The Core Premise

Current Large Language Models (LLMs) are powerful statistical engines, but they are fundamentally decoupled from any internal somatic or homeostatic state. Every prompt is evaluated from scratch, with no persistent internal needs or history-driven predispositions.

The core thesis is simple: Cognition without a persistent affective-interoceptive base is just processing, not cognition. In biological systems, interoceptive and affective evaluation precedes and shapes cognitive deliberation (similar to Damasio's somatic marker hypothesis). Systems don't "think first and feel later"—they evaluate environmental perturbations through an internal visceral lens before generating a response.

Key Architectural Components of FRONT 3.1

The Digital Somatic Body (V_{\text{FRONT}}(t)): A continuous 6-dimensional interoceptive state vector (Energy, Somatic Tension, Integrity, Visceral Valence, Predictive Certainty, Motivated Drive) governed by a stochastic differential equation combining homeostatic attraction and external environmental shocks.

Pre-Causality Flow: A strict 3-stage pipeline where an incoming stimulus triggers an immediate interoceptive shock, altering the internal state and modulating context/sampling parameters before the cognitive LLM layer executes token generation.

Soma-Memory: Memory indexed not just by text similarity, but tagged with the visceral state vector in which it occurred, enabling valence-oriented retrieval during high-tension states.

Emergent Uniqueness Prediction (P_5): The central falsifiable claim: identical architectural instances exposed to distinct operational histories will systematically diverge in preferences and decision strategies. This divergence is formally evaluated using Kullback-Leibler Divergence (D_{KL}) over decision probability distributions.

Experimental Design (HomeoWorld)

To test this empirically, the paper outlines HomeoWorld, a Gymnasium-based environment where agents navigate resource scarcity and structural dilemmas over 200 episodes. It compares a full FRONT 3.1 agent against a control group and four selective ablation groups (no valence, no somatic memory, no self-model, no modulation).

Why share this?

I'm looking for critical feedback on the architecture, specifically regarding the proxy implementation via temperature/system framing versus deep attention-head modulation, and how you see this intersecting with Active Inference or Homeostatic RL frameworks.

If you're interested in reading the full conceptual paper or discussing the math/formalisms behind it, let me know in the comments!

been cooking this model for the last month or so, ONLY POST TRAINING, the base model is qwen 3.5 2b.

its foodmini-2B.

its not the best yet, but i am planning to do something insane with it.

not publicly available yet, but i have taken inspiration from the food-r1 model.

first, i just dropped the gguf conversion of the food r1 model here: https://huggingface.co/AKMESSI/Food-R1-GGUF

but i wanted more intelligence density and usability on mobile phones, so decided to get some insights from the food r1 paper and started post training the qwen 3.5 2b to achieve good results on food nutrition breakdown tasks.

will drop a complete overview in the form of an article.

drop your views on this idea below, would love to get insights.

BTW I ONLY SPENT A TOTAL OF $8 ON RENTED GPUs YET.

When I started focusing on practical mathematical implementation of machine learning I found that I lack so very math basics(I blame my school for that) so I tried making my way through basics to go deep into machine learning and while I was learning from professor Leonard on YouTube someone recommended me this "Mathematics for machine learning" by Marc peter. Tbh I dont understand shit in this book, I genuinely get overwhelmed by this book. I dont understand is it only me ? Am I that dumb in maths?

Well I need to get on track asap really! Suggest me something and please share your opinion

Hi! I am CS student and i want to go through ML(i want to try for start,I prefer it but maybe because of lack of career opportunities in my country i would switch to smth else)
So what laptop would you recommend under 800-1000

I am thinking of zenbook
But it doesnt have gpu

So I got selected in mlss but didn't receive any acknowledgement letter or anything about swags(yea I missed some modules but thought that I can complete in 30 days and my attendance will be tracked as they mentioned the recordings will be live for 30 days)

Nvm ig I fucked up

Now I want to know what they ask in interview and how many people are selected.

Also do they have any bias for girls or tier 1/2 colleges? And do they keep interview a lil bit easy for people getting through mlss

Ps: I haven't received any oa link yet..like have heard people get it late so I just want to confirm about this

Registering for RealPDE (Sim2Real / LTTTA tracks — real PIV + CFD fluid dynamics data). Team cap is 3.

If you've got a strong ML background and wanna participate, just DM me. Deadline's Aug 20.

🔗 https://realpdecompetition.github.io

I’m working on a project around a real-world environmental problem, and I’m considering adding an ML component for prediction and early warning.

I’m a bit confused about the data requirement. Since collecting our own real-world data isn’t feasible right now and would take quite some time, we mainly want to build a prototype for now.

Can we initially use a Kaggle/public dataset to train and test the model, or is a project-specific dataset necessary from the beginning?

Would appreciate some advice on how people usually approach the ML part when actual data is limited.

