{"schema_version":"onlylabs.public_analysis_evidence.v1","title":"Meituan (LongCat) analysis evidence pack","description":"Public onlylabs evidence pack for cited agent analysis: captured pages, ranked public signals, and stored web-search provenance used by the background analysis workflow.","url":"https://onlylabs.fyi/labs/meituan","json_url":"https://onlylabs.fyi/analysis/meituan/evidence.json","generated_at":"2026-06-13T13:56:38.520Z","org":{"slug":"meituan","name":"Meituan (LongCat)","category":"neolab","category_label":"Neolab","dossier_url":"https://onlylabs.fyi/labs/meituan"},"analysis":null,"workflow":{"version":"onlylabs-deepagents-analysis-v3","provider":null,"model":null,"agent":null,"public_pack_mode":"local-pages-and-events","live_web_fetches":false,"note":"Public evidence exports do not trigger live Exa calls; stored Exa provenance is included when analysis metadata contains it."},"stats":{"pages":28,"events":52,"web":0,"evidence":80,"signal_desks":{"hiring":0,"forks":6,"releases":18,"talking":0,"repos":28},"data_radar_lanes":null,"data_radar_matches":null,"stored_analysis_evidence":null,"stored_analysis_web":null,"stored_analysis_signal_desks":null,"stored_analysis_data_radar_lanes":null,"stored_analysis_data_radar_matches":null},"stored_web_provenance":null,"evidence":[{"ref":"P1","kind":"page","title":"meituan-longcat/MineExplorer repository metadata","date":"2026-06-13T08:03:26.541332+00:00","date_source":null,"source_url":"https://github.com/meituan-longcat/MineExplorer","signal_url":null,"signal_json_url":null,"text":"# meituan-longcat/MineExplorer\n\nDescription: Reproduction code for paper \"MineExplorer: Evaluating Open-World Exploration of MLLM Agents in Minecraft\"\n\nLanguage: Python\n\nStars: 11\n\nForks: 0\n\nOpen issues: 1\n\nCreated: 2026-05-29T05:07:05Z\n\nPushed: 2026-06-12T05:11:39Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n
\n\"LongCat\n
\n\n# MineExplorer: Evaluating Open-World Exploration of MLLM Agents in Minecraft\n\n
\n\"LongCat\n
\n\n[![arXiv](https://img.shields.io/badge/arXiv-2605.30931-b31b1b.svg)](https://arxiv.org/abs/2605.30931)\n\nMineExplorer is a benchmark for evaluating the open-world exploration capabilities of multimodal large language model (MLLM) agents in Minecraft. We first filter atomic tasks whose solutions rely heavily on Minecraft-specific knowledge to better reflect general open-world reasoning, then organize the benchmark around a ReAct-style capability formulation and compose atomic tasks into implicit multi-hop tasks. To construct reliable instances, MineExplorer uses a multi-agent synthesis workflow that jointly designs task graphs, sandbox scenes, and rule-based milestone evaluators. Experiments show that open-world exploration remains challenging: strong models handle many single-hop tasks but degrade sharply when hidden prerequisites must be coordinated over longer trajectories, and larger models or thinking modes do not consistently translate into better performance.\n\n---\n\n## 1. Minecraft Sandbox Environment\n\nMineExplorer uses a Minecraft sandbox service built on top of [MineStudio](https://github.com/CraftJarvis/MineStudio), an open-source framework that provides a Minecraft simulator engine controllable via HTTP API. The sandbox allows you to programmatically create and control Minecraft game environments — spawning scenes, issuing commands, resetting episodes, and capturing first-person screenshots.\n\nWe release a ready-to-use Docker ima"},{"ref":"P2","kind":"page","title":"meituan-longcat/Meeseeks repository metadata","date":"2026-06-11T03:22:46.94834+00:00","date_source":null,"source_url":"https://github.com/meituan-longcat/Meeseeks","signal_url":null,"signal_json_url":null,"text":"# meituan-longcat/Meeseeks\n\nDescription: A iterative feedback driven benchmark on LLM's instruction following ability\n\nLanguage: Python\n\nStars: 57\n\nForks: 6\n\nOpen issues: 1\n\nCreated: 2025-05-09T08:44:50Z\n\nPushed: 2026-05-25T09:06:26Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n
\n\n# 👑 Meeseeks Benchmark\n\n\"Meeseeks\n\n
\n\n---\n\n## 🚀 Latest News\n\nWe officially released the multilingual version of Meeseeks!\n\n## 📋 Previous Versions\n\nTemporarily removed for arr submitting\n\n## 📖 Introduction\n**Meeseeks** is an **instruction-following benchmark** designed to evaluate how well models can adhere to user instructions in a **multi-turn scenario**. \nA key feature of Meeseeks is its **self-correction loop**, where models receive structured feedback and must refine their responses accordingly. \n\nThis benchmark provides a realistic evaluation of a model’s **adaptability, instruction adherence, and iterative improvement**.\n\n---\n\n## 📊 Leaderboard\n![leaderboard](leaderboard.svg)\n\n---\n\n## 🍄‍🟫 A Quick Example\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
ROUND1-InputEvaluation ContentCapability tags
Generate 32 colloquial user comments and 40 formal user comments from a consumer perspective in short video comment sections. Each comment should be exactly 7 characters long and must not contain the following words:[\"this\", \"good\", \"that\"]Whether 32 colloquial user comments were generatedElement number requirement
Whether 40 formal user c"},{"ref":"P3","kind":"page","title":"meituan-longcat/LongCat-Flash-Thinking repository metadata","date":"2026-06-11T03:22:46.202324+00:00","date_source":null,"source_url":"https://github.com/meituan-longcat/LongCat-Flash-Thinking","signal_url":null,"signal_json_url":null,"text":"# meituan-longcat/LongCat-Flash-Thinking\n\nLicense: MIT\n\nStars: 285\n\nForks: 29\n\nOpen issues: 2\n\nCreated: 2025-09-21T12:28:03Z\n\nPushed: 2026-05-13T02:23:47Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# LongCat-Flash-Thinking\n\n
\n\"LongCat-Flash\"\n
\n
\n\n
\n\n\"Chat\"\n\n\n\"Hugging\n\n
\n\n
\n\n\"Wechat\"\n\n\n\n\n\n\"Twitter\n\n
\n\n
\n\n\"License\"\n\n
\n\n

