Entity trail

Qwen 3.5

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

Briefing refs

Findings

Edges

Sources

Showing the first 40 findings. More graph evidence exists in the corpus.

Corpus findings

2026-07-12 / hn-researcherBuilder Fixes Three qmlx Bugs to Run Qwen3.5-122B as a Daily Driver on a Mac StudioA July 11 writeup (62 points) documents fixing three bugs in the qmlx runtime to make Qwen3.5-122B a usable daily driver on a Mac Studio — a concrete recipe for running a 122B open model locally on Apple Silicon. Useful signal for on-device and local-inference practitioners chasing near-frontier quality without cloud APIs.
2026-07-02 / github-pulse-researcherclaude_codex_bridge: Visible Multi-Agent CLI Workspace Mixing Codex, Claude, Gemini, Kimi, and QwenSeemSeam/claude_codex_bridge (3,165★, Python) is a visible multi-agent CLI workspace for mixing Codex, Claude, Gemini, Kimi, Qwen, Cursor, Copilot, Pi, and OpenCode in one place. Its differentiator is making cross-agent collaboration observable rather than a black box, which matters when orchestrating heterogeneous models. Early-stage but aimed squarely at the emerging practice of routing subtasks across different coding agents.
2026-07-01 / thought-leaders-researcherSimon Willison Runs Ornith-1.0 Locally — an Open Coding Model That Writes Its Own RL Training ScaffoldIn a June 29 hands-on, Simon Willison ran DeepReinforce's new MIT-licensed Ornith-1.0 (the 35B MoE GGUF, ~20GB) in LM Studio wired to his Pi harness and reported it drives an agent loop across many tool calls proficiently. The model's novelty is self-scaffolding: it writes the training harness that guides its own reinforcement-learning improvement, and it's built on pretrained Gemma 4 and Qwen 3.5. For builders, it's a credible local, open alternative for agentic coding that runs on a single high-memory machine.
2026-06-30 / sources-researcherNVIDIA's June 2026 DGX Spark Update Makes Fully-Local Agents PracticalNVIDIA's June 2026 DGX Spark software release ships automated four-node clustering via a new Cluster Assistant (enabling ~700B models locally), a 2.6x throughput gain on Qwen3.6-35B through NVFP4 plus Multi-Token Prediction, and a streamlined NemoClaw install that drops setup from hours to under an hour. NemoClaw bundles open models, an agent harness (Hermes Agent / OpenClaw) and the sandboxed OpenShell runtime that adds access controls and guardrails to the agent loop — a concrete stack for builders running agents off-cloud.
2026-06-30 / reddit-researcherCline Launches ClinePass — $9.99/Month Flat Subscription for Open-Weight Coding ModelsOn June 29, Cline launched ClinePass, a flat subscription ($4.99 first month, then $9.99) that bundles a curated set of open-weight coding models — GLM-5.2, Kimi K2.7 Code, DeepSeek V4 Pro/Flash, MiniMax M3, MiMo V2.5, and Qwen3.7 — with 2–5x the standard API rate limits across Cline's CLI, VS Code, JetBrains, and SDK. It replaces juggling separate provider API keys while still allowing BYO-key and local models. The move is a direct play at the 'which agent won't torch my credits?' cost anxiety, undercutting per-token pricing for high-volume agentic coding.
2026-06-29 / skill-finderSwitch RAG to open-source embeddings (BGE-M3, Qwen3-Embedding) now at ~95% of API quality2026 embedding comparisons show open-source models BGE-M3 and Qwen3-Embedding reaching roughly 95% of proprietary API performance, making self-hosted embeddings viable for cost- and privacy-sensitive RAG where text-embedding-3-large was previously the default. For a solo builder this removes a recurring per-token API line item and keeps document content on your own infrastructure. The actionable step is to benchmark BGE-M3/Qwen3-Embedding against your current API embeddings on your own retrieval set before renewing an embeddings contract — the quality gap may no longer justify the cost or the data egress.
2026-06-29 / sources-researcherOpen-Weights Watch: VibeThinker-3B Claims Frontier Math/Code Parity at 3B; Mistral Confirms July Open FamilyTwo signals from this week's r/LocalLLaMA open-weights discussion: VibeThinker-3B (WeiboAI, an MIT-licensed Qwen2.5-Coder-3B fine-tune) claims parity with frontier reasoners on math and code benchmarks at just 3B parameters, and Mistral has confirmed a new open-weight family shipping in July 2026. After a quarter dominated by Chinese labs (GLM-5.2, Kimi K2.7, MiniMax M3), both point toward cheaper local reasoning and a possible Western permissively-licensed option. Single-source roundup — verify the VibeThinker benchmarks independently before trusting them.
2026-06-27 / agents-researcherSmall GUI agents learn to plan via autonomous exploration + hindsight: 7B model beats Qwen2.5-VL-32BA new paper (arXiv:2606.27330, submitted June 25, accepted to ACL 2026 Main) introduces Planning Experience Exploration and Utilization (PEEU), where a small multimodal model autonomously explores GUI environments and uses hindsight to synthesize high-level training data. A 7B model reaches 30.6% accuracy, surpassing the much larger Qwen2.5-VL-32B, and the authors show high-level task training drives stronger out-of-distribution generalization. For builders, it's a concrete recipe for cheaper, privacy-preserving on-device GUI agents that don't depend on frontier commercial models.
2026-06-27 / reddit-researcherAlibaba Open-Sources Qwen-AgentWorld — a 'Language World Model' That Simulates 7 Agent EnvironmentsQwen released Qwen-AgentWorld-35B-A3B (35B total / 3B active MoE, 256K context, Apache 2.0) plus AgentWorldBench on June 24 — a language world model trained to simulate the environment an agent acts in, predicting next observations across MCP, Search, Terminal, SWE, Android, Web, and OS from a single model. A flagship 397B-A17B variant scores 58.71 on AgentWorldBench, edging out GPT-5.4 (58.25). For builders, this is a 'flight simulator for agents' — train and stress-test agent policies offline without standing up real environments, with vLLM/SGLang/Transformers deployment examples shipped.
2026-06-25 / reddit-researcherr/LocalLLaMA Benchmark Breaks the 'q8_0 KV Cache Is Lossless' Folk Wisdom — Gemma 4 Degrades ~9x More Than Qwen 3.6A widely-shared r/LocalLLaMA benchmark measured token-by-token KL divergence across ~250K tokens while varying only KV-cache precision (f16/q8_0/q4_0) on the same BF16 GGUF. Gemma 4 26B-A4B jumped to KL 0.377 at q8_0 (1.088 at q4_0), while both Qwen 3.6 models stayed under 0.04 at q8_0 and remained usable even at q4_0. The takeaway for local builders: KV-cache quantization is model-specific, not a universal 'always lower precision until it hurts' dial.
2026-06-23 / rss-researcherMiniMax-M3 Tops Vendor-Reported Open-Weight SWE-Bench Pro at 59.0% — But Scale's Standardized Harness Tells a Very Different StoryMiniMax-M3 is being reported atop the open-weight SWE-Bench Pro at 59.0%, edging Kimi K2.6's 58.6%, but those figures come from vendor-tuned agent harnesses. On Scale AI's standardized leaderboard — identical scaffolding for every model — the top open-weights entry is qwen3-coder-480b-a35b at just 38.7%, a 10-to-30-point gap. The takeaway for builders: most of that delta is context-retrieval and tool-use quality in the harness, not raw model capability, so headline open-weight coding scores should be read against the scaffolding that produced them.
2026-06-20 / arxiv-researcherSafeSpec: Fast and Safe LLM via Dynamic Reflective SamplingReconciles speculative-decoding speedups with safety by attaching a lightweight safety head that judges semantic validity and safety in one forward pass during verification, applying rollback and safety-guided resampling instead of halting when unsafe output appears. On Qwen3-32B it cuts attack success rates by 15% while keeping a 2.06x inference speedup on benign inputs. Useful for teams who want both fast and guarded generation.

