Sungkyunkwan University

Spatial transcriptomics analysis has long depended on cell segmentation, whose boundaries and transcript assignments are error-prone; segmentation-free methods avoid that step but often need an external single-cell reference or a preset number of cell types. SSAF-T chains segmentation-free cell typing to segmentation: the agent CeLLMan labels clusters from a literature-grounded knowledge graph and marker genes, the labels are spread across the tissue map, and that map constrains nucleus-seeded watershed segmentation. On public data including Visium HD mouse small intestine, Stereo-seq mouse brain, Xenium mouse kidney and MERFISH mouse liver, cell-type distributions match tissue anatomy, segmentation is comparable to Baysor and Cellpose, and runs are faster.

Spatial transcriptomics analysis has long depended on cell segmentation, whose boundaries and transcript assignments are error-prone; segmentation-free methods avoid that step but often need an external single-cell reference or a preset number of cell types. SSAF-T chains segmentation-free cell typing to segmentation: the agent CeLLMan labels clusters from a literature-grounded knowledge graph and marker genes, the labels are spread across the tissue map, and that map constrains nucleus-seeded watershed segmentation. On public data including Visium HD mouse small intestine, Stereo-seq mouse brain, Xenium mouse kidney and MERFISH mouse liver, cell-type distributions match tissue anatomy, segmentation is comparable to Baysor and Cellpose, and runs are faster.

GPUMAP splits UMAP's dimensionality-reduction pipeline into three interleavable operations, insert, update and embed, and runs them on a commodity GPU so that intermediate projections appear while computation continues, letting million-point datasets be inspected before the full run ends. On the 5-million-point HIGGS dataset, sequential RAPIDS UMAP produced its first projection only at 50.4 seconds, whereas GPUMAP produced an inspectable intermediate projection at 8.42 seconds and kept refining it; the cost is a total runtime about 1.6 times that of sequential execution.

GPUMAP splits UMAP's dimensionality-reduction pipeline into three interleavable operations, insert, update and embed, and runs them on a commodity GPU so that intermediate projections appear while computation continues, letting million-point datasets be inspected before the full run ends. On the 5-million-point HIGGS dataset, sequential RAPIDS UMAP produced its first projection only at 50.4 seconds, whereas GPUMAP produced an inspectable intermediate projection at 8.42 seconds and kept refining it; the cost is a total runtime about 1.6 times that of sequential execution.

The work introduces Relevant Evidence Decoding (RED), a training-free method that uses pointwise mutual information to decompose the joint audio-visual prediction into audio, video, and residual interaction components, then runs a question-only inference pass to determine the required evidence type and augments the original prediction with the corresponding contribution; across three audio-visual hallucination benchmarks and three AV-LLMs, RED improves over standard decoding by up to 7% on CMM, 6.3% on AVHBench, and 3.8% on SVHalluc, with an average relative time to first token of 1.5x standard decoding.

The work introduces Relevant Evidence Decoding (RED), a training-free method that uses pointwise mutual information to decompose the joint audio-visual prediction into audio, video, and residual interaction components, then runs a question-only inference pass to determine the required evidence type and augments the original prediction with the corresponding contribution; across three audio-visual hallucination benchmarks and three AV-LLMs, RED improves over standard decoding by up to 7% on CMM, 6.3% on AVHBench, and 3.8% on SVHalluc, with an average relative time to first token of 1.5x standard decoding.

The work introduces the Unlearning Depth Score (UDS), which first uses a retain-model baseline to identify layers encoding target knowledge and then measures via activation patching how much of that knowledge the unlearned model has erased on a 0-1 scale; in a meta-evaluation across 20 metrics on 150 unlearned models spanning 8 methods, UDS is reported to achieve the highest faithfulness and robustness, and case studies show it uncovers residual knowledge obscured from observational metrics by representational shifts, with erasure depth varying across prompt types.

The work introduces the Unlearning Depth Score (UDS), which first uses a retain-model baseline to identify layers encoding target knowledge and then measures via activation patching how much of that knowledge the unlearned model has erased on a 0-1 scale; in a meta-evaluation across 20 metrics on 150 unlearned models spanning 8 methods, UDS is reported to achieve the highest faithfulness and robustness, and case studies show it uncovers residual knowledge obscured from observational metrics by representational shifts, with erasure depth varying across prompt types.

The study introduces OncoAgent, a guideline-aware AI agent framework in which a large language model parses free-text clinical guidelines into an executable tool-call plan, generating three-dimensional target volumes without any expert-annotated training data; on planning CT from 40 mid-thoracic esophageal cancer patients (32 for training, 8 for testing), it achieved zero-shot CTV Dice of 0.842 and PTV Dice of 0.880, with no statistically significant difference from the fully supervised nnU-Net (GTV Prior) at 0.862 and 0.893 on primary metrics, and it was rated higher than that supervised baseline by blinded physicians on guideline compliance, modification effort, and clinical acceptability.

The study introduces OncoAgent, a guideline-aware AI agent framework in which a large language model parses free-text clinical guidelines into an executable tool-call plan, generating three-dimensional target volumes without any expert-annotated training data; on planning CT from 40 mid-thoracic esophageal cancer patients (32 for training, 8 for testing), it achieved zero-shot CTV Dice of 0.842 and PTV Dice of 0.880, with no statistically significant difference from the fully supervised nnU-Net (GTV Prior) at 0.862 and 0.893 on primary metrics, and it was rated higher than that supervised baseline by blinded physicians on guideline compliance, modification effort, and clinical acceptability.

Through empirical analysis, this work identifies a "similarity bias" in dot-product self-attention that systematically overlooks heterogeneous item relations carrying causal signal for target prediction, and proposes PRISM, a module that calibrates attention through K Perspective Lenses operating in two complementary views, an Affinity View and a Contrast View, consistently outperforming state-of-the-art baselines on seven real-world benchmarks.

Through empirical analysis, this work identifies a "similarity bias" in dot-product self-attention that systematically overlooks heterogeneous item relations carrying causal signal for target prediction, and proposes PRISM, a module that calibrates attention through K Perspective Lenses operating in two complementary views, an Affinity View and a Contrast View, consistently outperforming state-of-the-art baselines on seven real-world benchmarks.