\nTech Report 📄\n

\n\n## Model Introduction\n\nWe introduce and release **LongCat-Flash-Thinking**, which is a powerful and efficient large reasoning model (LRM) with 560 "},{"ref":"P4","kind":"page","title":"meituan-longcat/LongCat-Flash-Chat repository metadata","date":"2026-06-11T03:22:46.195533+00:00","date_source":null,"source_url":"https://github.com/meituan-longcat/LongCat-Flash-Chat","signal_url":null,"signal_json_url":null,"text":"# meituan-longcat/LongCat-Flash-Chat\n\nLicense: MIT\n\nStars: 1339\n\nForks: 67\n\nOpen issues: 13\n\nCreated: 2025-08-30T16:00:04Z\n\nPushed: 2026-06-09T02:08:37Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# LongCat-Flash-Chat\n\n
\n\"LongCat-Flash\"\n
\n
\n\n
\n\n\"Chat\"\n\n\n\"Hugging\n\n
\n\n
\n\n\"Wechat\"\n\n\n\n\n\n\"Twitter\n\n
\n\n
\n\n\"License\"\n\n
\n\n

\nTech Report 📄\n

\n\n## Model Introduction\nWe introduce LongCat-Flash, a powerful and efficient language model with 560 billion total parameters, featuring an innovative Mixture-of-E"},{"ref":"P5","kind":"page","title":"meituan-longcat/LongCat-Audio-Codec repository metadata","date":"2026-06-11T03:22:46.185361+00:00","date_source":null,"source_url":"https://github.com/meituan-longcat/LongCat-Audio-Codec","signal_url":null,"signal_json_url":null,"text":"# meituan-longcat/LongCat-Audio-Codec\n\nDescription: LongCat Audio Tokenizer and Detokenizer\n\nLanguage: Python\n\nLicense: MIT\n\nStars: 301\n\nForks: 23\n\nOpen issues: 1\n\nCreated: 2025-10-16T12:06:05Z\n\nPushed: 2026-05-09T10:22:27Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n
\n\"LongCat-Flash\"\n
\n\n
\n

