DREAM: Dynamic Retinal Enhancement with Adaptive Multi-modal Fusion for Expert Precision Medical Report Generation
Proposes DREAM framework for medical report generation from retinal images using adaptive multi-modal fusion with limited data.
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Proposes DREAM framework for medical report generation from retinal images using adaptive multi-modal fusion with limited data.
Proposes CDSA-Net for coronary digital subtraction angiography by decoupling vascular structure from background noise.
Reconciles theory-practice gap in online alignment methods by analyzing temperature-zero regret vs. KL-regularized regret criteria.
Framework for calibrating model-based summarization metrics without reference summaries or human annotations, addressing reliability in automatic evaluation.
Loss function approach for controlling summary generation across multiple quality dimensions while managing trade-offs between completeness and conciseness.
Odds-only probabilistic models for sports forecasting and market efficiency analysis via betting data conversion methods.
Study of using LLM-generated graph priors to improve agent coordination in multi-agent reinforcement learning without hand-specified topologies.
Reward-based optimization framework combining reasoning traces with preference alignment for faithful multi-role dialogue summarization.
Requirements engineering methodology using personas to design explainable multi-agent educational systems for clinical training scenarios.
Hybrid neural-symbolic framework combining LLM-based code synthesis with compiler feedback for automated vulnerability repair via adaptive routing.
Structural economic model of Bitcoin transaction fee formation using mempool queueing data and mechanism design theory.
Analysis of Mixture-of-Experts routing locality and KV cache sharing overlap across layers in multi-candidate code generation from shared prefixes.
Survey of decentralized trust mechanisms for IoT edge networks including federated learning, Zero Trust, and lightweight blockchain.
New CogBiasESC dataset for training LLMs to detect and address cognitive distortions in emotional support conversations.
Empirical study measuring representational change depth profiles across 240 fine-tuning runs on 15 transformer and state-space models.
Framework for autonomous spacecraft guidance using reasoning models to interpret mission intent and generate safe trajectories.
RosettaSearch: LLM-based inference-time optimization for protein sequence design using RosettaFold3 structure prediction rewards.
CognitiveBench: new benchmark measuring LLM performance on four cognitive dimensions (emotion, stance, thinking style, intention) jointly.
CCCL: GPU-native compression-coupled collective communication library for LLM training reducing overhead in tensor and expert parallelism.
Comparison of system prompt changes between Claude Opus 4.6 and 4.7, analyzed via git history visualization.
Asset pricing study arguing capacity sparsity and factor sparsity are complementary in high-dimensional financial feature discovery.
Hey all, Our ML team spent some time this week getting training and deployments working for Gemma-4, and wanted to document all the things we ran into along the way. * **PEFT doesn't recognize Gemma 4's custom layers.** Google wrapped vision/audio projections in a…
Benchmark of 10 frontier LLM agents on NYU CTF Bench offensive cybersecurity tasks across 7 providers, 200 challenges.
Lightweight VR cybersickness detection model using user-specific eye and head tracking data with low model complexity.
Bayesian and ensemble extensions of PINNs for uncertainty quantification in turbulent flow reconstruction from sparse data.
The company now offers robotaxi service in three cities, all of them in Texas, after launching in Austin last year and starting to offer rides without safety drivers in January 2026.
LLMs generate executable legal decision models from Dutch environmental law text using structured intermediate representations, evaluated on production Omgevingsloket platform.
FlowRefiner applies flow matching with ODE-based iterative refinement to improve autoregressive neural PDE solvers for 3D turbulent flow prediction.
Graph-of-Agents proposes graph-based multi-agent LLM orchestration with node sampling and decoupled communication to improve task performance over Mixture-of-Agents.
According to Nikkei Asia, even as suppliers ramp up DRAM production, manufacturers are only expected to meet 60 percent of demand by the end of 2027. SK Group chairman has even said that shortages could last until 2030. The world's largest memory makers - Samsung, SK Hynix, and…