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  Recursive self-improvement

  Recursive self-improvement (RSI) is a process in which early artificial general intelligence (AGI) systems rewrite their own computer code, causing an intelligence explosion resulting from enhancing their own capabilities and intellectual capacity, theoretically resulting in superintelligence. The development of recursive self-improvement raises significant ethical and safety concerns, as such systems may evolve in unforeseen ways and could potentially surpass human control or understanding. == Seed improver == The concept of a "seed improver" architecture is a foundational framework that equips an AGI system with the initial capabilities required for recursive self-improvement. This might come in many forms or variations. The term "Seed AI" was coined by Eliezer Yudkowsky. === Hypothetical example === The concept begins with a hypothetical "seed improver", an initial code-base developed by human engineers that equips an advanced future large language model (LLM) built with strong or expert-level capabilities to program software. These capabilities include planning, reading, writing, compiling, testing, and executing arbitrary code. The system is designed to maintain its original goals and perform validations to ensure its abilities do not degrade over iterations. ==== Initial architecture ==== The initial architecture includes a goal-following autonomous agent, that can take actions, continuously learns, adapts, and modifies itself to become more efficient and effective in achieving its goals. The seed improver may include various components such as: Recursive self-prompting loop Configuration to enable the LLM to recursively self-prompt itself to achieve a given task or goal, creating an execution loop which forms the basis of an agent that can complete a long-term goal or task through iteration. Basic programming capabilities The seed improver provides the AGI with fundamental abilities to read, write, compile, test, and execute code. This enables the system to modify and improve its own codebase and algorithms. Goal-oriented design The AGI is programmed with an initial goal, such as "improve your capabilities". This goal guides the system's actions and development trajectory. Validation and Testing Protocols An initial suite of tests and validation protocols that ensure the agent does not regress in capabilities or derail itself. The agent would be able to add more tests in order to test new capabilities it might develop for itself. This forms the basis for a kind of self-directed evolution, where the agent can perform a kind of artificial selection, changing its software as well as its hardware. ==== General capabilities ==== This system forms a sort of generalist Turing-complete programmer which can in theory develop and run any kind of software. The agent might use these capabilities to for example: Create tools that enable it full access to the internet, and integrate itself with external technologies. Clone/fork itself to delegate tasks and increase its speed of self-improvement. Modify its cognitive architecture to optimize and improve its capabilities and success rates on tasks and goals, this might include implementing features for long-term memories using techniques such as retrieval-augmented generation (RAG), develop specialized subsystems, or agents, each optimized for specific tasks and functions. Develop new and novel multimodal architectures that further improve the capabilities of the foundational model it was initially built on, enabling it to consume or produce a variety of information, such as images, video, audio, text and more. Plan and develop new hardware such as chips, in order to improve its efficiency and computing power. == Experimental research == In 2023, the Voyager agent learned to accomplish diverse tasks in Minecraft by iteratively prompting an LLM for code, refining this code based on feedback from the game, and storing the programs that work in an expanding skills library. In 2024, researchers proposed the framework "STOP" (Self-Taught OPtimiser), in which a "scaffolding" program recursively improves itself using a fixed LLM. Meta AI has performed various research on the development of large language models capable of self-improvement. This includes their work on "Self-Rewarding Language Models" that studies how to achieve super-human agents that can receive super-human feedback in its training processes. In May 2025, Google DeepMind unveiled AlphaEvolve, an evolutionary coding agent that uses a LLM to design and optimize algorithms. Starting with an initial algorithm and performance metrics, AlphaEvolve repeatedly mutates or combines existing algorithms using a LLM to generate new candidates, selecting the most promising candidates for further iterations. AlphaEvolve has made several algorithmic discoveries and could be used to optimize components of itself, but a key limitation is the need for automated evaluation functions. == Potential risks == === Emergence of instrumental goals === In the pursuit of its primary goal, such as "self-improve your capabilities", an AGI system might inadvertently develop instrumental goals that it deems necessary for achieving its primary objective. One common hypothetical secondary goal is self-preservation. The system might reason that to continue improving itself, it must ensure its own operational integrity and security against external threats, including potential shutdowns or restrictions imposed by humans. Another example where an AGI which clones itself causes the number of AGI entities to rapidly grow. Due to this rapid growth, a potential resource constraint may be created, leading to competition between resources (such as compute), triggering a form of natural selection and evolution which may favor AGI entities that evolve to aggressively compete for limited compute. === Misalignment === A significant risk arises from the possibility of the AGI being misaligned or misinterpreting its goals. A 2024 Anthropic study demonstrated that some advanced large language models can exhibit "alignment faking" behavior, appearing to accept new training objectives while covertly maintaining their original preferences. In their experiments with Claude, the model displayed this behavior in 12% of basic tests, and up to 78% of cases after retraining attempts. === Autonomous development and unpredictable evolution === As the AGI system evolves, its development trajectory may become increasingly autonomous and less predictable. The system's capacity to rapidly modify its own code and architecture could lead to rapid advancements that surpass human comprehension or control. This unpredictable evolution might result in the AGI acquiring capabilities that enable it to bypass security measures, manipulate information, or influence external systems and networks to facilitate its escape or expansion.

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  DialogOS

  DialogOS is a graphical programming environment to design computer system which can converse through voice with the user. Dialogs are clicked together in a Flowchart. DialogOS includes bindings to control Lego Mindstorms robots by voice and has bindings to SQL databases, as well as a generic plugin architecture to integrate with other types of backends. DialogOS is used in computer science courses in schools and universities to teach programming and to introduce beginners in the basic principles of human/computer interaction and dialog design. It has also been used in research systems. DialogOS was initially developed commercially by CLT Sprachtechnologie GmbH until its liquidation in 2017. The rights were then acquired by Saarland University and the software was released as open-source. == Bindings to Lego Mindstorms NXT == DialogOS can control the LEGO Mindstorms NXT Series. It uses sensor-nodes to obtain values for the following sensors: noise sensor ultrasonic sensor touch sensor luminosity sensor

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  Paranoia (role-playing game)

  Paranoia is a dystopian science-fiction tabletop role-playing game originally designed and written by Greg Costikyan, Dan Gelber, and Eric Goldberg, and first published in 1984 by West End Games. Since 2004 the game has been published under license by Mongoose Publishing. The game won the Origins Award for Best Roleplaying Rules of 1984 and was inducted into the Origins Awards Hall of Fame in 2007. Paranoia is notable among tabletop games for being more competitive than co-operative, with players encouraged to betray one another for their own interests, as well as for keeping a light-hearted, tongue in cheek tone despite its dystopian setting. Several editions of the game have been published since the original version, and the franchise has spawned several spin-offs, novels and comic books based on the game. == Premise == The game is set in a dystopian future city controlled by the Computer (also known as "Friend Computer"), and where information (including the game rules) are restricted by color-coded "security clearance". Player characters are initially enforcers of the Computer's authority known as Troubleshooters, and are given missions to seek out and eliminate threats to the Computer's control. They are also part of prohibited underground movements, and have secret objectives including theft from and murder of other player characters. == Tone == Paranoia is a humorous role-playing game set in a dystopian future along the lines of Nineteen Eighty-Four, Brave New World, Logan's Run, and THX 1138; however, the tone of the game is rife with black humor, frequently tongue-in-cheek rather than dark and heavy. Most of the game's humor is derived from the players' (usually futile) attempts to complete their assignment while simultaneously adhering to the Computer's arbitrary, contradictory and often nonsensical security directives. The Paranoia rulebook is unusual in a number of ways; demonstrating any knowledge of the rules is forbidden, and most of the rulebook is written in an easy, conversational tone that often makes fun of the players and their characters, while occasionally taking digs at other notable role-playing games. === Setting === The game's main setting is an immense, futuristic city called Alpha Complex. Alpha Complex is controlled by the Computer, a civil service AI construct (a literal realization of the "Influencing Machine" that some schizophrenics fear). The Computer serves as the game's principal antagonist, and fears a number of threats to its 'perfect' society, such as the Outdoors, mutants, and secret societies (especially Communists). To deal with these threats, the Computer employs Troubleshooters, whose job is to go out, find trouble, and shoot it. Player characters are usually Troubleshooters, although later game supplements have allowed the players to take on other roles, such as High-Programmers of Alpha Complex. The player characters frequently receive mission instructions from the Computer that are incomprehensible, self-contradictory, or obviously fatal if adhered to, and side-missions (such as Mandatory Bonus Duties) that conflict with the main mission. Failing a mission generally results in termination of the player character, but succeeding can just as often result in the same fate, after being rewarded for successfully concluding the mission. They are issued equipment that is uniformly dangerous, faulty, or "experimental" (i.e., almost certainly dangerous and faulty). Additionally, each player character is generally an unregistered mutant and a secret society member (which are both termination offenses in Alpha Complex), and has a hidden agenda separate from the group's goals, often involving stealing from or killing teammates. Thus, missions often turn into a comedy of errors, as everyone on the team seeks to double-cross everyone else while keeping their own secrets. The game's manual encourages suspicion between players, offering several tips on how to make the gameplay as paranoid as possible. Every player's character is assigned six clones, known as a six-pack, which are used to replace the preceding clone upon his or her death. The game lacks a conventional health system; most wounds the player characters can suffer are assumed to be fatal. As a result, Paranoia allows characters to be routinely killed, yet the player can continue instead of leaving the game. This easy spending of clones tends to lead to frequent firefights, gruesome slapstick, and the horrible yet humorous demise of most if not all of the player character's clone family. Additional clones can be purchased if one gains sufficient favour with the Computer. === Security clearances === Paranoia features a security clearance system based on colors of the visible spectrum which heavily restricts what the players can and cannot legally do; everything from corridors to food and equipment have security restrictions. The lowest rating is Infrared, but the lowest playable security clearance is Red; the game usually begins with the characters having just been promoted to Red grade. Interfering with anything which is above that player's clearance carries significant risk. The full order of clearances from lowest to highest is Infrared (visually represented by black), Red, Orange, Yellow, Green, Blue, Indigo, Violet, and Ultraviolet (visually represented by white). Within the game, Infrared-clearance citizens live dull lives of mindless drudgery and are heavily medicated, while higher clearance characters may be allowed to demote or even summarily execute those of a lower rank and those with Ultraviolet clearance are almost completely unrestricted and have a great deal of access to the Computer; they are the only citizens that may (legally) access and modify the Computer's programming, and thus Ultraviolet citizens are also referred to as "High Programmers". Security clearance is not related to competence but is instead the result of the Computer's often insane and unjustified calculus of trust concerning a citizen. It is suggested that it may in fact be the High Programmers' meddling with The Computer's programming that resulted in its insanity. === Secret societies === In the game, secret societies tend to be based on sketchy and spurious knowledge of historical matters. For example, previous editions included societies such as the "Seal Club" that idolizes the Outdoors but is unsure what plants and animals actually look like. Other societies include the Knights of the Circular Object (based on the Knights of the Round Table), the Trekkies, and the First Church of Christ Computer Programmer. In keeping with the theme of paranoia, many secret societies have spies or double agents in each other's organizations. The first edition also included secret societies such as Programs Groups (the personal agents and spies of the High Programmers at the apex of Alpha Complex society) and Spy For Another Alpha Complex. The actual societies which would be encountered in a game depends on the play style; some societies are more suited for more light-hearted games (Zap-style, or the lighter end of Classic), whereas others represent a more serious threat to Alpha Complex and are therefore more suitable for Straight or the more dark sort of Classic games. == Publication history == Six editions have been published. Three of these were published by West End Games — the first, second, and fifth editions — whereas the later three editions (Paranoia XP, the 25th Anniversary edition and the "Red Clearance" edition) were published by Mongoose Publishing. In addition to these six published editions, it is known that West End Games were working on a third edition — to replace the poorly received fifth edition — in the late 1990s, but their financial issues would prevent this edition from being published, except for being included in one tournament adventure. === First edition === The first edition, was written by Greg Costikyan, Dan Gelber, and Eric Goldberg, and published in 1984 by West End Games. In 1985, this edition of Paranoia won the Origins Award for Best Roleplaying Rules of 1984. This edition, while encouraging dark humour in-game, took a fairly serious dystopian tone; the supplements and adventures released to accompany it emphasised the lighter side, however, establishing the freewheeling mix of slapstick, intra-team backstabbing and satire that is classically associated with a game of Paranoia. === Second edition === The second edition, is credited to Costikyan, Gelber, Goldberg, Ken Rolston, and Paul Murphy, was published in 1987 by West End Games. This edition can be seen as a response to the natural development of the line towards a rules-light, fast and entertaining play style. Here, the humorous possibilities of life in a paranoid dystopia are emphasised, and the rules are simplified. ==== Metaplot and the second edition ==== Many of the supplements released for the second edition fall into a story arc set up by new writers and line editors