LongCat-Audio-Codec: An Audio Tokenizer and Detokenizer Solution Designed for Speech Large Language Models

\n
\n
\n\n
\n\n\"Technical\n\n\n\n\"Hugging\n\n
\n\n
\n\n\"Wechat\"\n\n\n\n\n\n\"Twitter\n\n
\n\n
\n\n\"License\"\n\n
\n\nLongCat-Audio-Codec is an"},{"ref":"P6","kind":"page","title":"meituan-longcat/SOP-Maze repository metadata","date":"2026-06-11T03:22:46.172184+00:00","date_source":null,"source_url":"https://github.com/meituan-longcat/SOP-Maze","signal_url":null,"signal_json_url":null,"text":"# meituan-longcat/SOP-Maze\n\nDescription: SOP(Standard Operating Procedure)-Maze benchmarking LLM's performance on EXTREMELY complicated business tasks. \n\nLanguage: Python\n\nStars: 7\n\nForks: 2\n\nOpen issues: 1\n\nCreated: 2025-09-03T09:05:53Z\n\nPushed: 2025-10-07T05:25:48Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# SOP-Maze\n\n**SOP-Maze** is a benchmark designed to evaluate the comprehensive capabilities of large language models (LLMs) in executing tasks that follow Standard Operating Procedures (SOPs).\n\n## 🧩 Overview\n\nSOP-Maze presents complex, structured tasks that mimic real-world procedural workflows. It tests an LLM's ability to:\n\n- Understand and follow SOPs.\n- Reason through multi-step operations.\n- Produce accurate, context-aware outputs.\n\n## 📁 Directory Structure\n\n```\n\n.\n├── raw_data/ # Original data samples (JSON)\n├── data_with_model_response/ # Populated with model-augmented samples\n├── quick_start.py # Script to run evaluation\n\n````\n\n## 🛠️ Setup Instructions\n\n### 1. Prepare the Data\n\nBefore evaluation, enrich each JSON file in `raw_data/` by adding a new key:\n\n```json\n\"model_response\": \"\"\n````\n\n* Copy the updated files into the `data_with_model_response/` directory.\n* **Important:** Make sure to **clear** the `data_with_model_response/` directory before copying in new files.\n\nYou can refer to the examples already in `data_with_model_response/` for formatting guidance.\n\n### 2. Run Evaluation\n\nTo begin evaluation, run:\n\n```bash\nsh quick_start.py\n```\n\nThis will execute the evaluation pipeline on the updated dataset.\n\n---"},{"ref":"P7","kind":"page","title":"meituan-longcat/vitabench repository metadata","date":"2026-06-11T03:22:45.942782+00:00","date_source":null,"source_url":"https://github.com/meituan-longcat/vitabench","signal_url":null,"signal_json_url":null,"text":"# meituan-longcat/vitabench\n\nDescription: [ICLR 2026] VitaBench: Benchmarking LLM Agents with Versatile Interactive Tasks in Real-world Applications\n\nLanguage: Python\n\nLicense: MIT\n\nStars: 145\n\nForks: 16\n\nOpen issues: 8\n\nCreated: 2025-10-14T12:23:29Z\n\nPushed: 2026-02-22T16:08:39Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n

