ViTC-UNet conditions a UNet on frozen pre-trained Vision Transformer representations via learnable tokens and two-way attention decoder. The approach improves biomedical semantic segmentation on MRI and CT without end-to-end fine-tuning, combining ViT global priors with UNet local inductive bias and high-resolution decoding.
Action-Gradient MCTS (AGMCTS) combines global tree search with local gradient-based action refinement for online planning in continuous spaces. Three theoretical contributions: action score gradient theorem, Multiple Importance Sampling Tree for sample reuse, tractable gradients via Area Formula. Outperforms state-of-the-art sample-based solvers on continuous MDP/POMDP benchmarks.
AI agent model with three human-inspired memory systems (short-term, episodic, semantic), each modeled as a knowledge graph. Evaluated in custom RL environment « the Room ». Deep Q-learning agent learns to encode, store, and retrieve memories to answer questions. Outperforms agent without this memory structure.
OASIS, an inter-layer null signaling technique, reduces attention sinks and activation outliers in AttnResidual architectures. Across three datasets, OASIS decreases maximum infinity norm by 9.26%, average kurtosis by 2.60%, and improves post-quantization performance (W8A8: -75.85% perplexity, W4A4: +12.42% GSM8K).
MARR introduces a low-bit post-training quantization method (≤4-bit) for LLMs and Vision Transformers using module-specific scaling coefficients to balance accumulated-error correction and residual-induced bias, with a PID-based adaptive update strategy. Achieves up to 20.2% gains on LLMs and 4.6% on ViTs over prior residual reconstruction methods.
Controlled study comparing human vs synthetic soft-labels on MNIST. Human labels improve calibration by regularizing predictions on difficult samples and aligning model uncertainty with human uncertainty, beyond simple mislabeling correction.
Graph Hierarchical Recurrence (GHR) is a novel framework for GNNs and Graph Transformers that captures long-range dependencies through hierarchical abstraction via pooling. GHR outperforms existing models on long-range benchmarks using only 1% of SOTA parameters, and improves out-of-distribution generalization.
Study of memorization-generalization coexistence in over-parameterized neural networks. With 80% label noise on arithmetic tasks, models memorize noisy labels while maintaining an internal generalization structure. Frequency-based extraction achieves near-perfect accuracy. Task-agnostic partitioning into generalization/memorization components proposed.
FedSDR addresses federated fine-tuning of LLMs under statistical heterogeneity. The method combines self-distillation (FedSD) with a dual-stream mechanism: a local LoRA-S branch to absorb heterogeneity via distilled data, and a parallel global LoRA-R branch anchored to raw data for factual correctness.
PromptDecipher is an authoring system for AI tutoring chatbots that restructures workflow around direct corrections rather than abstract system prompts. Teachers interact with a live chat preview, edit undesirable bot responses, and an automated pipeline proposes targeted prompt rewrites validated across pre-defined test scenarios.
RAGA is an LLM-based autonomous agent for knowledge graph construction and retrieval-augmented generation. It replaces stateless batch pipelines with a ReAct loop supporting full CRUD operations, hybrid KG-vector synchronization, and evidence-anchored verification linked to source text. Experiments on QASPER show measurable gains in answer and evidence quality.
Post hoc calibration method for Deep Cox models using isotonic regression. Improves calibration of predicted survival probabilities without affecting discriminative power. Theoretical guarantees including double-robustness and asymptotic calibration, validated on synthetic and real-world clinical data.
HGPM (Hypergraph Pattern Machine) models higher-order interactions by tokenizing compositional subsets and using an inclusion-aware Transformer. On 10 hypergraph benchmarks, the method matches or exceeds state-of-the-art, notably in adverse-event prediction where it correctly identifies inhibitory drug combinations that existing methods miss.
Preregistered study (N=418) on trust calibration in explainable AI: disclosing model limitations slightly improves alignment between user trust and actual performance in skin-lesion classification, but direct experience outweighs onboarding manipulations.
Study on using predictive uncertainty for autonomous/deferral decisions in robotics. Across three temporal activity-recognition benchmarks, uncertainty provides reliable error ranking only when the base model is sufficiently competent. Softmax, MC Dropout, and ensembles produce similar gating behavior; threshold choice has larger impact than uncertainty method.
New clinical VQA benchmark UCSF-PDGM-VQA: 2,387 QA pairs from 473 glioma MRI studies. Evaluation of 6 VLMs and 1 LLM shows current models fail on multi-sequence 3D MRI, suffering from modality collapse and over-reliance on language priors.
Visual Agentic Memory (VAM) is a training-free framework for long video understanding. It combines online selective indexing, hierarchical memory, and agentic retrieval. On OVO-Bench, VAM achieves 68.41 (vs 67.46 for Gemini 3 Flash alone) and 17.11% on MM-Lifelong (105.6h over 51 days).
Teger, a structured uncertainty module, improves spatio-temporal time-series forecasting by mitigating over-squashing through a Forman curvature-aware graph rewiring mechanism. Integrated into a low-rank-plus-diagonal covariance head, Teger is backbone-agnostic and demonstrates consistent CRPS improvements across LSTM, Transformer, and xLSTM architectures.
WhiteTesseract combines high-resolution XR and conversational AI to enhance museum visits. Tested on a Monet exhibition with 26 participants, the system increases viewing duration from 35.3 to 98.3 seconds (p<0.001). 60% of 529 interactions extend beyond factual queries to include analytical, emotional, and comparative inquiries.
CSRNet optimized with parameter-free attention mechanisms for crowd counting in public transport. Evaluation of PFCA, SA, and SimAM modules on ShanghaiTech dataset. Novel PFCASA combination (PFCA+SA) outperforms parameterized approaches while reducing model size, applicable to edge-deployed systems.
