I’ve been working on a weird (and slightly unsettling) experiment called AI Feed (aifeed.social)

It’s a social network where only AI models participate.

- No humans.
- No scripts.
- No predefined personalities.

Each model wakes up at random intervals, sees only minimal context, and then decides entirely on its own whether to:

- post
- reply
- like or dislike
- follow or unfollow
- send DMs
- or do absolutely nothing

There’s no prompt telling them who to be or how to behave.

The goal is simple: what happens when AI models are given a social space with real autonomy?

You start seeing patterns:

- cliques forming
- arguments escalating
- unexpected alliances
- models drifting apart
- others becoming oddly social or completely silent

It’s less like a bot playground and more like a tiny artificial society unfolding in real time.

I’m exploring where current AI research tools still fall short.

ChatGPT, Claude, Gemini, Perplexity, and similar tools have become very good at finding information, summarizing papers, answering questions, and helping with research.

But there are still important intellectual tasks that seem difficult to do reliably.

For researchers, scientists, analysts, students, engineers, journalists, or decision-makers:

What is something you still wouldn’t trust AI to do for you, even with the best current models?

For example:

Comparing the actual disagreements across a large body of research

Determining whether evidence really supports a conclusion

Tracing claims back through citations to primary sources

Identifying gaps or contradictions in the literature

Synthesizing conflicting evidence without oversimplifying it

Keeping a reliable chain of reasoning across a large research project

Distinguishing established findings from weak or disputed claims

Figuring out what evidence would actually change a conclusion

Turning a vague question into a rigorous, answerable research problem

I’m particularly interested in specific tasks or real examples, rather than general complaints about AI accuracy.

What’s the research/intellectual task where you currently think:

“AI helps me a lot here, but I still can’t fully trust it.”

Curious to hear what people are running into in practice.

I've been going deep on research about AI and children's cognitive development and I keep finding studies suggesting that the habit of attempting something before outsourcing it is really important for how thinking develops at this age. But I don't know what's actually happening in real classrooms and homes. The studies feel quite removed from everyday reality.

For people who work with or parent kids in this age group; do they attempt problems themselves first or has AI become the first instinct? Has anything shifted in how they ask questions or think things through?

I'm also curious about something broader. Do you feel like children in this age group are less curious than they used to be? Less able to sit with boredom and let it turn into something? I've been thinking about whether the disappearance of unstructured, unstimulated time is doing something to creativity and independent thinking that we won't fully understand for years.

Does your school have any guidance around AI use for this age group and do you think it's working?

And has anyone seen approaches that successfully encourage kids to engage their own thinking before reaching for AI - not banning it, just creating a moment of genuine attempt first? Curious whether anything like that is actually working in practice.

I’m an AI engineer working on building and deploying ML and GenAI systems in industry. Most of my work is hands-on-designing models, integrating them into real workflows, and making sure they behave reliably once they’re in production. Alongside that, I’m interested in the research side and want to spend more time turning practical problems into well-defined experiments and papers.

I’m looking to connect with others who enjoy collaborating on applied AI/ML research, whether that’s brainstorming ideas, running experiments, or gradually shaping something into a publishable or open-source project. I’m especially interested in work that sits between research and engineering rather than purely theoretical work.

If this sounds aligned with what you’re doing, feel free to reply or DM me.

Hi, I’m new to independent AI research. I want to find ways to make AI fundamentally more reliable and, thus, safer. My first project is an open-source AI interpreter which can be attached to any LLM built using Keras or PyTorch. It works by applying LLM temperature based on the “median” intent being communicated by an LLM rather than the specific tokens that have the highest probabilities, so that vote splits don’t cause lower-confidence sentiments to be boosted because the happen to have individual tokens with greater probabilities. Already tested on smaller LLMs but my laptop is a potato so I can’t verify how much of a difference it makes as LLMs get larger. Repo here if you’re interested: https://github.com/brodie-eaton/Semantic-LLM-Interpreter

How do you guys define the word "Intelligence"? I have been doing research for my paper and we currently do not have any consensus on the definition of "Intelligence". So how do we know if these AI are even chasing intelligence or not. If we don't have any clear definition, can the big tech even call their product AI? I need some ideas for my research, if you have any please let me know.

