Qiushi Sun
29 papers · 2022–2026 · 6 venues · across top CS/AI venues
Authors are matched by name, so researchers who share a name ("Yang Liu", "Wei Wang") appear as a single profile and their paper counts, venues and achievements are combined. How identity is handled.
Achievements
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π£ Hot Topic Early Bird π Interdisciplinary Bridge Β· Silver π Conference Polyglot Β· Silver πΊοΈ Taxonomy Completionist (56) π€ Dynamic Duo (10)
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Deep Specialist
(10)
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Dynamic Duo
(10)
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Renaissance Researcher
(6)
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Prolific Year Β· Prolific
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Century Club Β· Bronze
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Research topics
Artificial Intelligence
(24)
Core AI
(21)
Machine Learning
(16)
Learning Types Β· Machine Learning
(11)
Deep Learning
(10)
Natural Language Processing
(10)
Resources & Methods
(7)
Learning Types Β· Deep Learning
(6)
Optimization & Theory
(5)
Architectures
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Learning Paradigms
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Computer Science
(2)
Computer Vision
(2)
Keywords
large language model
(11)
vision-language model
(5)
code pre-trained model
(4)
abstract syntax tree
(3)
instruction tuning
(3)
transfer learning
(3)
syntax tree
(2)
uncertainty quantification
(2)
task automation
(2)
derivative-free optimization
(2)
reward model
(2)
attention mechanism
(2)
multi-task learning
(2)
reasoning chain
(2)
chain-of-thought reasoning
(2)
synthetic data generation
(2)
prompt engineering
(2)
multimodal learning
(2)
code generation
(2)
parameter-efficient learning
(2)
Papers
CodeEvo: Interaction-Driven Synthesis of Code-centric Data through Hybrid and Iterative Feedback
ACL 2026
OS-Oracle: A Comprehensive Framework for Cross-Platform GUI Critic Models
CVPR 2026
OS-Sentinel: Towards Safety-Enhanced Mobile GUI Agents via Hybrid Validation in Realistic Workflows
ACL 2026
OS-Symphony: A Holistic Framework for Robust and Generalist Computer-Using Agents
ACL 2026
AgentStore: Scalable Integration of Heterogeneous Agents As Specialized Generalist Computer Assistant
ACL 2025
CapArena: Benchmarking and Analyzing Detailed Image Captioning in the LLM Era
ACL 2025
Dynamic and Generalizable Process Reward Modeling
ACL 2025
Genius: A Generalizable and Purely Unsupervised Self-Training Framework For Advanced Reasoning
ACL 2025
Interactive Evolution: A Neural-Symbolic Self-Training Framework For Large Language Models
ACL 2025
KS-Lottery: Finding Certified Lottery Tickets for Multilingual Transfer in Large Language Models
NAACL 2025
OS-ATLAS: Foundation Action Model for Generalist GUI Agents
ICLR 2025
OS-Genesis: Automating GUI Agent Trajectory Construction via Reverse Task Synthesis
ACL 2025
Aggregation of Reasoning: A Hierarchical Framework for Enhancing Answer Selection in Large Language Models
COLING 2024
Automated Peer Reviewing in Paper SEA: Standardization, Evaluation, and Analysis
EMNLP 2024
Boosting Language Models Reasoning with Chain-of-Knowledge Prompting
ACL 2024
Explicit Memory Learning with Expectation Maximization
EMNLP 2024
Make Prompt-based Black-Box Tuning Colorful: Boosting Model Generalization from Three Orthogonal Perspectives
COLING 2024
Reasoning in Flux: Enhancing Large Language Models Reasoning through Uncertainty-aware Adaptive Guidance
ACL 2024
SeeClick: Harnessing GUI Grounding for Advanced Visual GUI Agents
ACL 2024
Structure-aware Fine-tuning for Code Pre-trained Models
COLING 2024
Symbol-LLM: Towards Foundational Symbol-centric Interface For Large Language Models
ACL 2024
TransCoder: Towards Unified Transferable Code Representation Learning Inspired by Human Skills
COLING 2024
Do Large Language Models Know What They Donβt Know?
ACL 2023
Evaluating and Enhancing the Robustness of Code Pre-trained Models through Structure-Aware Adversarial Samples Generation
EMNLP 2023
Exchange-of-Thought: Enhancing Large Language Model Capabilities through Cross-Model Communication
EMNLP 2023
Pass-Tuning: Towards Structure-Aware Parameter-Efficient Tuning for Code Representation Learning
EMNLP 2023
Uncertainty-aware Parameter-Efficient Self-training for Semi-supervised Language Understanding
EMNLP 2023
When Gradient Descent Meets Derivative-Free Optimization: A Match Made in Black-Box Scenario
ACL 2023
CAT-probing: A Metric-based Approach to Interpret How Pre-trained Models for Programming Language Attend Code Structure
EMNLP 2022