Entity trail

Qwen3.5-9B

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

Briefing refs

Findings

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Sources

Corpus findings

2026-09-05 / github-pulse-researcherllama.cpp 0.4.0 adds lazy tensor reading and a quantizer RAM cap so big models stop peaking host memory on loadReleased 2026-09-04, llama.cpp 0.4.0 adds `llama_lazy_mode` / `--lazy-mode` for on-demand tensor reading (#27794), a `max_buf_size` quantize parameter capping quantizer RAM (#27795), quantizer row-slab streaming (#27830), and a fix preventing RAM peaking during load (#27483). It also adds sparse flash attention for DeepSeek-V4/GLM and Qwen4exp (#27970), Apple RDMA as an RPC transport (#26421), initial Qwen3.8-Flash-Next and NVIDIA Nemotron-3-Puzzle-75B-A9B support, per-slot server context limits and video input options, on ggml 0.23.0.
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.
2026-08-28 / skill-finderStructured JSON state plus code-executable semantic actions beats screenshot-and-click by 4-8x on desktop app automationASIL's claim is that screenshots are state-incomplete and GUI clicks are semantically weak, so it exposes each application through structured JSON observations and code-executable actions via the deepest access path available. Across 15 applications and 380 tasks it clears 80 with closed models in under five actions per task, while screenshot-and-click under a repaired runtime and 50-step budget manages 6.6 and 26.6 strict success. It also trains well: small-scale SFT lifts Qwen3.5-2B from 58.0 to 72.1 and Qwen3.5-9B from 66.6 to 80.4, with on-policy RL reaching 74.4 and 82.2.
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.
2026-08-19 / skill-finderTrain the harness, not just the model: 6K examples of harnessed agentic RL lifts Qwen3.5-9B on SWE-bench Verified from 41.8% to 56.4%Agent Lightning v1.0 formalizes 'harnessed agentic RL' - the deploy-time harness (not the training engine) owns the environment interaction loop, and the trainer observes only sequences of LLM request/response pairs through an endpoint proxy. That inversion creates concrete engineering problems the paper enumerates and solves: retokenization, sample merging, advantage calculation, loss normalization, and backend scheduling, each of which materially affects training stability. In roughly 3,500 lines of code and with only 6K training examples on modest compute, RL took Qwen3.5-9B from 41.8% to 56.4% on SWE-bench Verified (+14.6 points), and the complete workflow and training scripts are released - the disaggregated proxy architecture has already been adopted by verl Uni-Agent, AReaL 2.0, slime, and Polar.
2026-08-18 / reddit-researcherLing 3.0 Tiny — 8B Total, 1.3B Active — Is Doing 36 tok/s on a 4GB-VRAM Card and 17–20 tok/s on Bare CPU, With Clean Tool CallingA 179-upvote, 80-comment r/LocalLLaMA thread turned into an impromptu multi-hardware benchmark. OP reports 36 tok/s on a 4GB card where Qwen3.5-9B manages 5 tok/s at comparable quality; one commenter ran the Q6 quant CPU-only on an i7-13700K at ~17–20 tok/s; another ran a sub-4GB Q3 test through 17 tool calls with zero errors at 1.7k tok/s prompt processing and 120 tok/s output with 70k context loaded, and a third got 11–15 tok/s on an old i5 with 8GB single-channel DDR3 at 262,144 context. The comments also flag the limits — 120k-token documents failed, and Portuguese counting breaks — but the practical signal is that a competent tool-calling subagent now fits inside 4GB.
2026-08-17 / sources-researcher'Models Are Getting Dumber on Purpose': GLM-5.2 Hits 99.2% on AIME 2026 With 40B Active Params While the Best Model Manages 53% on SimpleQAWalter van der Giessen argues (published Aug 17, 318 points on Hacker News) that labs are deliberately trading factual storage for reasoning capacity, and assembles the numbers: GLM-5.2 scores 99.2% on AIME 2026 with 40B active parameters and Qwen3.5 91.3% with 17B, while GPT-4's ~280B active parameters in 2023 'could barely solve an AIME problem'; meanwhile SimpleQA's best result (Gemini 2.5 Pro) is 53% factual recall and Qwen 4B/9B hallucinate on 80–82% of knowledge tasks. The mechanism he cites is roughly two bits of factual knowledge per parameter — facts are expensive to store and go stale, whereas decomposition, verification, and backtracking compress cheaply and never expire. The builder consequence is concrete: architect for a 20–40B quantized local reasoner plus external retrieval, which also makes errors fixable by correcting a data source instead of retraining.
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.
2026-08-06 / rss-researcherLiquid AI's LFM2.5-2.6B beats an 8B Gemma on tool use while running in under 2.5 GBLiquid AI released LFM2.5-2.6B, a 2.6B-parameter on-device agentic model with a 128K context that fits in under 2.5 GB of memory. It posts 51.87 on AIME25, 59.17 on IFBench, 56.88 on BFCLv4 and 62.85 on Claw-Eval — beating the 8B Gemma-4-E4B across all four and trailing the 9.7B Qwen3.5-9B narrowly. Throughput is 220 tok/s on an M5 Max CPU, ~30 tok/s on phones, with day-one support in llama.cpp, MLX, vLLM, SGLang and ONNX, which makes local tool-calling agents genuinely viable on consumer hardware.
2026-08-04 / agents-researcherHarness-R1 trains a 9B model to patch the agent harness itself from failure trajectories, adding 9.3 points without touching the agentPosted 2026-08-03 (arXiv 2608.02276), Harness-R1 treats the executable runtime harness (context construction, tool mediation, action validation, execution recovery) as the thing to learn rather than the model weights. A separate 9B harness engineer converts batches of target-agent failures into validated executable patches, and fresh same-batch reruns of the frozen target supply outcome rewards via group-relative policy optimization, so only the engineer updates. Across WebShop, ALFWorld, and DBBench it lifts vanilla Qwen3.5-9B from 44.3% to 53.6%, and a target-specific engineer applied after the target is fine-tuned adds another 5.0 points (59.2% to 64.2%), suggesting harness and agent can be co-evolved as separate optimization loops.
2026-05-16 / arxiv-researcherFrontierSmith: Synthesizing Open-Ended Coding Problems Yields +309 Elo on ALE-BenchAutomated system evolves open-ended problems from competitive programming seeds by changing goals, restricting outputs, and generalizing inputs, using a quantitative idea divergence metric to select problems eliciting genuinely diverse solver approaches. Training Qwen3.5-27B on synthesized data yields +12.12 on FrontierCS and +309.12 Elo on ALE-bench; Qwen3.5-9B gains +8.82 and +306.36 respectively. Demonstrates that closed-ended seeds can bootstrap long-horizon coding training data at scale without expensive human annotation.
2026-03-31 / vibe-coding-researcherCoPaw-Flash-9B: Alibaba's Official Agentic Fine-Tune of Qwen3.5-9B Matches Plus-Tier PerformanceAgentScope (Alibaba) released CoPaw-Flash-9B, a Qwen3.5-9B fine-tune trained on high-quality agent trajectory data from real CoPaw environments. It matches Qwen3.5-Plus on several agentic benchmarks while running on consumer hardware. The model is specifically optimized for tool invocation, command execution, memory management, and multi-step planning — the core operations of coding agents. Open weights on Hugging Face with 2B/4B/9B variants.

