Top AI talents are getting a new way of paying back.
Over the past two years, there has been a renewed awareness of robbery in the field of AI, Silicon Valley's price tag for robbery is comparable to the transfer of sports stars, and the country's biggest factories offer a year-on-year paycheck.
Today, entrepreneurial AI companies are starting to bring another kind of power benefit to the forefront of talent competition.
Today, Face-to-Face Intelligence released its latest "Advanced Four" Top Talent Program, aimed primarily at outstanding interns, with the sincerity to fill in: Star Project as the core technology backbone, equal sharing of R&D resources with full-time employees, unlimited research borders... One of the most striking of these is that qualified top tech interns can be awarded upfront at their "graduation" estimates.
This means that these young technological minds have acquired the right to a VC that may have belonged only to the top class in the past during their school years, and that they share the company's long-term growth results with time.
For top-level technicians, any measurement of the future must be based on the power of certainty. Therefore, what options really pose is not a simple question of income, but a longer-term question of judgment:
What is the long-term stake in front-end intelligence, and does it have strong enough technical underlying logic and performance capabilities?
The growth trajectory of front-end intelligence over the past year provides a perspective on this proposition.
First look at the numbers, then look at the path.
If you're a big model researcher or engineer choosing your next stop, start with a few hard numbers:
A set of figures comes from the open-source ecosystem: As of August, the MiniCPM series has been downloaded over 44 million times globally, with over 150,000 GitHub stars.
Another set of figures comes from real deployments: as of August this year, in the smartphone sector, the MiniCPM with Samsung flagship launched, which is the first large model on the domestic side to enter the global flagship product line of international headphone manufacturers; in the smartphone sector, the SuperMate has covered nearly 70 viable production scenarios, completed the production-level adaptation of five mainstream smartphone chips, and is expected to reach 300,000 car console systems by the end of 2026; in the body-smart sector, the related program has covered 6 major business-class tour scenarios, and is capable of identifying more than 30 routines, with a test success rate of more than 95%.
Over the past two years, the cost of training the cutting edge model has skyrocketed several times a year, high-quality content has been exhausted, and the industry consensus that "scale is justice" has created a visible rift.
At such nodes, a company that has been publicly betting on the "non-consensus" edge of the big model direction since 2023 has been increasing its valuation faster and faster. This explains at least one thing: the capital market has begun to price separately for "out-of-scale answers"; and, as the technical ceiling of Scaling Law has gradually skyrocketed, small-sized, high-intelligence models have begun to participate in the model pricing of the market.
Numbers are results, and more important than results is the path of generating numbers.
Many local victories.
Becoming the leading unicorn on the edge today
Face-to-face intelligence was the first large model company in the country to bet on "efficiency", emphasizing "efficiency", and is known in the industry as "DeepSeek in the edge area", which has grown rapidly over the past year due to its industry-leading technological awareness and judgment.
Overall, it's: the edge of the advance bet, the first "density law" with a solid open source.
As one of the earliest teams to engage in large-scale model technology training and R&D in the country, Facewall Intelligence made a consensus decision at the height of the "100 Models War" in 2023, when almost everyone in the industry was pursuing a large-scale cloud model of violent aesthetics.
This is not a temporary start-up, but a pioneering technological judgment of a long-term, deep-rooted model development:
In the age of large-scale parameters, the founders of the front-end intelligent team began to question the "efficiency" of large models. A related study published in 2021 by MoEfication shows that a compact large model is transformed into a rare model of equal parameters, and that only activating one to three percent of the parameters can maintain 95% performance. This is not the same as abandoning the large model, but moving the question from "how much more can the model do: how much better can the large model do under the given parameters, memory, power consumption and chip conditions?"
In 2024, face-to-face intelligence is the world's leading authority on the "Densing Law": Model intelligence density increases over time, reaching the parameters required for a specific level of intelligence, and shrinks by half every 3.5 months.
The technology, which has led the industry for at least two years, has been repeatedly cited by the industry since, as it ultimately supports everything that front-end smart today: the production of the MiniCPM "small artillery" series of models, the edge model of the smart terminal scene in cars and mobile phones and even robots, and the Chinese model in the Samsung Galaxy AI...
CEO Li Dahai likened this approach to "the light sculpting machine of the big model era": instead of seeking to create the largest model at once, he continued to create models of higher density, like chip systems.
Not only that, but it is also a firm believer in open-source technology. It has been continuously involved in building and contributing to the open-source community of Big Models since it started its OpenBMB community in 2021, making its self-development model's weight, code and training methods open to the community.
The spirit and contribution of face-to-face intelligence to open source reflects its equally open core to technological research, which is the ideal technology exploration country that countless talented young people look forward to.
The year 2026 is considered by many to be the anniversary of the arrival of end-to-end intelligence, and the year left behind a series of coordinates around "improving the density of model intelligence":
The language model MiniCPM5-2B was released on WAIC this year and, as of July 2026, is the world's number one under AA-Index list 4B, supporting mixed thinking with context on 512K and completing multi-chip adaptations.
