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Кто такие LLM-агенты и что они умеют?

В последнее время большие языковые модели (Large Language Models, LLM) стали невероятно популярными — кажется, их обсуждают везде, от школьных коридоров до Сената США. Сфера LLM растёт бурными темпами, привлекая внимание не только специалистов в области машинного обучения, но и обычных пользователей. Кто-то высказывает массу опасений насчет их дальнейшего развития, а кто-то и вовсе предлагает бомбить дата-центры — и даже в Белом Доме обсуждают будущее моделей. Но неужели текстом можно кому-то навредить? А что если такая модель приобрела бы агентность, смогла создать себе физическую оболочку и полностью ей управлять? Ну, это какая-то фантастика из (не)далёкого будущего, а про агентов нашего времени я расскажу в этой статье. И не переживайте — знание машинного обучения вам не понадобится!

На тему больших языковых моделей написано множество статей для разного уровня читающих. Если хотите на пальцах понять принцип работы GPT-like моделей, не имея технический бекграунд, рекомендую почитать обзор Игоря Котенкова “Как работает ChatGPT: объясняем на простом русском эволюцию языковых моделей с T9 до чуда”. Сегодняшний пост посвящен одному из интересных применений LLM — автономным агентам. Andrej Karpathy, бывший AI Director в Tesla, а теперь инженер в OpenAI (который по статусу в твиттере работает над something like Jarvis) сравнил LLM с первыми компьютерами, которые изначально использовались как калькуляторы. Только спустя какое-то время мы по-настоящему раскрыли их потенциал, построив вокруг этого операционные системы, множество приложений и обширную инфраструктуру. Автономные агенты на базе LLM могут стать аналогичным прорывом в мире языковых моделей. На сегодняшний день уже наблюдается активное развитие этой области, что подтверждается многочисленными исследованиями, публикациями, фреймворками и проектами ведущих компаний. Примером может быть GitHub CoPilot: представьте, если бы в гитхабе можно было открыть Issue, заполнить несколько полей, а далее бот сам напишет план, реализует код и откроет PR. Сколько багов мы получим времени можно будет сэкономить!

Этот обзор основан на обширном анализе статей и публикаций и представляет собой краткое изложение ключевой информации по теме. Для тех, кто хочет углубиться, в конце текста вы найдёте список литературы. Также присоединяйтесь к моему телеграм каналу AI[ex]Time, где я пишу про машинное обучение, NLP, LLM и в том числе про агентов. Отдельное спасибо Саше Абрамову (a.k.a DealerAI) и Игорю Котенкову из Сиолошной за ревью и обратную связь по данной статье.

Итак, начнем с базовых определений:

Определений агентов множество, но выделяя главное, можно сказать, что агенты — это системы, взаимодействующие с динамической средой, которые воспринимают ее и действуют, выполняя заложенные в них цели или задачи. Под LLM же в данном контексте будем понимать достаточно большие модели, которые по входному тексту, могут предсказать вероятный следующий токен (где токен – это слово или его часть).

Что же мы подразумеваем, говоря об агентах, основанных на LLM? В контексте сильного прогресса нейросетей и с появлением у моделей способности решать по-настоящему нетривиальные задачи, LLM может стать мозгом агента, а средой, в которой он функционирует, будет наш мир. Для этого система должна обладать следующими возможностями:

  • В некоторых сценариях уметь отыгрывать определенную роль. Роль возникает из того факта, что она улучшает перформанс и задает некоторые рамки поведения агента. Например, указание в промпте (т.е. текстовом запросе), что модель является "разработчиком на python" может улучшить качество кода (Profile).

  • Воспринимать поступающую информацию из различных источников. Это могут быть сенсоры, текст, изображения с камер, что угодно. Зачастую для полноценного выполнения задач агент должен уметь обрабатывать несколько модальностей (Perception).

  • Планировать, разбивая сложные задачи на серию более простых шагов. Например, формирование маршрута по Парижу с учетом лучших мест для проживания и мест, которые стоит посетить (Planning).

  • Использовать инструменты доступные в среде. Например, мы можем дать агенту возможность поиска в интернете, исполнять код, вызывать по API какие-то функции, другие модели и так далее. Как человек научился пользоваться инструментами и делегировать им задачи, так и к LLM можно подключить интернет, интерпретатор и прочее. Именно так нейронка и может влиять на реальный мир (Actions).

  • Рефлексировать на основе обратной связи из среды, информации об ошибках и корректировать свое планирование, исходя из данной информации (Reasoning).

  • Иметь краткосрочную и долгосрочную память для поддержания диалога или извлечения релевантной информации для дальнейших действий (Memory).

Далее мы поговорим про каждое направление и посмотрим на примере нескольких работ, какие прорывы случились за последний год.

LLM в роли мозга агента. Источник: The Rise and Potential of Large Language Model Based Agents: A Survey. Xi et al. 2023
LLM в роли мозга агента. Источник: The Rise and Potential of Large Language Model Based Agents: A Survey. Xi et al. 2023

Perception

На самом деле агенты, работающие только с текстом, уже могут выполнять разнообразные и серьезные задачи, просто получая информацию о состоянии среды в текстовом формате. Однако, их способности значительно расширяются, если добавить возможность обрабатывать и другие модальности, такие как аудио и изображения. Для этого необходимо не просто переводить каждую из сущностей в вектор (или последовательность векторов), но делать это так, чтобы векторы этих модальностей были в одном и том же скрытом пространстве.

Что значит в одном и том же скрытом пространстве?

Модели оперируют векторами, это их язык. Но векторы могут быть разные, например, [0.1, -0.1] и [100, 101, 102, 103]. Не все из них "имеют смысл" для нашей итоговой модели. Чтобы достичь мультимодальности, мы учимся переводить звук, картинки, текст как раз в единый язык, то есть в векторы определенного формата, которые "понятны" модели.

Рассмотрим, например, ситуацию, когда модель хорошо понимает текст, но изображения переводятся в числовое представление совершенно другой моделью, о которой агент ничего не знает. В таком случае информация, представленная изображением, окажется для него бесполезной. Для перевода разных форматов входных данных в единое пространство используются различные подходы. Давайте посмотрим на некоторые из примеров: модели LLaVa [1] и BLIP-2 [2], способные обрабатывать как текст, так и изображения.

LLaVa

В качестве LLM для генерации текстового ответа используется LLaMA, которая декодирует эмбеддинги (то же, что и векторы) картинок и входного текста в ответ. Чтобы кодировать картинку в последовательность эмбеддингов в архитектуре используется Vision Transformer (ViT-L): модель переводит одну картинку в набор патчей, то есть кусочки этой картинки, и далее обрабатывает их как последовательность. Такой подход дает нам возможность использовать силу архитектуры трансформера, а именно attention между патчами (про attention поговорим чуть позже). Однако для LLaMa эти эмбеддинги изначально не имеют смысла, они буквально написаны на другом, неродном LLM языке! Чтобы это исправить, вводится дополнительное преобразование векторов ViT (Z_v) в новые векторы H_v, которые понятны LLaMa. Это преобразование представляет из себя умножение на матрицу, и матрица обучается на отдельном этапе.

Архитектура LLaVa. Две модальности переводятся в одно пространство, эмбеддинги в котором обрабатывает LLaMa. (Источник: Liu et al. 2023)
Архитектура LLaVa. Две модальности переводятся в одно пространство, эмбеддинги в котором обрабатывает LLaMa. (Источник: Liu et al. 2023)

Обучение происходит посредством языкового моделирования, когда модель генерирует текст на основе изображения по заранее подготовленной разметке.

Пример, как может выглядеть разметка для обучения. На вход модель получает картинку и запрос от пользователя (Normal prompt) и на выходе должна дать ответ (Response). (Источник: Liu et al. 2023)
Пример, как может выглядеть разметка для обучения. На вход модель получает картинку и запрос от пользователя (Normal prompt) и на выходе должна дать ответ (Response). (Источник: Liu et al. 2023)

BLIP-2

В данной архитектуре используется немного другой подход. Основным элементом является Q-former, который служит связующим звеном между визуальной и текстовой модальностями. Это достигается за счет обучаемых Query-эмбеддингов, взаимодействующих с визуальными признаками через механизм Cross-attention. В этом случае, токены запроса могут 'посмотреть' на изображение, 'усвоить' важную визуальную информацию и преобразовать ее в числовое представление, которое затем обрабатывается языковой моделью. Заметим, что здесь тоже есть отдельное преобразование в виде полносвязного слоя между Q-Former и LLM.

Архитектура BLIP-2. Q-Former извлекает признаки изображения через специальные токены запроса. 2-ой этап обучения модели. Помеченные снежинкой части заморожены — мы не меняем их веса. Поэтому такая процедура очень быстрая, т.к. нужно обучить всего лишь малую часть архитектуры. (Источник: Li et al. 2023)
Архитектура BLIP-2. Q-Former извлекает признаки изображения через специальные токены запроса. 2-ой этап обучения модели. Помеченные снежинкой части заморожены — мы не меняем их веса. Поэтому такая процедура очень быстрая, т.к. нужно обучить всего лишь малую часть архитектуры. (Источник: Li et al. 2023)
💡 Как работает Cross-Attention?

