86 citations · 106 across the 10 of their papers we have counts for
10 papers
TurnSight: Turn-Level Hindsight Self-Distillation for Tool-Integrated Reasoning
Changle Qu, Sunhao Dai, Hengyi Cai +4
Tool-Integrated Reasoning (TIR) enables LLMs to solve complex tasks through iterative tool interactions. However, existing reinforcement learning methods often rely on trajectory-l…
KuaiLive-M3: A Multi-Modal, Multi-Domain, and Multi-Feedback Dataset for Live Streaming Recommendation
Ke Guo, Changle Qu, Jiayaqi Cheng +7
Existing public live streaming datasets suffer from three major limitations: they provide limited access to temporally evolving multimodal live content, overlook users' cross-domai…
Learning to Retrieve from Agent Trajectories
Yuqi Zhou, Sunhao Dai, Changle Qu +3
Information retrieval (IR) systems have traditionally been designed and trained for human users, with learning-to-rank methods relying heavily on large-scale human interaction logs…
MatchTIR: Fine-Grained Supervision for Tool-Integrated Reasoning via Bipartite Matching
Changle Qu, Sunhao Dai, Hengyi Cai +3
Tool-Integrated Reasoning (TIR) empowers large language models (LLMs) to tackle complex tasks by interleaving reasoning steps with external tool interactions. However, existing rei…
KuaiLive: A Real-time Interactive Dataset for Live Streaming Recommendation
Changle Qu, Sunhao Dai, Ke Guo +7
Live streaming platforms have become a dominant form of online content consumption, offering dynamically evolving content, real-time interactions, and highly engaging user experien…
Towards AI Search Paradigm
Yuchen Li, Hengyi Cai, Rui Kong +20
In this paper, we introduce the AI Search Paradigm, a comprehensive blueprint for next-generation search systems capable of emulating human information processing and decision-maki…
From Exploration to Mastery: Enabling LLMs to Master Tools via Self-Driven Interactions
Changle Qu, Sunhao Dai, Xiaochi Wei +5
Tool learning enables Large Language Models (LLMs) to interact with external environments by invoking tools, serving as an effective strategy to mitigate the limitations inherent i…
ReCODE: Modeling Repeat Consumption with Neural ODE
Sunhao Dai, Changle Qu, Sirui Chen +2
In real-world recommender systems, such as in the music domain, repeat consumption is a common phenomenon where users frequently listen to a small set of preferred songs or artists…
Tool Learning with Large Language Models: A Survey
Changle Qu, Sunhao Dai, Xiaochi Wei +5
Recently, tool learning with large language models (LLMs) has emerged as a promising paradigm for augmenting the capabilities of LLMs to tackle highly complex problems. Despite gro…
Towards Completeness-Oriented Tool Retrieval for Large Language Models
Changle Qu, Sunhao Dai, Xiaochi Wei +5
Recently, integrating external tools with Large Language Models (LLMs) has gained significant attention as an effective strategy to mitigate the limitations inherent in their pre-t…