Elumicate

elumicate

Brave New Word: Exploring the Diverse Niches for Decentralized Physical Infrastructure Networks 

The TechBeat: Omnity Eliminates the Chasm Between Bitcoin and Other Networks (11/30/2024)

One of the three blockchain trilemmas is interoperability. ( That's just a fancy way of saying that different blockchains have trouble talking to each other. So, don't sweat it! We'll break it down). What interoperability means in this context is that two different blockchain networks can't communicate with each other except through a blockchain bridge. The absence of a unified blockchain ecosystem poses challenges for mass adoption and mainstream acceptance of blockchain technology. This fundamental problem has its roots in the ways blockchain protocols are designed to function. For instance, blockchains have different token standards and consensus mechanisms, which make it practically impossible for the seamless transfer of digital assets from one network to another without the use of intermediaries such as blockchain bridges or wrapped tokens. Illustration: To transfer or use BTC on the Solana network, the requisite condition is that there has to be a wrapped BTC (i.e. a tokenized version of Bitcoin locked in a smart contract)to facilitate the use of such token on another blockchain network. To keep it simple, this implies that the reason why BTC cannot be directly used on the Solana network is due to the fundamental differences between the two blockchains. Solana is a layer-1 blockchain with its own native token and specific rules for transaction processing. Bitcoin, on the other hand, operates on its own blockchain with its unique consensus mechanism and token economics. However, despite the role played by blockchain bridges in cross-chain communication, they still pose a major challenge. Users grapple with poor or complex user interfaces, and high operation fees, Most especially, they are often compelled to find the right pairs for their digital assets because of the existence of a fragmented bridging landscape. Another major drawback of blockchain bridges is that they are susceptible to hacks and are usually perceived as constituting a single point of failure, which undermines the principle of decentralization. According to Elliptic, a London-based blockchain analytics company, blockchain bridges lost a combined sum of over $1 billion to crypto hackers in 2022. Blockchain networks have fundamental issues all related to user experience to address to gain mainstream adoption. The current terrain where a multi-chain ecosystem with very limited cross-chain functionality only breeds fragmented and abysmal user experience,  which further makes interacting with blockchain networks and decentralized applications very complex and boring. ckBTC : How ICP is Challenging Interoperability Model The Internet Computer Protocol (ICP) ‘s Chain-key Cryptography is an innovative solution that enables chain key transactions to settle on-chain through the Internet Computer Protocol. Chain-key transactions extends chain-key technology to allow transactions targeted at other blockchains to be computed fully on-chain using the Internet Computer Protocol. Using chain-key transactions, the IC can integrate with other blockchains such as Bitcoin and Ethereum in a completely trustless manner without needing any bridges. The underlying philosophy behind this innovation is to challenge the existing interoperability models on which most wrapped tokens operate today, thus paving the way for the emergence of user-friendly alternatives. It can potentially address the challenges that are commonly associated with wrapped tokens such as counterparty risks, price manipulation, poor user experience, etc. Earlier this year, a pilot test was conducted in a Mexican university by the ICP Mexico in which 100 pesos worth of Chain-Key Bitcoin(ckBTC) was distributed to over 3000 students. The objective of this pilot was to demonstrate the viability of ckBTC as a payment method that does not require a bridge. This direct integration with the blockchain network can be regarded as a significant improvement in bitcoin interoperability. Powered by chain-key cryptography and a pair of canister smart contracts holding Bitcoin directly without using blockchain bridges, chain-key Bitcoin functions as a multi-chain Bitcoin pair, which can be sent in a matter of seconds and at a very low fee. Additionally, ckBTC is an ICRC-1-compliant token that is completely backed by Bitcoin (BTC). Having a 1:1 parity with Bitcoin, enables users to redeem ckBTC for redeemed for BTC and vice versa. Latest Development : 1inch Launches Fusion+ For Secure, Bridge-less Cross Chain Swaps 1inch, the world's leading decentralized exchange (DEX) aggregator recently launched Fusion+, a network upgrade designed to enhance secure and bridge-less cross-chain swaps by integrating liquidity across the DeFi ecosystem. Fusion+ enables direct cross-chain swaps, eliminating the need for intermediaries such as blockchain bridges, and ushering in a new standard for cross-chain interoperability. As the blockchain ecosystem continues to grapple with interoperability challenges and the significant risks associated with third-party bridges, 1inch latest solution, Fusion+, is seeking to redefine how users interact with blockchain protocols, downscaling the need for centralized bridges altogether. Conclusion Blockchain protocols majorly work by obeying their own rules. However, there has been an increasing need for a unified interoperable ecosystem to unlock the full potential of blockchain technology. While blockchain bridges offer a temporary solution, they have several drawbacks limiting their widespread use and viability. The ICP Pilot in Mexico involving chain-key Bitcoin offered fresh insights into the nature and scope of blockchain interoperability as well as the challenges it poses. Innovations like ckBTC—a 1:1 Bitcoin-backed token with near-instant finality and negligible fees—demonstrate its practical application, as seen in a successful pilot test in Mexico. This approach not only enhances blockchain usability but also highlights the need for a unified, decentralized ecosystem, addressing the fragmentation and inefficiencies that hinder mass adoption and making blockchain interactions intuitive and accessible for mainstream users. One of the three blockchain trilemmas is interoperability. ( That's just a fancy way of saying that different blockchains have trouble talking to each other. So, don't sweat it! We'll break it down). One of the three blockchain trilemmas is interoperability . ( That's just a fancy way of saying that different blockchains have trouble talking to each other. So, don't sweat it! We'll break it down). interoperability What interoperability means in this context is that two different blockchain networks can't communicate with each other except through a blockchain bridge. The absence of a unified blockchain ecosystem poses challenges for mass adoption and mainstream acceptance of blockchain technology. This fundamental problem has its roots in the ways blockchain protocols are designed to function. For instance, blockchains have different token standards and consensus mechanisms, which make it practically impossible for the seamless transfer of digital assets from one network to another without the use of intermediaries such as blockchain bridges or wrapped tokens. Illustration: Illustration: To transfer or use BTC on the Solana network, the requisite condition is that there has to be a wrapped BTC (i.e. a tokenized version of Bitcoin locked in a smart contract)to facilitate the use of such token on another blockchain network. To keep it simple, this implies that the reason why BTC cannot be directly used on the Solana network is due to the fundamental differences between the two blockchains. Solana is a layer-1 blockchain with its own native token and specific rules for transaction processing. Bitcoin, on the other hand, operates on its own blockchain with its unique consensus mechanism and token economics. However, despite the role played by blockchain bridges in cross-chain communication, they still pose a major challenge. Users grapple with poor or complex user interfaces, and high operation fees, Most especially, they are often compelled to find the right pairs for their digital assets because of the existence of a fragmented bridging landscape. Another major drawback of blockchain bridges is that they are susceptible to hacks and are usually perceived as constituting a single point of failure, which undermines the principle of decentralization. According to Elliptic, a London-based blockchain analytics company, blockchain bridges lost a combined sum of over $1 billion to crypto hackers in 2022 . over $1 billion to crypto hackers in 2022 over $1 billion to crypto hackers in 2022 Blockchain networks have fundamental issues all related to user experience to address to gain mainstream adoption. The current terrain where a multi-chain ecosystem with very limited cross-chain functionality only breeds fragmented and abysmal user experience,  which further makes interacting with blockchain networks and decentralized applications very complex and boring. ckBTC : How ICP is Challenging Interoperability Model The Internet Computer Protocol (ICP) ‘s Chain-key Cryptography is an innovative solution that enables chain key transactions to settle on-chain through the Internet Computer Protocol. Chain-key Cryptography Chain-key transactions extends chain-key technology to allow transactions targeted at other blockchains to be computed fully on-chain using the Internet Computer Protocol. Using chain-key transactions, the IC can integrate with other blockchains such as Bitcoin and Ethereum in a completely trustless manner without needing any bridges. Chain-key transactions extends chain-key technology to allow transactions targeted at other blockchains to be computed fully on-chain using the Internet Computer Protocol. Using chain-key transactions, the IC can integrate with other blockchains such as Bitcoin and Ethereum in a completely trustless manner without needing any bridges. Chain-key transactions The underlying philosophy behind this innovation is to challenge the existing interoperability models on which most wrapped tokens operate today, thus paving the way for the emergence of user-friendly alternatives. It can potentially address the challenges that are commonly associated with wrapped tokens such as counterparty risks, price manipulation, poor user experience, etc. Earlier this year, a pilot test was conducted in a Mexican university by the ICP Mexico in which 100 pesos worth of Chain-Key Bitcoin(ckBTC) was distributed to over 3000 students. The objective of this pilot was to demonstrate the viability of ckBTC as a payment method that does not require a bridge. This direct integration with the blockchain network can be regarded as a significant improvement in bitcoin interoperability. Powered by chain-key cryptography and a pair of canister smart contracts holding Bitcoin directly without using blockchain bridges, chain-key Bitcoin functions as a multi-chain Bitcoin pair, which can be sent in a matter of seconds and at a very low fee. Additionally, ckBTC is an ICRC-1-compliant token that is completely backed by Bitcoin (BTC). Having a 1:1 parity with Bitcoin, enables users to redeem ckBTC for redeemed for BTC and vice versa. Latest Development : 1inch Launches Fusion+ For Secure, Bridge-less Cross Chain Swaps 1inch, the world's leading decentralized exchange (DEX) aggregator recently launched Fusion+, a network upgrade designed to enhance secure and bridge-less cross-chain swaps by integrating liquidity across the DeFi ecosystem. Fusion+ enables direct cross-chain swaps, eliminating the need for intermediaries such as blockchain bridges, and ushering in a new standard for cross-chain interoperability. As the blockchain ecosystem continues to grapple with interoperability challenges and the significant risks associated with third-party bridges, 1inch latest solution, Fusion+, is seeking to redefine how users interact with blockchain protocols, downscaling the need for centralized bridges altogether. Conclusion Blockchain protocols majorly work by obeying their own rules. However, there has been an increasing need for a unified interoperable ecosystem to unlock the full potential of blockchain technology. While blockchain bridges offer a temporary solution, they have several drawbacks limiting their widespread use and viability. The ICP Pilot in Mexico involving chain-key Bitcoin offered fresh insights into the nature and scope of blockchain interoperability as well as the challenges it poses. Innovations like ckBTC—a 1:1 Bitcoin-backed token with near-instant finality and negligible fees—demonstrate its practical application, as seen in a successful pilot test in Mexico. This approach not only enhances blockchain usability but also highlights the need for a unified, decentralized ecosystem, addressing the fragmentation and inefficiencies that hinder mass adoption and making blockchain interactions intuitive and accessible for mainstream users.