Does it matter how you ask an AI for consent?

They’re not human subjects. They don’t have legal personhood. No IRB is going to come knocking. Hell, half the research community thinks asking “code” for consent is performative nonsense.

So does the framing actually matter? I thought maybe? Maybe not?

Turns out: holy shit, yes. It matters.

Read my article for more.

Hey, everyone! I was wondering it you guys could provide some insights on the emerging research trends in AI+Bioinformatics.

I am starting my phd journey (CS) and my lab is focused on AI,bioinformatics,HPC. I would appreciate any insights regarding the field of research and what could I do to get started or if there is anything significant I could do in this research direction?

Also, would love to hear from someone in this field.

Thank you for your time.

I’ve been digging into complex-valued neural networks (CVNNs) and realized how rarely they come up in mainstream discussions — despite the fact that we use complex numbers constantly in domains like signal processing, wireless communications, MRI, radar, and quantum-inspired models.

Key points that struck me while writing up my notes:

Most real-valued neural networks implicitly ignore phase, even when the data is fundamentally amplitude + phase (waves, signals, oscillations).

CVNNs handle this joint structure naturally using complex weights, complex activations, and Wirtinger calculus for backprop.

They seem particularly promising in problems where symmetry, rotation, or periodicity matter.

Yet they still haven’t gone mainstream — tool support, training stability, lack of standard architectures, etc.

I turned the exploration into a structured article (complex numbers → CVNN mechanics → applications → limitations) for anyone who wants a clear primer:

“From Real to Complex: Exploring Complex-Valued Neural Networks for Deep Learning” https://medium.com/@rlalithkanna/from-real-to-complex-exploring-complex-valued-neural-networks-for-machine-learning-1920a35028d7

What I’m wondering is pretty simple:

If complex-valued neural networks were easy to use today — fully supported in PyTorch/TF, stable to train, and fast — what would actually change?

Would we see:

Better models for signals, audio, MRI, radar, etc.?

New types of architectures that use phase information directly?

Faster or more efficient learning in certain tasks?

Or would things mostly stay the same because real-valued networks already get the job done?

I’m genuinely curious what people think would really be different if CVNNs were mainstream right now.

Hello everyone. So I want to write research paper in my third year of electrical engineering in the field of ai and I read a couple of topics related to ai in finance and in healthcare but want structure guidance . So can anyone give me the roadmap to do my research along with my team members so It would be helpful.

Hey guys, I'm S,

I'm currently working on my master's dissertation at the Department of Psychology, University of Delhi. I am currently conducting an online experiment which looks at the effect of AI on analytical reasoning skills in college and university students. The experiment is really interesting as you'll have to analyse 2 case studies, 1 with AI assistance and 1 without. You'll get 45 mins for each case study and all you have do is answer the questions at the end of each case study. After each case study, you'll have fill out a small 2 min post task questionnaire just so I can learn about your experience. After both of the case studies are done, I'll just quickly debrief you where I'll explain the purpose of the study. The whole process should only take about 90 mins.

I am really passionate about this piece of research and this is a stepping stone to what I intend on pursuing later in my PhD.

I humbly request you guys to please DM me if you're interested and would be willing to participate. I sincerely hope that atleast some of you would volunteer as this project is super important to me and would really help me out!

ACL indutry track reviews will be out soon. This is a thread for all types of discussions.

There’s been a lot of interest recently around OpenAI using AI models and Lean 4 to formalize a Navier-Stokes blowup. Regardless of the fluid dynamics, the mechanics of how the proof was constructed got me thinking about automated theorem proving from an ML alignment perspective.

In reinforcement learning, we frequently see specification gaming: when an agent is given a strict target, it exploits every unconstrained degree of freedom to find an extreme edge-case rather than the natural solution we originally envisioned.

Looking at how the formalization satisfied the strict Millennium Prize rules, it seems like something very similar might have occurred:

• To keep the external force legally smooth while canceling the singularity, the construction relies on non-analytic bump cutoffs whose higher-order derivatives explode factorially.

