Entity trail

Qwen3.5

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

2026-09-10 / github-pulse-researcherEdge0 runs a 35B MoE on Apple Silicon in 2.9 GB of active memory by streaming experts off SSDEdge0-AI/Edge0 was created 2026-09-08 and reached 583 stars in two days (269 then 310 per GitHub's star history endpoint), with commits still landing this morning. It packages SSD expert offload, Recover-LoRA, and prerouter routing prediction into an MLX-backed framework: `edge0-35b` is a 4-bit 40-layer 256-expert model built on Qwen3.5-MoE 35B-A3B needing ~2.9 GB peak active memory against a ~23 GB on-disk checkpoint, scoring 79.2 average across AIME 2026, HumanEval, GPQA-Diamond, MMLU-Pro and IFBench versus 83.2 for the fp16 base. A CUDA backend is a reserved directory, not yet shipped.
2026-09-10 / reddit-researcherEvicting the expert cache during prefill made Qwen3.8-Flash-Next 2.2-2.5x faster, then the author found it loses time on short turnsPart 4 of a running 2x3090 optimization series: prefill was the weak spot at 80+ seconds to first token on an 8k prompt and 24 minutes on a 119k one, and releasing the 150-slot expert cache off the GPU while the prompt runs bought 2.2 to 2.5x. What makes the thread worth reading is the correction in the comments. A reader calculated the 2.8s release/restore only pays back past roughly 500 prefill tokens, and the author checked his own long-document run and found it was worse than that, 4.2s lost per short follow-up on a cached prefix, and committed to gating the swap on pending prompt length.
2026-09-10 / rss-researcherAWS published a working recipe for self-hosting the 2.4-trillion-parameter Qwen3.8 on HyperPod with vLLM and NVFP4The 2026-09-09 walkthrough covers deploying Qwen3.8-2.4T-A95B, a 2.4-trillion-parameter open-weight mixture-of-experts model with 95B active parameters, on Amazon SageMaker HyperPod: cluster provisioning, NVFP4 quantization, and an OpenAI-compatible endpoint with built-in reasoning support. Independent benchmark compilations put Qwen3.8-Max at 86.6 on Terminal-Bench 2.1, between GPT-5.6 Sol at 88.8 and Claude Opus 5 at 84.6, under Apache 2.0. The gap between a downloadable model and a closed frontier model on terminal coding tasks is now roughly two points, which changes the self-host calculus for anyone with GPU budget.
2026-09-10 / sources-researcherPrefilling 1% of GPT-5.5 Pro's reasoning into Qwen3.8 raises answer overlap by 18 points, and 27 points on STEMA v1.1 rerun of the reasoning-prefill experiment inserted the first 1% of GPT-5.5 Pro's reasoning into each target model's reasoning channel across 45 problems, leaving the visible answer freely generated, then measured how much of the teacher's answer appeared in the first 100 tokens. Qwen3.8 A95B jumped from 16.79% to 34.97% source recall (+18.18 pp), rising to +26.99 pp on the 15 STEM problems. DeepSeek V4 Flash moved -1.17 pp and Inkling +0.46 pp, so the effect is model-specific rather than a general artifact, which makes it usable evidence in the distillation-provenance argument.
2026-09-09 / reddit-researcherA 400MB Qwen3-0.6B running on a 2017 Samsung Note 8 is driving a real desktop Chrome browserAn r/LocalLLaMA post from someone building the page-perception layer (145 upvotes, 20 comments) reports testing 12 small local models and finding that Qwen3-0.6B at 400MB, running on a nine-year-old Samsung Note 8, is enough to drive a real desktop Chrome session rather than a simulated one. The claim inverts the usual browser-agent assumption that page understanding needs a frontier VLM; if it holds, the cost floor for a browser agent is a phone that was mid-range in 2017. Single-source, from a party with a stake in the result, so treat the framing carefully.
2026-09-09 / reddit-researcherNex shipped an Apache-2.0 computer-use family: Mini 35B, Pro 397B and a text-only 1.6T MaxThe Nex-N2.5 family went live September 8 with Apache-2.0 weights. Mini is a 35B/~3B-active multimodal MoE with 262K context, 256 routed experts (8 active plus a shared expert) and ~70GB of BF16 safetensors, deployable on two H100s; Pro at 397B scores 56.4 on OSWorld-2 against Qwen3.8-Max at 46.7, plus OSWorld-G 87.4, OSWorld-Verified 82.2 and Vision2Web 68.2. Max at 1.6T is text-only and scores 50.2 on AutomationBench v1.0.6, 0.1 behind Claude Opus 5. The r/LocalLLaMA thread for Mini drew 79 upvotes and 43 comments.