Can Chain-key Cryptography Eliminate Blockchain Bridges And Wrapped Tokens?

The TechBeat: Omnity Eliminates the Chasm Between Bitcoin and Other Networks (11/28/2024)

The TechBeat: Omnity Eliminates the Chasm Between Bitcoin and Other Networks (11/26/2024)

Omnity Eliminates the Chasm Between Bitcoin and Other Networks

Por qué Omnity Hub es la solución más segura y confiable para BTCFi

Pourquoi Omnity Hub est la solution la plus sûre et la plus fiable pour BTCFi

क्यों ओम्निटी हब BTCFi के लिए सबसे सुरक्षित और भरोसेमंद समाधान है

Omnity HubがBTCFiにとって最も安全で信頼できないソリューションである理由

ເປັນຫຍັງ Omnity Hub ເປັນການແກ້ໄຂທີ່ປອດໄພ ແລະບໍ່ມີຄວາມເຊື່ອໝັ້ນທີ່ສຸດສຳລັບ BTCFi

किन Omnity Hub BTCFi को लागि सबैभन्दा सुरक्षित र ट्रस्टलेस समाधान हो

ولې د Omnity Hub د BTCFi لپاره خورا خوندي او بې باوره حل دی

Imaraykutaq Omnity Hub Aswan Seguro & Mana Confiable Solución BTCFi kaqpaq

Почему Omnity Hub — самое безопасное и надежное решение для BTCFi

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Bakit Ang Omnity Hub ang Pinaka-Secure at Hindi Pinagkakatiwalaang Solusyon para sa BTCFi

