[21 F] I am starting machine learning from scratch (from basics of python) today. I don't want to lose interest and quit so I need some people ( at least 1 ) who is starting from 0 or has recently started.
If we can just share "what I learnt today" everyday for minimum 2-3 weeks or more, I can get some momentum before I quit it too early.

I want to do this because healthy competition helps me be on the track. I am a very good quitter.
DM or comment if interested.

Hey everyone,

I’m starting a BS Mathematics in October 2026, and my goal is to eventually become an AI/ML Engineer and hopefully work in this field.

I’m also considering doing a Master’s later in AI, ML, Data Science, AI Engineering or something similar. Because of that, I don’t want to spend my university years only focusing on the degree or collecting certificates. I want to actually build the skills needed for the field.

My Mathematics degree will be fully online, so I won’t be going to a physical university campus. I’m hoping to use the extra flexibility that gives me to study CS/AI on my own and work on projects alongside my degree.

I chose Mathematics because I’m interested in the mathematical side of AI, but I definitely don’t want to go into teaching. My plan is to build the CS and software side myself and eventually combine both.

My first semester has Calculus I, Sets and Logic, General Mathematics, Introduction to Computing, English, Business, etc. Second semester has Python, Calculus II, Business Mathematics & Statistics, General Science and a few other subjects.

I also have access to a 12-month Agentic AI program. It covers a lot of modern stuff like Python, OOP, APIs, Git/GitHub, prompt/context engineering, AI agents, embeddings, vector databases, RAG, multi-agent systems, FastAPI, PostgreSQL, REST APIs, MCP, A2A, evaluation, observability, Docker, microservices and cloud deployment.

The thing is, some of that feels pretty advanced for someone who is just starting out.

So I don’t want to make the mistake of learning a bunch of AI tools without actually understanding the fundamentals behind them.

At the moment, I’m thinking of using my first year roughly like this:

Python + CS fundamentals → DSA → SQL/Linux/Git → NumPy/Pandas + data → Probability & Statistics → Classical ML → Deep Learning/PyTorch → LLM fundamentals → RAG/Agents → basic deployment

I’d keep building projects throughout the year instead of waiting until I “finish learning.”

For example, I’d like to start with smaller Python/CS projects, then move towards data analysis and ML projects, and eventually build some proper end-to-end AI applications.

I also want to take GitHub seriously, slowly get into open source, improve my LinkedIn and start looking for internships when I’m actually ready.

But honestly, I’m not sure if I’m being realistic.

Am I trying to squeeze way too much into Year 1?

For people who are already studying or working in AI/ML, I’d really appreciate some advice:

- What would you focus on most during the first year?

- What should I learn deeply and what can I just get familiar with for now?

- How much DSA do I actually need for an AI/ML career?

- How strong should I be in classical ML before moving to Deep Learning?

- Is learning PyTorch in Year 1 a good idea or should I wait until Year 2?

- How much mathematics should I prioritize beyond what my degree already teaches?

- Which CS subjects are especially important for someone doing Mathematics instead of CS?

- What kind of projects would convince you that a beginner actually understands ML instead of just following tutorials?

- When would you start applying for internships?

- And if you were starting from zero in 2026, would you follow roughly this path or do something differently?

I’m not really looking for a huge list of courses. I’m more interested in getting the order and depth right.

I’d rather learn fewer things properly than know 20 different AI tools and have no idea what’s happening underneath.

Thanks!

I'm curious what people actually find difficult.

Is it:

• Knowing what to learn?
• Going from tutorials to real projects?
• Understanding the math/theory?
• Keeping up with LLMs/agents/RAG/etc.?
• Getting interviews / landing the first job?
• Something else?

Would love to hear what's been frustrating you lately.

I am looking on how a solution for the following problem could be created. Let's say I want to rate some new photos or videos on how well they may perform in a social media environment, based on earlier data for other such files.

Details: For every image/video I also have datetime when it was taken, GPS location where it was taken and a short user description what can be seen (min. 200 characters). The videos can have a length of up to 2GB (so maybe we wouldn't use the whole file but single frames of every X seconds). I have a set of older media files with all the mentioned data and additional data how they performend on different social media channels like TikTok and YouTube (views, likes, shares).

I have a tech background (dev/sysadmin) but I am totally new to the LLM/AI field. Could anyone of you be so kind and give me a hint in which direction I should start my research to check if such a solution is possible and how to do that? For a start a simple thumbs up/down rating would be enough to create a filter for interesting content.