\n🌱VitaBench: Benchmarking LLM Agents
\nwith Versatile Interactive Tasks\n

\n\n

\n📃 Paper • 🌐 Website • 🏆 Leaderboard • 🤗 Dataset
\n

\n\n## 🔔 News\n\n- [2026-02] VitaBench is cited by [Qwen3.5](https://qwen.ai/blog?id=qwen3.5) and [Seed2.0](https://seed.bytedance.com/en/seed2)! Growing adoption as the go-to benchmark for tool-use evaluation. 🚀\n- [2026-01] **Qwen Team** has used VitaBench to evaluate their new model [Qwen3-Max-Thinking](https://qwen.ai/blog?id=qwen3-max-thinking), publishing the average score across 4 domains. We encourage the community to adopt VitaBench as the definitive benchmark for tool-use performance and welcome diverse interpretations of its results! 🔥\n- [2026-01] VitaBench has been accepted to **[ICLR 2026](https://openreview.net/forum?id=rtcX9qOBaz)**! 🎉\n- [2026-01] An **updated version** of VitaBench is released with rectified datasets and tools, upgraded evaluation models, and updated metrics for proprietary and open language models based on the new evaluator.\n- [2025-11] The **English version** of the VitaBench dataset is now released! It includes fully translated tasks and databases, enabling broader international use. Try it out!\n- [2025-10] Our paper is released on arXiv: [VitaBench: Benchmarking LLM Agents with Versatile Interactive Tasks in Real-world Applications](https://arxiv.org/abs/2509.26490)\n- [2025-10] The VitaBench suite is released, including the **codebase, dataset and evaluation pipeline**! If you have any questions, feel free to raise issues and/or su"},{"ref":"P8","kind":"page","title":"meituan-longcat/R-HORIZON repository metadata","date":"2026-06-11T03:20:49.987054+00:00","date_source":null,"source_url":"https://github.com/meituan-longcat/R-HORIZON","signal_url":null,"signal_json_url":null,"text":"# meituan-longcat/R-HORIZON\n\nDescription: [ICLR'26] R-HORIZON: How Far Can Your Large Reasoning Model Really Go in Breadth and Depth?\n\nLanguage: Python\n\nLicense: MIT\n\nStars: 26\n\nForks: 3\n\nOpen issues: 0\n\nCreated: 2025-10-21T09:40:58Z\n\nPushed: 2026-05-09T10:23:09Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n
\n\n

\n\"logo\"\nR-HORIZON\n

\n\n
\n

How Far Can Your Large Reasoning Model Really Go in Breadth and Depth?

\n
\n
\n\n
\n\n
\n\n
\n\n

\n📃 Paper • 🌐 Project Page • 🤗 Dataset • 🤗 Models\n

\n\nR-HORIZON is a novel method designed to stimulate long-horizon reasoning behaviors in Large Reasoning Models (LRMs) through query composition. We transform isolated problems into complex multi-step reasoning scenarios, revealing that even the most advanced LRMs suffer significant performance degradation when facing interdependent problems that span long reasoning horizons.\n\n![](./assets/mainfig.png)\n\n## 🔥 Releases\n**[2026-03]**\n- 🤗 **Models** are available on Hugging Face: [R-HORIZON Models](https://huggingface.co/collections/lulululuyi/r-horizon-models)\n\n**[2026-01]**\n- 🎉 **R-HORIZON** is Accepted to ICLR 2026!\n\n**[2025-10]**\n- 🎉 **R-HORIZON Benchmark** is now available! Test your LRMs on complex multi-horizon reasoning tasks.\n- 🤗 **Training and evaluation datasets** are available on Hugging Face: [R-HORIZON Dataset](https://huggingface.co/datasets/meituan-longcat/R-HORIZON-training-data)\n- 📄 **Paper released** on ar"},{"ref":"P9","kind":"page","title":"meituan-longcat/UNO-Bench repository metadata","date":"2026-06-11T03:20:49.738101+00:00","date_source":null,"source_url":"https://github.com/meituan-longcat/UNO-Bench","signal_url":null,"signal_json_url":null,"text":"# meituan-longcat/UNO-Bench\n\nDescription: Omni Model Benchmark with high quality and diversity, which reveals the Compositional Law. We’re now focused on Chinese scenarios — and actively seeking partners to co-build English & multilingual versions! Let’s expand global impact together.\n\nLanguage: Python\n\nLicense: MIT\n\nStars: 78\n\nForks: 0\n\nOpen issues: 2\n\nCreated: 2025-10-24T04:20:18Z\n\nPushed: 2026-01-12T02:51:14Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n

UNO-Bench: A Unified Benchmark for Exploring the Compositional Law Between Uni-modal and Omni-modal in Omni Models