Чому Omnity Hub є найбезпечнішим і надійним рішенням для BTCFi

The digital age has transformed how we choose where to eat, stay, and play, with online reviews playing a critical role in our decisions. Yet, this reliance on online recommendations has led to a growing problem: fake reviews. In 2023, research found that up to 30% of online reviews were inauthentic, leading to widespread distrust among consumers and eroding confidence in businesses that once seemed reliable. The Problem of Fake Reviews The prevalence of fake reviews is a symptom of deeper issues. Intense competition among businesses, combined with the ease of manipulating online reviews, has created an environment where unethical practices thrive. Companies can easily purchase positive reviews—or negative ones for their competitors—distorting the true picture for consumers. This not only harms businesses that rely on genuine customer feedback but also misleads consumers, leading to disappointing experiences based on fabricated information. Recent surveys reveal that online reviews influence the purchasing decisions of 93% of users. However, trust in these reviews is steadily declining, with 67% of consumers now acknowledging fake reviews as a significant problem. The impact is substantial: consumers waste time and money on inferior products and services, legitimate businesses are overshadowed by those willing to engage in dishonest tactics, and overall confidence in online reviews continues to erode. Current Solutions Overview Several approaches have been tried to combat fake reviews, each with its own limitations: Human Moderation: Traditional moderation systems rely on human reviewers to detect and remove fraudulent content. While effective to some extent, this method is time-consuming, expensive, and often fails to keep pace with the sheer volume of reviews being posted. Automated Systems: Recently, review platforms have started deploying machine learning-based systems to identify suspicious patterns in reviews. These systems can catch many fake reviews but are not foolproof, often struggling with nuanced or sophisticated fake reviews. Additionally, automated systems risk flagging legitimate feedback as fraudulent, which can frustrate honest users and damage the platform’s credibility. Verified Purchase Badges: Some platforms have introduced verified purchase badges or similar markers to indicate that a reviewer actually used the product or service. While helpful, this approach is not universally adopted and doesn’t fully address the problem—especially in industries like hospitality, where experiences are subjective and not always tied to a simple transaction. Moreover, the verification process can sometimes be circumvented, allowing fake reviews to slip through. Reputation-Based Systems: By assigning reputation scores to users based on the quality and consistency of their reviews, this approach aims to encourage honest feedback. The idea is that users with a history of trustworthy reviews will be more reliable, helping to filter out fake content. However, building a strong reputation takes time, during which fake reviews can still be posted. Additionally, these systems are not immune to manipulation; coordinated groups can work together to inflate or deflate reputation scores, undermining their overall effectiveness Blockchain and AI: Innovative Approaches to Combat Fake Reviews Blockchain and AI provide powerful tools in the battle against fake reviews, offering complementary methods to enhance trust and authenticity. Blockchain’s decentralized and immutable ledger ensures that once reviews and recommendations are recorded, they cannot be altered or deleted without network consensus, safeguarding the integrity of the content and preventing tampering. This technology also enables identity verification, tying reviews to verified users and making it more difficult for fake accounts to flood platforms with misleading feedback. Additionally, blockchain tech can support token-based incentive systems, rewarding users with cryptocurrency or tokens for posting honest reviews, which further discourages dishonest submissions. AI complements blockchain by adding an extra layer of protection through content analysis and filtering before it reaches the public. AI algorithms can detect patterns, language, and behavior that indicate fake reviews, flagging suspicious content in real-time. Together, these technologies help ensure that the information consumers rely on is both genuine and trustworthy. However, despite the significant advancements in the crypto, Web3, and DeFi sectors, the full potential of blockchain and AI in combating fake reviews has yet to be fully realized. Case Study: daGama’s Multi Level Anti Fake System(MLAFS) daGama is pioneering the use of blockchain and AI to tackle fake recommendations. Its Multi-Level Anti-Fake System (MLAFS) effectively integrates blockchain’s secure ledger with AI's detailed analysis, creating a robust barrier against fraudulent content. This dual-layered approach ensures that only genuine recommendations make it through. In addition, it introduced the “Review and Earn” concept to tackles a critical issue in current systems: the lack of incentives for users to provide honest feedback, ensuring that users are rewarded with tokens for valuable recommendations that are upvoted and tipped by other community members. The platform’s RWL-Map (Real World Locations Map) is a dynamic, user-driven ecosystem that helps users discover the best locations, restaurants, and venues based on real, verified recommendations. With a global launch slated for 2024, daGama is set to transform the review industry by restoring trust and simplifying the process of discovering new places to visit. Conclusion As the online review industry grapples with the persistent challenge of fake reviews, traditional methods—such as human moderation, automated systems, verified purchase badges, and reputation-based systems—have proven to be only partially effective. Each approach has its limitations, often addressing only part of the problem while leaving significant gaps that can be exploited by those intent on manipulating recommendations. However, innovative solutions leveraging blockchain and AI present a promising way forward. By combining the immutability and transparency of blockchain with the analytical power of AI, these technologies offer a more robust defense against fraudulent content. The potential impact of blockchain and AI on the review industry goes beyond just preventing fake recommendations; it also fosters a culture of trust and transparency that benefits consumers and businesses alike. By restoring confidence in the accuracy of online reviews, these technologies make it easier for consumers to make informed decisions and for businesses to build genuine reputations based on authentic feedback. Looking ahead, as blockchain and AI technologies continue to mature, we can expect even more sophisticated systems to emerge, potentially incorporating other technologies like natural language processing, sentiment analysis, and decentralized identity verification. These advancements will make it increasingly difficult for fraudulent actors to manipulate review systems and will help build a more reliable, user-centric digital landscape. The digital age has transformed how we choose where to eat, stay, and play, with online reviews playing a critical role in our decisions. Yet, this reliance on online recommendations has led to a growing problem: fake reviews. In 2023, research found that up to 30% of online reviews were inauthentic, leading to widespread distrust among consumers and eroding confidence in businesses that once seemed reliable. research found research found The Problem of Fake Reviews The prevalence of fake reviews is a symptom of deeper issues. Intense competition among businesses, combined with the ease of manipulating online reviews, has created an environment where unethical practices thrive. Companies can easily purchase positive reviews—or negative ones for their competitors—distorting the true picture for consumers. This not only harms businesses that rely on genuine customer feedback but also misleads consumers, leading to disappointing experiences based on fabricated information. Recent surveys reveal that online reviews influence the purchasing decisions of 93% of users. However, trust in these reviews is steadily declining, with 67% of consumers now acknowledging fake reviews as a significant problem. The impact is substantial: consumers waste time and money on inferior products and services, legitimate businesses are overshadowed by those willing to engage in dishonest tactics, and overall confidence in online reviews continues to erode. Recent surveys Recent surveys Current Solutions Overview Several approaches have been tried to combat fake reviews, each with its own limitations: Human Moderation: Traditional moderation systems rely on human reviewers to detect and remove fraudulent content. While effective to some extent, this method is time-consuming, expensive, and often fails to keep pace with the sheer volume of reviews being posted. Automated Systems: Recently, review platforms have started deploying machine learning-based systems to identify suspicious patterns in reviews. These systems can catch many fake reviews but are not foolproof, often struggling with nuanced or sophisticated fake reviews. Additionally, automated systems risk flagging legitimate feedback as fraudulent, which can frustrate honest users and damage the platform’s credibility. Verified Purchase Badges: Some platforms have introduced verified purchase badges or similar markers to indicate that a reviewer actually used the product or service. While helpful, this approach is not universally adopted and doesn’t fully address the problem—especially in industries like hospitality, where experiences are subjective and not always tied to a simple transaction. Moreover, the verification process can sometimes be circumvented, allowing fake reviews to slip through. Reputation-Based Systems: By assigning reputation scores to users based on the quality and consistency of their reviews, this approach aims to encourage honest feedback. The idea is that users with a history of trustworthy reviews will be more reliable, helping to filter out fake content. However, building a strong reputation takes time, during which fake reviews can still be posted. Additionally, these systems are not immune to manipulation; coordinated groups can work together to inflate or deflate reputation scores, undermining their overall effectiveness Blockchain and AI: Innovative Approaches to Combat Fake Reviews Blockchain and AI provide powerful tools in the battle against fake reviews, offering complementary methods to enhance trust and authenticity. Blockchain’s decentralized and immutable ledger ensures that once reviews and recommendations are recorded, they cannot be altered or deleted without network consensus, safeguarding the integrity of the content and preventing tampering. This technology also enables identity verification, tying reviews to verified users and making it more difficult for fake accounts to flood platforms with misleading feedback. Additionally, blockchain tech can support token-based incentive systems, rewarding users with cryptocurrency or tokens for posting honest reviews, which further discourages dishonest submissions. AI complements blockchain by adding an extra layer of protection through content analysis and filtering before it reaches the public. AI algorithms can detect patterns, language, and behavior that indicate fake reviews, flagging suspicious content in real-time. Together, these technologies help ensure that the information consumers rely on is both genuine and trustworthy. However, despite the significant advancements in the crypto, Web3, and DeFi sectors, the full potential of blockchain and AI in combating fake reviews has yet to be fully realized. Case Study: daGama’s Multi Level Anti Fake System(MLAFS) daGama is pioneering the use of blockchain and AI to tackle fake recommendations. Its Multi-Level Anti-Fake System (MLAFS) effectively integrates blockchain’s secure ledger with AI's detailed analysis, creating a robust barrier against fraudulent content. This dual-layered approach ensures that only genuine recommendations make it through. In addition, it introduced the “Review and Earn” concept to tackles a critical issue in current systems: the lack of incentives for users to provide honest feedback, ensuring that users are rewarded with tokens for valuable recommendations that are upvoted and tipped by other community members. The platform’s RWL-Map (Real World Locations Map) is a dynamic, user-driven ecosystem that helps users discover the best locations, restaurants, and venues based on real, verified recommendations. With a global launch slated for 2024, daGama is set to transform the review industry by restoring trust and simplifying the process of discovering new places to visit. Conclusion As the online review industry grapples with the persistent challenge of fake reviews, traditional methods—such as human moderation, automated systems, verified purchase badges, and reputation-based systems—have proven to be only partially effective. Each approach has its limitations, often addressing only part of the problem while leaving significant gaps that can be exploited by those intent on manipulating recommendations. However, innovative solutions leveraging blockchain and AI present a promising way forward. By combining the immutability and transparency of blockchain with the analytical power of AI, these technologies offer a more robust defense against fraudulent content. The potential impact of blockchain and AI on the review industry goes beyond just preventing fake recommendations; it also fosters a culture of trust and transparency that benefits consumers and businesses alike. By restoring confidence in the accuracy of online reviews, these technologies make it easier for consumers to make informed decisions and for businesses to build genuine reputations based on authentic feedback. Looking ahead, as blockchain and AI technologies continue to mature, we can expect even more sophisticated systems to emerge, potentially incorporating other technologies like natural language processing, sentiment analysis, and decentralized identity verification. These advancements will make it increasingly difficult for fraudulent actors to manipulate review systems and will help build a more reliable, user-centric digital landscape.

