7 papers
EvoGraph-Mem: Failure-Aware Editable Graph Memory for Long-Term Language Agents
Yuxi Qian, Yuxiang Ren
Long-term memory is essential for language agents operating across extended interactions and evolving tasks. Existing memory-augmented agents mainly focus on storing and retrieving…
Mask-Proof: An LLM-based Automated Data Curation Pipeline on Mathematical Proofs
Jierui Zhang, Siyuan Tan, Xinhang Li +8
Large language models (LLMs) are increasingly capable of mathematical problem solving and can even assist with research-level proofs, yet we still lack a scalable and reproducible…
ScrapMem: A Bio-inspired Framework for On-device Personalized Agent Memory via Optical Forgetting
Jiale Chang, Yuxiang Ren
Long-term personalized memory for LLM agents is challenging on resource-limited edge devices due to high storage costs and multimodal complexity. To address this, we propose ScrapM…
DR-Eval: Towards Realistic and Reproducible Deep Research Evaluation
Qianqian Xie, Qingheng Xiong, He Zhu +16
Deep Research Agents (DRAs) aim to solve complex, long-horizon research tasks involving planning, retrieval, multimodal understanding, and report generation, yet their evaluation r…
LoTA-QAF: Lossless Ternary Adaptation for Quantization-Aware Fine-Tuning
Junyu Chen, Junzhuo Li, Zhen Peng +4
Quantization and fine-tuning are crucial for deploying large language models (LLMs) on resource-constrained edge devices. However, fine-tuning quantized models presents significant…
STORM-BORN: A Challenging Mathematical Derivations Dataset Curated via a Human-in-the-Loop Multi-Agent Framework
Wenhao Liu, Zhenyi Lu, Xinyu Hu +13
High-quality math datasets are crucial for advancing the reasoning abilities of large language models (LLMs). However, existing datasets often suffer from three key issues: outdate…