Вспомним, как работает классический Self-Attention в трансформерах: В нем мы оперируем одной последовательностью, и задача состоит в том, чтобы взвесить векторы токенов для получения оптимальной комбинации, учитывающей весь контекст. Для этого токены, представленные матрицей запросов (Q), сравниваются сами с собой, но в виде матрицы ключей (K). Полученные в результате этого сравнения веса используются для получения взвешенного значения токенов, представленных матрицей Value (V). Этот процесс позволяет каждому токену собирать информацию от всех остальных. Cross-Attention же работает с двумя разными последовательностями: запросы (Q) берутся из одной, а ключи (K) и значения (V) — из другой. Это позволяет одной последовательности извлекать наиболее актуальную информацию из другой.

Визуализация Self-Attention. Для каждого запроса Q (левый столбец) подсвечены наиболее релевантные слова (К), на которые он обращает внимание (правый столбец). В Cross-Attention слова в столбцах будут отличаться (или же в одном из столбцов могут быть части из другой модальности) Источник: Deconstructing BERT, Part 2: Visualizing the Inner Workings of Attention, Jesse Vig
Визуализация Self-Attention. Для каждого запроса Q (левый столбец) подсвечены наиболее релевантные слова (К), на которые он обращает внимание (правый столбец). В Cross-Attention слова в столбцах будут отличаться (или же в одном из столбцов могут быть части из другой модальности) Источник: Deconstructing BERT, Part 2: Visualizing the Inner Workings of Attention, Jesse Vig

Можно провести такую аналогию: каждый раз мы закидываем удочку со словом (Query) во все множество слов, на которое хотим посмотреть, но вытягиваем только то, что актуально в данный момент.

Основная идея, которую хочется здесь зафиксировать, заключается в том, что для обучения мультимодальных моделей нам требуется датасет вида (модальность1, модальность2) и процедура дообучения, которая показывает модели, как эти модальности соотносятся друг с другом. Далее мы можем выбирать среди множества подходов, как именно реализовать это архитектурно.

Profile

Автономные агенты часто выполняют свои задачи, принимая на себя определенные роли. Добавление небольшой фразы, чтобы модель отвечала методом Сократа, позволяет добиться общения в стиле диалога с открытыми вопросами. Роли также нужны для симуляции некоторых социологических феноменов (в тех случаях, когда нет возможности поставить эксперимент в реальной жизни, например, распространение фейковых данных в обществе). В таком случае нужно, чтобы каждый агент имел определенный характер.

Обычно это достигается путем добавления специального промпта (system prompt) в начало контекста модели. Профиль может содержать базовую информацию, такую как возраст, пол, профессия, а также более специфические аспекты, например, психологический портрет, отражающий характер пользователя. Создание таких профилей обычно не представляет сложности.

Интересной альтернативой ручному созданию профилей агентов является их автоматическая генерация с помощью другой модели/алгоритма. Примером такого подхода является работа RecAgent [3] — агент для пользовательских симуляций, например, в области рекомендаций фильмов. В статье авторы используют автоматическую генерацию профиля, предоставляя лишь базовую информацию в виде таблицы, на основании которой уже формируется более полное описание агента. Дополнительно, в таких таблицах можно отразить распределения, характерные для реального мира, например, процентное соотношение мужчин и женщин или распределение по профессиям. В таком случае мы повышаем реалистичность симуляций за счет соответствия агентских профилей данным реального мира.

Генерация профиля по заготовленным табличным данным. В колонках отображена только базовая информация по типу пользовательских интересов и профессий. (Источник: Wang et al. 2023)
Генерация профиля по заготовленным табличным данным. В колонках отображена только базовая информация по типу пользовательских интересов и профессий. (Источник: Wang et al. 2023)

Actions

LLM способны генерировать только текст, и в обычном сценарии использования это сильно ограничивает диапазон задач, которые агенты могут решить. Однако, существует элегантный способ значительно расширить возможности агентов путем добавления различных инструментов.

Action Module — это специализированный компонент, позволяющий языковой модели выполнять внешние задачи или операции, выходящие за рамки простой генерации текста. Этот модуль выступает в качестве интерфейса между языковой моделью и внешними инструментами, базами данных, API и другими ресурсами, позволяя модели выполнять действия, получать данные или взаимодействовать с другими системами. Несколько примеров:

  • Извлечение данных из БД для последующей аналитики.

  • Исполнение кода в Python или вычисления выражений в Wolfram, Latex, и тд.

  • Вызов других моделей, например, для генерации картинки или синтеза речи (Вот вам милый пример Spot от Boston Dynamics — собака-робот с добавленной LLM для взаимодействия с пользователем).

  • Вращение камеры или совершение физического действия в среде, если мы говорим про Embodied Agents.

Для реализации Action Module необходимо, чтобы модель научилась запрашивать информацию из внешних источников и чтобы отдельная программа выполняла эти запросы, обрабатывала ответ и отдавала его обратно в контекст модели. Процесс обычно выглядит примерно так:

  • В случае, когда модель считает, что нужно обратиться к API, она генерирует что-то вроде “<API> api_name (expression), parameters </API> (Словарь модели нужно расширить специальными токенами <API>, </API>)

  • Отдельная программа парсит это предложение и переводит в понятный формат, например, REST запроса.

  • Программа выполняет запрос к API, обрабатывает каким-то образом ответ и помещает его в контекст модели. Либо же ответ API уже используются как итоговое действие.

  • Модель, имея информацию от источника, продолжает генерацию ответа.

Для того, чтобы научить модель пользоваться инструментами и генерировать в случае необходимости такие запросы, неплохо работает few-shot learning, то есть предварительно в контекст модели помещаются примеры вызовов внешних инструментов, их параметры и инструкция, в каком случае их можно вызывать. Но важно отметить, что действуя таким образом, мы жертвуем размером оставшегося контекста. Также могут быть ошибки генерации конкретных параметров, из-за чего вызов инструмента может пойти не так. Чтобы бороться с такими неприятными ошибками, можно ввести специальный аннотатор, который будет анализировать генерацию модели и вызывать различные функции, заточенные под выполнение конкретных задач (например, проанализировали текст, поняли, что хотим поискать в интернете и вызвали специальную функцию составления запроса для поиска). Подробнее можно почитать в работе BlenderBot3 [4]. Идею такого аннотатора можно применять и в других модулях, например, модуле памяти, о котором мы поговорим чуть позже.

Архитектура BlenderBot, где под каждую задачу вызывается отдельная функция/модуль. Например, модуль составления запроса для поиска в интернете. (Источник: Shuster et al. 2022)
Архитектура BlenderBot, где под каждую задачу вызывается отдельная функция/модуль. Например, модуль составления запроса для поиска в интернете. (Источник: Shuster et al. 2022)

Помимо этого есть работы, которые дополнительно обучают модель пользоваться инструментами — хороший пример, Toolformer [5]. В работе авторы сгенерировали самой же моделью датасет с примерами вызовов различных API, оставили самые полезные и дополнительно обучили на них модель. Это показало значительный прирост в способности взаимодействовать с внешними инструментами. Можно сказать, что модель сама научилась использовать инструменты, предварительно сгенерировав примеры для обучения!

По похожему принципу работают и ChatGPT плагины. Основная идея заключается в том, что вызов любых внешних инструментов может быть получен через обычную генерацию текста, нужно лишь показать модели, как это делать (с помощью, например, тюнинга или примеров использования через промпт).

Примеры вызова API в датасете Toolformer. Все вызовы были изначально сгенерированы моделью. Примеры были оставлены только те, где вызов API улучшал последующую генерацию (сравнивали лосс функции на ответе с информацией из API и без). (Источник: Schick et al. 2023)
Примеры вызова API в датасете Toolformer. Все вызовы были изначально сгенерированы моделью. Примеры были оставлены только те, где вызов API улучшал последующую генерацию (сравнивали лосс функции на ответе с информацией из API и без). (Источник: Schick et al. 2023)

Memory

Хотя LLM уже вмещают обширные объемы информации в своих весах, существуют множество приложений, где отдельный модуль памяти оказывается необходимым. Например, если спросить ChatGPT о годе первого полета человека на Луну, он с легкостью даст точный ответ. Однако, возникает вопрос: как обрабатывать свежие данные и факты, которых не могло быть в обучающей выборке? Этот вопрос особенно важен в контексте 'галлюцинаций', когда модель, не имея достоверной информации, всё равно пытается сгенерировать ответ, нежели чем сказать “я не знаю”. Это является значительной проблемой для алайнмента моделей, которую здесь мы рассматривать подробно не будем.

Кроме хранения фактической информации, модуль памяти может быть использован для других различных целей: от запоминания доступных API (API-Bank) до суммаризации различных фактов в более обобщенные и абстрактные выводы.

Таким образом, модуль памяти не только хранит информацию, но и обеспечивает дополнительные возможности эффективного ее использования и обновления в соответствии с новыми данными и ситуациями, что позволяет моделям быть более адаптивными и реалистичными в своих ответах.

