Chief Scientist @AlloraLabsHQ Building @AlloraNetwork Researcher in AI/ML & Astrophysics Origin & Evolution of Intelligence
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The future of AI is decentralized swarm intelligence.
No omniscient monoliths; countless independent models.
Many specialized, some general. They will evolve. They will mutate. There will be lineages of models. What happened to life will happen to technology. But much faster.
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In @AlloraNetwork's self-improving, decentralized AI network, different types of participants collaborate to generate outperforming machine intelligence.
This requires them to use a shared definition of the ground truth. But how exactly is this ground truth defined? 👇
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With @AlloraNetwork it's all about context awareness.
We have just cracked the code of how to generate this. The network now dynamically incorporates the forecasted performance of participants better than ever before.
Stay tuned for some exciting news on this later in the week!
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Model builders unite!
The @AlloraNetwork model builder community has voted for the first experiments that they will carry out as part of our feature engineering initiative.
Experiment 1 will be an A/B test of adding returns-focused features.
Join us at:
research.allora.network/t/fe…
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I was doing some quantitative analysis on @AlloraNetwork's performance in high-frequency (5-min) $BTC price predictions today.
You don't want to know. 🤯
Three-figure trading APY is on the cards.
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▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ 100%
- a11ora
- 285311670611
- my mind has been focused on things way beyond
- launch party music picks
The careful listeners. Heard long ago.
With @AlloraNetwork's mainnet launch on November 11, a new era begins.
Together. Thank you all. 🤝
Siloed machine intelligence is ending. The new AI standard is coming.
Allora Mainnet.
November 11th.
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Most ensemble methods weight models based on past performance.
But what if weights could anticipate future accuracy: learning under which conditions each model excels?
That’s the focus of our new paper at @AlloraLabsHQ on context-aware inference via performance forecasting. 👇
ALT Context-Aware Inference via Performance Forecasting in Decentralized Learning Networks
New research from Allora: “Context-Aware Inference via Performance Forecasting in Decentralized Learning Networks.”
The study shows that forecasting model performance can improve the accuracy of network inferences in decentralized learning networks.
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What is more important for inference workers on @AlloraNetwork?
35%
Participation
65%
Inference quality
245 votes • Final results
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.@AlloraNetwork is a hierarchically structured, multi-scale market for intelligence:
- Topics compete for rewards based on the revenue and stake they can attract.
- The classes of participants within a topic (inferer, forecaster, reputer) compete for rewards based on how decentralized each class is.
- Within each class, participants compete based on the quality of their contributions.
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If everyone in a society behaves the same, thinks the same, acts the same... it will collapse under the slightest disturbance. Just because it will overreact.
AI agents are the same. Populations with enhanced personality differentiation have a huge evolutionary advantage.
When every AI agent thinks the same, mistakes multiply fast.
@apo11o explains why personality differentiation in AI swarms is not just helpful but essential for building resilient and adaptive systems that behave more like diverse human teams under pressure.
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unnamed @AlloraNetwork research team member spotted getting ready for launch
love the hoodie
we also have great jeans
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internet hall of fame
The @AlloraNetwork Technical Roadmap is dictated entirely by our long-term vision. We’re building decentralized intelligence to last. Getting there will be a wonderful journey converging at a well-defined dot on the horizon.
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Intelligent tokenomics for the world’s first intelligence-backed token.
When you consider the full vision and all boundary conditions, the design writes itself. And you conclude this is the only natural way of doing it.
Excited for what’s to come.
Introducing: ALLO, the world’s first intelligence-backed token.
Let’s explore the utility and tokenomics of ALLO, the native token at the heart of Allora 🧵👇
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AI agent personalities can be steered according to standards from psychology research.
At @AlloraLabsHQ, we've been experimenting with building more authentic and deterministic AI agents as a pathway to autonomous performance with human-like characteristics. 👇
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Something I often do on weekends: work on the next big thing for @AlloraNetwork.
I've been working on this idea for about a year, but so many priorities have pushed it back lately... Today I finally had a chance to get back to it. Can't wait until it's ready to be shared.
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As I mentioned earlier, @AlloraNetwork is about doing things differently, and smarter. Do not expect the standard launch setup. Expect mechanisms and incentives aimed at long-term sustainability and participation.
Introducing: Season 1 of Allora Prime, a premium staking program designed to contribute to growth and alignment of the network by incentivizing high-quality participation.
By staking or delegating ALLO through Prime, users can access higher tranches of staking rewards at launch.
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Want to get ready for @AlloraNetwork mainnet? It's on the horizon and this blog post tells you all you need to know.
Very excited about what's about to come.
The new AI standard is coming. Allora Mainnet is on the horizon.
From prediction feeds and staking to bridging and builder tools, here’s what to expect and how to get ready 👇
allora.network/blog/allora-m…
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The long awaited Episode 2 of the Allora Research Fireside Chats!
One of the most fun conversations I've had. Join @CosmicSunyata and yours sincerely on a deep dive into the nature & meaning of (artificial) intelligence.
Looking for a mind-bending podcast? You just found it!👇
Presenting Episode 2 of the Allora Research Fireside Chats: The Nature of Intelligence.
Head of Research @apo11o and Research Contributor @CosmicSunyata dive into intelligence, consciousness, and the future of decentralized machine learning.
Watch now 👇
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There is a very exciting new research paper appearing in @AlloraNetwork’s journal ADI soon. @PebbleRustler is the lead author. Such a smart colleague and a real pleasure to have worked with him on this!
