Saved in:
| Main Authors: | Ho, Sy-Tuyen, Liu, Minghui, Nghiem, Huy, Huang, Furong |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.30329 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
SoundnessBench: A Soundness Benchmark for Neural Network Verifiers
by: Zhou, Xingjian, et al.
Published: (2024)
by: Zhou, Xingjian, et al.
Published: (2024)
Agentic Critical Training
by: Liu, Weize, et al.
Published: (2026)
by: Liu, Weize, et al.
Published: (2026)
Imitate the Good and Avoid the Bad: An Incremental Approach to Safe Reinforcement Learning
by: Hoang, Huy, et al.
Published: (2023)
by: Hoang, Huy, et al.
Published: (2023)
MJ-Bench: Is Your Multimodal Reward Model Really a Good Judge for Text-to-Image Generation?
by: Chen, Zhaorun, et al.
Published: (2024)
by: Chen, Zhaorun, et al.
Published: (2024)
VeriGate: Verifier-Gated Step-Level Supervision for GRPO
by: Agrawal, Aakriti, et al.
Published: (2026)
by: Agrawal, Aakriti, et al.
Published: (2026)
Model Inversion Robustness: Can Transfer Learning Help?
by: Ho, Sy-Tuyen, et al.
Published: (2024)
by: Ho, Sy-Tuyen, et al.
Published: (2024)
Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights
by: Ho, Sy-Tuyen, et al.
Published: (2025)
by: Ho, Sy-Tuyen, et al.
Published: (2025)
Do Vision-Language Models Leak What They Learn? Adaptive Token-Weighted Model Inversion Attacks
by: Nguyen, Ngoc-Bao, et al.
Published: (2025)
by: Nguyen, Ngoc-Bao, et al.
Published: (2025)
Bias in the Tails: How Name-conditioned Evaluative Framing in Resume Summaries Destabilizes LLM-based Hiring
by: Nghiem, Huy, et al.
Published: (2026)
by: Nghiem, Huy, et al.
Published: (2026)
AgentCollabBench: Diagnosing When Good Agents Make Bad Collaborators
by: Mazumder, Aritra, et al.
Published: (2026)
by: Mazumder, Aritra, et al.
Published: (2026)
Revisiting Model Inversion Evaluation: From Misleading Standards to Reliable Privacy Assessment
by: Ho, Sy-Tuyen, et al.
Published: (2025)
by: Ho, Sy-Tuyen, et al.
Published: (2025)
Hypernetwork-Driven Low-Rank Adaptation Across Attention Heads
by: Diep, Nghiem T., et al.
Published: (2025)
by: Diep, Nghiem T., et al.
Published: (2025)
Good Allocations from Bad Estimates
by: Casacuberta, Sílvia, et al.
Published: (2026)
by: Casacuberta, Sílvia, et al.
Published: (2026)
DoRAN: Stabilizing Weight-Decomposed Low-Rank Adaptation via Noise Injection and Auxiliary Networks
by: Diep, Nghiem T., et al.
Published: (2025)
by: Diep, Nghiem T., et al.
Published: (2025)
SmartBench: Is Your LLM Truly a Good Chinese Smartphone Assistant?
by: Lu, Xudong, et al.
Published: (2025)
by: Lu, Xudong, et al.
Published: (2025)
Vertical Federated Learning in Practice: The Good, the Bad, and the Ugly
by: Wu, Zhaomin, et al.
Published: (2025)
by: Wu, Zhaomin, et al.
Published: (2025)
When Bad Data Leads to Good Models
by: Li, Kenneth, et al.
Published: (2025)
by: Li, Kenneth, et al.
Published: (2025)
Know Your Scientist: KYC as Biosecurity Infrastructure
by: Feldman, Jonathan, et al.
Published: (2026)
by: Feldman, Jonathan, et al.
Published: (2026)
Do Two AI Scientists Agree?
by: Fu, Xinghong, et al.
Published: (2025)
by: Fu, Xinghong, et al.
Published: (2025)
Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect
by: Tang, Kaihua, et al.
Published: (2020)
by: Tang, Kaihua, et al.
Published: (2020)
Agent Performing Autonomous Stock Trading under Good and Bad Situations
by: Luo, Yunfei, et al.
Published: (2023)
by: Luo, Yunfei, et al.
Published: (2023)
Sequence-Aware Inline Measurement Attribution for Good-Bad Wafer Diagnosis
by: Miyaguchi, Kohei, et al.
