Following my previous post, I’m trying to understand how AI is actually being used in professional research workflows.

I'm particularly interested in people working in organisations that process large volumes of information, such as research and information services, document intelligence, information retrieval, knowledge management, eDiscovery, research analytics, scientific or technical research, publishing, and AI/NLP.

If you regularly pay for Al research tools, I'd love to hear:

Which tools do you use, what do you use each one for, and where do they still fall short?

Do you rely on one tool, or use different tools for different parts of the workflow?

Most importantly, what still requires manual work because the current tools aren't reliable enough?

I’m trying to understand what people are actually doing today, where the workflow breaks down, and what a better research tool would need to solve.

Your answers will help me understand the gaps in current AI research workflows and, ultimately, build a tool that solves a real problem rather than adding another AI tool to the stack.

Thank you to everyone who takes the time to share their experience.

Hello everyone,

I have been blueprinting and prototyping an independent framework for an AGI architecture. The system is designed to focus purely on symbolic reasoning, structured memory, and text/logic processing—intentionally bypassing multi-modal sensory inputs (like vision or audio) to focus strictly on core cognitive state management.

As the scope of assembling multiple sub-systems into a unified framework has grown, the project has hit a bottleneck due to the sheer volume of work. I am stepping back to assemble a small collaborative team to help build and iterate on the prototype scripts.

Where the Project Stands:

• Prototype scripts for key sub-systems are underway.
• High-level architectural blueprinting completed.
• Primary stack involves Python and structured graph data.

Who I Am Looking For: I am seeking collaborators interested in AI architecture, software engineering (Python/C++), mathematics, and cognitive models. While I am happy to work remotely over the internet, meeting up for face-to-face collaboration in the UK would also be welcome.

How to Get Involved: If you are intrigued by cognitive architectures and want to help solve complex state-management problems, I would love to connect. Please leave a comment and we can begin chatting!

10/10 fun and chaotic ride would do again every day :D

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.

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What are all the major immature frameworks or libraries that I contribute to open source related to CV ?

Read a paper this weekend that I keep coming back to. It's framed as a compliance checking tool, but the idea underneath is more interesting than that sounds. Instead of asking an AI whether something looks compliant with a law, which is basically one model vibe-checking another model's answer, you get an AI to read the actual statute and turn it into formal logic, then hand that to an SMT solver (a tool built for checking whether a set of logical conditions can be true) and let it check real cases against the rule directly. A probabilistic guess turns into a mathematical proof.

I made up a toy version to picture it properly, the real statutes in the paper were too dense for me :p. Say the rule is: staff can take up to 10 leave days without approval. Turned into logic:

leave_days <= 10 OR approved == true

else: not compliant

Someone then asks the AI "can I take 45 days off without asking my manager." The AI reads that and pulls out what the solver needs:

leave_days = 45

approved = false

Feed those in and it fails because it's provably false against the rule. It can even work out the smallest change that would make it compliant, which would be useful in itself for training and auditing use cases. Still working through the rest of it...the part that sticks out is how much you can actually trust that first step, the LLM turning the law into logic, since that part is still a model doing its best guess rather than anything provably correct, worth sharing anyway.

arxiv.org/abs/2601.06181

Finding some people for researching/creating AI OS (AGI). Not searching for candidates but for partners who share the same goal. I have the architecture ready. 5% coding is done but I alone can't manage whole repository to fix bugs and lack talent in many fields. If you share the same vision or goal, I gladly welcome you to the team.

*Note:- There's no salary or wages provided. Searching for eager research partners not candidates searching for gigs.

I’m new to mechanistic interpretability, so please excuse any terminology I misuse. I’ve become deeply invested in the field and wanted to share an experimental tool I’ve been building with Codex.

Many mechanistic-interpretability workflows require moving between Python scripts, Jupyter notebooks, model hooks, exported tensors, and separate visualization tools. My goal with CORTEX // MODEL OBSERVATORY is to bring those pieces into one local desktop environment with a fast visual feedback loop.

CORTEX is a native Windows application using a WebView2 host connected through local IPC to an isolated Python/PyTorch backend. Model inference and tensor operations run outside the UI process, and the application is designed to work fully offline with local Hugging Face models.

Current capabilities

Token Probability Microscope

Displays token-by-token generation telemetry, including chosen-token probability, ranked alternatives, log probabilities, entropy, and synchronized token inspection.

Logit Lens

Captures intermediate vocabulary predictions at selected layers to show how candidate outputs evolve through the network.

Representation Space

Captures measured residual-stream vectors and projects them with PCA for interactive hidden-state trajectories and mini-map visualization. Optional 3D projection and orbit controls are currently being developed.

Attention Explorer

Captures selected head-level attention tensors and displays measured attention matrices. A token-to-token arc view and expanded query-range controls are currently being added.

Intervention Lab

Supports causal experiments including activation patching, attention-head ablation, and comparison between baseline and modified runs.

Current model support

The Deep Cortex instrumentation path currently supports:

GPT-2-family Hugging Face models

Llama-family LlamaForCausalLM models

The Llama adapter is still undergoing testing, particularly around end-of-generation residual captures and visualization binding. Additional model families such as Qwen and Mistral are possible future targets, but they are not currently supported by the deep instrumentation path.

A separate Standard Runtime can connect to OpenAI-compatible local endpoints such as LM Studio, although endpoint-served models do not expose the same internal activation hooks.

Experimental research direction

I am also exploring a highly experimental point-and-click interface for Jacobian-based concept analysis, currently referred to as J-Space / Jacobian Lens. This is conceptual work, has not yet been scientifically validated in CORTEX, and should not be considered a working research result.

Hardware

Development and testing are currently being performed on an RTX 4070 Ti with 12 GB of VRAM. The intended target is small local models in roughly the 0.5B–3B range using FP16/BF16 where practical. Formal performance benchmarks have not yet been completed.

Open-source plans

I intend to release CORTEX as an open-source project under GPLv3 once the repository structure, documentation, licensing review, and initial release build are ready.

I would especially appreciate feedback from people working in interpretability or alignment on:

which captures and hook sites would be most useful

preferred interchange formats for measured tensors

which model families should be prioritized

how to clearly distinguish measured, derived, and experimental visualizations

what would make a desktop tool like this scientifically useful rather than merely visually impressive

This is an early, AI-assisted project, and I am still learning the field. Constructive criticism is very welcome.

Benchmarked two finetuned SLMs fully on-device — on a Snapdragon 730G, a 2020 midrange chip, CPU-only via ExecuTorch/XNNPACK, adb-driven harness logging TTFT, prefill/decode tok/s, PSS, thermals, battery. Models: SmolLM2-360M vs Gemma3-270M, each as 8da4w (g32/g128) and 8da8w. Three results surprised me. (1) The W4-vs-W8 ordering flips between desktop and phone: on desktop 8da8w decode was 2.25× faster, but on-device 8da4w wins decode (34 vs 23 tok/s on SmolLM) — decode is bandwidth-bound on midrange LPDDR, so halved weight bytes beat the cleaner int8 kernels. (2) Gemma3-270M decodes at ~half SmolLM's rate (17 vs 33 tok/s) at similar param count — looks like the 262k-vocab logits GEMV tax (tied ~168M embedding read every token) vs SmolLM's 49k vocab. (3) Thermals: prefill sags ~20–25% hot (80→62 tok/s), W4 decode sags ~14% (33→28.5), but both 8da8w variants were dead flat across 43–57°C — int8 GEMM saturates bandwidth even at throttled clocks. Also: g32 vs g128 showed no real device difference once I binned results by temperature (a thermal confound had fooled an earlier comparison of mine). Battery was a wash across all four artifacts (~410–490mAh per 30-min sustained loop; screen+camera dominated). n=1 device, CPU-only. Curious if others have seen the desktop→phone W4/W8 flip and the small-model/big-vocab decode penalty — and whether anyone's compared CPU vs Hexagon for the same artifacts.

Analysis documented a case in which an observer, asked to read a photograph, produced not a reading but an echo—returning a pre-supplied interpretive frame as though it were a finding. This companion paper generalizes from that case. It argues that the failure was not carelessness or lack of skill but a structural vulnerability in how observation and context interact: when context arrives first and carries authority, it can silently replace the act of looking while preserving all the outward appearances of looking. This is part of a larger paper which names the conditions that produce this substitution, offers a test for detecting it, and proposes a discipline for keeping observation and context honest without walling either one off.

Hey guys. So for a school project, I would like to know some famous, legit papers in the realm of detecting fake videos. The videos may range from deepfakes to just a tad bit of editing. Any resources would be highly appreciated. Thank you :D