The multi-mode model MiniCPM-V 4.6 surpassed Ali Qwen3.5-0.8B with Google's Gemma4-E2B-it, which topped the global scale with 1.3B parameters when it was released in May 2026. Behind it are two major technological innovations: rebuilding the ViT architecture, cutting the computation of image encoding in half; 4x and 16x mixed vision Token compression, allowing performance and speed to be switched on demand.
The MiniCPM-o 4.5 is the industry's first full-duplex full-duplex model that supports side-sight, side-listening, and initiative, breaking the "speaker-like" iterative interaction pattern in traditional human-machine interaction. It is one of only two full-duplex models under 10B in the country, ahead of well-known teams such as the Thinking Machine Lab in the United States.
The Intelligent MiniCPM-Robot series incorporates this capability into the physical world. The General VLA Model RobotManip uses 1.5B parameters to overcome the 3-4B model's operational performance, halves the flow-through reasoning costs, and supports a 1-minute in-person memory; the RobotTrack, in collaboration with Nanjing University, is the first tracking model in the industry to be deployed locally in a real-world network disruption environment.
The VoxCPM 2.0 voice model covers 30 mainstream languages and eight Chinese dialects around the world, supports sound design and cloning, pulls the audio to a video level of 48000Hz, and meets the demand for video games, animation, video, audiobooks, and other scenarios that really challenge sound quality.
This year, in addition to the edge model, the exploration of front-end technology in front-end intelligence has expanded into the area of intelligent evolution:
In May, FaceWall Intel released the world's first fully AI-written, zero-human code-intervention production-level model training framework, ForgeTrain, on the H100, 10 percent faster than the self-developed Megatron, complete runway pre-training on the Huawei elevator, and training the world's first MiniCPM5-1B with performance below 2B.
Next, ForgeStencil, released in August, enabled AI's ability to evolve beyond AI to a broader computing world. With the dual-agent closure of Kernel Agent (autonomous research, simulation) and App Agent (location hotspots, verification and integration into real applications), it automatically optimizes 100+ real industrial and scientific computing software within a week, improving R&D efficiency by about 100 times.
From the AI self-writing training framework to AI self-optimizing industrial software, both studies point to the same conclusion: the software's own production methods are being rewritten. Front-end intelligence calls this new paradigm the Forge Engineering curve. When the AI curve of casting tools becomes the AI curve of casting tools, the speed of intelligent evolution is no longer limited to the number and experience of human engineers, but grows with computing power.
From data governance, model architecture and training frameworks, to chip adaptation, terminal deployments and robotic applications, front-end intelligence tries to solve not the local performance problems of a single model, but a complete link: how to continuously train deployable, adaptable and usable models with fewer parameters and lower computational costs, and raise the limits of intelligence at a faster rate.
This also reveals a fact: that the front-end intelligence is not only continually introducing smaller models, but is continually expanding the boundaries of the problem around a technological pathway. This means that on a technological pathway that is far from over, a large number of critical problems remain open, and that new participants into cutting edge research become inevitable; while the resources and inputs of the front-end intelligence are continually encoded, the judgment will truly fall into the hands of those who conduct experiments to support the exploration of technological talents.
What else is in the plan besides options?
For top candidates, the scoreboard is the past, and the option only answers half the question: "Is this company not worth joining?" and the other half is the same real question: "What will I do when I join?"
That's the other half of the answer to "Forever Four".
In recent years, the AI talent market has been undergoing subtle psychological changes: more and more AI researchers are tired of playing the screwdriver role in large organizational machines; instead, they want to get closer to the core of technology and take on more responsibilities and roles.
From the perspective of open team histories, technological achievements and open source projects, it presents a different organisational ecosystem. The most striking feature of this ecosystem is the tendency of young researchers to prioritize resources, decision-making and freedom of research.
Front-facing intelligence has always emphasized that scientific experimental methodology is superior to cumulative force, coupled with a simple set of rules of rhetoric. When discussing technical issues, the most effective "rhetoric" is not rank and qualifications, but solid experimental data and technical judgment logic.
This allows talented young people to make the most of their imagination, which has not yet been constrained by inherent paradigms, and to have the opportunity to transform research thinking into models, tools, papers or product capabilities.
"Forward Four" is a double pledge: options are about returns, and the value you create comes back to you in the form of ownership, and organizational ecology is about opportunities, and it's really possible to create that value.
In the technical narrative of front-end intelligence, there is a constantly moving mainline called "density" that uses smaller parameters to achieve stronger performance and drive greater intelligence with fewer resources. While the "Advanced Four" top talent program is essentially the same set of methodologies replicated in organizations: the other side of the efficient big model, the efficient organization with higher talent density.
A 1.3B model can win multiple times its opponents; a young team with a high enough talent density can do the same.
For the top brains who want to explore the boundaries of intelligence themselves, this is perhaps the best platform to get rid of the established gravity and start their own interstellar spaceflight.
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