MedEvoEval is an executable longitudinal evaluation framework that converts medical cases into action-gated simulated episodes to track how doctor agents evolve decision-making, resource use, and experience across multiple encounters.
Trimtokenator-lc: Towards adaptive visual token pruning for large multimodal models with long contexts.arXiv preprint arXiv:2512.22748, 2025
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
EVLA combines a Unified Co-State Encoder and Electro-aware Structured Reasoning Chain with physics-guided training to produce energy-optimal driving decisions, reporting +5.6% accuracy gains over fine-tuned VLM baselines on a driving QA benchmark.
citing papers explorer
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MedEvoEval: Evaluating Continual Evolution of Doctor Agents through Simulated Clinical Episodes
MedEvoEval is an executable longitudinal evaluation framework that converts medical cases into action-gated simulated episodes to track how doctor agents evolve decision-making, resource use, and experience across multiple encounters.
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EVLA: An Electro-Aware Multimodal Assistant for Physically-Grounded Driving Reasoning and Control
EVLA combines a Unified Co-State Encoder and Electro-aware Structured Reasoning Chain with physics-guided training to produce energy-optimal driving decisions, reporting +5.6% accuracy gains over fine-tuned VLM baselines on a driving QA benchmark.