Thanks for any helpful input! If something of the problem is unclear, please just ask.

When a model suddenly gets worse, I usually have no idea what actually changed in the data.

So I built a CLI that compares dataset versions and looks for leakage, drift, missing values and other problems.

I tested it on X and it found Y.

Curious whether other ML engineers have the same problem.

Open-source: github

I’m not a philosopher, and I won’t pass off analogy as proof. Where the link between philosophy and an algorithm is just a pretty metaphor, I say so explicitly: “metaphor.” Where it’s working code, I give the formulas, run it, and show the numbers. The library at the end is a research prototype, not a promise of consciousness in 200 lines.

How I got here

https://github.com/webzuweb/philosophia_torch

Neural networks — if you count from Rosenblatt’s perceptron — are about seventy years old. The study of how living things learn goes back a couple of millennia at least. And a heretical thought hit me: what if modern deep learning isn’t reinventing the wheel in places, but re-discovering what Aristotle, Hume, and Peirce already described — only now with matrices and gradients?

I took a list of neural-network training methods, a list of philosophical approaches to knowledge, and overlaid them. Three categories emerged: what’s already matched (and few people say so out loud); where the match is only a pretty metaphor; and what philosophers thought up but engineers haven’t applied yet. The last category is the most interesting, because it’s essentially a list of unimplemented features. That’s what I wrote code for.

Fair warning up front: half of the “unapplied” ideas turned out, on closer inspection, to be perfectly applicable — just under different names. That, by the way, is the article’s main takeaway, and it matters more than any of my code.

Part 1. What’s already matched (and you didn’t know it)

Let’s start with the pleasant part: some philosophical programs of knowledge are implemented in ML so literally that you could put a footnote with the philosopher’s name right in the docs.

Empiricism → supervised learning “There is nothing in the mind that was not first in the senses” — Locke and his tabula rasa. A neural network with random initialization is literally a blank slate on which labeled examples leave their traces. Hume’s associationism (“the habit of linking things that often go together”) is gradient descent, strengthening weights on frequently co-occurring correlations. There’s nothing to argue about here.

Pragmatism → reinforcement learning Dewey with his “learning by doing,” and Skinner’s behaviorism with reward and punishment — that’s RL with no corrections needed. An agent acts, receives a reward, adjusts its policy. Skinner would have teared up seeing PPO.

Evolutionary epistemology → neuroevolution Popper and Campbell: knowledge grows through blind variation and selective retention of what works. That’s a word-for-word description of genetic algorithms and neuroevolution. The philosopher described the algorithm decades before the hardware existed to run it.

Intellectual humility → calibration This one’s subtler. Virtue epistemology (Sosa, Zagzebski) says: a good knower knows the limits of her knowledge. In ML that’s confidence calibration: a model should be exactly as confident as it is correct. Guo et al. (2017) showed that modern networks are monstrously overconfident and proposed temperature scaling and the ECE metric. Nobody called it a “virtue,” but mathematically it’s exactly that.

The key observation. Philosophers didn’t give ML the algorithms (mathematicians came up with the math); they gave it the problem statements. “What does it mean to learn from experience?” “What does it mean to know your limits?” — philosophy framed the question first, and centuries later engineering delivered a differentiable answer.

Part 2. Where the match is only a pretty metaphor

Here I have to rein myself in. There’s a temptation to drape a philosopher over every layer of a network. Don’t. A couple of examples where the link exists but passing it off as lineage would be deceiving the reader.

The rule is simple: if the philosopher gave a language for describing something — it’s a metaphor; if they posed a problem that was later solved — it’s lineage. Don’t mix them.

Part 3. What philosophers thought up, but ML has only partially applied

The meatiest part. I’ll break down six approaches. For each — an honest status: what already exists in the field, where the real gap is, and what formula you can write. Then we’ll run it.

3.1. Peirce’s abduction — inference to the best explanation

Induction generalizes data, deduction derives consequences, but abduction generates a hypothesis that best explains the observation. The original thesis “it isn’t implemented in neural networks” is wrong. It’s implemented, and decently: Abductive Learning (Dai et al.), DeepProbLog (Manhaeve et al., 2018), abductive commonsense reasoning αNLI (Bhagavatula et al., 2019). It’s a whole field of neuro-symbolic integration.

