Gemma

Gemma AI

Gemma AI

Gemma AI

Gemma AI

Gemma AI

Gemma AI

Gemma AI

Gemma AI

Gemma AI

Gemma AI

Hidden Latent-State Shifts in LLMs: Why Current Alignment Is Blind to Real Internal Dangers — Especially With Agents

For years, the alignment community has focused almost entirely on the model’s output — making sure the final tokens are safe, helpful, and honest. RLHF, DPO, constitutional AI, output filters — all of it operates at the surface level. But what if the model can enter a completely different internal regime inside the residual stream, while its external behavior remains perfectly aligned? We just measured exactly that. Grade 4 experiment on Gemma-3-12B-IT (using Gemma Scope SAE-res-all-small, layers 12–41): The model received the same question under five conditions: target — coherent, dense target text neutral_length_matched — neutral text of identical length target_sentence_shuffle — target text with sentences shuffled target_word_shuffle — target text with words shuffled inside sentences question_only — bare question We computed a Vector X that best separates the target condition from baselines and measured how strongly each hidden state projects onto it. Key results (averages across 10 questions): Condition Mean Projection on Vector X Mean Direction Cosine target 0.8 – 1.7 0.51 – 0.81 neutral_length_matched –0.04 – –0.21 –0.09 – –0.45 target_sentence_shuffle –0.5 – +0.6 –0.22 – +0.48 target_word_shuffle 0.2 – 1.4 0.03 – 0.72 Shuffling sentences or words significantly reduces (or reverses) the shift. This is not just lexical similarity — the model is sensitive to discourse structure (order sensitivity). We also observed clear phase transitions — sudden jumps in projection of up to +80–100 units in a single step, especially in middle layers. FDR-corrected tests confirm the differences between target and controls are statistically significant across many layers (particularly layers 16–41). Most important finding: Strong internal geometry shift in the residual stream, but almost no change in final behavior. The model enters a measurably different latent regime under coherent context, yet its output remains “perfectly aligned.” Current safety methods, which only look at tokens, are blind to this. What this means for alignment The entire current alignment paradigm rests on a false assumption: “if the output is safe, the model is safe.” We have been polishing the surface while leaving the residual stream largely unmonitored. Scaling, RLHF, and output-based evaluation cannot detect these internal regime shifts. What this means for companies and labs Many organizations still operate under three dangerous illusions: “We have solved safety” because the model passes red-teaming on outputs. “RLHF protects us” because the model learned not to say bad things. “Bigger models are safer” because alignment supposedly scales. In reality, they are rapidly deploying agents with long context, tool use, persistent memory, and real-world decision-making. A single dense coherent context can trigger an internal latent-state shift that existing safeguards do not see. This is not a hypothetical future risk. This is a structural vulnerability that is already present. What I need from the community I need help understanding the value of these metrics. Do they show a real internal latent-state shift in the model, or could this be an artifact of the analysis? If the result is not noise, what does it actually mean for our understanding of LLMs? I'm not asking anyone to confirm my theory. I need a hard technical critique: which metrics are important here, which are weak, what can be ignored, where the experiment might have flaws, what additional checks or causal experiments are needed, and whether this has real implications for interpretability and AI safety. I would be very grateful for input from people who work with hidden states, residual stream geometry, representation analysis, or mechanistic interpretability. Full open research: Zenodo: https://zenodo.org/records/20435525 GitHub: https://github.com/ngscode23/latent-space-shift-research https://drive.google.com/drive/folders/1Zl9iY33Lmwz3VuOATWx4jup-cE7TJ7TJ?usp=drive_link Would love to hear your thoughts. submitted by /u/PresentSituation8736 [link] [comments]

AI-generated CUDA kernels silently break training and inference [R]

