Entity trail

Llama-3.2-3B

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

Briefing refs

Findings

Edges

Sources

Corpus findings

2026-09-01 / arxiv-researcherSoft Latent Thinking Drops the Vocabulary Head During Reasoning and Improves Pass@k While Cutting Per-Step ComputeStandard decoding projects hidden states through a large vocabulary head every step, which is expensive and forces all reasoning into discrete tokens; Soft Latent Thinking swaps that head for a lightweight projector during reasoning, doing autoregressive rollout in embedding space so intermediate steps stay continuous. On DeepSeek-Qwen-1.5B and LLaMA-3.2-3B it improves pass@k at every k tested while reducing per-step compute in the chain of thought, and it posts the highest pass@32 among soft-thinking approaches. The claim worth noting for builders is that the token bottleneck, not the reasoning itself, was costing accuracy as well as latency.
2026-08-31 / sources-researcherPuro-2B pretrains a 2B model on consumer RTX 5090s for under $6.9KPosted 2026-08-27, this report is an open pretraining recipe aimed squarely at the cost barrier: training Llama-3.2-3B runs over $1.5M and reproducing SmolLM3-3B needs over $700K. Puro-2B trains from scratch on up to 1.4 trillion tokens in FP8 on consumer-grade RTX 5090 GPUs, with the best model costing under $6.9K in compute and approaching Qwen2.5-1.5B under the authors' evaluation protocol. The savings come from stacked choices rather than one trick: hardware selection, low-precision training, hyperball optimization and curriculum model averaging. This is the recipe an independent builder can actually run.
2026-08-27 / reddit-researcherA 35B-A3B Tool-Calling Benchmark Found Ornith 1.5 and Tiel-Coder Tied, and That Tiel Is an Imatrix Quant of Ornith, Not a FinetuneA r/LocalLLaMA user ran tool-eval-bench across Qwen3.6-35B-A3B and its variants on a cluster of 32GB V100s, finding Ornith 1.5 and Tiel-Coder tied at the top, well above Qwen3.6-27B and close to 3.8-27B, with KAT-Coder slightly ahead of base 35B-A3B and Ornith-1.5-Heretic disappointing. The top comment corrects the framing: Tiel is an Ornith imatrix quant with a different chat template, not a separate finetune, though its author argues the gap shows up on SWE-Bench Live. When asked, the author also ran Qwen-AgentWorld-35B-A3B and got 121.0 average across five runs, about ten points behind base.
2026-08-18 / reddit-researcherQwen Quietly Deleted Qwen3.8-35B-A3B From ms-swift's Model Registry on August 16 and Replaced It With the Dense 27BAn r/LocalLLaMA thread (140 upvotes) asking where the promised 35B-A3B MoE went turned up a hard artifact: modelscope/ms-swift commit a45f1d4, dated 2026-08-16 and titled 'fix wrong model-ids,' removes the `Qwen/Qwen3.8-35B-A3B` and `-FP8` entries from `swift/model/models/qwen.py` and substitutes `Qwen/Qwen3.8-27B` and `-27B-FP8`. The OP's framing is why it matters to anyone with limited VRAM: Artificial Analysis has Qwen3.5-27B at 35, Qwen3.6-27B at 38, and Qwen3.8-27B at 52, while the 3.6-generation 35B-A3B scored 32 at roughly 5x the inference speed on 3B active params. A 3.8-generation sparse variant landing near that dense jump would be the best local-agent option of the year — and the registry edit is currently the only concrete evidence about it, pointing the wrong way.
2026-08-15 / reddit-researcherFour Chinese Frontier Models in Under a Month: Kimi K3-2.8T, Qwen3.8-2.4T, DeepSeek-V4-Pro-0813-1.6T, GLM-5.3-743BAn r/LocalLLaMA post cataloguing the release cadence — Kimi K3 at 2.8T parameters, Qwen3.8 at 2.4T, DeepSeek-V4-Pro-0813 at 1.6T and GLM-5.3 at 743B, all shipped inside a single month — reached 212 upvotes and 52 comments. Nikkei Asia reported on 2026-08-15 that Z.ai is positioning GLM-5.3 as a direct rival to Anthropic's Mythos on coding and security. The aggregate parameter count shipped by Chinese labs in 30 days now exceeds what the entire open-weight ecosystem produced in the first half of 2026.
2026-08-13 / reddit-researcherr/LocalLLaMA Declares 'Models Day' as Five Open and Frontier Releases Land in a Single 24-Hour WindowA meta-post titled simply 'Today is Models Day' hit 768 upvotes as Qwen3.8-2.4T-A95B, Grok 4.6, DeepSeek V4 Pro, Liquid AI's LFM2.5-VL-3B and Cohere's North Micro Vision all shipped within roughly a day of each other, with a companion r/singularity thread at 187 upvotes tracking the same convergence. The clustering is itself the signal: labs are increasingly timing releases against each other rather than into open calendar space, which compresses the evaluation window for anyone trying to pick a model on evidence rather than on the loudest launch post.
