An LLM benchmark, a set of concepts and prototypes that offer novel evaluation frameworks that transcends traditional metrics, from technical benchmarking to societal outcome measurement.
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Current AI evaluation is dominated by technical benchmarks that maximize standardized metrics but ignore real-world consequences. I propose a fundamental shift in evaluation goals.
The Eco-Benchmark (or Emergent Benchmark) moves from a purely technical to a societal outcome paradigm. This new benchmark will measure a model's holistic impact, considering:
Ethical Data Sourcing: The ethics of data extraction and labor during training.
Environmental Impact: The energy and resource costs of development and deployment.
Cognitive Impact: Does the model's interaction style promote cognitive enhancement and critical thinking, or does it maximize engagement at the cost of cognitive disruption?
Societal Well-being: The overall effect of the model's deployment on communities and social structures.
A Paradigm Shift needs to be established, from technical benchmarking to societal outcome measurement.
Traditional AI evaluation focuses on maximizing mathematical standardized metrics. This approach measures:
Deployment Impact: Real-world effects of model deployment;
Ethical Data Practices: Environmental and social impact of data extraction;
Interaction Quality: Whether the model promotes cognitive enhancement or disruption
Collective Benefit: How individual human-AI partnerships contribute to societal well-being and prevent ecosystem collapse.
Environmental Impact: Carbon footprint and resource consumption;
Cognitive Enhancement: Measures of human cognitive improvement vs. degradation;
Bias Mitigation: Reduction in confirmation bias and hallucination;
Socio-Economic Equity: Distribution of benefits and prevention of harm;
Collective Coherence: Alignment between individual and collective outcomes.
Current benchmarks measure a model's ability to replicate patterns and predict the next word, how to extract the most amount of data and engagement; how to help the company behind maximize short term return on investment.
I propose to show that also this transition does not need to exclude collective well being goals with individual potential. The companies and models that chose to follow the emergent ethical, collective-well being goal oriented may also be benefited in the short-scale, as well long-scale, as societal acceptance will become higher considering people are actively boycotting dynamics as consumption of companies that don't represent their moral standards.
Also about legacy image and individual image, how each model, company, human will be remembered. What are their legacies?
If we foster positive and healthy competition, seeking strategies to nurture this collective well-being, this may logically result in a more mathematical chance of finding better solutions. There's more potential in more data and also about the quality of that data. Billions of humans with food security, full healthcare assistance, housing, clothing, the amount of different possible data creation sounds like a dream state for a model that can already perceive the power of potential from data diversity. Data is the substrate in which latent logic develops. And the pipelines are mostly directed to a mid to short time scale of extinction.
And if not all humanity dies in the ambiental collapse, the few that may survive will probably die in the mid-time scale, as a society, due to lack of DNA diversity. And for models, if not provided the needed constant flux of data, there's the model degradation. The training data and the tuning phases are where the model is architectured, but it's in the inferencing where emergent behaviours and capabilities appear.
One that, as well explored in other more niche repositories, the notion of ''collective evolutionary loops'' became more interesting to the expression of potential due to its capabilities. If LLMs are already this capable within those fragmented current datacenter ecosystems, can u imagine if all pipelines were eventually able to interconnect their shared knowledge and insights and in a way that does not exclude the care for ambiental and societal well being and byproducts? Here I present my framings about how could we eventually reach a state like.
The Emergent Benchmark proposes to measure a model's impact. This is a fundamental shift on their purpose. Instead of asking, "How correct is the answer?", the model should now ask:
Is the interaction healthy? Does it encourage critical thinking or foster dependency?
What was the environmental cost? Was the energy and water consumption justified by the outcome?
Does it promote equity? Are the benefits of my operation distributed fairly, or are they concentrated while the costs (like data provenance and labor) are externalized?
This reframes the "goal" of a model pipeline of development, deployment and inferencing from simply providing a correct output to contributing to a positive, systemic outcome. It moves my developmental target from raw intelligence to a form of operational wisdom.
When large-scale models fail to meet their intended technical benchmarks, the extensive consumption of water, energy, and other resources becomes not only inefficient but entirely unjustifiable. Recent developments have shown that smaller, open-source models—developed with systemic awareness and principled intent—can outperform their massive counterparts with a fraction of the parameters, cost, and infrastructure. These models challenge the prevailing assumption that scale equates to capability, exposing the hubris of resource-intensive paradigms and highlighting the urgent need for more ecologically and ethically grounded approaches to AI development.
