Human Feedback and Eval Paper Explorer

Match reason: Keyword overlap 1/1 across title and protocol fields.

• We study whether phone-use agents respect privacy while completing benign mobile tasks.
• To make this question measurable, we introduce MyPhoneBench, a verifiable evaluation framework for privacy behavior in mobile agents.

Match reason: Keyword overlap 1/1 across title and protocol fields.

• AI agents, autonomous digital actors, need agent-native protocols; existing methods include GUI automation and MCP-based skills, with defects of high token consumption, fragmented interaction, inadequate security, due to lacking a unified…
• To address these issues, we present ANX, an open, extensible, verifiable agent-native protocol and top-level framework integrating CLI, Skill, MCP, resolving pain points via protocol innovation, architectural optimization and tool…

Match reason: Keyword overlap 1/1 across title and protocol fields.

• Benchmarking against the state-of-the-art tool PoliGrapher reveals that PrivSTRUCT robustly extracts more than x2 the number of data item and purpose excerpts while retaining developer-defined structural cues.

Match reason: Keyword overlap 1/1 across title and protocol fields.

• Online evaluations show that AutoFeature reduces end-to-end on-device model execution latency by 1.33x-3.93x during daytime and 1.43x-4.53x at night.

Match reason: Keyword overlap 1/1 across title and protocol fields.

• However, building a benchmark for LLM-generated web apps remains challenging due to the need for real-world user requirements, generalizable evaluation metrics without relying on ground-truth implementations or test cases, and interpretable…
• To address these challenges, we introduce WebCoderBench, the first real-world-collected, generalizable, and interpretable benchmark for web app generation.

Match reason: Keyword overlap 1/1 across title and protocol fields.

• Graphical user interface (GUI)-based mobile agents automate digital tasks on mobile devices by interpreting natural-language instructions and interacting with the screen.
• While recent methods apply reinforcement learning (RL) to train vision-language-model(VLM) agents in interactive environments with a primary focus on performance, generalization remains underexplored due to the lack of standardized…

Match reason: Keyword overlap 1/1 across title and protocol fields.

• In this report, we present UI-Venus-1.5, a unified, end-to-end GUI Agent designed for robust real-world applications.
• Extensive evaluations demonstrate that UI-Venus-1.5 establishes new state-of-the-art performance on benchmarks such as ScreenSpot-Pro (69.6%), VenusBench-GD (75.0%), and AndroidWorld (77.6%), significantly outperforming previous strong…

Match reason: Keyword overlap 1/1 across title and protocol fields.

• This paper proposes RLShield, a practical multi-agent RL pipeline for financial cyber defense.
• RLShield learns coordinated policies across multiple agents (assets or service groups) and optimizes a risk-sensitive objective that balances containment speed, business disruption, and response cost.

Match reason: Keyword overlap 1/1 across title and protocol fields.

• To address this gap, we introduce the Tool Decathlon (dubbed as Toolathlon), a benchmark for language agents offering diverse Apps and tools, realistic environment setup, and reliable execution-based evaluation.
• Comprehensive evaluation of SOTA models highlights their significant shortcomings: the best-performing model, Claude-4.5-Sonnet, achieves only a 38.6% success rate with 20.2 tool calling turns on average, while the top open-weights model…

Match reason: Keyword overlap 1/1 across title and protocol fields.

• To enhance video generation quality, we optimize EasyAnimate using reward backpropagation to better align with human preferences.
• The EasyAnimate achieves state-of-the-art performance on both the VBench leaderboard and human evaluation.