Papers by Silvio Savarese
LAVIS: A One-stop Library for Language-Vision Intelligence (2023.acl-demo)
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| Challenge: | a new open-source library for language-vision research and applications is available for free. |
| Approach: | They introduce LAVIS, an open-source deep learning library for LAnguage-VISion research and applications. |
| Outcome: | The proposed library is open-source and highly extensible and configurable. |
ActionStudio: A Lightweight Framework for Data and Training of Large Action Models (2025.emnlp-main)
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Jianguo Zhang, Thai Quoc Hoang, Ming Zhu, Zuxin Liu, Shiyu Wang, Tulika Manoj Awalgaonkar, Akshara Prabhakar, Haolin Chen, Weiran Yao, Zhiwei Liu, Juntao Tan, Juan Carlos Niebles, Shelby Heinecke, Huan Wang, Silvio Savarese, Caiming Xiong
| Challenge: | Existing infrastructure for efficient agentic data processing and model training remains underdeveloped. |
| Approach: | They propose a lightweight and extensible data and training framework for large action models . they propose to unify diverse agent trajectories using Unified Format 2.0 . |
| Outcome: | The proposed framework shows 9 higher throughput than existing frameworks and performs well across public and realistic agent benchmarks. |
PersonaBench: Evaluating AI Models on Understanding Personal Information through Accessing (Synthetic) Private User Data (2025.findings-acl)
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Juntao Tan, Liangwei Yang, Zuxin Liu, Zhiwei Liu, Rithesh R N, Tulika Manoj Awalgaonkar, Jianguo Zhang, Weiran Yao, Ming Zhu, Shirley Kokane, Silvio Savarese, Huan Wang, Caiming Xiong, Shelby Heinecke
| Challenge: | Existing research lacks direct access to such data, making benchmarking difficult due to privacy concerns. |
| Approach: | They propose a synthetic data pipeline that generates realistic user profiles and private documents and a benchmark to evaluate models' ability to understand personal information. |
| Outcome: | The proposed pipeline generates realistic user profiles and private documents, enabling PersonaBench, a benchmark for evaluating models’ ability to understand personal information. |
Translating Navigation Instructions in Natural Language to a High-Level Plan for Behavioral Robot Navigation (D18-1)
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Xiaoxue Zang, Ashwini Pokle, Marynel Vázquez, Kevin Chen, Juan Carlos Niebles, Alvaro Soto, Silvio Savarese
| Challenge: | Existing models for translating free-form natural language instructions to a high-level plan for behavioral robot navigation are difficult due to the variability in the way people describe routes. |
| Approach: | They propose an end-to-end deep learning model for translating free-form natural language instructions to a high-level plan for robot navigation. |
| Outcome: | The proposed model significantly outperforms baseline approaches on a new dataset containing 10,050 pairs of navigation instructions. |
CodeTree: Agent-guided Tree Search for Code Generation with Large Language Models (2025.naacl-long)
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| Challenge: | coding tasks require generated code to be fully executable and functionally correct . current agentic approaches struggle with multi-stage planning, generating, and debugging . |
| Approach: | They propose a framework for LLM agents to efficiently explore the search space in different stages of the code generation process. |
| Outcome: | The proposed framework achieves top results on 7 code generation benchmarks and a 31.9% solving rate on the SWEBench benchmark. |
Best-k Search Algorithm for Neural Text Generation (2023.acl-long)
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| Challenge: | Modern natural language generation paradigms require a decoding strategy to obtain quality sequences out of the model. |
| Approach: | They propose a deterministic search algorithm balancing quality and diversity . they investigate the vanilla best-first search algorithm and propose k-k search algorithm. |
| Outcome: | The proposed algorithm is parameter-free, lightweight, efficient, and easy-to-use. |
DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AI (2024.findings-eacl)
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Jianguo Zhang, Kun Qian, Zhiwei Liu, Shelby Heinecke, Rui Meng, Ye Liu, Zhou Yu, Huan Wang, Silvio Savarese, Caiming Xiong
| Challenge: | DialogStudio is the largest and most diverse collection of dialogue datasets . existing datasets lack diversity and comprehensiveness, authors say . |
| Approach: | They introduce DialogStudio: the largest and most diverse collection of dialogue datasets . DialogStuio aggregates more than 80 diverse dialogue dataset . |
| Outcome: | a new dataset is created to improve the quality and diversity of dialogue datasets . DialogStudio is the largest and most diverse collection of dialogue data . |
LAM SIMULATOR: Advancing Data Generation for Large Action Model Training via Online Exploration and Trajectory Feedback (2025.findings-acl)
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Thai Quoc Hoang, Kung-Hsiang Huang, Shirley Kokane, Jianguo Zhang, Zuxin Liu, Ming Zhu, Jake Grigsby, Tian Lan, Michael S Ryoo, Chien-Sheng Wu, Shelby Heinecke, Huan Wang, Silvio Savarese, Caiming Xiong, Juan Carlos Niebles
| Challenge: | Large Action Models (LAMs) face challenges due to the need for high-quality training data, especially for multi-steps tasks that involve planning, executing tool calls, and responding to feedback. |
| Approach: | They propose a framework for online exploration of agentic tasks with high-quality feedback . they use a dynamic task query generator and an extensive collection of tools to create a high-level feedback environment for LLM Agents. |
| Outcome: | The proposed framework achieves 49.3% performance improvement over baselines on toolbench and CRMArena. |
Contra4: Evaluating Contrastive Cross-Modal Reasoning in Audio, Video, Image, and 3D (2025.emnlp-main)
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Artemis Panagopoulou, Le Xue, Honglu Zhou, Silvio Savarese, Ran Xu, Caiming Xiong, Chris Callison-Burch, Mark Yatskar, Juan Carlos Niebles
