LLM

We introduce Kimi K2.5, an open-source multimodal agentic model designed to advance general agentic intelligence. The model focuses on …

Abstract: Long contexts challenge transformers as attention scores dilute across thousands of tokens and critical information is often lost in the middle. We reframe test-time adaptation as a budget-constrained memory consolidation problem and propose Gdwm (Gated Differentiable Working Memory), which introduces a write controller that estimates Contextual Utility, an information-theoretic measure of long-range contextual dependence, to allocate gradient steps efficiently.

Abstract: Reinforcement learning (RL) has shown great promise in enhancing LLM reasoning, but current approaches mainly focus on single domains with verifiable rewards. We propose RGR-GRPO, a rubric-driven RL framework for multi-domain reasoning that uses rubrics to provide fine-grained reward signals and offline guidance.

We introduce Vibe Coding as a formal discipline, formalizing it through a Constrained Markov Decision Process that captures the dynamic triadic relationship among human developers, software projects, and coding agents.

In this work, we propose a Dynamically Adaptive Density Control Strategy based on the degree of reconstruction of the background of the scene, which adaptive the spatial sample point generation strategy dynamically according to the training results and prevents the generation of redundant data in the model.

We introduce Kimi K2, a Mixture-of-Experts large language model with 32 billion activated parameters and 1 trillion total parameters. …

Abstract: Large language models (LLMs) have achieved remarkable progress, demonstrating unprecedented capabilities across various natural language processing tasks. However, the high costs associated with such exceptional performance limit the widespread adoption of LLMs, highlighting the need for prompt compression.

Large Language Models (LLMs) have made remarkable breakthroughs in reasoning, yet continue to struggle with hallucinations, logical …

TBD

作者提出CoT-Influx方法，一种对CoT的示例和内容进行优化从而提高LLMs推理能力的方法，其核心思想是通过剪枝最大化有效信息的输入。