2026-09-08 / arxiv-researcherRepeat-After-Me Gets 47% Attack Success on GPT-5.5 With Black-Box Image Prompt Injection That Emits Real Tool CallsarXiv 2609.04533 closes the gap between text and image prompt injection, which previously failed on frontier VLMs because harmful outputs require long, format-compliant strings like a parseable native tool call with exact function names and arguments. Repeat-After-Me exceeds 80% attack success on open-weight models including Qwen3.6-27B and 47% on commercial frontier VLMs including GPT-5.5, under a realistic setting where the benign user prompt is unrelated to the injected task. Injections optimized on one surrogate retain 43-46% of their success rate on two commercial victims, so surrogate-trained attacks transfer.
2026-09-08 / reddit-researcherA task-aware quantization pipeline puts Qwen3.8-27B at 99% of BF16 reasoning at 15% of the size, beating byte-matched Unsloth by 5.5 pointsTAK (Task Aware Knapsack) builds an imatrix from a task-specific corpus, finds the smallest size before collapse, then promotes and demotes tensors within a byte budget, with no pruning, fine-tuning or merging. On held-out reasoning data it scores 82.81% against 83.59% for BF16 and 77.34% for the byte-matched Unsloth UD IQ2_S, with larger gaps on smaller models: +11.72 on Qwen3.5-4B, +14.06 on Gemma 4 E4B, +19.53 on Gemma 3 4B QAT. The author edited the post to warn that coding is outside the reasoning-specialized quant's domain after users hit repetition loops.
2026-09-07 / arxiv-researcherA Small Draft Model Scoring an Agent's Own Output Predicts Failure Before Execution, Cutting Error Rate 6-8 PointsarXiv 2609.05274 inverts speculative decoding: a small open-weight draft model scores a black-box agent's already-generated trajectory in a single forward pass, needing no logits, weights, activations, or repeated sampling. Phase-aware features separating reasoning from action spans are calibrated against a verifiable objective to produce a failure-likelihood score. Wired into a pre-execution veto gate on Qwen3-Coder-480B and Claude 3.5 Sonnet, it cut execution error rate by 6-8 percentage points and token cost by 14-19%, transferring to out-of-distribution benchmarks without retraining.
2026-09-07 / github-pulse-researcherllama.cpp lands Qwen3-Next / Qwen3.5 conversion metadata, a QKV fusion flag, and two CUDA race fixes in 36 hoursggml-org/llama.cpp cut builds b10833 through b10839 between 2026-09-07T06:49Z and 11:14Z. #28208 writes explicit `recurrent_layers` during Qwen3-Next / Qwen3.5 HF-to-GGUF conversion, and #22780 adds a `--fuse-qkv` flag to fuse Q/K/V into a single QKV tensor at conversion time. Two correctness fixes touch CUDA concurrency: #28475 fixes races in `mmid` and `mmf`, and #27870 fixes a divergent barrier in f16 flash attention. Vulkan gained TQ1_0 support (#27765), type-aligned GET_ROWS (#28253) and rms_norm fusion (#28024), and #28068 corrects GDN normalization from `max` to `rsqrt`.
2026-09-07 / skill-finderAn injected image overwrote TOOLS.md in a default OpenClaw Discord deployment where text injection failedRepeat-After-Me is a black-box adaptive visual prompt injection that reaches attack success rates above 80% on Qwen3.6-27B and 47% on GPT-5.5 under a realistic setting where the benign user prompt is unrelated to the injected task and does not authorize it. Injections optimized on one surrogate retain 43-46% of their success on two commercial victims, and the authors demonstrate a real OpenClaw Discord deployment where an untrusted user's minimally injected image overwrites TOOLS.md, opening the door to remote code execution and secret exfiltration. The critical detail for builders is that this works in cases where adaptive textual prompt injection fails, so text-only injection filters are not covering the image path.
2026-09-07 / reddit-researcherA hands-on Q8 comparison puts DeepSeek-V4-Flash-Vision at 40% slower token generation but 2x faster task completion than Qwen3.8-Flash-NextAn r/LocalLLaMA practitioner running 2x Strix Halo 128GB over USB-C 4 with llama.cpp RPC compared both models at Q8_K_XL on real coding work (79 upvotes, 46 comments). DS-V4-Flash-Vision generates tokens ~40% slower yet finishes tasks about twice as fast, which the poster attributes to fewer hallucinated detours: a task Qwen finished in 25 minutes on medium took DeepSeek 12 minutes. The sharpest number is a failure mode, not a win: Qwen3.8-Flash-Next on 'xhigh' failed to complete in ~3 hours a task it finished in 25 minutes on 'medium,' so raising reasoning effort on that model is actively counterproductive for agentic coding.

