Multimodal long-chain-of-thought (CoT) generation requires models to dynamically route information between visual evidence and an expanding reasoning history. Existing hallucination mitigation methods often strengthen visual grounding or intervene in decoding, but typically treat grounding as a relatively static property of generation. We propose a training-free dynamic attention intervention framework that exploits the head-level sparsity, layer-level stratification, and trajectory-level dynamics of multimodal long-CoT decoding. Our method selects routing-sensitive heads using perturbation-only calibration, assigns visual-evidence and reasoning-history interventions to different layer regions, and continuously rescales selected head outputs according to their instantaneous token-group attention ratios. Across multiple MLLM architectures and multimodal hallucination benchmarks, our method consistently improves over vanilla decoding and visual contrastive decoding, achieving an average performance gain of 4.6%.

Retrieval-augmented LLM agents increasingly rely on curated skill banks: collections of reusable textual principles that guide decision making on complex tasks. Existing approaches typically expand these banks in an append-only fashion, continuously adding new skills without removing redundant, outdated, or harmful ones, resulting in inefficient and poorly curated repositories. In this paper, we formulate skill bank curation as a constrained multi-objective problem: a desirable bank must be useful for the agent, diverse in its content, and provide good coverage of the query distribution. To this end, we introduce SkillBrew, a multi-objective curation framework that formalizes skill bank curation as Pareto-aware optimization under a utility constraint, and solves it via a bi-level propose-then-verify loop. We evaluate our approach on three public benchmarks. Our findings suggest that treating skill banks as objects of principled curation, rather than ever-growing append-only logs, is an important step toward building self-improving LLM agents. Code is available at https://github.com/Applied-Machine-Learning-Lab/EMNLP26_SkillBrew.