…In the report, we summarize improvements in model design, multimodal training, reinforcement learning, toolchain expansion, and agent-framework integration, together with practical insights on building multimodal agents. GLM-5V-Turbo achieves strong…
…1) LLMConfig-Gym , a multi-fidelity environment encompassing four critical LLM experiment tasks, supported by over one million GPU hours of verifiable experiment outcomes; 2) A structured training pipeline that formulates configuration…
…AI-generated summary Controllable diffusion methods have substantially expanded the practical utility of diffusion models , but they are typically developed as isolated, backbone-specific systems with incompatible training pipelines, parameter formats, and…
…https://mobisoftinfotech.com/resources/blog/ai‑development/llm‑api‑pricing‑guide — which gives practical advice on LLM API usage, token‑based pricing, and how to plan costs when working with LLMs. Putting…
…Hao Wang , , , , Abstract SAEgis detects adversarial attacks on vision-language models using sparse autoencoders trained for reconstruction, achieving strong performance across domains without additional training. AI-generated summary Vision-language models (VLMs…
…AI-generated summary Large language models (LLMs) for code completion and generation are increasingly used in software development, yet they may reproduce training examples verbatim and without authorship attribution, raising legal and…
…AI-generated summary Training compute is increasingly outpacing the availability of high-quality data. This shifts the central challenge from optimal compute allocation to extracting maximum value from limited data. The widely…
…AI-generated summary On-Policy Distillation (OPD) has emerged as a dominant post-training paradigm for large language models, especially for reasoning domains. However, OPD remains unstable in practice due to the…
…AI-generated summary We present Darwin Family, a framework for training-free evolutionary merging of large language models via gradient-free weight-space recombination . We ask whether frontier-level reasoning performance can…
…AI-generated summary Model internals encode rich information about how a large language model (LLM) processes its training data; however, post-training data engineering largely relies on external signals and ignores rich…