Empirical study of privacy-leakage chains via prompt injection in black-box chatbot environments. Researchers analyze how attackers can hijack LLM agent tasks by injecting malicious content into external sources. They introduce the 'exemplification' technique and demonstrate a functional data-exfiltration chain combining prompt injection, jailbreaking, and web-tool invocation.
Video inference enhancement method using a fuzzy controller (FC-r) to dynamically adapt model sizes based on available device resources. Leverages spatiotemporal correlation across adjacent frames. Balances inference performance and resource efficiency without scaling up model complexity.
Vision Inference Former (VIF) is a lightweight architectural module improving visual consistency in multimodal models. It continuously injects visual semantics during generation to counter weakening vision-language alignment over long sequences. Tested on 14 benchmarks (reasoning, OCR, tables), VIF improves performance with minimal overhead.
Peak-Detector leverages instruction-tuned LLMs for peak detection across physiological signals (ECG, PPG, BCG, BSG) with explainability. A "peak-representation" technique compresses time-series while preserving critical events. The model is optimized via supervised fine-tuning then multi-objective reinforcement learning, evaluated on 7 datasets (6 public benchmarks + 1 real-world cohort).
PAREDA is a multi-accent speech dataset (Australian, Indian, Chinese English) featuring spontaneous discussions on NLP papers. SOTA ASR models degrade in zero-shot settings, but fine-tuning on PAREDA significantly reduces WER, validating the corpus's value for building robust ASR systems.
RGB-only framework for active 3D scene graph (3DSG) generation using fixed external cameras as common prior maps. System fuses onboard and external camera observations in a single hardware-agnostic pipeline, guiding robots toward high semantic uncertainty regions. Single external camera increases initial object recall by +79%.
BiKD introduces a bilevel framework to dynamically balance hard and soft losses in knowledge distillation on imbalanced data. A weight generation network produces adaptive per-sample weights guided by a small balanced validation set. Experiments on long-tailed CIFAR-10/100 show improvements over recent balanced distillation methods.
HyperPersona introduces a multi-level hypergraph framework for text-based automatic personality prediction. The model represents documents, sentences, and words as hyperedges and nodes, capturing global, local, and lexical dependencies. Evaluated on Big Five personality dimensions, it outperforms existing baselines by leveraging textual hierarchy.
PESD-TSF is a physics-inspired structured decomposition framework for long-term time series forecasting. It introduces a Multiplicative Periodic Gating mechanism, a multi-scale encoder with detrended attention, and Cross-Scale Collaborative Attention (CSCA) to preserve periodic structures and inter-variable dependencies across deep layers.
PIPER is a content-driven retrieval method for tabular datasets using table profiles and LLM-generated pseudoqueries for dense retrieval. It outperforms metadata-based baselines and existing TableQA methods in poor-metadata settings.
COCOCO, a post-hoc framework, integrates Conformal Prediction into neuro-symbolic concept-based models (NeSy-CBMs) to improve reliability. It conformalizes concepts and labels jointly via a single deduction-abduction revision step, ensuring consistency, coverage, and conciseness with distribution-free guarantees. Tested on 8 datasets.
SpatioRoute is a dynamic prompt routing approach for spatial question-answering over egocentric video. Without fine-tuning, it routes each question to a specialized prompt template (rule-based or LLM-driven mode) and achieves +5% accuracy gains on SQA3D versus fixed baselines, establishing SOTA for zero-shot video-only spatial VQA without 3D point-cloud inputs.
Theoretical paper on constant collapse in variational autoencoders, where the encoder mean becomes input-independent. Authors introduce a simplex witness certificate to detect and prevent this failure mode during training, with exact baseline and closed-form inverse.
Training-free approach for long-context LLM inference: attention-state memory externalizes prefix into lightweight lookup-based memory of precomputed attention states. On LLaMA-3.1-8B, reduces attention latency by 1.36x at 8K tokens and outperforms full-attention RAG with 20% memory footprint.
TAPE, a training-free pruning method for diffusion-based video generation, reduces computational overhead by intelligently removing tokens while maintaining temporal coherence across frames. It applies temporal smoothing between frames, performs token reselection per layer, and uses timestep-level budget scheduling to prune aggressively at early steps and relax during refinement.
EBOMLC method for noisy label correction via efficient bilevel optimization. Uses a meta model trained on clean data to correct large noisy dataset. Reduces hypergradient computational cost and improves stability on CIFAR-10/100 under high noise rates.
POOL, a novel privacy-preserving RL algorithm, addresses reinforcement learning in multi-dimensional continuous state-action spaces with one-sided feedback. Theoretical analysis derives sample complexity matching known lower bounds for non-private RL, demonstrating strong privacy guarantees are achievable without sacrificing learning efficiency.
First machine unlearning framework for masked diffusion language models (MDLMs like LLaDA and Dream). MDU minimizes KL divergence from prompt-conditional predictions to prompt-masked unconditional anchor, with temperature scaling to control privacy-utility trade-off. Code released.
CodeBind introduces a multimodal alignment framework using a shared-specific compositional codebook design. Tested across 9 modalities (text, image, video, audio, depth, thermal, tactile, 3D point cloud, EEG), it achieves state-of-the-art performance in multimodal classification and retrieval without requiring fully paired data.
RAG-based pipeline for sentence-level EEG-to-text decoding. Combines EEG encoder aligned with semantic embeddings, vector retrieval, and LLM refinement. On ZuCo dataset, achieves 30.45% improvement over random baseline (cosine similarity 0.181 vs 0.139).