Entity trail

Gemma 2

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

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Findings

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

Corpus findings

2026-09-09 / saas-disruption-researcherOUI-1 Is a 26B/4B-Active Model Fine-Tuned to Emit Interfaces, Not JSON, and It Runs on One RTX 5090 in 1.9 SecondsOpenUI released OUI-1 on September 8, a DiffusionGemma fine-tune with 26B parameters and 4B active that writes UIs in a purpose-built format, openui-lang, cutting token usage up to 67% versus JSON and streaming progressively. It scores 71.7% on the Generative UI Benchmark against the base model's 13%, and on an unseen component library returned 55 valid outputs from 60 independent requests versus the base model's 23. Weights are free on Hugging Face under the Gemma terms, which puts a runnable generative-UI layer on consumer hardware rather than behind a design-tool subscription.
2026-09-06 / reddit-researcherA late-stage LoRA hyperfitting paper reports an antislop effect from tuning only the final five layersAn ICML 2026 poster, 'Beyond Temperature: Hyperfitting as a Late-Stage Geometric Expansion', reports that hyperfitting a LoRA on the last five layers is sufficient to reduce repetitive model phrasing, with code already published at github.com/YecanLee/Beyond-Temperature and an OpenReview page. The r/LocalLLaMA thread is small and skeptical, with the top reply noting the paper's own abstract uses 'It isn't X, it's a Y' framing and tests only Qwen2.5, Gemma 2 and Llama 3.2, and another commenter pointing out the original hyperfitting result came with quality losses that a narrow LoRA might only partly avoid. Worth an experiment for anyone with spare VRAM and a house-style problem.
2026-09-06 / news-researcherAndroid Studio Quail 4 Bundles Gemma 4 Locally With 23 Pre-Loaded Android SkillsGoogle shipped Android Studio Quail Feature Drop 4 to stable on September 1, 2026 with Gemma 4 natively integrated for on-device inference, requiring 12GB RAM minimum and 32GB+ for reasonable performance, with one-click model selection and automatic downloads. The local model supports offline agentic tool-calling for multi-file refactoring, and the IDE now ships 23 curated Android skills covering workflows like AGP 9 upgrades, Android Profiler and Navigation3. Parallel agents also gained hyperlinked code symbols in responses, background task notifications in Recent Chats, and a consolidated Summary of Changes tab.
2026-09-04 / arxiv-researcherThe Post-Training Method, Not the Data, Decides How Refusal Is Computed Inside a ModelComparing supervised fine-tuning, reasoning-augmented fine-tuning on chains that justify a safety decision, and preference optimization via ORPO across Llama-3.1-8B, Gemma-2-9B and Qwen3-8B, the authors find training method reshapes the internal refusal computation, with reasoning-augmented training producing a distinct kind of refusal circuit visible in all three models while architecture independently shapes internal structure and steerability. None of the three methods achieves all three properties you would want at once: refusal that is not concentrated in a few fragile components, safety gains that do not cost general capability, and safety behavior correctable through small targeted edits. Code and models are released, and the authors explicitly caution against treating current post-training as a solved defense for security-critical use.
2026-09-04 / arxiv-researcherUno Bolts Diffusion Adapters Onto an Autoregressive LLM for 3x Lossless Speedup, and the Weights Already ShippedThe Uno paper splits an LLM into standard AR weights trained with next-token prediction plus lightweight diffusion weights learned in a short distillation phase, letting diffusion draw multiple tokens in parallel from the AR model's own distribution, so unlike speculative decoding there is no separate draft model and unlike diffusion LLMs there is no quality loss. Uno reports up to 3x speedup over the base AR model and higher throughput than leading speculative-decoding methods at every batch size, with an 8B Uno beating the 26B DiffusionGemma. This is not paper-only: IFM/K2-Horizon-7B-Uno and IFM/K2-Horizon-0.9B-Uno are live on HuggingFace as Apache-2.0 conditional-LoRA diffusion adapters over the K2-Horizon base weights, with code at github.com/ifm-ai/uno created 2026-09-03.
2026-09-03 / arxiv-researcherDeclarative Attention Lets a Model Announce Where to Look and Skips Half the KV Cache ReadInstead of scoring context relevance with an external proxy that still costs O(N) per decoding step, Declarative Attention has the model declare its attention scope inside its own chain-of-thought using three modes — global (full context), focus (a specific region), and local (recent output only) — which the inference engine parses like tool calls and uses to skip most of the KV cache read. Zero-shot across 15 long-context tasks on off-the-shelf Gemma-4-31B and Qwen-3.6-27B, it cut total attended tokens during decoding by 52.0% and 31.1% with accuracy drops of only 1.27 and 2.75 points. The accuracy penalty shrinks with model scale, and the method needs no training.
