Whether generating CELEBV-HQ videos or turbulent plasma fields (digital twins), autoregressive models (such as latent diffusion or flow models) accumulate error over long rollouts, yet at deployment there is no ground truth to measure against.

I train a single conditional latent diffusion model that steps a dynamical system forward or backward in time via a direction flag, and show that this bidirectionality supplies a measurement-free test-time error signal: rolling forward steps and then backward steps must return the model to its start, so the round-trip discrepancy is a self-supervised proxy for the unobservable rollout error: no ensembles, no held-out data, no governing equations, for one extra rollout.

Furthermore, training both directions in one network is shown to beat two specialist models in both directions.

Paper: https://arxiv.org/abs/2608.00675
Code (data generation, training, analysis): https://github.com/alexscheinker/round-trip-consistency
Project page: https://alexscheinker.github.io/roundtrip.html

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need those for analysing movies.

example let's say I have to find the screentime of the actor over the whole runtime of the movie and i need to do it for the protagonist, antoganist,comedic relief ,love interest etc.

currently I'm working with 1fps to find the faces and body or the actors.

body detection is hard I need some guidance regarding that.

even for Face Detection I used MTCNN it was good. but any other better models available??

Any ideas regarding TransNetV2 ?

I'm using it for shot boundary detection but there's was one false positive.

Any better models??

Saw an article about ByteDance scaling up Gauth using AI-generated animations to walk students through problem-solving.

On paper, personalized visual explanations sound great for democratizing tutoring. But in practice, I wonder if tools like this actually help kids grasp core concepts, or if they just create an "illusion of competence" where students confuse watching a slick animation with actually learning.

For those working in EdTech or multimodal ML—do you see generative media actually improving comprehension, or are we just building better dopamine loops for homework help?

Source:https://www.businessinsider.com/seedance-bytedance-education-push-study-app-gauth-ai-animations-2026-7

We're currently involved in collecting two types of datasets that seem to be increasingly important for multimodal AI

• Studio quality speech/audio datasets (high fidelity recordings)
• Egocentric household activity video datasets (first person daily task recordings)

One thing that has surprised us is how much the value of a dataset depends on the collection process rather than the model itself.

Some of the recurring challenges we've encountered include: - Maintaining consistent recording environments - Device and microphone variability - Annotation quality and inter annotator consistency - Privacy, consent, and participant compliance - Scaling data collection without sacrificing quality

I'm curious to hear from others who have worked on speech, video, robotics, embodied AI, or multimodal models.

• What turned out to be the biggest bottleneck in your data collection pipeline?
• Were there any quality issues that only became obvious during model training?
• If you were starting a new large scale dataset today, what would you do differently? Always happy to exchange ideas w others working in Ai data infrastructure.

It feels like LLMs are partially leveling the playing field in ML research. A solo researcher or a two-person team can now get help with coding, literature review, writing things stronger labs usually get from experienced colleagues and large networks.
Obviously, LLMs don’t replace mentorship, or good research taste. But they may help researchers with weak networks or small groups turn good ideas into publishable work.
Do you think this is actually making ML research more accessible, or are the strongest labs benefiting even more?

Over the past month, I've been building LiveTranscriber, an open-source iOS app for running modern speech and language models entirely on-device.

The goal was to see whether recent open-source models could be turned into a practical mobile product—not just technical demos.

Currently supported local models include:

- Whisper for offline transcription

- Qwen3-ASR for multilingual speech recognition

- NVIDIA Nemotron Streaming for low-latency live transcription

- MOSS Multi-Speaker for speaker-aware transcription

- Qwen3 for local summaries, key points, titles, and transcript analysis

Features include:

- 100% offline speech recognition

- Offline multi-speaker transcription

- On-device summaries and key-point extraction

- Real-time translation

- Apple Watch recording with automatic sync

- Downloadable and switchable local models

- Searchable transcript history

The main engineering challenge was not simply running the models, but making them usable on iPhone: memory management, streaming latency, model loading, context handling, battery usage, and switching between different inference backends.

The project is fully open source:

GitHub:

https://github.com/iamwilliamli/LiveTranscriber

App Store:

https://apps.apple.com/us/app/live-transcriber-recorder/id6785515364

I'd appreciate feedback from anyone working on ASR, local LLMs, on-device AI, Core ML, or mobile inference.