u/St_Cur

I just finished reading Five Little Pigs (sorry, this is the second novel of Agatha I read. The first one is Roger’s Case)

The ending is so long that I can’t wait to share it.

What I remember most is Caroline Crale’s vulnerability and resilience, and Elsa’s fatal ignorance of real emotions.

Elsa realizes that Caroline is pitying her （for a young girl and she simply didn't understand mature, complex emotions, and that ignorance drove her to her fatal mistake.)

The whole ending gives me this feeling of traditional Chinese artistic 'liubai' (the concept of leaving blank space in paintings). It’s beautifully understated, leaving so much unsaid, which gives the tragedy a profound, lingering resonance that is absolutely unforgettable

I am currently researching defenses against VLA visual front-end adversarial attacks. The paper "Exploring the Adversarial Vulnerabilities of Vision-Language-Action Models in Robotics" mentions three attack methods, which are

\- UADA: Action Difference Untargeted Attack

\- UPA: Position-Aware Untargeted Attack

\- TMA: Targeted Manipulation Attack

In my opinion, these attacks also exploit the need for VLA to chunk/tokenize action trajectories

So, if we extract the core ideas of the X-tokenizer paper from a defensive perspective, perhaps it would be something like this?

SRQ

\- It's clear that it can defend against UADA very well

\- But what about UPA and TMA?

Next-frame feature prediction

\- Can this solve UPA?

VL Contrastive Alignment

\- Can this be used to solve TMA?

I am currently researching defenses against VLA visual front-end adversarial attacks. The paper "Exploring the Adversarial Vulnerabilities of Vision-Language-Action Models in Robotics" mentions three attack methods, which are

- UADA: Action Difference Untargeted Attack

- UPA: Position-Aware Untargeted Attack

- TMA: Targeted Manipulation Attack

In my opinion, these attacks also exploit the need for VLA to chunk/tokenize action trajectories

So, if we extract the core ideas of the X-tokenizer paper from a defensive perspective, perhaps it would be something like this?

SRQ

- It's clear that it can defend against UADA very well

- But what about UPA and TMA?

Next-frame feature prediction

- Can this solve UPA?

VL Contrastive Alignment

- Can this be used to solve TMA?

I am currently researching defenses against VLA visual front-end adversarial attacks. The paper "Exploring the Adversarial Vulnerabilities of Vision-Language-Action Models in Robotics" mentions three attack methods, which are

• UADA: Action Difference Untargeted Attack

• UPA: Position-Aware Untargeted Attack

• TMA: Targeted Manipulation Attack

In my opinion, these attacks also exploit the need for VLA to chunk/tokenize action trajectories

So, if we extract the core ideas of the X-tokenizer paper from a defensive perspective, perhaps it would be something like this?

SRQ

• It's clear that it can defend against UADA very well

• But what about UPA and TMA?

Next-frame feature prediction

• Can this solve UPA?

VL Contrastive Alignment

• Can this be used to solve TMA?

Recently, I have been studying the optimization of the reasoning performance of meteorological models such as GenCast 、panggu and so on but I am completely 0 in this field.

At present, all I know is that the best time to do the model reasoning, followed by the lightweighting of the model, and finally the performance of the model itself.

But I don‘t know how to do it.

Where should I find the entry or information?

Is this question a little stupid?