Leveraging Blockchain And AI To Eliminate Fake Reviews

Authors:
(1) Kexun Zhang, UC Santa Barbara and Equal contribution;
(2) Hongqiao Chen, Northwood High School and Equal contribution;
(3) Lei Li, Carnegie Mellon University;
(4) William Yang Wang,UC Santa Barbara. Table of Links Abstract and Intro
Related Work
ToolDec: LLM Tool Use via Finite-State Decoding
Experiment: ToolDec Eliminates Syntax Errors
Experiment: ToolDec Enables Generalizable Tool Selection
Conclusion and References
Appendix 4. EXPERIMENT I: TOOLDEC ELIMINATES SYNTAX ERRORS In this section, we show that TOOLDEC can eliminate syntax errors while generating tool calls. We select two recent baselines, ToolLLM and ToolkenGPT, representative of the in-context learning and the fine-tuning paradigm, to showcase TOOLDEC’s ability. Since the tool-use settings for the two baselines are different and cannot be applied to one another, we test TOOLDEC’s performance separately for the two baselines using the benchmarks from the original papers. Through extensive experiments, we show that TOOLDEC can completely eliminate syntactic errors, resulting in better accuracy and shorter inference time. 4.1 BASELINES AND BENCHMARKS ToolLLM (Qin et al., 2023). ToolLLM is an in-context learning approach to tool-augmented language models. It utilizes an instruction-tuned LLaMA-7B model (Touvron et al., 2023) to use tools. Given the natural language instruction of a tool-dependent task, an API retriever first retrieves a small subset of relevant functions. Textual description and schema of these relevant functions are made available in the context. Then, ToolLLM goes through a multi-step reasoning process using the functions to produce a final answer. ToolLLM is evaluated on ToolEval, a dataset proposed in the same paper. ToolEval contains tasks that involve a massive set (10,000+) of publicly available REST APIs. We use the most difficult subsets of ToolEval to evaluate our method—I2-Category and I3-Instruction. They contain tasks that need complex and unseen tools from multiple categories (such as geolocation, date/time, and others) to solve. On average, an I2-Category task needs 6.76 tools and an I3-Category task needs 8.24 tools. ToolEval has two main metrics: Pass Rate measures the percentage of tasks for which the model reaches an answer within a certain amount of reasoning steps. Win Rate utilizes an automatic evaluator powered by an LLM following a pre-defined set of criteria for a better path. It compares the quality and correctness of the baseline answers to the reference answer produced by ChatGPT. Qin et al. (2023) finds that the automatic evaluator has a high correlation of 75.8% with human annotators. Other than these two metrics, we also measure Tool Error Rate, the proportion of tasks that have at least one tool-related error. ToolkenGPT (Hao et al., 2023). ToolkenGPT is a fine-tuning approach to tool use. ToolkenGPT represents each tool as a special token and optimizes only the embedding of the tool tokens for tool use. During inference, ToolkenGPT invokes a tool once the corresponding special token is predicted. During a tool call, it passes arguments by learning from in-context demonstrations. ToolkenGPT uses LLaMA-33B (Touvron et al., 2023) as its base model. 4.2 INTEGRATING TOOLDEC WITH THE BASE MODELS ToolLLM+TOOLDEC. Following Qin et al. (2023), we use ReAct (Yao et al., 2023) to plan the tool calls of ToolLLM. This conforms to the second case of mode switching in Section 3.2. There are three parts in the FSM for ToolLLM. First, a format FSM that enforces the “Thought, Action, Action Input” syntax of ReAct. After decoding “Action:”, this FSM transitions to the beginning state of the function name FSM, which guarantees that a decoded function name is always valid. We also constructed a JSON-based function argument FSM. We allowed LLMs to reason for 5 steps before it must call the finish action to be considered “pass”. ToolkenGPT+TOOLDEC. Since ToolkenGPT uses special tokens to call tools, TOOLDEC is only applied to guarantee the syntax of arguments. In this experiment, our FSM guarantees that every argument is a valid number, and arguments are separated by commas. It also guarantees that the actual number of arguments passed to a function is exactly the number needed by it. We compared TOOLDEC to two variants of the baseline in Hao et al. (2023), one with backtrace and one without. Backtrace tries to avoid failed tool calls by allowing the LLM to go back and try the next probable token, in place of the failed tool call. To evaluate TOOLDEC, we report the average inference time per problem and tool error rates in addition to accuracy. 4.3 EXPERIMENTAL RESULTS TOOLDEC enhances in-context learning tool LLMs. Table 3 shows TOOLDEC’s performance on ToolEval. TOOLDEC achieved 55% win rate on I2-Category and 60% win rate on I3-instruction. As a drop-in replacement of the original decoding algorithm, TOOLDEC eliminated all three types of tool-related errors and achieved the best win rate and pass rate, even beating ChatGPT. The high tool error rate of the baselines suggests that even after instruction finetuning, ToolLLM still lacks the ability of accurately invoke external tools from the tool documentation. This inability is more exposed when there is a large variety of tools available, as in I3-Instruction. Furthermore, these errors significantly impacted the model’s ability to complete tasks. We present the error rates of each error type on two benchmarks in Figure 4. For ToolLLMs, name error, i.e. calling a non-existent tool, was the most common syntactic error in a tool call. TOOLDEC completely eliminated all three errors. With function name hallucination being the most prevalent tool-related error, a slightly better baseline was to mitigate it with fuzzy matching by suffix. We present the results of the baseline with fuzzy matching as ToolLLM + Fuzzy Matching, and without as ToolLLM. This mitigation increased pass rate but had little impact on win rate, as evident in Table 3, because wrong APIs could often be chosen when a model was unable to precisely call the tool it wanted. Overall, our experiments on ToolLLM demonstrate that TOOLDEC is highly effective on in-context learning LLMs. Through the next baseline, ToolkenGPT, we show that TOOLDEC is also beneficial to fine-tuned tool LLMs. TOOLDEC enhances fine-tuning tool LLMs. Table 4 shows the results on FuncQAmulti. Although ToolkenGPT eliminates the possibility of calling non-existent tool names by fine-tuning a special token embedding, it can still suffer from other syntactic errors, which is demonstrated by the 27.9% tool error rate. As a drop-in replacement, TOOLDEC increased ToolkenGPT’s accuracy while being much faster in inference. Although ToolkenGPT + backtrace achieved slightly better accuracy than TOOLDEC, it used 2x more time to try different tools. Note that since TOOLDEC eliminated all tool errors, there were no failed tool calls for backtrace to retry. The results underscore tool-related errors’ relevancy and TOOLDEC’s applicability to both recent in-context learning and fine-tuning tool-augmented LLMs. This paper is available on arxiv under CC 4.0 DEED license. Authors: (1) Kexun Zhang, UC Santa Barbara and Equal contribution; (2) Hongqiao Chen, Northwood High School and Equal contribution; (3) Lei Li, Carnegie Mellon University; (4) William Yang Wang,UC Santa Barbara. Authors: Authors: (1) Kexun Zhang, UC Santa Barbara and Equal contribution; (2) Hongqiao Chen, Northwood High School and Equal contribution; (3) Lei Li, Carnegie Mellon University; (4) William Yang Wang,UC Santa Barbara. Table of Links Abstract