ICLR 2026 - Submissions

SubmissionsReviews

Submissions

Quantity AI Content: 0-10%10-30%30-50%50-70%70-90%90-100%All
Avg Rating: 0-11-22-33-44-55-66-77-88-99-10All

Summary Statistics

Quantity AI Content Count Avg Rating
0-10% 11864 (61%) 4.36
10-30% 3952 (20%) 4.14
30-50% 1846 (9%) 3.93
50-70% 1026 (5%) 3.75
70-90% 494 (3%) 3.39
90-100% 199 (1%) 2.90
Total 19490 (100%) 4.20
Title Abstract Avg Rating Quantity AI Content Reviews Pangram Dashboard
HiFo-Prompt: Prompting with Hindsight and Foresight for LLM-based Automatic Heuristic Design This paper investigates the application of Large Language Models (LLMs) in Automated Heuristic Design (AHD), where their integration into evolutionary frameworks reveals a significant gap in global co... 4.40 47% See Reviews View AI Dashboard
Ditto: Scaling Instruction-Based Video Editing with a High-Quality Synthetic Dataset Instruction-based video editing promises to democratize content creation, yet its progress is severely hampered by the scarcity of large-scale, high-quality training data. We introduce Ditto, a holist... 4.00 45% See Reviews View AI Dashboard
Constrained-Data-Value-Maximization: Utilizing Data Attribution for Effective Data Pruning in Low-Data Environments Attributing model behavior to training data is an evolving research field. A common benchmark is data removal, which involves eliminating data points with either low or high values, then assessing a ... 3.60 0% See Reviews View AI Dashboard
AgentMisalignment: Measuring the Propensity for Misaligned Behaviour in LLM-Based Agents As Large Language Model (LLM) agents become more widespread, associated misalignment risks increase. While prior research has studied agents’ ability to produce harmful outputs or follow malicious ins... 3.50 16% See Reviews View AI Dashboard
On the Impossibility of Retrain Equivalence in Machine Unlearning *Machine unlearning* seeks to selectively remove the "influence" of specific training data on a model’s outputs. The ideal goal is *Retrain Equivalence*--behavior identical to a model trained from scr... 4.50 0% See Reviews View AI Dashboard
TAMER: A Tri-Modal Contrastive Alignment and Multi-Scale Embedding Refinement Framework for Zero-Shot ECG Diagnosis Cardiovascular disease (CVD) diagnosis relies heavily on electrocardiograms (ECGs). However, most existing self-supervised uni-modal methods suffer from limited representational capacity, while multi-... 2.00 29% See Reviews View AI Dashboard
An Efficient Rubric-based Generative Verifier for Search-augmented LLMs Search augmentation empowers Large Language Models with retrieval capabilities to overcome the limitations imposed by static parameters. Recently, Reinforcement Learning leverages tailored reward sign... 4.50 0% See Reviews View AI Dashboard
VidEEG-Gen: A Dataset and Diffusion Framework for Video-Conditioned Privacy-Preserving EEG Generation Recent advancements in multimodal learning have revolutionized text, video, and audio processing, yet Electroencephalography (EEG) research lags due to data scarcity from specialized equipment and pri... 2.50 87% See Reviews View AI Dashboard
MultiCFV: Detecting Control Flow Vulnerabilities in Smart Contracts Leveraging Multimodal Deep Learning The introduction of smart contract functionality marks the advent of the blockchain 2.0 era, enabling blockchain technology to support digital currency transactions and complex distributed application... 4.50 0% See Reviews View AI Dashboard
RT-Remover: A Real-Time Video Object Removal by Composing Tracking and Removal in Auto-Regressive Diffusion Transformers With the rapid advancement of video diffusion, video editing techniques, especially video object removal, have garnered increasing attention. Existing methods generally rely on separate object trackin... 4.50 0% See Reviews View AI Dashboard
Neurocircuitry-Inspired Hierarchical Graph Causal Attention Networks for Explainable Depression Identification Major Depressive Disorder (MDD), affecting millions worldwide, exhibits complex pathophysiology manifested through disrupted brain network dynamics. Although graph neural networks that leverage neuroi... 3.00 67% See Reviews View AI Dashboard
