Entity trail

Qwen 3.8

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

2026-09-16 / reddit-researcherA screenshot showing a US government RAG system running a Qwen embedding model drew 254 upvotesAn r/singularity post (254 upvotes, 27 comments) sourced to x.com/tleilax___/status/2099788133558546576 shows a US government retrieval system using a Qwen embedding model for RAG lookup. The most-upvoted explanation is mundane and probably correct: Qwen embeddings are among the easiest models to run locally with broad tooling support, and individual agencies do not procure as a monolith. It is a single-source screenshot with no agency named, so treat it as an adoption anecdote rather than a policy signal.
2026-09-16 / reddit-researcherslotstream adds expert lookahead for SSD-streamed MoE on Macs, and the router beats a trained predictorcarloslfu (slotstream, 364 stars, pushed September 15) reports a 10%+ throughput gain for Qwen3.8-Flash-Next running with expert offloading on low-memory Macs by running the model's own router two layers ahead on the current hidden state to prefetch the next experts. The notable negative result is that he first trained a small model to predict experts N layers ahead and it lost to simply reusing the router; N=2 was the empirical sweet spot, and a correction model layered on top adds another 3-4%. slotstream's premise is running a 125B MoE that needs 104GB at 4-bit on a Mac with a fraction of that RAM, via MLX and Swift with an Ollama-compatible API.
2026-09-16 / reddit-researcherISTA-DASLab's GSQ-RCO quants shrink Qwen3.8-Flash-Next to 68-76GB, with a Q2_0 built for throughput over accuracyISTA-DASLab shipped GSQ-RCO GGUFs for Qwen3.8-Flash-Next (HF repo last modified September 15, 4,829 downloads), cutting the model from roughly 80-95GB to 68-76GB. Their IQ3_XXS operating point matches the base model exactly on AIME25 (100.00) and is within 0.51 on GPQA-Diamond and 1.14 on LiveCodeBench v6 at about a fifth of BF16 size. The unusual variant is Q2_0, which deliberately avoids lookup-table quant formats so decode cost stays flat, buying 3.4x prompt throughput and 1.9x lower end-to-end latency versus IQ2_XS for 0.09 points of task average. One commenter on dual 3090s reports it is currently slower in practice for them because tensor parallel and MTP support are missing.
2026-09-16 / reddit-researcherByteShape's Qwen3.8-27B quants hit 99.63% of BF16 at 3.84 bpw, and argue KL divergence is the wrong quant metricByteShape released full ShapeLearn GGUFs for Qwen3.8-27B (repo last modified September 15, 17,232 downloads, 60 likes) claiming 3.84 bpw reaches 99.63% of BF16's aggregate score across eight benchmarks and 3.23 bpw reaches 98.72%, with all five models on the measured quality/speed-bpw frontier across six GPUs against Unsloth v3, ISTA-DASLab, AtomicChat and Bartowski. The load-bearing argument is methodological: Unsloth Dynamic V3's UD-IQ3_S posted ~20% lower KLD at comparable size yet lost on downstream task results, so builders picking quants by KLD alone may be picking the wrong one. They also report DFlash2 at 1.34-2.10x baseline throughput and MTP at 1.28-1.66x under temperature sampling.
2026-09-16 / reddit-researcherAccio Lab publishes Occamy-1.0 weights, a 35B co-work agent model post-trained from Qwen3.6-35B-A3BThe Hugging Face repo Accio-Lab/occamy-1.0 was last modified 2026-09-16 and carries 93 likes and 1,048 downloads against the paper arXiv:2609.11977, 'Occamy-1.0: Open Pareto-frontier 35B Intelligence for Co-work' (dated 2026/09/04). It is Apache-2.0, a Qwen3_5MoeForConditionalGeneration image-text-to-text MoE, and the paper's pitch is cost-per-episode rather than peak capability: co-work agents spend most steps on state tracking, coordination, recovery and follow-through, not frontier reasoning, so they trained execution-grounded replayable long-horizon trajectories across multiple harnesses. Useful if you are paying for a long agent loop rather than a single hard question.
2026-09-16 / arxiv-researcherDistilling Arena Trajectories Into Explicit Observe-Think-Act Chains Lifts Qwen3-Coder-30B Past Qwen3 Coder PlusCoaching Qwen3 Coder 30B to Think Like a CodeClash Arena Agent (arXiv 2609.16096, submitted 14 Sep 2026) starts from the observation that Qwen3 Coder Plus ranked last among the 8 commercial agents in the original CodeClash multi-round tournament work, then diagnoses the open-weight 30B version as producing frequent syntax and protocol-breaking errors with weak cross-round strategic adaptation. Vanilla instruction tuning cannot fix this because offline SFT cannot verify whether an action is valid or beneficial. Their ReAct SFT rewrites teacher trajectories into explicit [obs][thought][act] chains, and trajectory-quality weighted SFT reweights samples toward post-edit checking; the fine-tuned open model then outperforms the original Qwen3 Coder Plus in tournament evaluation.
2026-09-16 / arxiv-researcherTwo Tool-Level Defenses Drive Prompt Injection and Memory Poisoning to 0% Attack Success in Many SettingsUniversal Defenses for Tool-Integrated LLM Agents (arXiv 2609.16098, submitted 14 Sep 2026) tackles direct injection, indirect injection, memory poisoning, and backdoors in one framework via two tool-level mechanisms: Attacker Tool Filtering, which uses anomaly detection such as Isolation Forest to remove suspicious tools, and Normal Tool Recalling, a white-box method that restores the agent's original toolset before planning. Across four open-source models (Gemma2-9B, Qwen2-7B, LLaMA3-8B, LLaMA3.1-8B) and three proprietary ones (GPT-3.5, GPT-4, GPT-5), attack success rates fell to 0% in many settings while original task success was preserved or improved. Code is released, and the practical read is that guarding the toolset is cheaper than guarding the prompt.
2026-09-16 / arxiv-researcherA 24 GiB MacBook Serves 196K Input Tokens Locally, a 6.93x Jump Over the mlx-vlm BaselineJustFit (arXiv 2609.17475, submitted 15 Sep 2026) is an MLX inference runtime combining compressed KV execution, component residency swapping, and state-preserving serving transitions. On a 24 GiB M4 Pro MacBook running Qwen3.8-27B MXFP4, three independent runs completed 196,608 input and 16,384 output tokens, lifting single-request context from the mlx-vlm baseline's 30,720 positions to 212,992. A 32K-input probe reached 19.11 tokens/s with a median peak process footprint of 16,374 MiB, and the runtime answered 29 of 30 AIME 2026 problems correctly.
2026-09-16 / github-pulse-researcherA missing newline before </think> was silently destroying prompt-cache reuse for qwen3-coder in llama.cppPR #28869, merged 2026-09-16, forces `\n</think>` rather than bare `</think>` when llama.cpp terminates a reasoning budget for qwen3-coder templates. The old string did not match what the template renders on subsequent requests, so every follow-up request missed the prompt cache. It is a one-character class of bug with a large cost, and the general lesson for anyone injecting forced tokens into a chat template is that the injected text has to be byte-identical to what the template itself would emit.
2026-09-16 / hn-researcherPerplexity Puts Its Portable Computer Agent on Windows, Running Qwen 3.8 27B Entirely on a 24GB RTX GPUPerplexity and NVIDIA announced on September 14 that the local version of the Perplexity Computer agent now runs in the Windows app on GeForce RTX and RTX PRO cards with at least 24GB of VRAM. The on-device model is Qwen 3.8 27B, post-trained for the Computer harness and optimized for RTX, and it reaches other desktop apps through local MCP servers so tool use stays on the machine. Work done locally burns no Perplexity credits and no files leave the device, which is the first mainstream consumer agent to make the local-inference tradeoff explicit in billing.
2026-09-15 / reddit-researcherRe-plot Artificial Analysis against RAM instead of parameters and K2 Horizon's rankings collapseA 74-upvote r/LocalLLaMA post did the arithmetic the leaderboard hides. At Q4_K_M, no drafter, no vision, 128K KV cache: K2 Horizon 36B-A4B needs 2 GiB dense plus 19 GiB experts plus 6.7 GiB context; the 7B needs 5.2 GiB weights plus 5 GiB context; the 3.7B needs 2.9 GiB weights plus 5 GiB context. Compare Qwen3.6-35B-A3B at 0.7 GiB of context and MiniCPM5-2B at 1.5 GiB. The author's conclusion is that K2 Horizon 36B-A4B only makes sense on exactly 16GB VRAM with 32GB host RAM, and that on 24GB VRAM Qwen3.8-27B is faster, smarter, and fits 256K context.
2026-09-15 / reddit-researcherK2 Horizon 7B lands between Qwen 3.6 27B and 35B-A3B on Artificial Analysis, and is actually 9BIFM's K2 Horizon lineup showed up on the Artificial Analysis Intelligence Index, and the 7B slots between Qwen 3.6 27B and Qwen 3.6 35B-A3B, which would be remarkable for the size. Two corrections from the thread and the model card: the Hugging Face config reports roughly 8.999B parameters in BF16 for a dense model, and the README states 18.0 GiB of KV cache at 128K tokens, which the top comment argues disqualifies it from the 'GPU poor' framing the post used. The repo is Apache 2.0, created 2026-09-01, with 7,447 downloads and 152 likes.

