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Tao Feng

10 papers hereh-index 453 citations12 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author10

Across the 10 of 10 papers where every author was matched, so the position is known.

fields
  • cs.LG4
  • cs.CL3
  • cs.AI1
  • cs.IR1
  • cs.SD1
same name
  • Tao Feng — 19 papers, h 13
  • Tao Feng — 15 papers, h 16
  • Tao Feng — 11 papers, h 14
  • Tao Feng — 11 papers, h 6
  • Tao Feng — 11 papers, h 5
  • Tao Feng — 10 papers, h 3

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

collaborators

5 papers

cs.IR2026

UniRec: Unified Multimodal Encoding for LLM-Based Recommendations

Zijie Lei, Tao Feng, Zhigang Hua +5

Large language models have recently shown promise for multimodal recommendation, particularly with text and image inputs. Yet real-world recommendation signals extend far beyond th…

cs.CL2025

ResearchArcade: Graph Interface for Academic Tasks

Jingjun Xu, Chongshan Lin, Haofei Yu +2

Academic research generates diverse data sources, and as researchers increasingly use machine learning to assist research tasks, a crucial question arises: Can we build a unified d…

cs.LG2025

PersonalizedRouter: Personalized LLM Routing via Graph-based User Preference Modeling

Zhongjie Dai, Tao Feng, Jiaxuan You

The growing number of Large Language Models (LLMs) with diverse capabilities and response styles provides users with a wider range of choices, which presents challenges in selectin…

cs.AI2025

Multi-Agent Evolve: LLM Self-Improve through Co-evolution

Yixing Chen, Yiding Wang, Siqi Zhu +5

Reinforcement Learning (RL) has demonstrated significant potential in enhancing the reasoning capabilities of large language models (LLMs). However, the success of RL for LLMs heav…

cs.CL2025

AcademicEval: Live Long-Context LLM Benchmark

Haozhen Zhang, Tao Feng, Pengrui Han +1

Large Language Models (LLMs) have recently achieved remarkable performance in long-context understanding. However, current long-context LLM benchmarks are limited by rigid context…

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