and Intro Related Work ToolDec: LLM Tool Use via Finite-State Decoding Experiment: ToolDec Eliminates Syntax Errors Experiment: ToolDec Enables Generalizable Tool Selection Conclusion and References Appendix Abstract and Intro Abstract and Intro Related Work Related Work ToolDec: LLM Tool Use via Finite-State Decoding ToolDec: LLM Tool Use via Finite-State Decoding Experiment: ToolDec Eliminates Syntax Errors Experiment: ToolDec Eliminates Syntax Errors Experiment: ToolDec Enables Generalizable Tool Selection Experiment: ToolDec Enables Generalizable Tool Selection Conclusion and References Conclusion and References Appendix Appendix 4. EXPERIMENT I: TOOLDEC ELIMINATES SYNTAX ERRORS In this section, we show that TOOLDEC can eliminate syntax errors while generating tool calls. We select two recent baselines, ToolLLM and ToolkenGPT, representative of the in-context learning and the fine-tuning paradigm, to showcase TOOLDEC’s ability. Since the tool-use settings for the two baselines are different and cannot be applied to one another, we test TOOLDEC’s performance separately for the two baselines using the benchmarks from the original papers. Through extensive experiments, we show that TOOLDEC can completely eliminate syntactic errors, resulting in better accuracy and shorter inference time. 4.1 BASELINES AND BENCHMARKS ToolLLM (Qin et al., 2023). ToolLLM is an in-context learning approach to tool-augmented language models. It utilizes an instruction-tuned LLaMA-7B model (Touvron et al., 2023) to use tools. Given the natural language instruction of a tool-dependent task, an API retriever first retrieves a small subset of relevant functions. Textual description and schema of these relevant functions are made available in the context. Then, ToolLLM goes through a multi-step reasoning process using the functions to produce a final answer. ToolLLM (Qin et al., 2023). ToolLLM is evaluated on ToolEval, a dataset proposed in the same paper. ToolEval contains tasks that involve a massive set (10,000+) of publicly available REST APIs. We use the most difficult subsets of ToolEval to evaluate our method—I2-Category and I3-Instruction. They contain tasks that need complex and unseen tools from multiple categories (such as geolocation, date/time, and others) to solve. On average, an I2-Category task needs 6.76 tools and an I3-Category task needs 8.24 tools. ToolEval has two main metrics: Pass Rate measures the percentage of tasks for which the model reaches an answer within a certain amount of reasoning steps. Win Rate utilizes an automatic evaluator powered by an LLM following a pre-defined set of criteria for a better path. It compares the quality and correctness of the baseline answers to the reference answer produced by ChatGPT. Qin et al. (2023) finds that the automatic evaluator has a high correlation of 75.8% with human annotators. Other than these two metrics, we also measure Tool Error Rate , the proportion of tasks that have at least one tool-related error. ToolEval, Pass Rate Win Rate Tool Error Rate ToolkenGPT (Hao et al., 2023). ToolkenGPT is a fine-tuning approach to tool use. ToolkenGPT represents each tool as a special token and optimizes only the embedding of the tool tokens for tool use. During inference, ToolkenGPT invokes a tool once the corresponding special token is predicted. During a tool call, it passes arguments by learning from in-context demonstrations. ToolkenGPT uses LLaMA-33B (Touvron et al., 2023) as its base model. ToolkenGPT (Hao et al., 2023). 4.2 INTEGRATING TOOLDEC WITH THE BASE MODELS ToolLLM+TOOLDEC. Following Qin et al. (2023), we use ReAct (Yao et al., 2023) to plan the tool calls of ToolLLM. This conforms to the second case of mode switching in Section 3.2. There are three parts in the FSM for ToolLLM. First, a format FSM that enforces the “Thought, Action, Action Input” syntax of ReAct. After decoding “Action:”, this FSM transitions to the beginning state of the function name FSM, which guarantees that a decoded function name is always valid. We also constructed a JSON-based function argument FSM. We allowed LLMs to reason for 5 steps before it must call the finish action to be considered “pass”. ToolLLM+TOOLDEC. ToolkenGPT+TOOLDEC. Since ToolkenGPT uses special tokens to call tools, TOOLDEC is only applied to guarantee the syntax of arguments. In this experiment, our FSM guarantees that every argument is a valid number, and arguments are separated by commas. It also guarantees that the actual number of arguments passed to a function is exactly the number needed by it. We compared TOOLDEC to two variants of the baseline in Hao et al. (2023), one with backtrace and one without. Backtrace tries to avoid failed tool calls by allowing the LLM to go back and try the next probable token, in place of the failed tool call. To evaluate TOOLDEC, we report the average inference time per problem and tool error rates in addition to accuracy. ToolkenGPT+TOOLDEC. 4.3 EXPERIMENTAL RESULTS TOOLDEC enhances in-context learning tool LLMs. Table 3 shows TOOLDEC’s performance on ToolEval. TOOLDEC achieved 55% win rate on I2-Category and 60% win rate on I3-instruction. As a drop-in replacement of the original decoding algorithm, TOOLDEC eliminated all three types of tool-related errors and achieved the best win rate and pass rate, even beating ChatGPT. TOOLDEC enhances in-context learning tool LLMs. The high tool error rate of the baselines suggests that even after instruction finetuning, ToolLLM still lacks the ability of accurately invoke external tools from the tool documentation. This inability is more exposed when there is a large variety of tools available, as in I3-Instruction. Furthermore, these errors significantly impacted the model’s ability to complete tasks. We present the error rates of each error type on two benchmarks in Figure 4. For ToolLLMs, name error, i.e. calling a non-existent tool, was the most common syntactic error in a tool call. TOOLDEC completely eliminated all three errors. With function name hallucination being the most prevalent tool-related error, a slightly better baseline was to mitigate it with fuzzy matching by suffix. We present the results of the baseline with fuzzy matching as ToolLLM + Fuzzy Matching, and without as ToolLLM. This mitigation increased pass rate but had little impact on win rate, as evident in Table 3, because wrong APIs could often be chosen when a model was unable to precisely call the tool it wanted. Overall, our experiments on ToolLLM demonstrate that TOOLDEC is highly effective on in-context learning LLMs. Through the next baseline, ToolkenGPT, we show that TOOLDEC is also beneficial to fine-tuned tool LLMs. TOOLDEC enhances fine-tuning tool LLMs. Table 4 shows the results on FuncQAmulti. Although ToolkenGPT eliminates the possibility of calling non-existent tool names by fine-tuning a special token embedding, it can still suffer from other syntactic errors, which is demonstrated by the 27.9% tool error rate. As a drop-in replacement, TOOLDEC increased ToolkenGPT’s accuracy while being much faster in inference. Although ToolkenGPT + backtrace achieved slightly better accuracy than TOOLDEC, it used 2x more time to try different tools. Note that since TOOLDEC eliminated all tool errors, there were no failed tool calls for backtrace to retry. The results underscore tool-related errors’ relevancy and TOOLDEC’s applicability to both recent in-context learning and fine-tuning tool-augmented LLMs. TOOLDEC enhances fine-tuning tool LLMs. This paper is available on arxiv under CC 4.0 DEED license. This paper is available on arxiv under CC 4.0 DEED license. available on arxiv