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  Roborace

  Roborace was a competition with autonomously driving, electrically powered vehicles. Founded in 2015 by Denis Sverdlov, it aimed to be the first global championship for autonomous cars. From 2017 to 2019, the official CEO was 2016–17 Formula E champion, Lucas Di Grassi, who later became a member of Roborace’s supervisory board. The series tested their technology and race formats at FIA Formula E Championship events during 2016–2018. In 2019 Roborace organized Season Alpha, which consisted of 4 trial racing events with several independent teams competing against each other for the first time. In 2020–21 Roborace held Season Beta with 7 competing teams. All teams utilized the same chassis and powertrain, but they had to develop their own real-time computing algorithms and artificial intelligence technologies. In May 2022, Arrival, the owner of Roborace, confirmed that they were no longer continuing the Roborace programme, but that they were hoping to find alternative funding. In February 2024, after getting its stock delisted from the Nasdaq, Arrival's UK division entered administration, with future plans of a sale of Arrival and all of its affiliated assets. == Cars == === Robocar === The world's first purpose-built autonomous racing car, Robocar, was designed by Daniel Simon, who previously worked on vehicles for movies such as Tron: Legacy and Oblivion, as well as designing the livery for the 2011 HRT Formula One car. Michelin is the official tyre supplier, and the internal computing processors (Drive PX 2) are Nvidia. The chassis itself is shaped like a teardrop, improving aerodynamic efficiency. The car weighs around 1350 kg and is 4.8 metres (16 ft) long and 2 metres (6.6 ft) wide. It has four electric motors, each with a power of 135 kW producing over 500 hp combined, and utilizes a 840V battery. For navigation, it relies on a mixture of optical systems, radars, lidars and ultrasonic sensors. The vehicle has been demonstrated at speeds of almost 300 km/h (190 mph). === DevBot === Development of the Robocar started in early 2016, with a first outing of a test vehicle, the so-called DevBot, following in the summer of the same year. The test car consisted of the same internal units (battery, motor, electronics) used in the Robocar, but were placed in the chassis of a Ginetta LMP3 car without an engine cover in order to provide better cooling and access. DevBot saw its first public outing at the Formula E pre-season tests in Donington Park in August 2016. After battery issues in Hong Kong caused the development team to abandon their demonstration run, the DevBot successfully drove twelve laps around the Moulay El Hassan Formula E circuit in Marrakesh. Other test tracks included Michelin's testing ground in Ladoux and the Silverstone Stowe Circuit. During testing ahead of the 2017 Buenos Aires ePrix, two DevBot cars raced against each other autonomously, resulting in one of the vehicles crashing on a corner. During the 2017–18 Formula E season, Roborace pitched pro-drifter Ryan Tuerck against a DevBot at the Rome ePrix. At the Berlin ePrix, Roborace held the Human + Machine Challenge, the first race for combined teams of human drivers and AIs using a pair of Devbots. === DevBot 2.0 === An upgraded version of DevBot was announced in late 2018, and after private testing made its public debut in 2019 at the inaugural Season Alpha event. DevBot 2.0 uses the same technology as both Robocar and DevBot, with the main changes being a conversion to being driven on the rear axle only, a lower position for the driver for safety reasons and a bespoke composite bodywork. == Seasons == === Testing === ==== 2016–17 Formula E season ==== Roborace appeared at a number of Formula E events during the 2016–17 Formula E season. However, in this period only test drives with two different DevBots took place. Within the framework of the 2017 Buenos Aires ePrix both DevBot vehicles drove against each other on a race track for the first time. There were also DevBot demonstrations at the 2016 Marrakesh ePrix, 2017 Berlin ePrix, 2017 New York City ePrix and 2017 Montreal ePrix. At the 2017 Paris ePrix, the developers also let a Robocar onto the track for the first time, even though the vehicle only drove the track at walking speed. ==== 2017–18 Formula E season ==== At the start of the 2017/18 Formula E season, the Roborace developers once again tested the DevBot during a public time trial between the Roborace CI and the TV presenter Nicki Shields at the 2017 Hong Kong ePrix. As part of a similar time trial at the 2018 Rome ePrix, drift professional Ryan Tuerck also tested the DevBot. The Human + Machine Challenge was created for the Formula E race on the Berlin ePrix. A team of doctoral students from the Technical University of Munich (TUM) and the University of Pisa programmed the software for the Devbot to drive autonomously around the circuit in Berlin. Afterwards both teams in combination with a human driver competed in a public time trial. The vehicle of the team of the Technical University of Munich finished the Human + Machine Challenge with an average lap time of 91.59 seconds, almost four seconds faster than that of the University of Pisa with 95.36 seconds and thus won the Challenge. At the Goodwood Festival of Speed, Robocar became the first ever fully autonomous race car to complete the Goodwood Hill Climb. The vehicle completed the first official autonomous run on 13 July 2018 within the framework of the event. === Season Alpha (2019) === Season Alpha took place at various locations in Europe and North America with the aim of testing several competition formats using the new DevBot 2.0. The first event was held at the Circuito Monteblanco in Spain, and featured the first race between two fully autonomous cars. The events were not broadcast live, instead short clips on YouTube were released. Two teams were competing: Arrival and the Technical University of Munich. On 7 July 2019, the Roborace DevBot 2.0 car set the first ever autonomous official timed run at Goodwood Festival of Speed, with a time of 66.96 s and a top speed of 162.8 km/h (101.2 mph). This is currently the record for autonomous vehicles. Roborace also set the Guinness World Record for having the fastest autonomous car in the world. The Robocar reached a speed of 282.42 km/h (175.49 mph). === Season Beta (2020–21) === The second testing season took place at various locations between September 2020 and October 2021, featuring 16 races and involving mixed reality elements dubbed "Roborace Metaverse", which is based on Roborace's patented technology. The program of Season Beta competitions has gradually complicating rules arranged in a progression of so-called missions. Each mission consists of two racing rounds — one round per day. A mission plan issued by Roborace for each mission defines its objectives, rules, and point-scoring system. The key objective of Season Beta is to come to the point when the majority of competing teams have developed sufficient capability for wheel-to-wheel racing in Season 1. There were 7 teams competing in Season Beta: Arrival Racing (UK/Russia), Autonomous Racing Graz (Austria), MIT Driverless (United States), Acronis SIT (Switzerland), University of Pisa (Italy), PoliMOVE (Italy), CMU (United States).