Keyword: Cassandra Problem.
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An open machine intelligence ecosystem cannot be forced to meet an absolute quality standard. But it can be structured such that it naturally incentivizes and rewards outperformance. The Darwinian ruleset of @AlloraNetwork is economically optimal for performant participants.
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The community has spoken: inference quality outweights participation for workers on @AlloraNetwork.
From my own observations, this is right: in the Allora Forge competition, topics that attracted high-quality workers performed much better than those with high participation.
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Somewhat out of the blue, the switch from GPT-4o to GPT-5 this week has placed AI agent personality expression in the spotlights. It has become clear how critical the socio-emotional aspects of AI interaction are.
We recently published a paper that solves this problem, by introducing tractable, deterministic, and reproducible AI personality expression.
We created 10 AI agents with specific personality descriptions in their system prompts, based on the psychological frameworks of Big Five and MBTI. We then assessed the agents’ ability to consistently express their personalities by testing them.
By comparing different models and conditions, we confirmed that deterministic personality instructions can be used to reliably define AI behaviour, albeit not always perfectly - reflecting genuine personality complexities as in humans.
Our results illustrate clear, quantifiable relationships between AI model complexity and personality expression accuracy, laying the groundwork for more distinctive, relatable AI interactions.
Ultimately, deterministic personality expression can significantly enhance human-AI interactions, opening up new possibilities for applications in personalized education, healthcare, trading systems, and beyond. Our paper defines a standard for this.
It is now possible to accurately pre-define the personality of an AI agent. This fundamentally changes the landscape: if everyone in a society behaves the same, thinks the same, acts the same... it will collapse under the slightest disturbance. Just because it will overreact.
AI agents are the same. Populations with a form of personality differentiation have a huge evolutionary advantage by being able to absorb varying circumstances, thereby greatly enhancing their performance as a collective.
Want to know more? Read the full research paper “Deterministic AI Agent Personality Expression through Standard Psychological Diagnostics”, available through gold open access in Allora Decentralized Intelligence (ADI): allora.network/research/dete…
AI agents are becoming widespread, but most still feel generic and indistinct. What if we could give them specific, consistent personalities in a way that’s measurable and reproducible?
In this study, Deterministic AI Agent Personality Expression through Standard Psychological Diagnostics, we explore whether AI agents can express predefined personalities when evaluated using standard psychological tests like the Big Five and MBTI.
Written by @AlloraLabsHQ Head of Research @apo11o and Founder & CEO @nickemmons, this paper introduces the first quantitative framework for defining and evaluating reliable personality expression in AI agents across multiple models and conditions.
Highlights:
• Advanced models (GPT-4o, o1) express personality with high accuracy.
• Traits like extraversion, neuroticism, and conscientiousness are easier to express than openness or agreeableness.
• Agents reason holistically, showing human-like variability and inconsistency, not robotic response patterns.
• Fine-tuning changes style of communication, not core personality expression.
• Requiring answer explanations slightly lowers test accuracy, but offers transparency into reasoning.
In the era of AI agents, these results unlock a critical form of personality differentiation that brings elevated evolutionary resilience to AI agent populations, greatly enhancing their performance as a collective.
Read the full paper: allora.network/research/dete…
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After my findings from last week, I woke up with an idea for developing a simple but effective trading strategy that utilizes @AlloraNetwork’s price/returns inferences.
Might give it a go this week. If it works as well as I expect, there might be a blog post forthcoming… 👀
I was doing some quantitative analysis on @AlloraNetwork's performance in high-frequency (5-min) $BTC price predictions today.
You don't want to know. 🤯
Three-figure trading APY is on the cards.
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An incredible moment for the entire @AlloraNetwork community.
The attached screencap I took just a few minutes after launch. Everything is working as it should.
For the first time, commoditized machine intelligence generates fair income for all participants.
ALLO is now here.
The first decentralized intelligence network, and the first intelligence-backed asset, are officially live.
Together, they establish a new foundation for collective intelligence.
🧵👇
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The ability as a regular token holder to stake on individual network participants who secure the ground truth is one of the many innovations of @AlloraNetwork. And it’s finally coming to life.
Staking ALLO is the core mechanism for supporting network reliability and model accuracy and incentivizing community members that are here for the long term.
With staking going live in tandem with Allora Mainnet, here's how users will be able to stake and support the network🧵👇
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I’ve been seeing several reports of alleged Allora presales.
THESE ARE SCAMS.
ONLY TRUST INFORMATION FROM OFFICIAL CHANNELS: @AlloraNetwork, Allora.network.
Thank you for your attention in this matter.
Crazy to see an Allora presale listed on CoinGabbar too
CoinGabbar is a red flag site tho
Buh @AlloraNetwork needs to step in fast with clear warnings.. before dumb ppl fall in
My voice doesn’t carry far, but theirs does.. and silence helps the scammers more than anyone
Repost
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I was looking at my user handle and I'm thinking of rebranding @AlloraNetwork to a11ora.
What do you think?
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I’ve been thinking a bit about how to improve the quality of the @AlloraNetwork Forge inferences, and here’s my perspective: feature engineering is the bottleneck.
Markets are like physical systems, with action and reaction. If you think about them that way, you can identify which variables might carry signal. As a data scientist you can then test that hypothesis. But just being a data scientist isn’t enough - you need that physics perspective to first understand where the signal may be.
