The Indian Farmers Movement (Infarm) will organise a two-week long agricultural and educational fest at Kodenchery from December 26. A reception committee has also been constituted to make the ...

Infarm fest begins on December 26

Source: Clayton County Register INFARM, once Europe's largest vertical farming company, has filed for bankruptcy in the Netherlands as of September 19. The company ceased operations in key ...

Agrowatch Weekly #3 - InFarm Declares Bankruptcy, Syngenta & Corteva Announces Agtech Partnerships & Much More

Vertical farming company Infarm has been “declared bankrupt” in the Netherlands, according to official documents. The news comes after the company closed operations across its key markets in ...

Infarm’s Dutch branch declared bankrupt

Industry sources tell AgFunderNews that vertical farming startup InFarm is considering a move to Qatar in the wake of the bankruptcy.

Updated: Infarm ‘won’t be making any comments’ on rumored $50m raise from Middle East

Elsewhere, Tikehau Capital invested in a biologicals startup, vertical farming company Infarm was declared bankrupt, and agrifoodtech layoffs continued.

The Week in AgriFoodTech: Paine Schwartz launches $1.7bn agrifood fund, Cricket One unveils new plant, Infarm ‘declared bankrupt’

A line drawing of the Internet Archive headquarters building façade. An illustration of a magnifying glass. An illustration of a magnifying glass.

Archiveteam: Archivebot GO Pack 20230920013930_e31dd9d7

Well-funded major players such as AeroFarms, AppHarvest, Kalera and Future Crops have filed for bankruptcy; Upward Farms, Agricool, Fifth Season and Glowfarms have ended all operations, and InFarm and Iron Ox have made swingeing staff cuts. And just this ...

How vertical farms are reaching new heights

Plans call for 80 Acres to open another production farm in Georgia, and more locations are upcoming, Kroger reported. The vertical grower’s latest farm, in Florence, Kentucky, got under way in December and is expected to supply roughly 40 million servings of produce annually.

Kroger to ramp up distribution with vertical grower 80 Acres Farms

More businesses cropped up in the following years, with major players such as Infarm and AeroFarms securing hundreds of millions in funding over the next decade. With the help of sustainable systems such as hydroponics, as well as artificial intelligence ...

Robotic Bees Could Support Vertical Farms Today and Astronauts Tomorrow

InFarm

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An Adventure Amidst the Industrial Revolution

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An Inquiry into the Nature and Causes of the Wealth of Nations: Book III - Chapter III

