EPIC mitigates imbalance via performance-aware expert migration and runtime expert activation, and then improves communication with topology-adaptive transport kernels and fine-grained computation-communication overlap.
Abstract
The Mixture-of-Experts (MoE) architecture scales large language models (LLMs) to trillions of parameters by activating only a small subset of experts per token. In practice, MoE inference is commonly deployed with Expert Parallelism (EP), which places whole experts on different GPUs to preserve kernel efficiency. However, production EP deployments often suffer from two bottlenecks: (1) expert workload imbalance, which creates computation and communication stragglers, and (2) communication inefficiency, where inter-GPU transfers dominate latency even after balancing. We present EPIC, an experience-driven EP inference system that addresses these issues progressively for real deployments. EPIC mitigates imbalance via performance-aware expert migration and runtime expert activation, and then improves communication with topology-adaptive transport kernels and fine-grained computation-communication overlap. EPIC has been deployed at scale across O(10K) GPUs in our online inference service for both open-source models (e.g., Qwen3-Coder and DeepSeek-R1) and internal models, reducing communication time and per-token latency by up to 40% and 21%, respectively.
LifeSciBench is introduced, a benchmark of 750 expert-authored tasks designed to evaluate whether language models can handle realistic life science research work, with each constituent task paired with a human expert-written rubric.
Amelia Liu, Andrew Ho, Anne Marie Droste et al.· bioRxiv· 2 citations
TestifAI, a deep learning testing framework for efficient and accurate estimation of robustness against combinations of perturbations, is proposed and partial model tomography is introduced, a novel approach to reconstructing model behaviour in a multi-perturbation space from tests that apply only a small number of perturbations.
Arooj Arif, T. Hartung, E. Botoeva et al.· 1 citation
A rapidly advancing precision-therapy pipeline-including antisense oligonucleotides to upregulate the intact allele, AAV-based gene replacement, CRISPR-mediated transcriptional activation, epigenetic modulators, and rational pathway-targeted small molecules-offers realistic prospects for disease modification.
This paper proposes the Evaluation Context Protocol (ECP), an early-stage, vendor-neutral framework intended to act as a portable evaluation contract layer for agentic systems and describes an open-source reference implementation that includes adapters for LangChain, LlamaIndex, CrewAI, and PydanticAI.
Large Language Models show potential in their diagnostic accuracy and consequent ability to reduce clinician burden, and may provide the greatest benefit when used to optimise referral quality at source, improving both clinician and potentially LLM triage downstream.
K. Surendran, I. Aziz, Glyndwr Jenkins· Current Surgery Reports· 0 citations
Background Ambient AI documentation tools, known as scribes, are entering routine clinical practice at scale, but the evidence comparing the notes they produce against clinician-written notes is dominated by single-site, single-language studies that rely on human review to find errors, a method known to miss most documentation errors. Methods We conducted a paired simulation across five countries and languages (Cambridge/English, Barcelona/Spanish, Milan/Italian, Paris/French, Cologne/German; 385 paired consultations, 770 notes). From each actor-performed consultation, an AI scribe (Heidi) and a junior-to-middle-grade clinician independently produced a note. Notes were scored on the PDQI-9 by evaluators blinded to authorship. Documentation errors were identified by two methods of deliberately different sensitivity - clinician adjudication, and a calibrated automated reviewer externally validated against a blinded ten-clinician panel - then graded for clinical risk by a three-model panel. The co-primary outcomes were PDQI-9 total and Critical+High error burden, the latter reported under both detection arms. The analysis plan was registered before any pooling across sites. Results AI notes scored higher than clinician notes on the PDQI-9 (40.6 vs 35.6; difference +5.08, 95% CI 4.6-5.6; Cohen dz=0.55), consistently across all five sites (dz 0.41-0.75), and were less dispersed (5.7% of AI vs 27.8% of clinician notes fell below the study pre-specified low-score threshold (<32)). On the principal safety outcome - the paired probability that a note carried [≥]Critical+High error - clinician notes were affected more often under both detection arms: 61.0% versus 24.4% by the calibrated reviewer (relative risk 2.50, 95% CI 2.09-3.00) and 21.8% versus 6.2% by clinician adjudication (relative risk 3.50, 95% CI 2.32-5.27). The difference was largest for omissions. Unaided clinician review identified roughly 12% of the errors the calibrated reviewer retained, and a smaller fraction in AI notes than in clinician notes. Conclusions In this simulation, AI-generated notes scored higher on documentation quality, varied less, and carried fewer clinically significant errors than notes written on the same consultations by junior-to-middle-grade clinicians. The magnitude of the safety difference depends on the sensitivity of error detection, so we report both detection regimes and bound rather than point-estimate the absolute error rate. Extension to live practice, consultant-authored documentation, and notes as filed after clinician editing remains to be established.
H. Bergman, V. Liu, B. Austin et al.· medRxiv· 0 citations
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.
