Stepwise model routing improves the efficiency of Large Reasoning Models (LRMs) by assigning each reasoning step to a suitable model. Recent methods formulate routing as a sequential decision process and train the router with reinforcement learning. However, although they model routing as a process, they still supervise the router with outcome rewards. Such rewards only reflect final answer correctness and fail to evaluate intermediate routing decisions, which can weaken performance and generalization. To address this gap, we propose RoRo, a rubric-guided process reward framework for stepwise model routing. RoRo first collects diverse routing trajectories and constructs preference pairs based on outcome, cost, and process quality. It then trains a Rubricor to generate a query-specific evaluation rubric and a Judge to score routing trajectories under this rubric through alternating optimization. The resulting process rewards are combined with outcome rewards to optimize the routing policy via GRPO. Experiments on five reasoning benchmarks under both same-family and cross-family settings show that RoRo consistently outperforms strong baselines and achieves better accuracy and cost trade-offs.

Long-video understanding remains challenging for multimodal large language models (MLLMs), as informative visual evidence is often sparsely distributed across many redundant frames. Although sampling more frames can improve coverage, it also increases visual encoding overhead and consumes more visual tokens, making compact frame selection essential. However, existing methods often sample candidate frames uniformly before selection, which may require decoding most frames of the video. Moreover, simply selecting frames by query relevance may overlook the global temporal structure needed for coherent video understanding. To address these limitations, we propose VISA, a VIdeo Skeleton-Aware framework for efficient long-video frame selection. VISA exploits sparse I-frame anchors from the native GOP structure to avoid dense candidate decoding and constructs a video skeleton that preserves the coarse temporal progression. Query relevance is aggregated over this skeleton to select informative anchors, followed by cross-GOP refinement to obtain final frames. Extensive experiments show that VISA consistently improves long-video understanding, achieving up to 8.8% relative gain over uniform sampling. Meanwhile, VISA reduces average end-to-end latency by 32.9%–53.6% compared with recent frame selection baselines on LongVideoBench, demonstrating both effectiveness and efficiency for long-video frame selection.

LLM-based multi-agent systems (MAS) increasingly use latent collaboration to avoid the information loss and repeated encoding-decoding overhead of natural-language communication. However, directly forwarding all sender latents makes the receiver-side context scale with both the number of agents and the reasoning length, increasing computation, memory usage, and collaboration latency. A natural solution is latent compression. But we find that cross-agent redundancy remains unresolved in existing latent compression approaches, which typically compress each sender independently and then concatenate the results. We propose LatCom, a cross-agent latent compression framework for efficient multi-agent latent collaboration. LatCom maps multiple sender latents into a fixed number of receiver-readable and task-relevant slots. Rather than reconstructing all sender hidden states, it optimizes the compressed latents for receiver-side task utility. LatCom trains the compressor in two stages: single-sender readability learning first establishes a latent interface interpretable by the frozen receiver, and multi-sender fusion learning then trains the compressor to fuse complementary evidence and remove redundancy across agents. Experiments on multiple benchmarks with the Qwen-4B model show that LatCom improves average inference speed by 2.46x over LatentMAS, reduces token usage by 70.3%, and increases average accuracy by 0.46%.