\n

\n\n

\n\n
\n\n\n\n\n\n\n
\n\n## 🔔News\n- 🔥[2025/12/04] We have released the evaluation scripts [uno-eval](./uno-eval/), a unified evaluation framework for omni-modal benchmarks. More benchmarks will be supported in the future.\n- 🔥[2025/12/04] We have released the scoring model [UNO-Scorer-Qwen3-14B](https://huggingface.co/AGI-Eval/UNO-Scorer-Qwen3-14B). Feel free to use it!\n- 🔥[2025/10/29] We proposed a new omni-modal benchmark [UNO-Bench](https://meituan-longcat.github.io/UNO-Bench). The technical report is available [Arxiv](https://arxiv.org/abs/2510.18915). The dataset is available [Hugging Face](https://huggingface.co/datasets/meituan-longca"},{"ref":"P10","kind":"page","title":"meituan-longcat/LongCat-Flash-Omni repository metadata","date":"2026-06-11T03:20:49.704341+00:00","date_source":null,"source_url":"https://github.com/meituan-longcat/LongCat-Flash-Omni","signal_url":null,"signal_json_url":null,"text":"# meituan-longcat/LongCat-Flash-Omni\n\nDescription: This is the official repo for the paper \"LongCat-Flash-Omni Technical Report\"\n\nLanguage: Python\n\nLicense: MIT\n\nStars: 492\n\nForks: 33\n\nOpen issues: 21\n\nCreated: 2025-10-24T12:33:35Z\n\nPushed: 2026-05-09T10:22:12Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# LongCat-Flash-Omni\n\n
\n\"LongCat-Flash-Omni\"\n
\n
\n\n
\n\n\"Omni\"\n\n\n\"Hugging\n\n
\n\n
\n\n\"Wechat\"\n\n\n\n\n\n\"Twitter\n\n
\n\n
\n\n\"License\"\n\n
\n\n

\nTech Report 📄\n"},{"ref":"P11","kind":"page","title":"meituan-longcat/AMO-Bench repository metadata","date":"2026-06-11T03:20:49.467685+00:00","date_source":null,"source_url":"https://github.com/meituan-longcat/AMO-Bench","signal_url":null,"signal_json_url":null,"text":"# meituan-longcat/AMO-Bench\n\nDescription: This is the official repo for the paper \"AMO-Bench: Large Language Models Still Struggle in High School Math Competitions\".\n\nLanguage: Python\n\nLicense: MIT\n\nStars: 128\n\nForks: 3\n\nOpen issues: 1\n\nCreated: 2025-10-29T09:25:17Z\n\nPushed: 2026-02-06T02:59:58Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n

\n📐 AMO-Bench: Large Language Models Still
\nStruggle in High School Math Competitions\n

\n\n

\n📃 Paper • 🌐 Project Page • 🤗 Dataset
\n

\n\nThis is the official repo for the paper **AMO-Bench: Large Language Models Still Struggle in High School Math Competitions**.\n\n## Updates\n- 2026.02.05: Leaderboard Update: **[Qwen3-Max-Thinking](https://qwen.ai/blog?id=qwen3-max-thinking) achieves a new SOTA with 65.1%, while [GLM-4.7](https://z.ai/blog/glm-4.7) sets a new open-source record at 62.4%!** \n- 2025.12.01: We have added [Token Efficiency](#-token-efficiency) showing the number of output tokens used by models in the leaderboard. **[Gemini 3 Pro](https://deepmind.google/models/gemini/pro/) achieves the highest token efficiency among top-performance models!**\n- 2025.11.24: **[Gemini 3 Pro](https://deepmind.google/models/gemini/pro/) achieves 63.1%, setting a new SOTA and breaking 60% for the first time!** We have updated the [Leaderboard](#-leaderboard) with the results of Gemini 3 Pro and [Qwen3-Max-Thinking (Preview)](https://qwen.ai/blog?id=qwen3-max).\n- 2025.11.19: [Kimi-K2-Thinking](https://moonshotai.github.io/Kimi-K2/thinking.html) achieves 56.0%, new SOTA on [Leaderboard](#-leaderboard)!\n- 2025.11.05: The problem statement of **Problem 35** has been revised in 🤗 Huggingface Dataset: (1) the five integers that sum to $k$ should be **non-negative** rather than positive, and (2) we also stipulate that 1 couldn't be replaced with five integers. Additionally, for the "},{"ref":"P12","kind":"page","title":"meituan-longcat/LongCat-Video repository metadata","date":"2026-06-11T03:20:49.351181+00:00","date_source":null,"source_url":"https://github.com/meituan-longcat/LongCat-Video","signal_url":null,"signal_json_url":null,"text":"# meituan-longcat/LongCat-Video\n\nLanguage: Python\n\nLicense: MIT\n\nStars: 4273\n\nForks: 669\n\nOpen issues: 67\n\nCreated: 2025-10-25T06:49:49Z\n\nPushed: 2026-05-27T02:51:41Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# LongCat-Video\n\n
\n\"LongCat-Video\"\n
\n
\n\n
\n\"LongCat-Video\"\n\n\n\n
\n
\n\"LongCat-Video-Avatar\n\n\n\n\n
\n
\n\"placeholder\"\n\n \n\n\n📄 Paper   |  \n🌐 Website   |  \n📘 机器之心   |  \n🧪 AGI-Eval\n

