arXiv:2605.06191v1 Announce Type: new Abstract: The work in this paper evaluates zero-shot and few-shot large language models (LLMs) for safety-critical clinical action extraction using the CLIP discharge-note dataset, with particular emphasis on transitions of care and post-discharge patient safety. To manage the complexity of clinical documentation, we introduce a two-stage extraction framework that decomposes discharge […]
SSMamba: A Self-Supervised Hybrid State Space Model for Pathological Image Classification
arXiv:2604.15711v2 Announce Type: replace-cross Abstract: Pathological diagnosis is highly reliant on image analysis, where Regions of Interest (ROIs) serve as the primary basis for diagnostic evidence, while whole-slide image (WSI)-level tasks primarily capture aggregated patterns. To extract these critical morphological features, ROI-level Foundation Models (FMs) based on Vision Transformers (ViTs) and large-scale self-supervised learning (SSL) […]
Joint Consistency: A Unified Test-Time Aggregation Framework via Energy Minimization
arXiv:2605.06219v1 Announce Type: new Abstract: This paper studies test-time aggregation, an approach that generates multiple reasoning traces and aggregates them into a final answer. Most existing methods rely on evaluation signals collected from candidate traces in isolation or answer frequencies, while ignoring comparative interactions among candidates. We propose Joint Consistency (JC), formulated as a constrained […]
Conversation for Non-verifiable Learning: Self-Evolving LLMs through Meta-Evaluation
arXiv:2601.21464v2 Announce Type: replace-cross Abstract: Training large language models (LLMs) for non-verifiable tasks, such as creative writing, dialogue, and ethical reasoning, remains challenging due to the absence of ground-truth labels. While LLM-as-Judge approaches offer a scalable alternative to human feedback, they face a fundamental limitation: performance is constrained by the evaluator’s own quality. If the […]
WaferSAGE: Large Language Model-Powered Wafer Defect Analysis via Synthetic Data Generation and Rubric-Guided Reinforcement Learning
arXiv:2604.27629v3 Announce Type: replace Abstract: We present WaferSAGE, a framework for wafer defect visual question answering using small vision-language models. To address data scarcity in semiconductor manufacturing, we propose a three-stage synthesis pipeline incorporating structured rubric generation for precise evaluation. Starting from limited labeled wafer maps, we employ clustering-based cleaning to filter label noise, then […]
Addressing Labelled Data Scarcity: Taxonomy-Agnostic Annotation of PII Values in HTTP Traffic using LLMs
arXiv:2605.06305v1 Announce Type: new Abstract: Automated privacy audits of web and mobile applications often analyse outbound HTTP traffic to detect Personally Identifiable Information (PII) leakage. However, existing learning-based detectors typically depend on scarce, manually labelled traffic and are tightly coupled to fixed label taxonomies, limiting transferability across domains and evolving definitions of PII. This paper […]
HDTree: Generative Modeling of Cellular Hierarchies for Robust Lineage Inference
arXiv:2506.23287v2 Announce Type: replace-cross Abstract: In single-cell research, tracing and analyzing high-throughput single-cell differentiation trajectories is crucial for understanding biological processes. Key to this is the robust modeling of hierarchical structures that govern cellular development. Traditional methods face limitations in computational cost, performance, and stability. VAE-based approaches have made strides but still require branch-specific network […]
More Than Can Be Said: A Benchmark and Framework for Pre-Question Scientific Ideation
arXiv:2605.06345v1 Announce Type: new Abstract: AI research agents have shown strong potential in automating literature search and manuscript refinement, yet most assume a clear and actionable initial input, operating only after a research question has been made explicit. In contrast, human research often begins with tacit friction, a sense of misalignment before a question can […]
Towards Metric-Faithful Neural Graph Matching
arXiv:2605.06588v1 Announce Type: cross Abstract: Graph Edit Distance (GED) is a fundamental, albeit NP-hard, metric for structural graph similarity. Recent neural graph matching architectures approximate GED by first encoding graphs with a Graph Neural Network (GNN) and then applying either a graph-level regression head or a matching-based alignment module. Despite substantial architectural progress, the role […]
HEDP: A Hybrid Energy-Distance Prompt-based Framework for Domain Incremental Learning
arXiv:2605.05776v1 Announce Type: new Abstract: Domain Incremental Learning is a critical scenario that requires models to continuously adapt to new data domains without retraining. However, domain shifts often cause severe performance degradation. To address this, we propose Hybrid Energy-Distance Prompt, a domain-incremental framework inspired by Helmholtz free energy. HEDP introduces an energy regularization loss to […]
Self-Consistency Is Losing Its Edge: Diminishing Returns and Rising Costs in Modern LLMs
arXiv:2511.00751v2 Announce Type: replace Abstract: Self-consistency — sampling multiple reasoning paths and selecting the most frequent answer — was designed for an era when language models made frequent, unpredictable errors. This study argues that the technique has become increasingly wasteful as models grow stronger, and may degrade performance on problems that modern models already solve […]
Von Neumann Networks
arXiv:2605.05780v1 Announce Type: new Abstract: In the mid-twentieth century, mathematician and polymath John von Neumann created a computational system on an array of cells as a simple model of the human brain, where each cell had one of a finite set of roles or states that he predicted would be modelled by a diffusion process. […]