Actualités sur Mistral et LLM - 13 avril 2026

Mistrallundi 13 avril 2026

4 articles analysés par IA / 10 total

Articles pertinents

LLM Dictionary: A reference to contemporary LLM vocabulary [P]

There is now so much technical knowledge about the transformer/LLM/AI space that each niche tends to have it's own vocabulary with scattered information sources. This is my small attempt at addressing the problem of scattered information sources that are published once rather than maintained over time. LLM dictionary is built to be extensible by design and owned by the community. Add one json file to create an entry and that's it (the contributing card has everything you need) Link: https://llmdict.is-cool.dev/ Github: https://github.com/aditya-pola/llmdict https://i.redd.it/lvfkhcg0ixug

Reddit - r/MachineLearning · 13/04/2026 09:45:04

Grooming Loop (GL): Rule-based detection of progressive influence in message sequences (preprint + demo) [D]

I’ve published a short preprint describing a rule-based method for detecting progressive emotional constraint across message sequences. The model identifies trajectories where an expressed state is reframed, replaced, and stabilized over multiple messages. Preprint: https://doi.org/10.5281/zenodo.19550683 Demo: https://www.transmissionorigindiagnostics.com Looking for feedback on robustness and failure cases. submitted by /u/Meditativetrain [link] [comments]

Reddit - r/MachineLearning · 13/04/2026 08:24:32

Implementation details of Backpropagation in Siamese networks. [D]

Hey Folks, Could someone please share correct implementation of backprop in siamese networks? The explanation on the original paper is not super detailed. I found this random implementation on github, ref. The inputs are passed one after the other, loss is computed for the last two inputs and the weight is updated after. Is this the correct implementation? Another implementation I could think of is to have two copies of same networks like Bi-encoder. Two inputs are passed simultaneously, loss is backprop'd and weights are updated for both the networks, and both network weights are replaced

Reddit - r/MachineLearning · 13/04/2026 06:10:00

[ICML 2026] Extending the deadline for reviewer final justifications while not extending for Author-AC comments was a huge mistake [D]

Just as the title says, I believe the decision to extend the deadline for reviewers to post their final justifications while not allowing authors to contact their ACs was a big misstep. I have a reviewer who, in their final justification is questioning the reliability of experimental setup and evaluation, as was as the fairness of comparison, issues that were never brought up during the initial review or their response to our rebuttal. It seems as though they were looking for reasons to justify not wanting to move their score from weak accept. It now feels like, despite having otherwise strong

Reddit - r/MachineLearning · 13/04/2026 03:48:01