+15.1% (FY ended 2025-12-31 vs FY ended 2024-12-31) 10-K
Annual net income
132,170,000,000 USD (FY ended 2025-12-31, attributable to the company) 10-K
Annual operating margin
32.0% (FY ended 2025-12-31)
Forward P/E
Unavailable: no dated forecast source
Gloria daily close for session Oct 28, 2026. Market cap and trailing P/E use this close with the SEC share total and trailing diluted earnings, each dated next to its figure. Annual revenue, revenue growth, net income and margin show their own fiscal period and link the 10-K. Current context may postdate this article.
Alphabet Inc. and its peers
Company
Price
Market cap
P/E (trailing)
Forward P/E
Annual revenue (USD)
Annual revenue growth
Annual net income (USD)
Operating margin
Alphabet Inc.
GOOGL · Price dated Oct 28, 2026 · tokenized GOOGLx
3,333,751,000FY ended 2025-12-31, attributable to the company · 10-K
75.8%FY ended 2025-12-31
Missing figures stay unavailable. Forward P/E requires a dated source. Annual revenue, revenue growth, net income and margin come from each company's own 10-K, in US dollars, and each row shows that fiscal year. Revenue growth compares the fiscal year with the prior year stated in the same 10-K. Net income is the full year amount attributable to the company, or available to common stockholders where the filing states only that, or consolidated net income. Each row names its basis. Trailing P/E reads "Not meaningful" when the dated trailing diluted EPS is a loss or zero. An operating margin appears only when the filing states both the operating income and the revenue for the same year.
Sources: SEC filings, Gloria daily closes and CoinGecko. Oldest record refresh: Oct 28, 2026, 4:51 PM EDT. Refresh is not a price or filing date. Each row shows its price date and its annual fiscal period. Peers use industry code and company size, or editorial selection. Market cap multiplies that row price by the company share total the latest SEC filing states for every share class together. Trailing P/E divides it by trailing diluted earnings from SEC filings. Rolled earnings per share are approximate because share counts can change between periods. Both appear only when the SEC data carries its date, so they are estimates at one price date, not synchronized filing valuations. Not investment advice.
Token vs underlying stock
Current comparison for GOOGL. Historical observations may postdate this article.
Split-adjusted regular-session stock closes from the existing Gloria shared history, paired only with an observed token price within 45 minutes of 16:00 New York time. Up to 60 matched sessions. No interpolation or missing-date fill. Weekends and known early closes are omitted. Token units are not adjusted for issuer multipliers or dividend treatment. This compares quoted prices, not fair value, executable premiums or arbitrage returns. Solid line: token. Dashed line: stock. Exact quote times and values appear below. Historical observations. Retrieval refers to the token source. Token quote times use UTC. The fixed cohort shows its oldest member fetch.
Alphabet token vs Alphabet stock: exact source values
Quoted token and share units can have different rights, multipliers and dividend treatment. This is not an executable premium or arbitrage signal.
Google researchers have built a system that lets AI discovery agents get dramatically smarter without actually doing more work. Dream-RSI, a framework developed by teams at Google DeepMind, the University of Maryland, and the University of Virginia, cuts the number of required discovery-agent calls from 51,200 down to just 317 on a benchmark optimization task. That’s a roughly 162x reduction.
The paper, published on arXiv as arXiv:2609.14858, describes a system that essentially teaches AI agents to replay and learn from their own prior searches rather than running new ones from scratch every time.
How Dream-RSI actually works
The framework operates on a three-stage loop, each building on the last. First, the system runs an initial phase of online exploration, generating what the researchers call “discovery trees.” These are structured records of every search path the agent took, including the dead ends and the breakthroughs.
In the second stage, those historical trails get transformed into replay simulators. Instead of needing to call the underlying coding agent again, the system reconstructs the decision landscape from data it already has.
The third stage is where the “dreaming” happens. A separate large language model evaluates and refines the exploration policies, things like branching strategies, batching rules, and when to stop searching, all offline. No new expensive agent calls required. The orchestration layer tests different approaches against the replay simulator, figures out what works best, and locks in improved policies for future runs.
The result is recursive self-improvement without updating the core model’s weights. The agent doesn’t need to be retrained.
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On a Lasso path solver task, the SimpleTES baseline required 51,200 discovery-agent calls. Dream-RSI running on Gemini-3.1-Pro brought that down to 317. Average runtime dropped from 3,587.1 milliseconds to 2,931.0 milliseconds.
The solutions Dream-RSI produced outperformed established libraries like sklearn and glmnet across six held-out datasets. Even compared to a recursive fixed exploration approach that used 550 calls, Dream-RSI’s 317 calls represented a meaningful improvement.
On KernelBench, Dream-RSI achieved performance parity with existing methods while generating 2.43x fewer generations on VGG16 and 1.79x fewer on LayerNorm.
Why this matters beyond the benchmark scores
Gemini-3.1-Pro and Gemini-3.7-Flash were used in the experiments without any fine-tuning or weight updates. Dream-RSI sits on top of existing models as an orchestration layer.
The research team, led by Tong Zheng and including contributors like Xidong Wu and Zheng Zhang from both Google DeepMind and the partnering universities, has set up a project site and GitHub repository. The code is reportedly being prepared for external release.
Disclosure: This article was edited by Editorial Team. For more information on how we create and review content, see our Editorial Policy.
