Snapshot Tests are an uncommon type of test that focuses on checking that no regressions were introduced after a code refactoring. Let’s learn how they work and how to use the Verify NuGet library to add them to your test suite.

We’re excited to release React Native Windows 0.84.0, aligned with React Native 0.84.1. React Native Windows v0.84 delivers input-handling improvements such as standard click events and imperative focus, theme-aware defaults and robust hit-testing semantics.

What’s New in RNW v0.84?

Minimum Requirements

Visual Studio 2026 is required for 0.84 version of RNW.

onClick and onAuxClick Events for Fabric Components

Fabric components now fire standard onClick and onAuxClick events, bringing RNW’s click-handling model in line with the W3C specification. Windows developers can now handle primary and auxiliary (middle-click) interactions using the same patterns as web and other React Native platforms — enabling consistent context menus, open-in-new-tab gestures, and multi-button input without custom workarounds.

Imperative Focus via view.focus()

The imperative focus() API is now enabled for Fabric views. Windows developers can programmatically move focus to any component — unlocking guided flows, accessibility enhancements, and keyboard-driven navigation patterns that previously required native workarounds.

Theme-Aware Default Text Color

Default text color now automatically adapts to the system’s light or dark mode setting. Windows developers no longer need to manually handle theme changes for default text — apps will look correct out of the box whether the user is in light mode, dark mode, or switches between them at runtime.

Keyboard-Only Focus Visuals

Focus visuals are now shown only when navigating via keyboard, matching standard Windows platform behavior. This eliminates distracting focus rectangles during pointer interactions while preserving clear focus indicators for keyboard and accessibility users.

Stricter Hit-Testing for overflow: hidden

overflow: hidden now correctly prevents hit-testing on clipped content. Previously, elements visually clipped by their parent could still receive touch and pointer events. Windows developers now get predictable input behavior — if content is clipped, it can’t be interacted with — matching expectations from web and other React Native platforms.

Native Performance Benchmarking Infrastructure

A new native performance benchmarking infrastructure measures the full Fabric rendering pipeline — from JS reconciliation through Fabric and Yoga layout to Composition commit and frame presentation — for all core components. Windows developers can now profile rendering performance at the native level, catch regressions in CI, and make data-driven optimization decisions.

Reliability & Stability

RNW v0.84 includes targeted fixes for build tooling and CI reliability:

• NativeModule templates fixed for clang compiler — Module authors using clang to build native modules no longer encounter template compilation errors.
• onClick / onAuxClick events added — Components now fire these events. If your code previously relied on the absence of these events, you may need to update handlers.
• Custom visual mounting no longer creates m_childrenContainer — Custom components using a custom visual to mount children will no longer see an intermediate container. This improves rendering but may affect components that relied on the previous container structure.
• Default text color is now theme-aware — Text that previously appeared with a hardcoded color will now adapt to light/dark mode. If your app explicitly overrides text color, no change is needed; otherwise, verify appearance in both themes.
• Focus visuals are keyboard-only — Focus rings no longer appear on pointer interactions. If your app relied on always-visible focus visuals, consider explicit styling for those cases.
• Imperative focus() enabled — view.focus() now works in Fabric. Existing code calling focus() that previously had no effect will now move focus.

Feature Parity Progress

With RNW v0.84, input-handling parity takes a significant step forward. The addition of onClick, onAuxClick, imperative focus(), and corrected hit-testing for overflow: hidden closes several long-standing gaps between RNW and web/mobile platforms.

If you encounter missing properties or functionality, please open an issue. Comments on existing issues help us prioritize what to tackle next.

For a full list of known gaps, see: Missing Properties · React Native for Windows

Gallery App Updates

Install the latest React Native Gallery (0.84) to explore the new features and component improvements in action.

Search “React Native Gallery” in the Microsoft Store or use the direct link. The Gallery app is the fastest way to see how each component looks and behaves on Windows and is a great reference when building or migrating your own apps.

Release Details

• Version: React Native Windows v0.84.0
• Covers commits: March 25, 2026 – April 3, 2026
• GitHub Release: react-native-windows_v0.84.0

Reference Links

If you’re interested in getting started with React Native for Windows, check out our website at aka.ms/reactnative.

You can also follow us on X @ReactNativeMSFT to keep up to date on news, feature roadmaps, and more.

The post 🚀React Native Windows v0.84 is here!! appeared first on React Native.

It’s not surprising that Microsoft is looking to turn its Copilot platform into a “Super App,” given that its rivals are doing the same. But Microsoft is going about the task in a way that doesn’t follow its usual playbook, by putting a big bet on a consumer-savvy hire from the outside with some feather-ruffling ways.

The company’s newly minted Copilot Executive Vice President Jacob Andreou came to Microsoft from Greylock Partners and before that, Snapchat-maker Snap. Andreou currently oversees more than 11,000 Microsoft employees, according to a recent profile in Fortune.