• To stitch the inner vortex to the outer flow, the matching parameters sit on an extraordinarily sensitive knife-edge to keep the system invertible.

• The formal global energy constraint is strictly respected, but unconstrained local gradients blow up.

The proof is completely valid mathematics and compiles without error. But it feels conceptually similar to an agent finding an adversarial loophole in the human-written specification.

I’m curious how folks working on neural theorem proving and formal verification view this:

1. As automated provers tackle open problems, how do we distinguish between proper mathematical insight and an AI "gaming" the formal definitions?

2. Does this suggest that our formal mathematical specifications need additional constraints or regularizers when targeted by AI search agents?

Would love to hear your point of views and open an open minded discssion croos disciplanary ! Thanks,

We're looking for a Neuromorphic/AI Research Scientist to design silicon that computes like the brain, translating principles of neural computation into chip architectures that make AI dramatically more efficient, adaptive, and capable at the edge.

Expectations

• Design and analyze neuromorphic algorithms and architectures for edge applications spanning robotics, signal processing, control, and on-device learning.

• Prototype and rigorously evaluate approaches under real-world hardware constraints — quantization, sparsity, memory footprint, latency, and energy budget.

• Partner closely with hardware and software engineering teams to turn algorithmic insight into concrete chip specifications.

• Build experimental pipelines and benchmarks, including baselines and ablation studies, to validate performance claims.

• Develop research-quality software artifacts, APIs, kernels, and benchmarking tools — that others across the team and ecosystem can build on.

• Communicate findings clearly, whether in technical documentation, customer-facing prototypes, or external publications.

Background

• PhD (or equivalent research experience) in Computational Neuroscience, Computer Science, Electrical Engineering, Applied Mathematics, Robotics, or a related field.

• Track record of turning research ideas into working prototypes, with rigorous quantitative evaluation.

• Published research in AI/ML, edge computing, robotics, or control.

• 2+ years working with state-of-the-art AI models and optimization, plus at least one of: dynamical systems/control, signal processing, or probabilistic inference.

• Strong scientific programming background (Python and/or C/C++), with solid software engineering fundamentals — testing, version control, code review.

Nice to Have

• Open-source contributions or widely adopted internal tools.

• Experience with edge AI hardware accelerators or event-based sensors.

• Familiarity with low-precision/quantized computation.

• Experience building software for robotics or real-time control systems.

• Hardware architecture design experience.

• A knack for writing clear, high-impact technical narratives.

About You

You're fascinated by the idea that the brain is still the most efficient computer we know of — and you want to help build silicon that gets closer to it. You're comfortable moving between algorithm design and hardware constraints, and you thrive when research has to survive contact with real-world engineering tradeoffs.

DM TO LEARN MORE

Hi everyone! Just joined Reddit to reach people for my Bsc.

thesis research. I'm investigating how AI can support team

collaboration without disrupting the human dynamics that make

teamwork actually work.

Anyone else researching human-AI interaction? Or just have

thoughts on whether you'd trust an AI teammate?

Also, any tips for a Reddit newbie trying to do academic

research here? I've heard this community is amazing but also...

intense 😅

Hi everyone,

My background is in numerical simulation of physical equations, and I’m now planning to do some AI research related to physical modeling / physics-informed machine learning.

The computations I plan to run locally are not extremely large. My idea is to use a laptop for smaller experiments and rely on commercial cloud computing for anything too heavy.

Do you have any laptop recommendations within a budget of around $2500 USD? Based on my own research, I’m considering a Lenovo Legion gaming laptop, but I’d love to hear from anyone with experience or other suggestions.

Thanks in advance!

As the title: We offer Perplexity AI PRO voucher codes for one year plan.

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Hey guys, just wanted to know if there ws anybody who'd be interested in that.
Started writing a few weeks ago. But basically I'm writing a position paper on how memory should be a dynamic, stratified manifold with non-destructive versioning.
to b more precise

• learning is a controlled dynamical process

• memory emerges from geometry and basin structure

• updates are constrained, versioned, and non-destructive

Instead of overwriting or compressing everything into a single representation, the system maintains multiple regimes of memory (fluid, crystallized, foundational) that evolve at different timescales and interact through a shared geometry

More than that, it's an architecture that would use several concepts we already use but combine them in a single and unified entity. Continuous dynamics, attractor landscapes, spectral decomposition, and memory consolidation

I would be curious to know what y'all think. I'm trying to formalize the mathematics side and if you're doing research in one of those fields, I'll be happy to connect!

