AI Dataset2026
Evaluates language models that generate 3D objects from text by comparing their outputs in human preference tests. Scores cover creativity, coherence, prompt alignment, and visual preference.
AI Agent Papers2026
Evaluates whether shopping simulators reproduce real-user behavior across long interactive journeys, then checks whether simulated policy effects align with a production A/B test.
AI Agent Papers2026
Zimu Wang, Xiaobo Wang +12University of California, Berkeley, University of Science and Technology of China +5
Builds and compares agent-evolution strategies on shared infrastructure, keeping mutable agent resources separate from protected evaluation. The best observed overall pass@1 gain was 13.5 percentage points, but results varied by setting and did not always transfer to held-out tasks.
AI Agent Papers2026
Evaluates AI agent harness reliability and failure attribution using inspectable execution traces and evidence from multiple sources. Its 95 tasks cover seven dimensions and score task performance separately from harness reliability.
Connects scene understanding, long-horizon visual planning, and robot control by alternating language reasoning with video prediction. An agentic variant learns when generated visual evidence is worth using.
AI Agent Papers2026
Synthesizes multi-step tool interactions and trains agents only on actions supported by information visible at decision time. Tests across five Qwen3 scales show gains on Tau1, Tau2, and BFCL.
Simulates closed-loop interactions between people and wearable AI assistants to test whether multimodal models can guide real-world tasks. Its 120 multi-turn benchmark tasks are scored against verifiable simulator states.
Trains a spatial vision-language model to transfer across categorical, metric, and temporal reasoning tasks by replaying heterogeneous supervision chronologically. It also evaluates adaptive frame selection as a separate inference-time improvement.
AI Video Papers2026
Infers implicit user intent from continuous video and adapts both response timing and content as goals change. Its benchmark covers explicit, implicit, and dynamic intents, while hierarchical memory tracks state across sustained interactions.
AI Agent Papers2026
Coordinates world creation, trajectory exploration, and Harness evolution to improve multimodal agents. It organizes tasks by shared capabilities rather than surface categories, reporting mean gains of 34 points on training benchmarks and 26 on unseen benchmarks.
AI Video Papers2026
Generates continuing video segments from multi-turn natural-language instructions and camera control. Its segment-wise causal attention preserves scene context while confining action instructions to their intended time windows.