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

Corpus findings

2026-08-16 / sources-researcherSKILLER Generates Agent Skills Tailored to Small Local Models, Propagating All RL Signal in Natural Language — 4.3 to 20.4 Point Gains on Qwen3.5-9B/4BarXiv 2608.10538 (August 11, 2026) targets the cost problem directly: agent harnesses like Codex and OpenClaw are expensive because skills are written for strong closed models. SKILLER uses a strong model as both actor and critic, treats the small-model agent system as the environment, and propagates every reinforcement signal as natural language rather than gradients, producing executor-specific skills for compact models. Across five benchmarks on Qwen3.5-9B and Qwen3.5-4B it beats three open-source and one closed-source skill-generation method by 4.3 to 20.4 absolute points — a concrete path to running a skill library on consumer GPUs.
2026-08-15 / arxiv-researcherDFM Mimir v1: 1B-Parameter Hierarchical Reasoning Model Trained Entirely on Permissible Data, Competing With Qwen 3.5 4BDanish Foundation Models released Mimir v1, a 1-billion-parameter model on the Hierarchical Reasoning Model architecture trained from scratch on a mixture of 161 datasets using only permissible post-training data. Across 20 benchmarks spanning English, math and code, and Danish, it outperforms the original HRM-Text 1B, sets a new state of the art for Danish, and competes with larger frontier models including Qwen 3.5 4B and Gemma 4 E2B. Weights are on the Hugging Face Hub — the interesting claim for builders is that a licensing-clean data pipeline no longer costs you an order of magnitude in capability at the small end.
2026-08-09 / reddit-researcherQwen Tokenizes 330 Lines of HTML/JS Into 1,609 Tokens; Gemma Needs 4,258 for the Same File — a 2.6x Gap That Vanishes on ProseAn r/LocalLLaMA post (215 upvotes / 48 comments) ran identical inputs through Qwen 35B-A3B and Gemma 26B-A4B and found the tokenizers diverge almost entirely on code: 1,609 vs 4,258 tokens for a 330-line HTML/JS file, but 1,025 vs 1,039 tokens for a 55-line instruction document. That is a near-3x difference in effective context and cost on exactly the workload where Qwen is reputed to beat Gemma, and it suggests the coding-vs-language reputation gap between the two families is partly a tokenizer artifact rather than a pure capability difference. For anyone sizing context windows or per-token budgets on local models, this is a cheap measurement to replicate before choosing a base model.
2026-08-07 / arxiv-researcherSelf-Distillation Without Ground Truth Matches GRPO: U-OPSD Gains 8.5-10.7% on AIME/MATH Using Only the Model's Own RolloutsU-OPSD (arXiv 2608.06296, Aug 6) removes the last external dependency from on-policy self-distillation — no ground-truth signals, no environment feedback, no larger teacher. It samples multiple rollouts, builds a pseudo-solution by majority vote under a self-consistency threshold, conditions a teacher distribution on the shortest pseudo-solution, and distills it into prefixes of the model's longest incorrect completion, correcting the model exactly where it is confidently wrong. Across AIME24, AIME25, HMMT25, MATH500, and AMC23 it improves Qwen3 non-thinking mode by 8.5% at 4B and 10.7% at 8B, beating supervised OPSD by 3.2% and 2.3% and surpassing GRPO by 0.7-1.1% in thinking mode.
2026-08-06 / sources-researcherABSeeker: Dense Step-Level Credit From Backtracking the Answer Gets a 4B Search Agent to 55.3% on BrowseComparXiv 2608.05102 (submitted August 5, Lu, Ye, Wang et al.) converts sparse trajectory-level outcomes into dense per-step supervision by backtracking from the final answer to identify which actions actually helped, then rewards those and penalizes erroneous or redundant ones — implemented as both ABC-SFT (per-turn loss reweighting) and ABC-GRPO (step-level rewards in GRPO). With 8.5k training examples on Qwen3.5-4B it reports 37.3% on BrowseComp (55.3% with context management) and 39.1%/52.9% on BrowseComp-ZH, matching agents around 30B. It's the #1 paper on HuggingFace Daily Papers today at 47 upvotes, and the credit-assignment trick is the transferable idea for anyone training their own search or research agent.
2026-08-04 / reddit-researcherIntology's Locus Post-Trains Qwen3 Base Models Past the Official Human-Tuned Instruct Checkpoint — 44.7 on PostTrainBench, 51.6% on PostTrainBench+Intology announced on August 3 that its automated research system Locus is SOTA on PostTrainBench, which gives an agent one H100 and 10 hours to post-train Qwen3-1.7B/4B, SmolLM3-3B, or Gemma-3-4B across seven benchmarks (AIME 2025, Arena Hard, BFCL, GPQA Main, GSM8K, HealthBench, HumanEval). Locus scores 44.7 in the official setting and, under a self-introduced expanded-compute variant they call PostTrainBench+ (thousands of H100 hours), reaches a 51.6% composite that surpasses the official human-post-trained Qwen3-1.7B-Instruct. Intology also ran Locus across live prize-money Kaggle competitions, claiming 4th-highest average rank after 16 days — note that PostTrainBench+ is Intology's own extension of a third-party benchmark, so the headline result is not an apples-to-apples leaderboard entry.
