paper_id string | arxiv_id string | title string | markdown dict | reviews list | scores dict | metadata dict | meta_review dict | decision dict |
|---|---|---|---|---|---|---|---|---|
At9JmGF3xy | 2410.14445v2 | Toward Generalizing Visual Brain Decoding to Unseen Subjects | {
"content": "## Abstract\n\nAbstract Visual brain decoding aims to decode visual information from human brain activities. Despite the great progress, one critical limitation of current brain decoding research lies in the lack of generalization capability to unseen subjects. Prior works typically focus on decoding br... | [
{
"id": "LvH5g44B1B",
"initial_rating": 6,
"confidence": 4,
"soundness": 3,
"contribution": 3,
"presentation": 3,
"summary": "This paper presents a new brain decoding paradigm for zero-shot brain decoding. Multiple experimental design exposes that brain decoding models can be generalized... | {
"rating": "1;5;5;6",
"rating_avg": 4.25,
"confidence": "4;3;4;4",
"confidence_avg": 3.75,
"soundness": "1;3;3;2",
"soundness_avg": 2.25,
"contribution": "1;2;2;2",
"contribution_avg": 1.75,
"presentation": "2;3;3;3",
"presentation_avg": 2.75
} | {
"primary_area": "",
"track": "main",
"venue": "ICLR 2025 Poster",
"venueid": "ICLR.cc/2025/Conference",
"processed_at": "2026-01-14T22:16:01.152437"
} | {
"id": "47gyeczZzP",
"metareview": "This submission contributes a method for brain-based decoding generalizing across subjects. The study is a vision task, mapping brain signals to CLIP embeddings of the images. The submission generated interest and discussion from the reviewers, in particular the across-subject g... | {
"decision": "Accept (Poster)"
} |
AvOhBgsE5R | 2405.17013v3 | Motion-Agent: A Conversational Framework for Human Motion Generation with LLMs | {
"content": "## Abstract\n\nAbstract While previous approaches to 3D human motion generation have achieved notable success, they often rely on extensive training and are limited to specific tasks. To address these challenges, we introduce Motion-Agent , an efficient conversational framework designed for general huma... | [
{
"id": "pLRINSNPMO",
"initial_rating": 5,
"confidence": 4,
"soundness": 2,
"contribution": 2,
"presentation": 2,
"summary": "In this paper, the authors propose Motion-Agent, a conversational framework for human motion that utilizes large language models (LLMs). By incorporating MotionLL... | {
"rating": "5;6;6;6;8",
"rating_avg": 6.2,
"confidence": "4;2;5;3;4",
"confidence_avg": 3.6,
"soundness": "2;3;3;3;4",
"soundness_avg": 3,
"contribution": "2;3;3;3;3",
"contribution_avg": 2.8,
"presentation": "2;3;3;3;4",
"presentation_avg": 3
} | {
"primary_area": "",
"track": "main",
"venue": "ICLR 2025 Poster",
"venueid": "ICLR.cc/2025/Conference",
"processed_at": "2026-01-14T22:16:01.153809"
} | {
"id": "x3r9BJ9eyJ",
"metareview": "The submission introduces a framework for human motion modeling based on conversations. Reviewers were in general positive about the submission, appreciating the novel idea and the good writing. The rebuttal was also helpful in convincing the reviewers to level up their suppor... | {
"decision": "Accept (Poster)"
} |
Ax3uliEBVR | 2405.15429v4 | E(n) Equivariant Topological Neural Networks | {
"content": "## Abstract\n\nAbstract Graph neural networks excel at modeling pairwise interactions, but they cannot flexibly accommodate higher-order interactions and features. Topological deep learning (TDL) has emerged recently as a promising tool for addressing this issue. TDL enables the principled modeling of a... | [
{
"id": "MWQqN6Ngv6",
"initial_rating": 5,
"confidence": 3,
"soundness": 3,
"contribution": 2,
"presentation": 4,
"summary": "This paper introduces an equivariant model within the framework of topological deep learning. The architecture generalizaes the equivariant graph neural network a... | {
"rating": "5;5;6;6",
"rating_avg": 5.5,
"confidence": "3;3;4;4",
"confidence_avg": 3.5,