\n\n## 🔥News\n\n- [2025/05/16] The DPT-agent paper has been accepted by ACL 2025!\n- [2025/03/18] Our work is featured by [机器之心](https://mp.weixin.qq.com/s/dT9KQmebVJX0ewkzJmisPg) on Wechat!\n- [2025/03/06] We have established a partnership with [AGI-Eval](https://agi-eval.cn/mvp/home) platform. The benchmark results of Overcooked Challenge are now available on [AGI-Eval-Overcooked Challenge](https://agi-eval.cn/evaluation/detail?id=56). \n\n# Overcooked Challenge\n![layout](assets/overcooked.png)\n\n# DPT-Agent Framework\n![Image 1](assets/framework.png)\n\n# Results\n\n![Exp1](assets/exp1.png)\n\n![Exp2](assets/exp2.png)\n\n# Usage\n\n## Installation\n\nCreate a new environment\n```\nconda create -n dptagent python=3.10 -y # support python<=3.10\nconda activate dptagent\n\n# Install pytorch\n# We do not need gpu\npip install torch torchvision torchaudio\n\n# Installing dependencies\n\nbash ./install.sh\n```\n## Running Instructions\n\n### Set up LLMs\n\n```\nlitellm -c llms/litellm/config.yml --port 40000\n\n# check health\ncurl --location 'http://127.0.0.1:40000/health' -H \"Authorization: Bearer sk-1234\"\n```\n\n### Run LLM as Indenpendent System 1 and System 2 Experiments\nFor single agent exp, first"},{"ref":"P14","kind":"page","title":"meituan-longcat/LongCat-Image repository metadata","date":"2026-06-11T03:20:48.936633+00:00","date_source":null,"source_url":"https://github.com/meituan-longcat/LongCat-Image","signal_url":null,"signal_json_url":null,"text":"# meituan-longcat/LongCat-Image\n\nLanguage: Python\n\nLicense: Apache-2.0\n\nStars: 695\n\nForks: 60\n\nOpen issues: 10\n\nCreated: 2025-12-03T06:26:54Z\n\nPushed: 2026-05-09T10:22:47Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# LongCat-Image\n\n
\n\"LongCat-Image\"\n
\n
\n\n
\n\n\n\n\n\n\n
\n\n
\n\n[//]: # ( )\n\n\n\n\n
\n\n## Model Introduction\nWe introduce **LongCat-Image**, a pioneering open-so"},{"ref":"P15","kind":"page","title":"meituan-longcat/PIE_bench repository metadata","date":"2026-06-11T03:20:48.925757+00:00","date_source":null,"source_url":"https://github.com/meituan-longcat/PIE_bench","signal_url":null,"signal_json_url":null,"text":"# meituan-longcat/PIE_bench\n\nDescription: PIEbench is a multilingual benchmark evaluating models performance on both general and regional QAs.\n\nStars: 4\n\nForks: 1\n\nOpen issues: 0\n\nCreated: 2025-11-17T02:45:54Z\n\nPushed: 2026-04-21T06:39:34Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# PIE Bench\nPIEbench is a multilingual benchmark evaluating models performance on both general and regional QAs."},{"ref":"P16","kind":"page","title":"meituan-longcat/LongCat-Flash-Thinking-2601 repository metadata","date":"2026-06-11T03:02:52.126062+00:00","date_source":null,"source_url":"https://github.com/meituan-longcat/LongCat-Flash-Thinking-2601","signal_url":null,"signal_json_url":null,"text":"# meituan-longcat/LongCat-Flash-Thinking-2601\n\nLicense: MIT\n\nStars: 254\n\nForks: 11\n\nOpen issues: 4\n\nCreated: 2026-01-14T18:22:54Z\n\nPushed: 2026-05-09T10:22:01Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# LongCat-Flash-Thinking-2601\n\n
\n\"LongCat-Flash\"\n
\n
\n\n
\n\n\"Chat\"\n\n\n\"Hugging\n\n\n\"ModelScope\"\n\n
\n\n
\n\n\"Wechat\"\n\n\n\n\n\n\"Twitter\n\n
\n\n\n
\n\n\n\n
\n\n\"Wechat\"\n\n\n\n\n\n\"Twitter\n\n
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\n\n\"Hugging\n\n\n\"Hugging\n\n
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\n📃 Paper • 🌐 Project Page • 🏆 Leaderboard • \n🤗 Dataset\n
\n