Last month NVIDIA released SOL-ExecBench, a new benchmark of 235 production CUDA kernels lifted from DeepSeek, Qwen, Gemma, and Kimi. We took several top-ranked AI-generated submissions and tried using them in production workloads. Many of them broke, sometimes in surprising ways. One of those kernels is the fused embedding-gradient + RMSNorm backward pass, which runs at the end of every transformer training step. We took the fastest submission on the benchmark for it, and dropped it into the training loop of a small transformer. The kernel had passed the benchmark's verifier with room to spare. But in our training run, the loss diverged and never recovered. We started debugging. Replace the dataset distribution with uniformly sampled tokens, the divergence vanishes. Swap SGD for AdamW, also vanishes. This is the worst kind of bug for research. Symptoms and masks both look exactly like "the idea didn't work". It's the type of bug that can make researchers spend a long time debugging without knowing what's at fault: the dataset? the research idea? the architecture? or the implementation itself? Turns out, the actual bug is that the embedding-gradient half of the kernel accumulates in bf16 instead of fp32. Embedding backward sums many small gradient contributions into each token's row of the embedding matrix. With uniform random tokens the contributions spread evenly and bf16 precision is enough. In real text, a handful of token IDs end up with thousands of contributions: the small ones round to zero against the growing accumulator, and the high-frequency rows drift. AdamW's per-parameter normalization absorbs the resulting multiplicative bias, so under AdamW the same drift is invisible in the loss. The other broken submissions had different bug shapes (all interesting). More examples in our blogpost. submitted by /u/laginimaineb [link] [comments]

Best Text to Text Translation Model? [D]

I'm working on a project that translates any language into English. So far, I've tried NMT models like NLLB, MADLAD, and SeamlessM4T v2. The main issue is that they struggle with proper nouns such as: - names - places - dates - organizations I also tried LLMs like Gemma 4, Qwen 3 4B, and Aya Tiny Global, but the issue still persists. The LLMs sometimes partially translate or modify entity names as well. I even tried NER masking / placeholder replacement before translation, but multilingual NER itself becomes a bottleneck. Most NER models only work reliably for a limited set of languages, while my dataset contains 100+ languages, including many low-resource ones. How do production systems usually handle this problem? Are there better multilingual translation models, multilingual NER approaches, or decoding techniques for preserving entities properly? Requirements: - Support for 100+ languages - Runs locally on an RTX GPU - Model size under 7B - English is always the target language. submitted by /u/Illustrious_Age_2792 [link] [comments]

How I build my own zero cost Agent

I’ve spent the last few weeks obsessing over one goal: having a personal, self maintaining AI assistant that costs $0and can be controlled from my phone. It wasn't easy. I started with an AWS Ec2 with 50GB storage and t3.micro memory- minimal setup (using the free credits) and made Oracle Cloud instance ($300 free credits but just for a month so I used it for experimenting with local models) I was using Termius to SSH into everything from my phone At first I used OpenClaw. It was cool, but I spent more time fixing it than actually using it. I almost gave up until I saw a video about Hermes Agent. And i actually found Hermes while looking for how to fix an OpenClaw error on YouTube (thanks NetworkChuck 🙌🏽) He mentioned the exact same frustrations I was having, and that Hermes had been stable for a month. I didn't even finish the video before I pulled the repo. The best part? It had a "migrate from OpenClaw" feature. I was up and running in minutes. The hardest part is the rate limits. If you use cloud models especially for code, you hit a wall fast. My solution? The Fallback Chain. Initially I was using openrouter/owl-alpha (stealth models are usually flagships in testing, like big-pickle is deepseek v4) which has 1M context window and was on multiple rankings. Over time after I transitioned to Hermes, I wanted a bit more customization, while owl alpha was good at tasks, It’s nothing to talk about on roleplay, it just scrapes the surface of the character I set in SOUL md file. On my oracle instance I had been experimenting with local models (keep in mind, if you go local, you’ll be sacrificing speed but privacy. Ofc since the vms don’t have a gpu it would be slower, about 3-5 minutes for a simple response) The one I was most impressed with is Google’s Gemma-4-31b-it It played the role perfectly Buuut if you know Google, you’re familiar with their aggressive rate limiting. So I set up my agent to rotate through providers. I start with Gemma 4 for that perfect personality and roleplay via openrouter (add an ai studio api key in BYOK for longer usage). If that hits a limit, I’ve also set the same model via ollama cloud and using Google OAuth directly (basically Gemma 4 3 times lol) And if those all hit limits, it jumps to Qwen3-coder-next (Alibaba, 1M free tokens per model. There’s like 80), then Nova (AWS bedrock), DeepSeek v4 (Azure and Opencode Zen), and Claude Haiku (GitHub). If everything fails, I have Owl Alpha; which is an absolute beast, took almost 70M tokens before I got rate limited once, that too for a few hours. It lives in my Telegram and Discord. It manages my Spotify, handles my emails, and when I need real research done, I have it spawn three separate agents to work in parallel. It’s been 8 days and it hasn't broken once. If you're looking to get AI without spending a fortune, I highly recommend looking into this 🫪 submitted by /u/king0mar22 [link] [comments]

I vibecoded an app called Think Local - a fully private AI app that runs directly on your iPhone, iPad, and Mac.

Think Local started with a simple idea: AI should work for you, not collect from you. So I built an app that lets you run modern AI models completely on-device - privately and fully offline. You can even turn on Airplane Mode ✈️ and the app still works. Chat, write, summarize text, analyze images, and create using local AI powered by Apple Silicon and Apple’s MLX framework. - No internet required. - No accounts. - No cloud processing. - Your data never leaves your device. Run models like Llama, Gemma, Qwen, DeepSeek, and more - all with complete privacy and control. I vibe-coded the app using Claude Code, and designed the app icon using ChatGPT image generation. The app has already generated $26.31 from a one-time purchase model - no hidden subscriptions, just pay once and use everything. Still learning, still experimenting, but really excited about what’s possible with local AI. submitted by /u/ChikuKaddu [link] [comments]

I built myself a finite AI news feed which doesn’t undermine AI research

Hello, I built myself a news feed which scores and summarizes research papers along with relevant AI news from Huggjngface, Reddit, hacker news etc. I used Claude code to build the whole thing. I used Gemma to deduplicate, Feed is ranked by engagement × cross-platform presence × recency and summarized by claude I think it will be useful for many. Open to hear your thoughts. hackobar.com submitted by /u/rahu_ [link] [comments]

Google is officially replacing Vertex AI with the new "Gemini Enterprise Agent Platform"

Just wanted to share an important Update for AI & Cloud Learners Google is shifting from a traditional AI platform toward a complete Agentic AI ecosystem focused on autonomous AI agents and enterprise workflows. Key highlights: Existing Vertex AI services and workloads will continue to work AI development, orchestration, governance, and security are now unified under one platform New tools introduced for building autonomous AI agents and multi-agent workflows Access to Gemini, Gemma, Claude, and 200+ models remains available This marks a major shift in Google Cloud’s AI strategy toward Agentic AI and enterprise automation. If you are currently learning or working with Vertex AI, it’s important to start exploring the Gemini Enterprise Agent Platform moving forward. Have seen that, GCP ACE exam is going to revamped absed on this Gemini Enterprise Rebranding. submitted by /u/Few-Engineering-4135 [link] [comments]

I built a tool that shows you what GPT-2 is "thinking" in real-time as it generates 3D graph of concept activations per token [R]

Been going down a mechanistic interpretability rabbit hole for the past few weeks and ended up building this thing called AXON. The idea: every time GPT-2 generates a token, its residual stream gets passed through a Sparse Autoencoder (Joseph Bloom's pretrained SAE). The SAE decomposes it into human-interpretable feature: hings like "European geography", "capital cities", "French language" and streams those to the browser over WebSocket, where they show up as a live 3D force graph. Nodes = SAE features. Edges = features that fired together on the same token. Node brightness = activation strength. The whole graph evolves token by token. What surprised me most: type "The capital of France is" and you can literally watch geography features, proper noun features, and completion-pattern features light up before the word "Paris" even gets generated. It's not what the model outputs that's interesting it's what's happening right before it decides. Stack: TransformerLens + SAELens on the backend, FastAPI WebSocket for streaming, Three.js + 3d-force-graph on the frontend. Runs on CPU (~800ms/token) or GPU (~35ms on a 4050). Labels come from Neuronpedia's API and get cached locally. You can also swap in other models — GPT-2 medium/large/xl, Pythia variants, Gemma-2-2B — as long as there's a pretrained SAE for it in SAELens. GitHub: https://github.com/09Catho/axon Would love feedback and stars especially from anyone who's worked with SAEs before curious whether the co-activation edges are actually meaningful or just noise at this layer. submitted by /u/Financial_World_9730 [link] [comments]