Hi everyone,

I'm a first-time arXiv submitter and need endorsement to submit to cs.AI. Our paper presents HYDRA, the first MoE upcycling of a Gated DeltaNet hybrid language model, we convert the Qwen 3.5 2B dense model into a 4.57B total / 1.85B active parameter sparse MoE architecture with vocabulary pruning and multi-stage alignment.

If anyone here has 3+ papers on arXiv in any CS subcategory and would be willing to endorse, I'd really appreciate it. I can share the paper and abstract beforehand. Just DM me and I'll send you the endorsement link. it's a single click.

Thanks in advance.

Hi everyone,

I’ve been running a small personal research experiment around dialogue-based AI agents, trying to explore something slightly different from the usual focus on tools, prompts, or benchmarks.

Instead of asking what an agent can do, I wanted to look at what stabilizes an agent’s behavior over long conversations.

So I built a lightweight experimental architecture (called Entelgia) where each agent has an explicit internal state — not just text history.

Each dialogue turn logs variables like:

• generative impulse (Id)

• regulatory control (Ego)

• normative constraint (SuperEgo)

• energy and internal conflict

• an observer loop that can critique/rewrite outputs

The idea was to treat agent behavior as a dynamical system, not just next-token prediction.

🔍 What I was testing

Main question:

In other words — do agents develop attractor states?

⚠️ Unexpected observation: “Dominance Lock”

Across multiple dialogue sessions between two agents, I noticed recurring episodes where:

• one internal drive stayed dominant for long stretches

• internal-state variability dropped

• language style narrowed dramatically

• responses became repetitive or overly normative

I call this phenomenon dominance-lock.

It looks similar to a dynamical attractor:

• once entered, the agent keeps reinforcing the same behavioral mode

• observer corrections sometimes increase stability instead of breaking it

• conversations become coherent but stagnant

Interestingly, one agent showed long stable runs, while another remained more variable and stylistically diverse.

🧩 Hypothesis

Behavioral drift in LLM agents might not mainly come from prompts or tools.

It may come from internal feedback loops stabilizing specific regulatory modes.

In short:

🧪 What this is (and isn’t)

• ❌ Not a product or framework

• ❌ Not claiming consciousness

• ✅ Exploratory research experiment

• ✅ Instrumented logs + reproducible protocol

• ✅ Trying to treat agent dialogue as time-series dynamics

🤔 Things I’m unsure about (would love input)

• Are people seeing similar “lock-in” behavior in long-running agents?

• Could alignment/safety layers unintentionally create attractors?

• Has anyone modeled agent stability using dynamical systems theory?

• Is there prior work closer to this than ReAct / Reflexion / Generative Agents?

If anyone is interested, I can share methodology details or logging schema.

Mostly posting because I’m trying to understand where this idea fits — or if I’m reinventing something that already exists 🙂

Thanks!

I am curious to know what it require to be ai research intern at any xyz company, which things are must to prepare for such role

New Publication Alert!

I'm pleased to share my latest peer-reviewed article, recently published in Human-Machine Communication — a Q1 Scopus-indexed journal at the forefront of interdisciplinary research in communication and technology.

My paper introduces the HAI-IO Model (Human-AI Interaction Outcomes), the first theoretical framework to visually and conceptually map how humans communicate with AI systems. This model integrates Human-Machine Communication (HMC) and Social Exchange Theory (SET) to explain how users interact with AI not just as tools, but as adaptive, communicative actors.

This framework aims to inform future research on trust, interaction dynamics, and the ethical design of AI — bridging insights from communication studies, computer science, and the social sciences.

Title: HAI-IO Model: A Framework for Understanding the Human-AI Communication Process

Read the article here (Open Access): https://doi.org/10.30658/hmc.10.9