6 papers
Scaling Agentic Capabilities, Not Context: Efficient Reinforcement Finetuning for Large Toolspaces
Karan Gupta, Pranav Vajreshwari, Yash Pandya +3
Agentic systems operating over large tool ecosystems must plan and execute long-horizon workflows under weak or non-verifiable supervision. While frontier models mitigate these cha…
Fara-7B: An Efficient Agentic Model for Computer Use
Ahmed Awadallah, Yash Lara, Raghav Magazine +9
Progress in computer use agents (CUAs) has been constrained by the absence of large and high-quality datasets that capture how humans interact with a computer. While LLMs have thri…
Adaptive LLM Routing under Budget Constraints
Pranoy Panda, Raghav Magazine, Chaitanya Devaguptapu +2
Large Language Models (LLMs) have revolutionized natural language processing, but their varying capabilities and costs pose challenges in practical applications. LLM routing addres…
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…
eARCO: Efficient Automated Root Cause Analysis with Prompt Optimization
Drishti Goel, Raghav Magazine, Supriyo Ghosh +5
Root cause analysis (RCA) for incidents in large-scale cloud systems is a complex, knowledge-intensive task that often requires significant manual effort from on-call engineers (OC…
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…