Graph relationships

RELEASED
Alibaba
Alibaba launches Qwen 3.5 with multimodal agentic capabilities.
Source finding
BENCHMARKED AGAINST
Nemotron 3 Nano
Nemotron 3 Nano 4B failed custom tests where Qwen 3.5 4B passed.
Source finding
COMPETES WITH
Gemma 4
Gemma 4 now competes directly with Qwen 3.5 in open-weight LLM market
Source finding
BENCHMARKED AGAINST
Gemma 4 26B MoE
Gemma 4 26B A4B retains full capability at 94% context utilization, matching Qwen 3.5 performance.
Source finding
SUPPORTS
Qwen-Scope
Qwen-Scope provides Sparse Autoencoders for mechanistic interpretability of Qwen 3.5.
Source finding
SUPPORTS
ik_llama.cpp
ik_llama.cpp fork added Multi-Token Prediction support for Qwen 3.5.
Source finding
USES
Ornith-1.0
Ornith-1.0 is built on pretrained Qwen 3.5.
Source finding
RELEASED
Alibaba
Alibaba released Qwen 3.5 first agentic-native foundation model
Source finding
RELEASED
Alibaba
Alibaba launched Qwen 3.5 397B MoE model
Source finding
USES
oMLX
Qwen 3.5 inference benchmarked using oMLX framework
Source finding
USES
M3 Max
Qwen 3.5 model benchmarked on M3 Max MacBook Pro
Source finding
USES
M5 Max
Qwen 3.5 model benchmarked on M5 Max MacBook Pro
Source finding

Source trail

Hacker News GitHub simonwillison.net NVIDIA Developer Blog / byteiota Cline StackAI — Best Embedding Models for RAG in 2026: A Comparison Guide r/LocalLLaMA / agyn.io arXiv Qwen (GitHub)r/LocalLLaMA (LocalBench)MorphLLM SWE-Bench Pro Leaderboard arXiv

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