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.
Enhancing llm efficiency: Targeted pruning for prefill-decode disaggregation in inference.arXiv preprint arXiv:2509.04467, 2025
3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3verdicts
UNVERDICTED 3representative citing papers
SpectrumKV applies per-token mixed-precision KV cache transfer (FP16/INT8/INT4) with a model-specific probe for INT4 tolerance, achieving better perplexity and retrieval than PDTrim at equivalent budgets on Qwen2.5-7B, Mistral-7B, and Gemma-2-9B.
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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SpectrumKV: Per-Token Mixed-Precision KV Cache Transfer for Prefill-Decode Disaggregated LLM Serving
SpectrumKV applies per-token mixed-precision KV cache transfer (FP16/INT8/INT4) with a model-specific probe for INT4 tolerance, achieving better perplexity and retrieval than PDTrim at equivalent budgets on Qwen2.5-7B, Mistral-7B, and Gemma-2-9B.
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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.