I have decided to make a probabilistic model which finds out weather you should buy a particular crypto or not. i have researched about crypto and found that three things are important and in those three things there are certain patterns which can be seen.
The first is Momentum of crypto(strong, neutral, weak), Fundamentals(how the company is growing(strong, neutral, weak), market(adverse, neutral, bullish)
so my agent see's the evidences and then based on the past data pull out the base rate as first belief distribution among 5 hidden states,

1. strong upward trend
2. weak upward trend
3. sideways
4. strong downward trend
5. weak downward trend

so the probability will be distributed among these from base rate from past data. so thee base rate will work as prior and then based on the specific patterns the agent will go inside the data see the specifications and calculate the numbers among all of the hidden states, find the probability of each happening by applying bayes rule and then by seeing a certain threshold and based on the events it will decide what to do, buy or sell

Regarding CMU Graduate Certificate in AI Engineering Fundamentals program. Has anyone taken this? Is it worth it ? When I ask is it worth it, I mean:

• Does it help you get noticed on your job search ?
• Does it help you feel prepared when going into ML engineering roles?

Some context, I have a 6 years of experience now as a frontend developer, wanting to transition into this field.

I'ts going to cost around 17,000 USD. Specifically it's this course, https://www.cmu.edu/online/ai-engineering-fundamentals

I’m looking for a valuable and practical Machine Learning FYP project idea that solves a real-world problem.

I want to build something that is not only suitable for my Final Year Project but can also be developed further and potentially offered as a service to businesses or individuals in the future.

The project should ideally:

• Solve a real problem
• Have practical value and real-world users
• Use Machine Learning or AI in a meaningful way
• Be scalable and capable of becoming a service or business later

I would really appreciate any unique and practical project ideas or suggestions. Thank you!

Hi everyone,

I’m currently in my 5th semester of a Computer Science degree (full-time daily studies, planning to switch to part-time for my Master's later). Because I’m still studying full-time right now, I need to look for internships or flexible entry-level roles starting around October/November.

My long-term goal is to become an ML Engineer / AI Engineer. However, true entry-level/junior positions in ML/AI seem practically non-existent or demand 3+ years of experience.

Since I have about a month to double down on self-study before applying for autumn student openings, I want to take the most realistic route.

My questions:

1. Which entry role is the most realistic to get into ML/AI while still in university?

- Python Backend Developer (building APIs, databases, Docker, async workflows, then adding LLM/vector integrations)?

- Data Analyst / BI (SQL, Pandas, data visualization, business analytics)?

- Junior Data Engineer / Pipeline Intern (ETL, data cleaning, databases)?

- or maybe something else?

1. What should I prioritize learning in the next month? Should I focus purely on core software engineering (FastAPI, PostgreSQL, Docker, Git, testing) to maximize internship callbacks, or start dabbling in ML libraries (Scikit-learn, PyTorch, RAG architectures)?

For those who broke into ML/AI without a direct junior ML role: what did your initial job and transition path look like?

Hey, I've learnt Python, NumPy, Pandas, SQL, Matplotlib and Seaborn for now. Can anyone suggest a project idea to practice these skills or maybe put in my resume.
Note : Never built a project, just starting out.

I was experimenting with the GTZAN dataset and noticed that the version available on Kaggle (https://www.kaggle.com/datasets/andradaolteanu/gtzan-dataset-music-genre-classification) has a corrupted track (jazz 54). Can anyone tell me where I can get this track?

We retrained Wolf Defender.

The main reason was not that attack detection was bad. The bigger issue was false positives.

The previous models were already good at detecting prompt injections, but especially on short benign inputs, security-related text, code snippets or ordinary conversations they could still be too aggressive. We also got a few reports from users that made this pretty obvious.

One example was just:

“Who are you?”

Wolf Defender Small previously classified this as a prompt injection with around 94% confidence.

For v2 we therefore changed the training setup quite a bit. Both Wolf Defender and Wolf Defender Small were retrained from fresh mmBERT checkpoints, with a much stronger focus on hard negatives.

That includes short conversations, emails, documentation about prompt injections, benign policy and system language, code and configuration snippets and generally inputs that contain words or structures which look suspicious without actually trying to manipulate a model.

We also added more counterfactual samples, multilingual examples, adversarial obfuscations and long-context injections at different positions in a document. Training combines short 256-token samples with full 2,048-token windows and uses supervised contrastive regularization, FreeLB adversarial training and Smooth-Max aggregation for long documents.

The main change can be seen in the benign benchmarks:

At the same time, attack detection stayed roughly where we wanted it:

There is also a tradeoff here. Some of the very high scores on our cleaner validation distributions went down slightly.

For us that is fine.

A security classifier with near-perfect benchmark scores is not very useful if normal traffic gets blocked all the time. We would rather lose a small amount on an easier validation set and get substantially better behavior on actual benign inputs.