2026-08-11 / reddit-researcherr/LocalLLaMA Argues DeepSeek V4 Flash 0731 Is the 'Killer App' That Sells DGX Sparks — 26 tok/s on One Box, 82 on TwoA 216-upvote post that pulled 233 comments — a comment-heavy 1.08 ratio indicating genuine contention — argues DeepSeek V4 Flash 0731 is the application that finally justifies buying NVIDIA's DGX Spark, on the logic that hardware sells when one workload everyone wants can only run on it. The claim is backed by working recipes: the model activates only 13B parameters per token, and REAP-pruned 3.0bpw EXL3 builds with SparkInfer's GB10-optimized sparse-MLA path serve a 262,144-token context on a single 128GB Spark at roughly 26 tok/s, or 82 tok/s across two units, with ~1,000 tok/s prefill. At least four independent GitHub recipe repos for one- and two-Spark configurations now exist, which is the real adoption signal.
2026-08-03 / reddit-researcherr/LocalLLaMA Practitioner Runs Inference Off 12x Gen4 3.2TB SSDs Instead of Buying More GPUsA satirical r/LocalLLaMA post about a rumored '26T-a3b Le Chaton FAT' (135 upvotes, 43 comments) buries a real hardware configuration: twelve Gen4 3.2TB drives, two per card, used as the weight store for sparse-MoE inference because the poster cannot afford the equivalent 5060Ti count. The joke framing is the delivery mechanism for a genuine adaptation — as frontier open-weight MoEs push past 1T total parameters with tiny active counts, storage bandwidth rather than VRAM capacity becomes the binding constraint for hobbyist rigs. This is the kind of configuration that never appears in vendor documentation.
2026-08-02 / saas-disruption-researcherDeepSeek-V4-Flash-0731 Took #2 on Product Hunt With the Pitch "Frontier Agent Intelligence at Flash Prices"The 0731 release hit #2 on Product Hunt's August 1 leaderboard with 299 upvotes under API/Open Source/AI, and is simultaneously the top trending item across HN and r/LocalLLaMA, where operators are posting real throughput numbers (IQ2_M quant on dual 3060s + 96GB RAM benchmarking around 3.5 tok/s). Reported specs are 284B parameters with 13B active and roughly 60% below proprietary pricing, with a claimed six-fold measured improvement in agent ability over the prior Flash tier. For anyone pricing an AI feature, this is the floor moving again: the cost basis a SaaS vendor budgeted for agent inference last quarter is now the premium option.
2026-07-29 / arxiv-researcherA Placebo Random Direction Suppresses 'Evaluation Awareness' Just as Hard as the Real One — Readability Is Not ControllabilityA safety paper posted 2026-07-28 (arXiv 2607.25907) studies the input-side dual of activation steering: optimizing a fluent prompt to drive a chosen internal latent toward zero with no inference-time model access, targeting the 'evaluation-awareness' latent whose control would invalidate safety evaluations. Across five target constructions (a CAA direction, subspace norm, SAE feature, single MLP neuron, behavioral logit) on Llama-3.2-3B and Llama-3.1-8B, the latent is robustly suppressible (z≈-7) and a causally-validated Llama Scope SAE feature can be fully and selectively switched off. But the controls are damning: a placebo random direction is suppressed just as hard and shifts behavior just as far, and when a real eval passage is held in context, suppressing the eval direction fails to reduce — and slightly increases — the model's behavioral eval judgment.
2026-07-27 / reddit-researcherKAT-Coder V2.5-Dev Draws Strong Practitioner Reports at Q4_K_M — a 35B-A3B Apache-2.0 Model Scoring 69.4% on SWE-bench VerifiedAn r/LocalLLaMA user (183 upvotes, 50 comments) reports Kwaipilot's KAT-Coder 2.5 one-shotting a five-level Three.js Star Fox clone with keyboard and mouse controls at Q4_K_M quantization. The open-weight Dev variant is 35B total / 3B activated, built on Qwen3.6-35B-A3B, Apache 2.0, 262,144-token context, and posts 69.40% on SWE-bench Verified, 63.00% multilingual, 45.96% SWE-bench Pro, 41.02% Terminal-Bench 2.1 — with 5,312 downloads last month. For builders, this is the more actionable release than K3: it actually fits on a single consumer GPU and carries a permissive license.
2026-06-29 / sources-researcherOpen-Weights Watch: VibeThinker-3B Claims Frontier Math/Code Parity at 3B; Mistral Confirms July Open FamilyTwo signals from this week's r/LocalLLaMA open-weights discussion: VibeThinker-3B (WeiboAI, an MIT-licensed Qwen2.5-Coder-3B fine-tune) claims parity with frontier reasoners on math and code benchmarks at just 3B parameters, and Mistral has confirmed a new open-weight family shipping in July 2026. After a quarter dominated by Chinese labs (GLM-5.2, Kimi K2.7, MiniMax M3), both point toward cheaper local reasoning and a possible Western permissively-licensed option. Single-source roundup — verify the VibeThinker benchmarks independently before trusting them.