Published: (2025)
by: Miyaguchi, Kohei, et al.
Published: (2025)
Are Synthetic Time-series Data Really not as Good as Real Data?
by: Fu, Fanzhe, et al.
Published: (2024)
by: Fu, Fanzhe, et al.
Published: (2024)
Beware! The AI Act Can Also Apply to Your AI Research Practices
by: Wernick, Alina, et al.
Published: (2025)
by: Wernick, Alina, et al.
Published: (2025)
The Good, the Bad, and the Ugly: The Role of AI Quality Disclosure in Lie Detection
by: Bhattacharya, Haimanti, et al.
Published: (2024)
by: Bhattacharya, Haimanti, et al.
Published: (2024)
Idea2Plan: Exploring AI-Powered Research Planning
by: Huang, Jin, et al.
Published: (2025)
by: Huang, Jin, et al.
Published: (2025)
FADE: Why Bad Descriptions Happen to Good Features
by: Puri, Bruno, et al.
Published: (2025)
by: Puri, Bruno, et al.
Published: (2025)
Tell Your Model Where to Attend: Post-hoc Attention Steering for LLMs
by: Zhang, Qingru, et al.
Published: (2023)
by: Zhang, Qingru, et al.
Published: (2023)
On Zero-Initialized Attention: Optimal Prompt and Gating Factor Estimation
by: Diep, Nghiem T., et al.
Published: (2025)
by: Diep, Nghiem T., et al.
Published: (2025)
The Good, The Bad, and The Hybrid: A Reward Structure Showdown in Reasoning Models Training
by: Sahoo, Subramanyam
Published: (2025)
by: Sahoo, Subramanyam
Published: (2025)
Ambient Diffusion Omni: Training Good Models with Bad Data
by: Daras, Giannis, et al.
Published: (2025)
by: Daras, Giannis, et al.
Published: (2025)
The AI Data Scientist
by: Akimov, Farkhad, et al.
Published: (2025)
by: Akimov, Farkhad, et al.
Published: (2025)
Bad Values but Good Behavior: Learning Highly Misspecified Bandits and MDPs
by: Banerjee, Debangshu, et al.
Published: (2023)
by: Banerjee, Debangshu, et al.
Published: (2023)
Superintelligent Agents Pose Catastrophic Risks: Can Scientist AI Offer a Safer Path?
by: Bengio, Yoshua, et al.
Published: (2025)
by: Bengio, Yoshua, et al.
Published: (2025)
Conformalized Exceptional Model Mining: Telling Where Your Model Performs (Not) Well
by: Du, Xin, et al.
Published: (2025)
by: Du, Xin, et al.
Published: (2025)
Can AI Scientist Agents Learn from Lab-in-the-Loop Feedback? Evidence from Iterative Perturbation Discovery
by: Wainrib, Gilles, et al.
Published: (2026)
by: Wainrib, Gilles, et al.
Published: (2026)
GUIDE: Towards Scalable Advising for Research Ideas
by: Liu, Yaowenqi, et al.
Published: (2025)
by: Liu, Yaowenqi, et al.
Published: (2025)
Towards a Medical AI Scientist
by: Wu, Hongtao, et al.
Published: (2026)
by: Wu, Hongtao, et al.
Published: (2026)
When Good Equations Get Bad Scores: Improving Symbolic Regression Through Better Parameter Optimization
by: Wang, Boxiao, et al.
Published: (2026)
by: Wang, Boxiao, et al.
Published: (2026)
Simulation, Modelling and Classification of Wiki Contributors: Spotting The Good, The Bad, and The Ugly
by: Méndez, Silvia García, et al.
Published: (2024)
by: Méndez, Silvia García, et al.
Published: (2024)
Similar Items
-
SoundnessBench: A Soundness Benchmark for Neural Network Verifiers
by: Zhou, Xingjian, et al.
Published: (2024) -
Agentic Critical Training
by: Liu, Weize, et al.
Published: (2026) -
Imitate the Good and Avoid the Bad: An Incremental Approach to Safe Reinforcement Learning
by: Hoang, Huy, et al.
Published: (2023) -
MJ-Bench: Is Your Multimodal Reward Model Really a Good Judge for Text-to-Image Generation?
by: Chen, Zhaorun, et al.
Published: (2024) -
VeriGate: Verifier-Gated Step-Level Supervision for GRPO
by: Agrawal, Aakriti, et al.
Published: (2026)