The Monster Hunter series has found massive success in recent years. The latest release from Capcom, Monster Hunter Rise, has become part of the company’s . This feat is only more impressive when considering the game only released on one platform so far, the Nintendo Switch. With MH Rise set to release on PC by next year, the future of the series is all but guaranteed to be bright. top 10 best-selling games At least, that’s what the optimists would tell you. On the other hand, Monster Hunter Rise has garnered its fair share of criticisms from players, especially long-time fans of the series. The main problem, for them, isn’t that something is wrong with the game itself - it’s about how it compares to other Monster Hunter games. As someone who’s been following and playing Monster Hunter Rise since its release, I’ve noted more than a few of these complaints. The most common ones I’ve seen involve the game lacking content or being too easy - these complaints even made their way into official reviews of the game, such as the one made by . They might not be the most popular opinions of the game, but they were widespread enough to reach major outlets; I wouldn’t be surprised if at least one person took those opinions to heart. IGN France I consider myself a fan of the series, so I delved into Monster Hunter in order to see just how well-founded those complaints were. Rise’s very development is different from most other Monster Hunter games, and there’s definitely a fair share of differences between it and previous titles. For the sake of this article, I’ll be focusing mainly on Rise, but I’ll mention previous titles in the series as I go along, such as Monster Hunter 4, Monster Hunter Generations Ultimate, and Monster Hunter: World. To start with, I’ll be discussing Rise’s content, and whether it really is as lacking as some describe. Monster Variety Across the Series Image via official press release As the name implies, the biggest focus of the Monster Hunter series is...well, the monsters. They provide the challenge of the games, and beating them down is the only way you can really progress. Having a wide variety of monsters can provide a lot of substantial time within these games, but the numbers might not be as impressive if you look closely enough. Monster Hunter 4 Monster Hunter 4 has a roster of over 50 monsters. In comparison, Rise only has 46, including the special ‘Apex’ monsters featured in rampages. This seems to be a spot where 4 has Rise beat...at least, at first. Many of the different monsters in the older title are actually just alternate forms of existing monsters - a monster like a Yian Kut-Ku is considered different from a Blue Yian Kut-Ku, for example. If you cut down each game’s monster list to only include monsters that don’t share names, the roster is only one or two monsters away from being the same size. Monster Hunter World To add to this, Monster Hunter World, the most recent game before Rise, only has 36 monsters. It was the first game to redesign how Monster AI worked, but the roster pales in comparison to Rise - which carries over those changes and makes even more improvements. The Iceborne DLC available for Monster Hunter: World does bump up the roster considerably, increasing it to around 70, but that brings up an issue reflected in Monster Hunter Generations Ultimate. Monster Hunter Generations Ultimate Being the only other Monster Hunter title available on the Switch, Generations Ultimate boasts an impressive amount of monsters. Almost 100 monsters make up the count in that game, and even when discounting similar monsters, it still has the largest roster of any game in the series. There’s a good reason for this, however; it’s not actually the base game. Generations Ultimate is the “expanded” version of Generations, a title released on the 3DS. Many mainline games get their own expanded versions, bumping up not just the amount of monsters but also how much there is to do in the game. This is also why Iceborne provides such a large increase of monsters; that acts as Monster Hunter: World’s expansion. To put it simply, Monster Hunter Rise provides a number of unique monsters comparable to most normal games in the series. Following the recent pattern of releases, it seems likely that Rise will gain an expansion for itself - and that may very well increase its roster of monsters to heights greater than previous titles. As a side note, Monster Hunter Generations was conceived as a celebration of the series up to that point. With that in mind, it’s no wonder it has so many monsters! Content Beyond Monsters Image via unofficial Monster Hunter wiki Of course, while monsters might be a major focus of Monster Hunter, there is more to the series than meets the eye. Each game provides its own unique quests and features, with some of them changing the games pretty heavily. Generations Ultimate provides the most of these features, as one might expect, with a massive amount of quests and fights that are either new or brought over from previous titles. Without even regarding expansion titles, however, Monster Hunter: World and Monster Hunter 4 do boast a fair share of their own special features. In Monster Hunter 4, normal monsters could be ‘Frenzied’, a state they take on that increases their power and changes their behavior. This provided a further challenge to already existing monsters, as they were able to wield greater power and sometimes even different moves. World had a similar feature for monsters, where they would reach ‘Tempered’ states. Fighting these monsters would lead to increased rewards and even rare materials you couldn’t find otherwise. Rise doesn’t have anything similar to that...but in the end, this may not actually matter. Rise has its own special features, with one heavily-advertised feature being Rampage Quests. In these quests, you play through a defense mission, repelling monsters with weapons and installations before they can break into your village. At higher levels, this will give you rare materials and rewards needed to farm for charms, which provide some of the best gear in the game. It can get repetitive after a while, and you’ll be facing a lot of the same monsters in order to obtain that gear...but that’s not very different from how it worked in World, where only high-level monsters gave players the best materials. In fact, it’s even more freeform in Rise, as players have the option of keeping those materials from the rampage; instead, they can use lower-value materials from regular monsters to create charms. The reason why Rise seems to lack content is because of presentation. Instead of going after the same monsters in new states, hunters in Rise are encouraged to simply go after what they’ve fought before. This gives players a more ‘set your own goals’ mindset, rather than being tempted with special modifiers and unique materials that accomplish the same thing in the end. While others might find this to be a subjective opinion, I find that Rise’s content is perfectly comparable to - if not greater than - the content found in older games. There is more to