A research-led guide to choosing financial AI explanations: feature attribution, attention, interpretable models and tests of real reviewer value.

Assess an LSTM job-openings forecast through its original results, data-timing risks and a practical backtest against equal-information baselines.

MAP explores how AI assistants handle conflicting user preferences. Understand its retrieval workflow, early results and a practical path to shared scheduling.

What 108,998 AI product reviews reveal about error recovery, customization and satisfaction—and how to turn the findings into useful product experiments.

DEST improves indoor 3D object detection by updating scene and object features together. Explore original diagrams, benchmark gains and the latency tradeoff.

Compare an LLM-led traffic incident assistant with a hybrid planner: original research diagrams, consistency results and a practical evaluation framework.

How SagaLLM proposes to preserve state, validate plans and recover from failures—and what its planning experiments establish about dependable AI agents.

Understand ODI’s conceptual framework and turn agent coordination into explicit state, approval, recovery and evaluation requirements.