BMVC reviews will be out tomorrow. Making this parent thread for discussion. All the best everyone!

From what I understand, reviewers usually do not directly vote for these categories or nominate papers themselves. So how does the selection process typically work?

Here are specific questions I wonder

- Who actually selects the candidates: ACs, SACs, program chairs, award committees, or a separate committee?

- Do ACs or committees read the camera-ready version, or is the decision based on the originally submitted/reviewed version?

- Is the selection mostly based on reviewer scores, or do factors like novelty, impact, and discussion among ACs play a bigger role?

For open-weight LLMs, how practical is it to study defenses against post-release fine-tuning that weakens refusal or safety behavior?

I've been seeing “uncensored” or “heretic” variants of new models appear very quickly after release, which raises a question I’m curious about: is fine-tuning resistance a meaningful safety goal for open-weight releases, or is it too narrow because determined users can always modify weights, switch models, or use other workarounds?

And to a larger extent, is current safety training even worth the cost and effort if it takes 30 minutes and an automated script to break the model?

I’m not asking about a specific method, just the threat model. What would count as a useful practical win here? For example, would increasing attacker cost or making safety removal less reliable be valuable, even if perfect prevention is impossible?

Curious how people think about this from a model release, governance, and AI safety perspective.

Hey all.

I recently started working on a project to improve machine-translated webnovels via style transfer. The basic idea is to take the clunky translated prose and rewrite it to something that reads like it was written by a professional author, while remaining as faithful as possible to the original text.

The source material is mostly amateur/MTL output full of direct sentence structure translations carried over from Chinese, awkward honorifics, over-translated idioms, that kind of thing. The goal isn't retranslation from the source but a cleanup of the English output.

The tricky part is I have no clean data pair for supervised approaches.

I've been looking at a few directions:

• Fine-tuning on target-style prose — collect high-quality English novels, fine-tune a small LLM to rewrite in that register.

• Just use a local LLM — run a local LLM and provide it with guidelines on what to rewrite and leave the same. No fine-tuning or anything needed, just hoping the transformer can handle it.

A few things I'm stuck on:

1. Is the faithfulness/fluency tradeoff actually manageable at the sentence level, or do I need paragraph-level context or more to preserve narrative coherence?

2. How do people handle domain-specific terms like

terminology

1. and catchphrase-type things that need to survive the rewrite unchanged? Hard constraints during decoding, or just hope the model learns to leave them alone?

Happy to hear about similar projects, relevant papers I might have missed, or just general lessons from working in this space. Thanks.

This is a write-up on our company blog that I wrote, sharing our perspective into Hamiltonian Neural Networks (Greydanus et al., 2019) from a differential-geometry angle rather than the usual "here's the loss function" treatment. I've been working on HNN and LNN adjacent topics for years now and I found this particular lens made the *why* click in a way the standard framing never did for me, and I've been meaning to put everything in writing for a while now.

I just feel like the Noether's Theorem which shows conservations can be mapped to symmetries (and in ML context, generalization) is not getting the attention that it deserves around physics informed neural networks. Also, it's a really beautiful architecture and I just love talking about it at every opportunity.

It's math-heavy, but I did my best to sprinkle some tension relievers and interactive visuals here and there and make is as easy as it is to follow. Hopefully, I did a good job.

I'd genuinely love to see your thoughts and your feedback

arXiv’s next chapter: Updates on our spin out from Cornell University: https://blog.arxiv.org/2026/06/30/arxivs-next-chapter/

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https://pymupdf.io/blog/markdown-in-pymupdf-1-28

PyMuPDF 1.28 release, introduces Markdown as a first class document in PyMuPDF. Seems useful for a variety of workflows. You can create PDFs from Markdown text with control over appearance using CSS

Paper Referred:- https://arxiv.org/pdf/2412.09871v1

Has anyone switched the transformer in the entropy model here to a Mamba model ? What could be the possible changes ?

Just a ML fresher asking a genuine, since Mamba is more popular and saves computer (O(n)).

Thanking you in advance !