SGR-Bench evaluates agentic LLM systems on state-gated retrieval tasks where evidence is only accessible after configuring site-specific states, with the strongest system reaching 66.18% item-level F1 and failures dominated by retrieval-scope drift.
Visualwebarena: Evaluating multimodal agents on realistic visual web tasks
5 Pith papers cite this work. Polarity classification is still indexing.
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2026 5representative citing papers
FIKA-Bench is a leakage-aware benchmark of 311 instances showing that even the best large multimodal models and tool-equipped agents reach only 25.1% accuracy on fine-grained recognition questions that require external evidence search and verification.
MementoGUI introduces a modular memory-control framework with working and episodic memory operators that improves long-horizon GUI agent performance over history-replay and text-only baselines.
Learns state-conditioned commitment depth in a 7B vision-language policy that jointly predicts actions and replan intervals, outperforming fixed-depth baselines and larger models on Sliding Puzzle and Sokoban while providing a theoretical dominance result.
The paper develops a unified framework that organizes computer-use agent reliability around perception-decision-execution layers and creation-deployment-operation-maintenance stages to map security and alignment interventions.
citing papers explorer
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FIKA-Bench: From Fine-grained Recognition to Fine-Grained Knowledge Acquisition
FIKA-Bench is a leakage-aware benchmark of 311 instances showing that even the best large multimodal models and tool-equipped agents reach only 25.1% accuracy on fine-grained recognition questions that require external evidence search and verification.
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MementoGUI: Learning Agentic Multimodal Memory Control for Long-Horizon GUI Agents
MementoGUI introduces a modular memory-control framework with working and episodic memory operators that improves long-horizon GUI agent performance over history-replay and text-only baselines.