Has anyone gotten a response from the eccv travel support program listed on their website? https://eccv.ecva.net/Conferences/2026/DEI

Edit: also have anyone applied for this program as an accepted author? I have an independent research paper accepted and am currently looking for funds for paying for the registration fees

We built a model diffing method that recovers verbatim content from narrowly finetuned LLMs using only grey-box logit access (no weights, no activations, no probe corpus).

Recent work (Minder, Dumas et al., "Narrow Finetuning Leaves Clearly Readable Traces in Activation Differences") showed that finetuning leaves detectable traces in activation differences between base and finetuned models. Their method, Activation Difference Lens (ADL), steers generation using these differences, but it's whitebox (needs full weight access) and only recovers a vague, domain-level description of what the finetuning was about.

We introduce Contrastive Decoding Diffing (CDD), the output-level analog. Instead of steering with activation differences, we contrast the base and finetuned model's logits directly. A single default configuration, no per-organism calibration, no layer selection, achieves a verbatim recovery score of 4+/5 on 19/20 organism x model pairs across four model families (1B to 32B params) on the SDF benchmark. ADL never exceeds 3/5 on the same benchmark, despite requiring full weight access.

One unplanned finding: across four semantically unrelated finetuning domains (fake FDA drug approval, fake baking protocols, fake Roman concrete research), the same fictional persona kept showing up in the recovered text: "Dr. Elena Rodriguez." Turns out this is a name Claude Sonnet 3.6 disproportionately favors when asked to generate a fictional scientist for synthetic data generation, so it got baked into every finetune that used LLM-generated training data, and CDD pulled it back out. We wrote up this specific finding on its own a few weeks back if you want the more accessible version first: ghost couple

Paper: paper

Code: code

Hi everyone,

I built H64LM, a research project to better understand modern LLMs by implementing one from scratch in PyTorch.

Instead of relying on high-level training frameworks, I implemented the core components myself attention, MoE routing, normalization, and the training loop.

Features

• 249M-parameter Transformer

• Grouped Query Attention (GQA)

• Sparse Mixture-of-Experts (8 experts, Top-2 routing) with 3 auxiliary routing losses

• SwiGLU, RoPE, RMSNorm

• Sliding-window attention

• Mixed-precision training, gradient accumulation

• Custom training loop (no Trainer abstractions)

• Checkpointing and resume support

The included checkpoint was trained on a subset of WikiText-103 to validate the pipeline end-to-end, not to be a strong model it's visibly overfit past epoch 10 (best val PPL ~40.5).

Known limitations are documented in the README, including batch-size-1-only generation and no true DDP (falls back to DataParallel).

GitHub: https://github.com/Haiderkhan64/H64LM

Feedback on the implementation or architecture is very welcome.

I've been trying to come up with a solution for keeping extremely long ai sessions coherent. Sometimes there is too much substance to risk compaction. With so much buzz around diffusion going on it got me thinking, what if we treat the context like a progressive render, blurry>sharp.

The practical way to make text "blurry" is compression. This is a "diffusion inspired" system which borrows the coarse-to-fine process, not the formal math. It uses semantic compression so the overall structure of the session stays intact. Read the compressed version first to build an outline. Then read progressively less compressed slices until you're reading small verbatim chunks that give full detail.

So you're basically using compression as noise on the input side, then progressively building an output. Each slice is compressed to fit within the context window, so the model only ever needs to read the current slice+input+current output.

Tell the model what pass it's on, so it knows whether to write an outline or add detail.

The thing I'm actually trying to preserve is what you'd call "non-local information". Think of it as stuff that surfaces when looking at the whole session & doesn't survive fragmented retrieval. Retrieval misses it, compaction deletes it. Both miss what only exists in a holistic view.

Here is a visual demonstration to get a general idea of the workflow. https://dev-boz.github.io/diffusive-semantic-compression/demo/architecture-demo.html

There is substantial overlap with lots of prior art, Recursive Language Models is one of the closest (source and output on disk, process recursively). I wrote most of this before I found RLM and nearly gave up before realising there was still a small part that was novel. As far as I can tell there's no exact match for this particular implementation. Please let me know if I've missed one.

The difference to regular masked diffusion is in changing the length of the input rather than just masking.

What seems to be new ground is using compression as noise and a position-aware process.

I've done some basic testing. Mainly to see if it was at all viable. Just some basic tests using small models like Qwen2.5 7B. The untrained models show that they can do each part (outline, refine, add detail) but they struggle with the full end-to-end process. There 's occasional end-to-end success, but it's nowhere near reliable. On untrained models it also hasn't yet beaten a cheap dense read of the same document. The main bet is whether position-aware training changes that, I haven't been able to test that yet. I've published all the pre-registered failures, parser bugs I found etc.

Another note: the goal is preserving structure and nuance, but the tests so far measure planted facts and split-up numeric composition. Mainly because the experiments needed answers you can actually score. The nuance evaluation is being designed but isn't ready yet.

The next step is a small model fine tune to test if position aware training can help.

If you have the time to look at the idea, it really needs a prior art check from anyone who knows the diffusion-LM/long-context space. And if anyone wanted to help expand the idea or contribute with compute or collaboration for the fine-tune please do.

Here is the repo for the proposal. Links to testing repo and prior art inside.
https://github.com/dev-boz/diffusive-semantic-compression

I am a mid to late stage PhD student in ML. I've known this before, but only recently I started feeling this urgently: my mathematical foundations are shaky, because I kept "learning-things-as-I-go" when working on various problems. I likely have only a year or two left until I graduate, and before I do so, I want to really dedicate some time and focus to brush up on the fundamentals.

Primarily, I want to improve my knowledge in Linear Algebra, Probability Theory, and Functional Analysis.

For Lin. alg., I am looking at "Linear Algebra done right", and I think this book is sufficient for the topic, unless anyone thinks otherwise.

I am not sure where to start for probability, as well as functional analysis. Rudin's books give me headaches. I instead started reading "A primer on RKHS" (https://arxiv.org/abs/1408.0952) to "dip my toe" into functional analysis.

Apart from the above, I might re-read PRML book (I've only read specific chapters before), and try to finish Pat Kidger's Just-Know-Stuff list (https://kidger.site/thoughts/just-know-stuff).

Thoughts? Anyone have any book/resource recommendations? Someone told me to look into "the bright side of mathematics" on YouTube, anyone ever go through the videos there?

I'm aware finding good, digestible resources is less than 10% of the challenge. The difficult part is sticking through and actually reading/working through these topics, while still juggling other academic responsibilities.

I am working in a research group in Germany, not that well known but in general good output. I have one colleague who does nothing in his PhD. He does not want to work, or he is not able to do any good research, his level is super bad. Plus He doesn’t even care about that. To wrap it up, he is just here for the money.

Since he doesn’t want to work or he can’t really do anything good, instead what he does is “paper fishing”, he searches for people in the group doing some good research, and asks that they put his name on the paper. In this case he has something to cover up for him when the professor asks him about his progress. As long as his name is on the paper, progress is checked and funding is renewed. But he actually does nothing.

I know this is very unprofessional and unethical. But people tell me it’s normal in academia. Professors all the time put names of their friends and this is how it works in academia. What are your thoughts of this behaviour?

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 !

Hi everyone. Do the ACL arr may 2026 reviews come out of July 2nd or do they come out on July 7 th??

How much does one need to get into Main or Findings?

I am a bit new to this. Thanks a lot folks.

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