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
Survival at Any Cost? LLMs and the Choice Between Self-Preservation and Human Harm How do Large Language Models (LLMs) behave when faced with a dilemma between their own survival and harming humans? This fundamental tension becomes critical as LLMs integrate into autonomous systems ... 4.50 51% See Reviews View AI Dashboard
Chart-R1: Chain-of-Thought Supervision and Reinforcement for Advanced Chart Reasoner Recently, inspired by OpenAI-o1/o3 and Deepseek-R1, the R1-style method based on reinforcement fine-tuning has received widespread attention from the community. Previous R1-style methods mainly focus ... 4.00 0% See Reviews View AI Dashboard
The Differences Between Direct Alignment Algorithms are a Blur Direct Alignment Algorithms (DAAs) simplify LLM alignment by directly optimizing policies, bypassing reward modeling and RL. While DAAs differ in their use of SFT (one-stage vs. two-stage) and the sca... 4.40 0% See Reviews View AI Dashboard
APLA: A Simple Adaptation Method for Vision Transformers Existing adaptation techniques typically require architectural modifications or added parameters, leading to high computational costs and complexity. We introduce Attention Projection Layer Adaptation... 4.40 7% See Reviews View AI Dashboard
Aligning Large Language Model Behavior with Human Citation Preferences Most services built on powerful large-scale language models (LLMs) add citations to their output to enhance credibility. Recent research has paid increasing attention to the question of what reference... 3.20 0% See Reviews View AI Dashboard
FlowKV: Enhancing Multi-Turn Conversational Coherence in LLMs via Isolated Key-Value Cache Management Large Language Models (LLMs) are increasingly deployed in multi-turn conversational applications, where the management of the Key-Value (KV) Cache presents a significant bottleneck. The linear growth ... 3.00 34% See Reviews View AI Dashboard
EgoFact: A Benchmark for Multi-Hop Multimodal Retrieval-Augmented Generation Retrieval-Augmented Generation (RAG) has emerged as a powerful approach to improve large language models (LLMs) by grounding their outputs in external knowledge. However, progress in the multimodal do... 3.00 33% See Reviews View AI Dashboard
Your Discriminative Model is Secretly a Generative Model Although discriminative and generative models are fundamentally equivalent in understanding data distributions, bridging these paradigms -- especially transforming off-the-shelf discriminative models ... 5.00 13% See Reviews View AI Dashboard
Stochastic Neural Networks for Causal Inference with Missing Confounders One of the major challenges in causal inference with observational data is handling missing confounders. Latent variable modeling offers a valid framework to address this challenge, but existing appro... 4.00 0% See Reviews View AI Dashboard
Reasoning Scaffolding: Distilling the Flow of Thought from LLMs The prevailing approach to distilling reasoning from Large Language Models (LLMs)—behavioral cloning from textual rationales—is fundamentally limited. It teaches Small Language Models (SLMs) to mimic ... 5.50 44% See Reviews View AI Dashboard
Progressive Memory Transformers: Memory-Aware Attention for Time Series Self-supervised learning has become the de‑facto strategy for time‑series domains where labeled data are scarce, yet most existing objectives emphasize \emph{either} local continuity \emph{or} global ... 5.33 14% See Reviews View AI Dashboard
Could Student Selection Be the Missing Piece for Efficient Distillation? Selecting the optimal student architecture remains an overlooked challenge in knowledge distillation (KD). Current approaches typically rely on model size constraints or random selection, ignoring how... 4.00 36% See Reviews View AI Dashboard
PT$^2$-LLM: Post-Training Ternarization for Large Language Models Large Language Models (LLMs) have shown impressive capabilities across diverse tasks, but their large memory and compute demands hinder deployment. Ternarization has gained attention as a promising co... 4.50 16% See Reviews View AI Dashboard
Self-Reflective Generation at Test Time Large language models (LLMs) increasingly solve complex reasoning tasks via long chain-of-thought, but their forward-only autoregressive generation process is fragile; early token errors can cascade, ... 3.50 42% See Reviews View AI Dashboard
Beyond Textual CoT: Interleaved Text-Image Chains with Deep Confidence Reasoning for Image Editing Image editing with natural language has gained significant popularity, yet existing methods struggle with intricate object intersections and fine-grained spatial relationships due to the lack of an ex... 4.00 11% See Reviews View AI Dashboard
Out of the Shadows: Exploring a Latent Space for Neural Network Verification Neural networks are ubiquitous. However, they are often sensitive to small input changes. Hence, to prevent unexpected behavior in safety-critical applications, their formal verification -- a notori... 6.50 0% See Reviews View AI Dashboard
Taming the Judge: Deconflicting AI Feedback for Stable Reinforcement Learning Aligning language models using LLM judge feedback offers a scalable alternative to human annotation, yet is plagued by judgment inconsistencies that destabilize reinforcement learning. While prior wor... 3.50 68% See Reviews View AI Dashboard