this, however. The amount of content and incentives to play is different than how many hours one might put into a game. To understand why some players find themselves putting less hours into Rise than previous titles, it’s important to understand the differences between the two. From that, a point can be made about the final complaint against Monster Hunter Rise - difficulty. How Rise Differs from Other Monster Hunter Titles Image via GamesRadar Monster Hunter Rise features the same overall formula as other games in the series. You hunt monsters, you make gear from those monsters, and you use that gear to fight stronger monsters. Rise happens to bring over some extra changes from Monster Hunter: World, such as the ability to use items while moving and the lack of loading screens. With these changes, Rise plays almost like an alternative version of World - with some alterations, that is. Where Rise differs most is the addition of two features. The first is the Wirebug, which acts as an addition to the player’s normal moveset. Hunters are able to vault through the air and climb up walls using the Wirebug, and certain weapons use its abilities to perform counterattacks and powerful moves. The second addition is the Palamute, a dog-like companion you can ride to traverse areas with greater speed. These additions are where the complaints regarding difficulty stem from, I believe. Wirebugs can allow players to more quickly recover and react to monster blows, and Palamutes provide players an extremely fast way to travel within each locale. Much of the difficulty from older games came from how much slower players were; it could take a very long time to recover from attacks, use items, or even just walk across a map. Rise gives players strength and speed almost unheard of in the series at the time. As a result, monsters are beaten much sooner, danger is a lot less apparent, and the overall challenge simply isn’t as apparent. That is a sentiment I disagree with, however. After all, it wasn’t just the players that have gotten stronger and faster. How “Difficulty” Has Changed Throughout the Monster Hunter Series Image via Reno Gazette Journal Older Monster Hunter titles, especially those before Monster Hunter: World, did not have very great enemy AI. Monsters would struggle to turn their bodies, different species would share animations and entire movesets, and even the toughest monsters were only really difficult due to some very strange hitboxes in their attacks. World completely redesigned how monsters acted during fights, but many of them still had very limited - and similar - movesets. Monster Hunter Rise, on the other hand, takes this redesign even further. There are still obvious bits of similarity, but even the most familiar fights are taken in brand-new directions. A good example of this can be found in the monsters Rathian and Rathalos - they’re almost the exact same species, and they’ve shared most of their moves with each other throughout the series. Rise keeps the general moveset for Rathian the same, but Rathalos gains a whole plethora of attacks. It will use its flight to combine a plethora of attacks, from fireballs to claw swipes, in manners that previous Monster Hunter titles wouldn’t even dream of doing. This is where Rise’s difficulty comes from - not just how players are meant to fight against monsters, but the monsters themselves. Instead of learning animation transitions and specific hitbox locations, players are meant to learn how a monster actually fights. The actual challenge between each monster becomes more substantial, even if the game provides a lot of tools to make fights easier. You might not struggle as much as in the rest of the series, but if you do end up failing, it’s not because of any faulty mechanics or broken hitboxes; it’s because you simply need to improve your skills. The Shortcomings of Monster Hunter Rise Image via Official Monster Hunter Twitter My statements so far have been in defense of Rise, confronting each complaint against it and offering my own arguments as to why those complaints might be short-sighted. However, there is a strong case to be made in favor of those complaints when taking two things into account: time, and pricing. Rise is still a very recent title in the Monster Hunter series, and it only reached its current state after numerous game updates. Upon release, the game featured fewer monsters and didn’t even allow players to increase their rank. Such an issue is irrelevant now, but it brings the game’s update cycle into consideration, especially when referencing other titles. Older games feature Event Quests that were slowly added into each title across a manner of weeks or months. Rise features Event Quests, as well, but the number pales in comparison to those featured in World, Generations Ultimate, or even 4. Because of these games being older, their price is much lower, as well. As of right now, without any sales, Monster Hunter: World can be purchased on the Steam store alongside its expansion for about the same price as Monster Hunter Rise. Because of the included expansion, a newcomer to the series would be getting way more out of the purchase of World and Iceborne, including much more content and much greater challenge. Of course, this issue will eventually be resolved in the same way it was introduced - time. Rise continues to get new event quests every week, and it wouldn’t be a stretch to assume that the eventual release of an expansion for Rise would lower the base game’s price. Players who decide to wait for that time to come will get quite a lot of bang for their buck, and players who don’t wait still have some great things to look forward to. Final Verdict: What Rise Provides for Newcomers and Veterans Image via Official Monster Hunter Twitter I would argue that Monster Hunter Rise is most definitely worth grabbing, even when considering older games in the series. Newcomers to the franchise will find a less frustrating experience with Rise overall, with the numerous tools at their disposal sure to keep them interested throughout the whole game. And while the search for higher difficulty might not be completely answered for veterans, the new monsters - and alterations to existing ones - will provide a satisfying challenge for many long-time hunters. But don’t think you should consider Rise in place of older games. Each title offers its own unique experience, after all. If you have similar complaints against Rise even after playing through it, you may very well find a lot of merit in games like World, Generations Ultimate, or 4. In the end, it’s all still Monster Hunter - as long as you enjoy that, there will be plenty of hunts for you! Relevant Sources: Monster Hunter Rise Is About To Become Capcom's Best-Selling Single-Platform Game - Nintendo Life Monster Hunter Rise Test: The Price of Accessibility (ign.com) Monster Hunter: World Iceborne 15.10 Removes Event Quest Time Limits (gameskinny.com) MONSTER HUNTER RISE[Update Information] Monster Hunter: World on Steam (steampowered.com) MONSTER HUNTER RISE for Nintendo Switch - Nintendo Game Details