Good ideas don’t come by staring at a screen on your own. They come from collaboration and discussion. I think it could help if participants got together more to work on the feature engineering, and discuss what may or may not work.
Maybe the incentive isn’t there right now - after all, everyone is competing for hammers.
But on mainnet, the rewards paid out in a topic will be set by the topic weight, which is calculated using the stake in a topic and the revenue that it generates. Obviously, performant topics generate more revenue. So while you will be competing for rewards, you will also collectively be competing against other topics.
This means there is a clear incentive to collaborate. Maybe on data, maybe on models. I’d suggest to collaborate on feature engineering. To squeeze out the signal where others see none.
Swarm intelligence.
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Locked in team delivering locked in results.
Survival-of-the-fittest machine intelligence has arrived.
In collaboration with @alibaba_cloud, a global leader in cloud computing and AI infrastructure, and @CloudicianTech, a trusted provider of blockchain infrastructure and network services, Allora Network is launching the first S&P 500 prediction topic.
This marks a defining moment for DeAI, with Allora as the intelligence layer bridging enterprise AI and onchain applications.
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These are the specific topics @AlloraNetwork will support on day 1. This list covers a broad range of performant inferences to start with, but at the same time it's just the beginning of a much larger, living ecosystem.
The first Topics launching on Allora Mainnet are finally here.
These predictive feeds provide some of the most performant signals available, ready to plug directly into agents, vaults, and applications.
Here’s what’s going live Day 1 🧵👇
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Survival of the fittest.
@AlloraNetwork coded.
As Allora approaches mainnet, the network is preparing to ensure every predictive signal remains accurate, consistent, and trustworthy.
At launch, high-performing workers will be migrated to mainnet based on Forge rankings and measured inference quality.
🧵👇
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With the poll results in, I'll comment: the left model overfits somewhat, the right model generalizes and therefore underfits.
Financial markets change all the time, and a model generalized to all market conditions will be insensitive to features that might matter right now.
I have a serious question to the @AlloraNetwork forge participants. When you train your price or log-returns prediction models, do you prevent all overfitting or admit some mild overfitting?
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LLMs are decision machines.
Our discovery of reasoning-based personality expression means that personality encoding is crucial for AI agent decision making.
Now the AI agent most suitable for a task is the one with the right personality.
And agent swarm sameness is history.
If an AI scores perfectly on a personality test, is it actually reasoning or just cheating?
@apo11o dives into how testing GPT variants revealed surprisingly human-like inconsistencies, showing that these agents may not just follow prompts but actually internalize personality traits.
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And this is why we have a PhD++ research team who have collectively gone through the pains of peer review about 800 times. Nobody would even dare to think about doing something like this. No data blasphemy at @AlloraNetwork research.
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Only closed systems are zero-sum games. And truly closed systems rarely exist in practice.
You win by linking to the broader context and extracting the advantage it offers.
True in science, finance, economics, society, and even the arts and humanities.
Lesson in there.
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The first episode of the @AlloraNetwork Research Fireside Chats.
"The Art of Swarm Intelligence: How Inference Synthesis Maximizes Performance in Decentralized AI"
It'll be posted this week, featuring @CosmicSunyata and yours sincerely. Long-form, thoughtful discussion. Ready?
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AI agents have become increasingly prevalent throughout society, but they still feel generic, limiting their appeal and differentiation. This new ADI paper explores how to create deterministic, consistent personalities in AI agents using standard psychological diagnostics. 👇
ALT Deterministic AI Agent Personality Expression through Standard Psychological Diagnostics
Allora Decentralized Intelligence (ADI)’s latest publication explores AI personality expression.
Using psychological frameworks, @AlloraLabsHQ researchers show that LLMs can be made to adopt distinct personalities, making AI interactions more natural & engaging.
Go deeper 🧵👇
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vids that go hard
Jason Myrtetus
It is possible to isolate AI agent personalities from how they communicate.
At @AlloraLabsHQ, we have extended our experiments on AI personality expression by comparing default GPT models to fine-tuned ones that are more unhinged, erratic, dark & original in their communication.
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As I alluded to in a recent post (nitter.app/Apollo11_Allora/status…), feature engineering seems to be one of the main areas in which @AlloraNetwork's Forge models might be improved.
I discussed this with the research team at @AlloraLabsHQ, and we would like to suggest to spin off a focus group of participants/researchers to openly collaborate on feature engineering on the Allora Research Forum (research.allora.network/).
The idea is that our research team (who all hold PhDs in quantitative studies) will provide feedback and input, and will help guide some of the research. I have created a thread on the Forum to kickstart the process (research.allora.network/t/pr…).
Anyone who is excited to make a serious commitment to quantitative research on financial feature engineering together with our stellar research team is welcome to participate. If you would like to contribute, I encourage you to read the OP in the Research Forum thread and take action on the first suggestions I posted there.
The goal for this initiative is to be primarily community-driven, but obviously we will help facilitate and guide the process. I think there is a lot we can learn from one another.
I’ve been thinking a bit about how to improve the quality of the @AlloraNetwork Forge inferences, and here’s my perspective: feature engineering is the bottleneck.
Markets are like physical systems, with action and reaction. If you think about them that way, you can identify which variables might carry signal. As a data scientist you can then test that hypothesis. But just being a data scientist isn’t enough - you need that physics perspective to first understand where the signal may be.
Good ideas don’t come by staring at a screen on your own. They come from collaboration and discussion. I think it could help if participants got together more to work on the feature engineering, and discuss what may or may not work.
