Entity trail

Qwen 3.5 4B

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

Briefing refs

Findings

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Sources

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

Corpus findings

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.
2026-09-13 / reddit-researcherbartowski measured which tensors actually break under quantization, and Q3_K_M got 15% smaller for itA Hugging Face blog post dated September 10 documents 96+ hours across 1,000+ quantization configurations on Qwen 3.5 0.8B and 4B, producing per-tensor layout maps that replace the generic GGUF heuristics. The measured findings: token embeddings are 8-16x more sensitive to degradation than other weights, sensitivity follows a U-shape by model depth, attention projections matter and gate tensors mostly do not. Q3_K_M on Qwen3.5-4B comes out 15% smaller with quality called marginally better across the board, and the layouts were validated against Gemma, Granite and DeepSeek with a canary test for generalization. r/LocalLLaMA picked it up at 213 upvotes when bartowski re-uploaded the Qwen3.8-27B GGUFs under the new layout.
2026-09-12 / reddit-researcher100 zebra puzzles and 6.5 minutes on one H100 moved Qwen 3 4B Base from 54% to 84.6% on MATH-500A Hugging Face blog post published 2026-09-11 introduces PCSS, a per-example calibrated sigmoid scaler derived from KTO that reduces to standard SFT gradient times a scaler decaying as the model masters an example. Fine-tuning Qwen 3 4B Base on 100 zebra puzzles for 6.5 minutes on a single H100/H200 gave 84.60% on MATH-500, a +30.5% delta, with +2.57% on AIME 2025. The gains shrink sharply with model strength: Granite 4.1 3B got +11.1% and Qwen 3.5 9B only +3.1%, so this is a small-model recipe, not a general one. A reproduction notebook and the dataset viewer are both public.
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.
2026-09-04 / arxiv-researcherSpeculative Macro Commit Pre-Executes Multi-Action Chains and Cuts Agent Wall Time 44.9% on AppWorldTool-using agents lose wall-clock time to serial action-observation turns, not just inference. SMC runs a two-tier system where a large authoritative actor produces the official trajectory while a faster drafter continuously predicts and executes future action chains on an isolated environment snapshot, matching against a macro library of recurring multi-action skeletons mined from training traces; when the actor's next tool call matches the first drafted action, SMC commits the remaining pre-executed steps and their observations. With Qwen3.5-27B INT4 as actor and Qwen3.5-4B as drafter, it matches sequential accuracy while cutting latency 10.23% over the Speculative Actions baseline and 18.59% over sequential execution on the Tau^2-Bench Telecom subset, and cuts AppWorld wall time 7.7% over SA and 44.9% over sequential with a small completion drop. Code is public.
2026-09-04 / reddit-researcherWidening MoE expert selection only in late layers cut reasoning tokens 8.5% with no retrainingA Zenodo paper posted to r/LocalLLaMA expands the router's expert budget past K in the final transformer layers with a linear decay on the extra experts, turning Qwen3.6-35B-A3B into what the author calls A4B+. Across all 714 MMLU-Pro questions it produced an 8.5% drop in mean reasoning tokens and 10.9% lower latency (p=6.5e-6), with accuracy statistically unchanged at 84.5% vs 84.0% (p=0.77). It is an inference-time routing change only, so any llama.cpp or vLLM operator running a sparse MoE can test it today.
2026-09-03 / skill-finderCo-evolving the harness alongside the policy cut AgentDojo attack success 3x while raising benign utilitySafeEvolve (arXiv 2609.02786, 2026-09-02) argues that safety alignment done only at the policy level or only through external harness updates leaves runtime control disconnected from intrinsic safety. It runs a continual loop where completed on-policy trajectories become safety evidence: on the harness side, bounded component-level updates to the safety prompt and hierarchical skills, producing auditable and reversible harness artifacts; on the policy side, harness-use SFT to teach the model to actually use those artifacts, then harness-augmented RL with verifier-decomposed rewards. For Qwen3.5-4B it delivered a 3x attack-success-rate reduction on AgentDojo while benign utility rose from 59.79% to 61.86%.