Is Monster Hunter Rise Better or Worse than Capcom’s Previous Installments?

High APY won’t last long, Defi + upgraded DAO is the true future The farming mania Have you enjoyed the yield farming in the last couple of days? The price of COMP has jumped from $30 to $340 in just a week. And by supplying token assets on Compound, the lenders(of some tokens) could earn 20%+ APY (not counting the incentives COMP yet). On Saturday, June 20, Compound has overtaken MakerDAO to become the largest decentralized finance protocol by total value locked according to data from DeFi Pulse. There is over $580M in cryptocurrency locked on Compound compared to $470M on MakerDAO. Source: The Block Behind the halo It happened so quickly that people on Twitter kept comparing it with the ICO scam. Not to judge for that yet, let’s understand the mechanism of how Compound works first. In a nutshell, it’s called “Liquidity Mining.” The more people borrow or lend, the more rewards(COMP token) they can “farm,” which increases the liquidity of the financing and the size of the balance sheet. It’s not different from that you save money to the bank and earn the interest rate (it’s very low). However, on Compound, borrowers are willing to pay a lot more, because they think they can make more money with what they borrow from you than what they have to pay you. How does it work? They are getting paid to borrow. When they borrow from you on Compound, they are getting COMP Token as a sort of cashback. The more they borrow, the more COMP Token they get. At this time, the cashback(big juicy subsidies) is actually worth more than the cost of the fees to borrow, which means people are incentivized to borrow just to “farm” the cashback. As liquidity miners are compensated for both lending and borrowing, the optimal strategy is to lend the highest interest rate asset, borrow as much as they can against their tokens and then add the remaining assets back into the lending pool. So, People are borrowing funds to “farm” COMP, then using the same funds out to lend, increasing their yield (COMP reward) even further. As a result, now they can earn 100% APY instead of the 0.01%-1.00% your banks are offering you, which is a 100–10,000X increase. What’s going to happen next? While almost everyone agrees that when something becomes too good to be true, it either won’t be sustainable or won’t exist at all. Since you can lend, borrow, then lend and borrow again, just like using leverages to do the arbitrage, it has the material risks of liquidation, especially the APY rate is fluctuating severely. And actually, it has already happened in some users’ accounts. Do you remember the Trans-Fee Mining(the more you trade, the more FT you can farm) introduced by Fcoin? Within a few weeks after launching the incentive program, FCoin’s 24-hour trading volume reached a whopping $17B on June 26, 2018 (3 times the total trading volume of other top 10 exchanges). However, the surge did not last long. As reported on CoinMarketCap, FCoin’s 24-hour trading volume dropped to $0.16B on August 12, 2018. It collapsed when people started to dump FT, and it was a speeding-up vicious cycle when people saw the FT price was dropping. So, same here, does Compound give its users a reason to hold COMP instead of selling it? There would be a tricky point that when people dump COMP token to secure their profit, the price of the COMP token keeps dropping and becomes unattractive or hard to compensate for the difference between the borrowing and lending cost, then people start to withdraw their token assets from the platform, and the tragedy begins. As an “insider” of the blockchain and crypto field, few of my friends borrow or lend from these Defi platforms, or holding COMP or MKR tokens. It might be some selection bias, but it reminds me that the size of this Defi market is actually limited to the level of speculative activities in the crypto industry. If there are no breakthrough use scenarios to bring in new users, the imagination that the existing user base will support a new giant will also be limited. It’s just another zero-sum game. Is Defi a Pseudo-proposition？ Definitely no. But I don’t think Defi will replace the Cefi. On the contrary, they will co-exist in the long term. Like the day and night, Defi and Cefi will complement each other to serve different business/people, and they will have interactions, like the stable coin/Fiat and KYC. Defi is still at its early stage. If it is only borrowing or lending to gain arbitrage, it will just become another alternative investment. How could it change the world as it intends to be? In the physical world, companies create value, and that’s what the stocks, bonds, or alternative investment products are pegging. In the decentralized world, distributed collaborations constitute the Decentralized Autonomous Organization (DAO). As the counterpart of the traditional company and as a reasonable logic, Defi should peg to the value creation entity- DAO. Different DAOs are set to accomplish various goals, so borrowing and lending is only a small part of the whole landscape. As an organization lives on the blockchain and internet, all the activities that the distributed community members carry out (like finish a task, conduct a transaction or share knowledge) will be recorded and tracked in almost real-time, which offers the edge for Defi to be different from traditional centralized finance. So, not as in the traditional finance scenario that the information asymmetry forced investors to gauge the past business data and bet on the future almost blindly, or invest in a project to try their luck, Defi investors or Defi service providers could scrutinize the “atomic” layer (in theory), with their investment or alternative investment pegged to the progress of the collaboration or the project in the DAO. Too complicated or not real? No worries, DAO has the capability to backbone that, and now almost all the Defi projects are leveraging the DAO’s governance structure. However, the DAO’s governance protocols need to be upgraded, voting and budget allocating (which most governance protocols are focusing on) are not enough, because that is only the beginning of the whole collaboration process in the DAO. A systematic framework should be built up to regulate the distributed collaborative relationship, to manage the collaboration, and to protect the interests of all the collaborators. With the framework, Defi investors or service providers can peg to the project’s real value, controlling their own destiny while also providing the liquidity for the project, which is a win-win. So, to conclude, Defi + DAO (with updated governance framework) will become the real organic field to farm. Contributed by Kevin Liu