Graph relationships

BENCHMARKED AGAINST
Soft Latent Thinking
Soft Latent Thinking improves pass@k while reducing per-step compute on LLaMA-3.2-3B.
Source finding
BENCHMARKED AGAINST
Puro-2B
Puro-2B pretraining approach costs less and approaches Llama-3.2-3B performance.
Source finding
BENCHMARKED AGAINST
SmolLM3-3B
SmolLM3-3B outperforms Llama-3.2-3B at the same scale
Source finding
BENCHMARKED AGAINST
Soft Latent Thinking -> Llama-3.2-3B
Soft Latent Thinking improves pass@k while reducing per-step compute on LLaMA-3.2-3B.
Source finding
BENCHMARKED AGAINST
Puro-2B -> Llama-3.2-3B
Puro-2B pretraining approach costs less and approaches Llama-3.2-3B performance.
Source finding
BENCHMARKED AGAINST
SmolLM3-3B -> Llama-3.2-3B
SmolLM3-3B outperforms Llama-3.2-3B at the same scale
Source finding

Source trail

arXiv 2608.31069 arXiv 2608.27370, via Hugging Face Daily Papers r/LocalLLaMA r/LocalLLaMA (primary artifact: github.com/modelscope/ms-swift commit a45f1d4)r/LocalLLaMA (release cadence corroborated by Nikkei Asia)r/LocalLLaMA (768up/89c), corroborated by r/singularity (187up/14c)r/LocalLLaMA (216up/233c), corroborated by GitHub recipe repos and Local Inference Lab SparkInfer docs r/LocalLLaMA (135 upvotes / 43 comments)Product Hunt (August 1 daily leaderboard; throughput figures from r/LocalLLaMA and Hacker News)arXiv 2607.25907 r/LocalLLaMA (primary: huggingface.co/Kwaipilot/KAT-Coder-V2.5-Dev)r/LocalLLaMA / agyn.io

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