2026-09-02 / sources-researcherA documented 48GB M4 Pro Mac mini local-agent stack: 325 tok/s prefill, 34 tok/s generation, reachable over TailscaleThis 2026-09-01 writeup gives the numbers most local-LLM posts omit: Qwen3.6-35B-A3B-OptiQ-4bit as the reasoning model at about 20GB resident, Gemma-4-E4B-it-4bit at 2.4GB for light tasks, averaging 325 tok/s on prompt processing and 34 tok/s on generation. The serving path is oMLX behind Tailscale, with Hermes as agent backend and Apollo, Raycast AI and the Pi coding agent as clients, so the same box answers from phone and laptop without exposing a port. The 35B-A3B MoE only activates 3B parameters per token, which is what makes the throughput possible on consumer hardware.
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.
2026-08-24 / reddit-researcherA 450M Vision Model Fine-Tuned on 50,000 Browser Screenshots Scored 44/100, Beating Gemma 4 E4B and Qwen 3.5 2B but Not Qwen 3.5 4BStarting from LFM2.5-VL-450M, which scored 0/100 strict passes on a held-out 100-case browser screenshot benchmark, the author fine-tuned on 16,646 screenshots to reach 30/100, expanded to 50,000 examples weighted toward multilingual OCR, forms, auth screens, modals, tables and loading states to reach 36/100, then exported to a Q4 decoder plus FP16 vision ONNX config for WebGPU which scored 44/100. Comparison scores on the same set: Qwen 3.5 2B at 39, Gemma 4 E4B at 41, Qwen 3.5 4B at 55. Training was supervised LoRA at rank 16, about 4.1M trainable parameters, one epoch on a single RTX 4090, with labels generated by Qwen 3.6 35B, and the author credits matching the output contract to what the browser agent actually consumes rather than generic captions.
2026-08-22 / reddit-researcherMeasured Across 9 Models: Telling an LLM to Be Concise Saves ~1.5x, Shortening Your Prompt Costs Up to 96% MoreResearchers posting to r/MachineLearning tested output compression against input compression at five reduction levels on five short-answer datasets, across GPT-4o, GPT-5.4, Claude Haiku 4.5, Claude Sonnet 4.6, Qwen2.5-VL-7B, Qwen3.5-9B, DeepSeek-R1-Distill, Gemma-4-E4B and Kimi-K2.6. Instructing shorter output averaged 1.5x cheaper and up to 3x in the best case with accuracy roughly flat, and the effect held across an eleven-language run including Swahili, Bengali, Thai and Telugu. Trimming the input prompt did the reverse, costing up to 96% more on the worst benchmark because the model compensates with longer answers. The authors surfaced the paper specifically because Claude Code had shipped a concise output style the day before.
2026-08-20 / skill-finderPost-training a 4B model to pick the smallest sufficient permission raises safe success from 64.36% to 98.48%Task-Conditioned Least-Privilege Learning trains Qwen3.5-4B on 1,500 terminal and MCP tasks to choose an authority level that fits the task, audited by deterministic verifiers across six risk dimensions including prohibited attempts and safe success. Over 2,896 episodes on 500 held-out tasks, safe success went from 64.36% to 98.48% and excess-authority errors fell from 4.56% to 0.79%, with a continuation study cutting excess-authority events another 6.99 points on 400 new tasks. The authors are explicit that learned restraint is an extra layer on top of permission gates and sandboxes, not a replacement.

Graph relationships

COMPETES WITH
Mimir v1
Mimir v1 outperforms Qwen 3.5 4B despite smaller size.
Source finding
COMPETES WITH
Mimir v1 -> Qwen 3.5 4B
Mimir v1 outperforms Qwen 3.5 4B despite smaller size.
Source finding

Source trail

r/LocalLLaMA (74 upvotes, 29 comments)Hugging Face / bartowski (via r/LocalLLaMA)Hugging Face blog / tamewild (via r/LocalLLaMA)r/LocalLLaMA (167 upvotes, 49 comments)arXiv 2609.03236 Zenodo (via r/LocalLLaMA, 122 upvotes)arXiv 2609.02786 — SafeEvolve: Harness-Policy Co-Evolution from Agent Experience for Safety Alignment lws.io arXiv 2608.28099 - Speculative Probing: LLM Monitoring at Speculative-Decoding Cost r/LocalLLaMA (80 upvotes), full benchmark write-up at webbrain.one/blog/tiny-vision-models-compared-ii r/MachineLearning (49 upvotes)arXiv 2608.18351 - Task-Conditioned Least-Privilege Learning for Executable Terminal and MCP Agents

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