1 citations · 3 across the 15 of their papers we have counts for
15 papers
ContinualSkillBench: Can LLM Agents Truly Evolve Their Capabilities?
Tianyi Guan, Yiding Wang, Haotong Yang +5
Modern agent frameworks equip large language models with external skill libraries to solve complex tasks. However, it remains unclear whether these systems can effectively evolve t…
BLADE: Boundary-Expanded and Layer-Adaptive Dynamic Exit for Efficient LLM Reasoning
Keshu Fu, Keqin Peng, Jun Bai +6
Large language models often improve task performance by generating long reasoning traces, but the resulting computation is frequently wasted on redundant verification and revision.…
Xetrieval: Explaining Dense Retrieval through Feature Elicitation
Zhixin Cai, Jun Bai, Yang Liu +7
Dense retrieval enables effective semantic matching, yet the semantic factors underlying its relevance scores remain opaque. Sparse autoencoders (SAEs) support feature-level explan…
$OneMillion-Bench: How Far are Language Agents from Human Experts?
Qianyu Yang, Yang Liu, Jiaqi Li +19
As language models (LMs) evolve from chat assistants to long-horizon agents capable of multi-step reasoning and tool use, existing benchmarks remain largely confined to structured…
The AI Hippocampus: How Far are We From Human Memory?
Zixia Jia, Jiaqi Li, Yipeng Kang +12
Memory plays a foundational role in augmenting the reasoning, adaptability, and contextual fidelity of modern Large Language Models and Multi-Modal LLMs. As these models transition…
Adaptive Preference Optimization with Uncertainty-aware Utility Anchor
Xiaobo Wang, Zixia Jia, Jiaqi Li +2
Offline preference optimization methods are efficient for large language models (LLMs) alignment. Direct Preference optimization (DPO)-like learning, one of the most popular approa…
TongSearch-QR: Reinforced Query Reasoning for Retrieval
Xubo Qin, Jun Bai, Jiaqi Li +2
Traditional information retrieval (IR) methods excel at textual and semantic matching but struggle in reasoning-intensive retrieval tasks that require multi-hop inference or comple…
RuleReasoner: Reinforced Rule-based Reasoning via Domain-aware Dynamic Sampling
Yang Liu, Jiaqi Li, Zilong Zheng
Rule-based reasoning is acknowledged as one of the fundamental problems of reasoning. While recent studies show that large reasoning models (LRMs) have remarkable reasoning capabil…
ReflectEvo: Improving Meta Introspection of Small LLMs by Learning Self-Reflection
Jiaqi Li, Xinyi Dong, Yang Liu +6
We present a novel pipeline, ReflectEvo, to demonstrate that small language models (SLMs) can enhance meta introspection through reflection learning. This process iteratively gener…
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…
LooGLE: Can Long-Context Language Models Understand Long Contexts?
Jiaqi Li, Mengmeng Wang, Zilong Zheng +1
Large language models (LLMs), despite their impressive performance in various language tasks, are typically limited to processing texts within context-window size. This limitation…