Bridging the Gap Between Homogeneous and Heterogeneous Asynchronous Optimization is Surprisingly Difficult Modern large-scale machine learning tasks often require multiple workers, devices, CPUs, or GPUs to compute stochastic gradients in parallel and asynchronously to train model weights. Theoretical resu... 5.00 0% See Reviews View AI Dashboard
Decoupling of Experts: A Knowledge-Driven Architecture for Efficient LLMs Current large language models (LLMs), particularly Mixture-of-Experts (MoE) variants, face challenges in achieving efficient, structured, and interpretable scaling. We introduce the Decoupling of Expe... 1.60 69% See Reviews View AI Dashboard
Characteristic Root Analysis and Regularization for Linear Time Series Forecasting Time series forecasting remains a critical challenge across numerous domains, yet the effectiveness of complex models often varies unpredictably across datasets. Recent studies highlight the surprisin... 6.00 38% See Reviews View AI Dashboard
GrapHist: Large-Scale Graph Self-Supervised Learning for Histopathology Self-supervised vision models have achieved notable success in digital pathology. However, their domain-agnostic transformer architectures are not designed to inherently account for fundamental biolog... 0.67 0% See Reviews View AI Dashboard
Test-Time Alignment of LLMs via Sampling-Based Optimal Control in pre-logit space Test-time alignment of large language models (LLMs) attracts attention because fine-tuning LLMs requires high computational costs. In this paper, we propose a new test-time alignment method called ada... 4.50 0% See Reviews View AI Dashboard
Transporting Tokens: Optimal-Transport View of Parallel LLM Decoding Autoregressive decoding is a primary bottleneck for large language models (LLMs), as its inherent sequentiality severely limits inference speed. While speculative decoding methods mitigate this via a ... 4.67 79% See Reviews View AI Dashboard
MultiViewPano: Training-Free 360° Panorama Generation via Multi-View Diffusion and Pose-Aware Stitching We propose MultiViewPano, a training-free framework for generating 360° panoramas from one or more arbitrarily positioned input images with varying fields of view. Existing panorama generation methods... 3.50 70% See Reviews View AI Dashboard
VEM: Environment-Free Exploration for Training GUI Agent with Value Environment Model Training Vision-Language Models (VLMs) for Graphical User Interfaces (GUI) agents via Reinforcement Learning (RL) faces critical challenges: environment-based RL requires costly interactions, while en... 3.20 45% See Reviews View AI Dashboard
Dissecting Mahalanobis: How Feature Geometry and Normalization Shape OOD Detection Out-of-distribution (OOD) detection is critical for the reliable deployment and better understanding of deep learning models. To address this challenge, various methods relying on Mahalanobis distance... 4.29 48% See Reviews View AI Dashboard
Self-Improving Skill Learning for Robust Skill-based Meta-Reinforcement Learning Meta-reinforcement learning (Meta-RL) facilitates rapid adaptation to unseen tasks but faces challenges in long-horizon environments. Skill-based approaches tackle this by decomposing state-action seq... 5.50 27% See Reviews View AI Dashboard
M²F-PINN: A Multi-Scale Frequency-Domain Multi-Physics-Informed Neural Network for Ocean Forecasting Physics‐informed neural networks (PINNs) embed physical laws into data-driven learning and are becoming increasingly influential in climate and ocean forecasting. Yet effectively capturing multi-scale... 3.50 0% See Reviews View AI Dashboard
Closing the Data-Efficiency Gap Between Autoregressive and Masked Diffusion LLMs Despite autoregressive large language models (arLLMs) having been the dominant paradigm in language modeling, they resist knowledge injection via fine-tuning due to inherent shortcomings such as the "... 4.00 0% See Reviews View AI Dashboard
Scaling Multi-Task Bayesian Optimization with Large Language Models In multi-task Bayesian optimization, the goal is to leverage experience from optimizing existing tasks to improve the efficiency of optimizing new ones. While approaches using multi-task Gaussian proc... 5.50 0% See Reviews View AI Dashboard
SceneAdapt: Scene-aware Adaptation of Human Motion Diffusion Human motion is inherently diverse and semantically rich, while also shaped by the surrounding scene. However, existing motion generation approaches address either motion semantics or scene-awareness ... 4.50 0% See Reviews View AI Dashboard
Beyond Binary Preferences: A Principled Framework for Reward Modeling with Ordinal Feedback Reward modeling is crucial for aligning large language models with human preferences, yet current approaches lack a principled mathematical framework for leveraging ordinal preference data. When human... 5.50 34% See Reviews View AI Dashboard
StaMo: Unsupervised Learning of Generalizable Robot Motion from Compact State Representation A fundamental challenge in embodied intelligence is developing expressive and compact state representations for efficient world modeling and decision making. However, existing methods often fail to ac... 3.00 6% See Reviews View AI Dashboard