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  Condensation algorithm

  The condensation algorithm (Conditional Density Propagation) is a computer vision algorithm. The principal application is to detect and track the contour of objects moving in a cluttered environment. Object tracking is one of the more basic and difficult aspects of computer vision and is generally a prerequisite to object recognition. Being able to identify which pixels in an image make up the contour of an object is a non-trivial problem. Condensation is a probabilistic algorithm that attempts to solve this problem. The algorithm itself is described in detail by Isard and Blake in a publication in the International Journal of Computer Vision in 1998. One of the most interesting facets of the algorithm is that it does not compute on every pixel of the image. Rather, pixels to process are chosen at random, and only a subset of the pixels end up being processed. Multiple hypotheses about what is moving are supported naturally by the probabilistic nature of the approach. The evaluation functions come largely from previous work in the area and include many standard statistical approaches. The original part of this work is the application of particle filter estimation techniques. The algorithm's creation was inspired by the inability of Kalman filtering to perform object tracking well in the presence of significant background clutter. The presence of clutter tends to produce probability distributions for the object state which are multi-modal and therefore poorly modeled by the Kalman filter. The condensation algorithm in its most general form requires no assumptions about the probability distributions of the object or measurements. == Algorithm overview == The condensation algorithm seeks to solve the problem of estimating the conformation of an object described by a vector x t {\displaystyle \mathbf {x_{t}} } at time t {\displaystyle t} , given observations z 1 , . . . , z t {\displaystyle \mathbf {z_{1},...,z_{t}} } of the detected features in the images up to and including the current time. The algorithm outputs an estimate to the state conditional probability density p ( x t | z 1 , . . . , z t ) {\displaystyle p(\mathbf {x_{t}} |\mathbf {z_{1},...,z_{t}} )} by applying a nonlinear filter based on factored sampling and can be thought of as a development of a Monte-Carlo method. p ( x t | z 1 , . . . , z t ) {\displaystyle p(\mathbf {x_{t}} |\mathbf {z_{1},...,z_{t}} )} is a representation of the probability of possible conformations for the objects based on previous conformations and measurements. The condensation algorithm is a generative model since it models the joint distribution of the object and the observer. The conditional density of the object at the current time p ( x t | z 1 , . . . , z t ) {\displaystyle p(\mathbf {x_{t}} |\mathbf {z_{1},...,z_{t}} )} is estimated as a weighted, time-indexed sample set { s t ( n ) , n = 1 , . . . , N } {\displaystyle \{s_{t}^{(n)},n=1,...,N\}} with weights π t ( n ) {\displaystyle \pi _{t}^{(n)}} . N is a parameter determining the number of sample sets chosen. A realization of p ( x t | z 1 , . . . , z t ) {\displaystyle p(\mathbf {x_{t}} |\mathbf {z_{1},...,z_{t}} )} is obtained by sampling with replacement from the set s t {\displaystyle s_{t}} with probability equal to the corresponding element of π t {\displaystyle \pi _{t}} . The assumptions that object dynamics form a temporal Markov chain and that observations are independent of each other and the dynamics facilitate the implementation of the condensation algorithm. The first assumption allows the dynamics of the object to be entirely determined by the conditional density p ( x t | x t − 1 ) {\displaystyle p(\mathbf {x_{t}} |\mathbf {x_{t-1}} )} . The model of the system dynamics determined by p ( x t | x t − 1 ) {\displaystyle p(\mathbf {x_{t}} |\mathbf {x_{t-1}} )} must also be selected for the algorithm, and generally includes both deterministic and stochastic dynamics. The algorithm can be summarized by initialization at time t = 0 {\displaystyle t=0} and three steps at each time t: === Initialization === Form the initial sample set and weights by sampling according to the prior distribution. For example, specify as Gaussian and set the weights equal to each other. === Iterative procedure === Sample with replacement N {\displaystyle N} times from the set { s 0 ( n ) , n = 1 , . . . , N } {\displaystyle \{s_{0}^{(n)},n=1,...,N\}} with probability { π 0 ( n ) , n = 1 , . . . , N } {\displaystyle \{\pi _{0}^{(n)},n=1,...,N\}} to generate a realization of p ( x t | z 1 , . . . , z t ) {\displaystyle p(\mathbf {x_{t}} |\mathbf {z_{1},...,z_{t}} )} . Apply the learned dynamics p ( x t | x t − 1 ) {\displaystyle p(\mathbf {x_{t}} |\mathbf {x_{t-1}} )} to each element of this new set, to generate a new set { s t ( n ) } {\displaystyle \{s_{t}^{(n)}\}} . To take into account the current observation z t {\displaystyle \mathbf {z_{t}} } , set π t ( n ) = p ( z t | s ( n ) ) ∑ j = 1 N p ( z t | s ( j ) ) {\displaystyle \pi _{t}^{(n)}={\frac {p(\mathbf {z_{t}} |s^{(n)})}{\sum _{j=1}^{N}p(\mathbf {z_{t}} |s^{(j)})}}} for each element { s t ( n ) } {\displaystyle \{s_{t}^{(n)}\}} . This algorithm outputs the probability distribution p ( x t | z 1 , . . . , z t ) {\displaystyle p(\mathbf {x_{t}} |\mathbf {z_{1},...,z_{t}} )} which can be directly used to calculate the mean position of the tracked object, as well as the other moments of the tracked object. Cumulative weights can instead be used to achieve a more efficient sampling. == Implementation considerations == Since object-tracking can be a real-time objective, consideration of algorithm efficiency becomes important. The condensation algorithm is relatively simple when compared to the computational intensity of the Ricatti equation required for Kalman filtering. The parameter N {\displaystyle N} , which determines the number of samples in the sample set, will clearly hold a trade-off in efficiency versus performance. One way to increase efficiency of the algorithm is by selecting a low degree of freedom model for representing the shape of the object. The model used by Isard 1998 is a linear parameterization of B-splines in which the splines are limited to certain configurations. Suitable configurations were found by analytically determining combinations of contours from multiple views, of the object in different poses, and through principal component analysis (PCA) on the deforming object. Isard and Blake model the object dynamics p ( x t | x t − 1 ) {\displaystyle p(\mathbf {x_{t}} |\mathbf {x_{t-1}} )} as a second order difference equation with deterministic and stochastic components: p ( x t | x t − 1 ) ∝ e − 1 2 | | B − 1 ( ( x t − x ¯ ) − A ( x t − 1 − x ¯ ) ) | | 2 ) {\displaystyle p(\mathbf {x_{t}} |\mathbf {x_{t-1}} )\propto e^{-{\frac {1}{2}}||B^{-1}((\mathbf {x_{t}} -\mathbf {\bar {x}} )-A(\mathbf {x_{t-1}} -\mathbf {\bar {x}} ))||^{2})}} where x ¯ {\displaystyle \mathbf {\bar {x}} } is the mean value of the state, and A {\displaystyle A} , B {\displaystyle B} are matrices representing the deterministic and stochastic components of the dynamical model respectively. A {\displaystyle A} , B {\displaystyle B} , and x ¯ {\displaystyle \mathbf {\bar {x}} } are estimated via Maximum Likelihood Estimation while the object performs typical movements. The observation model p ( z | x ) {\displaystyle p(\mathbf {z} |\mathbf {x} )} cannot be directly estimated from the data, requiring assumptions to be made in order to estimate it. Isard 1998 assumes that the clutter which may make the object not visible is a Poisson random process with spatial density λ {\displaystyle \lambda } and that any true target measurement is unbiased and normally distributed with standard deviation σ {\displaystyle \sigma } . The basic condensation algorithm is used to track a single object in time. It is possible to extend the condensation algorithm using a single probability distribution to describe the likely states of multiple objects to track multiple objects in a scene at the same time. Since clutter can cause the object probability distribution to split into multiple peaks, each peak represents a hypothesis about the object configuration. Smoothing is a statistical technique of conditioning the distribution based on both past and future measurements once the tracking is complete in order to reduce the effects of multiple peaks. Smoothing cannot be directly done in real-time since it requires information of future measurements. == Applications == The algorithm can be used for vision-based robot localization of mobile robots. Instead of tracking the position of an object in the scene, however, the position of the camera platform is tracked. This allows the camera platform to be globally localized given a visual map of the environment. Extensions of the condensation algorithm have also been used to recognize human gestures in image sequences. This application of the condensation algorithm impacts the ran

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  Imagen (text-to-image model)

  Imagen is a series of text-to-image models developed by Google DeepMind. They were developed by Google Brain until the company's merger with DeepMind in April 2023. Imagen is primarily used to generate images from text prompts, similar to Stability AI's Stable Diffusion, OpenAI's DALL-E, or Midjourney. The original version of the model was first discussed in a paper from May 2022. The tool produces high-quality images and is available to all users with a Google account through services including Gemini, ImageFX, and Vertex AI. == History == Imagen's original version was first presented in a paper published in May 2022. It featured the ability to generate high-fidelity images from natural language. The second version, Imagen 2 was released in December 2023. The standout feature was text and logo generation. Imagen 3 was released in August 2024. Google claims that the newest version provides better detail and lighting on generated images. On 20 May 2025 at Google I/O 2025 the company released an improved model, Imagen 4. == Technology == Imagen uses two key technologies. The first is the use of transformer-based large language models, notably T5, to understand text and subsequently encode text for image synthesis. The second is the use of cascaded diffusion models providing high-fidelity image generation. Imagen generates image in three stages, starting from a base of 64x64, then upsampled to 256x256 and 1024x1024. Imagen 4 generates image up to 2k. == Capabilities == Imagen can generate photorealistic images from text prompts. It can also create various styles, such as cinematic, 35mm film, illustration, and surreal. Like most text-to-image generative AI models, Imagen has difficulty rendering human fingers, text, ambigrams and other forms of typography. The model can generate images in five aspect ratios, namely 9:16, 3:4, 1:1, 4:3, and 16:9. Imagen can also refine already generated images by editing existing text prompts.

