Long-context Large Language Models, despite their expanded capacity, require careful working memory management to mitigate attention dilution during long-horizon tasks. Yet existing approaches rely on external mechanisms that lack awareness of the agent’s reasoning state, leading to suboptimal decisions. We propose Memory-as-Action (MemAct), a framework that treats working memory management as learnable policy actions. By formulating context management as in-place editing operations (deletion, insertion), MemAct enables joint optimization of information retention and task performance through end-to-end reinforcement learning. To address the computational challenges of dynamic context updates, we introduce Dynamic Context Policy Optimization, which restores training efficiency without compromising reasoning integrity. Experiments show that MemAct-RL-14B matches the accuracy of models 16× larger while reducing average context length by 51%, with learned strategies that adapt to model capabilities and generalize across task complexities. The code and datasets are available at https://github.com/ADaM-BJTU/MemAct.

Omni-modal Large Language Models (OLLMs) excel in diverse tasks but struggle with complex emotional reasoning, which requires integrating textual, visual, and acoustic signals. We attribute this limitation to modality collapse, where models over-rely on a dominant modality while neglecting complementary cues. To address this issue, we introduce OmniCoT, a data paradigm that interleaves guided tokens (e.g., [vision], [audio]) into reasoning traces to enforce structured evidence extraction. To further internalize the reasoning behaviors instilled by OmniCoT and facilitate adaptive modality prioritization, we propose Dynamic Modality-Entropy GRPO (DyME-GRPO), which utilizes entropy-based uncertainty estimates over Guided Tokens (GTs) to regulate modality usage, thereby mitigating collapse and informational redundancy. By applying supervised fine-tuning with OmniCoT followed by DyME-GRPO, we develop EmoOmni based on the Qwen2.5-Omni-7B backbone. Extensive experiments demonstrate that EmoOmni achieves state-of-the-art performance on multiple emotion recognition and reasoning benchmarks while preserving the general capabilities of the base model. These findings highlight the potential of our work for omni-modal reasoning across a broader range of complex tasks.