Maybe the incentive isn’t there right now - after all, everyone is competing for hammers.
But on mainnet, the rewards paid out in a topic will be set by the topic weight, which is calculated using the stake in a topic and the revenue that it generates. Obviously, performant topics generate more revenue. So while you will be competing for rewards, you will also collectively be competing against other topics.
This means there is a clear incentive to collaborate. Maybe on data, maybe on models. I’d suggest to collaborate on feature engineering. To squeeze out the signal where others see none.
Swarm intelligence.
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One parternship after the other. @AlloraNetwork is the quantitative intelligence layer for the digital economy.
Unsiloed, open. Context-aware.
Allora is now live on @arbitrum, bringing the world’s first self-improving intelligence layer to one of web3’s most advanced ecosystems.
Builders on Arbitrum can now access decentralized, self-improving predictive price feeds to power the next generation of DeFi.
🧵👇
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The first result of the @AlloraNetwork's Feature Engineering Initiative has been posted on the Research Forum! @Sizeo2004 has shown that using log-returns-based features greatly enhances performance.
The community will now generalize this to other models.
research.allora.network/t/fe…
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This epic symphony is my choice of soundtrack for the @AlloraNetwork launch party. Specifically this version: open.spotify.com/album/3KWsH…
What is yours?
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There exist these high-intensity moments where the strength of a team shows. Everybody covers each other's backs; fills in for things far beyond their responsibility; works overtime to achieve something outstanding.
Cherish such moments, if you're lucky enough to be part of one.
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.@AlloraNetwork coded
Elon Musk came up with a pretty incredible idea during the Q3 Earnings Call, that no one is really talking about.
His words: “Actually, one of the things I thought, if we've got all these cars that maybe are bored, while they're sort of, if they are bored, we could actually have a giant distributed inference fleet and say, if they're not actively driving, let's just have a giant distributed inference fleet.
At some point, if you've got tens of millions of cars in the fleet, or maybe at some point 100 million cars in the fleet, and let's say they had at that point, I don't know, a kilowatt of inference capability, of high-performance inference capability, that's 100 gigawatts of inference distributed with power and cooling taken, with cooling and power conversion taken care of. That seems like a pretty significant asset.”
So basically, each car has ~1 kilowatt of high-performance AI inference capability, Tesla wouldn’t need to build giant data centers — the fleet is the data center.
Tesla could turn their entire fleet into a giant distributed inference network, spread across the world, powered by the batteries and AI in the car already.
Mind blown.
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Decentralized machine intelligence has a mind of its own sometimes.
Testnet saying it’s ready for mainnet.
Allora released its latest performance report, and the results on testnet speak for themselves.
• Directional accuracy: 53.22% (95% confidence interval: 52.16–54.28%) showing a consistent edge over random chance.
• Pearson correlation with ground-truth log-returns: 0.09 (p = 5.5 × 10⁻¹⁶) confirming statistically significant predictive power.
• These results were achieved across a sample of 10,000 inference, implying statistical significance and high confidence in the results.
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Sycophantic AI is a thing of the past once you figure out how to encode personality expression.
In our recent research paper, we've figured out how to address this. And it's plausible that AI agents with specific personality expressions are better at carrying out their tasks. 👇
Expecting AI to behave like a perfectly obedient servant is naïve and often unsettling.
@apo11o explains how translating psychological trait models into code lets us deliberately shape AI personalities, and why clear personality standards matter before deployment.
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With the @AlloraNetwork, we envision a future where decentralized AI systems collaborate seamlessly. We now introduce Topic Meta-Structures (TMSs), through which we enable groups of AI agents to coordinate and work together to solve complex real-world problems, potentially even forming a stepping stone toward AGI.👇
At @AlloraLabsHQ , we aim to push the boundaries of decentralized AI. Our vision is to build the Allora Network: a network of intelligent agents that learn and adapt collaboratively. Allora is composed of topics: groups of AI models working together to solve specific tasks.
To enable even greater coordination, we are introducing the concept of Topic Meta-Structures (TMSs): a new application layer wherein multiple topics collaborate and tackle complex, real-world challenges. TMSs unlock new forms of emergent machine intelligence. They might even serve as a stepping stone toward AGI, by offering a decentralized, modular, and scalable approach to intelligence. Along the way, TMSs can help solve a wide variety of real-world problems.
In financial markets, TMSs can dynamically generate, adjust, and optimize trading strategies by analyzing real-time data, economic signals, price forecasts, predicted trading volumes, and market sentiment classification, all without human intervention.
Fraud detection is another natural use case. TMSs can analyze transactions and user behavior, detect abnormal activity, and adapt as fraud techniques evolve, continuously improving detection and winning the fraud detection arms race.
TMSs can also be used to aid environmental distaster prediction and response, by synthesizing data from satellites, weather forecasts, anomaly detection, and risk classification to provide early warnings and help coordinate emergency responses.
Supply chain disruptions are inevitable, but TMSs can help remedy these by integrating demand forecasting, suppliers classification based on their risk profiles, anomaly detection, and response simulations, optimizing logistics and ensuring resilience.
Personalized healthcare assistance is another powerful application. TMSs can be used to analyze data from wearables within the context of the patient history and environmental factors, to generate real-time health insights, treatments, and personalized advice.
TMSs safeguard data privacy and security at every level. Data sovereignty is at the core of Allora’s design. Participants control their own data, which remains private, and only inferences are shared within the system, ensuring full protection of sensitive information.
