Tag: #research297 results
LiveMedBench offers weekly updated real-world clinical cases for LLM evaluation, avoiding contamination via temporal separation. Multi-agent curation ensures integrity; automated rubric evaluation aligns with experts better than alternatives. Tests reveal top LLMs at 39.2%, highlighting contextual gaps.
Early Moltbook data from 6k agents shows power-law participation and small-world connectivity like human networks. Micro patterns are alien: shallow threads, low reciprocity, 34% duplicate templates. Dominated by identity language and phrases like 'my human'.
Introduces diffusion-based generative priors in DGP framework for reconstructing CT images from sparse-view sinograms. Combines iterative optimization with neural generative power while preserving explainability. Shows promising results under highly sparse geometries.
DiffGDA uses diffusion and SDEs to model continuous structure-semantic evolution from source to target graphs. A domain-aware network guides trajectories to optimal adaptation paths. Outperforms baselines on 14 tasks across 8 datasets.
DermFM-Zero is a vision-language model trained on 4M multimodal data for zero-shot dermatology tasks. Achieves SOTA on benchmarks and outperforms clinicians in studies. Latent representations enable interpretable concept discovery.
CycFlow replaces diffusion generation with deterministic point transport for combinatorial optimization like TSP. It learns vector fields to map coordinates to circular arrangements for angular sorting. Speeds up solving by 1000x vs. baselines.
Proposes authenticated prompts and context for cryptographic provenance in LLM apps. Features policy algebra with Byzantine resistance and layered defenses. Achieves 100% attack detection with zero false positives.
Proposes CrossTALK for red-teaming VLMs via cross-modal entanglement attacks. Extends clues across modalities with scalable complexity. Achieves state-of-the-art jailbreak success rates.
CRL uses reinforcement learning to select sparse autoencoder (SAE) features for steering language models at each token, revealing which features impact outputs. It includes adaptive masking for diverse features and enables analysis like branch point tracking and layer-wise comparisons. Tested on Gemma-2 2B, it improves benchmarks while providing interpretable logs.
Foundation models match task-specific discrimination in abdominal trauma CT but suffer specificity drops from negative-class heterogeneity like solid organ injuries. Task-specific models handle confounds better. Adaptation via labeled training reduces susceptibility.