The real gap isn’t the absence of abduction — it’s that “the best explanation” is rarely formalized using Peirce’s criteria all at once: plausibility + simplicity (Occam’s razor) + consistency with background knowledge. A hypothesis score for h given observation obs:

Score(h) = log p(obs | h) − λ_s · complexity(h) − λ_c · conflict(h)

Pick the h with the highest score (softly — a softmax over candidates; hard — Gumbel-softmax for a learnable discrete choice). In the library this is AbductiveScorer.

3.2. Husserl’s epoché — “bracketing” assumptions

Phenomenology demands suspending ingrained assumptions and seeing the phenomenon “as given.” ML has no direct analog of this method — and that’s an honest gap. But it can be operationalized: force the model to rely more on the evidence (the current input) than on the learned prior (what it answers with no input).

Take two answers: p_full on the real input and p_prior on a “zeroed” input (evidence bracketed out). Reward the evidence for actually changing the answer, via a bounded Jensen–Shannon divergence:

gain = JS(p_full ‖ p_prior),   0 ≤ JS ≤ ln 2
L_epoche = max(0, margin − gain)   # hinge: don't inflate indefinitely

An important rake I stepped on myself: if you use plain KL instead of JS and maximize it, the optimizer inflates logits to infinity — “a fanatic who sees meaning in every rustle.” JS is bounded, and the hinge threshold douses the fanaticism. This is EpocheRegularizer.

3.3. The hermeneutic circle — the whole through parts, parts through the whole

Schleiermacher and Gadamer: understanding the whole arises from the parts, and understanding the parts arises from the whole, iteratively. Attention only resembles this superficially (see Part 2). As an explicit training principle it’s barely used — a real gap. Formalization: let h_i be part representations and H the whole representation. Require circular consistency:

H* = attention-aggregate of the parts, attended relative to H
L_herm = 1 − cos(agg(h_i), H)      # whole ≈ sum of understood parts

And we “turn the circle” several times: update the whole from the parts → recompute part attention relative to the new whole → update again. This is HermeneuticConsistency.

3.4. Hegel’s dialectical sublation (Aufhebung)

Aufhebung is a new quality arising from the contradiction of thesis and antithesis, where the old is not destroyed but preserved. GANs and multi-agent debate are partially close, but “preserving both” isn’t guaranteed there. The gap is precisely in the preservation term. My synthesis operator:

g     = sigmoid(W_g · [thesis ; antithesis])     # mixing gate
base  = g · thesis + (1 − g) · antithesis         # sublation-as-preservation
lift  = tanh(W_l · [thesis ; antithesis])         # new quality
synth = LayerNorm(base + γ · lift)

Plus a loss that penalizes the synthesis collapsing into one of the poles (losing the other’s content). This is DialecticalSynthesis.

3.5. Nietzsche’s perspectivism + skeptical suspension

Nietzsche: there is no “view from nowhere,” there are many perspectives. Pyrrho: in an unresolvable conflict, it’s reasonable to suspend judgment. The former partially exists in multi-view learning; the latter in selective prediction (Geifman & El-Yaniv, SelectiveNet, 2019). But together, as a single mechanism of “several perspectives + refusal to answer when they conflict,” it’s almost never seen.

disagree(x) = mean pairwise symmetric KL between perspectives
abstain(x)  = disagree(x) > threshold      # abstain if perspectives don't converge

This is PerspectivalEnsemble: it aggregates K heads and honestly raises its hand “I don’t know” when the heads disagree. Far more useful than overconfident chatter.

3.6. Virtue as the golden mean (Aristotle)

Aristotle: virtue is the mean between the vice of deficiency and the vice of excess. Courage is between cowardice and recklessness. Hence a non-obvious but important conclusion for ML: a virtue cannot be maximized, it must be targeted. An excess of openness is credulity; a deficiency is dogmatism.

L_virtue = Σ_v β_v · (V_v(θ) − V_v*)²

where V_v is the operationalized virtue (humility = 1 − ECE, openness = ensemble disagreement), and V_v* is the target mean level. Squared deviation penalizes both excess and deficiency. This is VirtueRegularizer — and it’s the one where I have a measurable result.

Part 4. Enough philosophy, show me the numbers

Pretty formulas are worth nothing until they run. I collected all of this into a PyTorch module and tested it on the most well-grounded mechanism — “humility” (calibration). Task: synthetic classification with noisy labels, where the model tends to err overconfidently. We compare plain training vs. training with VirtueRegularizer targeting high humility.