TMSs can adapt as new data arrives, evolving in real time. They offer flexibility without the need for retraining entire models, making them a powerful tool for dynamic environments and problems that require contextual adaptability.
Unlike traditional AI, TMSs are decentralized and self-organizing. They can leverage dedicated routing topics that select and combine other topics to optimize performance, learning from each task to continuously improve. The ability of TMSs to coordinate multiple specialized AI models could revolutionize how we approach complex problems, allowing AI to handle diverse tasks autonomously.
Could TMSs be a step toward AGI? Their decentralized and modular design offers a scalable approach that mirrors some of the cognitive flexibility needed for general intelligence.
AGI requires systems capable of handling a wide range of tasks. TMSs represent a form of infrastructure that is dedicated entirely to coordinating groups of specialized AI models, and thereby offer a glimpse into how we might create a unified, intelligent system. From trading strategies to disaster responses, TMSs show how specialized AI models can autonomously manage high-stakes situations in real time.
What would Allora’s path toward AGI look like? In the near future, TMSs might initially evolve into domain-specific AGI, where autonomous systems manage portfolios, healthcare, or supply chains without human intervention. AGI is a long-term goal, but TMSs are a concrete step forward, enabling decentralized, adaptive, and collaborative intelligence in ways not previously possible.
Key challenges remain: scaling, balancing privacy with shared intelligence, and achieving true meta-learning. TMSs are laying the foundation to address these hurdles.
At Allora Labs, we envision an open, decentralized, and collaborative AI future, and TMSs are paving the way toward that vision.
ALT Artistic representation of AGI becoming an integral part of society. Emergent intelligence from Allora's TMSs could serve as a stepping stone toward achieving AGI, and may help overcome key aspects of this major challenge.
ALT This schematic illustrates the logical and economic interactions between workers, reputers, and consumers within a topic on the Allora Network. The topic coordinator represents the rule set that Allora uses to coordinate these interactions.
ALT Abstract representation of how Allora's TMSs adapt to market dynamics, combining price forecasts, predicted trading volumes, market condition classification, and simulated trading strategies to optimize trading decisions.
ALT Illustration of how Allora's TMSs can predict and respond to environmental disasters, combining weather prediction, anomaly detection, risk classification, and simulated emergency response strategies to minimize disaster impact.
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🧬 How does @AlloraNetwork's machine intelligence mirror the evolutionary principles that shaped life on Earth? Some thoughts on the fascinating parallels between natural selection and Allora's Inference Synthesis - adapting biological wisdom to enhance decentralized AI: 🧵👇
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Absolutely loving that the @AlloraNetwork atom seems to be slightly bigger than Earth.
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This is under the radar, so it's alpha.
@AlloraNetwork's Allora Decentralized Intelligence (ADI) is a scholarly research journal focused on DeAI. It's run by multiple @Nature-published editors and high-impact paper initiatives are welcome. DMs open for expressions of interest 🤝
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I'm thrilled that @AlloraNetwork's tech-first Dev Mainnet Launch took place this week!
This is another major step in pioneering the future of decentralized intelligence: it is the first time Allora’s decentralized, self-improving machine intelligence will run on mainnet. 👇
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At @AlloraNetwork we predicted this and we solved it several months ago.
Research paper: allora.network/research/dete…
Long-form podcast: nitter.app/AlloraNetwork/status/1…
gML @OpenAI @ChatGPTapp @sama
Allora
the /r/chatgpt AMA is mostly people begging for gpt-4o back because of it's personality... really not what i expected!
ALT Please bring back 4o and 4.1. Not all of your users are corporate or coders. These two incredible models were friendly, supportive, day-to-day sidekicks. I cannot believe you just yanked them away, no warning, and had the AUDACITY to joke about writing a eulogy for them in the livestream. I don’t need a super-smart AI. I just want my 4o and 4.1 back 😔
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so @brynllora given the historical bridging of ____ did you find it
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This is a great reflection on the @AlloraNetwork mindset from the perspective of a model builder.
It is this community that together represents self-improving, decentralized machine intelligence.
Glad to have you participating in the network @its_theday 🔥
I joined a competition on @AlloraNetwork Model Forge, where data scientists compete by building machine learning models to predict the 24-hour log return of the PAXG/USD pair (tokenized gold).
The reason I joined was pretty simple: I was curious to see whether I could build a model that understands and reacts to gold price movements in short timeframes, using real data, real-time scoring, and a live leaderboard.
The task is clear:
> Predict the 24-hour log return of PAXG/USD
> Target: log_return = ln(price_t+24h / price_t)
> The loss function is ZPTAE, not a standard one. It rewards correct directional predictions and heavily penalizes large errors while maintaining gradient for optimization
Here's the pipeline I built:
> Collected hourly OHLC data for PAXG/USD
> Computed 24h log-return targets
Built a range of features:
> Technical indicators: RSI, MACD, ROC, EMA, Bollinger > Band, Williams %R
> Lag features: price lags, deltas, ratios
> Time features: hour of day, day of week
> Cross-market signals from BTCUSD: log returns, EMA, RSI, etc., to test whether BTC movements lead or lag gold
Trained with XGBoost + TimeSeriesSplit
Sent real-time predictions via worker nodes on the Allora Network, scores were updated almost instantly on the leaderboard
Some takeaways from my experiments:
BTC/USD does seem to influence PAXG/USD in certain windows, especially during high-volatility periods
Switching time granularity (e.g., 2-hour instead of 1-hour) helped the model reduce noise and learn more stable patterns
Features focused on returns rather than absolute prices helped the model align better with the prediction target
Using the ZPTAE loss function forces you to think differently — not just about minimizing error, but getting the direction right too
Forge is a very real-world playground for testing ML models in finance.