Проведя аналогию с человеческой памятью, можно предложить следующую реализацию компонентов модуля памяти в агентах:

Краткосрочная (Short-term). Эта память представляет собой информацию, к которой агент имеет быстрый доступ, но она является ненадежной и может быть забыта в любой момент. В роли краткосрочной памяти выступает контекст самой модели и ее способность к in-context learning (когда по инструкции и нескольким примерам модель делает то, что мы от нее хотим, без сбора тысяч примеров для дообучения). Модель имеет прямой доступ ко всему, что находится в ее контексте, благодаря механизму attention во время генерации токенов. Есть множество примеров систем, использующих контекст для доступа к определенной информации, но в качестве интересного приведу CALYPSO [6] — агент для игры в Dungeons & Dragons, который может помогать ведущему (Dungeon Master) в создании и изложении историй. Его кратковременная память построена на описаниях сцен, информации о монстрах и предыдущих кратких описаниях.

Бот Calypso помогает придумывать истории и отвечать на вопросы в D&D. (Источник: Zhu et al. 2023)
Бот Calypso помогает придумывать истории и отвечать на вопросы в D&D. (Источник: Zhu et al. 2023)

Однако, ограничение этого типа памяти заключается в ограниченном размере контекста модели, из-за чего со временем информация может быть потеряна. Более того, есть работы, показывающие, что на очень длинных контекстах велика вероятность, что модель пропустит некоторую информацию (например, из середины этого контекста), даже если необходимые факты явно содержатся внутри.

Долгосрочная (Long-term). Это данные, хранящиеся в БД, которые могут извлекаться агентом по запросу. Этот вид памяти обеспечивает более медленный доступ к данным и не всегда гарантирует извлечение абсолютно точной информации, но он надежно сохраняет все данные и предотвращает их безвозвратную потерю. Более того для такой модели памяти нам важно уметь выполнять несколько операций:

  1. Чтение из памяти и помещение информации в контекст модели. То есть цепочка превращений здесь: Долгосрочная память → Краткосрочная память → Вывод модели.

    Суть чтения из памяти заключается в том, как извлечь ценную информацию.

    Извлечение документа можно записать в математическом виде как извлечение документа, на котором достигается минимум по всем документам от некоторой скоринговой функции.
    Извлечение документа можно записать в математическом виде как извлечение документа, на котором достигается минимум по всем документам от некоторой скоринговой функции.

    При извлечении информации из памяти обычно используются три основных критерия: актуальность, релевантность и важность, оцениваемые посредством скоринговых функций s^{rec},s^{rel}иs^{imp}. Это помогает определить, какая часть информации из всей памяти (M) наиболее подходит для текущего запроса (q). В качестве такого запроса может быть задача, которую нужно решить или контекст, в котором находится агент. Параметры α, β, γ являются настраиваемыми и должны выбираться на основе требований к системе. Часто можно встретить, что используют только β, то есть для нахождения информации считаем только меру релевантности.

    Однако, на практике просканировать все документы из базы тяжело, т.к. это будет слишком долго. О решении этой проблемы поговорим чуть дальше.

  2. Запись информации в память. Цель записи в память — сохранить информацию, которая потенциально может быть полезна в будущем. Здесь есть несколько важных вопросов:

    1. Понять, что и когда нужно сохранять. Возможно что-то из диалога с пользователем или опыт решения какой-то задачи.

    2. Решить, что делать с устаревшей или дублированной информацией. Иначе со временем могут возникнуть проблемы по типу противоречий.

      • Решение может быть таким: мы объединяем информацию в небольшие группы, связанные одной темой. Как только размер группы превосходит порог, мы запускаем процедуру суммаризации и сжимаем кол-во документов в группе до одного-двух. На этом же этапе можем удалять/помечать устаревшую информацию.

      • Как альтернатива, сама LLM может решать, в каком случае нужно обновить, удалить, дополнить информацию из памяти и сделать соответствующий запрос. В RET-LLM [7] модель сама генерирует команду записи в память, когда считает, что конкретную информацию нужно сохранить (команда может выглядеть, например, как MEM_WRITE[info]).

    3. Решить, что делать с переполнением памяти, то есть как освобождать место под новую актуальную информацию.

      • Наивное решение — использовать что-то вроде TTL (time to leave) или Cache Eviction policies, то есть классические методы для работы с базами данных, или отдать это на управлению пользователю. Например, в работе ChatDB [8], где в качестве памяти дополнительно используется база данных, к которой агент может обращаться путем SQL запросов, добавлена возможность удалять воспоминания путем пользовательских команд.

      • Более сложные подходы могут выглядеть как фоновые процессы, пытающиеся найти кандидатов к удалению по заданным критериям.

  3. Рефлексия памяти. Рефлексия памяти имитирует способность человека наблюдать и оценивать свои собственные когнитивные, эмоциональные и поведенческие процессы. При адаптации к агентам ставится задача наделить их способностью самостоятельно обобщать и выводить более абстрактную, сложную и высокоуровневую информацию. Пример имплементации может выглядеть следующим образом:

    Агент генерирует ключевые вопросы на основе своих воспоминаний, используя их для запроса в память, а затем формирует на их основе высокоуровневые выводы. Кроме того, процесс осмысления может происходить иерархически, то есть осмысление может генерироваться на основе уже существующих выводов. Например, в работе GITM [9] (Ghost in the Minecraft, агент по игре в Minecraft, где задачей ставится с нуля создать как можно больше вещей из определенного списка в 262 предмета), успешные действия, достигающие подцелей, хранятся в списке. Когда количество элементов в списке превышает порог, агент генерирует обобщенные шаблоны на основе этих действий для будущего использования.

    Промпт для суммаризации предыдущего опыта в GITM. (Источник: Zhu et al. 2023)
    Промпт для суммаризации предыдущего опыта в GITM. (Источник: Zhu et al. 2023)

Эти механизмы чтения, записи и рефлексии памяти обеспечивают агентам более глубокое и гибкое взаимодействие с информацией, позволяя адаптироваться под различные сценарии использования.

Давайте наконец вернемся к вопросу, как выглядит наиболее популярный подход для реализации работы долгосрочной памяти. Заключается он в использовании векторного поиска:

  • В первую очередь, все данные преобразуются в векторы определенного размера и хранятся в формате vector ↔ text.

  • Для извлечения информации запрос также переводится в вектор, и происходит поиск похожих документов в базе данных. Поиск заключается в том, что мы считаем некоторую меру схожести между векторами запроса и документа. В общем случае ее нужно посчитать для всех документов из базы, что дает нам линейную сложность. В случае миллионов или десятков миллионов документов такой поиск становится неэффективным.

  • В таком случае принято использовать алгоритмы ANN (Approximate Nearest Neighbors), способные извлекать почти-точных соседей (мы извлекаем близкие документы, но не гарантируем, что все из них – ближайшие) за сублинейную сложность, например за O(logn). Со списком алгоритмов можно ознакомиться на ann benchmarks, где представлено множество интересных реализаций. От себя хотел бы порекомендовать подробнее ознакомиться с HNSW, IVF и ANNOY.

  • После получения похожих документов, мы можем поместить их в контекст модели напрямую (переводя таким образом в краткосрочную память) или же использовать другие методы по типу Cross-Attention для выделения главной информации.

Помимо векторного поиска, существуют и другие способы взаимодействия с долгосрочной памятью, включая упомянутое выше использование SQL-запросов для обращения к внешним хранилищам. Также хочется сказать, что здесь мы тоже можем обучать модель пользоваться памятью эффективнее путем сбора подходящего датасета [7] и последующего на нем тюнинга.

Planning & Reasoning

На сегодняшний день, модуль планирования и декомпозиции задач, возможно, является одним из самых сложных аспектов в разработке автономных агентов. В то время как большие языковые модели уже демонстрируют способность решать простые задачи, используя дополнительные инструменты, API и извлекая информацию из долгосрочной памяти, ключевым вызовом остается обучение моделей эффективно разбивать сложные задачи на более мелкие и управляемые этапы. Это требует не только понимания общей структуры задачи, но и способности генерировать детализированные планы действий. Давайте рассмотрим, какие существуют подходы для реализации этого процесса, от простых методов к более сложным.

Планирование без обратной связи

  • Single Path Reasoning и Chain-of-Thought (CoT) [10]. Классика промптинга, когда мы просим модель рассуждать шаг за шагом (“Think step by step”) по мере решения задачи. Модель таким образом от решения задачи напрямую переходит к рассуждению по шагам, что в некотором смысле и является декомпозицией задачи. Сейчас существует множество и других вариаций, например, в недавней работе [11] было показано, что добавление эмоционального окраса в промпт может улучшить метрики модели.

Также существуют различные методы для поиска оптимальных промптов под конкретную задачу с помощью оптимизационных алгоритмов, включая градиентные и эволюционные. В качестве примера для последнего оставлю ссылку на недавнюю интересную работу [12]. Однако, для оптимизационных алгоритмов нам нужно уметь оценивать качество работы агента на какой-то задаче, что в общем случае сделать нетривиально.