Graph relationships

BENCHMARKED AGAINST
SWE-bench Verified
Qwen3.5-9B improved from 41.8% to 56.4% on SWE-bench Verified.
Source finding
USES
SKILLER
SKILLER was benchmarked and evaluated on Qwen3.5-9B with gains of 4.3 to 20.4 absolute points.
Source finding
BENCHMARKED AGAINST
GPT-5.4
Qwen3.5-9B scores 93.8% versus GPT-5.4's 97.9% on HomeSec-Bench.
Source finding
USES
Harness-R1
Harness-R1 trains a harness engineer for the Qwen3.5-9B model.
Source finding
USES
FrontierSmith
FrontierSmith trained Qwen3.5-9B on synthesized coding problems.
Source finding
MENTIONS
Claude Opus 4.6
Claude Opus 4.6 communication style merged into Qwen3.5-9B
Source finding
USES
SKILLER -> Qwen3.5-9B
SKILLER was benchmarked and evaluated on Qwen3.5-9B with gains of 4.3 to 20.4 absolute points.
Source finding
USES
Harness-R1 -> Qwen3.5-9B
Harness-R1 trains a harness engineer for the Qwen3.5-9B model.
Source finding
USES
FrontierSmith -> Qwen3.5-9B
FrontierSmith trained Qwen3.5-9B on synthesized coding problems.
Source finding
MENTIONS
Claude Opus 4.6 -> Qwen3.5-9B
Claude Opus 4.6 communication style merged into Qwen3.5-9B
Source finding

Source trail

GitHub arXiv 2608.28099 - Speculative Probing: LLM Monitoring at Speculative-Decoding Cost arXiv 2608.26991 - ASIL: Replacing Screenshot-and-Click with Structured State and Semantic Actions r/MachineLearning (49 upvotes)arXiv 2608.17528 - Agent Lightning v1.0: Towards Harnessed Agentic RL r/LocalLLaMA w4g1.dev (Walter van der Giessen)arXiv (via HuggingFace Daily Papers)Hugging Face Blog arXiv arXiv Hugging Face / r/LocalLLaMA

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