Currently, most agentic systems are driven by mainstream closed-source large language models (LLMs), such as GPT-5 and Gemini 3, resulting in high operational costs. As a result, deploying customized small language models (SLMs) to replace LLMs in agent components has attracted increasing attention. However, the limited capabilities of SLMs often render them inadequate for agent tasks. To address this, we propose Edit4Agency, a novel framework that utilizes model editing to efficiently and effectively enhance agent sub-capabilities of SLMs. By formalizing tasks as a sequence generation problem triggered by specific conditions, the framework embeds task triggers into system prompts and performs model editing through localized parameter updates, eliminating the need for large-scale retraining. Extensive experiments across multiple datasets and case studies in agent systems demonstrate that model editing, with only 50 data samples, enables a base model without instruction tuning to acquire new skills such as tool calling while maintaining its general capabilities. The results show that Edit4Agency outperforms instruction-tuned SLMs, constrained decoding, and SLMs trained with large-scale supervised fine-tuning or reinforcement learning methods.

Large language models (LLMs) have achieved remarkable success across a wide range of natural language processing tasks, yet their performance remains heavily biased toward high-resource languages. Tibetan, despite its cultural significance and large speaker population, is still substantially underrepresented. In this work, we present a comprehensive pipeline for advancing Tibetan language modeling through large-scale data curation and continual pre-training. We construct a 72 GB high-quality Tibetan corpus, the largest to date, and adapt Qwen2.5-7B through balanced multilingual continual pre-training with Tibetan, Chinese, and English, followed by multilingual instruction tuning. To further scale capacity efficiently, we extend the dense model to a 50B-A10B Mixture-of-Experts architecture. Due to the absence of standardized Tibetan benchmarks, we build multiple evaluation datasets via high-quality translation and human verification. Experimental results show that both dense and MoE models consistently outperform existing open-source and Tibetan-focused models of similar scale across diverse tasks. Our work advances Tibetan-centric LLM research and provides transferable insights for extending LLMs to other low-resource languages. We will release the model weights, evaluation benchmarks, and detailed data processing documentation in the follow-up.

With the increasing capability of large language models (LLMs), LLM-as-a-judge has emerged as a new evaluation paradigm. Compared with traditional automatic and manual evaluation, LLM evaluators exhibit better interpretability and efficiency. Despite this, existing LLM evaluators suffer from limited use scenarios and poor flexibility. To mitigate these issues, we propose Praetor, a fine-grained generative LLM evaluator with instance-level customazable evaluation criteria. To train Praetor, we curate a large-scale dataset guided with a hierarchical guideline covering a wide range of tasks and instance-level evaluation criteria. We train Praetor on this dataset in a multi-task learning fashion, which enables to evaluate LLMs in either pointwise grading or pairwise comparison way and support two languages simultaneously with a high flexibility of setting evaluation criteria. Extensive experiments demonstrate that Praetor outperforms previous LLM evaluators and instruction-tuned LLMs on multiple benchmarks, setting new SOTA results. It also exhibits the potential for generating critiques as scalable feedback to further improve LLMs. Our model and related resources are released at https://github.com/tjunlp-lab/Praetor.