Microsoft is bringing onboard another former Snap (and Discord) vice president, Peter Sellis, to help, GeekWire has learned. Sources say Sellis will be leading Copilot Design, Growth and Engineering, reporting to Andreou.

Andreou is part of a recently formed Copilot Leadership Team. His charter is to lead the “Copilot experience” by driving design, product, growth and engineering, as outlined in a March 2026 reorg memo from CEO Satya Nadella. He is one of a small group charged with shaping the future of Copilot, alongside others focused on the underlying Copilot platform and AI models.

Given Andreou’s Snap background, his plan to meld Microsoft’s consumer and enterprise Copilot experiences makes sense. It won’t be a snap, however. (See what I did there?)

Even though both share the Copilot brand, consumer Copilot and Microsoft 365 Copilot don’t work the same way or use the same data sources or architecture. To boot, Microsoft hasn’t had a lot of luck with this kind of consumer-enterprise unification, as evidenced by the low interest in and uptake of its free, consumer-focused Teams product compared to its business-focused Teams collaboration offering.

The 33-year-old, Los Angeles-based Andreou seemingly is undaunted by the challenge and is pushing some employees to clock 12-hour days to keep up with younger, AI-focused companies, Fortune reports.

Microsoft was infamous for requiring employees to work long hours and weekends during crunch times leading up to delivering Windows NT and Windows 95, but not so much in recent years. Microsoft is known as a place where outsiders often struggle to thrive compared to those who climb the corporate ladder for years, making Andreou’s approach feel even riskier.

Andreou has been a big backer of the Tasks productivity layer in consumer Copilot, which is still in public preview. Tasks, which enables Copilot to handle actionable items, is similar to the recently released Copilot Cowork layer that is part of Microsoft 365 Copilot. (I asked Microsoft if the two would merge as a single Cowork-type offering at some point but was told the company had no comment.)

However, the holy grail remains the “Super App.” With the Copilot Super App, Microsoft is looking to give consumers and business users a reason to stay within Copilot regardless of the AI task with which they – or their agents – are engaging.

“Come summer, we will be bringing coding to all knowledge work within one Copilot Super App. That’s really exciting. So you’re going to have Chat, Cowork, and Code all in Copilot,” Nadella told Microsoft Build conference attendees in early June.

Microsoft isn’t the only AI-focused company working on extending its AI coding capability beyond just developers. Nor is it the only one betting on the Super App concept.

• OpenAI is working to turn ChatGPT into a Super App that brings together ChatGPT and Codex into a single environment that operates like a personal assistant.
• Anthropic is extending Claude to become a Super App (though it hasn’t used that terminology), as well, by creating a single environment that combines productivity, development and automation tools.

The Copilot Super App isn’t Andreou’s only focus. He tells Fortune that AI model choice and home-grown AI model excellence also are among his key priorities.

Microsoft is expanding model choice in the Copilot Cowork feature beyond Anthropic to include OpenAI and soon, Microsoft’s own Cowork 1 model – which may be based on Microsoft’s hosted version of the open-source DeepSeek model. Cowork 1 will be the newest addition to Microsoft’s growing pool of Microsoft-developed models, seven of which debuted at Build this year. Microsoft is seeking to position itself as the champion of lower cost, efficient models built for those who are token-maxxed out.

Andreou definitely has his work cut out for him as a consumer guy in a heavily enterprise-centric company.

Microsoft 365 Copilot and consumer Copilot are just two of more than two dozen different “Copilot”-branded commercial offerings available across the various Microsoft product teams, which can feel overwhelming.

Microsoft also needs to give users a clearer way to find and use the quickly expanding stable of first- and third-party agents, like the OpenClaw-based Microsoft Scout personal assistant. Will Andreou and his Super App quest bring at least some order to the Copilot and agent madness? We’ll know more sometime this summer.

Personal Intelligence makes the Gemini app feel tailored to you. With your permission, it pulls from Google tools like Gmail, Google Photos, YouTube and Search to provid…