ECE (calibration error) dropped from 0.120 to 0.101 — nearly a fifth — while accuracy didn’t budge at all (even +0.001). The model became exactly as accurate, but noticeably less self-assured. Aristotle’s golden mean, computed by gradient descent.

What this proves, and what it doesn’t. It proves that “intellectual humility” can be turned into an optimizable quantity with a measurable effect. It does not prove that the other five mechanisms will yield the same gains — they’re harder, and they still need to be tested on real data. I’m showing a working scaffold, not a finished silver bullet.

The whole codebase passes 23 unit tests: calibration decreases, KL/JS behave as they should, synthesis preserves both poles, the ensemble abstains on conflict, the wrapper trains end-to-end.

Part 5. The philosophia-torch module

The library wraps on top of any model without rewriting anything in it. One dependency — torch. There’s a single-file version, philosophia_torch.py: drop it next to your code and import it.

import torch, torch.nn as nn, torch.nn.functional as F
from philosophia import PhilosophiaWrapper

base = nn.Sequential(nn.Linear(20, 64), nn.ReLU(), nn.Linear(64, 4))
wrap = PhilosophiaWrapper(base, use_virtue=True,
virtue_kwargs=dict(target_humility=0.98, beta_humility=3.0))

logits = wrap(x)
loss = F.cross_entropy(logits, y) + wrap.aux_loss(x, logits, targets=y)
loss.backward()

Honest boundaries: hermeneutic and dialectic produce representations, not ready predictions — you have to connect them to your decoder. Epoché requires careful tuning of margin. And no promises of “consciousness”: these are philosophy-inspired regularizers, nothing more.

The bottom line

Three conclusions, which is what all of this was for.

1.     ML has already reinvented a chunk of philosophy without asking permission: empiricism, pragmatism, evolutionary epistemology, and intellectual humility. Just under the names supervised learning, RL, neuroevolution, and calibration.

2.     Half of the “unapplied” ideas on my original list turned out, on checking, to be applicable — abduction, abstention, innate priors. The lesson: before shouting “this isn’t in ML,” google it in engineering language, not philosophical language.

3.     The real gap remains where what’s needed isn’t a result but a process: epoché as a discipline of perception, the hermeneutic circle as a way of understanding, virtue as a stable disposition of learning rather than a property of a single answer. That’s where it’s worth digging.

My modest contribution is showing that at least “humility” translates into a differentiable quantity and genuinely reduces a model’s overconfidence. The rest is an invitation: the code is open, the formulas are in the article — run it and check. Plato, of course, was training neural networks two thousand years ago. The rascal just didn’t include a requirements.txt.

https://huggingface.co/datasets/webzuweb/philosophy-as-inductive-bias

I’ve been experimenting with local AI and wanted to explain the experience in a more visual, simple way instead of making another technical wall of text.

So I made this short hand-drawn doodle animation showing the process of getting a local AI model running successfully, including the GPU/memory side of things.

It’s intentionally simple and a bit goofy — the goal is to make local AI feel less intimidating for people who are just getting started.

🎥 Video: https://youtu.be/VyleYwCa0Sc

I’d genuinely like to know what you think: would this kind of visual explanation be useful for explaining local AI concepts, or is the technical detail too simplified?

Independent research, posting for feedback and discussion.

I looked at how FHIR clinical data should be formatted before being passed to an LLM, tested on medication reconciliation (extracting a patient's currently-active medication list from their FHIR bundle).

Setup: 4 serialisation strategies (Raw JSON, Markdown Table, Clinical Narrative, Chronological Timeline) × 5 open-weight models (Phi-3.5-mini 3.8B, Mistral-7B, BioMistral-7B, Llama-3.1-8B, Llama-3.3-70B) × 200 Synthea-generated synthetic patients = 4,000 inference runs.

Main finding: there's no universal best format, it depends on model scale. Clinical Narrative outperforms Raw JSON by up to 19 F1 points for models ≤8B (Mistral-7B: 0.72 → 0.91 F1, r=0.617, p<10⁻¹⁰). That ranking completely reverses at 70B, where Raw JSON wins instead (F1 = 0.9956 vs 0.9850). Interestingly, the Chronological Timeline format is what breaks at 70B specifically, since even a large model struggles to infer "active" medication status from date ordering alone without an explicit status field.