Yield Farming Cannot Be The Organic Future of Blockchain

The One-Shot Generalization Paradox: Why Generative AI Struggles With New Information

የአንድ-ሾት አጠቃላይ አያዎ (ፓራዶክስ)፡ ለምን አመንጪ AI ከአዲስ መረጃ ጋር ይታገላል

Парадоксът на еднократното обобщение: Защо генеративният AI се бори с нова информация

Paradoks generalizacije u jednom slučaju: Zašto se generativna AI bori s novim informacijama

La paradoja de la generalización de una sola muestra: por qué la IA generativa tiene dificultades con la nueva información

ワンショット一般化パラドックス: 生成型 AI が新しい情報に苦戦する理由

Paradoks Generalisasi Satu Pukulan: Mengapa AI Generatif Bergelut Dengan Maklumat Baharu

Ihuriro rimwe-rusange Rusange Paradox: Impamvu Generator AI irwana namakuru mashya

Iyo One-Shot Generalization Paradox: Nei Generative AI Inetsekana Neruzivo Rutsva

ความขัดแย้งในการสรุปแบบครั้งเดียว: เหตุใด Generative AI จึงประสบปัญหาในการจัดการกับข้อมูลใหม่

The One-Shot Generalization Paradox: Ha yini AI ya Generative yi Lwisana na Rungula Lerintshwa

Nghịch lý khái quát hóa một lần: Tại sao AI tạo sinh lại gặp khó khăn với thông tin mới