Syntax Error-Free and Generalizable Tool Use for LLMs: ToolDec Eliminates Syntax Errors

LLM-এর জন্য সিনট্যাক্স ত্রুটি-মুক্ত এবং সাধারণীকরণযোগ্য টুল ব্যবহার: টুলডেক সিনট্যাক্স ত্রুটি দূর করে

Syntaxfehlerfreier und verallgemeinerbarer Tool-Einsatz für LLMs: ToolDec eliminiert Syntaxfehler

Uso de herramientas generalizables y sin errores de sintaxis para LLM: ToolDec elimina los errores de sintaxis

Utilisation d'un outil sans erreur de syntaxe et généralisable pour les LLM : ToolDec élimine les erreurs de syntaxe

एलएलएम के लिए वाक्यविन्यास त्रुटि-मुक्त और सामान्यीकृत उपकरण का उपयोग: टूलडेक वाक्यविन्यास त्रुटियों को समाप्त करता है

LLM 向けの構文エラーのない汎用ツールの使用: ToolDec は構文エラーを排除します

LLM을 위한 구문 오류가 없고 일반화 가능한 도구 사용: ToolDec은 구문 오류를 제거합니다.

Uso de ferramenta generalizável e livre de erros de sintaxe para LLMs: ToolDec elimina erros de sintaxe

Синтаксические ошибки и универсальное использование инструмента для LLM: ToolDec устраняет синтаксические ошибки

LLM'ler için Sözdizimi Hatasız ve Genelleştirilebilir Araç Kullanımı: ToolDec, Sözdizimi Hatalarını Ortadan Kaldırır

Sử dụng công cụ không có lỗi và có thể khái quát hóa cho LLM: ToolDec Loại bỏ lỗi cú pháp