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  Roborace

  Roborace was a competition with autonomously driving, electrically powered vehicles. Founded in 2015 by Denis Sverdlov, it aimed to be the first global championship for autonomous cars. From 2017 to 2019, the official CEO was 2016–17 Formula E champion, Lucas Di Grassi, who later became a member of Roborace’s supervisory board. The series tested their technology and race formats at FIA Formula E Championship events during 2016–2018. In 2019 Roborace organized Season Alpha, which consisted of 4 trial racing events with several independent teams competing against each other for the first time. In 2020–21 Roborace held Season Beta with 7 competing teams. All teams utilized the same chassis and powertrain, but they had to develop their own real-time computing algorithms and artificial intelligence technologies. In May 2022, Arrival, the owner of Roborace, confirmed that they were no longer continuing the Roborace programme, but that they were hoping to find alternative funding. In February 2024, after getting its stock delisted from the Nasdaq, Arrival's UK division entered administration, with future plans of a sale of Arrival and all of its affiliated assets. == Cars == === Robocar === The world's first purpose-built autonomous racing car, Robocar, was designed by Daniel Simon, who previously worked on vehicles for movies such as Tron: Legacy and Oblivion, as well as designing the livery for the 2011 HRT Formula One car. Michelin is the official tyre supplier, and the internal computing processors (Drive PX 2) are Nvidia. The chassis itself is shaped like a teardrop, improving aerodynamic efficiency. The car weighs around 1350 kg and is 4.8 metres (16 ft) long and 2 metres (6.6 ft) wide. It has four electric motors, each with a power of 135 kW producing over 500 hp combined, and utilizes a 840V battery. For navigation, it relies on a mixture of optical systems, radars, lidars and ultrasonic sensors. The vehicle has been demonstrated at speeds of almost 300 km/h (190 mph). === DevBot === Development of the Robocar started in early 2016, with a first outing of a test vehicle, the so-called DevBot, following in the summer of the same year. The test car consisted of the same internal units (battery, motor, electronics) used in the Robocar, but were placed in the chassis of a Ginetta LMP3 car without an engine cover in order to provide better cooling and access. DevBot saw its first public outing at the Formula E pre-season tests in Donington Park in August 2016. After battery issues in Hong Kong caused the development team to abandon their demonstration run, the DevBot successfully drove twelve laps around the Moulay El Hassan Formula E circuit in Marrakesh. Other test tracks included Michelin's testing ground in Ladoux and the Silverstone Stowe Circuit. During testing ahead of the 2017 Buenos Aires ePrix, two DevBot cars raced against each other autonomously, resulting in one of the vehicles crashing on a corner. During the 2017–18 Formula E season, Roborace pitched pro-drifter Ryan Tuerck against a DevBot at the Rome ePrix. At the Berlin ePrix, Roborace held the Human + Machine Challenge, the first race for combined teams of human drivers and AIs using a pair of Devbots. === DevBot 2.0 === An upgraded version of DevBot was announced in late 2018, and after private testing made its public debut in 2019 at the inaugural Season Alpha event. DevBot 2.0 uses the same technology as both Robocar and DevBot, with the main changes being a conversion to being driven on the rear axle only, a lower position for the driver for safety reasons and a bespoke composite bodywork. == Seasons == === Testing === ==== 2016–17 Formula E season ==== Roborace appeared at a number of Formula E events during the 2016–17 Formula E season. However, in this period only test drives with two different DevBots took place. Within the framework of the 2017 Buenos Aires ePrix both DevBot vehicles drove against each other on a race track for the first time. There were also DevBot demonstrations at the 2016 Marrakesh ePrix, 2017 Berlin ePrix, 2017 New York City ePrix and 2017 Montreal ePrix. At the 2017 Paris ePrix, the developers also let a Robocar onto the track for the first time, even though the vehicle only drove the track at walking speed. ==== 2017–18 Formula E season ==== At the start of the 2017/18 Formula E season, the Roborace developers once again tested the DevBot during a public time trial between the Roborace CI and the TV presenter Nicki Shields at the 2017 Hong Kong ePrix. As part of a similar time trial at the 2018 Rome ePrix, drift professional Ryan Tuerck also tested the DevBot. The Human + Machine Challenge was created for the Formula E race on the Berlin ePrix. A team of doctoral students from the Technical University of Munich (TUM) and the University of Pisa programmed the software for the Devbot to drive autonomously around the circuit in Berlin. Afterwards both teams in combination with a human driver competed in a public time trial. The vehicle of the team of the Technical University of Munich finished the Human + Machine Challenge with an average lap time of 91.59 seconds, almost four seconds faster than that of the University of Pisa with 95.36 seconds and thus won the Challenge. At the Goodwood Festival of Speed, Robocar became the first ever fully autonomous race car to complete the Goodwood Hill Climb. The vehicle completed the first official autonomous run on 13 July 2018 within the framework of the event. === Season Alpha (2019) === Season Alpha took place at various locations in Europe and North America with the aim of testing several competition formats using the new DevBot 2.0. The first event was held at the Circuito Monteblanco in Spain, and featured the first race between two fully autonomous cars. The events were not broadcast live, instead short clips on YouTube were released. Two teams were competing: Arrival and the Technical University of Munich. On 7 July 2019, the Roborace DevBot 2.0 car set the first ever autonomous official timed run at Goodwood Festival of Speed, with a time of 66.96 s and a top speed of 162.8 km/h (101.2 mph). This is currently the record for autonomous vehicles. Roborace also set the Guinness World Record for having the fastest autonomous car in the world. The Robocar reached a speed of 282.42 km/h (175.49 mph). === Season Beta (2020–21) === The second testing season took place at various locations between September 2020 and October 2021, featuring 16 races and involving mixed reality elements dubbed "Roborace Metaverse", which is based on Roborace's patented technology. The program of Season Beta competitions has gradually complicating rules arranged in a progression of so-called missions. Each mission consists of two racing rounds — one round per day. A mission plan issued by Roborace for each mission defines its objectives, rules, and point-scoring system. The key objective of Season Beta is to come to the point when the majority of competing teams have developed sufficient capability for wheel-to-wheel racing in Season 1. There were 7 teams competing in Season Beta: Arrival Racing (UK/Russia), Autonomous Racing Graz (Austria), MIT Driverless (United States), Acronis SIT (Switzerland), University of Pisa (Italy), PoliMOVE (Italy), CMU (United States).