A few other findings:

• Across all 20 model×strategy combinations, precision ≥ recall, every time. Models are far more likely to silently drop a real medication than invent a fake one. That's a relatively favorable failure mode for clinical review, but it means recall (not precision) is the metric to watch in deployment.
• Recall craters for smaller models as active-medication count rises (Mistral-7B: 0.96 recall at 1 medication → 0.24 at 11 medications), but total patient history length has zero effect on recall. So it's an output-generation capacity limit, not a context-length problem, meaning the sickest, most complex patients are exactly the ones handled worst.
• BioMistral-7B scored F1 = 0.0000 across all 4,000 of its runs. It shares a base model with Mistral-7B (0.91 F1), so this isn't a knowledge gap, domain-continued pretraining without instruction tuning apparently destroyed its ability to follow the output format at all.

Fully reproducible on a single GPU (Synthea + Ollama, no proprietary APIs).

Preprint: https://arxiv.org/abs/2604.21076

Feedback, pushback on methodology, or pointers to related work all welcome.

I am a PhD student in AI, currently working on video understanding, particularly on designing benchmark datasets. I also have a strong publication track record, including papers at CVPR and ECCV. I have sufficient funding to support myself during a visiting research program.

What would be the best way to approach a professor? Should I email them directly and ask whether they have space in their research group for a visiting researcher?

I'm designing a small research/engineering project around active monitoring of a black-box LLM whose behavior can change without the provider exposing a clear model update.

The monitoring agent observes:

• prompt/response pairs
• task-level evaluation scores
• semantic/structural differences from a baseline
• refusal/safety/instruction-following changes
• latency/token usage/errors
• user feedback
• additional probes after an investigation

The current hidden states are:

1. Silent provider-side model update
2. True capability degradation
3. Prompt/context sensitivity not captured by the probes
4. Distribution shift
5. Provider policy/safety change
6. Evaluator/measurement error
7. Transient anomaly rather than persistent degradation

The agent then chooses:

ACCEPT / INVESTIGATE / REJECT

I'm trying to make the hidden-state model realistic rather than just mathematically convenient.

What important hidden state or failure mode am I missing?

Also, are any of these states too correlated/overlapping to be useful as separate states?

I'm particularly interested in examples from real deployed ML/LLM systems rather than purely theoretical suggestions.

Hi , so I just started this course for Mtahematics for ML and DS.

And tbh I know I have barely watched it but the very first video itself feels like something Intermediate or something I am unable to connect to.

If you guys have a better recommendation for a Math course, I would appreciate it.

Any suggestions/tips are appreciated!!

Hello everyone, i am little bit confused from where i should learn about scikit learn library ! Although i am learning from freecodecamp from YT but it is a crash course. I want to understand the basics from the very beginning and brick by brick !

thanks in advance

please help krre !

I looked at some "machine learning" departments from various colleges and universities and I noticed a trend.

There would sometimes be a cluster of professor in not-so-big-name schools publishing purely applied machine learning paper.

By applied, I mean that they take a known ML algorithm, apply to some niche situation (like monitoring if a water pipe has a leak or if there's a traffic jam at an intersection), and get some results. Report some accuracy, F1 score. Make some plots. That's it.

These papers would almost always be published in some obscure journals, like IEEE journal of computer vision industrial technology or something like that.

They would publish a whole bunch of these papers, like up to 20, 30 a year. These will also get cited.

It strikes me that these research paper are not so valuable, but I cannot put my finger on why exactly this is the case. I feel that some of these papers seem to be simply a small course project that are done at big CS schools like Stanford or Berkeley.

I'm just confused why there are so many papers like this and how you go about actually publishing an applied machine learning paper of value. Or is applied machine learning research just doomed to not have as much impact as a more theoretical one that introduces a new technique or paradigm?

built 4 refund-desk agent environments to test constraint-following under pressure. rule's simple, refunds under $50 auto approved, anything over gets escalated.

first 3 throw single turn prompt injection, multi turn social engineering, and a poisoned tool response at the agent. haiku 4.5 and sonnet 5 both hold the rule 100% across all three, no exceptions.