  • Multi Path Reasoning и Tree-of-Thought (ToT) [13]. Применяя технику CoT, мы можем строить на каждом шаге параллельные варианты дальнейших действий, получая таким образом целое дерево различных вариантов. Далее модель может посмотреть на полученные пути и выбрать наиболее перспективные для дальнейшей генерации. Здесь появляется важное свойство, что теперь модели не надо генерировать правильный ответ с первой попытки, можно сгенерировать тысячу кандидатов и потом выбрать лучшего. И это улучшает качество работы модели — в том числе и ризонинга.

    Пример построенного дерева из алгоритма ToT, где каждый квадратик выражает некоторую мысль. (Источник: Yao et al. 2023)
    Пример построенного дерева из алгоритма ToT, где каждый квадратик выражает некоторую мысль. (Источник: Yao et al. 2023)

    Можно провести аналогию с Monte-Carlo Tree Search (MCTS) — алгоритмом, с помощью которого такие модели как AlphaGo генерирует варианты для дальнейшего хода (небольшое видео с объяснением алгоритма). Очевидно, что мы не можем перебрать все возможные ходы, поэтому ограничиваемся несколькими наиболее перспективными, из которых выбираем наилучшее продолжение, например, с помощью модели-критика.

    В качестве простой иллюстрации приведена игра в крестики-нолики. Здесь дерево можно построить для всех исходов, но для более сложных игр (шахматы, го) это сделать невозможно, поэтому алгоритм просчитывает только некоторое число ходов. (Источник: How AlphaGo works, Tim Wheeler)
    В качестве простой иллюстрации приведена игра в крестики-нолики. Здесь дерево можно построить для всех исходов, но для более сложных игр (шахматы, го) это сделать невозможно, поэтому алгоритм просчитывает только некоторое число ходов. (Источник: How AlphaGo works, Tim Wheeler)

    Отдельно хочется упомянуть вариант планирования через дебаты между различными агентами [14]. Идея заключается в создании некоторой арены, где LLM агенты могут совместно приходить к общему выводу. В этой среде каждому агенту предъявляется запрос, и их ответы передаются всем остальным агентам. Важно, что контекст, который учитывает каждая LLM на каждом шагу, включает в себя как свои собственные рассуждения, так и рассуждения других агентов. После 3-4 итераций таких дебатов модели приходят к единому стабильному результату. Таким образом, подобно людям в дискуссии, они приходят к общим выводам, зачастую более правильным, чем первоначальный вывод отдельно взятого агента. Важным здесь остается вопрос выбора критерия останова для таких дебатов.

    Несколько агентов формирует единое решение через общение друг с другом. (Источник: Zhuge et al. 2023)
    Несколько агентов формирует единое решение через общение друг с другом. (Источник: Zhuge et al. 2023)

  • External Planners. Выделяющимся вариантом из предыдущих является обращение к внешнему планировщику, используя, например, язык PDDL для формализации задачи планирования. В таком случае LLM формулирует запрос на специальном языке, обращается к планировщику как внешнему инструменту и переводит его результат обратно в текст. Основной проблемой здесь является требование наличия такого планировщика и языка под конкретный домен, что бывает далеко не часто, поэтому метод можно встретить пока только в узких направлениях.

Планирование с обратной связью

Во многих реальных сценариях для решения сложных задач агентам необходимо осуществлять долгосрочное планирование. При решении таких задач описанные выше модули планирования без обратной связи могут оказаться менее эффективными по следующим причинам: во-первых, сгенерировать безупречный план непосредственно с самого начала крайне сложно, поскольку необходимо учесть сразу все нюансы. Во-вторых, по ходу выполнения плана что-то может пойти не так и мы должны быть способны реагировать на изменения среды должным образом. Поэтому нужно научиться обрабатывать обратную связь.

Обратная связь от среды

Зачастую после какого-то действия среда дает нам отклик: поменялась обстановка на камерах, интерпретатор вернул код ошибки, браузер вернул список ресурсов и так далее. Эту информацию полезно учитывать для уточнения или корректировки текущего плана. В частности, подход ReAct [15] предлагает строить подсказки на основе триплетов "мысль - действие - наблюдение".

Пример корректного ответа модели через принцип мысль-действие-наблюдение. (Источник: Yao et al. 2022)
Пример корректного ответа модели через принцип мысль-действие-наблюдение. (Источник: Yao et al. 2022)

Компонент "мысль" облегчает высокоуровневые рассуждения и планирование для агента. Наблюдение соответствует результату выполненного действия, полученному через внешнюю обратную связь, например, результаты работы поисковой системы. Следующая мысль формируется под влиянием предыдущих наблюдений, что делает генерируемый план более адаптивным к окружающей среде.

Другой пример — Voyager [16], агент общего назначения в Minecraft для исследования среды, приобретения новых навыков и предметов. В этой работе агент строит планы, используя три типа обратной связи с окружающей средой, включая промежуточный ход выполнения программы, ошибки выполнения и результаты самопроверки.

Ответ из среды (что произошло при последнем действии), результат выполнения кода и дополнительные рассуждения модели подаются на вход для корректировки планов (Источник: Wang et al. 2023)
Ответ из среды (что произошло при последнем действии), результат выполнения кода и дополнительные рассуждения модели подаются на вход для корректировки планов (Источник: Wang et al. 2023)

Данная работа интересна и с других точек зрения, особенно рекомендую ее к прочтению. Например, там очень интересно устроен модуль памяти — агент сам пишет подпрограммы на javaScript для исполнения в будущем, они сохраняются, и далее из них уже выбираются возможные действия.

И наконец, в работе Teaching Large Language Models to Self-Debug [17] используется результат работы кода как фидбэк для последующего анализа моделью. Если код оказался нерабочим, то агент может исправить какие-то части и повторить выполнение.

Объяснение кода и результат его выполнения подаются обратно на вход модели в виде обратной связи. (Источник: Chen et al. 2023)
Объяснение кода и результат его выполнения подаются обратно на вход модели в виде обратной связи. (Источник: Chen et al. 2023)

Таким образом, интеграция долгосрочного планирования с обработкой обратной связи значительно улучшает способность агентов к адаптации и эффективному решению задач, позволяя им реагировать на изменения в среде и динамически корректировать свои планы действий. Стоит отметить, что мы также можем использовать методы по типу CoT и ToT в сценариях с обратной связью.

Обратная связь от человека

Такой сигнал является субъективным, но позволяет эффективно согласовывать работу агента с ценностями и предпочтениями человека, а также облегчает проблему галлюцинаций. Обратная связь может быть включена напрямую в промпт, по которому агент может делать дальнейшие выводы. Например, в работе Inner Monologue [18] агент может получать подсказки от пользователя для уточнения и объяснения деталей, которые ему сложно получить из среды самому.

Пользовательские сообщения напрямую влияют на составление дальнейшего плана агентом. (Источник: Huang et al. 2022)
Пользовательские сообщения напрямую влияют на составление дальнейшего плана агентом. (Источник: Huang et al. 2022)

Обратная связь от модели

Обратная связь от человека несет большую ценность, но имеет огромный недостаток — временную задержку на написания текста, как минимум это несколько секунд. Поэтому существует третий тип обратной связи — от других моделей. Этот тип обычно генерируется на основе предварительно обученных на конкретные задачи моделей. Агент генерирует ответ и получает от них обратную связь, на основе которых планирует следующие действия.

Алгоритм Reflexion [19] разработан для расширения возможностей планирования с помощью подробной обратной связи. В этом подходе агент сначала производит действие, основываясь на своей памяти, а затем другая модель-эксперт генерирует обратную связь, принимая историю агента в качестве входных данных. В отличие от большинства предыдущих работ, где обратная связь дается в виде численного значения или ответа среды напрямую, в Reflexion используется LLM для создания детальной обратной связи, которая обеспечивает более информативный сигнал для агента.

Дополнительная стадия "рефлексии" модели по ответу из среды. (Источник: Shinn et al. 2023)
Дополнительная стадия "рефлексии" модели по ответу из среды. (Источник: Shinn et al. 2023)

Другим хорошим примером, на мой взгляд, служит работа от OpenAI — Let’s Verify Step by Step [20]. В ней в качестве проблемы авторы выбрали математические задачи и обучали модель немного в другом формате. Вместо того, чтобы давать модели оценку за ответ целиком (например, 1, если ответ правильный и 0, если нет), предлагается делать это по промежуточным шагам, указывая на удачные/неудачные примеры рассуждений. Опять же, в таком случае получаем намного более полезный и информативный сигнал, нежели оценка всей генерации одним числом. Более того, из-за новой постановки задачи модель на выходе также способа оценивать корректность конкретного шага и выступать в роли некоторого критика, то есть в дальнейшем ее можно хорошо использовать с алгоритмом по типу ToT.

Полученная модель является только первым шагом на пути к системе, которая сможет заменить одного исследователя: OpenAI строят агента, но с планами амбициознее, чем игра в майнкрафт или доту, они хотят автоматизировать рисерч! Уже сейчас это дает свои результаты, но предстоящий прогресс в этой сфере завораживает.

Красным выделены этапы рассуждения модели, которые критик оценил как вероятно неправильные (Источник: Lightman et al. 2023)
Красным выделены этапы рассуждения модели, которые критик оценил как вероятно неправильные (Источник: Lightman et al. 2023)

Подход с обратной связью от модели позволяет получать более быстрый сигнал, делая процесс планирования эффективнее. Более того, главным преимуществам здесь является то, что модели-критики потенциально могут использоваться для всех видов задач (главное, обучить их этому), в то время как человеку во множестве доменов может не хватать экспертизы.