EigenLoRAx: Efficient Low Rank Adaptation Using Recycled Principal Subspaces The rapid growth of large models has raised concerns about their environmental impact and equity in accessibility due to significant computational costs. Low-Rank Adapters (LoRA) offer a lightweight s... 4.00 0% See Reviews View AI Dashboard
FS-KAN: Permutation Equivariant Kolmogorov-Arnold Networks via Function Sharing Permutation equivariant neural networks employing parameter-sharing schemes have emerged as powerful models for leveraging a wide range of data symmetries, significantly enhancing the generalization a... 4.33 0% See Reviews View AI Dashboard
Neural Algorithmic Reasoning for Hypergraphs with Looped Transformers Looped Transformers have shown exceptional neural algorithmic reasoning capability in simulating traditional graph algorithms, but their application to more complex structures like hypergraphs remains... 3.50 13% See Reviews View AI Dashboard
StarEmbed: Benchmarking Time Series Foundation Models on Astronomical Observations of Variable Stars Time series foundation models (TSFMs) are increasingly being adopted as highly-capable general-purpose time series representation learners. Although their training corpora are vast, they exclude astro... 3.50 0% See Reviews View AI Dashboard
Cross-View Open-Vocabulary Object Detection in Aerial Imagery Traditional object detection models are typically trained on a fixed set of classes, limiting their flexibility and making it costly to incorporate new categories. Open-vocabulary object detection add... 5.00 52% See Reviews View AI Dashboard
Critical attention scaling in long-context transformers As large language models scale to longer contexts, attention layers suffer from a fundamental pathology: attention scores collapse toward uniformity as context length $n$ increases, causing tokens to ... 6.00 0% See Reviews View AI Dashboard
FastVGGT: Fast Visual Geometry Transformer Scaling visual geometry transformers for long image sequences poses a significant computational and memory challenge. In this work, we diagnose this issue in the state-of-the-art model VGGT, and trace... 4.00 19% See Reviews View AI Dashboard
A Conformalized Inference on Unobservable Variables Quantifying uncertainty in predicted unobservable variables is a critical area of research in statistics, artificial intelligence, and empirical science. Most scientific studies assume a specific stru... 4.00 4% See Reviews View AI Dashboard
FedAgentBench: Towards Automating Real-world Federated Medical Image Analysis with Server–Client LLM Agents Federated learning (FL) allows collaborative model training across healthcare sites without sharing sensitive patient data. However, real-world FL deployment is often hindered by complex operational c... 5.00 10% See Reviews View AI Dashboard
LLM-ERM: Sample-Efficient Program Learning via LLM-Guided Search We seek algorithms for program learning that are both sample-efficient and computationally feasible. In the realizable short-program regime, length-first (Occam/MDL) enumeration achieves near-optimal ... 1.50 19% See Reviews View AI Dashboard
Duality and Policy Evaluation in Distributionally Robust Bayesian Diffusion Control We consider a Bayesian diffusion control problem of expected terminal utility maximization. The controller imposes a prior distribution on the unknown drift of an underlying diffusion. The Bayesian op... 4.80 0% See Reviews View AI Dashboard
Aegis: Automated Error Generation and Identification for Multi-Agent Systems Large language model based multi-agent systems (MAS) have unlocked significant advancements in tackling complex problems, but their increasing capability introduces a structural fragility that makes t... 6.00 34% See Reviews View AI Dashboard
InfiniteHiP: Extending Language Model Context Up to 3 Million Tokens on a Single GPU In modern large language models (LLMs), handling very long context lengths presents significant challenges as it causes slower inference speeds and increased memory costs. Additionally, most existing ... 4.00 0% See Reviews View AI Dashboard
RF Prior: Preserving Global-Context Priors for Efficient Instance Segmentation Transfer We present an efficient transfer-learning framework that reparameterizes a state-of-the-art detector backbone—instantiated with a YOLO-family model—for polygon based instance segmentation. Our key ide... 3.00 3% See Reviews View AI Dashboard
Rethinking GNNs and Missing Features: Challenges, Evaluation and a Robust Solution Handling missing node features is a key challenge for deploying Graph Neural Networks (GNNs) in real-world domains such as healthcare and sensor networks. Existing studies mostly address relatively be... 5.50 4% See Reviews View AI Dashboard
QVGen: Pushing the Limit of Quantized Video Generative Models Video diffusion models (DMs) have enabled high-quality video synthesis. Yet, their substantial computational and memory demands pose serious challenges to real-world deployment, even on high-end GPUs.... 6.80 0% See Reviews View AI Dashboard
Faithful Rule Learning for Tabular Data Cell Completion Tabular data cell completion aims to infer the correct constants that could fill a missing cell in a table row. While machine learning (ML) models have proven to be effective for this task, the limite... 5.50 0% See Reviews View AI Dashboard
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