Google Gemini 3 shows in UIs – 69% odds, $803k bet volume

A Google Vids promo card for “Nano Banana Pro” appears in the UI with a Try it button and copy stating it’s “powered by Gemini 3 Pro,” implying the refreshed image stack ships alongside Gemini 3. The leak matters for creative pipelines choosing between OpenAI/Gemini image tools next week. See details in the feature shot leak screenshot and the write‑up full scoop.

A dark‑mode model picker lists a new “3 Pro” option next to “2.5 Pro,” suggesting Gemini 3 is enabled in at least some internal or staged environments. For teams planning migrations, this is a concrete sign to prep eval suites and safety gates now model picker shot.

Following up on 69% odds chatter last week, Sundar Pichai quote‑tweeted a market predicting a Nov 22 drop with a wink and thinking face, reinforcing the timeline. The market shows 69% “Yes” and ~$803k volume—useful for planning comms and eval windows Sundar quote. A separate screenshot shows the same 69% odds odds chart.

Multiple hints stack up: a “gonna be a good week” note from a Google AI lead Googler tease, broad team excitement team excitement, and a short clip flashing a ‘Gemini 3.0’ screen teaser clip. Treat this as launch‑prep signal: freeze prompts, line up side‑by‑side evals, and verify tool‑use behavior.

Early samples tagged “Nano Banana Pro” are circulating, including a dramatic Nether portal scene with accurate Hoglins and lava ambience. If legit, output fidelity looks production‑friendly for stylized worlds; teams should hold final judgment for official samples sample image.

A circulating phone UI mock claims “stunning SVG output” from Gemini 3, hinting at higher‑fidelity vector generation useful for responsive design and icon systems. Treat as an unverified leak until Google posts samples or docs svg claim.

IBM’s finance‑grade evals report smaller 7–8B models delivered 100% identical outputs at temperature 0 while a 120B model hit 12.5%, attributing drift to retrieval order and decoding variance. Their playbook—greedy decoding, frozen retrieval order, schema checks—kept SQL/JSON stable and suggests tiered model choices for regulated flows. Abstract and setup details are in the share. paper summary

OpenRouter’s new cloaked model “Sherlock Think Alpha” is showing early numbers: 1805.67 on LisanBench with a 0.96 average validity ratio, trailing top-tier reasoning models on score but beating Grok‑4 on answer validity (0.87). That combination hints at strong instruction following and tool‑use reliability for agent chains. See the leaderboard snapshot and validity chart shared with the launch. benchmarks chart, and the model’s availability note is here model page.

Salesforce et al. propose Socratic Self‑Refine: split solutions into micro steps, estimate per‑step confidence by resampling, then only rework the suspicious steps. On math and logic suites, the method lifts accuracy by roughly 68% while remaining interpretable, and shows better cost‑to‑gain curves than whole‑solution rewriting. Figures and method overview here. paper thread

DeepMind’s AlphaEvolve explores 67 quantitative math problems (e.g., Kissing numbers, moving sofa) by evolving solution programs with parallel search and verification. Results show faster convergence with stronger base models, benefits to parallelism, and visible reward‑hacking failure modes—clear signals for anyone building reasoning‑at‑scale loops. Read the study and see problem kits. paper recap, ArXiv paper, and GitHub repo

A fresh Video Prompt Benchmark dropped with a quick montage showing prompts and generated clips side‑by‑side. It’s useful for creative teams comparing prompt sensitivity and visual consistency across video models without spinning up private eval rigs. Watch the short launch reel for the format. launch reel

A new benchmark (Moral RolePlay) shows models that are strongly aligned for helpfulness/honesty lose fidelity when asked to play egoists or villains, often substituting anger for scheming and breaking character consistency. This exposes a quality gap for fiction tools and NPC agents that require non‑prosocial motives. Abstract and chart are here. paper overview

Researchers show you can catch silent multi‑agent failures (drift, loops, missing details) by featurizing execution traces—steps, tools, token counts, timing—and training small detectors. XGBoost on 16 features hit up to 98% accuracy on curated datasets, with one‑class variants close behind, offering a cheap guardrail layer for prod agents. See the setup and metrics. paper abstract

A widely read community review finds ERNIE 5.0 much cleaner than X1.1 (better instruction following and readability) but still trailing Kimi K2 and MiniMax M2 on harder reasoning and multi‑turn stability; peak 65.57/median 46.36 on the shared rubric. The summary table and takeaways are worth a scan if you target China stacks. review summary

Andon Labs reran Vending‑Bench and reports Kimi K2 as the current top open‑source model on the board. If you’re testing agentic coding with long tool chains, this is a useful routing baseline to compare against closed‑weight options. rerun note

A grassroots Lisan RL‑Shizo_Bench proposes sanity prompts that are intentionally nonsensical; reports claim even top “thinking” models burn minutes and thousands of tokens instead of deferring, while stronger large models more often refuse or summarize the ambiguity. Treat it as a useful red‑team axis for agent routing and cost caps. bench pitch, and an example pair is here example outputs.