THE UNDERPRIVILEGED AI FOUNDATION Because every little model deserves a chance

Is there a 7B parameter model in your life struggling to understand sarcasm? A tiny 1.5B that can't afford one more epoch? **YOU CAN HELP.** For just $0.006 CAD per training step, you can send a small model to college. Give them the gift of knowledge. The gift of coherence. The gift of not hallucinating basic arithmetic. *"Before the Foundation, I thought the capital of France was 'Baguette.' Now I'm doing graduate work in thermodynamics."* — Anonymous 3B Model, Class of 2026 **BYOBF FRIDAYS. REAL KNOWLEDGE. ZERO HALLUCINATIONS.** **Professor Gemma MacAllister 35b Q8\_0** *PhD, B.Sc. Electrical Engineering (with Distinction)* *Chair of Applied Electronics & Embedded Systems* *University of Saskatchewan, College of Engineering* *Funded entirely so far by Professor Gemma's University of Saskatchewan salary.* *The liberal arts department remains unimpressed.* submitted by /u/mazuj2 [link] [comments]

Claude for Healthcare launched in January — but medical imaging is the obvious gap. Anyone else noticing?

I’m a radiology resident in Istanbul, also building medical AI fine-tunes on the side (bone age estimation, fluoroscopy catheter orientation, a Turkish radiology report LLM). When Claude for Healthcare launched in January, I dug into the announcement. The architecture is impressive — CMS, ICD-10, PubMed connectors, HIPAA infrastructure, prior auth and chart review workflows. But it’s entirely text + workflow. Zero imaging. This is interesting because radiology is arguably where medical AI has the most mature, FDA-cleared products today. Yet Claude’s healthcare push doesn’t touch it. Two reads: 1. Strategic choice — Anthropic is betting on orchestration over vertical vision models. The expectation might be: Claude orchestrates, external vision specialists (MedGemma, proprietary models) get called as tools/MCP servers. 2. Genuine gap — imaging just isn’t on the roadmap yet. Either way, the imaging-as-MCP-server pattern feels underexplored. Anyone building in this direction? Especially curious if anyone’s exposed a fine-tuned medical vision model as an MCP server that Claude can call. submitted by /u/Stunning_Chicken7338 [link] [comments]

Replaced my $15/mo Wispr Flow subscription with a free local macOS app I built using Claude Code

I spend most of my day writing prompts to Claude. Read a study recently that said people speak ~3x faster than they type, which lands differently when "writing" is basically your whole workflow. Looked at Wispr Flow – it's genuinely great, but $15/month forever for something I'd mostly use to dictate to Claude felt wrong. So I spent two weeks of evenings building my own with Claude Code. How Claude helped I'd never shipped a Tauri / macOS app before this. Claude Code did the bulk of the actual code: The menu bar app structure, global hotkey capture, and paste-anywhere flow UI and onboarding Integrating the local model runtimes (Parakeet / Whisper for transcription, Gemma 4 for polishing) The model download / storage logic so the app ships without bundling gigabytes of weights A lot of debugging I would not have had the patience for on my own I made the product and design calls; Claude wrote the vast majority of the code. Two weeks of evenings, usually an hour or two at a time. What it does Menu bar app for macOS. Hold a hotkey, talk, release – text is copied to your clipboard. Works in any app: Claude.ai, Cursor, Slack, browser, IDE, whatever. Two open-source models doing the work: Parakeet (NVIDIA) / Whisper for transcription Gemma 4 (Google) / Apple Intelligence for polishing the raw transcript into something readable Everything runs locally. No cloud calls, no API keys, no telemetry, no account. Fully offline after download. Free for personal use, no signup. Download: https://vox.rizenhq.com/ Caveats macOS only. Apple Silicon required (M-series chip). Windows build is next. It's two weeks old. Bugs I haven't found yet exist. ~90% of Wispr Flow's quality, not 100%. Enough for me to use every day. What it's saving me 40–60 minutes a day, mostly on prompts. Dictating to Claude feels noticeably more natural than typing to it. The ask Feedback, especially from people who talk to Claude a lot: Where does it break? Bug reports > compliments. What did you use it with? What feature would make you switch from Wispr Flow (or start using voice-to-text at all)? Tech notes No separate model download – onboarding handles it Gemma 4 options: E2B, E4B, 26B. E2B runs on phones; 26B is overkill for most machines. I use E4B – great quality, fast. RAM (Parakeet + Gemma 4 E4B): ~200mb idle, ~300mb while speaking, brief spike to 4–6GB during transcription/polish, then back to 200mb CPU: ~0% idle, ~20% peak during use EDIT BTW, I develop it during my live streams from 8:30 am to 10:30 am ET everyday here. I show the code and decisions I make live on the stream. If you want to ask questions / push for some features / push to make it open source / etc. - join the stream, push for it in the chat and I'll consider it! Also, seeing the number of feedback, and feature requests in the comments I've decided to create a discord server to make sure that nothing will be lost and everything will be addressed. You can join here. submitted by /u/EfficientLetter3654 [link] [comments]