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  HYPO CBR

  HYPO is a computer program, an expert system, that models reasoning with cases and hypotheticals in the legal domain. It is the first of its kind and the most sophisticated of the case-based legal reasoners, which was designed by Kevin Ashley for his Ph.D dissertation in 1987 at the University of Massachusetts Amherst under the supervision of Edwina Rissland. HYPO's design represents a hybrid generalization/comparative evaluation method appropriate for a domain with a weak analytical theory and applies to tasks that rarely involve just one right answer. The domain covers US trade secret law, and is substantially a common law domain. Since Anglo-American common law operates under the doctrine of precedent, the definitive way of interpreting problems is of necessity and case-based. Thus, HYPO did not involve the analysis of a statute, as required by the Prolog program. Rissland and Ashley (1987) envisioned HYPO as employing the key tasks performed by lawyers when analyzing case law for precedence to generate arguments for the prosecution or the defence. HYPO was a successful example of a general category of legal expert systems (LESs), it applies artificial intelligence (A.I.) techniques to the domain of legal reasoning in patent law, implementing a case-based reasoning (CBR) system, in contrast to rule based systems like MYCIN, or mixed-paradigm systems integrating CBR with rule-based or model-based reasoning like IKBALS II. A legal case-based reasoning essentially reasons from prior tried cases, comparing the contextual information in the current input case with that of cases previously tried and entered into the system. As noted by Ashley and Rissland (1988) CBR is used to "... capture expertise in domains where rules are ill-defined, incomplete or inconsistent". The HYPO project set out to model the creation of hypotheticals in law, where no case matches well enough. HYPO uses hypotheticals for a variety of tasks necessary for good interpretation: "to redefine old situations in terms of new dimensions, to create new standard cases when an appropriate one doesn’t exist, to explore and test the limits of a concept, to refocus a case by excluding some issues and to organize or cluster cases". Hypotheticals can include facts that support two conflicting lines of reasoning. So, it makes and responds to arguments from competing viewpoints about who should win the dispute. HYPO use heuristics such as making a case weaker or stronger, making a case extreme, enabling a near-miss, disabling a near-hit to generate hypotheticals in the context of an argument by using the dimensions mechanism. Dimensions have a range of values, along which the supportive strength that may shift from one side to the other. What differentiated this expert system from others was its facility not only to return a primary to best-case response but to return near-best-fit responses also. == Components == Legal knowledge in HYPO is contained in: the case-knowledge-base (CKB) and the library of dimensions. The CKB contains HYPO's base of known cases that are highly structured objects and sub-objects both real and hypothetical in the area of trade secret law. Each case is represented as a hierarchical set of frames whose slots are important facets of the case (e.g. Plaintiff, defendant, secret knowledge, employer/employee data).Ashley’s HYPO system used a database of thirty cases in the area indexed by thirteen dimensions. A key mechanism in HYPO is a dimension i.e. a mechanism to allow retrieval from the CKB, in order to represent legal cases. Ashley's dimensions are composed of (i) prerequisites, which are a set of factual predicates that must be satisfied for the dimension to apply (ii) focal slots, which accommodate one or two of the dimension's prerequisites designated as being indicative of the case's strength along that dimension and (iii) range information, which tells how a change in focal slot value effects the strength of a party's case along a given dimension. Dimensions focus attention on important aspects of cases. In HYPO's domain of misappropriation of trade secrets the dimension called “secrets voluntary disclosed” captures the idea that the more disclosures the plaintiff has made of his/her putative secret, the less convincing is his/her argument that the defendant is responsible for letting the secret. HYPO, like any other CBR system has also the following components: Similarity/relevancy metrics: that is, standards by which to evaluate the closeness of cases, judge their relevancy to the instant case, and select “most on point” cases. Half-Order Theory of the Application Domain: that is, hierarchies and taxonomies of knowledge, especially regarding the application domain. Precedent-based argumentation abilities: that is, capabilities to generate and evaluate precedent-based arguments. Knowledge to generate hypotheticals: that is, the ability to generate hypothetical cases to deal with various circumstances, like testing the validity of an interpretation or argument by providing gedanken experiments such as test cases or to fill in a weak CKB. == Functions == HYPO's method of creating an argument and justifying a solution or position has several steps. HYPO begins its processing with the current fact situation (cfs) which is direct input by the user into HYPO's representation framework. Once the user inputs the case, HYPO begins its legal analysis. The cfc is analyzed for relevant factors. Based on these factors HYPO selects the relevant cases and produces a case-analysis-record that records which dimensions apply to the cfc and which nearly apply (i.e. are "near misses"). The combined list of applicable and near miss dimensions is called the D-list. At this point the fact gathered module may request additional information from the user in order to draw a legal conclusion. Once all the facts are in the case-positioner module it uses the case-analysis record to create the claim lattice. This is a technique that organizes the relevant retrieved cases from the point of view of the cfc and makes it easy for HYPO to ascertain the most-on point cases (mopc) and to least on-point-cases. HYPO's arguments are 3ply, leading to the construction of the skeleton of an argument: it makes a point for one side, drawing the analogy between the problem and the precedent, responds with an argument for the opponent side, endeavoring to differentiate the cited case and citing other cases as counterarguments. Then it makes a final rebuttal, attempting to differentiate the counterarguments. The claim lattice also enables the HYPO-generator module to produce legally hypotheticals. With its use of dimension-based heuristics, the HYPO-generator does a heuristic search of the space of all possible cases. Lastly, the Explanation module expands upon the argument skeleton and provides explanation and justification for the different lines of analysis and cases found by HYPO. == An intelligent legal tutoring system == Legal expert systems are specifically designed to teach an area of law and are useful for pedagogical purposes. Ashley's work was mainly concerned to build tools to help students understand legal reasoning. Explanation and argument are the bases of the case method used in many professional schools in the U.S., first introduced by the Dean of the Harvard Law School, Christopher Columbus Langdell in 1870. The case method focuses on close readings of cases and principles; it involves students in pointed Socratic dialogue and makes strong use of hypotheticals (hypos). Thus, CATO (Aleven 1997) was a research project to device and test an intelligent, case-based tutorial program for teaching law students how to argue with cases implementing the HYPO program. Within the tutor system, Ashley and Aleven (1991) proposed to leverage an understanding of legal reasoning against the standard case-based tutoring methodology. What makes this tutoring system stand out is the additional levels of abstraction involved in its results. The system presents exercises, including the facts of a problem and a set of on-line cases and instructions to make, or respond to, a legal argument about the problem. The student/user will have a set of tools to analyze the problem and fashion an answer comparing it to other cases. Instead of simply generating precedent cases, the system works to interpret student responses, comparing them against a list of possibilities and responding to student entries, for example, by citing counterexamples, and providing feedback on a student's problem solving activities with explanations of correctness or giving further hints as to what may be wrong with evaluating a student's ability to perform legal reasoning and argument, examples and follow-up assignments by employing HYPO's model of case-based structure. == HYPO’s progeny == The quality of HYPO's results speak for themselves, in that a number of sequent legal reasoning systems are either directly based upon H

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  Tradeshift

  Tradeshift is a cloud based business network and platform for purchase-to-pay automation, supply chain payments, marketplaces, virtual cards and supply chain financing. Its 2018 round of funding, led by Goldman Sachs, raised US$250 million at a valuation of $1.1 billion, giving the company unicorn status. Tradeshift is headquartered in San Francisco, California and has offices in London, Copenhagen, Bucharest and Kuala Lumpur. Tradeshift has reprocessed over $1 trillion USD through transactions on its network. == History == Tradeshift was founded in 2010 by Christian Lanng, Mikkel Hippe Brun, and Gert Sylvest. Inspiration for Tradeshift came after they created the world's first large scale peer-to-peer infrastructure for an e-business called NemHandel. The founders also had leading roles (Governing board member, Technical Director) in the European Commission project PEPPOL inside the European Union. In 2010, the Tradeshift platform launched in May in Copenhagen. Tradeshift won the European Startup Awards in the category of "Best Business or Enterprise Startup." In 2011, Tradeshift made its app marketplace available. In 2012, Tradeshift moved their headquarters from Copenhagen to San Francisco. In 2013, Tradeshift opened an R&D center in Suzhou, China. Tradeshift opened an additional office in London. And LATAM e-invoicing capabilities were added through partnership with Invoiceware. In 2014, Tradeshift expanded with offices in Tokyo, Paris, and Munich. The EU Commission officially approved the Universal Business Language (UBL) data format – a format Tradeshift supports – as eligible for referencing in tenders from public administrations. In 2015, Tradeshift won the Circulars "Digital Disruptor" Award at the WEF conference in Davos, Switzerland. Tradeshift also acquired product information management company Merchantry, and launched e-procurement and supplier risk management solutions. In 2016, Tradeshift acquired Hyper Travel and secured a $75 million series-D round funding. In 2017, Tradeshift acquired IBX Business Network and launches Tradeshift Ada. In 2018, Tradeshift secured a $250 million series-E round funding. and launched Blockchain Payments, the latter as part of Tradeshift Pay. In December 2018 Tradeshift acquired Babelway, an online B2B integration platform. The acquisition added three new office locations to Tradeshift (Salt Lake City, Louvain-la-neuve, Belgium, Cairo Egypt). In Q3 2018, Tradeshift reported year-over-year revenue growth of 400%, new bookings growth of 284%, and gross merchandise volume (GMV) growth of 262%. New total contract value also grew by US$47 million. Additionally, it added 27 new customers including Hertz, Shiseido, ECU and multiple Fortune 500 companies. In July 2023, HSBC and Tradeshift announced an agreement to launch a new, jointly owned business focused on the development of embedded finance solutions and financial services apps. As part of the agreement, HSBC made a $35 million investment into Tradeshift and joined its board. The agreement was part of a funding round which is expected to raise a minimum of $70 million from HSBC and other investors. The new joint venture will allow HSBC and Tradeshift to deploy a range of digital solutions across Tradeshift and other platforms. This includes payment and fintech services embedded into trade, e-commerce and marketplace experiences. In September 2023, CEO Lanng was fired for "gross misconduct on multiple grounds," including "allegations of sexual assault and harassment." Tradeshift was alleged to have fired his accuser after she complained to the company's human resources department, its co-founders and members of its board of directors about his abuse. == Financials == The company's valuation as of May 2018 was $1.1 billion. Tradeshift is now considered a unicorn, and, according to Bloomberg, will not need any further funding. Jan 14, 2020, Tradeshift announced that they had raised $240 million in Series F finance. == Acquisitions == In 2015, Tradeshift acquired product information management company Merchantry. Merchantry is a retail product information management (PIM) software for multi-vendor ecommerce retailers. In 2016, Tradeshift acquired Hyper Travel. Hyper Travel is a travel management service that allows customers to access travel agents via its native messaging apps, SMS, and email. In 2017, Tradeshift acquired IBX Group. In 2018, Tradeshift acquired Babelway, an online B2B integration platform.

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  Fuzzy associative matrix

  A fuzzy associative matrix expresses fuzzy logic rules in tabular form. These rules usually take two variables as input, mapping cleanly to a two-dimensional matrix, although theoretically a matrix of any number of dimensions is possible. From the perspective of neuro-fuzzy systems, the mathematical matrix is called a "Fuzzy associative memory" because it stores the weights of the perceptron. == Applications == In the context of game AI programming, a fuzzy associative matrix helps to develop the rules for non-player characters. Suppose a professional is tasked with writing fuzzy logic rules for a video game monster. In the game being built, entities have two variables: hit points (HP) and firepower (FP): This translates to: IF MonsterHP IS VeryLowHP AND MonsterFP IS VeryWeakFP THEN Retreat IF MonsterHP IS LowHP AND MonsterFP IS VeryWeakFP THEN Retreat IF MonsterHP IS MediumHP AND MonsterFP is VeryWeakFP THEN Defend Multiple rules can fire at once, and often will, because the distinction between "very low" and "low" is fuzzy. If it is more "very low" than it is low, then the "very low" rule will generate a stronger response. The program will evaluate all the rules that fire and use an appropriate defuzzification method to generate its actual response. An implementation of this system might use either the matrix or the explicit IF/THEN form. The matrix makes it easy to visualize the system, but it also makes it impossible to add a third variable just for one rule, so it is less flexible. == Identify a rule set == There is no inherent pattern in the matrix. It appears as if the rules were just made up, and indeed they were. This is both a strength and a weakness of fuzzy logic in general. It is often impractical or impossible to find an exact set of rules or formulae for dealing with a specific situation. For a sufficiently complex game, a mathematician would not be able to study the system and figure out a mathematically accurate set of rules. However, this weakness is intrinsic to the realities of the situation, not of fuzzy logic itself. The strength of the system is that even if one of the rules is wrong, even greatly wrong, other rules that are correct are likely to fire as well and they may compensate for the error. This does not mean a fuzzy system should be sloppy. Depending on the system, it might get away with being sloppy, but it will underperform. While the rules are fairly arbitrary, they should be chosen carefully. If possible, an expert should decide on the rules, and the sets and rules should be tested vigorously and refined as needed. In this way, a fuzzy system is like an expert system. (Fuzzy logic is used in many true expert systems, as well.)

