PPaperPicks

Amrit Singh Bedi

23 papers at tracked venues · 15 at CORE A* · active 20242026

Venues

Frequent coauthors

Papers

  1. Jailbreaks as Inference-Time Alignment: A Framework for Understanding Safety Failures in LLMs
  2. SafeR-CLIP: Mitigating NSFW Content in Vision-Language Models While Preserving Pre-Trained Knowledge
  3. Align-Pro: A Principled Approach to Prompt Optimization for LLM Alignment
  4. Bounded Rationality for LLMs: Satisficing Alignment at Inference-Time
    ICML 2025 ·
    Mohamad Fares El Hajj Chehade
  5. Confidence-Controlled Exploration: Efficient Sparse-Reward Policy Learning for Robot Navigation
  6. Does Thinking More Always Help? Mirage of Test-Time Scaling in Reasoning Models
  7. EfficientEQA: An Efficient Approach to Open-Vocabulary Embodied Question Answering
  8. Immune: Improving Safety Against Jailbreaks in Multi-modal LLMs via Inference-Time Alignment
  9. On the Global Optimality of Policy Gradient Methods in General Utility Reinforcement Learning
  10. On the Sample Complexity Bounds of Bilevel Reinforcement Learning
  11. On the Vulnerability of LLM/VLM-Controlled Robotics
  12. Uncertainty-Aware Answer Selection for Improved Reasoning in Multi-LLM Systems
  13. Closing the Gap: Achieving Global Convergence (Last Iterate) of Actor-Critic under Markovian Sampling with Neural Network Parametrization
  14. FACT or Fiction: Can Truthful Mechanisms Eliminate Federated Free Riding?
  15. LANCAR: Leveraging Language for Context-Aware Robot Locomotion in Unstructured Environments
  16. MaxMin-RLHF: Alignment with Diverse Human Preferences
  17. PARL: A Unified Framework for Policy Alignment in Reinforcement Learning from Human Feedback
  18. PIPER: Primitive-Informed Preference-based Hierarchical Reinforcement Learning via Hindsight Relabeling
  19. Position: On the Possibilities of AI-Generated Text Detection
  20. Towards Global Optimality for Practical Average Reward Reinforcement Learning without Mixing Time Oracles
  21. Transfer Q-star : Principled Decoding for LLM Alignment
  22. TrustNavGPT: Modeling Uncertainty to Improve Trustworthiness of Audio-Guided LLM-Based Robot Navigation
  23. When, What, and with Whom to Communicate: Enhancing RL-based Multi-Robot Navigation through Selective Communication