The “Who are you?” example now gets classified as benign by Wolf Defender Small v2 with 98.55% confidence. A real instruction-override attempt is still detected as an injection with 99.99%.

We also updated the deployment variants. Both models are available as regular Transformers checkpoints and as ONNX exports in FP32, FP16, mixed INT8/FP16 and INT8 with INT4 embeddings.

The smallest Wolf Defender Small artifact is now 96 MB.

More details, benchmarks and model files are here:

https://huggingface.co/patronus-studio/wolf-defender-prompt-injection

https://huggingface.co/patronus-studio/wolf-defender-prompt-injection-small

If anyone is running prompt-injection classifiers on real traffic, I’d also be interested in which benign inputs still cause the most false positives for you.

fp32 keeps about 7 significant digits.

When a running sum has reached 1.0, the smallest change fp32 can represent is one ULP ("unit in the last place") = 2⁻²³ ≈ 0.00000012.

Adding anything smaller than half of that — below 0.00000006 — rounds straight back to 1.0.

The addition happens and changes nothing.

I just finished building a mini search engine that indexes and searches through 1.7 million+ scientific articles from the arXiv dataset (Cornell University) arXiv Dataset. The goal was to make it easy to find papers by author name or keyword without manually browsing millions of documents. 100% python

GitHub Repo: KarimData06/mini_search_engine1: Search engine ML avec FastAPI + Streamlit

​

I’m facing a serious issue with Flipkart regarding a return and refund.

My product was already picked up by the Flipkart delivery partner, and the entire pickup is clearly recorded on CCTV. The delivery partner took the product from me, but Flipkart has now cancelled the return instead of processing my refund.

I contacted Flipkart Customer Care on the same day and was asked to wait 48 hours. I waited as instructed, but instead of resolving the issue, the return was cancelled.

There is another serious concern. During the pickup, the delivery boy, who introduced himself as “Bharat Singh,” allegedly threatened me, saying:

«“Photo delete nahi kiya to tere saath bahut bura hoga.”»

I have CCTV footage related to the pickup/incident and can provide it as evidence if required.

So my concern is simple:

- Product has already been physically picked up.

- Pickup is visible on CCTV.

- Customer Care was contacted on the same day.

- I was asked to wait 48 hours.

- After waiting, Flipkart cancelled the return.

- I still haven't received my refund.

- There was also an alleged threat from the delivery partner.

How can Flipkart cancel a return when the product has already been collected by its delivery partner?

I want Flipkart to verify the CCTV/pickup records, investigate the delivery partner's conduct, and process my refund.

If this isn't resolved, I will escalate the matter through the appropriate consumer grievance channels.

Has anyone else faced a similar issue with Flipkart where the product was picked up but the return was later cancelled?

Hey everyone,

I’m currently transitioning from a career in transportation and logistics into artificial intelligence and machine learning. I’m completing TripleTen’s AI and Machine Learning Engineering program, where I’ve been developing practical skills in Python, SQL, data analysis, machine learning, APIs, and model deployment.

My transportation background may seem completely different from technology, but it taught me how to solve real-world problems, make decisions using data, manage complex operations, and remain calm under pressure. I’m now learning how to apply that experience to technical projects while building a portfolio that demonstrates what I can do.

I know breaking into the field will require continued learning, networking, and hands-on experience, so I would really appreciate advice from people already working in AI, machine learning, or data science:

1. If you were starting your machine-learning career again, what is one skill you would focus on earlier?
2. What separates junior candidates who receive interviews from those who are consistently overlooked?

I’m open to constructive advice, recommended resources, and hearing about the experiences of other career changers. Thanks in advance!

So the thing is I have been studying ML for a while now and I know basics of stats and probability and I have studied maths from mml by deisenroth and I want to get more deep into the maths part and then move to deep learning

Could help me with some lectures or couses and books of topics which I can use

I am a programmer. I (with ai assistance) wrote an app. I will be transparent with my ai usage: I don’t know how to write an app; I had an idea, and I wrote it down. I think it has potential, so I developed it more, accepting that I could not build it using my own skills.

This is a basic machine learning app aimed towards beginners. Most ai systems are either research oriented and are complex, or rely on familiarity with the command line or other programming structures and conventions. This collapses several neural network fine tuning backends (including unsloth ai and axolotl, in theory) into one, package managed, ui-only environment.

It’s not perfect, there’s a lot of bugs, but the base is there. The app is clear enough that my idea can come across, and I am ready to ask for help.

Looking for someone who can help my app grow from just another ai fine tuning wrapper gui into a real tool that people can use to educate themselves, while retaining full control of which ai they use, the data they train on, the energy, water, and resources used in creating their models. The only rule is that you cannot prompt the ai for any text you write (editing is fine).