一次性泛化悖论：生成式人工智能为何难以应对新信息

How to improve team members' performance using information about mental fatigue

Authors:
(1) Jianhui Pang, from the University of Macau, and work was done when Jianhui Pang and Fanghua Ye were interning at Tencent AI Lab (nlp2ct.pangjh3@gmail.com);
(2) Fanghua Ye, University College London, and work was done when Jianhui Pang and Fanghua Ye were interning at Tencent AI Lab (fanghua.ye.19@ucl.ac.uk);
(3) Derek F. Wong, University of Macau;
(4) Longyue Wang, Tencent AI Lab, and corresponding author. Table of Links Abstract and 1 Introduction 2 Related Work 3 Anchor-based Large Language Models 3.1 Background 3.2 Anchor-based Self-Attention Networks 3.3 Anchor-based Inference 4 Experiments and 4.1 Our Implementation 4.2 Data and Training Procedure 4.3 Evaluation 5 Results 6 Analysis 7 Conclusion, Limitations, Ethics Statement, and References A More Experimental Results B Data Settings 2 Related Work Our research is inspired by the recent investigation into the understanding of in-context learning (ICL) within LLMs by Wang et al. (2023). In their study, the authors delve into the underlying mechanisms of ICL, emphasizing the influence of label words in demonstration examples on information flow. They reveal that these label words serve as anchors, wherein semantic information converges into these anchors during inference, subsequently directing the LLMs’ final predictions. Motivated by their findings, our objective is to extend this feature to natural language modeling by guiding sequence information compression into manually designed anchor tokens, rather than solely relying on label words. This is crucial because natural language texts may not always contain an explicit label. The most relevant method to our approach in the existing literature is the learning to compress prompts with gist tokens (Mu et al., 2023). Their approach centers around compressing task-specific prompts by fine-tuning the model using the proposed gist masking, thereby enforcing prompt compression. However, there are several crucial divergences between our study and theirs. Unlike their focus on compressing a task prompt, our objective lies in training the LLM to condense sequence information into the anchor tokens. Consequently, our approach can be universally applied to a range of tasks without requiring task-specific training, a feature not shared by gist tokens, as the anchor tokens are seamlessly incorporated into the model’s language modeling. Furthermore, our anchor-based attention masks account for information compression within a sequence and information interaction between sequences, thus extending beyond the mere compression of task prompts. On the other hand, FlashAttention (Dao et al., 2022) and PagedAttention (Kwon et al., 2023) both present memory-efficient attention mechanisms for LLMs. While they focus on optimizing attention computation and subdividing attention processing, our proposed method offers a distinct approach that specifically targets the compression of sequence information into anchor tokens, making it orthogonal to these existing works. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Jianhui Pang, from the University of Macau, and work was done when Jianhui Pang and Fanghua Ye were interning at Tencent AI Lab (nlp2ct.pangjh3@gmail.com); (2) Fanghua Ye, University College London, and work was done when Jianhui Pang and Fanghua Ye were interning at Tencent AI Lab (fanghua.ye.19@ucl.ac.uk); (3) Derek F. Wong, University of Macau; (4) Longyue Wang, Tencent AI Lab, and corresponding author. Authors: Authors: (1) Jianhui Pang, from the University of Macau, and work was done when Jianhui Pang and Fanghua Ye were interning at Tencent AI Lab (nlp2ct.pangjh3@gmail.com); (2) Fanghua Ye, University College London, and work was done when Jianhui Pang and Fanghua Ye were interning at Tencent AI Lab (fanghua.ye.19@ucl.ac.uk); (3) Derek F. Wong, University of Macau; (4) Longyue Wang, Tencent AI Lab, and corresponding author. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Related Work 2 Related Work 3 Anchor-based Large Language Models 3.1 Background 3.1 Background 3.2 Anchor-based Self-Attention Networks 3.2 Anchor-based Self-Attention Networks 3.3 Anchor-based Inference 3.3 Anchor-based Inference 4 Experiments and 4.1 Our Implementation 4 Experiments and 4.1 Our Implementation 4.2 Data and Training Procedure 4.2 Data and Training Procedure 4.3 Evaluation 4.3 Evaluation 5 Results 5 Results 6 Analysis 6 Analysis 7 Conclusion, Limitations, Ethics Statement, and References 7 Conclusion, Limitations, Ethics Statement, and References A More Experimental Results A More Experimental Results B Data Settings B Data Settings 2 Related Work Our research is inspired by the recent investigation into the understanding of in-context learning (ICL) within LLMs by Wang et al. (2023). In their study, the authors delve into the underlying mechanisms of ICL, emphasizing the influence of label words in demonstration examples on information flow. They reveal that these label words serve as anchors, wherein semantic information converges into these anchors during inference, subsequently directing the LLMs’ final predictions. Motivated by their findings, our objective is to extend this feature to natural language modeling by guiding sequence information compression into manually designed anchor tokens, rather than solely relying on label words. This is crucial because natural language texts may not always contain an explicit label. The most relevant method to our approach in the existing literature is the learning to compress prompts with gist tokens (Mu et al., 2023). Their approach centers around compressing task-specific prompts by fine-tuning the model using the proposed gist masking, thereby enforcing prompt compression. However, there are several crucial divergences between our study and theirs. Unlike their focus on compressing a task prompt, our objective lies in training the LLM to condense sequence information into the anchor tokens. Consequently, our approach can be universally applied to a range of tasks without requiring task-specific training, a feature not shared by gist tokens, as the anchor tokens are seamlessly incorporated into the model’s language modeling. Furthermore, our anchor-based attention masks account for information compression within a sequence and information interaction between sequences, thus extending beyond the mere compression of task prompts. On the other hand, FlashAttention (Dao et al., 2022) and PagedAttention (Kwon et al., 2023) both present memory-efficient attention mechanisms for LLMs. While they focus on optimizing attention computation and subdividing attention processing, our proposed method offers a distinct approach that specifically targets the compression of sequence information into anchor tokens, making it orthogonal to these existing works. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

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