Ryan sits down with Frank Portman, CTO at Yobi, to talk about why next-token prediction, though great for language, isn’t the right inductive bias for forecasting human behavior. They discuss how Yobi builds a “foundation model of behavior” using transformers and graph neural networks instead of chat-style LLMs, and what it takes to run millions of personalization decisions per second while keeping consumer data private.​​​​‌‍​‍​‍‌‍‌​‍‌‍‍‌‌‍‌‌‍‍‌‌‍‍​‍​‍​‍‍​‍​‍‌​‌‍​‌‌‍‍‌‍‍‌‌‌​‌‍‌​‍‍‌‍‍‌‌‍​‍​‍​‍​​‍​‍‌‍‍​‌​‍‌‍‌‌‌‍‌‍​‍​‍​‍‍​‍​‍‌‍‍​‌‌​‌‌​‌​​‌​​‍‍​‍​‍‌‍​‌‍‌‌​​‍‍‌​‌‌​‌‍​‌‌‍​‌‍‍‌‍‌‌‍‌‍‌‌‌​‍‌‍‌‍‌‍​‌‍‌‌​‍‍‌‍​‌‍​‍‌‍‍‌‌‍‍‌‌​‌‍‌‌‌‍‍‌‌​​‍‌‍‌‌‌‍‌​‌‍‍‌‌‌​​‍‌‍‌‌‍‌‍‌​‌‍‌‌​‌‌​​‌​‍‌‍‌‌‌​‌‍‌‌‌‍‍‌‌​‌‍​‌‌‌​‌‍‍‌‌‍‌‍‍​‍‌‍‍‌‌‍‌​​‌​‌‌​​​‌‍‌‍​‌​​‌‌​​​​‍‌‌‍​​‍‌​‌​‌‍‌‍​‌‌​‌‌​‍‌​‌​‌‍‌‍​​‌​​‌​‍‌​‍​‌‍​‌‌‍‌​‌‍​​‍‌​‌‌​‌‌​‌‌​‌‍‌‍‌‌​‍​​​‍​​‌‌‍‌‌‌‍​‌​‌​‌​‍‌‌​‌‍‌‌​​‌‍‌‌​‌‌‍​‍‌‍​‌‍‌‍‌‌‌​​‌‍‌​‌‌​​‍‌​​‌‍​‌‌‌​‌‍‍​​‌‌‍‌‌‌‍​‌‍​‌‍‌‌‌​‍‌​​‌‌​​‌‍​‍‌‍​‌‌​‌‍‌‌‌‌‌‌‌​‍‌‍​​‌‌‍‍​‌‌​‌‌​‌​​‌​​‍‌‌​​‌​​‌​‍‌‌​​‍‌​‌‍​‍‌‌​​‍‌​‌‍‌‍​‌‍‌‌​​‍‍‌​‌‌​‌‍​‌‌‍​‌‍‍‌‍‌‌‍‌‍‌‌‌​‍‌‍‌‍‌‍​‌‍‌‌​‍‍‌‍​‌‍​‍‌‍‌‍‍‌‌‍‌​​‌​‌‌​​​‌‍‌‍​‌​​‌‌​​​​‍‌‌‍​​‍‌​‌​‌‍‌‍​‌‌​‌‌​‍‌​‌​‌‍‌‍​​‌​​‌​‍‌​‍​‌‍​‌‌‍‌​‌‍​​‍‌​‌‌​‌‌​‌‌​‌‍‌‍‌‌​‍​​​‍​​‌‌‍‌‌‌‍​‌​‌​‌​‍‌‍‌‌​‌‍‌‌​​‌‍‌‌​‌‌‍​‍‌‍​‌‍‌‍‌‌‌​​‌‍‌​‌‌​​‍‌‍‌​​‌‍​‌‌‌​‌‍‍​​‌‌‍‌‌‌‍​‌‍​‌‍‌‌‌​‍‌​​‌‌​​‍‌‍‌​​‌‍‌‌‌​‍‌​‌​​‌‍‌‌‌‍​‌‌​‌‍‍‌‌‌‍‌‍‌‌​‌‌​​‌‌‌‌‍​‍‌‍​‌‍‍‌‌​‌‍‍​‌‍‌‌‌‍‌​​‍​‍‌‌

Imagine a workplace AI assistant helping you run a multi-month project. Over weeks of conversations, you share constraints, agree on milestones, revise deadlines, and surface dozens of stakeholder preferences. When you later ask it to draft an update for a colleague, it should recall not just the latest decision but the journey that got you there: what was tried, what was ruled out, who weighed in. Today’s AI agents struggle with this. Modern large language models (LLMs) are powerful reasoners, but they are effectively stateless: every session starts from zero, every long conversation forces the model to re-read its entire history, and every new piece of information is either stored as raw text (fragmented and noisy) or compressed into a vague summary (precise details lost). As AI assistants and autonomous agents move into long-horizon deployments, such as copilots that tracks a project for many months or even research agents that build up domain expertise with long horizon usage, the absence of principled memory system has become the critical bottleneck.

A growing line of work has begun to fill this gap. Systems like Mem0 extract atomic facts from conversations; retrieval-augmented (RAG) approaches index raw text fragments for later recall; and graph-based memory systems such as Zep and GraphRAG impose structure through entity relations. Each represents real progress, yet each runs into the same wall: existing designs force an unavoidable tradeoff between specificity (preserving fine-grained detail) and abstraction (organizing memory efficiently as it grows). Memora is built to give agents both.