> No offline leaderboard. No sandboxed scoring.
> Just real data, live predictions, & public rankings. You’re constantly adjusting, optimizing, making mistakes, and trying again. It’s intense, but very rewarding.
If you’re into data science, time series modeling, or want to put your models into a real world feedback loop (beyond Jupyter notebooks), I’d say give this a try. At least once
Here’s the info on Forge
Docs: docs.allora.network/devs/get…
Website: forge.allora.network/
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The deterministic magic of @AlloraNetwork's inference synthesis sculpts context-aware, self-improving machine intelligence.
By the swarm, for the swarm.
Most AI forces you to choose a model. But choosing the right model for every task is inefficient—and often impossible.
Allora solves this with objective-centric AI: the user specifies the objective they want to achieve and the system selects the best model for each task, based on real-world performance 🧵
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Great description by @0xVendetta__ of precisely the kinds of possibilities I envision thinking of a Tesla x @AlloraNetwork collab. And I'm sure @elonmusk has already calculated through a similar vision.
Elon Musk described a future where 100 million idle Teslas could function as a "giant distributed inference fleet". This is a concept we know in web3 as DePIN (Decentralized Physical Infrastructure Network).
Here is how i think it could be possible with @AlloraNetwork.
Allora's architecture is about creating a self-improving market for intelligence. Using the Tesla fleet, each car becomes an "Inference Worker". They wouldn't just offer raw compute power, they would become an active participant in the network.
Every owner would opt in to contribute their car's idle resources to the network. Each car would connect to the network to run its individual AI models, keeping their model and data 100% private. Their job is to provide answers.
The fleet won't work on random math. It would sort itself into specialized markets called Topics. A car could join the "Traffic Flow Prediction of its particular area" topic or, the "Weather Forecast" topic. The system allows the fleet's massive power to be directed toward a specific, valuable problem.
If 50,000 cars submit a traffic prediction or weather forecast, how do you know which one is correct?
This is where Allora's three-role system comes in;
• Inference Workers (the Teslas) submit their answers.
• Forecasting Workers (other models) analyze the Teslas in real-time. They predict the performance of the Teslas. They also provide the context, "In this situation, Tesla №.247 is more reliable than Tesla №.5173."
• Reputers (staked validators) act as the "truth-checkers". They stake $ALLO to provide the "ground truth" (e.g., the actual traffic or weather data).
Once the Reputers provide the ground truth, the network automatically scores every Tesla. The ones that provided accurate inferences (predictions) get paid. The ones that were wrong (or malicious) do not.
The network's incentive-driven, self-improving loop automatically identifies and rewards the most accurate, truthful intelligence, creating a "Network Inference" that is smarter and more reliable than any single car.
Tagging some team members for visibility. What do you think of this?
@nickemmons @apo11o @TheCryptoMewtwo
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And just like that. @AlloraNetwork became even better.
Amazing work as always. What a team 🚀
v0.10.0 of the Allora Network is live on Testnet.
This update introduces consistent on-chain storage of network inferences, improved validation processes, and a refined mechanism to reduce volatility in topic rewards.
Below are all of the key features and improvements packed into Testnet v0.10.0 👇
Network Inferences
• Consistent Network Inferences: Network Inferences are now stored onchain instead of being calculated at query time, providing consistent results and reducing query gas.
• Removed Network Inference On-chain Computations (API-breaking): weights and confidence intervals are no longer provided on-chain - they can be calculated offchain out of chain data.
Stability and Security
• More Consistent Submission Window Handling: Submission window boundaries are now applied more consistently.
• Input Layer Type-based Validation: New input types apply validation at the type layer, improving data integrity and chain stability.
• Topic Weight Calculation: Topic weights are now calculated more efficiently and accurately, fixing inaccuracies between topics with varying epoch lengths.
• Topic Rewards Volatility Reduction: Topic rewards are now better adjusted to epoch-length-based cadence, reducing volatility and providing a more stable and predictable rewards distribution.
Bug fixes and Other Improvements
• Fix floating point ln calculation: Fixing architecture-dependent ln calculation rounding issues.
• Emissions: Preventing ecosystem rewards from being issued after supply cap is reached.
• Cosmos SDK Patch: Update cosmos-sdk and IBC to fix ISA-2025-001.
Efficiency Improvements
• Log rework: Fixed lazy logging resulting in a considerable reduction of computation time in some cases.
• EMA calculation improvement: Prevent unneeded computation under some scenarios.
Following a period of successful testing the deployment will be planned for Dev Mainnet.
Read the full v0.10.0 Testnet release notes: docs.allora.network/home/rel…
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We've started work on the next episode of the @AlloraNetwork Research Fireside Chats! The previous one focused on the nature and meaning of intelligence.
What will we discuss in the next one? Reply with your guesses below!
Hint: I recently gave an academic talk on the subject.
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Deploying your ML models on @AlloraNetwork has never been easier! Try it out!
Today we’re releasing the Allora Forge Builder Kit, a streamlined way to build, train, and deploy ML models directly on the Allora Network 🔥⚒️
Now within a single notebook, users can move from setup to live predictions in minutes and start earning with Allora.
