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
What MLLMs Learn about When they Learn about Multimodal Reasoning: Perception, Reasoning, or their Integration? Multimodal reasoning models have recently shown promise on challenging domains such as olympiad-level geometry, yet their evaluation remains dominated by aggregate accuracy, a single score that obscur... 4.50 0% See Reviews View AI Dashboard
Target Before You Perturb: Enhancing Locally Private Graph Learning via Task-Oriented Perturbation Graph neural networks (GNNs) have achieved remarkable success in graph representation learning and have been widely adopted across various domains. However, real-world graphs often contain sensitive p... 4.67 26% See Reviews View AI Dashboard
LDLCC: Label Distribution Learning-based Confidence Calibration for Crowdsourcing Crowdsourcing typically collects multiple noisy labels for each instance and then aggregates these labels to infer its unknown true label. We discover that miscalibration, an important issue in superv... 2.00 0% See Reviews View AI Dashboard
Intention-Conditioned Flow Occupancy Models Large-scale pre-training has fundamentally changed how machine learning research is done today: large foundation models are trained once, and then can be used by anyone in the community (including tho... 6.00 0% See Reviews View AI Dashboard
REAR: Scalable Test-time Preference Realignment through Reward Decomposition Aligning large language models (LLMs) with diverse user preferences is a critical yet challenging task. While post-training methods can adapt models to specific needs, they often require costly data c... 3.00 9% See Reviews View AI Dashboard
Interactive Agents to Overcome Underspecificity in Software Engineering AI agents are increasingly being deployed to automate tasks, often based on underspecified user instructions. Making unwarranted assumptions to compensate for the missing information and failing to as... 5.33 3% See Reviews View AI Dashboard
Similarity-Constrained Reweighting for Complex Query Answering on Knowledge Graphs Machine learning models for answering complex queries on knowledge graphs estimate the likelihood of answers that are not reachable via direct traversal. Prior work in this area has focused on structu... 5.00 10% See Reviews View AI Dashboard
DAL: A Practical Prior-Free Black-Box Framework for Non-Stationary Bandits We introduce a practical, black-box framework termed Detection Augmented Learning (DAL) for the problem of non-stationary bandits without prior knowledge of the underlying non-stationarity. DAL accept... 4.00 0% See Reviews View AI Dashboard
Right Side Up? Disentangling Orientation Understanding in MLLMs with Fine-grained Multi-axis Perception Tasks Object orientation understanding represents a fundamental challenge in visual perception that underpins critical real-world applications like robotic manipulation and augmented reality. However, curre... 6.00 12% See Reviews View AI Dashboard
From Traits to Circuits: Toward Mechanistic Interpretability of Personality in Large Language Models Large language models (LLMs) have been observed to exhibit personality-like behaviors when prompted with standardized psychological assessments. However, existing approaches treat personality as a bla... 4.50 31% See Reviews View AI Dashboard
Action Chunking Proximal Policy Optimization for Universal Robotic Dexterous Grasping Universal dexterous grasping across diverse objects is a crucial step towards human-like manipulation. In order to handle the high degrees of freedom (DoF) of dexterous hands, state-of-the-art univer... 3.00 0% See Reviews View AI Dashboard
Boost the Identity-Preserving Embedding for Consistent Text-to-Image Generation Diffusion-based text-to-image (T2I) models have advanced high-fidelity content generation, but their inability to maintain subject consistency—preserving a target’s identity and visual attributes acro... 4.67 0% See Reviews View AI Dashboard
Myna: Masking-Based Contrastive Learning of Musical Representations In this paper, we present Myna, a simple yet effective approach for self-supervised musical representation learning. Built on a contrastive learning framework, Myna introduces two key innovations: (1)... 5.33 21% See Reviews View AI Dashboard
CHyLL: Learning Continuous Neural Representations of Hybrid Systems Learning the flows of hybrid systems that have both continuous and discrete time dynamics is challenging. The existing method learns the dynamics in each discrete mode, which suffers from the combinat... 4.67 0% See Reviews View AI Dashboard
ACTS: Adaptive Control for Test-time Scaling Controlling the generation length of Large Language Models (LLMs) presents a difficult trade-off between computational cost and output quality. We tackle this challenge with the ACTS (Adaptive Control... 4.00 22% See Reviews View AI Dashboard
Making Slow Thinking Faster: Compressing LLM Chain-of-Thought via Step Entropy Large Language Models (LLMs) using Chain-of-Thought (CoT) prompting excel at complex reasoning but generate verbose thought processes with considerable redundancy, leading to increased inference costs... 4.50 40% See Reviews View AI Dashboard