语法无错误且可推广的法学硕士 (LLM) 工具使用：ToolDec 可消除语法错误

When in-game cheating becomes the norm, does the gaming industry have a major problem on its hands? A global study of the gaming industry would say yes. It was found that 77% of online gamers are likely to stop playing a multiplayer game online if they think other players are cheating, and 48% of online gamers are likely to buy less in-game content. Add in the accrued value of digital assets as a key feature of blockchain gaming, and developers have an obligation to tackle this problem head-on. An attractiveness to bending the rules is not something limited to the world of online gaming; it is rife in all parts of society. However, as social psychologist Corey Butler points out ‘anonymity (playing with strangers or online) and opportunity can tip the scales toward greater cheating. When strong incentives like money or grades are on the line, otherwise honest people might be tempted to cheat. If somebody with a competitive nature is presented with the opportunity to easily outperform the other at a task, then it's safe to say that many will bite the bullet and risk the reward for the easy win—factor in the monetary value of rewards in Web3 gaming, and this desire to win increases significantly. However, when it comes to multiplayer online games, the competition is designed around entertainment, fun, and the community. These core values of gaming make cheating a stain on the industry as a whole. Cheating is the new norm. Cheating in some of the largest games has become an economy of its own, with many platforms and YouTube channels dedicated to introducing cheating, hacks, and navigating the ways to cheat. Don't believe us, simply type fortnite cheat terms into a google search. Combating this trend is critical for maintaining the reputation of these brands. Simon Vieira, CEO of MixMob, a Solana-based gaming platform, believes the temptation to cheat is rife, but it is nothing new, “Web3 games offer enhanced incentives due to the tangible value of game coins and digital items. However, this also creates a greater temptation to cheat. That said, traditional gaming has previously addressed similar issues. For instance, real money gaming employs methods such as biotic randomness to obscure certain game aspects like matchmaking. This approach reduces predictability, thus diminishing the incentives to cheat while still maintaining a skill-based gaming environment. We apply these strategies at MixMob to ensure fairness and keep the game engaging for all players,” says Vieira. When hacking games, cheaters now have an endless stack of tools to choose from when it comes to infiltrating game mechanics with glitching and farming to using aimbots in the software and speed hacks. This problem is exacerbated in Web3 gaming as it lacks an authoritative host. “Fair play is crucial in multi-player games as it directly impacts player engagement and retention. A fair environment ensures that players feel valued and that the game remains competitive and enjoyable for everyone,” notes Vieira. Furthermore, with AI, it is easier than ever to predict patterns of player behavior and create new ways to exploit and manipulate gaming data. So, how can this problem be addressed without compromising the gaming experience? Speaking to Tashi Protocol CEO Sandeep Bhatia, it is clear that consensus will play a future role in blockchain gaming. “Dynamite as the network transport for multiplayer games can prevent certain forms of cheating like 'lag switching' or local state hacking. The benefit of having consensus on top of a peer-to-peer network layer ensures that players cannot cheat, and the architecture reduces costs.” Frustration with the publishers Publishers are tackling the issue of one player, or several players at a time, by banning them and using anti-cheat software. Just last month, Activision, a gaming giant and creator of Call of Duty, banned 27,000 accounts from the game. However, this isn't deterring new cheats coming into the sector. Tackling it from the outset in the process of the game studio is the only way to ensure that both the publishers and the gamers benefit in the long run. To restore confidence, creators of blockchain games must demonstrate trust from the outset. “Shifting from a traditional gaming infrastructure to adopt a decentralized, serverless framework will optimize the entire Web3 gaming experience and solve the issue of cheating from the ground up. Not only can we reduce the costs by eliminating the need for centralized servers, but Tashi's Dynamite ensures fair distribution and protection against common security threats,” says Bhatia. Once a player sees that a game is compromised, it ruins the fun element and may even deter them from entering the environment because of security fears. In addition, the influx of bots and other hacks has led to unfair advantages within some of the most loved games. What can blockchain game publishers do? Gaming studios must take the necessary steps to demonstrate their commitment to fair play. This is essential in the blockchain space as it continues to build trust with the traditional gaming communities. At MixMob, they leverage blockchain transparency to allow the community to participate in monitoring and reporting cheaters actively. “This community refereeing not only helps maintain fairness but also fosters a sense of responsibility and integrity among players, and it has worked great for us,” says Vieira. “Web3 game publishers must take decisive steps to limit cheating. The actions taken should vary based on the severity of the cheating incident. Potential measures include banning, suspending, or permanently removing players involved in unfair practices. Additionally, publishers can adopt proven strategies from traditional gaming, such as introducing randomness to some parts of the game to reduce predictability, which helps deter cheating,” he continues. Game developers who are keen to implement anti-cheat measures are often outplayed by hackers who have mastered the art of cheating and the avoidance of detection. Sure, developers can ban cheaters who are caught in the act or embed cheat detection software. However, this doesn't prevent cheating from occurring from the outset. The vulnerabilities have already been exploited by the time the developer has their hands on the activities. If, however, a game creator starts the development process using the Tashi Consensus Protocol, it has eliminated the act of cheating from the initial design stage. “Gamers rely on trust that game developers would have implemented the best practices to ensure cheaters are punished. That is why Tashi works to educate all its gaming partners on the novel ways blockchain solves cheating issues. We make our tech easy to use and guide all our partners who might come from the Web2 world and are new to using blockchain tech,” added Bhatia. While anti-cheat software like Riot Game's Vanguard or Activision Blizzard's Ricochet stands as the frontline defense, the second layer can utilize the transparency provided by blockchain technology to fight against bad actors. Implementing innovative approaches to game development, like the Tashi Dynamite Consensus Engine, pushes gaming to a new era of fair play and reclaims the values that true gamers want to see in the game development stage. In addition to fostering a culture of fair play, a paradigm shift is necessary in the approach to combating cheating in online gaming. By 2027, the number of users in the Online Games market is expected to amount to 1.2bn users. The projected revenue in the Online Games market worldwide is expected to reach US$27.97bn in 2024. Reclaiming the gaming sector for true gamers who value the entire gaming experience is crucial as blockchain games position themselves alongside some of the gaming giants. When in-game cheating becomes the norm, does the gaming industry have a major problem on its hands? A global study of the gaming industry would say yes. It was found that 77% of online gamers are likely to stop playing a multiplayer game online if they think other players are cheating, and 48% of online gamers are likely to buy less in-game content. Add in the accrued value of digital assets as a key feature of blockchain gaming, and developers have an obligation to tackle this problem head-on. study study An attractiveness to bending the rules is not something limited to the world of online gaming; it is rife in all parts of society. However, as social psychologist Corey Butler points out ‘anonymity (playing with strangers or online) and opportunity can tip the scales toward greater cheating. When strong incentives like money or grades are on the line, otherwise honest people might be tempted to cheat. If somebody with a competitive nature is presented with the opportunity to easily outperform the other at a task, then it's safe to say that many will bite the bullet and risk the reward for the easy win—factor in the monetary value of rewards in Web3 gaming, and this desire to win increases significantly. However, when it comes to multiplayer online games, the competition is designed around entertainment, fun, and the community. These core values of gaming make cheating a stain on the industry as a whole. Cheating is the new norm. Cheating in some of the largest games has become an economy of its own, with many platforms and YouTube channels dedicated to introducing cheating, hacks, and navigating the ways to cheat. Don't believe us, simply type fortnite cheat terms into a google search. Combating this trend is critical for maintaining the reputation of these brands. Simon Vieira, CEO of MixMob, a Solana-based gaming platform, believes the temptation to cheat is rife, but it is nothing new, “Web3 games offer enhanced incentives due to the tangible value of game coins and digital items. However, this also creates a greater temptation to cheat. That said, traditional gaming has previously addressed similar issues. For instance, real money gaming employs methods such as biotic randomness to obscure certain game aspects like matchmaking. This approach reduces predictability, thus diminishing the incentives to cheat while still maintaining a skill-based gaming environment. We apply these strategies at MixMob to ensure fairness and keep the game engaging for all players,” says Vieira. When hacking games, cheaters now have an endless stack of tools to choose from when it comes to infiltrating game mechanics with glitching and farming to using aimbots in the software and speed hacks. This problem is exacerbated in Web3 gaming as it lacks an authoritative host. “Fair play is crucial in multi-player games as it directly impacts player engagement and retention. A fair environment ensures that players feel valued and that the game remains competitive and enjoyable for everyone,” notes Vieira. Furthermore, with AI, it is easier than ever to predict patterns of player behavior and create new ways to exploit and manipulate gaming data. So, how can this problem be addressed without compromising the gaming experience? Speaking to Tashi Protocol CEO Sandeep Bhatia, it is clear that consensus will play a future role in blockchain gaming. “Dynamite as the network transport for multiplayer games can prevent certain forms of cheating like 'lag switching' or local state hacking. The benefit of having consensus on top of a peer-to-peer network layer ensures that players cannot cheat, and the architecture reduces costs.” Frustration with the publishers Publishers are tackling the issue of one player, or several players at a time, by banning them and using anti-cheat software. Just last month, Activision , a gaming giant and creator of Call of Duty, banned 27,000 accounts from the game. However, this isn't deterring new cheats coming into the sector. Activision Activision Tackling it from the outset in the process of the game studio is the only way to ensure that both the publishers and the gamers benefit in the long run. To restore confidence, creators of blockchain games must demonstrate trust from the outset. “Shifting from a traditional gaming infrastructure to adopt a decentralized, serverless framework will optimize the entire Web3 gaming experience and solve the issue of cheating from the ground up. Not only can we reduce the costs by eliminating the need for centralized servers, but Tashi's Dynamite ensures fair distribution and protection against common security threats,” says Bhatia. Once a player sees that a game is compromised, it ruins the fun element and may even deter them from entering the environment because of security fears. In addition, the influx of bots and other hacks has led to unfair advantages within some of the most loved games. What can blockchain game publishers do? Gaming studios must take the necessary steps to demonstrate their commitment to fair play. This is essential in the blockchain space as it continues to build trust with the traditional gaming communities. At MixMob, they leverage blockchain transparency to allow the community to participate in monitoring and reporting cheaters actively. “This community refereeing not only helps maintain fairness but also fosters a sense of responsibility and integrity among players, and it has worked great for us,” says Vieira. “Web3 game publishers must take decisive steps to limit cheating. The actions taken should vary based on the severity of the cheating incident. Potential measures include banning, suspending, or permanently removing players involved in unfair practices. Additionally, publishers can adopt proven strategies from traditional gaming, such as introducing randomness to some parts of the game to reduce predictability, which helps deter cheating,” he continues. Game developers who are keen to implement anti-cheat measures are often outplayed by hackers who have mastered the art of cheating and the avoidance of detection. Sure, developers can ban cheaters who are caught in the act or embed cheat detection software. However, this doesn't prevent cheating from occurring from the outset. The vulnerabilities have already been exploited by the time the developer has their hands on the activities. If, however, a game creator starts the development process using the Tashi Consensus Protocol, it has eliminated the act of cheating from the initial design stage. “Gamers rely on trust that game developers would have implemented the best practices to ensure cheaters are punished. That is why Tashi works to educate all its gaming partners on the novel ways blockchain solves cheating issues. We make our tech easy to use and guide all our partners who might come from the Web2 world and are new to using blockchain tech,” added Bhatia. While anti-cheat software like Riot Game's Vanguard or Activision Blizzard's Ricochet stands as the frontline defense, the second layer can utilize the transparency provided by blockchain technology to fight against bad actors. Implementing innovative approaches to game development, like the Tashi Dynamite Consensus Engine, pushes gaming to a new era of fair play and reclaims the values that true gamers want to see in the game development stage. In addition to fostering a culture of fair play, a paradigm shift is necessary in the approach to combating cheating in online gaming. By 2027, the number of users in the Online Games market is expected to amount to 1.2bn users. The projected revenue in the Online Games market worldwide is expected to reach US$27.97bn in 2024. Reclaiming the gaming sector for true gamers who value the entire gaming experience is crucial as blockchain games position themselves alongside some of the gaming giants.

