Dañu koy yeesal bis bu nekk3926 jaar-jaar yuñ firnde
Xibaar AI. Bu amul xumbaay.
IA buñu saytu bu baax ci lu jëm ci genne ay fasoŋu porodiwi, coppite ci politik, gestu ci kaaraange, ak toxu usine yi, ap ekipu njang buy def te Yàlla tax moo ko leeral ci Àngle bu leer.
Sourcing buñ firndeel
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.
Angale bu leer
Li xewoon, lu tax mu am solo, ak li ñu wara seetaan - te amul jargon.
Amul filler
Su siñaal bi sew, dunu siiwal dara ludul padding feed bi.
Yeneen jaar-jaar
9 jaar-jaarYeesal
JIT-Agent dafay laaj ñu defar harnais ci saasi ngir ndawu IA yi
Benn preprint bu bees bu arXiv dafay wane JIT-Agent, muy xeetu model buñ defar ngir defar, defar ak gëna suqali losisel yiy tegtal agent IA yi. Auteur yi dañu wax ni amnañu ay benefiis yu bari ci famiy model lakk yu bari, waaye liggéey bi des na preprint buñu xoolaat te amul benn validation ci source bi.arxiv.orgYeesal
$R^3$ dafay tàggat robot yi ñu jëfandikoo làkk wi ñuy xalaat suñuy jaay xel
Benn arXiv preprint dugal na $R^3$, ab pexem ginaaw tàggat buy jëfandikoo xalaat làkk bu amul benn coow ngir tegtal politiku jëfandikoo robot. Auteur yi dañu wax ci benefiis yi ci ñaari benchmark yuñ saytu, ci noonu lañu bàyyi performance real-world ak dayo benefiis yooyu ñu leeralul.arxiv.orgYeesal
Audit gisna ay benchmark IA physique di séddoo ay leeral yu bari
Benn preprint bu bees bu arXiv dafa xamle ni ay benchmark yu bari ci IA physique dañuy natt ay leeral yuy jaxasoo, te loolu mën na soppi ni gëstukat yi di rangee ay model ak di tànnee ay suite evaluation.arxiv.orgYeesal
Radiology Vision-Language Models Show Hidden Failures Under Data Shifts, Preprint Finds
A new preprint reports that medical vision-language models can appear reliable on familiar data while failing cross-dataset transfer, multimodal alignment and shortcut tests.arxiv.orgYeesal
ProViP uses head-aware pruning to cut visual tokens in vision-language models
A new arXiv preprint proposes ProViP, a training-free method that progressively removes redundant visual tokens and focuses pruning on the attention heads most useful for selecting critical visual information. In one reported LLaVA-1.5-7B experiment, it retained 95.9% of the original performance while delivering a…arxiv.orgYeesal
Frozen Hematology AI Models Lose Accuracy and Calibration Under Acquisition Shift, Study Finds
An audit of 15 frozen hematology, pathology and general-vision foundation models reports steep drops in cross-dataset accuracy and confidence calibration when white-blood-cell images come from different acquisition conditions.arxiv.orgYeesal
LiDAR-SAM2 uses a video foundation model to create 4D LiDAR labels without human annotation
A new preprint introduces LiDAR-SAM2, which transfers video segmentation from SAM2 to temporally consistent 4D LiDAR labeling using multi-view projection and spatio-temporal aggregation.arxiv.orgYeesal
SHIFT-LLM reports a training-free way to recover accuracy after pruning LLM layers
A new preprint describes SHIFT-LLM, a post-pruning correction method that uses lightweight linear adapters to approximate the computations removed from large language models. The authors report accuracy gains of up to 15.7 points on Llama-3.1-8B-Instruct across seven zero-shot benchmarks.arxiv.orgYeesal
YOLOEZ offers a no-code workflow for AI-based structural-defect inspection
A new paper introduces YOLOEZ, an open-source graphical tool that combines image labeling, YOLO model training and defect inference in one no-code workflow for structural inspection.arxiv.org
Benn nettali bu am njariñ ayu-bis bu nekk
Weyal IA te doo dundu ci feed bi.
Wutal xibaar IA buñ firndeel ci ayu-bis bi, done yu baax, jumtukaay yu am njariñ, tànneefi jàng, ak liggéey IA yu bees.
Yegg ci nit ñiy jàng IA
Nga jël ab liggéeykat IA wala nga genne ab produit IA bu am njariñ? Tegal ko ci kanamu nit ñi ñëw fi ngir jàng ak jëf.
Publie ab liggey IAYonnee ab jumtukaayu IA