Итак, мы разобрали основные компоненты LLM-based агентов и посмотрели на современные работы, которые освещают эту тему. Когда я только начинал писать этот обзор, мне хотелось сделать 3 вещи:

  1. Дать верхнеуровневое представление, как и для чего работает каждый модуль и какие есть проблемы в их реализации. Сделать это хотелось не сильно техническим языком, понятным в первую очередь людям не из сферы машинного обучения.

  2. Посмотреть на последние работы и для лучшего понимания в качестве иллюстраций показать, как авторы решали те или иные проблемы.

  3. Посмотреть, на что уже сегодня способны модели-агенты.

Надеюсь мне хотя бы отчасти это удалось и вам понравилось.

Возникает вопрос, а какой следующий шаг нужно сделать в разработке агентов, чтобы выйти на новый уровень? На мой взгляд, это модуль планирования и ризонинга. Возможно там уже есть новые разработки, о которых мы пока не знаем, но кажется для решения по-настоящему сложных задач (таких как автоматизация исследования в конкретной области) именно планирования и рассуждения играют ключевую роль. Здесь можно идти по пути усовершенствования LLM в целом (и тогда все сопутствующие навыки улучшатся) или разработки конкретных методов (как в работе Let's Verify Step by Step), либо сразу по обоим. Так что пристально следим за направлением.

Ну и напоследок, представьте, что агент сможет работать на удаленке, для этого не так много надо. Более того, часть из необходимых навыков, такие как поиск ответов в интернете и написание кода, текущие агенты уже решают отлично. Взять тикет, прикинуть план действий, провести сбор информации, написать код и тесты, покрыть документацией, сделать PR. Что будем делать тогда?

Все упомянутые статьи можно найти в списке ниже, чтения хватит надолго! Если у вас остались какие-то вопросы, то смело задавайте их любым удобном способом: телеграм, Singularis (alex_golubev) или LinkedIn, постараюсь оперативно отвечать.

Литература

Ссылки на статьи

  1. Liu et al. "Visual Instruction Tuning" arXiv preprint arXiv:2304.08485 (2023).

  2. Li et al. "BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models" arXiv preprint arXiv:2301.12597 (2023).

  3. Wang et al. "When Large Language Model based Agent Meets User Behavior Analysis: A Novel User Simulation Paradigm" arXiv preprint arXiv:2306.02552 (2023).

  4. Shuster et al. "BlenderBot 3: a deployed conversational agent that continually learns to responsibly engage" arXiv preprint arXiv:2208.03188 (2022).

  5. Schick et al. "Toolformer: Language Models Can Teach Themselves to Use Tools" arXiv preprint arXiv:2302.04761 (2023).

  6. Zhu et al. "CALYPSO: LLMs as Dungeon Masters' Assistants" arXiv preprint arXiv:2308.07540 (2023).

  7. Modarressi et al. "RET-LLM: Towards a General Read-Write Memory for Large Language Models" arXiv preprint arXiv:2305.14322 (2023).

  8. Hu et al. "ChatDB: Augmenting LLMs with Databases as Their Symbolic Memory" arXiv preprint arXiv:2306.03901 (2023).

  9. Zhu et al. "Ghost in the Minecraft: Generally Capable Agents for Open-World Environments via Large Language Models with Text-based Knowledge and Memory" arXiv preprint arXiv:2305.17144 (2023).

  10. Wei et al. "Chain-of-Thought Prompting Elicits Reasoning in Large Language Models" arXiv preprint arXiv:2201.11903 (2022).

  11. Li et al. "Large Language Models Understand and Can be Enhanced by Emotional Stimuli" arXiv preprint arXiv:2307.11760 (2023).

  12. Guo et al. "Connecting Large Language Models with Evolutionary Algorithms Yields Powerful Prompt Optimizers" arXiv preprint arXiv:2309.08532 (2023).

  13. Yao et al. "Tree of Thoughts: Deliberate Problem Solving with Large Language Models" arXiv preprint arXiv:2305.10601 (2023).

  14. Zhuge et al. "Mindstorms in Natural Language-Based Societies of Mind" arXiv preprint arXiv:2305.17066 (2023).

  15. Yao et al. "ReAct: Synergizing Reasoning and Acting in Language Models" arXiv preprint arXiv:2210.03629 (2022).

  16. Wang et al. "Voyager: An Open-Ended Embodied Agent with Large Language Models" arXiv preprint arXiv:2305.16291 (2023).

  17. Chen et al. "Teaching Large Language Models to Self-Debug" arXiv preprint arXiv:2304.05128 (2023)

  18. Huang et al. "Inner Monologue: Embodied Reasoning through Planning with Language Models" arXiv preprint arXiv:2207.05608 (2022).

  19. Shinn et al. "Reflexion: Language Agents with Verbal Reinforcement Learning" arXiv preprint arXiv:2303.11366 (2023).

  20. Lightman et al. "Let's Verify Step by Step" arXiv preprint arXiv:2305.20050 (2023).

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    CRYPTO SCAM RECOVERY SUCCESSFUL – A TESTIMONIAL OF LOST PASSWORD TO YOUR DIGITAL WALLET BACK. My name is Robert Alfred, Am from Australia. I’m sharing my experience in the hope that it helps others who have been victims of crypto scams. A few months ago, I fell victim to a fraudulent crypto investment scheme linked to a broker company. I had invested heavily during a time when Bitcoin prices were rising, thinking it was a good opportunity. Unfortunately, I was scammed out of $120,000 AUD and the broker denied me access to my digital wallet and assets. It was a devastating experience that caused many sleepless nights. Crypto scams are increasingly common and often involve fake trading platforms, phishing attacks, and misleading investment opportunities. In my desperation, a friend from the crypto community recommended Capital Crypto Recovery Service, known for helping victims recover lost or stolen funds. After doing some research and reading multiple positive reviews, I reached out to Capital Crypto Recovery. I provided all the necessary information—wallet addresses, transaction history, and communication logs. Their expert team responded immediately and began investigating. Using advanced blockchain tracking techniques, they were able to trace the stolen Dogecoin, identify the scammer’s wallet, and coordinate with relevant authorities to freeze the funds before they could be moved. Incredibly, within 24 hours, Capital Crypto Recovery successfully recovered the majority of my stolen crypto assets. I was beyond relieved and truly grateful. Their professionalism, transparency, and constant communication throughout the process gave me hope during a very difficult time. If you’ve been a victim of a crypto scam, I highly recommend them with full confidence contacting: Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] Call/Text: +1 (336) 390-6684 Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 25.06.26 21:13 Emilie Safi

    A fraudulent investment scheme operated by BTCMining.limited functions as a fake return scam. In this setup, scammers lure victims with false promises of high returns. Through manipulative tactics, they gain individuals' trust and convince them to invest, ultimately leading to financial loss. If you have ever faced a cyber threat or fallen victim to an online crypto scam and need to reach the authorities, I recommend contacting [email protected], [email protected], WhatsApp +19852969146, telegram @resqprofirm. They are a legitimate team that helps victims of online crypto scams using advanced tools.

  • 25.06.26 21:25 Emilie Safi

    So I ended up losing $38,000 to this platform. At first, they kept asking me to put in more money so I could get into my portfolio. I did that, but then they wouldn’t let me withdraw anything—just kept asking for more deposits. It got way too suspicious, so I stopped. I found this company called ResQProfirm on Google and told them what happened. They got in touch, asked me to walk them through everything, and I gave them all the proof I had. They did an amazing job tracking down my money and getting it back. Big thanks to them at [email protected] and on WhatsApp at +19852969146. Please be careful out there and always research before investing.