Graph relationships

SUPPORTS
llama.cpp
llama.cpp added HF-to-GGUF conversion support for Qwen3.5.
Source finding
BENCHMARKED AGAINST
Spark-X2.5-4B
Spark-X2.5-4B scored 65.1 on BFCL-V4 and 44.4 on SWE-Bench Pro versus Qwen3.5.
Source finding
BENCHMARKED AGAINST
Qwen3-30B
Qwen 3.5 9B model outperforms Qwen3-30B on benchmarks.
Source finding
RELEASED
Alibaba
Alibaba released Qwen 3.5 series natively multimodal with 262K context under Apache 2.0 license.
Source finding
USES
Flash-MoE
Flash-MoE runs Qwen3.5-397B on 48GB MacBook Pro at 4.4 tok/s
Source finding
BENCHMARKED AGAINST
Claude 4.6 Opus
Community distilled Qwen3.5 models from Claude 4.6 Opus reasoning chains via Unsloth.
Source finding
USES
Claude Code
Qwen3.5-27B runs autonomously for 9+ minutes in Claude Code environments.
Source finding
BENCHMARKED AGAINST
Mistral Small 4
Mistral Small 4 received lukewarm community reception compared to Qwen3.5.
Source finding
USES
Vitalik Buterin
Vitalik Buterin tested Qwen3.5:35B locally for coding tasks.
Source finding
SUPPORTS
llama.cpp
llama.cpp achieves lossless KV cache compression on Qwen3.5 at 4x compression
Source finding
SUPPORTS
Unsloth Studio
Unsloth Studio supports Qwen3.5.
Source finding
COMPETES WITH
Tesseract
Vision language models like Qwen3.5 are replacing traditional OCR engines
Source finding

Source trail

GitHub r/LocalLLaMA AWS Machine Learning Blog wsxiaoys (GitHub Gist)r/LocalLLaMA Hugging Face / Nex (via r/LocalLLaMA)arXiv 2609.04533 — Repeat-After-Me: Black-Box Adaptive Visual Prompt Injection r/LocalLLaMA (167 upvotes, 49 comments)arXiv 2609.05274 GitHub r/LocalLLaMA (79 upvotes, 46 comments)llm-bench.io (via r/LocalLLaMA)

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