brandoncarl
Joined 14 years ago
Joined 14 years ago
The topic of LLM jailbreaking is an important one. As machines increase in power their ability to benefit society increases, as does their ability to harm society.
This paper intends to address the question: "Is it possible to create a perfect universal guardian against LLM jailbreaks?". This means:
Universal: applies to all prompts and input combinations Perfect: is able to successfully distinguish harmful versus not harmful Computable: is feasible to compute We show that this combination does not exist and Request for Comment.
A more feasible approach involves post-hoc filtering of results, which involves an easier problem of decideability. In order to implement such approaches, both reasoning traces an intermediate code changes would likely need to be hidden from the user until the results are determined.
Note: this paper was written in conjunction with ChatGPT 5.5 Pro. The topic of the reducibility of such problems to The Halting Problem has been top of mind for me for some time. In one sense, it is a trivial conclusion. Yet it is one of high importance.
However, as document lengths increase: 1. It is wasteful to put the entire context in memory 2. It can reduce accuracy by providing less relevant information 3. Increase the possibility of errors during edits
Inspired by Cloudflare's Markdown Content Type and Code Mode, we propose a lightweight document object model for Markdown to: 1. Reduce costs 2. Increase the accuracy of locating key sections 3. Increase speed 4. Increase the accuracy of editing documents
https://www.amazon.com/gp/product/B07F1DB2F7/ref=ppx_yo_dt_b...
On timing – I just had to start planning a bit better. In worst case I'd start the meeting without video and turn on a minute or two in. Well worth it for me.
I've had dozens of people comment how clear my camera is. Many now purchased a similar setup. I used OBS for a long time since it was free, but the quality wasn't close.
If you're in a line of work that requires you to stream it's a worthwhile investment by a great company that just keeps getting better.
As you can see, the rates don't map to any significant overcollateralization.
To do this correct you'd need purchases and sales versus assets.
All that said, it's why I put a rough estimate of 30% (instead of 60%) and hadn't included any losses in broader commercial paper (which has dropped). So the overall estimate is defensible.
Also agree on Basel 2-like risk. The primary issue with Tether is the marketing around stability and the transparency. If it was treated like any other "money market" with better NAV reporting, position reporting and rules around withdrawals then we'd mitigate some of the risks.
There are attention mechanisms that help to mitigate this, but you can clearly see in the chart that Tibo posted that the attention mechanisms they are using are still scaling quadratically.
This becomes an optimization problem for token t:
min f[cost, quality loss] = compaction cost[t] + compaction quality loss[t] + token cost[t]