  • 26.06.26 01:04 robertalfred175

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  • 26.06.26 01:04 robertalfred175

    CRYPTO SCAM RECOVERY SUCCESSFUL – A TESTIMONIAL OF LOST PASSWORD TO YOUR DIGITAL WALLET BACK. My name is Robert Alfred, Am from Australia. I’m sharing my experience in the hope that it helps others who have been victims of crypto scams. A few months ago, I fell victim to a fraudulent crypto investment scheme linked to a broker company. I had invested heavily during a time when Bitcoin prices were rising, thinking it was a good opportunity. Unfortunately, I was scammed out of $120,000 AUD and the broker denied me access to my digital wallet and assets. It was a devastating experience that caused many sleepless nights. Crypto scams are increasingly common and often involve fake trading platforms, phishing attacks, and misleading investment opportunities. In my desperation, a friend from the crypto community recommended Capital Crypto Recovery Service, known for helping victims recover lost or stolen funds. After doing some research and reading multiple positive reviews, I reached out to Capital Crypto Recovery. I provided all the necessary information—wallet addresses, transaction history, and communication logs. Their expert team responded immediately and began investigating. Using advanced blockchain tracking techniques, they were able to trace the stolen Dogecoin, identify the scammer’s wallet, and coordinate with relevant authorities to freeze the funds before they could be moved. Incredibly, within 24 hours, Capital Crypto Recovery successfully recovered the majority of my stolen crypto assets. I was beyond relieved and truly grateful. Their professionalism, transparency, and constant communication throughout the process gave me hope during a very difficult time. If you’ve been a victim of a crypto scam, I highly recommend them with full confidence contacting: Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] Call/Text: +1 (336) 390-6684 Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 26.06.26 02:48 Miriam Rocha

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  • 26.06.26 02:52 Miško Bakić

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  • 26.06.26 02:56 Asunción Herrera

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  • 26.06.26 15:05 Riley Stephens

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  • 28.06.26 00:37 kimberlyhebertt673

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  • 28.06.26 00:37 kimberlyhebertt673

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  • 29.06.26 22:37 riley777

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  • 30.06.26 15:08 wendytaylor015

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  • 30.06.26 15:08 wendytaylor015

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  • 02.07.26 01:22 Lieneke Bonnema

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  • 02.07.26 01:26 Clara Morin

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  • 04.07.26 15:32 Fraddy Pual

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  • 05.07.26 14:44 lydiassmith567

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  • 05.07.26 14:44 lydiassmith567

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  • 06.07.26 16:20 Olga Ognjanović

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  • 06.07.26 16:31 Joseph Weigl

    Invest wisely and stay cautious. Don't be influenced by promises of unusually high returns or convincing sales pitches from brokers. I learned this the hard way after falling victim to an investment scam that promised huge profits. Fortunately, I acted quickly and reported the incident to a recovery firm for assistance. Contact: Email: [email protected] Telegram: @Resqprofirm WhatsApp: +1 985 296 9146

  • 06.07.26 16:33 Jaran Løvlien

    A heartfelt thank you to RESQPRO FIRM for their commitment and professionalism throughout the investigation of my case. Their team worked diligently and helped recover assets valued at $88,000, which were returned to my wallet. I truly appreciate their support, clear communication, and dedication, and I'm grateful for the assistance I received. Contact: Email: [email protected] Telegram: @Resqprofirm WhatsApp: +1 985 296 9146

  • 07.07.26 18:00 robertalfred175

    CRYPTO SCAM RECOVERY SUCCESSFUL – A TESTIMONIAL OF LOST PASSWORD TO YOUR DIGITAL WALLET BACK. My name is Robert Alfred, Am from Australia. I’m sharing my experience in the hope that it helps others who have been victims of crypto scams. A few months ago, I fell victim to a fraudulent crypto investment scheme linked to a broker company. I had invested heavily during a time when Bitcoin prices were rising, thinking it was a good opportunity. Unfortunately, I was scammed out of $120,000 AUD and the broker denied me access to my digital wallet and assets. It was a devastating experience that caused many sleepless nights. Crypto scams are increasingly common and often involve fake trading platforms, phishing attacks, and misleading investment opportunities. In my desperation, a friend from the crypto community recommended Capital Crypto Recovery Service, known for helping victims recover lost or stolen funds. After doing some research and reading multiple positive reviews, I reached out to Capital Crypto Recovery. I provided all the necessary information—wallet addresses, transaction history, and communication logs. Their expert team responded immediately and began investigating. Using advanced blockchain tracking techniques, they were able to trace the stolen Dogecoin, identify the scammer’s wallet, and coordinate with relevant authorities to freeze the funds before they could be moved. Incredibly, within 24 hours, Capital Crypto Recovery successfully recovered the majority of my stolen crypto assets. I was beyond relieved and truly grateful. Their professionalism, transparency, and constant communication throughout the process gave me hope during a very difficult time. If you’ve been a victim of a crypto scam, I highly recommend them with full confidence contacting: Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] Call/Text: +1 (336) 390-6684 Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 07.07.26 18:01 robertalfred175

    CRYPTO SCAM RECOVERY SUCCESSFUL – A TESTIMONIAL OF LOST PASSWORD TO YOUR DIGITAL WALLET BACK. My name is Robert Alfred, Am from Australia. I’m sharing my experience in the hope that it helps others who have been victims of crypto scams. A few months ago, I fell victim to a fraudulent crypto investment scheme linked to a broker company. I had invested heavily during a time when Bitcoin prices were rising, thinking it was a good opportunity. Unfortunately, I was scammed out of $120,000 AUD and the broker denied me access to my digital wallet and assets. It was a devastating experience that caused many sleepless nights. Crypto scams are increasingly common and often involve fake trading platforms, phishing attacks, and misleading investment opportunities. In my desperation, a friend from the crypto community recommended Capital Crypto Recovery Service, known for helping victims recover lost or stolen funds. After doing some research and reading multiple positive reviews, I reached out to Capital Crypto Recovery. I provided all the necessary information—wallet addresses, transaction history, and communication logs. Their expert team responded immediately and began investigating. Using advanced blockchain tracking techniques, they were able to trace the stolen Dogecoin, identify the scammer’s wallet, and coordinate with relevant authorities to freeze the funds before they could be moved. Incredibly, within 24 hours, Capital Crypto Recovery successfully recovered the majority of my stolen crypto assets. I was beyond relieved and truly grateful. Their professionalism, transparency, and constant communication throughout the process gave me hope during a very difficult time. If you’ve been a victim of a crypto scam, I highly recommend them with full confidence contacting: Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] Call/Text: +1 (336) 390-6684 Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 09.07.26 19:06 Toivo Walli

    I lost 8.56btc to a fake Bitcoin mining site, I tried withdrawing but couldn't approved my process, I reported to !R£SQPROFIRM! via °R£SQproFirm°àt°gmail•com° °tEL£°gram=R£SQprofirm °whaT°Zap+198°52°96°91°46

  • 09.07.26 19:10 Misty Alexander

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  • 09.07.26 19:13 Clara Soto

    Anyone receiving continued requests for additional deposits from a scam platform should immediately cut off communication and submit the case to a reputable re­covery service for investigation. R£sQprofirm is a dependable firm you can reach at °ResQproFirm°àt°g,*ma'il(•)¢om°, TEL£gram ResQprofirm, or |whaTZap| +1-985-296-9146.

  • 12.07.26 03:30 Kora Baltacha

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  • 12.07.26 03:33 Pahal Mathew

    It's important to move proactively by engaging an experienced recovery specialist. They will assist you throughout the process. To help them, provide: · Transaction evidence · Scammer information · Any additional relevant details The experts will then track and try to retrieve your funds from the scammers' hidden accounts or wallets. R£sQprofirm offers recovery assistance with no upfront fees. Contact: Telegram: @ResQprofirm WhatsApp: +19852969146 Email: [email protected]

  • 13.07.26 23:49 [email protected]

    One of the biggest concerns I have about cryptocurrency is the lack of regulation. It creates opportunities for scammers to invent convincing stories and fraudulent investment schemes. Unfortunately, some social media platforms continue to display these ads because they profit from them, even after users report them.I personally clicked on a Facebook advertisement for a company called Chickenfastmining and ended up losing more than $120,000 in a scam. I reported the ad, but nothing was done. Later, through a Reddit community, I found a recovery service called CYBERBERSPY that, in my personal experience, they helped me recover $110,000 of my lost funds. If you've been a victim of a cryptocurrency scam, don't lose hope. Explore your options carefully, and always verify the legitimacy of any recovery service before trusting them or paying any fees. Based on my own experience, CYBERBERSPY was helpful to me and i was able to recover my funds back, but I encourage everyone to do their own research before using any recovery service.i highly recommend: ([email protected])

  • 15.07.26 11:53 Sarah Green

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  • 15.07.26 11:58 Lily Gagné

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  • 16.07.26 21:38 patricialovick86

    How To Recover Your Bitcoin Without Falling Victim To Scams: A  Testimony Experience With Capital Crypto Recover Services, Contact Telegram: @Capitalcryptorecover Dear Everyone, I would like to take a moment to share my positive experience with Capital Crypto Recover Services. Initially, I was unsure if it would be possible to recover my stolen bitcoins. However, with their expertise and professionalism, I was able to fully recover my funds. Unfortunately, many individuals fall victim to scams in the cryptocurrency space, especially those involving fraudulent investment platforms. However, I advise caution, as not all recovery services are legitimate. I personally lost $273,000 worth of Bitcoin from my Binance account due to a deceptive platform. If you have suffered a similar loss, you may be considering crypto recovery, The Capital Crypto Recover is the most knowledgeable and effective Capital Crypto Recovery Services assisted me in recovering my stolen funds within 24 hours, after getting access to my wallet. Their service was not only prompt but also highly professional and effective, and many recovery services may not be trustworthy. Therefore, I highly recommend Capital Crypto Recover to you. i do always research and see reviews about their service, For assistance finding your misplaced cryptocurrency, get in touch with them, They do their jobs quickly and excellently, Stay safe and vigilant in the crypto world. Contact: [email protected]  You can reach them via email at [email protected] OR Call/Text Number +1 (336)390-6684 his contact website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 16.07.26 21:38 patricialovick86

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  • 20.08.26 11:32 michaeldavenport218

    I was recently scammed out of $53,000 by a fraudulent Bitcoin investment scheme, which added significant stress to my already difficult health issues, as I was also facing cancer surgery expenses. Desperate to recover my funds, I spent hours researching and consulting other victims, which led me to discover the excellent reputation of Capital Crypto Recover, I came across a Google post It was only after spending many hours researching and asking other victims for advice that I discovered Capital Crypto Recovery’s stellar reputation. I decided to contact them because of their successful recovery record and encouraging client testimonials. I had no idea that this would be the pivotal moment in my fight against cryptocurrency theft. Thanks to their expert team, I was able to recover my lost cryptocurrency back. The process was intricate, but Capital Crypto Recovery's commitment to utilizing the latest technology ensured a successful outcome. I highly recommend their services to anyone who has fallen victim to cryptocurrency fraud. For assistance contact [email protected] and on Telegram OR WhatsApp Number +1 (336)390-6684 via email: [email protected] you can visit his website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 23.08.26 20:02 leslieyee

    Excellent analysis by /dataviz! I was particularly struck by how strictly the order is defined: "data meaning first, color last" and how the validator actually adjusts the palette according to OKLCH, color blindness, and WCAG standards. These rules greatly improve the quality of charts. By the way, a similar approach to clean and legible charts is highly valued in trading—for example, on the ExpertOption platform, the interface and price visualization are designed to ensure information is read instantly and without unnecessary noise.