Low Rank Transformer for Multivariate Time Series Anomaly Detection and Localization Multivariate time series (MTS) anomaly diagnosis, which encompasses both anomaly detection and localization, is critical for the safety and reliability of complex, large-scale real-world systems. The ... 5.50 0% See Reviews View AI Dashboard
LRIM: a Physics-Based Benchmark for Provably Evaluating Long-Range Capabilities in Graph Learning Accurately modeling long-range dependencies in graph-structured data is critical for many real-world applications. However, incorporating long-range interactions beyond the nodes' immediate neighborho... 6.67 0% See Reviews View AI Dashboard
Heteroscedastic Variational Bayesian Last Layers: Modeling Input-Dependent Noise in Sparse-Data Regression Bayesian Neural Networks (BNNs) have been extensively studied for uncertainty quantification. To train BNNs efficiently, Variational Bayesian Last Layer (VBLL) provides a sampling-free, deterministic ... 2.67 6% See Reviews View AI Dashboard
Team, Then Trim: An Assembly-Line LLM Framework for High-Quality Tabular Data Generation While tabular data is fundamental to many real-world machine learning (ML) applications, acquiring high-quality tabular data is usually labor-intensive and expensive. Limited by the scarcity of observ... 4.00 9% See Reviews View AI Dashboard
TriSpec: Ternary Speculative Decoding via Lightweight Proxy Verification The enhanced reasoning capabilities of Large Language Models (LLMs) have led to longer response sequences, yet the inference efficiency is fundamentally limited by their serial, autoregressive generat... 4.50 0% See Reviews View AI Dashboard
A Convergence Analysis of Adaptive Optimizers under Floating-point Quantization The rapid scaling of large language models (LLMs) has made low-precision training essential for reducing memory, improving efficiency, and enabling larger models and datasets. Existing convergence the... 4.80 22% See Reviews View AI Dashboard
CLIP as a Prior Teacher: Breaking the Label Dependency in Semi-Supervised Learning Semi-supervised learning (SSL) has shown remarkable potential in scenarios with limited labeled data. However, our study reveals that existing SSL approaches remain inherently label-dependent—their ab... 4.00 4% See Reviews View AI Dashboard
OmniSpatial: Towards Comprehensive Spatial Reasoning Benchmark for Vision Language Models Spatial reasoning is a key aspect of cognitive psychology and remains a bottleneck for current vision-language models (VLMs). While extensive research has aimed to evaluate or improve VLMs' understand... 5.50 N/A See Reviews
RA-SpaRC: Robust Adaptation with Sparse Plus Low-Rank Compressors Parameter-Efficient Fine-Tuning (PEFT) methods, such as Low-Rank Adaptation (LoRA), are widely adopted for their efficiency. However, LoRA assumes model updates are inherently low-rank, which introduc... 3.50 8% See Reviews View AI Dashboard
UniOMA: Unified Optimal-Transport Multi-Modal Structural Alignment for Robot Perception Achieving generalizable and well-aligned multimodal representation remains a core challenge in artificial intelligence. While recent approaches have attempted to align modalities by modeling condition... 4.00 22% See Reviews View AI Dashboard
ZoomV: Temporal Zoom-in for Efficient Long Video Understanding Long video understanding poses a fundamental challenge for large video-language models (LVLMs) due to the overwhelming number of frames and the risk of losing essential context through naive downsampl... 4.67 0% See Reviews View AI Dashboard
Faster Vision Transformers with Adaptive Patches Vision Transformers (ViTs) partition input images into uniformly sized patches regardless of their content, resulting in long input sequence lengths for high-resolution images. We present Adaptive Pat... 5.00 0% See Reviews View AI Dashboard
Vision-on-Demand: Efficient Visual Language Understanding with Intermittent Attention Existing approaches for improving the efficiency of Large Vision-Language Models (LVLMs) are largely based on the concept of visual token reduction. This approach, however, creates an information bott... 4.00 0% See Reviews View AI Dashboard
