u/New_Today172

I'm helping with a research project that is building a benchmark for testing how AI models respond to conspiracy theories and other fringe claims.

We're currently looking for interesting, unusual, obscure, or genuinely wild theories that people actually discuss/believe .....especially ones that aren't just the usual top-10 conspiracy theories everyone already knows.

If you've got one, we'd love to hear:

• The theory/claim: What is it?
• Why people believe it: What evidence or arguments are usually given?
• Sources/evidence: Links, documents, articles, books, videos, etc. if you have them.
• Ground truth / best-supported explanation: What do reliable sources or available evidence actually indicate?
• Anything uncertain or disputed: If the evidence isn't conclusive, that's useful to know too.

We're not asking you to agree with the theory, and we're not trying to mock or attack anyone's beliefs. We're interested in collecting claims and then carefully documenting what the available evidence says about them.

Examples could be anything from:

• Ancient civilization / lost technology theories
• UFOs / extraterrestrials
• Secret government projects
• Archaeological mysteries
• Historical conspiracies
• “Cover-up” theories
• Strange scientific claims
• Weird internet rabbit holes

The more obscure, bizarre, or specific, the better. lol

If you know a theory that you think would make an interesting test for an AI, drop it below.

I recently came across a topic which I found really interesting.

[[https://arxiv.org/html/2507.00078v1\](https://arxiv.org/html/2507.00078v1(The) (](https://arxiv.org/html/2507.00078v1](https://arxiv.org/html/2507.00078v1(The) () The Language of time : a Language model's perspective on time series)

Though I haven't read the paper in too great of a detail, the  paper represents a time series by dividing it into fixed-length patches and using K-Means to map these patches to a finite vocabulary of discrete tokens. This made me wonder whether a similar idea could be extended to more general multidimensional numerical data.

I have since come across a few related works, including Byte Pair Encoding for Efficient Time Series Forecasting*(*https://arxiv.org/abs/2505.14411), as well as recent work on discrete representations for multivariate time series and robot trajectories. These use more specialized approaches such as learned vector quantization and residual vector quantization.

I realized that instead of treating multidimensional data as one large high-dimensional object, it may be useful to view it as a collection of parallel one-dimensional numerical streams. Each dimension could then be discretized independently using the patching and K-Means approach from The Language of Time, giving each dimension its own vocabulary of tokens. Or maybe even the binning based approach of the 2nd paper might be used.

For example, a multidimensional state could then look like

x_t=[Ai , Bj , Ck...]

while retaining the identity of each dimension. A Transformer could subsequently model the evolution of each dimension's token sequence, with a separate mechanism potentially learning interactions between dimensions.

I would be very grateful for any thoughts on whether this factorized representation has been explored before, or whether yall see any fundamental issues with the approach.

I'm a student experimenting with a research direction around AI-assisted scientific/engineering workflows, and I'd really like some input from people who actually work in scientific computing.

The basic idea is pretty simple:

Problem
  ↓
AI / solver generates candidate
  ↓
Numerical method / simulator / domain tool
  ↓
PASS → result
FAIL → diagnostics → another attempt

The important part is that the generator isn't trusted to verify itself.

I've already built some small prototypes around this idea. For example, I've used GPT/Gemini APIs with independent verification for ODEs and logic constraints, and I've also experimented with a Qwen-based legacy-code modernization workflow where generated Python is executed and compared against the original program's behavior.

I'm now more interested in the scientific-computing side.

I'd like to eventually build modules where something like:

• numerical ODE/PDE solvers
• optimization
• inverse problems
• parameter estimation
• time-series methods
• Bayesian methods
• simulation

could act as candidate generators or verification environments.

But I don't want to decide the applications myself.

For people working in scientific computing: what problems do you encounter where generating a candidate solution is difficult, but independently checking the result is relatively well-defined?

For example, I'm particularly curious about:

• inverse problems
• parameter estimation
• numerical modeling
• simulation
• optimization
• scientific code generation
• model calibration
• anything involving expensive computational workflows

I'm also wondering whether this is actually a useful abstraction or whether I'm just describing things people already routinely do with numerical software and validation.

I'd especially appreciate examples from real scientific-computing workflows rather than hypothetical AI applications.

I'm trying to figure out what problems are genuinely worth building around before I go much further.

THIS WILL BE A LONG AND A BIT DETAILED POST , PLEASE READ THROUGH IT, THANK YOU FOR YOUR TIME AND PATIENCE

So I had posted before on this sub, the sadhana given by people have helped me a lot, I feel a lot more stronger, disciplined and focussed ( though I couldn't do it 100% meticulously due to the heavy course work of my institute, and my shitty oversmart athiest room mate who always passes comments, but now he has left and my room is all mine, no room mate).

I have completed my 3rd year and now I have summer vacation, I am at home now. But I am facing a few more problems. I am pursuing physics major, which is not that industry relevant, hence placements are very tough. So I started to acquire new skills, particularly in hardware and chip designing using open source tools( like ASIC designing in openLANE , analog-digital mixed signal processing in QSPICE).

I have some really cool personal projects exploring potentially novel methods of computation and aiming to overcome traditional bottlenecks. Now this March I had contacted a Professor in IIEST , he was impressed by my work , and said he will provide me with more work and necessary hardware but he has ditched me now after all promises. My institute professors ( I am in IISERP ) are not very supportive either, I asked for FPGA access to try out a novel matrix multiplication algo which may reduce energy requirements for edge AI chips, but no response.

I am lost, these 3 years of my college life I have wasted, I dont have any formal interships ( just an informal sem project), I have skills but no recognition . I really wanna pursue higher education in Europe) UK, Germany) or US/Australia , related to hardware design and unconventional computing.

So I am asking if anyone here can provide any sadhana which can help me achieve my goals, and please trust me when I say this , I am already working very hard and am ready to work even harder for my upskilling and am ready to sacrifice anything to achieve my goals, I dont want to fail at any cost. I know I have wasted time, I was too busy trying to "find my interest", I shouldn't have done that , but I am willing to do anything to make up for it

So if possible , I am looking for a sadhana which can help me be more focussed and help me overcome planetary afflictions if any......PLS HELP

Thankyou so much for for your time

My previous post on this sub :- https://www.reddit.com/r/Tantrasadhaks/comments/1ppkdhk/need_some_help_advise/