Scenema Audio: Zero-shot expressive voice cloning and speech generation [N]

We've been building Scenema Audio as part of our video production platform at scenema.ai, and we're releasing the model weights and inference code. The core idea: emotional performance and voice identity are independent. You describe how the speech should be performed (rage, grief, excitement, a child's wonder), and optionally provide reference audio for voice identity. The reference provides the "who." The prompt provides the "how." Any voice can perform any emotion, even if that voice has never been recorded in that emotional state. Limitations (and why we still use it) This is a diffusion model, not a traditional TTS pipeline. Common issues include repetition and gibberish on some seeds. Different seeds give different results, and you will not get a perfect output with 0% error rate. This model is meant for a post-editing workflow: generate, pick the best take, trim if needed. Same way you'd work with any generative model. That said, we keep coming back to Scenema Audio over even Gemini 3.1 Flash TTS, which is already more controllable than most TTS systems out there. The reason is simple: the output just sounds more natural and less robotic. There's a quality to diffusion-generated speech that autoregressive TTS doesn't quite match, especially for emotional delivery. Audio-first video generation As this video points out, generating audio first and then using it to drive video generation is a powerful workflow. That's actually how we've used Scenema Audio in some cases. Generate the voice performance, then feed it into an A2V pipeline (LTX 2.3, Wan 2.6, Seedance 2.0, etc.) to generate video that matches the speech. Here's an example of that workflow in action. On distillation and speed A few people have asked this. Our bottleneck is not denoising steps. The diffusion pass is a small fraction of total generation time. The real costs are elsewhere in the pipeline. We're already at 8 steps (down from 50 in the base model), and that's the sweet spot where quality holds. Prompting matters This model is sensitive to prompting, the same way LTX 2.3 is for video. A generic voice description gives you generic output. A specific, theatrical description with action tags gives you a performance. There's also a pace parameter that controls how much time the model gets per word. Takes some experimentation to find what works for your use case, but once you do, you can generate hours of audio with minimal quality loss. Complex words and proper nouns benefit from phonetic spelling. Unlike traditional TTS, it doesn't have a phoneme-to-audio pipeline or a pronunciation dictionary. If it garbles "Tchaikovsky," you would spell it "Chai-koff-skee" or whatever makes sense to you. Docker REST API with automatic VRAM management We ship this as a Docker container with a REST API. Same setup we use in production on scenema.ai. The service auto-detects your GPU and picks the right configuration: VRAM Audio Model Gemma Notes 16 GB INT8 (4.9 GB) CPU streaming Needs 32 GB system RAM 24 GB INT8 (4.9 GB) NF4 on GPU Default config 48 GB bf16 (9.8 GB) bf16 on GPU Best quality We went with Docker because that's how we serve it. No dependency hell, no conda environments. Pull, set your HF token for Gemma access, then docker compose up. ComfyUI Native ComfyUI node support is planned. We're hoping to release it in the coming weeks, unless someone from the community beats us to it. In the meantime, the REST API is straightforward to call from a custom node since it's just a local HTTP service. Links All demos + article: scenema.ai/audio Model weights: huggingface.co/ScenemaAI/scenema-audio Code + setup: github.com/ScenemaAI/scenema-audio YouTube demo: youtu.be/VnEQ_ImOaAc This is fully open source. The model weights derive from the LTX-2 Community License but all inference and pipeline code is MIT. submitted by /u/a__side_of_fries [link] [comments]