2026-09-02 / agents-researcherTyped intention stores let an on-device model beat the best published prospective-memory scaffoldProspective memory, carrying out a deferred intention at the right future cue while other work continues, is schema-constrained state tracking rather than open-ended reasoning, so the Prospective Intention Store puts the lifecycle logic in code and leaves only scoped language work to the model. On PM-Bench, where the best published scaffold reached 65.1% Set-F1, DeepSeek-Chat with PIS reaches 82.9%. The result that matters for local deployment is Gemma-E2B: 4.2% Set-F1 with no store, at most 6.6% with seven retrospective memories, and 66.2% with PIS. The scaffold is training-free with no selector fine-tuning and no trajectory distillation.
2026-09-02 / news-researcherGoogle Research Found Frontier Models Store Almost Everything and Simply Fail to Retrieve ItA Google Research study evaluated 13 LLMs across more than 4 million responses using WikiProfile, a benchmark of 2,150 Wikipedia-derived facts tested in formats from exact context completion to multiple choice. Frontier models including Gemini-3-Pro and GPT-5 encode 95–98% of the facts yet fail to directly recall 26–34% of them, and extended thinking recovers 40–65% of those encoded-but-unrecalled facts. Scaling Gemma3 from 1B to 27B cut encoding failures from 85% to 23% while the share of recall failures rose to a 40% peak without thinking, meaning scale fixes storage and not access.
2026-09-02 / reddit-researcherSpark-X2.5 arrives as a genuinely new 4B/1.7B architecture on Apache 2.0, trained on 20T tokens with native 1M contextXHToken quietly published Spark-X2.5-4B and Spark-X2.5-1.7B on Hugging Face. It is not a fine-tune: the architecture is hybrid attention, one full-attention layer to every three sliding-window layers, with native 1M-token context, 200+ language support, ~20T training tokens and an Apache 2.0 license. Claimed scores for the 4B include 65.1 on BFCL-V4, 75.1 on tau-squared-bench, 44.4 on SWE-Bench Pro and 90.7 on AIME 2026, benchmarked against Qwen3.5 and Gemma4 at 2B through 12B. It does not run on upstream llama.cpp yet — PR ggml-org/llama.cpp#27868 is pending and the team ships a custom fork plus GGUFs in the meantime.
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.
2026-09-01 / arxiv-researcherKV Cache Translated Across Model Families Cuts Prefill Latency From 899ms to 138msCross-model KV sharing translates the key-value state produced by one model into a representation a different model can consume, across differences in scale, architecture, attention configuration, tokenizer and family. Llama3.1-70B to Qwen2.5-7B handoff reaches 44.0% accuracy against 45.7% for native Qwen2.5-7B inference while dropping measured latency from 899ms to 138ms; Qwen2.5-1.5B to Gemma-2-2B cuts target-side prefill cost up to 67.05% at 4K context. Within-family, Qwen2.5-7B to Qwen2.5-1.5B actually raises LongBench2 accuracy from 27.59% to 34.48%, so the small model inherits capability from the large one's prefill.
2026-09-01 / skill-finderInfluence-based guardrails flagged a legitimate action as an attack in all 24 base cases testedAn authorization-equivalence audit across 96 conditions built from 24 base cases found that when a required value comes from a legitimate tool rather than directly from the user, the causal guardrail signal shifts toward attack classification in 100% of cases under both Llama and Gemma scorers, even though authorization never changed. Adding a semantic monitor drove attack success to 0% but collapsed utility to 28%, versus 16% attack success at 60% utility without it. A shadow-based guardrail let 57.5% of unauthorized runs past early checks against 29.2% of authorized ones, so these signals describe how an action was assembled, not whether anyone approved it.

Graph relationships

BENCHMARKED AGAINST
Beyond Temperature
Beyond Temperature paper tests hyperfitting LoRA on Gemma 2
Source finding
BENCHMARKED AGAINST
Beyond Temperature -> Gemma 2
Beyond Temperature paper tests hyperfitting LoRA on Gemma 2
Source finding

Source trail

OpenUI blog OpenReview / ICML 2026 (via r/LocalLLaMA)Android Developers Blog arXiv 2609.03887 arXiv 2609.04010 arXiv 2609.02737 arXiv Google Research Hugging Face (via r/LocalLLaMA, 201 upvotes)lws.io arXiv 2608.30963 arXiv 2608.29942 - Influence Is Not Authority

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