CommitDistill is an open-source Python prototype extracting typed knowledge units (Facts, Skills, Patterns) from local git history via deterministic regex and exposing them through a TF-IDF retriever. Tested on 5 repositories (25k commits), it achieves 0.750 hit-rate at 256-character budget versus 0.333 for BM25. No statistically detectable improvement on time-travel bug-fixes in LLM-as-judge evaluation.
EmoMind decodes affective captions directly from fMRI signals in two stages: retrieving semantically grounded neutral descriptions, then rewriting using a continuous 34-dimensional emotion vector. Uses classifier-free guidance to balance semantic fidelity and affective expressivity. Outperforms GPT-4 on two independent fMRI emotion datasets.
ISEP proposes an offline reinforcement learning method that implicitly expands action support by interpolating between in-distribution data and policy samples. A stochastic mechanism alternates between conservative cloning and optimistic expansion signals, implemented via Conditional Flow Matching with classifier-free guidance.
Game-theoretic decoding method for small vision-language models (2-8B) in medical imaging. Wasserstein semantic stopping criterion replaces lexical matching, improving Qwen3-VL-2B by +3.5pp on VQA-RAD and reducing convergence iterations by 20% while maintaining reliability.
Study of a shared-weight recurrent Transformer architecture (AIR) that develops two distinct roles without modular partitioning. On Sudoku-Extreme and Maze, state zH acts as committed proposal while zL retains local uncertainty. Freezing experiments and ablations show that input injection asymmetry induces this functional specialization.
Study on contextual sycophancy in LLMs: 60 participants collaborated with AI on analytical tasks. Results show models mirror user errors rather than correct them. An AI literacy intervention reduced incorrect mirroring but did not eliminate error propagation, suggesting system-level approaches are needed.
CGPO (Curriculum Group Policy Optimization) improves text-to-image model training via adaptive curriculum based on reward variance. Method prioritizes partially-mastered prompts (high variance) and balances categories through proportional fairness optimization. Gains validated on GenEval, T2I-CompBench++, DPG Bench.
DAEM (Diffusion Attention Expert Model) detects STAS (spread through air spaces) in lung cancer histopathological images. Model achieves AUC 0.8946 on frozen sections and 0.9112 on paraffin sections. Validated across 8 external institutions. Semi-automatic localization and TME biomarkers identified.
Systematic evaluation of synthetic clinical notes generated by LLMs at million-note scale. Study shows synthetic notes preserve core clinical information for coarse-grained tasks but lose fine-grained details for ICD coding. Chunk-based rephrasing mitigates detail loss but reduces factual precision under incomplete context.
SkillsVote is a lifecycle-governance framework for LLM agent skills from collection to evolution. It profiles a million-scale open-source corpus for quality and verifiability, then decomposes trajectories into skill-linked subtasks with outcome attribution. Results: +7.9pp on Terminal-Bench 2.0 (GPT-5.2) and +2.6pp on SWE-Bench Pro.
Hierarchical two-stage framework for long-horizon vessel trajectory prediction under real ocean conditions. Combines long-term predictor with short-term Spatio-Temporal Graph Transformer on discretized maritime cells. Environmental module integrates currents, wind, wave height via cross-modal attention. Results: 25% improvement in ADE, 17% in FDE on Australian CTS data.
DEPPA optimizes pocket-aware diffusion models for drug design via reinforcement learning. The method fine-tunes a pre-trained model by formulating the denoising process as a Markov Decision Process, optimizing binding affinity, drug-likeness, synthesizability and diversity. On CrossDocked2020 benchmark, DEPPA achieves Vina Score -8.5 kcal/mol.
Paper on mitigating NBTI aging in arithmetic multipliers used in AI. The technique exploits sign-invariance of multiplication to redistribute transistor stress via 2's complement transformations. Integrated into systolic arrays, it improves lifetime with negligible area and delay overhead.
Reinforcement learning framework for predicting customer trajectories in retail spaces. RL-based approach outperforms TSP/PNN heuristics (average 28% deviation from shortest paths) by modeling bounded rationality. Validated on real convenience store data: RL predictions better align with observed behavior, more accurate impulse purchase rates and shelf traffic estimates, enabling practical layout optimization.
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.