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TOPIC · ENTITY #6876

frontier LLMs

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RESEARCH · 1 SOURCE · arXiv cs.AI

MEA: Reward-driven multi-agent system for faithful model explanations (arXiv:2610.02480v1)

This paper introduces MEA, a two-agent framework (Proposer and Actor) that selects and configures explanation tools and is optimized end-to-end against perturbation-based faithfulness rewards, producing natural-language explanations across tabular, text, and vision modalities. Evaluated on six datasets, MEA reportedly outperforms post-hoc explainers, agentic, and closed-source baselines and yields faithfulness gains of roughly +28% (tabular), +21% (text), and +34% (vision) over the untrained backbone, while the authors find that frontier LLMs often produce unfaithful explanations.

7.0

MODELS · 1 SOURCE · arXiv cs.AI

Hapi: a U-Net Swin Transformer for continental-scale 24–72h hydrological forecasts

Hapi is a multivariable U-Net Swin Transformer that forecasts discharge, surface runoff, snow water equivalent, and soil wetness across the contiguous United States at 0.05° resolution for 24–72 hour lead times. On 2024 test data using reconstructed ERA5‑Land inputs it outperformed an operational physics-based model and a state-of-the-art AI model in flood detection, was validated against 3,881 USGS gauges and a Hurricane Helene case study, and runs a four-variable 72-hour forecast in an average 0.11 s on a single A100 GPU.

8.0