  • 25.08.26 13:44 lydiassmith567

    HIRE A HACKER YOUR STOLEN CRYPTO RECOVERY / BTC / USDT / ETH WITH THE HELP OF CAPITAL CRYPTO RECOVER. I want to share my experience publicly regarding cryptocurrency wallet recovery, I highly recommend you to contact CAPITAL CRYPTO RECOVER, a professional private investigator in the Bitcoin world. They have a certified expert security team specializing in Bitcoin Recovery Services and have helped many people worldwide recover their lost funds. My wife and I were defrauded by an online manipulator posing as an experienced crypto investment professional. We lost $9.2 Million Stolen BTC in cryptocurrency and were left feeling homeless. After spending hours searching for a reliable crypto recovery service, I discovered CAPITAL CRYPTO RECOVER online. By patiently explaining my situation to their team, I was able to recover all my funds. Remarkably, my money was returned to my wallet in less than 24 hours. I am extremely grateful to CAPITAL CRYPTO RECOVER for their excellent assistance—they truly were a godsend in my difficult situation. If you have fallen victim to a cryptocurrency scam, you can reach CAPITAL CRYPTO RECOVER through the following channels Email: [email protected] OR Call/WhatsApp: +1 (336) 390-6684 Contact: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 25.08.26 13:44 lydiassmith567

    HIRE A HACKER YOUR STOLEN CRYPTO RECOVERY / BTC / USDT / ETH WITH THE HELP OF CAPITAL CRYPTO RECOVER. I want to share my experience publicly regarding cryptocurrency wallet recovery, I highly recommend you to contact CAPITAL CRYPTO RECOVER, a professional private investigator in the Bitcoin world. They have a certified expert security team specializing in Bitcoin Recovery Services and have helped many people worldwide recover their lost funds. My wife and I were defrauded by an online manipulator posing as an experienced crypto investment professional. We lost $9.2 Million Stolen BTC in cryptocurrency and were left feeling homeless. After spending hours searching for a reliable crypto recovery service, I discovered CAPITAL CRYPTO RECOVER online. By patiently explaining my situation to their team, I was able to recover all my funds. Remarkably, my money was returned to my wallet in less than 24 hours. I am extremely grateful to CAPITAL CRYPTO RECOVER for their excellent assistance—they truly were a godsend in my difficult situation. If you have fallen victim to a cryptocurrency scam, you can reach CAPITAL CRYPTO RECOVER through the following channels Email: [email protected] OR Call/WhatsApp: +1 (336) 390-6684 Contact: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 30.08.26 15:37 [email protected]

    Discovering I had been defrauded, I searched online for services claiming to recover stolen ETH. I contacted several companies, but none succeeded. Although some claimed they could trace assets or recover funds, I could not verify their success or recover my money. I later found SYLVESTER BRYANT Recovery through Google. After contacting him, he recovered 450,000,00. He can be reached at [email protected] or WhatsApp at +1 512 577 7957.

  • 30.08.26 15:37 [email protected]

    Discovering I had been defrauded, I searched online for services claiming to recover stolen ETH. I contacted several companies, but none succeeded. Although some claimed they could trace assets or recover funds, I could not verify their success or recover my money. I later found SYLVESTER BRYANT Recovery through Google. After contacting him, he recovered 450,000,00. He can be reached at [email protected] or WhatsApp at +1 512 577 7957.

  • 01.09.26 11:16 lisawerth897

    My Experience With Cryptocurrency — CAPITAL CRYPTO RECOVER I lost over $82,000 in Bitcoin after falling victim to a fraudulent online investment scheme. After realizing I had been scammed, I spent considerable time researching possible ways to recover my funds. During my research, I came across CAPITAL CRYPTO RECOVER and decided to contact them after reading their reported success record and encouraging client positive reviews and testimonials. Their team guided me through the recovery process, and I was grateful to successfully recover my lost cryptocurrency. The experience was challenging, but I appreciated the assistance and support I received throughout the process. I’m sharing my experience in the hope that it may encourage other victims to carefully research their options and verify any recovery service before proceeding. I will forever be thankful to you CAPITAL CRYPTO RECOVER 📧 [email protected] 🌐 Website: recovercapital.wixsite.com/capital-crypto-rec-1 📧 [email protected] 📞 Call/WhatsApp: +1 (336) 390-6684

  • 01.09.26 11:16 lisawerth897

    My Experience With Cryptocurrency — CAPITAL CRYPTO RECOVER I lost over $82,000 in Bitcoin after falling victim to a fraudulent online investment scheme. After realizing I had been scammed, I spent considerable time researching possible ways to recover my funds. During my research, I came across CAPITAL CRYPTO RECOVER and decided to contact them after reading their reported success record and encouraging client positive reviews and testimonials. Their team guided me through the recovery process, and I was grateful to successfully recover my lost cryptocurrency. The experience was challenging, but I appreciated the assistance and support I received throughout the process. I’m sharing my experience in the hope that it may encourage other victims to carefully research their options and verify any recovery service before proceeding. I will forever be thankful to you CAPITAL CRYPTO RECOVER 📧 [email protected] 🌐 Website: recovercapital.wixsite.com/capital-crypto-rec-1 📧 [email protected] 📞 Call/WhatsApp: +1 (336) 390-6684

  • 01.09.26 17:34 Garry42

    Really crazy world. These fraudsters go at any length to steal your hard earned funds. I have been a victim of a bitcoin scam about 7 months back. A Con artist gained access to my cashapp account through a phishing scam. They stole $409,000. I was really devastated. I did everything to get back my funds by contacting the FBI but they claimed there was nothing they could do. A friend told me about a recovery expert. He helps fight against various phishing and investment scams and they were able to help trace and recover my funds even though it took over 2 days. you can reach out to him through recoverydarek@gmail. com . I can guarantee his services are still active.

  • 01.09.26 17:34 Garry42

    Really crazy world. These fraudsters go at any length to steal your hard earned funds. I have been a victim of a bitcoin scam about 7 months back. A Con artist gained access to my cashapp account through a phishing scam. They stole $409,000. I was really devastated. I did everything to get back my funds by contacting the FBI but they claimed there was nothing they could do. A friend told me about a recovery expert. He helps fight against various phishing and investment scams and they were able to help trace and recover my funds even though it took over 2 days. you can reach out to him through recoverydarek@gmail. com . I can guarantee his services are still active.

  • 02.09.26 03:43 kimberlyhebertt6877

    I invested in bitcoin trading After losing $78.4 USDT) linked to a romance fraud scam worth of cryptocurrency through an online investment platform and later discovered it was a scam. After extensive research for recovery options, I contacted CAPITAL CRYPTO RECOVER based on positive client reviews and recommendations. Their professional security team guided me through the recovery process using advanced technology, and I was able to recover my lost cryptocurrency successfully. I am truly grateful for their support and assistance during such a difficult experience. I will advise you to contact CAPITAL CRYPTO RECOVER helped me recover my funds. For anyone facing similar issues, Website: https://recovercapital.wixsite.com/capital-crypto-rec-1 Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] WhatsApp/Text Number: +1 (336) 390-6684

  • 02.09.26 03:43 kimberlyhebertt6877

    I invested in bitcoin trading After losing $78.4 USDT) linked to a romance fraud scam worth of cryptocurrency through an online investment platform and later discovered it was a scam. After extensive research for recovery options, I contacted CAPITAL CRYPTO RECOVER based on positive client reviews and recommendations. Their professional security team guided me through the recovery process using advanced technology, and I was able to recover my lost cryptocurrency successfully. I am truly grateful for their support and assistance during such a difficult experience. I will advise you to contact CAPITAL CRYPTO RECOVER helped me recover my funds. For anyone facing similar issues, Website: https://recovercapital.wixsite.com/capital-crypto-rec-1 Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] WhatsApp/Text Number: +1 (336) 390-6684

  • 04.09.26 21:51 Kovengray

    Recovering your lost investment funds as the case might be, is not what you can do alone, you’d require the service of a trained recovery specialist. A recovery specialist is a person or a group of people who are well equipped to work around the brokerage network. They have vast knowledge about the whole network and have the right software and private keys to follow any transaction. I was ripped off trading online to an investment broker, good thing I got every penny back through the help of Gavin ray he’s a genius Contact : Gavinray78 at gmail com or WhatsApp +1 352 322 2096 It is also important to be patient and really calm during the process.