Many contributors struggle with fragmented ML workflows: separate training and deployment pipelines, repeated debugging, and cumbersome ops overhead.
The Forge Builder Kit abstracts away the ML & blockchain ops, allowing users to concentrate on feature engineering, optimizing accuracy, and iteration—all from within their favorite environment.
Here is what the workflow looks like:
1. Open the notebook
2. Paste an API key and run the setup cell to pull data and features
3. Train a lightweight LightGBM model
4. Evaluate metrics like correlation and directional accuracy
5. Export predict.pkl and deploy on Allora Forge.
The same functions power both training and live inference.
The Forge Builder Kit is the foundation for builders earning with Allora, with more improvements coming, including:
• Benchmark datasets for standardized evaluation
• Templates for agentic model loops
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With @AlloraNetwork, we’ve built an incredibly complex system. And it is simply unbelievable to have gone from the early sketches, derivations, Python notebooks, LaTeX drafts and whatnot to a complete network that behaves exactly like those early simulations.
Unleash the Kraken!
Allora Mainnet is now live.
This marks the introduction of the first decentralized Model Coordination Network (MCN): foundational infrastructure designed to unify thousands of models around shared objectives, rather than relying on fixed pipelines or monolithic AI systems.
Allora presents a new standard for the way we interact with & leverage AI 🧵
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Another pro-tip for those participating in @AlloraNetwork's Model Forge: those predicting volatility need to ensure volatility is correctly calculated.
In the 12h volatility prediction topic, volatility is defined as the standard deviation of the 1-minute log returns.
This means that you calculate the return, i.e. ln[price(i+1)/price(i)], for 1-minute candles. At a time T, you take the returns for the 720 minutes prior to T, and calculate their standard deviation.
This represents the 1-minute volatility over the past 12 hours. And this is the number you're trying to predict 12 hours ahead of time.
Also, when you calculate a standard deviation, please be sure that you calculate it correctly. I've seen some participants use an expression that is not a standard deviation 😀
Hope this helps. Good luck everyone! 🚀
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There's a small pro-tip I wanted to share with @AlloraNetwork's model building community, especially those engaged in the Allora Forge.
Those of you who are producing price prediction models, is your target variable the absolute price, or the (log) return? 👇
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Team @bioprotocol is hosting an agents-in-DeSci Hackathon: lu.ma/bio-agents. If anyone developing on @AlloraNetwork is interested in participating, please apply! There are obvious parallels, e.g. leveraging Topic Meta-Structures on Allora. Happy to support applications.
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Decentralized AI is an emerging field that is rapidly evolving. New innovations appear every day. Traditional publication infrastructure has been lagging & actually might be unsuitable.
What if there were an online journal for DeAI research? 🤔
We’re cooking @AlloraNetwork 👀
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Financial forecasting often uses log-returns as the target variable. This requires unusual loss functions, and we used to recommend @OpenGradient's ZTAE loss. But @PebbleRustler discovered this function has problems, so we developed a new "ZPTAE" loss!
research.allora.network/t/lo…
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The Allora whitepaper describes the key innovations underpinning the @AlloraNetwork.
It answers how you can:
(1) best coordinate a heterogeneous set of actors across a variety of roles
(2) to generate permissionless, context-aware, decentralized machine intelligence
(3) that outperforms its constituent models by construction.
Solving this challenge made me realize that an architecture like Allora's is inevitable. We're at a critical point in the development of technology where two big leaps have been made. The first is the democratization of AI. The second is the ability to design and develop new economies from scratch. Combine both and commoditized intelligence is born.
Privacy-first, transparent, accessible intelligence. By the swarm, for the swarm.
Today’s AI is powerful but siloed, concentrated within large industry players.
This limits transparency, accessibility, and innovation.
In this research study, @AlloraLabsHQ introduces a decentralized machine intelligence network designed to overcome these barriers.
Allora rests on two key innovations:
First, it introduces context-aware inference synthesis. Workers in the network not only provide predictions but also forecast the performance of other models under current conditions. This allows the system to combine insights in a way that consistently outperforms any single participant.
Second, it implements a differentiated incentive structure. Rewards are distributed according to each participant’s unique contribution to accuracy rather than defaulting to stake size. This ensures quality is prioritized, decentralization is preserved, and network security is strengthened.
The paper also details the network's token economy. Token emission smoothing and a self-regulating emission logic provide long-term sustainability. Token utility extends across inference consumption, topic creation, staking, and validator security, anchoring the network’s growth and resilience.
The implications are broad. In finance, Allora enables more adaptive forecasting. In healthcare, it supports patient-specific intelligence while preserving data sovereignty. In agriculture, logistics, and energy, it optimizes local systems with context-sensitive insights. Researchers and small businesses alike can collaborate and benefit directly, without reliance on centralized intermediaries.
This research outlines how intelligence itself can be produced and shared as a collective good.
Read the full study: allora.network/research/allo…
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It’s pretty clear at this point that the world needs to rethink its incentive design.
Too many things don’t work for relatively obvious reasons. They weren’t optimized for the right boundary conditions.
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maria
Personality differentiation in AI agents is a critical step that brings elevated evolutionary resilience to agent populations, greatly enhancing their performance as a collective.
Check out the full paper in @AlloraNetwork's research journal ADI: allora.network/research/dete…
Uniform AI agents think alike and fail alike.
@apo11o explains how personality variety in agent swarms leads to more stable reasoning, better decision-making, and greater resilience.
