1 citations · 1 across the 6 of their papers we have counts for
8 papers
Enhancing Long-Video VLM Embeddings with Query-Aware Streaming Latent Reasoning
Haozhe Chi, Song Jin, Yang Jin +1
Long-video embedding requires capturing sparse query-relevant evidence under a limited visual-token budget. Uniform sampling can miss brief events in videos spanning minutes or hou…
Learning from Repaired Reasoning: Root-Cause-Guided On-Policy Distillation
Chenglei Shen, Haoyang Yao, Weijie Yu +3
On-policy self-distillation (OPSD) uses reference solutions as privileged hindsight to supervise student-generated reasoning trajectories. However, reference-based guidance may exp…
Allocate Before You Embed: Adaptive Visual Input Allocation for Video Embeddings
Song Jin, Zhongtao Jiang, Chenglei Shen +5
Large-scale video retrieval requires embedding models to encode long and diverse videos under tight visual-input and inference budgets. Existing methods typically sample a small, f…
FinRpt: Dataset, Evaluation System and LLM-based Multi-agent Framework for Equity Research Report Generation
Song Jin, Shuqi Li, Shukun Zhang +1
While LLMs have shown great success in financial tasks like stock prediction and question answering, their application in fully automating Equity Research Report generation remains…
ViPER: Empowering the Self-Evolution of Visual Perception Abilities in Vision-Language Model
Juntian Zhang, Song Jin, Chuanqi Cheng +8
The limited capacity for fine-grained visual perception presents a critical bottleneck for Vision-Language Models (VLMs) in real-world applications. Addressing this is challenging…
TagPR: Tag-Guided Process Supervision for Personalization Reasoning in Large Language Models
Song Jin, Juntian Zhang, Ruyu Lyu +7
Recent advancements have endowed Large Language Models with impressive general reasoning capabilities. However, these reasoning models often perform worse than non-reasoning models…
Beyond Static Testbeds: An Interaction-Centric Agent Simulation Platform for Dynamic Recommender Systems
Song Jin, Juntian Zhang, Yuhan Liu +6
Evaluating and iterating upon recommender systems is crucial, yet traditional A/B testing is resource-intensive, and offline methods struggle with dynamic user-platform interaction…
PEER: Expertizing Domain-Specific Tasks with a Multi-Agent Framework and Tuning Methods
Yiying Wang, Xiaojing Li, Binzhu Wang +10
In domain-specific applications, GPT-4, augmented with precise prompts or Retrieval-Augmented Generation (RAG), shows notable potential but faces the critical tri-lemma of performa…