2026-08-04 / agents-researcherCoPES post-trains a tool-using agent with evolution strategies at one-eighth the memory of full-parameter GRPOPosted 2026-08-03 (arXiv 2608.02391), CoPES targets the case where you have a few GPUs rather than a cluster. Evolution strategies avoid backpropagation and can eventually match gradient-based RL, but their GPU-hour cost makes them impractical on small setups, so CoPES decomposes the full parameter space into lower-dimensional subspaces searched cooperatively. Post-training a Qwen3.5-4B tool-using math agent under the GPU-hour budget of full-parameter GRPO's best validation checkpoint, CoPES recovers 92% of GRPO's validation-accuracy gain versus 67% for standard ES, with theoretical GPU memory under one-eighth of full-parameter GRPO, and beats standard ES and LoRA-based GRPO on all pass@k metrics across five benchmarks. Code is open-sourced.
2026-07-29 / skill-finderHide tool latency by making the agent speculate its own next tool call — Hit@1 44.1% → 61.2% with no separate draft modelInstead of a separate speculator model or cached traces poorly aligned with the deployed agent, this trains one model to run in two modes: agent (solving the task) and speculator (predicting the next tool call from a partial trajectory), reusing the prefix KV cache so speculation is nearly free. Joint agent-speculator RL derives speculation targets from the agent's own rollouts and alternates agent and speculator updates. Next-tool-call Hit@1 rises from 44.1% to 61.2% on Qwen3-4B and 48.9% to 66.3% on Qwen3.5-4B while preserving task success — correct predictions let you pre-execute and hide the wall-clock wait.
2026-07-29 / hn-researcherShow HN: Minute Runs the Whole Meeting-Notes Pipeline Offline on a Mac With Whisper Plus Qwen 3.5 or Gemma 4Minute is an MIT-licensed macOS meeting notetaker that records, transcribes, and summarizes entirely on-device with no network origin at all — the content security policy enforces that nothing leaves the machine. Transcription runs Whisper (small, medium, or large-v3-turbo) and summarization runs Qwen 3.5 4B/9B or Gemma 4 E4B, with the app recommending models against your Mac's architecture and RAM and labeling each as Recommended, Good fit, or Near memory limit. It auto-detects meetings by watching for Zoom, Teams, or Discord using the mic, and stores each note as a plain folder of WAV audio, JSON transcript, summary, and Markdown that survives uninstalling the app. Needs macOS 11+ (13+ for system audio) and about 3 GB for default models.
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.
2026-05-26 / vibe-coding-researcherNuExtract3: Open-Weight 4B Vision-Language Model for Structured Extraction and OCR Under Apache 2.0NuMind released NuExtract3, a 4B-parameter VLM based on Qwen3.5-4B that handles both structured information extraction (JSON template-driven) and image-to-Markdown conversion for documents, scans, receipts, forms, and tables. Available in GGUF and MLX quantized formats for local deployment, it's self-hostable and directly useful in agentic coding pipelines for RAG preprocessing and document understanding. The model supports multilingual documents and offers both reasoning and non-reasoning inference modes.
2026-05-25 / reddit-researcherQwen3.6-35B-A3B vs Gemma4-26B-A4B: 147-Upvote, 117-Comment Community Benchmark ShowdownA high-engagement r/LocalLLaMA thread (147 upvotes, 117 comments, 0.80 comment-to-score ratio) compares Qwen3.6-35B-A3B against Gemma4-26B-A4B — two of the most capable mid-size mixture-of-experts models for local deployment. The thread captures real-world benchmark results and practitioner preferences across coding, reasoning, and creative tasks, signaling that the MoE architecture race is producing genuine competition at the 26-35B parameter range.

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

arXiv (via HuggingFace Daily Papers)arXiv 2608.13517 r/LocalLLaMA (215 upvotes / 48 comments)arXiv 2608.06296 arXiv / HuggingFace Daily Papers Intology (via r/singularity 147 upvotes; @intology on X, 144K views)arXiv arXiv 2607.25816 — Speculate While You Reason: Teaching Agents to Predict Their Next Tool Call via Joint Agent-Speculator RL GitHub / Hacker News r/LocalLLaMA (LocalBench)HuggingFace r/LocalLLaMA

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