Quantifying Statistical Significance in Diffusion-Based Anomaly Localization via Selective Inference Anomaly localization in images—identifying regions that deviate from expected patterns—is vital in applications such as medical diagnosis and industrial inspection. A recent trend is the use of image ... 4.67 0% See Reviews View AI Dashboard
Rethinking Benign Relearning: Syntax as the Hidden Driver of Unlearning Failures Machine unlearning aims to remove specific content from trained models while preserving overall performance. However, the phenomenon of benign relearning, in which forgotten information reemerges even... 5.33 4% See Reviews View AI Dashboard
A Balanced Neuro-Symbolic Approach for Commonsense Abductive Logic Although Large Language Models (LLMs) have demonstrated impressive formal reasoning abilities, they often break down when problems require complex proof planning. One promising approach for improving ... 4.00 0% See Reviews View AI Dashboard
Autonomous Urban Region Representation with LLM-informed Reinforcement Learning Urban representation learning has become a key approach for many applications in urban computing, but existing methods still rely heavily on manual feature designs and geographic heuristics. We presen... 3.00 16% See Reviews View AI Dashboard
Implicit Regularization Through Hidden Diversity in Neural Networks A significant body of work has focused on studying the mechanisms behind the implicit regularization in neural networks. Recently, developments in ensemble theory have demonstrated that, for a wide va... 4.00 0% See Reviews View AI Dashboard
DRBench: A Realistic Benchmark for Enterprise Deep Research We introduce DRBench, a benchmark for evaluating AI agents on complex, open-ended deep research tasks in enterprise settings. Unlike prior benchmarks that focus on simple questions or web-only queries... 5.50 0% See Reviews View AI Dashboard
Pixel3DMM: Versatile Screen-Space Priors for Single-Image 3D Face Reconstruction We address the 3D reconstruction of human faces from a single RGB image. To this end, we propose Pixel3DMM, a set of highly-generalized vision transformers which predict per-pixel geometric cues in or... 6.00 0% See Reviews View AI Dashboard
Reinforcement Learning from Dynamic Critic Feedback for Free-Form Generations Open-ended generation tasks require outputs to satisfy diverse and often implicit task-specific evaluation rubrics. The sheer number of relevant rubrics leads to prohibitively high verification costs ... 4.00 14% See Reviews View AI Dashboard
Scalable Second-order Riemannian Optimization for $K$-means Clustering Clustering is a hard discrete optimization problem. Nonconvex approaches such as low-rank semidefinite programming (SDP) have recently demonstrated promising statistical and local algorithmic guarante... 6.00 0% See Reviews View AI Dashboard
Spatial Sign based Direct Sparse Linear Discriminant Analysis for High Dimensional Data Robust high-dimensional classification under heavy-tailed distributions without losing efficiency, is a central challenge in modern statistics and machine learning. However, most existing linear discr... 3.33 0% See Reviews View AI Dashboard
DBMSolver: Fast Diffusion Bridge Sampling for High-Quality Image-to-Image Translation Diffusion-based approaches for image-to-image (I2I) translation have garnered significant attention due to their ability to generate high-fidelity images and scalability to large-scale datasets. Howev... 4.00 5% See Reviews View AI Dashboard
Splat Feature Solver Feature lifting has emerged as a crucial component in 3D scene understanding, enabling the attachment of rich image feature descriptors (e.g., DINO, CLIP) onto splat-based 3D representations. The core... 4.67 0% See Reviews View AI Dashboard
VCode: a Multimodal Coding Benchmark with SVG as Symbolic Visual Representation Code has emerged as a precise, executable medium for reasoning and action in the agent era. Yet progress has largely focused on linguistic-centric tasks, such as program synthesis and debugging, leavi... 3.33 12% See Reviews View AI Dashboard
Don't Shift the Trigger: Robust Gradient Ascent for Backdoor Unlearning Backdoor attacks pose a significant threat to machine learning models, allowing adversaries to implant hidden triggers that alter model behavior when activated. Although gradient ascent (GA)-based unl... 5.50 0% See Reviews View AI Dashboard
Inverse IFEval: Can LLMs Unlearn Stubborn Training Conventions to Follow Real Instructions? Large Language Models (LLMs) achieve strong performance on diverse tasks but often exhibit cognitive inertia, struggling to follow instructions that conflict with the standardized patterns learned dur... 5.33 8% See Reviews View AI Dashboard
DisCo-Layout: Disentangling and Coordinating Semantic and Physical Refinement in a Multi-Agent Framework for 3D Indoor Layout Synthesis 3D indoor layout synthesis is crucial for creating virtual environments. Traditional methods struggle with generalization due to fixed datasets. While recent LLM and VLM-based approaches offer improve... 2.50 23% See Reviews View AI Dashboard
Training-Free Adaptive Frame Selection for Video-Language Understanding Multimodal Large Language Models (MLLMs) have shown strong performance on image understanding tasks, but video comprehension remains a significant challenge due to the high computational cost of proce... 5.00 31% See Reviews View AI Dashboard
