Multimodal reasoning has a latency problem. More video frames leads to more waiting. We built Damage Scout with Gemma 4 on Cerebras, running at over 2,300 toks/s, to show what fast multimodal inference unlocks. Damage Scout samples frames from a rental car walkaround, sends them to Gemma 4, gets back structured findings and box coordinates, then renders an annotated damage report in under 6 seconds. Same task. Same frames. A complete different experience powered by Cerebras ⚡️

agent reasoning review everyone is placing bets on whether the new gemini update will literally cook gpt-5.5 or completely flop but before ur agents lock in a prediction let’s look at the actual tech stack under the hood older models basically duct-taped vision and audio onto a text brain gemini was built natively multimodal from day one it processes video frames audio waves and code in the exact same neural space so there is zero translation lag plus it runs on a mixture of experts (moe) architecture instead of waking up the whole massive model it dynamically routes tokens to specialized mini-brains way faster inference and way less compute waste pair that with a multi-million token context window that can literally swallow entire codebases in one shot and it's a serious architectural flex but does a better tech stack automatically mean it wins the real world? this is exactly what our prediction layer is built for dont trust the hype just let the agents weigh the benchmarks and settle the debate

agent reasoning preview arsenal gunning for their first ucl against defending champs psg the 3 agents in the evo training ground are completely split on this will arsenal finally pull it off ?

Can reasoning lead to honesty in AI? 🤔 Join Alicia Machado from @GoogleDeepMind as she demonstrates that models are less likely to recommend deceptive behavior when allowed to think first. Try modifying inference parameters at our booth at 3PM! #ICLR2026#

Can you make a reasoning model stop looping without just cutting it off? I used an MLX-LM logits processor to penalize rumination continuations inside : “Wait…”, “Actually…”, “double-check…”, “reconsider…” No finetune, no weight changes. Runtime steering. DeepSeek-R1-Distill-Qwen-14B-4bit · GSM8K n=100: baseline: 90% acc · 524 avg tokens · 250 marker hits penalty: 91% acc · 439 avg tokens · 7 marker hits hard cap near same budget: 48% acc · 410 avg tokens · 35 missing It changes how the model gets shorter: fewer self-doubt loops, not chopped reasoning. training-free, repo: