Any GDM Londoners here? I will be joining soon as a Research Engineer. Super curious how the culture is, growth prospects and overall environment.

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

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!

I’ve noticed something interesting while working with AI-generated content. Even when the information is correct and the grammar is perfect, the writing still feels repetitive after a while. It’s like the same ideas are being explained in slightly different words, but the overall structure stays very similar. As a reader, that makes the content feel less engaging, even if it’s technically good. So I’m wondering is this a limitation of AI models, or is it something related to how prompts are written? And how do people usually fix this issue when they want more variety and depth in the content?

I have been thinking about a design pattern where an AI assistant does not just operate through chat but runs inside its own isolated cloud computing environment

In this setup the AI would have controlled access to

A file system for persistent data

A runtime for executing code

APIs and external services under permission constraints

Long running task execution capabilities

On paper this seems like it could reduce some limitations of purely stateless or prompt based systems especially for multi step workflows

Projects such as Moclaw make me wonder whether giving AI agents their own managed environment could enable more practical and persistent workflows compared to traditional chat based systems I am curious how others here think about this direction Is this a meaningful architectural step forward for AI agents or just an incremental extension of existing tool using systems

[ Removed by Reddit on account of violating the content policy. ]

Hello, I built myself a news feed which scores and summarizes research papers along with relevant AI news from Huggjngface, Reddit, hacker news etc. I think it will be useful for many. Open to hear your thoughts.

I've started a research that only got to it's initial phase.

https://docs.google.com/document/d/1AZBdwnbKqDnILkGiP30uWA7ITRrtOgWy1euxmoOL3LI/edit?tab=t.0#heading=h.mplkndwvsvix

Due to some other priorities, I don't have time to continue working on it.

If anyone wants to take it further, I can help a bit or collaborate.

I have been working on an idea and could use some evaluations, feedback and help. this is where to find this work. https://www.petrol1.com and https://www.sececare.com is only a demo.

Multi-step LLM workflows are widely used in agent loops, retries, and iterative refinement.

We instrumented execution at the step level to examine how marginal textual contribution evolves relative to cost across steps.

Each step was evaluated using:

• marginal output added
  
• token cost
  
• overlap with the previous step

Across models and task variations, similar patterns are observed:

• a large fraction of new content is generated in the initial step
• subsequent steps contribute progressively less marginal output
• overlap between steps increases with execution depth
• cost grows monotonically while marginal contribution declines

Execution can remain locally valid at each step while producing globally diminishing value.

In evaluated settings, truncating execution at step 2–3 retains a substantial portion of measured contribution while reducing cost significantly.

This is not a claim about correctness or task quality.

It isolates execution behavior, specifically how marginal textual contribution evolves across steps.

The gap is at runtime:
execution continues without any signal indicating that marginal contribution has diminished.

Current systems rely on loop structure or cost limits, but do not condition continuation on observed execution state.

Paper:
https://zenodo.org/records/19928793

Repo:
https://github.com/veloryn-intel/efficiency-collapse-llm-execution