The decline of proficient practitioners poses an existential threat to numerous ancient writing systems, risking the loss of the cultural heritage they encode. Among these systems is Sawndip, a logographic script whose computational preservation faces severe challenges due to the scarcity of standardized corpora. In this paper, we present the first systematic framework for low-resource Sawndip-Chinese translation and bilingual conversational modeling. We introduce a newly curated dataset comprising sentence-level Sawndip-Chinese parallel data and character-level lexical definitions. Building upon this foundation, we propose a knowledge-enhanced supervised fine-tuning paradigm that incorporates lexical knowledge during training. To address the additional challenges of Chinese-to-Sawndip generation, we further develop a manually curated template-based substructure substitution strategy for synthesizing reverse-direction data. Bilingual conversational data is then constructed through a dimension-conditioned dialogue generation and multi-candidate back-translation pipeline, enabling controllable and diverse dialogue synthesis. Experimental results show that our final unified model effectively supports both Sawndip-Chinese translation and bilingual conversational abilities. Ultimately, this work provides a foundational reference for the digital preservation of Sawndip, offering broader insights into the computational documentation of globally-endangered languages.

Nowadays, developing reliable DeepResearch-style long-form report generation remains challenging, as training and evaluation lack verifiable reward signals. Accordingly, rubric-based evaluation has become a common practice. However, existing approaches either rely on coarse, pre-defined rubrics that lack sufficient granularity or depend on manually constructed query-specific rubrics that are costly and difficult to scale. In this paper, we propose a pipeline to train preference-grounded query-specific rubric generators tailored for DeepResearch report generation. We first construct a dataset of DeepResearch-style queries annotated with human preferences over paired reports, and train rubric generators via reinforcement learning with a hybrid reward combining preference consistency, format validity, and LLM-based rubric evaluation. We evaluate the resulting rubric generators in two stages. First, on a held-out human-preference test set, the learned rubrics discriminate preferred from rejected reports more effectively than generic, prompted, or SFT-trained rubric alternatives. Second, when used as reward signals to train DeepResearch systems, our rubric generators yield substantial performance gains under both a simple single-agent ReAct framework and a complex multi-agent workflow on the DeepResearch Bench. Our code and model are available at https://huggingface.co/fdu-lcz/rubric_generator.

As LLM-based agents are increasingly used in long-term interactions, cumulative memory is critical for enabling personalization and maintaining stylistic consistency. However, most existing systems adopt an “all-or-nothing” approach to memory usage: incorporating all relevant past information can lead to Memory Anchoring, where the agent is trapped by past interactions, while excluding memory entirely results in under-utilization and the loss of important interaction history. We show that an agent’s reliance on memory can be modeled as an explicit and user-controllable dimension. We first introduce a behavioral metric of memory dependence to quantify the influence of past interactions on current outputs. We then propose Steerable Memory Agent, SteeM, a framework that allows users to dynamically regulate memory reliance, ranging from a fresh-start mode that promotes innovation to a high-fidelity mode that closely follows interaction history. Experiments across different scenarios demonstrate that our approach consistently outperforms conventional prompting and rigid memory masking strategies, yielding a more nuanced and effective control for personalized human-agent collaboration.