64 citations · 140 across the 13 of their papers we have counts for
13 papers
Kimi K2.5: Visual Agentic Intelligence
Kimi Team, Tongtong Bai, Yifan Bai +333
We introduce Kimi K2.5, an open-source multimodal agentic model designed to advance general agentic intelligence. K2.5 emphasizes the joint optimization of text and vision so that…
ToolOrchestra: Elevating Intelligence via Efficient Model and Tool Orchestration
Hongjin Su, Shizhe Diao, Ximing Lu +13
Large language models are powerful generalists, yet solving deep and complex problems such as those of the Humanity's Last Exam (HLE) remains both conceptually challenging and comp…
VideoAgentTrek: Computer Use Pretraining from Unlabeled Videos
Dunjie Lu, Yiheng Xu, Junli Wang +12
Training computer-use agents requires massive amounts of GUI interaction data, but manually annotating action trajectories at scale is prohibitively expensive. We present VideoAgen…
MMTEB: Massive Multilingual Text Embedding Benchmark
Kenneth Enevoldsen, Isaac Chung, Imene Kerboua +83
Text embeddings are typically evaluated on a limited set of tasks, which are constrained by language, domain, and task diversity. To address these limitations and provide a more co…
Learn-by-interact: A Data-Centric Framework for Self-Adaptive Agents in Realistic Environments
Hongjin Su, Ruoxi Sun, Jinsung Yoon +3
Autonomous agents powered by large language models (LLMs) have the potential to enhance human capabilities, assisting with digital tasks from sending emails to performing data anal…
Spider 2.0: Evaluating Language Models on Real-World Enterprise Text-to-SQL Workflows
Fangyu Lei, Jixuan Chen, Yuxiao Ye +13
Real-world enterprise text-to-SQL workflows often involve complex cloud or local data across various database systems, multiple SQL queries in various dialects, and diverse operati…
BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrieval
Hongjin Su, Howard Yen, Mengzhou Xia +12
Existing retrieval benchmarks primarily consist of information-seeking queries (e.g., aggregated questions from search engines) where keyword or semantic-based retrieval is usually…
EVOR: Evolving Retrieval for Code Generation
Hongjin Su, Shuyang Jiang, Yuhang Lai +5
Recently the retrieval-augmented generation (RAG) has been successfully applied in code generation. However, existing pipelines for retrieval-augmented code generation (RACG) emplo…
Generative Representational Instruction Tuning
Niklas Muennighoff, Hongjin Su, Liang Wang +5
All text-based language problems can be reduced to either generation or embedding. Current models only perform well at one or the other. We introduce generative representational in…
OpenAgents: An Open Platform for Language Agents in the Wild
Tianbao Xie, Fan Zhou, Zhoujun Cheng +13
Language agents show potential in being capable of utilizing natural language for varied and intricate tasks in diverse environments, particularly when built upon large language mo…
Lemur: Harmonizing Natural Language and Code for Language Agents
Yiheng Xu, Hongjin Su, Chen Xing +13
We introduce Lemur and Lemur-Chat, openly accessible language models optimized for both natural language and coding capabilities to serve as the backbone of versatile language agen…
One Embedder, Any Task: Instruction-Finetuned Text Embeddings
Hongjin Su, Weijia Shi, Jungo Kasai +7
We introduce INSTRUCTOR, a new method for computing text embeddings given task instructions: every text input is embedded together with instructions explaining the use case (e.g.,…
Selective Annotation Makes Language Models Better Few-Shot Learners
Hongjin Su, Jungo Kasai, Chen Henry Wu +8
Many recent approaches to natural language tasks are built on the remarkable abilities of large language models. Large language models can perform in-context learning, where they l…