Graph-Driven Uncertainty Quantification in Text-to-Image Diffusion Models In this paper, we explore the problem of uncertainty quantification (UQ) in text-to-image generation models, focusing on the propagation of uncertainty through a graph-based structure of diffusion mod... 2.00 96% See Reviews View AI Dashboard
3D Medical Image Segmentation with Anatomy-Guided Conditioning from Surrounding Structures Accurate segmentation of complex anatomical structures in 3D medical images is challenged by low contrast, unclear features, and complex topology. We propose Anatomy-Guided Conditioning (AGC), which i... 3.33 40% See Reviews View AI Dashboard
How Learning Dynamics Drive Adversarially Robust Generalization? Despite significant progress in adversarially robust learning, the underlying mechanisms that govern robust generalization remain poorly understood. We propose a novel PAC-Bayesian framework that expl... 4.67 15% See Reviews View AI Dashboard
ConstrainPrompt: Code-Based Assurance of Prompt-Defined Constraints Large language models (LLMs) are increasingly used in applications where outputs must satisfy hard, application–critical constraints (e.g., JSON format, lexical inclusion, and length limits). When the... 4.50 13% See Reviews View AI Dashboard
TopoFormer: Topology Meets Attention for Graph Learning We introduce *TopoFormer*, a lightweight and scalable framework for graph representation learning that encodes topological structure into attention-friendly sequences. At the core of our method is *To... 5.20 24% See Reviews View AI Dashboard
Toward Effective Tool-Integrated Reasoning via Self-Evolved Preference Learning Tool-Integrated Reasoning (TIR) enables large language models (LLMs) to enhance their internal reasoning ability by integrating external tools. However, models with TIR often exhibit suboptimal behavi... 6.00 0% See Reviews View AI Dashboard
ARMOR: Conceptual Augmentation for Robust Multi-Concept Erasure in Stable Diffusion via Model Retrieval Stable Diffusion enables high-quality synthesis but raises risks around copyright, misinformation, and explicit content. Concept erasure helps mitigate these risks by fine-tuning model weights, yet ex... 3.50 15% See Reviews View AI Dashboard
Modeling Student Learning with 3.8 Million Program Traces As programmers write code, they often edit and retry multiple times, creating rich “interaction traces” that reveal how they approach coding tasks and provide clues about their level of skill developm... 3.50 0% See Reviews View AI Dashboard
Optimal Sub-data Selection for Nonparametric Function Estimation in Kernel Learning with Large-scale Data This paper considers estimating nonparametric functions in a reproducing kernel Hilbert space (RKHS) for kernel learning problems with large-scale data. Kernel learning with large-scale data is comput... 3.50 0% See Reviews View AI Dashboard
Focusing on the Riskiest: Gaussian Mixture Models for Safe Reinforcement Learning Reinforcement learning under safety constraints remains a fundamental challenge. While primal–dual formulations provide a principled framework for enforcing such constraints, their effectiveness depen... 4.80 67% See Reviews View AI Dashboard
Learning Non-Gradient Diffusion Systems via Moment-Evolution and Energetic Variational Approaches This paper proposes a data-driven learning framework for identifying governing laws of generalized diffusions with non-gradient components. By combining energy dissipation laws with a physically consi... 3.50 0% See Reviews View AI Dashboard
PromptFE: Automated Feature Engineering by Prompting Automated feature engineering (AutoFE) liberates data scientists from the burden of manual feature construction. The semantic information of datasets contains rich context information for feature engi... 3.50 0% See Reviews View AI Dashboard
A Simple "Motivation" Can Enhance Reinforcement Finetuning of Large Reasoning Models Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a powerful learn-to-reason paradigm for Large Reasoning Models to tackle complex tasks. However, current RLVR paradigm is still no... 4.50 0% See Reviews View AI Dashboard