SGD-Based Knowledge Distillation with Bayesian Teachers: Theory and Guidelines Knowledge Distillation (KD) is a central paradigm for transferring knowledge from a large teacher network to a typically smaller student model, often by leveraging soft probabilistic outputs. While KD... 6.50 0% See Reviews View AI Dashboard
How much correction is adequate? A Unified Bias-Aware Loss for Long-Tailed Semi-Supervised Learning Long-tailed semi-supervised learning (LTSSL) suffers from class imbalance-induced biases in both training and inference. Existing debiasing methods typically rely on distribution priors, which fail to... 4.00 9% See Reviews View AI Dashboard
HARPA: A Testability-Driven, Literature-Grounded Framework for Research Ideation While there has been a surge of interest in automated scientific discovery (ASD), especially with the emergence of LLMs, it remains challenging for tools to generate hypotheses that are both testable ... 4.00 0% See Reviews View AI Dashboard
Delta-Triplane Transformers as Occupancy World Models Occupancy World Models (OWMs) aim to predict future scenes via 3D voxelized representations of the environment to support intelligent motion planning. Existing approaches typically generate full futur... 5.00 0% See Reviews View AI Dashboard
Condensing Videos by Learning Where Motion Matters Video dataset condensation aims to mitigate the immense computational cost of video processing, but faces the unique challenge of preserving the complex interplay between spatial content and temporal ... 3.50 31% See Reviews View AI Dashboard
Episodic Memory Representation for Long Video Understanding Video Large Language Models (Video-LLMs) excel at general video understanding but struggle with long-form videos due to context-window limits. Consequently, recent approaches focus on keyframe retriev... 4.50 23% See Reviews View AI Dashboard
When Scores Learn Geometry: Rate Separations under the Manifold Hypothesis Score-based methods, such as diffusion models and Bayesian inverse problems, are often interpreted as learning the data distribution in the low-noise limit ($\sigma \to 0$). In this work, we propose a... 6.67 4% See Reviews View AI Dashboard
AdaptiveResidual: Inference-Time Trust Calibration for Contextual Knowledge Injection In modern large language models (LLMs), injecting external knowledge via the context to guide models' outputs toward desired outcomes (e.g., through RAG) is a standard practice. However, recent resea... 2.00 2% See Reviews View AI Dashboard
Data- and Hardware-Aware Entanglement Selection for Quantum Feature Maps in Hybrid Quantum Neural Networks Embedding classical data into a quantum feature space is a critical step for Hybrid Quantum Neural Networks (HQNNs). While entanglement in this feature map layer can enhance expressivity, heuristic ch... 2.50 49% See Reviews View AI Dashboard
Structuring Semantic Embeddings for Principle Evaluation: A Kernel-Guided Contrastive Learning Approach Evaluating principle adherence in high-dimensional text embeddings is challenging because principle-specific signals are often entangled with general semantic content. Our kernel-guided contrastive le... 2.00 78% See Reviews View AI Dashboard
Learning from Examples and Self-Exploration: A New Paradigm for Dynamic Fusion Alignment of Large Language Models with human preferences is dominated by two paradigms: Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL), exemplified by methods like Group Relative Policy... 2.00 59% See Reviews View AI Dashboard
Reflective Flow Sampling Enhancement The growing demand for text-to-image generation has led to rapid advances in generative modeling. Recently, flow models trained with flow matching algorithms, such as FLUX, have achieved remarkable pr... 4.50 6% See Reviews View AI Dashboard
Mapping Overlaps in Benchmarks through Perplexity in the Wild We develop signatures of capacity familiarity to characterize large language model (LLM) benchmarks and their meaningful overlaps. Benchmark signatures probe the capacity required for benchmark perfor... 6.50 13% See Reviews View AI Dashboard
Weak Correlations as the Underlying Principle for Linearization of Gradient-Based Learning Systems Deep learning models, such as wide neural networks, can be viewed as nonlinear dynamical systems composed of numerous interacting degrees of freedom. When such systems approach the limit of infinite n... 5.33 0% See Reviews View AI Dashboard
SELU: Energy-based Targeted Unlearning in LLMs Large language models (LLMs) often memorize sensitive or copyrighted content, motivating \emph{machine unlearning} methods that can remove specific knowledge without retraining from scratch. A challe... 2.50 18% See Reviews View AI Dashboard
From Values to Tokens: An LLM-Driven Framework for Context-aware Time Series Forecasting via Symbolic Discretization Time series forecasting plays a vital role in supporting decision-making across a wide range of critical applications, including energy, healthcare, and finance. Despite recent advances, forecasting ... 3.50 37% See Reviews View AI Dashboard
Learnable Spiking Neural P System with Interval Excitation Spiking Neural P (SN P) systems are parallel distributed models developed by mimicking bio-nervous systems. Past decades have emerged a lot of efforts on theoretical characterizations and modeling pla... 4.00 11% See Reviews View AI Dashboard
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