Dañu koy yeesal bis bu nekk3929 jaar-jaar yuñ firnde
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Bépp jaar-jaar dafay lëkkale ak firnde yi gëna am doole: balluwaay yu njëkk yi suñu ko amee, luko moy rapoor yuñ joxe ci anam wu leer.
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Yeneen jaar-jaar
9 jaar-jaarYeesal
EventTime paper proposes AI method to estimate market losses after cybersecurity disclosures
An arXiv preprint introduces EventTime, a machine-learning framework that combines market history, event metadata and contrastive learning to estimate short-term financial losses after cybersecurity disclosures.arxiv.orgYeesal
Preprint dafay xalaat anam wu yomb ngir gëna mëna xayma wóolu LLMs black-box
Benn arXiv preprint dafa wax ni classifiers yu yomb yuñ tàggat ci model confiance scores ak njariñu laaj yu noonu mel mën nañu gëna xam ndax tontu model làkk yu mag yi jub nañu.arxiv.orgYeesal
Këyitu FleetSieve dafay digle ay profil yuñ jagleel flot LLM yu xam SLO
Benn arXiv preprint dafay wane FleetSieve, ab pexem profilage buy xoole ci natt yi mëna soppi LLM flot allocations boole ci xayma kàttan ak geen-latency yam.arxiv.orgYeesal
SAGE-XGBoost paper reports stronger landslide and wildfire mapping with scarce data
An arXiv preprint proposes SAGE-XGBoost, combining spatial graph embeddings, data augmentation and XGBoost for natural-hazard susceptibility mapping with limited labeled data. It reports substantial gains over Spatial XGBoost in landslide and wildfire case studies, while key validation details remain unknown.arxiv.orgYeesal
FlashPrefill V2 paper reports large long-context serving speedups with block-sparse attention
An arXiv paper describes FlashPrefill V2, a block-sparse attention system for long-context LLM serving, and reports speedups of up to 47.26x over FlashAttention-2 at 128K context on NVIDIA H20 GPUs.arxiv.orgYeesal
SWE-bench Science benchmark finds coding agents struggle with scientific software
A new arXiv preprint introduces a 119-task benchmark for testing coding agents on scientific software and reports that the best-performing agent scored below 50% on pass@1.arxiv.orgYeesal
ArXiv këyit dafay laaj tàggat yaram bu lalu ci jaar-jaar yu am solo ngir ndawu LLM yu am horizon bu yàgg
MileGPO dafay jëfandikoo gis-gis yu am solo ak firnde yu am ci gox bi ngir gëna mëna joxe leble suñuy tàggat ndawu liggéey yu am modelu làkk ci liggéey yu gudd te bari jéego. Auteur yi dañuy wax ci resultaa yi gëna xarañ ci ALFWorld ak WebShop, waaye waxtaan yi ñu ngi yam ci jàngat yi ci këyit bi.arxiv.orgYeesal
Preprint tests whether LLM agents know when to remember, verify or ask
A new benchmark evaluates whether language-model agents correctly decide when interaction-derived information should be saved, checked, used temporarily or clarified with a user.arxiv.orgYeesal
Saytu yoon ci diggante làkk yi ci misaalu làkk Watermarking
Dañu nara amal benn kaadaru jàngat bu bees ngir watermarking ci misaali làkk yu yaatu, mu lalu ci yoon ci diggante làkk yi.arxiv.org
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