JudgeLRM: Large Reasoning Models as a Judge Large Language Models (LLMs) are increasingly adopted as evaluators, offering a scalable alternative to human annotation. However, existing supervised fine-tuning (SFT) approaches often fall short in ... 4.00 13% See Reviews View AI Dashboard
Adaptive Dual Prompting: Hierarchical Debiasing for Fairness-aware Graph Neural Networks In recent years, pre-training Graph Neural Networks (GNNs) through self-supervised learning on unlabeled graph data has emerged as a widely adopted paradigm in graph learning. Although the paradigm is... 4.00 13% See Reviews View AI Dashboard
Adaptive Residual-Update Steering for Low-Overhead Hallucination Mitigation in Large Vision-Language Models Large Vision-Language Models (LVLMs) often suffer from object hallucination, generating text inconsistent with visual inputs, which can critically undermine their reliability. Existing inference-time ... 4.00 7% See Reviews View AI Dashboard
Adaptive Multi-Scale Attention-Based LSTM Coupling for Early Detection This paper introduces a novel adaptive, attention-coupled Long Short-Term Memory (LSTM) architecture developed specifically for real-time scenario recognition and prediction in complex automotive elec... 2.50 100% See Reviews View AI Dashboard
OASIS: An Optimized Approach to Systematic Calibration Data Selection Post-training pruning is a critical technique for compressing Large Language Models. However, as shown in previous research, its effectiveness is highly sensitive to the small set of calibration data ... 3.50 32% See Reviews View AI Dashboard
ParaFlow: Parallel Sampling for Flow Matching Models Flow Matching (FM) models, including state-of-the-art architectures like Stable Diffusion 3 and Flux, have achieved remarkable success in high-fidelity data generation. However, their inference proces... 2.00 27% See Reviews View AI Dashboard
Buckingham $\pi$-Invariant Test‑Time Projection for Robust PDE Surrogate Modeling PDE surrogate models such as FNO and PINN struggle to predict solutions across inputs with diverse physical units and scales, limiting their out-of-distribution (OOD) generalization. We propose a $\{p... 4.50 4% See Reviews View AI Dashboard
Query-Efficient Zeroth-Order Algorithms for Nonconvex Optimization Zeroth-order optimization (ZO) has been a powerful framework for solving black-box problems, which estimates gradients using zeroth-order data to update variables iteratively. The practical applicabil... 4.00 0% See Reviews View AI Dashboard
Bridging Temporal and Semantic Gaps: Prompt Learning on Temporal Interaction Graphs Temporal Interaction Graphs (TIGs) are widely utilized to represent real-world systems like e-commerce and social networks. While various TIG models have been proposed for representation learning, the... 4.00 0% See Reviews View AI Dashboard
ReasonEdit: Towards Reasoning-Enhanced Image Editing Models Recent advances in image editing models have shown remarkable progress. A common architectural design couples a multimodal large language model (MLLM) encoder with a diffusion decoder, as seen in syst... 4.50 13% See Reviews View AI Dashboard
Arboreal Neural Network Recent advancements in deep learning and Large Language Models (LLMs) have significantly influenced fields such as Natural Language Processing (NLP), Computer Vision (CV), and audio analysis. However,... 5.00 29% See Reviews View AI Dashboard
VEAttack: Downstream-agnostic Vision Encoder Attack against Large Vision Language Models Large Vision-Language Models (LVLMs) have demonstrated capabilities in multimodal understanding, yet their vulnerability to adversarial attacks raises significant concerns. To achieve practical attack... 6.00 0% See Reviews View AI Dashboard
EvA: Evolutionary Attacks on Graphs Even a slight perturbation in the graph structure can cause a significant drop in the accuracy of graph neural networks (GNNs). Most existing attacks leverage gradient information to perturb edges. Th... 4.50 0% See Reviews View AI Dashboard
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