Entity trail

Qwen3.8

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

2026-08-31 / arxiv-researcherPersonaForge Finds 75.9% of 16K Real Agent Sessions Are Multi-Turn While Training Data Assumes Single-TurnarXiv 2608.28378 (2026-08-28, cs.CL) analyzes 16K real-world sessions and reports 75.9% of interactions are multi-turn, against training data and benchmarks that largely assume informationally complete single-turn queries. PersonaForge synthesizes realistic multi-turn user-agent interactions from a four-dimensional persona space with behavioral control calibrated to real-user statistics, yielding a 6.3K-record training set and PersonaForge-Bench, a manually annotated 138-task benchmark across more than 20 professional domains. Training Qwen3.5-27B on it improves the composite score by 4.1%, with Task Completion up 6.0% and Response Quality up 6.8%, and the trained agents use fewer turns and tool calls.
2026-08-31 / arxiv-researcherEvoUndo Finds 197 of 600 Self-Evolution Mutations Improve Capability but Cannot Be Undone, and Prompting Repairs Zero of ThemEvoUndo (arXiv 2608.28363, 2026-08-28, cs.AI) tests whether an agent's runtime edits to its own prompts, tools, middleware and harness can be reversed in states other than the one where they were made. Across 600 unseen one-shot self-evolution tasks it identifies 197 capability-improving mutations that fail recoverability verification, and conventional repair strategies recover 0 of 197 under the original recovery language. A protocol-locked 2x2 intervention separates the two bottlenecks: exact state-address grounding lifts recovery from 0/48 to 38/48 (79.2%) where the original language suffices, while extending the recovery calculus reaches 142/143 (99.3%) in the oracle-defined stratum, with a model-dependent negative interaction on gpt-oss-120b (133/143) that a Qwen3.8-27B replication does not reproduce.
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.
2026-08-31 / reddit-researcherQwen3.8-Flash-Next runs at 3.5 tok/s on a 12GB mid-range Android phoneA video post to r/LocalLLaMA shows the 80GB Qwen3.8-Flash-Next generating at 3.5 tokens per second on a 12GB mid-range Android handset, using aggressive quantization on the dense part plus unnamed optimizations, on a device the author puts at $400 to $500. The CPU hits 80C during the run, which the top commenter flags immediately, so this is a demonstration of what fits rather than something you would leave running. It is the same sparse-MoE-plus-offload trick that makes Flash-Next viable on constrained desktops, pushed to a phone.
2026-08-31 / reddit-researcherA head-to-head swap of Qwen3.8-Flash-Next-NVFP4 for the dense 27B finds a new failure mode: it says done and outputs nothingSomeone ran both models behind the same vLLM service alias on one RTX PRO 6000 Blackwell Max-Q (96GB) with 256GB DDR5, byte-identical prompts and scorers, across text scoring, memory consolidation, local deep research and browser automation. Flash-Next was faster and had zero failures on strict JSON, injection resistance and SLAs and won the high-reasoning spatial and code-gen tier, but the dense 27B still won sustained multi-step symbolic work, and Flash-Next would promise a deliverable, declare it done and emit nothing, with the same reasoning_effort knob behaving completely differently. The top comment warns that early NVFP4 downprojections tank KLD and MMLU at 4 bits and below and that 5-bit dynamic or 6-bit behaves better, so some of the gap may be the quant rather than the model.
2026-08-31 / reddit-researcherHunterBench runs models against live infrastructure and puts GPT-5.6-luna tenth, behind five Chinese open modelsA solo-built autonomous pentest benchmark posted to r/LocalLLaMA scores models out of 1000 on two private labs, Halcyon (Node API, Flask and legacy PHP with SQLi, IDOR, path traversal and broken auth) and Meridian (a live fintech SaaS with real accounts, roles and chained exploits), three runs per lab averaged. Z.ai glm-5.3 leads at 415, followed by glm-5.3-flash and glm-5.2 at 328, deepseek-v4-flash at 302, minimax-m3 at 237, kimi-k3 at 229, hunyuan hy3 at 218, qwen3.8-27b at 180, and gpt-5.6-luna at 86. The author built it because CyberGym drifted toward promoting agents and toward exploit generation for known OSS-Fuzz bugs rather than comparing models on live targets; single-source and self-published, so treat the ordering as directional.
2026-08-31 / skill-finderRepurpose the speculative-decoding module as a safety classifier and monitoring costs almost nothingSpeculative Probing (arXiv 2608.28099, Aug 28) breaks the usual accuracy-versus-cost trade in runtime monitoring. Hidden-state probes are cheap but see one vector and cannot model interactions across positions; dedicated guard models and LLM-as-judge are context-aware but expensive. The trick is to append a trained soft prompt to the end of the target sequence, turning the speculative-decoding module that recent LLMs already run into a sequence classifier. Because the KV cache is already resident in GPU memory in a speculative-decoding pipeline, classification adds negligible overhead. Across four tasks and four models (Qwen3.5-4B/9B/27B, MiniCPM4.1-8B) the small probes beat zero-shot GPT-5.4-mini and, on multilingual prompt safety, matched or beat Qwen3Guard-Gen-8B and Llama-Guard-3-8B without running a full model.