"soundness": "2;2;3;3",
"soundness_avg": 2.5,
"contribution": "2;2;3;2",
"contribution_avg": 2.25,
"presentation": "4;3;2;3",
"presentation_avg": 3
} | {
"primary_area": "",
"track": "main",
"venue": "ICLR 2025 Poster",
"venueid": "ICLR.cc/2025/Conference",
"processed_at": "2026-01-14T22:16:01.155047"
} | {
"id": "HEvSnP3YRl",
"metareview": "This paper extends TNN to ETNN, similar to the extension of GNN to EGNN. There have been very intensive discussions among the authors and reviewers, and the authors put a lot of efforts during the process. Eventually, I believe all the major issues have been resolved. I agree wi... | {
"decision": "Accept (Poster)"
} |
B5iOSxM2I0 | 2407.11606v3 | The Foundations of Tokenization: Statistical and Computational Concerns | {
"content": "## Abstract\n\nAbstract Tokenization—the practice of converting strings of characters from an\nalphabet into sequences of tokens over a vocabulary—is a critical step in\nthe NLP pipeline. The use of token representations is widely credited with\nincreased model performance but is also the source of many... | [
{
"id": "mGyUM7cIdu",
"initial_rating": 8,
"confidence": 3,
"soundness": 3,
"contribution": 3,
"presentation": 4,
"summary": "Disclaimer: I previously reviewed an earlier version of this paper during its submission to NeurIPS 2024.\n\nThis paper proposes a formal approach to tokenization... | {
"rating": "5;5;6;8",
"rating_avg": 6,
"confidence": "3;1;3;3",
"confidence_avg": 2.5,
"soundness": "3;3;3;4",
"soundness_avg": 3.25,
"contribution": "2;2;3;3",
"contribution_avg": 2.5,
"presentation": "2;3;3;4",
"presentation_avg": 3
} | {
"primary_area": "",
"track": "main",
"venue": "ICLR 2025 Poster",
"venueid": "ICLR.cc/2025/Conference",
"processed_at": "2026-01-14T22:16:01.157264"
} | {
"id": "YZ4v8wpZED",
"metareview": "This paper presents a foundational perspective on tokenization and established some formal properties of tokenization; the authors established these properties via the utilization of stochastic maps, which is novel. \n\nStrengths:\n- Very clear exposition.\n- Novel work where th... | {
"decision": "Accept (Poster)"
} |
B6AQzaQCsl | 2312.02132v2 | Hot PATE: Private Aggregation of Distributions for Diverse Tasks | {
"content": "## Abstract\n\nAbstract The Private Aggregation of Teacher Ensembles (PATE) framework is a versatile approach to privacy-preserving machine learning. In PATE, teacher models that are not privacy-preserving are trained on distinct portions of sensitive data. Privacy-preserving knowledge transfer to a stu... | [
{
"id": "Xjx7kbMwAU",
"initial_rating": 6,
"confidence": 2,
"soundness": 2,
"contribution": 3,
"presentation": 1,
"summary": "The paper introduces HotPATE, a method based on the Private Aggregation of Teacher Ensemble with the distinction that the method forgoes independent teacher data ... | {
"rating": "3;5;5;6",
"rating_avg": 4.75,
"confidence": "3;4;3;3",
"confidence_avg": 3.25,
"soundness": "2;3;3;3",
"soundness_avg": 2.75,
"contribution": "3;2;4;3",
"contribution_avg": 3,
"presentation": "1;2;2;4",
"presentation_avg": 2.25
} | {
"primary_area": "",
"track": "main",
"venue": "Submitted to ICLR 2025",
"venueid": "ICLR.cc/2025/Conference/Rejected_Submission",
"processed_at": "2026-01-14T22:16:01.158129"
} | {
"id": "hanFTGGTZq",
"metareview": "The paper proposes a transformation that can be applied before PATE to increase consistency of expert predictions in case they share a low-probability prediction. The authors suggest this allows using private aggregation for new tasks such as aggregating next-word predictions fr... | {
"decision": "Reject"
} |
B6HtEFoJiG | 2405.15557v1 | Learning from Linear Algebra: A Graph Neural Network Approach to Preconditioner Design for Conjugate Gradient Solvers | {