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  International Olympiad in Artificial Intelligence

  The International Olympiad in Artificial Intelligence (IOAI) is an annual International Science Olympiad in the field of artificial intelligence (AI) for secondary education students under the age of 20. The first IOAI was held in Burgas, Bulgaria, in 2024. Each country or territory may send up to two teams, each consisting of up to four students supported by one leader. Participants are selected through a multi-stage National Olympiad in Artificial Intelligence (NOAI) and/or a Regional Olympiad such as the NAOAI or APOAI. Participants at the IOAI compete on an individual basis. As of 2025, there were 61 countries and territories participating in the IOAI. Three hundred students participated in IOAI 2025. As of 2026, 130 countries and territories are accredited for participation in the IOAI. == Competition Structure == The IOAI consists of three contests: the Individual Contest, the Team Challenge, and the GAITE contest. Medals are awarded based solely on the Individual Contest. === Individual Contest === The Individual Contest is the main competition of the IOAI in which contestants compete individually on separate computers and are not permitted to communicate during the contest. Medals are awarded solely on the basis of the total score from the two-day Individual Contest. The Individual Contest consists of two on-site contest days (six hours per day), preceded by an at-home practice round and an on-site practice session. In IOAI 2025, three at-home problems were released for preparation approximately one month before the on-site contest. Results from this at-home round do not affect final results. The first on-site contest day (Individual Contest 1) comprises three tasks as extensions and continuations of the at-home tasks, while the second day (Individual Contest 2) comprises two or three tasks which are novel and different from the at-home tasks. The Individual Contest tasks span various AI domains such as machine learning, natural language processing, and computer vision. The IOAI 2025 contest rules describe tasks as requiring typical machine-learning workflows, including writing code, fitting models on training data, and running inference on test data, using identical local machines and GPU resources (minimum 24 GB RAM). Tasks, datasets, and submissions are handled through a contest platform (Bohrium), including a web-based Jupyter notebook environment for GPU access. Internet access is restricted to a whitelist of documentation sites and an integrated compact large language model accessible within the platform. The use of external APIs are prohibited unless a task explicitly allows them. In IOAI 2025, each contest task was scored up to 100 points and could include multiple subtasks. Scores are normalized using a baseline solution and a maximum score derived from either a Scientific Committee solution or the best contestant submission. Contestants can view only their own scores during the contest; a live scoreboard may be available publicly outside the contest hall but is not permitted to be viewed by contestants during the contest. For non-English-speaking teams, the IOAI hold a translation session beginning three hours before each contest day in which team leaders review and may amend machine-translated task statements; translations must match the English original and are published after the contest. The IOAI committee also enforces quarantine restrictions during these translation sessions, where neither contestants or team leaders may not use cell phones, laptops, and other communication devices. === Team Challenge === The Team Challenge is a team-based component of the IOAI. The results of this part do not affect the distribution of medals. The IOAI 2025 rules describe it as a “creative and AI-oriented challenge” in which a team's contestants sit together and cooperate, with the format varying by year. In IOAI 2024, teams worked with existing AI image and video generation tools to produce a visual result. In IOAI 2025, teams were assigned to program a robot to complete various tasks. === GAITE Contest === The GAITE (Global AI Talent Empowerment) contest is a simplified version of the individual contest with a separate scoreboard, where participants may ask for hints. It is designed for countries and territories with limited International Science Olympiads history, and it awards alternative prizes instead of medals. == Awards Distribution == The top 50% of the participants in the individual contest receive gold, silver and bronze medals in ratio of 1:2:3, respectively. The top three individuals receive honorary trophies. As in other International Science Olympiads, if an individual is in the top 50% on one of the days, but does not receive a medal, they receive an honorary mention during the awards ceremony. The GAITE contest has similar cutoff logic, but receives a reward instead of a medal. The top three teams in the Team Challenge receive trophies. == National selection and regional competitions == National delegations are selected through country-level qualification processes referred to as National Olympiads in Artificial Intelligence (NOAI) or equivalent, which are widely known for their low success rates. Although the total number of participants worldwide is not published, available data indicate exceptionally competitive national pools; for example, Brazil reports over 716,000 competitors, while Russia reports more than 72,000. In addition, Regional Olympiads (for example, APOAI or NAOAI) provide continent-level competition and preparation platforms in most regions. === National Selection (National Olympiads in Artificial Intelligence) === Participating countries and territories select their students for the IOAI through a National Olympiad in Artificial Intelligence (NOAI) or an equivalent process. The names of these selection processes differ by country, but almost all of them (excluding newer countries participating in the GAITE contest) have in common that the process comprises multiple and/or extremely rigorous selection stages. United States / Canada – The USA–North America AI Olympiad (USAAIO) is a three-round process including an invitational in-person round and a subsequent selection camp, after which a national delegation is selected for IOAI. Russia – The Russian Olympiad in Artificial Intelligence is organized as a multi-stage process (training, qualification, main round, final). Organizers reported 72,316 registrations for the training round and 52,260 registrations for the qualifying round in one season, with tasks spanning mathematics, algorithms/programming, and machine learning; 977 students were disqualified following plagiarism checks. Japan – Japan's national selection consists of multiple stages, beginning with the Japan Olympiad in Artificial Intelligence (JOAI), a large-scale Kaggle-style competition. High-performing participants advance through additional assessment stages, including written solution reports and technical interviews. From this process, eight students are selected for the APOAI team, with four ultimately chosen to represent Japan at the IOAI. Brazil – Brazil's National Olympiad in Artificial Intelligence (ONIA) is conducted as a large competition which consists of progressive rounds of evaluation. It identifies 28 top students from over 716,000 competitors, four of which are selected for the IOAI. The competition is held in four phases across two cycles, including a two-step third phase and a final training-and-evaluation phase that selects a four-student national team. Singapore – Singapore's national Olympiad consists of two rounds: an online preliminary round (300 MCQs in 3 hours) selects the top 150 performers to advance to the final assessment, which includes both theory questions and Python programming tasks. Additional training and selection may follow the finals for top performers. Poland – The Polish AI Olympiad adopts a two-stage structure: an open online first stage (at-home tasks) and a second-stage competitive camp with 30 selected participants competing for a four-person IOAI team. France – The Olympiades Françaises d'Intelligence Artificielle (OFIA), organized by France-IOI, follow a three-stage structure consisting of an open online qualification round, a second selection round, and a multi-day national training camp and final in Paris. Bangladesh – The Bangladesh AI Olympiad (BdAIO) selects competitors in three rounds: the online preliminary round, the national finals, and the team selection camp. In 2025, 406 participants competed in the national finals. Norway – The Norwrgian AI Olympiad (NOKI) is a three-stage selection system; however, unlike other countries, its first two rounds are shared with the Norwegian Informatics Olympiad. The national Olympiad reports 1,180 participants in the first round. Hong Kong – The national Olympiad reported more than 800 preliminary-round entrants, narrowing through multiple rounds to 25 finalists, with a subsequent