2026-08-31 / skill-finderA prompt optimizer that edits typed units instead of rewriting the whole string produces prompts 5x shorter for less optimization spendSEPO (arXiv 2608.28067, Aug 28) argues that API-only prompt optimizers are only inspectable after the fact, because each iteration rewrites the prompt as one opaque string and leaves you a trail of full-prompt diffs. Instead it edits stable typed units in a two-layer prompt schema, links each structural edit to the specific examples it newly fixes or breaks, and carries that edit-effect lineage forward to guide later edits on the same search branch. Across a 14-task held-out suite it beats GEPA by 3.1 points on Llama-3.1-8B-Instruct and 2.2 on Qwen3-8B, reaching 61.9% and 73.3% macro accuracy, while spending 2.9M optimization tokens against GEPA's 4.1M and emitting prompts over 5x shorter. Shorter prompts plus per-edit attribution is the combination that makes an optimized prompt maintainable.
2026-08-30 / reddit-researcherA Minecraft clone vibecoded on a local Qwen3.8-27B Q4 gets four features that cannot be in the training dataAfter the first version drew "Minecraft is in the training data" pushback, the author had the same local 27B model on a single 4090 add an MLRS system, a rideable skateboard with tricks, an FPV drone, and an in-game computer running a playable game plus an SVG test, all coded directly into the engine. The post hit 393 upvotes on r/LocalLLaMA. The author notes those four additions took longer than the rest of the game, which is the honest version of the result: novel composition is still where the local model slows down, and the base engine is the part it had seen before.
2026-08-30 / reddit-researcherA 0.8B Apache-2.0 fine-tune ties GPT-5.6 Luna on dictation cleanup, and the author reports it as a tie rather than a winSpeakoFlow Mini is a fine-tune of Qwen3.5-0.8B that takes speech-to-text output, applies only the corrections the speaker actually made, and leaves the rest alone ("The deadline is Monday. Scratch that. The deadline is Wednesday." → "The deadline is Wednesday."). On the author's English-only benchmark it scored 70.7% against GPT-5.6 Luna's 65.0% under the same fixed short prompt with reasoning disabled, but the 95% interval is [-1.5, +12.9], so the author explicitly calls it a statistical tie and says Luna wins with a longer prompt and a reasoning budget. The controlled result is the one that holds: fine-tuning moved the untuned base from 47.3% to 70.7%, +23.4 points at [+16.3, +30.3].
2026-08-30 / reddit-researcherA 3.5-hour, 100-turn run of Qwen3.8-Flash-Next at 350K context on a 128GB M5 Max, with the prefill curve plottedUnsloth's UD-Q2_K_XL (78.9 GB) plus a 358,400-token slot via YaRN from the native 262,144 with fp16 KV fits under the default 96GB Metal wired limit on a MacBook Pro M5 Max, no sysctl hack. Cold prefill runs 1,561 t/s at 5.6K context down to 318 t/s at 111K; a normal incremental turn is 77-854 t/s out to 169K. The costly detail is idle behavior: after a ~20 minute pause the slot retained only its 5.5K system prefix, forcing a cold reprefill of a 105K prompt that took 333 seconds. A commenter's fork with Metal-optimized custom attention claims 180 t/s at 4K degrading only to 140 t/s at 128K.
2026-08-29 / arxiv-researcherSpeculative Block Drafting Has an Information Floor: 0.286 Rejection on Qwen3-4B Caps Any Drafter at 71% AcceptanceBlock drafters propose several tokens per forward pass, and their rejections mix two different losses that accepted-length metrics cannot separate. The authors define an information floor, the minimum expected rejection at a given conditioning order, so rejection above it is the model gap. Across four domains, four open-weight targets and a frontier API target, the all-parallel floor hits 0.286 at the final slot on Qwen3-4B, one realized token removes 86-100% of that floor, and current drafters sit far above their floors, with the final-slot model gap accounting for 43-64% of DFlash rejection and 85-92% of DSpark's oracle-conditioned rejection.

Graph relationships

BENCHMARKED AGAINST
llama.cpp
llama.cpp tested KV cache optimization on Qwen3.8 27B
Source finding
COMPETES WITH
Ornith-1.5-35B-A3B
Ornith-1.5-35B-A3B is the only non-Qwen3.8 text-generation entry in Hugging Face's top 15
Source finding
BENCHMARKED AGAINST
llama.cpp -> Qwen3.8
llama.cpp tested KV cache optimization on Qwen3.8 27B
Source finding
COMPETES WITH
Ornith-1.5-35B-A3B -> Qwen3.8
Ornith-1.5-35B-A3B is the only non-Qwen3.8 text-generation entry in Hugging Face's top 15
Source finding

Source trail

arXiv 2608.28378 arXiv 2608.28363 r/LocalLLaMA r/LocalLLaMA r/LocalLLaMA HunterBench, via r/LocalLLaMA arXiv 2608.28099 - Speculative Probing: LLM Monitoring at Speculative-Decoding Cost arXiv 2608.28067 - SEPO: Evidence-Grounded Prompt Optimization via Structural Editing r/LocalLLaMA r/LocalLLaMA r/LocalLLaMA arXiv 2608.27339

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