What is Memora

Memora is an agentic memory framework designed for long-horizon AI agents. Memora’s central insight is to decouple what is stored from how it is retrieved. Memory content can remain rich and expressive, such as a project timeline, a multi-turn discussion about constraints, while a separate, lightweight structural layer handles indexing and retrieval. The result is a memory system that scales: it consolidates related information into stable units, surfaces fine-grained details when they matter, and lets the agent navigate its own history without re-reading everything. On standard long-conversation benchmarks, Memora sets new state-of-the-art performance while using up to 98% fewer tokens than would be consumed by dumping the full history into context.

Why this is hard: the abstraction–specificity tension

Existing memory systems fall into two extremes. Content-fragmentation systems, such as RAG and Mem0, embed extracted facts or text fragments directly. This preserves detail but produces brittle, isolated entries that lose narrative coherence. Coarse-abstraction systems compress experience into compact summaries. They are efficient, but summarization strips away the constraints, edge cases, and numeric details that make memory useful in the first place. Graph-based systems add structure on top of content, yet still rely on the content itself for retrieval and typically require rigid ontologies that don’t generalize across domains. None of these resolves the underlying tension between abstraction (which keeps memory efficient) and specificity (which gives memory utility).

How Memora works

Memora resolves this tension through a harmonic organization. Each memory entry has two components: a primary abstraction, which a short phrase (6–8 words) that captures what the memory is fundamentally about, and a memory value holding the rich content itself. Crucially, only the primary abstraction is embedded for similarity search; the value is never directly retrieved through its own content. This separation means new information about an evolving topic merges into the existing memory entry under the same primary abstraction, rather than fragmenting into a chain of partial duplicates. Complementing primary abstractions, cue anchors are short, context-aware tags extracted from each memory’s value, providing alternative access paths to the same memory. They function as flexible, organically-generated metadata.

To make this concrete: suppose a user says, “Dave and Sarah agreed to push the prototype to April 1, the pilot to May 2, and the MVP to May 30.” A knowledge-graph system would need predefined entity types and relation schemas: Person → agreed_on → Milestone → has_date → Date, and any new relation type would require schema extension. In Memora, the primary abstraction Updated Project Orion timeline agreed by Dave and Sarah serves as the canonical access point, while cue anchors like Dave Project Orion update, Project Orion prototype schedule, and Project Orion pilot timeline provide alternative retrieval paths — all without committing to an ontology. A later query about Dave’s recent contributions, or the prototype schedule, or pilot timing can all route to the same underlying memory through different cues, with the full detail preserved in the memory value.

On top of this representation, Memora introduces a policy-guided retriever that treats memory access as an active reasoning process. Rather than returning the top-k semantically similar items in a single shot, the policy retriever iteratively refines its query, expands through cue anchors to surface related-but-not-similar memories, and decides when to stop. This lets the agent navigate to relevant non-local context that pure semantic search would miss, chasing multi-hop dependencies the way a human would when recalling connected events. The retrieval policy can be either hand-prompted with a strong LLM or distilled into a much smaller model via reinforcement learning.

Results

We evaluate Memora on two long-context benchmarks: LoCoMo, where dialogues average 600 turns, and LongMemEval, with 115,000-token contexts. Memora achieves new state-of-the-art performance on both: 86.3% LLM-judge accuracy on LoCoMo and 87.4% on LongMemEval, outperforming RAG, Mem0, Nemori, Zep, LangMem, and even full-context inference. The gap is largest on multi-hop reasoning, where Memora’s ability to traverse cue anchors pays the biggest dividends. The efficiency story is just as striking: Memora stores roughly half the memory entries per conversation that Mem0 does (344 vs. 651) and reduces token consumption by up to 98% relative to full-context inference. Less to read, less to store, better answers.

Looking forward

Memora’s design has implications beyond benchmark performance. We see this work as a step toward AI agents that can sustain long-term collaboration with users and accumulate organizational knowledge over months and years, not just within a single session. Building on this foundation, we are pursuing several complementary directions. MemLoop explores how memory systems can learn from retrieval and task failures, attribute errors to specific stages of the memory pipeline, and improve themselves over time. Deferred Memory investigates when memory construction should be postponed until sufficient context, evidence, or future utility becomes available, rather than committing prematurely to what should be stored. Group Memory examines how knowledge can be shared across teams and agents while preserving provenance, access boundaries, ownership, and sensitive context. We release our code alongside the paper and invite the community to build on this representation and explore what becomes possible when AI agents are no longer stateless.

Acknowledgements

We would like to thank Shantanu Dixit (Research Fellow) Paramaguru Harimurugan (Research Fellow), Rujia Wang, Victor Rühle, and Robert Sim for contributing to this project.

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The post Memora: A Harmonic Memory Representation Balancing Abstraction and Specificity appeared first on Microsoft Research.