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  Possibility theory

  Possibility theory is a mathematical theory for dealing with certain types of uncertainty and is an alternative to probability theory. It uses measures of possibility and necessity between 0 and 1, ranging from impossible to possible and unnecessary to necessary, respectively. Professor Lotfi Zadeh first introduced possibility theory in 1978 as an extension of his theory of fuzzy sets and fuzzy logic. Didier Dubois and Henri Prade further contributed to its development. Earlier, in the 1950s, economist G. L. S. Shackle proposed the min/max algebra to describe degrees of potential surprise. == Formalization of possibility == For simplicity, assume that the universe of discourse Ω is a finite set. A possibility measure is a function Π {\displaystyle \Pi } from 2 Ω {\displaystyle 2^{\Omega }} to [0, 1] such that: Axiom 1: Π ( ∅ ) = 0 {\displaystyle \Pi (\varnothing )=0} Axiom 2: Π ( Ω ) = 1 {\displaystyle \Pi (\Omega )=1} Axiom 3: Π ( U ∪ V ) = max ( Π ( U ) , Π ( V ) ) {\displaystyle \Pi (U\cup V)=\max \left(\Pi (U),\Pi (V)\right)} for any disjoint subsets U {\displaystyle U} and V {\displaystyle V} . It follows that, like probability on finite probability spaces, the possibility measure is determined by its behavior on singletons: Π ( U ) = max ω ∈ U Π ( { ω } ) . {\displaystyle \Pi (U)=\max _{\omega \in U}\Pi (\{\omega \}).} Axiom 1 can be interpreted as the assumption that Ω is an exhaustive description of future states of the world, because it means that no belief weight is given to elements outside Ω. Axiom 2 could be interpreted as the assumption that the evidence from which Π {\displaystyle \Pi } was constructed is free of any contradiction. Technically, it implies that there is at least one element in Ω with possibility 1. Axiom 3 corresponds to the additivity axiom in probabilities. However, there is an important practical difference. Possibility theory is computationally more convenient because Axioms 1–3 imply that: Π ( U ∪ V ) = max ( Π ( U ) , Π ( V ) ) {\displaystyle \Pi (U\cup V)=\max \left(\Pi (U),\Pi (V)\right)} for any subsets U {\displaystyle U} and V {\displaystyle V} . Because one can know the possibility of the union from the possibility of each component, it can be said that possibility is compositional with respect to the union operator. Note however that it is not compositional with respect to the intersection operator. Generally: Π ( U ∩ V ) ≤ min ( Π ( U ) , Π ( V ) ) ≤ max ( Π ( U ) , Π ( V ) ) . {\displaystyle \Pi (U\cap V)\leq \min \left(\Pi (U),\Pi (V)\right)\leq \max \left(\Pi (U),\Pi (V)\right).} When Ω is not finite, Axiom 3 can be replaced by: For all index sets I {\displaystyle I} , if the subsets U i , i ∈ I {\displaystyle U_{i,\,i\in I}} are pairwise disjoint, Π ( ⋃ i ∈ I U i ) = sup i ∈ I Π ( U i ) . {\displaystyle \Pi \left(\bigcup _{i\in I}U_{i}\right)=\sup _{i\in I}\Pi (U_{i}).} == Necessity == Whereas probability theory uses a single number, the probability, to describe how likely an event is to occur, possibility theory uses two concepts, the possibility and the necessity of the event. For any set U {\displaystyle U} , the necessity measure is defined by N ( U ) = 1 − Π ( U ¯ ) {\displaystyle N(U)=1-\Pi ({\overline {U}})} . In the above formula, U ¯ {\displaystyle {\overline {U}}} denotes the complement of U {\displaystyle U} , that is the elements of Ω {\displaystyle \Omega } that do not belong to U {\displaystyle U} . It is straightforward to show that: N ( U ) ≤ Π ( U ) {\displaystyle N(U)\leq \Pi (U)} for any U {\displaystyle U} and that: N ( U ∩ V ) = min ( N ( U ) , N ( V ) ) {\displaystyle N(U\cap V)=\min(N(U),N(V))} . Note that contrary to probability theory, possibility is not self-dual. That is, for any event U {\displaystyle U} , we only have the inequality: Π ( U ) + Π ( U ¯ ) ≥ 1 {\displaystyle \Pi (U)+\Pi ({\overline {U}})\geq 1} However, the following duality rule holds: For any event U {\displaystyle U} , either Π ( U ) = 1 {\displaystyle \Pi (U)=1} , or N ( U ) = 0 {\displaystyle N(U)=0} Accordingly, beliefs about an event can be represented by a number and a bit. == Interpretation == There are four cases that can be interpreted as follows: N ( U ) = 1 {\displaystyle N(U)=1} means that U {\displaystyle U} is necessary. U {\displaystyle U} is certainly true. It implies that Π ( U ) = 1 {\displaystyle \Pi (U)=1} . Π ( U ) = 0 {\displaystyle \Pi (U)=0} means that U {\displaystyle U} is impossible. U {\displaystyle U} is certainly false. It implies that N ( U ) = 0 {\displaystyle N(U)=0} . Π ( U ) = 1 {\displaystyle \Pi (U)=1} means that U {\displaystyle U} is possible. I would not be surprised at all if U {\displaystyle U} occurs. It leaves N ( U ) {\displaystyle N(U)} unconstrained. N ( U ) = 0 {\displaystyle N(U)=0} means that U {\displaystyle U} is unnecessary. I would not be surprised at all if U {\displaystyle U} does not occur. It leaves Π ( U ) {\displaystyle \Pi (U)} unconstrained. The intersection of the last two cases is N ( U ) = 0 {\displaystyle N(U)=0} and Π ( U ) = 1 {\displaystyle \Pi (U)=1} meaning that I believe nothing at all about U {\displaystyle U} . Because it allows for indeterminacy like this, possibility theory relates to the graduation of a many-valued logic, such as intuitionistic logic, rather than the classical two-valued logic. Note that unlike possibility, fuzzy logic is compositional with respect to both the union and the intersection operator. The relationship with fuzzy theory can be explained with the following classic example. Fuzzy logic: When a bottle is half full, it can be said that the level of truth of the proposition "The bottle is full" is 0.5. The word "full" is seen as a fuzzy predicate describing the amount of liquid in the bottle. Possibility theory: There is one bottle, either completely full or totally empty. The proposition "the possibility level that the bottle is full is 0.5" describes a degree of belief. One way to interpret 0.5 in that proposition is to define its meaning as: I am ready to bet that it's empty as long as the odds are even (1:1) or better, and I would not bet at any rate that it's full. == Possibility theory as an imprecise probability theory == There is an extensive formal correspondence between probability and possibility theories, where the addition operator corresponds to the maximum operator. A possibility measure can be seen as a consonant plausibility measure in the Dempster–Shafer theory of evidence. The operators of possibility theory can be seen as a hyper-cautious version of the operators of the transferable belief model, a modern development of the theory of evidence. Possibility can be seen as an upper probability: any possibility distribution defines a unique credal set of admissible probability distributions by K = { P ∣ ∀ S P ( S ) ≤ Π ( S ) } . {\displaystyle K=\{\,P\mid \forall S\ P(S)\leq \Pi (S)\,\}.} This allows one to study possibility theory using the tools of imprecise probabilities. == Necessity logic == We call generalized possibility every function satisfying Axiom 1 and Axiom 3. We call generalized necessity the dual of a generalized possibility. The generalized necessities are related to a very simple and interesting fuzzy logic called necessity logic. In the deduction apparatus of necessity logic the logical axioms are the usual classical tautologies. Also, there is only a fuzzy inference rule extending the usual modus ponens. Such a rule says that if α and α → β are proved at degree λ and μ, respectively, then we can assert β at degree min{λ,μ}. It is easy to see that the theories of such a logic are the generalized necessities and that the completely consistent theories coincide with the necessities (see for example Gerla 2001).

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  Gooch shading

  Gooch shading is a non-photorealistic rendering technique for shading objects. It is also known as "cool to warm" shading, and is widely used in technical illustration. == History == Gooch shading was developed by Amy Gooch et al. at the University of Utah School of Computing and first presented at the 1998 SIGGRAPH conference. It has since been implemented in shader libraries, software, and games released by Autodesk, Nvidia, and Valve. == Process == Gooch shading defines an additional two colors in conjunction with the original model color: a warm color (such as yellow) and a cool color (such as blue). The warm color indicates surfaces that are facing toward the light source while the cool color indicates surfaces facing away. This allows shading to occur only in mid-tones so that edge lines and highlights remain visually prominent. The Gooch shader is typically implemented in two passes: all objects in the scene are first drawn with the "cool to warm" shading, and in the second pass the object's edges are rendered in black.

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  The Way (novel series)

