6 citations · 7 across the 8 of their papers we have counts for
9 papers
Beneath the Surface: What We Overlook When We Talk About Agent Evolution
Xiaobo Wang, Zimu Wang, Siyu Ye +13
Agent harness evolution enables recursive self-improvement (RSI) without updating model weights. We examine whether higher benchmark scores yield reliable, efficient, and transfera…
UniCode: Augmenting Evaluation for Code Reasoning
Xinyue Zheng, Haowei Lin, Shaofei Cai +3
Current coding benchmarks often inflate Large Language Model (LLM) capabilities due to static paradigms and data contamination, enabling models to exploit statistical shortcuts rat…
When Large Multimodal Models Confront Evolving Knowledge: Challenges and Explorations
Kailin Jiang, Yuntao Du, Yukai Ding +7
Large Multimodal Models (LMMs) store vast amounts of pretrained knowledge but struggle to remain aligned with real-world updates, making it difficult to avoid capability degradatio…
LIFT: A Novel Framework for Enhancing Long-Context Understanding of LLMs via Long Input Fine-Tuning
Yansheng Mao, Yufei Xu, Jiaqi Li +5
Long-context understanding remains challenging for LLMs due to limited context windows. This paper introduces Long Input Fine-Tuning (LIFT), a framework that improves the long-cont…
LIFT: Improving Long Context Understanding Through Long Input Fine-Tuning
Yansheng Mao, Jiaqi Li, Fanxu Meng +3
Long context understanding remains challenging for large language models due to their limited context windows. This paper introduces Long Input Fine-Tuning (LIFT) for long context…
Mars: Situated Inductive Reasoning in an Open-World Environment
Xiaojuan Tang, Jiaqi Li, Yitao Liang +3
Large Language Models (LLMs) trained on massive corpora have shown remarkable success in knowledge-intensive tasks. Yet, most of them rely on pre-stored knowledge. Inducing new gen…
In-Context Editing: Learning Knowledge from Self-Induced Distributions
Siyuan Qi, Bangcheng Yang, Kailin Jiang +5
In scenarios where language models must incorporate new information efficiently without extensive retraining, traditional fine-tuning methods are prone to overfitting, degraded gen…
RAM: Towards an Ever-Improving Memory System by Learning from Communications
Jiaqi Li, Xiaobo Wang, Wentao Ding +4
We introduce an innovative RAG-based framework with an ever-improving memory. Inspired by humans'pedagogical process, RAM utilizes recursively reasoning-based retrieval and experie…
JARVIS-1: Open-World Multi-task Agents with Memory-Augmented Multimodal Language Models
Zihao Wang, Shaofei Cai, Anji Liu +9
Achieving human-like planning and control with multimodal observations in an open world is a key milestone for more functional generalist agents. Existing approaches can handle cer…