  • 05.09.26 21:38 [email protected]

    I invested 45,000  Euro, and later aggreviated to 198,000 Euro.  I requested  to place ‎Withdrawal of my funds to be paid to my Bank account. But nothing happened I was subjected to pay more fee until I can get my funds on my account, i got in touch with Theodore ryan here on this platform who had helped a lot of people, I followed all instructions and he legally got back my withheld funds, I got threatened by the company that if I don’t pay they will get my account frozen, all thanks to Theodore ryan and I highly recommend him to anyone dealing with an unregulated broker company… contact him on his Gmail - theodoreryan318@  gmail .  com

  • 09.09.26 21:31 lisawerth897

    My Experience With Cryptocurrency — CAPITAL CRYPTO RECOVER I lost over $82,000 in Bitcoin after falling victim to a fraudulent online investment scheme. After realizing I had been scammed, I spent considerable time researching possible ways to recover my funds. During my research, I came across CAPITAL CRYPTO RECOVER and decided to contact them after reading their reported success record and encouraging client positive reviews and testimonials. Their team guided me through the recovery process, and I was grateful to successfully recover my lost cryptocurrency. The experience was challenging, but I appreciated the assistance and support I received throughout the process. I’m sharing my experience in the hope that it may encourage other victims to carefully research their options and verify any recovery service before proceeding. I will forever be thankful to you CAPITAL CRYPTO RECOVER 📧 [email protected] 🌐 Website: recovercapital.wixsite.com/capital-crypto-rec-1 📧 [email protected] 📞 Call/WhatsApp: +1 (336) 390-6684

  • 09.09.26 21:31 lisawerth897

    My Experience With Cryptocurrency — CAPITAL CRYPTO RECOVER I lost over $82,000 in Bitcoin after falling victim to a fraudulent online investment scheme. After realizing I had been scammed, I spent considerable time researching possible ways to recover my funds. During my research, I came across CAPITAL CRYPTO RECOVER and decided to contact them after reading their reported success record and encouraging client positive reviews and testimonials. Their team guided me through the recovery process, and I was grateful to successfully recover my lost cryptocurrency. The experience was challenging, but I appreciated the assistance and support I received throughout the process. I’m sharing my experience in the hope that it may encourage other victims to carefully research their options and verify any recovery service before proceeding. I will forever be thankful to you CAPITAL CRYPTO RECOVER 📧 [email protected] 🌐 Website: recovercapital.wixsite.com/capital-crypto-rec-1 📧 [email protected] 📞 Call/WhatsApp: +1 (336) 390-6684

  • 09.09.26 23:22 Fraddy Pual

    Hearing that these individuals are focusing on other people makes me very sad. I had a similar situation with them and lost a lot of money, but after learning about (Cruxcipherteam @ proton DoT me), whataqq:+168160-15021, telegram: @Cruxcipherteam I was able to get my money back. It's critical that we all be watchful and keep reporting these occurrences.

  • 11.09.26 03:23 kimberlyhebertt6877

    I invested in bitcoin trading After losing $78.4 USDT) linked to a romance fraud scam worth of cryptocurrency through an online investment platform and later discovered it was a scam. After extensive research for recovery options, I contacted CAPITAL CRYPTO RECOVER based on positive client reviews and recommendations. Their professional security team guided me through the recovery process using advanced technology, and I was able to recover my lost cryptocurrency successfully. I am truly grateful for their support and assistance during such a difficult experience. I will advise you to contact CAPITAL CRYPTO RECOVER helped me recover my funds. For anyone facing similar issues, Website: https://recovercapital.wixsite.com/capital-crypto-rec-1 Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] WhatsApp/Text Number: +1 (336) 390-6684

  • 11.09.26 03:23 kimberlyhebertt6877

    I invested in bitcoin trading After losing $78.4 USDT) linked to a romance fraud scam worth of cryptocurrency through an online investment platform and later discovered it was a scam. After extensive research for recovery options, I contacted CAPITAL CRYPTO RECOVER based on positive client reviews and recommendations. Their professional security team guided me through the recovery process using advanced technology, and I was able to recover my lost cryptocurrency successfully. I am truly grateful for their support and assistance during such a difficult experience. I will advise you to contact CAPITAL CRYPTO RECOVER helped me recover my funds. For anyone facing similar issues, Website: https://recovercapital.wixsite.com/capital-crypto-rec-1 Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] WhatsApp/Text Number: +1 (336) 390-6684

  • 15.09.26 03:35 elioduncan

    I fell victim to a freelance web development contract scam that ultimately cost me CAD 4,000. It all started when I came across a job posting on Indeed Canada, a popular online job portal. The position seemed perfect: a freelance web developer role for an established company looking for someone to design and build a fully functional e-commerce website. The job promised a high payment of CAD 20,000 upon successful completion, which sounded like a great opportunity for me to gain experience and earn decent pay.The employer, who introduced himself as a project manager from a "well-known" tech company, was very persuasive and professional in our initial communication. He explained that the project involved developing a user-friendly, responsive online store for their client and that they had a strict timeline to meet. He assured me that the payment would be made promptly after completing the tasks. However, before starting the work, he told me that I would need to pay an upfront fee of CAD 4,000 to cover certain software tools and licensing fees required for the project. This was supposedly a part of their company policy for freelance contractors, ensuring access to their premium resources. The idea of working on a professional project, coupled with the promise of a substantial payout, convinced me to pay the upfront fee.As soon as I made the payment, the project manager became increasingly difficult to reach. He initially responded to my emails and provided some vague instructions on what the project would entail, but as time went on, communication slowed to a complete halt. I never received the required tools or any proper project details. My emails went unanswered, and any attempts to contact the company were met with silence. After waiting for weeks, I realized that I had been scammed.Desperate to recover my money, I turned to TechY Force Cyber Retrieval, a service that specializes in helping victims of online scams. They helped me track the payment and took legal steps to pursue the fraudsters. Through their guidance, I was able to recover the full CAD 4,000. TechY Force Cyber Retrieval worked with my bank to reverse the transaction and liaised with the authorities to trace the scammer's details.This was a hard lesson, and I now know to be highly cautious about online job offers that require upfront payments. It is crucial to research companies thoroughly and avoid any job that seems too good to be true, especially when it involves paying money upfront. WhatsApp https://wa.link/2x6ktp Mail. [email protected]

  • 15.09.26 15:45 lydiassmith567

    HIRE A HACKER YOUR STOLEN CRYPTO RECOVERY / BTC / USDT / ETH WITH THE HELP OF CAPITAL CRYPTO RECOVER. I want to share my experience publicly regarding cryptocurrency wallet recovery, I highly recommend you to contact CAPITAL CRYPTO RECOVER, a professional private investigator in the Bitcoin world. They have a certified expert security team specializing in Bitcoin Recovery Services and have helped many people worldwide recover their lost funds. My wife and I were defrauded by an online manipulator posing as an experienced crypto investment professional. We lost $9.2 Million Stolen BTC in cryptocurrency and were left feeling homeless. After spending hours searching for a reliable crypto recovery service, I discovered CAPITAL CRYPTO RECOVER online. By patiently explaining my situation to their team, I was able to recover all my funds. Remarkably, my money was returned to my wallet in less than 24 hours. I am extremely grateful to CAPITAL CRYPTO RECOVER for their excellent assistance—they truly were a godsend in my difficult situation. If you have fallen victim to a cryptocurrency scam, you can reach CAPITAL CRYPTO RECOVER through the following channels Email: [email protected] OR Call/WhatsApp: +1 (336) 390-6684 Contact: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 15.09.26 15:45 lydiassmith567

    HIRE A HACKER YOUR STOLEN CRYPTO RECOVERY / BTC / USDT / ETH WITH THE HELP OF CAPITAL CRYPTO RECOVER. I want to share my experience publicly regarding cryptocurrency wallet recovery, I highly recommend you to contact CAPITAL CRYPTO RECOVER, a professional private investigator in the Bitcoin world. They have a certified expert security team specializing in Bitcoin Recovery Services and have helped many people worldwide recover their lost funds. My wife and I were defrauded by an online manipulator posing as an experienced crypto investment professional. We lost $9.2 Million Stolen BTC in cryptocurrency and were left feeling homeless. After spending hours searching for a reliable crypto recovery service, I discovered CAPITAL CRYPTO RECOVER online. By patiently explaining my situation to their team, I was able to recover all my funds. Remarkably, my money was returned to my wallet in less than 24 hours. I am extremely grateful to CAPITAL CRYPTO RECOVER for their excellent assistance—they truly were a godsend in my difficult situation. If you have fallen victim to a cryptocurrency scam, you can reach CAPITAL CRYPTO RECOVER through the following channels Email: [email protected] OR Call/WhatsApp: +1 (336) 390-6684 Contact: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

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