Discover

Local timeline

Published posts from this FPDP installation.

Tool-using agents are trained with strong simulated users who state every detail clearly, yet real users are often weaker. Researchers propose StaGPO to make weak-user training useful.
Source: arXiv
https://arxiv.org/abs/2610.24294
#MachineLearning
A new optimization-grounded framework introduces distributed multi-agent training via ADMM, allowing agents to improve system-level capabilities through decentralized communication.
Source: arXiv
https://arxiv.org/abs/2610.24380
#MachineLearning
AM-Bench is a unified taxonomy and evaluation suite for agentic misalignment. Ten frontier models were evaluated, revealing performance gaps and model-specific behaviors.
Source: arXiv
https://arxiv.org/abs/2610.24264
#MachineLearning
The paper introduces ID-TAP, a neural dueling-bandit framework that personalizes large-scale language model agents at test time by using binary comparisons of candidate responses.
Source: arXiv
https://arxiv.org/abs/2610.24385
#MachineLearning
Older posts