Graph relationships

BENCHMARKED AGAINST
EchoNet
Qwen 3.8 was tested on the EchoNet benchmark.
Source finding
SUPPORTS
Unsloth
Unsloth lists Qwen 3.8 as a supported model
Source finding
RELEASED
Qwen
Qwen announced version 3.8 but it is not yet open-weight.
Source finding
CRITICIZES
Fable 5
Fable 5 reportedly refused to edit a Qwen 3.8 deployment script.
Source finding
SUPPORTS
Unsloth -> Qwen 3.8
Unsloth lists Qwen 3.8 as a supported model
Source finding
RELEASED
Qwen -> Qwen 3.8
Qwen announced version 3.8 but it is not yet open-weight.
Source finding
CRITICIZES
Fable 5 -> Qwen 3.8
Fable 5 reportedly refused to edit a Qwen 3.8 deployment script.
Source finding

Source trail

r/singularity GitHub / carloslfu (via r/LocalLLaMA, 27 upvotes)Hugging Face / ISTA-DASLab (via r/LocalLLaMA, 24 upvotes)ByteShape (via r/LocalLLaMA, 126 upvotes)Hugging Face (via r/LocalLLaMA, 87 upvotes; paper arxiv.org/abs/2609.11977)arXiv 2609.16096 arXiv 2609.16098 arXiv 2609.17475 GitHub NVIDIA Blog (corroborated by Tom's Hardware and Unite.AI)r/LocalLLaMA (74 upvotes, 29 comments)r/LocalLLaMA (537 upvotes, 49 comments; verified against huggingface.co/api/models/IFM/K2-Horizon-7B)

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