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Otter is a 15.3M-parameter human chess AI that extends Maia 2 by conditioning move predictions on game history and time pressure, achieving higher accuracy than Maia 2 with fewer parameters. Trained on 6.1 billion positions from Lichess games, it demonstrates that treating chess as a time-aware, sequential activity improves prediction of human play.
This paper introduces GigaChat Audio, a time-aware large audio language model that answers questions with explicit timestamps for up to 120 minutes of audio, using interleaved periodic time markers and synthetic supervision. The model achieves strong temporal grounding accuracy on benchmarks and the authors release model weights and datasets.
Presents LemonHarness, an integrated execution framework for long-horizon LLM agents that constrains state-changing operations within a clearly defined workspace, introduces a reusable rule knowledge base, and adds time-aware execution. Achieves 84-86% accuracy on Terminal-Bench 2.0.
Proposes TMR-GGNN, a time-aware multi-relational graph neural network for credit card fraud detection that handles imbalanced data and evolving fraud patterns via contrastive learning and focal loss.