Benchmark of LLMs on legal precedent treatment classification. Expert-annotated dataset of 239 real-world legal citations. Gemini 2.5 Flash achieves 79.1% on high-level classification, GPT-5-mini 67.7% on fine-grained schema. Novel Average Severity Error metric to measure practical impact of misclassifications.
MR-SLAM is a mixed reality system using Meta Quest 3 to teleoperate three TurtleBot3 robots in collaborative SLAM. The operator views the real world in passthrough with spatially anchored information panels. Three SLAM Toolbox instances merge occupancy grids in real time via ROS 2, achieving 94.7% cross-instance consistency and 8.83 Hz scan rate.
Comparative study of 29 data augmentation techniques for 2D echocardiography segmentation on U-Net. Anatomically plausible geometric transformations (affine, shift-scale-rotate, perspective, horizontal flip) improve cross-dataset generalization, while aggressive intensity augmentations degrade it. Pairwise combinations outperform individual augmentations.
Prompt2Fingerprint introduces a framework for LLM fingerprinting via parameter generation. Instead of fine-tuning each model separately, a specialized generator maps textual descriptions to low-rank parameter increments in a single forward pass, eliminating the computational overhead of existing methods.
Hybrid framework combining 1D and 2D CNNs with attention-guided fusion for ECG-based biometric recognition. Evaluation on ECG-ID, MIT-BIH, PTB: 99.56%, 100%, 99.89% accuracy. Multi-session tests (Heartprint, 10 years): 98.54%-99.09% same-session, 53-56% cross-session.
ReTAMamba is a Mamba-based model for predicting irregular clinical time series. It estimates observation reliability from missingness and elapsed time, integrates multi-resolution information via Chronological Weaving, and uses a budgeted token router. On MIMIC-IV, eICU, and PhysioNet 2012, it improves AUPRC by 7.51%, 7.80%, and 10.15% respectively.
SafeLens introduces a two-tier video moderation architecture (fast-and-slow) to reduce inference costs. The framework filters SafeWatch dataset to 2.4% via influence-guided filtering and augments with Chain-of-Thought traces. It outperforms SafeWatch-8B, OmniGuard-7B, GPT-5.4, and Gemini-3.1-pro on real and AI-generated video benchmarks.
PEIRA is a non-contrastive self-supervised learning method analyzing JEPA dynamics through a regularized linear regressor. It minimizes an explicit objective based on the trace of the optimal regressor, ensuring stable non-collapsed equilibria aligned with canonical correlation subspaces. Competitive results on ImageNet-1K and CIFAR-10.
arXiv study demonstrates that color features alone (RGB/HSV histograms, statistical moments) achieve 89% accuracy in binary cancer/benign classification in histopathology, excluding morphological information. Authors propose these simple features as lightweight pre-screening tool before complex deep learning models.
A label-wise attention model trained on combined ICD-9 and ICD-10 data improves rare medical code prediction by 27% micro F1 (18K rare codes) and macro metrics on frequent codes, despite version mismatch. Version-independent approach to automate clinical coding.
Fre-Res introduces adaptive video-token compression for video MLLMs. The framework separates spatial details (high-fidelity anchors) from temporal evolution (residual-frequency tokens via 1D-DCT). A Spatial-Guided Absorber aligns frequency dynamics with visual embeddings. Results: near full-token performance with substantial reduction in token length across short and long-video benchmarks.
A supervised method called Manifold Probe discovers representation manifolds in superposition within neural networks. Tested on Llama 2-7b, it identifies linear manifolds for time and space, and demonstrates causal control by steering model completions about release years of movies and songs.
Investigation of invariant and equivariant semi-supervised learning (FixMatch, Dense FixMatch) for multi-task training on partially labeled datasets. Evaluation on Cityscapes and BDD100K for object detection and semantic segmentation. Equivariant approaches outperform supervised baselines, especially with limited labeled samples per task.
GA-S2S combines T5-small with a Relational Graph Attention Network (RGAT) for knowledge graph link prediction. The model jointly encodes textual features and full k-hop subgraph topology around the query entity. On CoDEx, GA-S2S outperforms Seq2Seq baselines with 19% relative accuracy gain.
StrLoRA introduces a streaming continual visual instruction tuning framework for MLLMs. Unlike existing methods restricted to predefined tasks, StrCVIT handles data streams with dynamic, interleaved tasks. StrLoRA employs two-stage expert routing with task-aware selection and token-wise weighting, stabilized via routing-stability regularization.
Study evaluating three off-the-shelf LLMs to estimate difficulty of educational items without response data. Across 6 primary-school mathematics domains, Spearman correlations show moderate-to-strong alignment with empirical difficulties. Pairwise comparisons outperform absolute judgements; adding token probabilities and few-shot examples improves results.
Bayesian-Monte Carlo probabilistic framework for dynamic construction project schedule updating. Models activity durations with lognormal distributions, updates them via Bayesian inference, and propagates uncertainty through Monte Carlo simulation. Demonstrates improved forecasting accuracy over deterministic CPM methods on PSPLIB benchmarks.
UniAlign is a model-agnostic framework improving robustness of network traffic classification under distribution shifts. It combines domain alignment fine-tuning and stable model ensembling, achieving 2.51% accuracy and 2.71% F1 improvements on three public datasets, requiring only 12.4–53.9% of baseline training time.
FedNL reformulates federated learning as a three-level nested optimization system. Embeds Titans-based linear attention for zero-shot test-time adaptation without additional training. Tested on Non-IID MMLU and long-context benchmarks with constant inference memory.
UVTran, a transformer-based framework, solves N-sided hole filling in CAD by predicting an auxiliary projection surface via cross-attention biased toward nearby control points, voxelizing coordinates, and progressive-resolution training. On benchmark, it improves tolerance-satisfaction rate by 12% over industrial and academic baselines while producing fairer trimmed surfaces.