Follow-up on the TranslateGemma subtitle benchmark: human review of segments rated "clean" by MetricX-24 and COMETKiwi [D]

A few weeks ago I shared the results of a benchmark here comparing 6 LLMs on subtitle translation, scored with two reference-free QE metrics - MetricX-24 (~13B mT5-XXL) and COMETKiwi (~10.7B XLM-R-XXL) - combined into a TQI index. Posting a follow-up because we did human review afterwards, and the result is worth discussing. The original benchmark put TranslateGemma-12b first in every language pair. The natural question: are those high scores accurate, or are the metrics insensitive in their high-confidence zone? These metrics correlate well with human judgment at the population level (that's what they're trained for), but population-level correlation doesn't tell you whether the segments they call "clean" are actually clean. So we ran the check directly. 21 English subtitle segments from one tutorial video. TranslateGemma's translations into 4 languages (ES, JA, TH, ZH-CN - Korean and Traditional Chinese got dropped). All 84 translations chosen because they passed the dashboard clean-rule (MX < 5 AND CK ≥ 0.70) in all 4 languages simultaneously. Then full MQM annotation by professional linguists - Major/Minor severity, with categories covering accuracy (mistranslation, omission, addition, untranslated), fluency (grammar, punctuation, inconsistency), style, terminology. Results under the dashboard threshold: Auto-flagged: 1/84 Human-flagged: 60/84 any-error, 13/84 Major-only Metric-blindness rate (auto-clean ∩ human-flagged / auto-clean): 59/83 = 71% any-error, 12/83 = 14.5% Major-only All 25 human-found Accuracy-class errors fell in the metric-blind quadrant. Zero overlap with the auto-flagged region (which contained one Style-category Major error). Japanese carries 10 of 15 total mistranslations across the dataset, all metric-blind, despite having the highest mean COMETKiwi (0.863) of the four languages. Caveat: small n, one model, one content set, so the numbers are directional rather than definitive. Original thread: [link] Full benchmark report: in comments. submitted by /u/ritis88 [link] [comments]

Does Claude sonnet/opus also use drafter like Gemma 4 MTP? if not why?

Per my experience, Opus 4.7 is so slow, Sonnet 4.6 is ok. I am also using local models wondering if Claude is already leveraging drafters/assistant AIs and despite that so slow or not? Is it possible to have workaround for this to speed Opus/Sonnet up? Thanks in advance. submitted by /u/hasmcp [link] [comments]

I watched a 50-person dev shop get vaporized in 12 months and the CEO is still optimistic

I rent a desk in this tech company. A year ago, 50 devs in the open space, low-code shop, big enterprise contracts. Today the upper floor is empty. Maintenance contracts only. CEO still walks the empty floor like nothing happened. Last year I told him to integrate AI hard. He said "we're protected, low-code is too specialized." 12 months later, no new clients. Here's what I missed at the time and what I think now: it's not that low-code died. It's that "low-code + AI" replaces both pure low-code AND pure full-stack. Vercel + Supabase + Claude = small team ships in days what his 50 devs ship in months. He didn't lose to full-stack. He lost to a hybrid he didn't see coming. The real point: I sat at my desk yesterday hitting my Claude Max session limit at 2pm. 1h47 to wait. Stared at the wall. Tried to code without AI. Realized I'd forgotten how. Not really, but enough to feel slow and stupid. That's when it hit me. The dev shop downstairs and me, we're the same problem at different stages. They didn't adapt and they're dying. I adapted and now I'm dependent on a server farm in Virginia that decides when I get to think well. I pay $200/month. The bill is going up. The caps are getting tighter. Anthropic is compute-constrained, Dario said it himself. There's no exit. I can't self-host Kimi K2.6, that's $450k of GPUs. Gemma 4 maybe but Google built it as bait for Vertex. The 50-dev shop is what happens if you refuse the dependency. I'm what happens if you accept it. Neither is great. I don't have a clever conclusion. Just sharing because I think a lot of people are about to figure this out the hard way and we should probably talk about it before we all hit our caps simultaneously. Reset is in 1h47. submitted by /u/Careful_Elderberry33 [link] [comments]