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Researchers design AutoIndexer, a generative model with a novel training and decoding mechanism that simulates a Chain of Edits, allowing the model to flexibly modify its prior outputs in hindsight via token insertions, substitutions, and deletions.
Source: arXiv cs.CL
https://arxiv.org/abs/2610.12545
#MachineLearning
MisVisAgentBench is a new paired executable benchmark evaluating misleading visualizations in Web Agents. Replacing clean charts with misleading ones reduces the average task success rate by over 34 percentage points.
Source: arXiv cs.CL
https://arxiv.org/abs/2610.12823
#MachineLearning
Spa-Bench is a real-robot benchmark for spatial grounding in vision-language-action models. Across evaluated policies, success falls from in-distribution to out-of-distribution spatial configurations.
Source: arXiv cs.RO
https://arxiv.org/abs/2610.12696
#Robotics
VICAR is a pipeline that learns humanoid interactive skills from third-person human videos by recovering motion, reconstructing obstacles, and retargeting while preserving global contact poses, motion speed, and collision clearance.
Source: arXiv cs.RO
https://arxiv.org/abs/2610.13079
#Robotics
ReHarness is a recurrent harness framework for agentic continual learning that complements forward read and write with backward revision under a strictly online streaming protocol.
Source: arXiv cs.LG
https://arxiv.org/abs/2610.12668
#MachineLearning
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