activity
20232026
most citedFormally Specifying the High-Level Behavior of LLM-Based Agents

2 citations · 6 across the 15 of their papers we have counts for

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Showing 2025Show all

8 papers · 1 filter

cs.CL2025

ToolRM: Outcome Reward Models for Tool-Calling Large Language Models

Mayank Agarwal, Ibrahim Abdelaziz, Kinjal Basu +4

As large language models (LLMs) increasingly interact with external tools, reward modeling for tool use has emerged as a critical yet underexplored area of research. Existing rewar…

eess.AS2025★ 1 cited

Granite-speech: open-source speech-aware LLMs with strong English ASR capabilities

George Saon, Avihu Dekel, Alexander Brooks +21

Granite-speech LLMs are compact and efficient speech language models specifically designed for English ASR and automatic speech translation (AST). The models were trained by modali…

cs.CL2025

Putting It All into Context: Simplifying Agents with LCLMs

Mingjian Jiang, Yangjun Ruan, Luis Lastras +2

Recent advances in language model (LM) agents have demonstrated significant potential for automating complex real-world tasks. To make progress on these difficult tasks, LM agent a…

cs.LG2025

Activated LoRA: Fine-tuned LLMs for Intrinsics

Kristjan Greenewald, Luis Lastras, Thomas Parnell +6

Low-Rank Adaptation (LoRA) has emerged as a highly efficient framework for finetuning the weights of large foundation models, and has become the go-to method for data-driven custom…

cs.AI2025

A Library of LLM Intrinsics for Retrieval-Augmented Generation

Marina Danilevsky, Kristjan Greenewald, Chulaka Gunasekara +13

In the developer community for large language models (LLMs), there is not yet a clean pattern analogous to a software library, to support very large scale collaboration. Even for t…

cs.IR2025

Granite Embedding Models

Parul Awasthy, Aashka Trivedi, Yulong Li +19

We introduce the Granite Embedding models, a family of encoder-based embedding models designed for retrieval tasks, spanning dense-retrieval and sparse retrieval architectures, wit…

cs.CV2025★ 1 cited

Granite Vision: a lightweight, open-source multimodal model for enterprise Intelligence

Granite Vision Team, Leonid Karlinsky, Assaf Arbelle +60

We introduce Granite Vision, a lightweight large language model with vision capabilities, specifically designed to excel in enterprise use cases, particularly in visual document un…

cs.SD2025

A Non-autoregressive Model for Joint STT and TTS

Vishal Sunder, Brian Kingsbury, George Saon +5

In this paper, we take a step towards jointly modeling automatic speech recognition (STT) and speech synthesis (TTS) in a fully non-autoregressive way. We develop a novel multimoda…