SPIN Unprocessed October 15, 2026 ai_technology research
DecisionRobust: Benchmarking Robustness for JEV Style Decision Models Across Text and Vision
View original on arxiv.orgOverview
arXiv:2610.19118v1 Announce Type: new Abstract: Emerging decision models such as JEV provide typed interfaces that map task instructions and observations directly to choices, judgments, and scores. As these models are increasingly used for automated decision-making, understanding their reliability under adversarial manipulation becomes critical. We introduce DecisionRobust, a benchmark for systematically evaluating the robustness of decision models across textual and visual inputs. Our benchmark
SpinGraph analysis pending — check back after processing.
Ask AI about this story
Opens with the SpinGraph .md URL and structured context — one click, prompt included.
More from arXiv Computation and Language
View all →- Copy-Paste Language Models
- Do Syntax Heads Influence Model Behavior?
- Titan-Llama: Retrofitting Pre-trained LLMs with External Neural Memory Modules
- DUALVIEW: Leveraging Broad and Output-Specific Evidence for LLM-based Multi-Output Text Regression
- Weighted Ensemble Sampling for Rare-Event Estimation in Language Models
- NEPSEE: A Double-Annotated Benchmark for Nepali Sentiment Analysis
Markdown (.md) · JSON-LD schema (.json) · Machine-readable for AI & GEO