| Challenge: | a recent study shows that multimodal models can reason across multiple modalities . a limited number of models are able to reason across a variety of inputs . |
| Approach: | They propose a dataset for contrastive cross-modal reasoning across four modalities . they use human annotations and a mixture-of-models round-trip-consistency filter . |
| Outcome: | a new model evaluates models on multiple modalities to determine which one best answers a natural language prompt . the model must select the one that best satisfies the query and then fine-tune it . state-of-the-art models still achieve only 56% accuracy overall and 42% in four-modal settings . |
LATTE: Learning to Think with Vision Specialists (2025.emnlp-main)
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Zixian Ma, Jianguo Zhang, Zhiwei Liu, Jieyu Zhang, Juntao Tan, Manli Shu, Juan Carlos Niebles, Shelby Heinecke, Huan Wang, Caiming Xiong, Ranjay Krishna, Silvio Savarese
| Challenge: | Open-source vision-language models excel on simple question-answering tasks, but struggle with complex questions that require both perception and reasoning. |
| Approach: | They propose a family of vision-language models that have LeArned to Think wiTh vision spEcialists by offloading perception to state-of-the-art vision models. |
| Outcome: | The proposed model achieves 4-5% gains over baselines across 6 benchmarks covering both perception and reasoning abilities. |
MCPEval: Automatic MCP-based Deep Evaluation for AI Agent Models (2025.emnlp-demos)
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Zhiwei Liu, Jielin Qiu, Shiyu Wang, Jianguo Zhang, Zuxin Liu, Roshan Ram, Haolin Chen, Weiran Yao, Shelby Heinecke, Silvio Savarese, Huan Wang, Caiming Xiong
| Challenge: | Existing evaluation frameworks suffer from limitations such as static task benchmarks, limited scope, and inadequate integration with practical applications. |
| Approach: | They propose an open-source, Model Context Protocol-based evaluation framework specifically tailored for comprehensive and systematic assessment of LLM-powered agents. |
| Outcome: | The proposed framework uncovers nuanced performance patterns and identify domain-specific strengths and weaknesses, providing valuable insights beyond traditional binary success metrics. |
CRMArena: Understanding the Capacity of LLM Agents to Perform Professional CRM Tasks in Realistic Environments (2025.naacl-long)
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Kung-Hsiang Huang, Akshara Prabhakar, Sidharth Dhawan, Yixin Mao, Huan Wang, Silvio Savarese, Caiming Xiong, Philippe Laban, Chien-Sheng Wu
| Challenge: | Existing benchmarks for evaluating CRM agents on work-related tasks are limited due to data privacy concerns. |
| Approach: | They propose a benchmark to evaluate AI agents on real-world CRM tasks . they use 16 commonly used industrial objects with high interconnectivity to simulate real data distributions. |
| Outcome: | The new benchmark evaluates AI agents on real-world customer service tasks . it includes 16 commonly used industrial objects with high interconnectivity . the results highlight the need for enhanced agent capabilities in function-calling and rule-following . |
Long Document Summarization with Top-down and Bottom-up Inference (2023.findings-eacl)
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| Challenge: | Recent models infer latent representations of words or tokens with a transformer encoder, which is bottom-up and thus does not capture long-distance context well. |
| Approach: | They propose a method to infer latent representations of words or tokens in documents . they assume a hierarchical structure of a document where top-level captures long range dependency . |
| Outcome: | The proposed model can summarize an entire book and achieve competitive performance on a wide range of document summarization benchmarks. |
xLAM: A Family of Large Action Models to Empower AI Agent Systems (2025.naacl-long)
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Jianguo Zhang, Tian Lan, Ming Zhu, Zuxin Liu, Thai Quoc Hoang, Shirley Kokane, Weiran Yao, Juntao Tan, Akshara Prabhakar, Haolin Chen, Zhiwei Liu, Yihao Feng, Tulika Manoj Awalgaonkar, Rithesh R N, Zeyuan Chen, Ran Xu, Juan Carlos Niebles, Shelby Heinecke, Huan Wang, Silvio Savarese, Caiming Xiong
| Challenge: | Autonomous agents powered by large language models (LLMs) have attracted significant research interest, but there are few standards for developing specialized models for agent tasks. |
| Approach: | They propose a series of large action models with dense and mixture-of-expert architectures that unifies, augments, and synthesizes diverse datasets to enhance agent generalizability and performance. |
| Outcome: | The proposed models outperform GPT-4, Claude-3, and many other models in terms of tool use and outperformed GPT-based models on multiple agent ability benchmarks. |
Plug-and-Play VQA: Zero-shot VQA by Conjoining Large Pretrained Models with Zero Training (2022.findings-emnlp)
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| Challenge: | Existing approaches require substantial adaptation of pretrained language models for vision-language reasoning tasks. |
| Approach: | They propose to use natural language and network interpretation as an intermediate representation that glues pretrained models together. |
| Outcome: | The proposed framework outperforms the Flamingo model on VQAv2 and GQA by 8.5%. |
SlackAgents: Scalable Collaboration of AI Agents in Workspaces (2025.emnlp-demos)
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Zhiwei Liu, Weiran Yao, Zuxin Liu, Juntao Tan, Jianguo Zhang, Frank Wang, Sukhandeep Nahal, Huan Wang, Shelby Heinecke, Silvio Savarese, Caiming Xiong
| Challenge: | Existing open-source frameworks like LangChain and LlamaIndex fail to integrate into daily workflows, resulting in limited daily usage for work. |
| Approach: | They propose a multi-agent library for scalable management and collaboration of AI agents on Slack. |
| Outcome: | The proposed framework offers instant AI integration into organizational workflows and facilitates scalable collaboration, allowing for effective communication and task orchestration. |