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9 storiesInnovation
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.orgInnovation
Preprint proposes a low-cost way to improve confidence estimates for black-box LLMs
An arXiv preprint says simple classifiers trained on model confidence scores and similar-query outcomes can better predict whether large language model responses are correct.arxiv.orgInnovation
FleetSieve paper proposes targeted profiling for SLO-aware LLM fleets
An arXiv preprint presents FleetSieve, a profiling method that targets measurements likely to change LLM fleet allocations while accounting for capacity and tail-latency limits.arxiv.orgInnovation
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.orgInnovation
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.orgInnovation
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.orgInnovation
ArXiv paper proposes milestone-based training for long-horizon LLM agents
MileGPO uses milestone discovery and local evidence to improve credit assignment when training language-model agents on long, multi-step tasks. The authors report state-of-the-art results on ALFWorld and WebShop, but the claims remain limited to the paper’s experiments.arxiv.orgInnovation
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.orgInnovation
Auditing Cross-Lingual Fairness in Language Model Watermarking
A new evaluation framework for watermarking schemes in large language models is proposed, focusing on cross-lingual fairness.arxiv.org
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