Study on automated classification of invalid bug reports and no-code fix generation. Researchers propose a standardized taxonomy and benchmark, testing vanilla LLMs, RAG, and agentic web search. RAG achieves 0.66 weighted F1 for subclassification; agentic web search reaches 68.9% Judge LLM success rate for fix generation.
CATA introduces a continual machine unlearning method for vision-language models (VLMs). It represents each unlearning request as a task vector and aggregates historical vectors by suppressing conflicting components, ensuring forgetting effectiveness, model fidelity, and persistence against knowledge re-emergence.
New SPBM method for constrained optimization in deep learning. Combines penalty methods, barrier methods, and exponential dual averaging to handle non-convexity and non-smoothness. Demonstrates effectiveness on fairness, physics-informed networks, and symbolic knowledge integration with linear overhead up to 10k constraints.
Framework for active 3D scene graph generation from RGB cameras only, without depth sensors. Unifies perception and planning around a structured representation. On Replica dataset, achieves F1-score parity with depth-based baselines. Semantic-driven viewpoint selection detects 2× more objects than geometric frontier baseline.
SAGE, a self-evolving framework, improves spatial reasoning in VLMs by enforcing logical consistency through geometric and linguistic duality operations. Applied as a lightweight GRPO post-training stage, it corrects inconsistencies under predictable transformations and shows gains on video and spatial reasoning benchmarks.
Theoretical paper proposing optimizers respecting symmetries of modern neural architectures. Introduces equivariant update rules for embeddings, LM heads, SwiGLU MLPs, and MoE routers. Validation on dense and sparse MoE models (Qwen3, Gemma 3, OLMoE, gpt-oss) shows improved validation loss vs AdamW.
Robotic clay sculpting planning method using visually-aligned representation. System models deformable material dynamics capturing textures and lighting, enabling long-horizon planning (>100 actions) without per-goal retraining. Tested on three materials with various end-effectors.
SENSE is a generative diffusion-based framework that jointly synthesizes realistic urban satellite imagery and aligned building energy consumption and height maps. Tested on NYC, Boston, Lyon, and Busan, it generates annotated synthetic data using <20% labeled data, improving prediction performance by 10% IoU and reducing error by 3-11% NMBE.
Agentic AI framework for peer-based mental health support in military operations. Recovered soldiers trained as peer facilitators supervise specialized AI agents (symptom triage, interventions, documentation) in air-gapped environments. Prototype developed with U.S. Army Health Center. Goal: reduce evacuations, accelerate care, maintain human oversight.
Study of trade-offs between estimation accuracy, differential privacy, and communication cost in federated learning. Proposes FedHybrid and FedNewton, improvements over FedAvg and FedSGD with finite-sample MSE upper bounds and minimax lower bounds. Evaluation on logistic regression and neural networks (MNIST, CIFAR-10).
COOPO is a hybrid offline-online reinforcement learning algorithm that cycles between KL-regularized offline training and online fine-tuning. Periodic returns to offline training eliminate catastrophic forgetting and distribution drift. On D4RL benchmarks, COOPO reduces online interactions while improving final returns compared to state-of-the-art hybrids.
Implementation study of Google Notebook LM in an English for Academic Purposes course (106 students, Hong Kong). Generated videos, podcasts, and infographics via RAG. Students rated visual and multimodal content highly; video preference correlated positively with academic performance. High cognitive load negatively associated with grades.
Reversa is a reverse documentation engineering framework converting legacy systems into operational specifications for AI agents. A multi-agent pipeline extracts implicit business rules, synthesizes architecture, and generates traceable specifications with confidence marking. Case study: COBOL-to-Go ATM migration producing 517 claims, 10 identified gaps, and 53 Gherkin scenarios.
An agentic framework uses an LLM to assist users in real-time re-optimization of OR models. The LLM translates requests into structured model modifications, selects re-optimization techniques, and returns implementable solutions. Tested on supply chain and university exam scheduling.
Position paper arguing that a three-layer probabilistic architecture (semantic intent/policy compliance, environmental validity, dynamical feasibility) is structurally required for safe LLM agent deployment. Each layer must independently certify one safety dimension via composable probabilistic guarantees.
BESplit introduces a Split Federated Learning framework to mitigate non-IID data effects. The method combines Evidential Aggregation for client contribution reweighting, Bias-Compensated Collaboration for representation alignment, and Dual-Teacher Distillation for model synchronization. Experiments on 5 benchmarks show improvements in accuracy and convergence stability.
Theoretical paper on mechanistic interpretability of vision models. Proposes a distributional framework using KL-minimal optimization to interpret internal feature activations, addressing biases in heuristic methods (top-K retrieval, regularized optimization). Implementation via energy-guided diffusion posterior sampling, validated on DINOv3.
Vision-OPD introduces regional-to-global self-distillation to improve fine-grained visual understanding in MLLMs. The framework transfers the model's privileged perception on evidence-centered crops to its full-image policy via KL divergence minimization between token distributions. Competitive results on fine-grained visual understanding benchmarks without external models or ground-truth labels.
Multilingual OCR-aware fine-tuning framework for MLLMs combining synthetic OCR-to-translation data generation, LoRA-based SFT, and structured visual chain-of-thought reasoning. Significantly improves extraction of small, blurred, occluded text on receipts, menus, documents under degraded visual conditions. Outperforms GPT-5 and Gemini on OCR grounding and hallucination reduction.
Learning lifted STRIPS+ action models from partial traces with minimal observability assumptions. Authors relax prior work by allowing partial observability of both actions and states. Three cases formalized: no state observability, full observability of selected predicates, local observability of predicates. Completeness results and experiments provided.