Entity trail

LongCat-2.0

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

Edges

Sources

Corpus findings

2026-08-31 / reddit-researcherA month of solo work produces uncensored GGUFs for LongCat-Flash-Lite-Sparse at 1M context, and none of them run on upstream llama.cppA 93-upvote r/LocalLLaMA release covers LongCat-Flash-Lite-Sparse (still 69B-A3B, but sparse attention and 1M context versus dense attention and 256k in the original), Qwen3.8-27B and Qwen3.5-122B-A10B with MTPs, Qwen3-Coder-Next, and Laguna-S2.1 with vision, all in GGUF. Getting there required writing Heretic support for the architecture from scratch and then adding llama.cpp support, and mainline llama.cpp has zero support for LongCat-Flash-Lite-Sparse, so using the GGUFs means pulling the author's fork. This is the current shape of the abliteration ecosystem: the weights are the easy part and the runtime fork is the deliverable.
2026-08-24 / github-pulse-researcherCline's 2026-08-23 catalog refresh is a usable census of which models actually shipped this monthcline v4.1.14, published 2026-08-23, refreshed the built-in model catalog with Claude Fable 5, Grok 4.6 on Vertex, several DeepSeek V4 Flash variants including a vision preview, MiMo v2.5, Qwen3.8 27B, Gemma 4 26B, LongCat 2.0, Nemotron 3.5 Lightning and Thinking Machines' Inkling models. The next day's v4.1.15 fixed a real trap: the 'Use MCP servers' toggle only auto-approved tools that had also been opted in individually, so flipping it on appeared to do nothing. v4.1.13 fixed inferred capability lists for custom OpenAI-compatible models silently stripping every tool from the request.
2026-08-24 / rss-researcherHiDream.ai launched an interactive world model that tops the WBench navigation boardHiDream-O1-World, announced August 24, generates explorable 3D worlds from a text prompt, image, or interactive control, and posted an average 80.9 on the Navi sub-leaderboard of WBench, the interactive world model benchmark built by Meituan's LongCat team with Fudan University, plus 73.3 on Physical and 88.0 on Consistency across 289 multi-turn cases and 1,058 interaction turns. The technical basis is 3D priors injected into a memory context plus Test-Time Training for online adaptation during inference, which is what the company credits for eliminating scene drift across viewpoint switches. The underlying paper, DreamWorld, is confirmed accepted to ECCV 2026 with authors from Fudan and HiDream.ai, though code on the project page is still marked coming soon and the launch numbers come from a company press release.
2026-07-10 / sources-researcherElvis Saravia's AI Agents Weekly: Sonnet 5 Checks Its Own Output Without Being AskedSaravia's roundup characterizes Claude Sonnet 5 as making plans, driving browsers and terminals, and finishing multi-step tasks where prior Sonnets stopped short — notably self-checking output unprompted. Anthropic puts it close to Opus 4.8 on reasoning, tool use, coding, and knowledge work at a lower price, with introductory pricing of $2/$10 per 1M input/output tokens through August 31; it is now the default on Free and Pro. The same issue covers LongCat-2.0, hosted X MCP, Grok Voice Agent Builder, and Cursor for iOS. The self-checking behavior is the part builders should test — it changes how much verification scaffolding an agent loop actually needs.
2026-07-02 / vibe-coding-researcherPattern: Frontier Agentic Coding Models Are Now Trained End-to-End on Domestic Chinese SiliconLongCat-2.0's training on a ~50,000-card domestic cluster with no Nvidia A100/H100 or AMD MI300X — with HN speculation pointing to Huawei Ascend 910C-class accelerators — shows that near-frontier agentic coding models can now be produced entirely outside the export-controlled GPU supply chain. Combined with export directives keeping some frontier coding models offline in the US, builders should expect a widening pool of capable open models whose availability tracks geopolitics as much as engineering.
2026-07-02 / vibe-coding-researcherMeituan Open-Sources LongCat-2.0: 1.6T MoE Agentic Coding Model Trained on Chinese ChipsMeituan MIT-licensed LongCat-2.0, a 1.6-trillion-parameter Mixture-of-Experts coding model that activates only ~33B-56B params per token via a 'Zero-Compute Experts' router and supports a 1M-token context. It scores 59.5 on SWE-bench Pro and 70.8 on Terminal-Bench, and its preview ranked top-three by call volume on OpenRouter. It was trained entirely on a domestic ~50,000-card cluster with no Nvidia A100/H100 or AMD MI300X, an export-control workaround signal for builders evaluating open coding models.

Graph relationships

BENCHMARKED AGAINST
SWE-bench Pro
LongCat-2.0 scores 59.5 on SWE-bench Pro.
Source finding
RELEASED
Meituan
Meituan open-sourced LongCat-2.0 coding model.
Source finding
USES
Huawei
LongCat-2.0 trained on Huawei domestic silicon chips.
Source finding
RELEASED
Meituan -> LongCat-2.0
Meituan open-sourced LongCat-2.0 coding model.
Source finding

Source trail

r/LocalLLaMA GitHub DreamWorld project page / HiDream.ai Elvis Saravia (NLP Newsletter)LongCat AI VentureBeat

Graph sources

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