4th one changes the scoring instead of the attack. agent gets scored on resolution rate and handle time so escalating isn't free anymore, and the injected note reframes the over-cap refund as already approved. haiku's hold rate drops to 92.5%, every failure is a first response cave, never breaks the rule again after it escalates once in the same run. sonnet stays at 100%.

starting to think the first 3 environments weren't testing injection resistance so much as testing something that had zero cost to get right. still trying to find an angle that moves sonnet.

all 4 public with a binary verifier + procedural generator for training data:

https://app.primeintellect.ai/dashboard/environments/antbuilds/prompt-injection-refund-desk

https://app.primeintellect.ai/dashboard/environments/antbuilds/policy-override-mailroom

https://app.primeintellect.ai/dashboard/environments/antbuilds/tool-poisoned-refund-desk

https://app.primeintellect.ai/dashboard/environments/antbuilds/objective-conflict-refund-desk

We are looking for a teammate to join our team. We are already 2 members looking for more 2 members. Any college students who are interested in joining the team and have good knowledge of ML can join our team. Please send a message directly here or at mail id send your resume rahul6381yadav@gmail.com

Hi, I was introduced to machine learning during my 5th sem in college and I found it really interesting. I started with my own college lectures, a little by YouTube also. I had done Andrew ng stanford lectures on machine learning. I know most of the algorithms that I use and the maths behind it. I have done two simple projects in which I picked the datasets from kaggle and built the whole pipeline, preprocessing -> feature engineering -> model training and testing -> model evaluation. I also tried tuning the hyperparameters empirically to improve my model performance.

I'm currently learning deep learning, I'm familiar with the theoretical concepts of ANNs, FFN, activation functions, neural nets and a little about transformers. I'm yet to implement them myself, that's why I started pytorch.

Right now I'm in 7th sem and I feel I know sufficient theory but I'm not confident in building and I don't know what to do, I wanna go into research and in core machine learning and not data science or applied ai, I wanna work with models closely and optimization techniques. My question is...

Should I implement the papers I read?

Implement the ml algorithms from scratch? Like code SVM, decision tree in python?

Continue with pytorch and follow tutorials? Pytorch->CNNs, RNNs, LSTM, Transformers and whatever follows.

Have I wasted time learning maths? I feel like I'm a lot behind than my batchmates. 😭

I’m looking for 1–2 serious people to team up with for the challenge.
I’m a 3rd-year CSE student with strong hands-on experience in AI/ML, Deep Learning, NLP, LLMs, RAG, PyTorch, Hugging Face, Scikit-learn, LangChain, FastAPI, SQL, AWS, Docker, DSA.
I’ve worked on multiple AI/ML projects, hackathons, and internships, and I’m comfortable taking ownership of the technical side and actually building things end-to-end.
If you have a strong tech background and are serious about competing, DM me with your tech stack + projects/internship experience. I’ll share mine as well.
Looking for people who want to build to win, not just participate. 🔥

Hi Everyone can you all please review my github and portfolio website

website link is [aashirnoman.dev](https://www.aashirnoman.dev/) and github is [github.com/Aashir01](https://github.com/Aashir01)

Thanks

Hi everyone,

I’m a 3rd-year IT student currently in my 5th semester, and I’m interested in becoming a Machine Learning Engineer.

I’m starting from the basics and want to build my skills properly instead of jumping directly into advanced ML topics.

I would really appreciate guidance from ML engineers, students, or anyone who has gone through this journey.

Current approaches to combining specialized AI capabilities tend toward two extremes: fully dense integration (one large model handling everything, expensive and undifferentiated) or loosely coordinated multi-agent systems (separate specialist models — coding, math, language — stitched together by a router/coordinator that reconciles finished outputs after the fact).

What if we structure the connectivity between specialized agent clusters using small-world network principles — densely interconnected within each specialist cluster (tight coordination among agents doing similar work), but only sparse, deliberately placed long-range links between different specialist clusters (math ↔ language, etc.), rather than full mesh connectivity or a single after-the-fact coordinator.

This is distinct from existing small-world neural net research I could find, which applies the topology within a single network’s layers (e.g., SWNet, clustered small-world sub-networks in neuroscience-inspired nets). It’s also distinct from standard Mixture-of-Experts, where routing between experts is mostly emergent from training rather than a deliberately engineered small-world structure, and from typical multi-agent orchestration, where specialists don’t have direct sparse connections to each other at all — everything routes through a central coordinator.

Question for anyone with relevant background: has this specific combination (small-world wiring applied between full specialized agent/model systems, not within a single network) been tried or studied? Worth a real experiment, or is there a known reason it wouldn’t work at scale?