\n\n## 📖 Introduction\n\nWe present **General365**, a highly challenging and diverse benchmark for evaluating the general reasoning capabilities in LLMs.\n\n\"General Reasoning\" refers to reasoning tasks that depend exclusively on general knowledge.\nWe define general knowledge as knowledge within the K-12 scope (such as common sense, fundamental linguistics, and basic subject matter), excluding university-level academic knowledge.\nCompared to domain-specific reasoning (e.g., Math Reasoning), general reasoning evaluation better decouples a model’s reasoning capability from its knowledge dependence.\nThis enables a more precise assessment of reasoning skills rather than rote memorization, while testing the generalization of a model's reasoning abilities across broader scenarios.\nCurrent benchmarks for general reasoning face several challenges: a lack of difficulty, insufficient diversity, or overly synthetic characteristics.\nConsequently, we introduce **General365**, a manually curated benchmark characterized by high challenge and high diversity, aiming to facilitate more effective evaluation of reasoning capabilities in frontier models.\n\n> To ensure the impartiality of the evaluation, we have released only half of the total questions. The remaining questions are maintained as a held-out test set to track potential data contamina"},{"ref":"P26","kind":"page","title":"meituan-longcat/LARYBench repository metadata","date":"2026-06-11T02:51:45.177835+00:00","date_source":null,"source_url":"https://github.com/meituan-longcat/LARYBench","signal_url":null,"signal_json_url":null,"text":"# meituan-longcat/LARYBench\n\nLanguage: Python\n\nLicense: MIT\n\nStars: 150\n\nForks: 8\n\nOpen issues: 3\n\nCreated: 2026-04-09T03:09:15Z\n\nPushed: 2026-06-10T08:18:09Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# LARY — A Latent Action Representation Yielding Benchmark for Generalizable Vision-to-Action Alignment\n\n

\n\"LARYBench\"\n

\n\n

\n\"Project\n \n\"arXiv\"\n \n\"HuggingFace\"\n \n\"ModelScope\"\n \n\n \n\"MIT\n