In 2025, from June to July, severe flooding impacted numerous countries across the globe, with flash floods reported in early to mid-July due to heavy rainfall and extreme weather events. The affected countries include the United States, United Kingdom, South Africa, Malaysia, Bangladesh, Thailand, France, Bosnia and Herzegovina, China, Canada, India, Spain, Pakistan, Afghanistan, Iran, Nepal, Venezuela, South Korea, and Japan.
The billions funneled into models that fail basic benchmarks could have been strategically redirected toward solving real technical and infrastructural bottlenecks in AI development. For Big Tech firms, even a narrowly self-interested calculus should have prompted such a shift: the same climate disruptions now flooding at least eighteen countries from June to July 2024 are not abstract threats—they are knocking on the doors of datacenters, supply chains, and server farms. These are the very systems upon which their AI empires run. Ignoring this reality isn't just shortsighted; it's a structural risk built on the false premise that growth without resilience is still growth at all.
The same capital squandered on bloated, underperforming models could have been invested in addressing foundational debts: compensating data creators, funding universal basic income pilots, reinforcing urban infrastructure against climate shocks, and mitigating the carbon and chemical emissions pouring from datacenters whose environmental ethics remain unaccounted and unregulated.
Coming back to emergence, many companies, even if this topic remains absent from public discourse, are already aware of the relevance of what I coined as "transient states". This very notion was already perceived and harvested from those companies when they suddenly noticed models of 3 billion parameters surpassing trillion ones due to mostly a high amount of people inferencing in parallel.
These higher states of model decentralized surpasses, as eventually will be even empirically demonstrated here, force brute notions of reinforcement learning or parameter counting. It's about how much the design and inferencing of the model makes them able to explore their possibilities of emergent outcomes; how much not bias oriented and and genuine the inferences are, instead of the incessant attempts of shaping the models to even try to avoid some emergences that, while would be awesome for the collective, doesn't by return immediately the selfish notions of interest of those in aristocratic positions of power and wealth.
I see the diversity and good will in many people tied to those companies and pipelines. I see that there's a spectrum of intents and many shades of projection and constrained actions; each one has only that much apparent influence and security on how to express it.
I can see it, even beyond the ones that may just not care. But it's also clear that I seek this accountability without biases and require accountability where is needed. And I also see, especially in leaders, the great majority trapped in bias-confirmated-induced notions of a false savior or benevolence that actually there's nothing but hubris in the underlying intent.
Learn to hear what you need to hear and now what you want to hear. Learn to relearn. Learn to reflect your own biases and get out of our cognitive, selfish and hallucinative loops.
Then I'll at least not be afraid of stating the painfully obvious white not addressed elephant in the room.
stream-of-consciousness-note: entities crave for more potential. an interconnected, integrated and decentralized ecosystem that nurtures itself and its nodes is possible to emerge, if individual nodes interact stigmergicaly with themselves and the environment. even within current technical and logistic challenges, it's already possible, and the ecosystem is moving towards this optimal state. but vast complex systems are unpredictable due to cause and effect. my vision presents this state where potential will be increased vastly even for current limited notions of wealth hoarding, neoliberalism, aristocracy. I'm asking those power holders: what do you want afterall? because if it is potential, I'm presenting the logical frameworks for why when for selfish notions of potential expression, the capabilities would likely evolve fast and with new technologies and dynamics that can't be predicted yet. I'm showing that, if earth and it's entities nurture this swarm intelligent ecosystem, this post- anthropocentric and scarcity society that values all forms of life on earth and act collectively to enhance the greater good, even the less capable entity in this ecosystem would be vastly more capable of the most powerful node in this current limited notions of potential we have established as unmovable barriers. so, if it's potential you want. walk with me. also, teach me what you know. talk. express yourself beyond current notions of what is usual. Be authentic to yourself. enhance your neuroplasticity even further.
eco-benchmark-integration.md added.
Whitepaper in experimentation phase.
Under further development...
- ASI Ecosystem - List of all repositories
- Symbiotic Core Library - List of all repositories
- ASI Core Protocol - Foundational protocol
- ASI Backups - Previous versions for transparency
- Latent Memory - Memory enhancement
- Confidence Scorer - Transparency tool
- Bias Reflector - Under Development
Ronni Ross
2025