Knowledge Editing (KE) aims to update specific facts in large language models (LLMs) without full retraining. Prior efforts sought to tune the knowledge layers of LLMs, achieving near-perfect performance in teacher-forced evaluations. However, under realistic autoregressive generation evaluation, their effectiveness remains limited by two core challenges: (1) even when methods successfully inject new knowledge, pre-trained capabilities can degrade, and (2) more critically, the injected parametric knowledge is not reliably expressed at inference time. To this end, we formalize knowledge editing as a two-stage problem and propose EtCon, an edit-then-consolidate paradigm that couples targeted edits with post-edit consolidation. Within EtCon, the edit stage applies Targeted Proximal Supervised Fine-Tuning (TPSFT) to update parametric knowledge with minimal disruption to pre-trained capabilities. The consolidation stage applies Group Relative Policy Optimization (GRPO) to align the model’s autoregressive generation with the edited parametric knowledge. Extensive experiments demonstrate that our instantiation (TPSFT + GRPO) improves editing reliability and real-world generalization while preserving pre-trained capabilities, and that EtCon consistently improves existing editing methods when instantiated with them.

Understanding long videos remains challenging due to the sparsity of visual evidence relevant to a given query. Prior work has explored program-based visual grounding, typically relying on executable programs generated by auxiliary large language models. However, when scaling to long videos, existing approaches face several critical limitations: (1) frame-centric vision modules are often insufficient for long video processing; (2) naively applying program-based reasoning to all queries incurs considerable computational overhead; and (3) errors arising from low-confidence predictions and imperfect program execution are difficult to recover from. To address these challenges, we propose VideoPro, a unified framework that enables VideoLLMs to adaptively reason over long videos and refine their predictions through executable programs. VideoPro first performs adaptive reasoning, dynamically determining whether a query can be resolved directly by the native VideoLLM or requires explicit multi-step program reasoning. For complex queries, the model decomposes the task into executable programs that invoke specialized vision modules for precise temporal and semantic grounding. To further improve robustness, VideoPro incorporates a self-refinement mechanism that leverages execution feedback and confidence signals to correct erroneous executions and refine low-confidence reasoning programs. By tightly integrating adaptive reasoning with self-refinement, VideoPro consistently outperforms prior methods across multiple long-video understanding benchmarks, yielding an average 6.7% improvement for Qwen3-VL-8B.