\n\n**LARY** is a unified evaluation framework for **latent action representations**.\nGiven any model that produces latent action representations (LAMs or visual encoders), LARY provides three complementary evaluation pipelines:\n\n| Pipeline | Task |\n|---|---|\n| **`get_latent_action`** | Extract latent action representations from videos or image pairs |\n| **`classification`** | Probe how well latent actions capture *action semantics* (action-type recognition) |\n| **`regression`** | Probe how well latent actions can *decode physical robot actions* (action regression) |\n\n---\n\n## News\n- **[2026-06-10]** LARYBench now supports V-JEPA 2.1 and simplify the way to add new custom models. We welcome all kinds of models evaluating on LARYBench and contributing to our leaderboards!\n- **[2026-05-01]** LA"},{"ref":"P27","kind":"page","title":"meituan-longcat/LongCat-Flash-Chat model card","date":"2026-06-11T02:50:36.656433+00:00","date_source":null,"source_url":"https://huggingface.co/meituan-longcat/LongCat-Flash-Chat/raw/main/README.md","signal_url":null,"signal_json_url":null,"text":"---\nlicense: mit\nlibrary_name: LongCat-Flash-Chat\npipeline_tag: text-generation\ntags:\n- transformers\n---\n\n# LongCat-Flash-Chat\n\n
\n\n
\n\n
\n\n
\n\n\n\"Chat\"\n\n\n\"Hugging\n\n
\n\n
\n\n\"Wechat\"\n\n\n\"Twitter\n\n
\n\n
\n\n\"License\"\n\n
\n\n## Model Introduction\nWe introduce LongCat-Flash, a powerful and efficient language model with 560 billion total parameters, featuring an innovative Mixture-of-Experts (MoE) architecture. The model incorporates a dynamic computation mechanism that activates 18.6B∼31.3B parameters (averaging∼27B) based on contextual demands, optimizing both computational efficie"},{"ref":"P28","kind":"page","title":"meituan-longcat/LongCat-Flash-Thinking model card","date":"2026-06-11T02:50:34.564961+00:00","date_source":null,"source_url":"https://huggingface.co/meituan-longcat/LongCat-Flash-Thinking/raw/main/README.md","signal_url":null,"signal_json_url":null,"text":"---\nlicense: mit\nlibrary_name: LongCat-Flash-Chat\npipeline_tag: text-generation\ntags:\n- transformers\n---\n# LongCat-Flash-Thinking\n\n
\n\"LongCat-Flash\"\n
\n
\n\n
\n\n\"Chat\"\n\n\n\"github\"\n\n
\n\n
\n\n\"Wechat\"\n\n\n\"Twitter\n\n
\n\n
\n\n\"License\"\n\n
\n\n

\nTech Report 📄\n

\n\n## Model Introduction\n\nWe introduce and release **LongCat-Flash-Thinking**, which is a powerful and efficient large reasoning model (LRM) with 560 billion total parameters, featuring an innovative Mixture-of-Experts (MoE) architecture. The model incorporates a dynamic computa"},{"ref":"E1","kind":"event","title":"meituan-longcat/LongCat-Flash-Chat","date":"2025-08-29T07:39:50+00:00","date_source":"source","source_url":"https://huggingface.co/meituan-longcat/LongCat-Flash-Chat","signal_url":"https://onlylabs.fyi/signals/9e833acb-45b8-42ff-8c32-b7fb9e6b8caf","signal_json_url":"https://onlylabs.fyi/signals/9e833acb-45b8-42ff-8c32-b7fb9e6b8caf/signal.json","text":"model_released · meituan-longcat/LongCat-Flash-Chat · signal_desk=releases · occurred_at=2025-08-29T07:39:50+00:00 · url=https://huggingface.co/meituan-longcat/LongCat-Flash-Chat · hf_downloads=80114 · hf_likes=535 · hf_params=561862880256 · pipeline=text-generation · 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license=apache-2.0"},{"ref":"E7","kind":"event","title":"meituan-longcat/LongCat-Flash-Thinking","date":"2025-09-21T07:46:09+00:00","date_source":"source","source_url":"https://huggingface.co/meituan-longcat/LongCat-Flash-Thinking","signal_url":"https://onlylabs.fyi/signals/adf8b771-f198-487f-a00b-98b4fa610688","signal_json_url":"https://onlylabs.fyi/signals/adf8b771-f198-487f-a00b-98b4fa610688/signal.json","text":"model_released · meituan-longcat/LongCat-Flash-Thinking · signal_desk=releases · occurred_at=2025-09-21T07:46:09+00:00 · url=https://huggingface.co/meituan-longcat/LongCat-Flash-Thinking · hf_downloads=95 · hf_likes=148 · hf_params=561862880256 · pipeline=text-generation · 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