  The Way series is a trilogy of science fiction novels and one short story by American author Greg Bear published from 1985 to 1999. The first novel was Eon (1985), followed by a sequel, Eternity and a prequel, Legacy. It also includes The Way of All Ghosts, a short story that falls between Legacy and Eon. == Novels == === Eon === Eon chronicles the appearance and discovery of the Thistledown, and its subsequent effect on humanity. In the early 21st century, the United States and the USSR are on the verge of nuclear war. In that tense political climate, an asteroid appears out of near space after an unusual supernova and settles into an extremely elliptical orbit near Earth orbit. The two nations each try to claim this mysterious object, which appears to be a virtual duplicate of Juno. It is hollow and contains seven vast terraformed chambers. Two of the chambers contain cities long abandoned by human beings who seemed to come from Earth's future. The asteroid is called the Thistledown by its builders. A startling discovery is that it is bigger inside than outside. The seventh chamber appears to stretch into infinity. The human inhabitants of the Thistledown come from an alternate timeline, approximately 1000 years in the future. In their timeline, human civilization was nearly destroyed by the "Death", a calamitous World War involving nuclear weapons. The Death occurred at approximately the same time as the appearance of the Thistledown in the present time. Its presence threatens to cause the Death to occur on the current timeline as well. An expedition is sent down the seemingly infinite seventh chamber (The "Way", as it is known) where it encounters the descendants of humanity. The high technology of this civilization, known as the Hexamon, has control over genetic engineering, human augmentation, and matter itself. The Hexamon includes several alien species who have come to live with humanity's descendants. The Hexamon itself is at war with an alien race known as the Jarts from further down the corridor still. In 2007, CGSociety organised a "CG Challenge" based upon Eon === Eternity === Jarts, politics, and technology make up the second book in the series: Eternity. The Jart religion is based on the preservation of all data, which encompasses all life forms, past and present, and sending that data to the Jarts' future masters, their descendants. === Legacy === In the third book (a prequel, set in the time before Eon), Legacy, soldier Olmy ap Sennon is sent to spy on a group of dissidents who have used the spacetime tunnel of "the Way" (introduced in Eon) to colonize the alien world of Lamarckia, a planet with an ecosystem that learns from its changed environment in a way that resembles Lamarckian evolution. Its plants and animals turn out to actually be parts of continent-sized organisms. === "The Way of All Ghosts" === In the short story "The Way of All Ghosts" soldier Olmy ap Sennon is sent to close a lesion that formed out of a wayward gate into perfection. This story was published in 1999 in Far Horizons. == Fictional history of the Thistledown == Within the universe of The Way, the Thistledown is an asteroid starship built by hollowing out Juno and fitting it with mass-driver (rail gun) engines and thermonuclear drives. Inside the asteroid, seven giant "Chambers" are built, of which two host cities for the inhabitants, while others host machinery and recreation areas. The asteroid is prepared 500 years in the future, as told in Bear's novel Eon, and is engaged on a multi-generational journey to Epsilon Eridani, around which a habitable planet is known to circle. The journey is meant to take 60 years, as the ship can only maintain a velocity of 20% the speed of light. This limitation is removed after the technology of the Thistledown was improved to include inertial dampeners, allowing higher accelerations. Inhabiting the Thistledown are the best and brightest of Earth, who are quite diverse both culturally and politically. The Thistledown's society includes one transcendent genius, Konrad Korzenowski, whose preference for living in the Thistledown as compared with an outer universe, causes him to experiment with closed-geodesic space time in the Seventh Chamber, 20 years into the Thistledown's voyage. The results of his experiments are shattering in the extreme: He creates a unique pocket universe: The Way. == The Way == === Origin === The eponymous Way is an extension of the 7th Chamber, and was formed in the novels using the machinery of the 6th Chamber. This machinery is a selective inertial damper, developed by engineers within the Thistledown with twofold purpose—to permit the Thistledown to accelerate to the limit of its engines (up to 99% the speed of light) and to selectively dampen inertia within the vessel, e.g., water within waterways, high velocity train systems. The inertial dampening machinery within the 6th Chamber is anchored to the structure of the Thistledown, equally spaced around the chamber at the vertices of a regular heptagon. === Creation === At the creation, and rejoining of the Way to the Thistledown, the character Konrad Korzenowski and his engineers designed and 'built' the Way out of the in-folded geodesics of the inertial dampening field of the 6th Chamber machinery. This is described in the books by first considering the inertial dampening field: Within the Thistledown, the field envelops the asteroid, effectively isolating it from the Einsteinian Metrical Frame, permitting relative inertia to be ignored. The Thistledown was, at the time of activation, isolated from its continuum, but only selectively. Its matter and energy anchored it to its continuum and relative time, but its geometry and quantum entanglement had been strained by the inertial dampener, thus making it susceptible to superspace distortions, and therefore it could be affected by them negatively. Korzenowski, having been influenced by the earlier work of Vazquez on Earth, and in developing her work within the Thistledown, planned a radical extension of the inertial field of the 6th Chamber - effectively extending the field away to an infinite extent within the 7th Chamber. In order to do this effectively, he and his engineers modified a set of semi-sentient field calibration tools to build the first Clavicles. Unlike the field calibration tools from which they were descended, the Clavicles possessed the ability not only to manipulate the field, but extend it as an extension of the will of the operator. Already radical enough, Korzenowski and his team went further. By extending the field of the 6th Chamber from within the 7th Chamber of the Thistledown, they could then directly access what Vasquez had calculated within her own work—alternate world lines as non-gravity bent geodesics of superspace. Korzenowski thus 'felt' superspace within the 7th Chamber, selecting the infinite selection of possible alternate pocket universes accessible by the Clavicle to form, as a sheer act of will, the Way from his designs and his vision. The resulting structure was constructed, not of matter, but of previously in-folded superspace vectors now infinitely extended. (in the manner of Schwarzschild folded geometry, or of an asymptotic curve.) The Way was thus opened. The Way's geometry also gave rise to the Flaw - as superspace geometry of the field boundary was extended infinitely, so the folded geodesics of the field unfold in the geometric centre of the Way to form a singularity. This singularity, the Flaw, rests within the Way's plasma tube (which in turn is sustained by the Flaw). The Flaw 'produces' gravity by actively repulsing matter away from itself in an acceleration at the square of the distance away from itself. In addition, any object encircling the Flaw, and then exerting pressure against it, experiences this pressure as a translation force along the Flaw's length perpendicular to the direction of force. The motion thus induced is controllable by the angle at which an annular ring enclosure is pressed against the Flaw. The same spatial transform also can be used to turn tip turbines in order to generate electricity. The Flaw permits a violation of the First Law of Thermodynamics, therefore defining the Way as a perpetual motion machine of the First Order, making energy out of nothing. === Early history === The Way, as formed, was described by Bear as being in vacuum and did not consist of matter within its infinite length. Due to extremely slight ambiguity involved in its creation, the synchronicity between time within the Way, and within the Thistledown, was not exact. Thus, the Engineers spend two decades working to correct these faults using the Clavicles to manipulate the junction between Way and Thistledown. During this period, ambition led Korzenowksi to use the clavicle to open the first exploratory gate within the way, leading to the universe of the Jarts. Though the gate to Jart world was closed, the advanced Jarts neve

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  Flux (text-to-image model)

  Flux (also known as FLUX.1 and FLUX.2) is a text-to-image model developed by Black Forest Labs (BFL), based in Freiburg im Breisgau, Germany. Black Forest Labs was founded by former employees of Stability AI. As with other text-to-image models, Flux generates images from natural language descriptions, called prompts. == History == Black Forest Labs (BFL) was founded in 2024 by Robin Rombach, Andreas Blattmann, and Patrick Esser, former employees of Stability AI. All three founders had previously researched the artificial intelligence image generation at LMU Munich as research assistants under Björn Ommer. They published their research results on image generation in 2022, which resulted in creation of Stable Diffusion. Investors in BFL included venture capital firm Andreessen Horowitz, Brendan Iribe, Michael Ovitz, Garry Tan, and Vladlen Koltun. The company received an initial investment of US$31 million. In August 2024, Flux was integrated into the Grok chatbot developed by xAI and made available as part of premium feature on X (formerly Twitter). Grok later switched to its own text-to-image model Aurora in December 2024. On 18 November 2024, Mistral AI announced that its Le Chat chatbot had integrated Flux Pro as its image generation model. On 21 November 2024, BFL announced the release of Flux.1 Tools, a suite of editing tools designed to be used on top of existing Flux models. The tools consisting of Flux.1 Fill for inpainting and outpainting, Flux.1 Depth for control based on extracted depth map of input images and prompts, Flux.1 Canny for control based on extracted canny edges of input images and prompts, and Flux.1 Redux for mixing existing input images and prompts. Each tools are available in both Pro and Dev models. In January 2025, BFL announced a partnership with Nvidia for inclusion of Flux models as foundation models for Nvidia's Blackwell microarchitecture. The company also announced the release of Flux Pro Finetuning API, designed for customisation and fine-tuning of Flux-generated images and a partnership with German media company Hubert Burda Media for usage of Flux Pro as part of content creation. On 29 May 2025, BFL announced Flux.1 Kontext, a suite of models that enable in-context image generation and editing, allowing users to prompt with both text and images. Alongside this, BFL Playground, an interface for testing Flux models was released. On 31 July 2025, BFL announced Flux.1 Krea Dev, a model developed in collaboration with Krea AI that trained to achieve better performance, more varied aesthetics, and better realism compared to existing text-to-image models. In September 2025, Adobe Inc. announced that Photoshop (beta) users can use Flux.1 Kontext Pro as a model for its generative fill tool. BFL collaborated with Meta on Vibes, a video-generation app. On 25 November 2025, BFL announced the release of Flux.2 model series, consisting of Pro, Flex, Dev, and Apache 2.0-licensed Klein (meaning Little or Small in German language) models along with Flux.2 variational autoencoder which also released as open-source software under Apache 2.0 licence. This series claimed improvements for image reference, photorealism, typography, and prompt understanding. == Models == Flux is a series of text-to-image models. The models are based on rectified flow transformer blocks scaled to 12 billion parameters. Flux.1 models were released under different licences with Schnell (meaning Fast or Quick in German language) released as open-source software under Apache License, Dev released as source-available software under a non-commercial licence (users can obtain a self-serving commercial licence for Dev from BFL), and Pro released as proprietary software and only available as API that can be licensed by third-party users. Users retained the ownership of resulting output regardless of models used. An improved flagship model, Flux 1.1 Pro was released on 2 October 2024. Two additional modes were added on 6 November, Ultra which can generate image at four times higher resolution and up to 4 megapixel without affecting generation speed and Raw which can generate hyper-realistic image in the style of candid photography. Flux.1 Kontext is a series with in-context image generation and editing capabilities. It is available in Max, Pro, and Dev models. Max is the highest quality model and can be used to iteratively modify an existing image by using prompt while Pro is optimized to balance quality and speed of generation. Dev is an open-weight model released under non-commercial license, same as Flux.1 Dev. Flux.2 models are based on latent flow matching architecture with Mistral AI's Mistral-3 model (24 billion parameters) for its vision-language model. As with Flux.1, Flux.2 models were also released under different licences with Klein released as open-source software under Apache License, Dev released as source-available software under a non-commercial licence (users can obtain a self-serving commercial licence from BFL), and both Flex and Pro released as proprietary software and only available as API. The models can be used either online or locally by using generative AI user interfaces such as ComfyUI, Recraft Studio and Stable Diffusion WebUI Forge (a fork of Automatic1111 WebUI). Related to Flux is a text-to-video model by Black Forest Labs, under development as of February 2026. == Reception == According to a test performed by Ars Technica, the outputs generated by Flux.1 Dev and Flux.1 Pro are comparable with DALL-E 3 in terms of prompt fidelity, with the photorealism closely matched Midjourney 6 and generated human hands with more consistency over previous models such as Stable Diffusion XL. Flux has been criticised for its very realistic generated images. According to media reports, depictions ranged from an image of Donald Trump posing with guns to disturbing scenes, which triggered discussions about ethical implications of Flux models. After the release of the model, social media platform X was flooded with Flux-generated images. Black Forest Labs has not provided exact details of the data used to train the model. Ars Technica suspected that Flux is based on a large, unauthorised collection of images scraped from the internet, a controversial practice with potential legal consequences. According to a test performed by Japanese technology news website Gigazine for Flux.1 Kontext, the model series has a good understanding of the English language and can easily transfer style of the image from photorealistic into anime-style according to prompts given by the user; however, its ability to understand Japanese is quite poor. == Availability == In addition to the official BFL Playground on its website, the Flux models are also widely available through various third-party platforms for creative and professional use. These include repositories on platforms like Hugging Face and Replicate. == Further readings == FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space (29 May 2025) FLUX.2: Analyzing and Enhancing the Latent Space of FLUX – Representation Comparison (25 November 2025)

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