult examples during supervised fine-tuning. This data-centric design turns diagnostic failures into targeted training signal while remaining simple to apply with standard offline fine-tuning. Experiments on BFCLv3 show that FUSE substantially improves a compact Qwen3-based model across standard and stress categories, including missing functions, missing parameters, and long-context interactions. Evaluation on BFCLv4 provides initial evidence that the same refinement strategy can transfer to selected agentic function-calling tasks.

Recently, Large Language Models (LLMs) have shown strong potential in recommendation tasks due to their broad world knowledge and reasoning capabilities. However, applying them to serendipity-oriented recommendation remains challenging, mainly due to a domain gap of LLMs in modeling personalized user behavior and the scarcity of labeled serendipitous interactions. In this paper, we introduce SOLAR (Serendipity-Optimized Language model Aligned for Recommendation), a two-stage framework that addresses these challenges. To alleviate label scarcity, we adopt a weak supervision strategy: a sequential ID-based recommender generates candidate items, which are then reranked by an LLM acting as a preference judge to produce serendipity-aware pseudo-labels. To bridge the domain gap, we propose a domain-adaptive instruction tuning method (SUN) that aligns LLMs with recommendation tasks. Experiments on three real-world datasets show that SOLAR consistently improves both accuracy and serendipity over strong baselines, showing its effectiveness in enabling more diverse, user-centric recommendations. Code and dataset are released at https://github.com/SOLAR2025ARR/SOLAR.