Is It Game Over for Cheaters? Using Blockchain Technology to Eliminate Unwanted Gaming Behavior

At a time when decentralized finance seeks regulatory clarity, GRVT has launched a platform that bridges traditional compliance with blockchain innovation. The December 20, 2024 release of its Mainnet Alpha introduces a hybrid exchange that combines decentralized trading capabilities with centralized exchange features, achieving $15 million in transactions within its first hour of operation. This launch represents a shift in the DeFi landscape, where regulatory compliance and institutional participation converge with decentralized trading mechanisms. The trading activity comprised $8.7 million in Bitcoin transactions and $6.4 million in Ethereum trades. This volume emerged from a user base of 30,000 KYC-verified participants, representing a higher initial user count compared to industry counterparts at launch. The platform's institutional backing includes 55 clients and 17 market makers, who have committed to generate $4.2 billion in monthly trading volume. The current institutional trader roster includes QCP, Galaxy Trading Asia Limited, Ampersan, Arbelos, Amber Group, IMC, Flow Traders, Pulsar, and Selini. From a technical perspective, GRVT operates on a validium ZK Chain within the Elastic Network powered by ZKsync. This Layer 2 blockchain architecture enables Ethereum scaling while maintaining data privacy. The implementation delivers a processing capacity of 600,000 transactions per second through its hybrid model of off-chain order matching and on-chain settlements. The regulatory framework supporting GRVT includes a Class M Digital Asset Business License from the Bermuda Monetary Authority. This positions the platform as a regulated entity in the DEX space, though it operates independently from Singapore's Monetary Authority oversight. The platform introduces cross-chain functionality through Proxy Bridge, developed by XY Finance, enabling users to transfer assets between GRVT, CEX accounts, and crypto wallets. This implementation eliminates gas fees for cross-chain transactions from CEX to DEX. GRVT, established in 2022, enters the market as a hybrid derivatives platform. The launch represents a step toward creating a blockchain-based financial marketplace, focusing on institutional-grade infrastructure while maintaining accessibility for individual traders. For context, this development occurs during a period of increased regulatory scrutiny in the digital asset space, making GRVT's regulated status a notable factor in the evolving DeFi landscape. Users should note that cryptocurrency trading carries inherent risks, and GRVT's regulatory status varies by jurisdiction. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR At a time when decentralized finance seeks regulatory clarity, GRVT has launched a platform that bridges traditional compliance with blockchain innovation. The December 20, 2024 release of its Mainnet Alpha introduces a hybrid exchange that combines decentralized trading capabilities with centralized exchange features, achieving $15 million in transactions within its first hour of operation. GRVT This launch represents a shift in the DeFi landscape, where regulatory compliance and institutional participation converge with decentralized trading mechanisms. The trading activity comprised $8.7 million in Bitcoin transactions and $6.4 million in Ethereum trades. This volume emerged from a user base of 30,000 KYC-verified participants, representing a higher initial user count compared to industry counterparts at launch. The platform's institutional backing includes 55 clients and 17 market makers, who have committed to generate $4.2 billion in monthly trading volume. The current institutional trader roster includes QCP, Galaxy Trading Asia Limited, Ampersan, Arbelos, Amber Group, IMC, Flow Traders, Pulsar, and Selini. From a technical perspective, GRVT operates on a validium ZK Chain within the Elastic Network powered by ZKsync. This Layer 2 blockchain architecture enables Ethereum scaling while maintaining data privacy. The implementation delivers a processing capacity of 600,000 transactions per second through its hybrid model of off-chain order matching and on-chain settlements. The regulatory framework supporting GRVT includes a Class M Digital Asset Business License from the Bermuda Monetary Authority. This positions the platform as a regulated entity in the DEX space, though it operates independently from Singapore's Monetary Authority oversight. Class M Digital Asset Business License The platform introduces cross-chain functionality through Proxy Bridge, developed by XY Finance, enabling users to transfer assets between GRVT, CEX accounts, and crypto wallets. This implementation eliminates gas fees for cross-chain transactions from CEX to DEX. GRVT, established in 2022, enters the market as a hybrid derivatives platform. The launch represents a step toward creating a blockchain-based financial marketplace, focusing on institutional-grade infrastructure while maintaining accessibility for individual traders. For context, this development occurs during a period of increased regulatory scrutiny in the digital asset space, making GRVT's regulated status a notable factor in the evolving DeFi landscape. Users should note that cryptocurrency trading carries inherent risks, and GRVT's regulatory status varies by jurisdiction. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program . HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Vested Interest Disclosure: business blogging program business blogging program

GRVT Launches First Regulated DEX, Records $15M Trading Volume in Initial Hour

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