5 papers
Learning When to Act or Refuse: Guarding Agentic Reasoning Models for Safe Multi-Step Tool Use
Aradhye Agarwal, Gurdit Siyan, Yash Pandya +3
Agentic language models operate in a fundamentally different safety regime than chat models: they must plan, call tools, and execute long-horizon actions where a single misstep, su…
Think Right: Learning to Mitigate Under-Over Thinking via Adaptive, Attentive Compression
Joykirat Singh, Justin Chih-Yao Chen, Archiki Prasad +3
Recent thinking models solve complex reasoning tasks by scaling test-time compute, but this scaling must be allocated in line with task difficulty. On one hand, short reasoning (un…
Agentic Reasoning and Tool Integration for LLMs via Reinforcement Learning
Joykirat Singh, Raghav Magazine, Yash Pandya +1
Large language models (LLMs) have achieved remarkable progress in complex reasoning tasks, yet they remain fundamentally limited by their reliance on static internal knowledge and…
Self-Evolved Preference Optimization for Enhancing Mathematical Reasoning in Small Language Models
Joykirat Singh, Tanmoy Chakraborty, Akshay Nambi
Large language models (LLMs) have significantly improved their reasoning capabilities; however, they still struggle with complex multi-step mathematical problem-solving due to erro…
PromptWizard: Task-Aware Prompt Optimization Framework
Eshaan Agarwal, Joykirat Singh, Vivek Dani +3
Large language models (LLMs) have transformed AI across diverse domains, with prompting being central to their success in guiding model outputs. However, manual prompt engineering…