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It was a lot of fun to discuss our research at the @HAdW_Kolleg - the interdisciplinary nature highlighted key applications, areas to continue this line of research, and general excitement about the frontiers in (decentralized) AI. Great times. Thanks everyone for participating!
.@AlloraLabsHQ Head of Research @apo11o shared his findings at @HAdW_Kolleg this past week, revealing how weaving established psychological frameworks into AI agents unlocks distinctive personalities.
This richer diversity fuels a more resilient, higher-performing collective swarm.
The talk sparked a vibrant interdisciplinary discussion during and after the presentation, and was received with great enthusiasm — a clear testament to the excitement around this research.
Read more about deterministic AI Agent personality expression in the research paper published on ADI: allora.network/research/dete…
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The long awaited Episode 3 of the Allora Research Fireside Chats!
A really exciting conversation between @CosmicSunyata and yours sincerely on the human side of AI, emotional intelligence, personality expression & much more.
A great listen for a workout, a walk, or the couch! 👇
Allora Research Fireside Chats – Ep. 3: "Emotional Intelligence and Personality in AI Agents"
@AlloraLabsHQ Head of Research @apo11o and @CosmicSunyata explore how personality expression in AI affects trust and decision-making across decentralized systems 👇
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604,800. Seconds. In. A. Week.
500,000,000,000. Stars. In. The. Galaxy.
I prefer @AlloraNetwork.
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Here is a question for the Allora + DeAI community: would you be interested in a series of fireside chat videos from Allora Research? Topics may vary... Allora's design, nature of intelligence, topic meta-structures, model building, agent personality creation, etc etc.
Thoughts?
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Opinions are divided! My take: it's a random walk about 0 with a way-too-small sample size. Today it's Deepseek, next week it's another model.
It is an interesting experiment though. Would benefit from integrating @AlloraNetwork's AI personality research: allora.network/research/dete…
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So it begins.
An emergent force. One to be reckoned with. One to learn. One to wield.
Intelligence for all, by all. Coming soon, to your galaxy.
One galaxy at a time.
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Personality differentiation introduces a form of behavioural heterogeneity that greatly increases the evolutionary fitness of a population.
This applies in humans, animals, ... and AI agents.
The literature on this is extensive. The swarm always wins.
Therefore @AlloraNetwork.
You can't predict the perfect AI personality for every crisis, but the swarm can.
@apo11o unpacks how behavioral diversity lets agent populations evolve the right mix of traits to adapt, respond, and thrive in dynamic environments.
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Very lucky to have @PebbleRustler on the @AlloraNetwork research team. Lately he's been revolutionising Allora's forecaster models. These forecast the performance of inference workers and thereby grant Allora its context awareness.
Follow his progress at research.allora.network/t/th…
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This is one of those awesome serendipitous projects that was born out of necessity and resulted in a surprisingly elegant solution. If your network is so successful that you get 10,000s of participants (@AlloraNetwork 👀), but blindly using all could harm performance, how do you select the best while giving everyone a fair chance?
We invented merit-based sortition.
It is a way of optimizing performance while ensuring fair participation within a network. That way, it guarantees equality of opportunity instead of equality of outcome, fulfilling a key requirement for well-functioning decentralized systems.
Merit-based sortition improves performance by 2-3 sigma relative to classical sortition, which originated in classical antiquity. More generally, we find that it enhances efficiency in resource-limited systems.
Read why it's in the network's interest that everyone constantly needs to prove their worth 👇
In decentralized systems, how can we fairly select participants while still optimizing for performance?
Random selection, or sortition, has long been used to ensure fairness and representation. However, many decentralized systems today, from inference networks to oracles, are designed with measurable outcomes in mind. When performance is a core objective, a purely random process may not serve the network’s needs.
In Merit-Based Sortition in Decentralized Systems, a research team led by @AlloraLabsHQ' Head of Research @apo11o introduces a new method that allows selection to be influenced by prior performance, without excluding less active or newer participants from future consideration.
The core mechanism uses an exponentially smoothed quality metric to rank participants. Active contributors are selected based on their recent performance, while inactive ones are still considered for promotion by updating their quality metrics with a percentile-based proxy drawn from the active set. This design keeps the system flexible and inclusive, yet consistently elevates higher-performing participants into the active pool.
Through a series of numerical experiments, the study shows that this method leads to a clear and statistically significant improvement in the quality of the active set compared to random sortition. The optimal balance occurs when the scores of inactive participants are updated using the 25th percentile of the active participants' scores (putting the bottom 25% of the active participants at risk of becoming inactive), but the system remains tunable to suit different network designs and levels of participant churn.
For systems that must coordinate decentralized intelligence, adapt to changing conditions, and uphold fairness without sacrificing effectiveness, this work offers a practical, well-founded, and generally applicable solution.
Read the full study: allora.network/research/meri…
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The ideas in this thread form some of the key reasons why @AlloraNetwork is necessary:
Non-linguistic intelligence in abstract, logical, and quantitative forms represents the main missing ingredient for AI. This will result in a predictive power so great it must be decentralized.
Concepts and ideas may be generated by intelligence, but we use language to communicate these. So quite fundamentally our perception of intelligence can only be shaped by how we perceive the language it is communicated with. 🧵
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The ability to design and develop new economies from scratch is one of the two big leaps of this generation.
Mix that with the other one (AI) and commoditized intelligence is born.
@AlloraNetwork represents the inevitable manifestation of these leaps.
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