"content": "## Abstract\n\nAbstract Large linear systems are ubiquitous in modern computational science. The main recipe for solving them is iterative solvers with well-designed preconditioners. Deep learning models may be used to precondition residuals during iteration of such linear solvers as the conjugate gradi... | [
{
"id": "dPcENkaeTC",
"initial_rating": 3,
"confidence": 4,
"soundness": 2,
"contribution": 2,
"presentation": 3,
"summary": "The paper titled \"Learning from Linear Algebra: A Graph Neural Network Approach to Preconditioner Design for Conjugate Gradient Solvers\" presents a novel method... | {
"rating": "3;5;5;6",
"rating_avg": 4.75,
"confidence": "4;3;5;5",
"confidence_avg": 4.25,
"soundness": "2;2;3;3",
"soundness_avg": 2.5,
"contribution": "2;2;2;3",
"contribution_avg": 2.25,
"presentation": "3;3;3;3",
"presentation_avg": 3
} | {
"primary_area": "",
"track": "main",
"venue": "Submitted to ICLR 2025",
"venueid": "ICLR.cc/2025/Conference/Rejected_Submission",
"processed_at": "2026-01-14T22:16:01.158881"
} | {
"id": "o2mt5puAD0",
"metareview": "Here the authors propose a method for designing preconditioners based on training a GNN. While some of the reviewers point out the novelty of using GNNs for learning preconditioners, the reviewers also note weaknesses in the scope and applicability of the work beyond the specifi... | {
"decision": "Reject"
} |
B7eHRsuTSh | 2404.11213v1 | Revisiting Noise Resilience Strategies in Gesture Recognition: Short-Term Enhancement in Surface Electromyographic Signal Analysis | {
"content": "## Abstract\n\nAbstract Gesture recognition based on surface electromyography (sEMG) has been gaining importance in many 3D Interactive Scenes. However, sEMG is easily influenced by various forms of noise in real-world environments, leading to challenges in providing long-term stable interactions throug... | [
{
"id": "X876UxQ7ta",
"initial_rating": 1,
"confidence": 5,
"soundness": 2,
"contribution": 1,
"presentation": 2,
"summary": "The authors propose a Short Term Enhancement Module (STEM) to improve noise resilience in sEMG-based gesture recognition. The results show that the proposed metho... | {
"rating": "1;5;6;8;8",
"rating_avg": 5.6,
"confidence": "5;3;4;4;3",
"confidence_avg": 3.8,
"soundness": "2;2;3;3;3",
"soundness_avg": 2.6,
"contribution": "1;2;3;3;3",
"contribution_avg": 2.4,
"presentation": "2;2;3;3;3",
"presentation_avg": 2.6
} | {
"primary_area": "",
"track": "main",
"venue": "Submitted to ICLR 2025",
"venueid": "ICLR.cc/2025/Conference/Rejected_Submission",
"processed_at": "2026-01-14T22:16:01.159615"
} | {
"id": "MgJmuP8ca3",
"metareview": "The paper proposes a method for gesture recognition from sEMG. The work tackles an important area and shows strong results. The paper is easy to follow and understand. The shortcomings of the paper initially raised by the reviewers include the lack of novelty in individual compo... | {
"decision": "Reject"
} |
B8aHIDSi7E | 2410.01309v1 | Getting Free Bits Back from Rotational Symmetries in LLMs | {
"content": "## Abstract\n\nAbstract Current methods for compressing neural network weights, such as decomposition, pruning, quantization, and channel simulation, often overlook the inherent symmetries within these networks and thus waste bits on encoding redundant information.\nIn this paper, we propose a format ba... | [
{
"id": "hClVPujTrb",
"initial_rating": 6,
"confidence": 3,
"soundness": 3,
"contribution": 3,
"presentation": 2,
"summary": "In this paper, the authors highlight that the rotational symmetries of SliceGPT introduce redundancies. Based on SliceGPT, they propose further compressing the we... | {
"rating": "5;5;6;6",
"rating_avg": 5.5,
"confidence": "3;3;2;3",
"confidence_avg": 2.75,
"soundness": "2;3;3;3",
"soundness_avg": 2.75,
"contribution": "2;3;3;3",
"contribution_avg": 2.75,
"presentation": "2;3;3;2",
"presentation_avg": 2.5
} | {
"primary_area": "",
"track": "main",
"venue": "Submitted to ICLR 2025",
"venueid": "ICLR.cc/2025/Conference/Rejected_Submission",
"processed_at": "2026-01-14T22:16:01.160333"
} | {
"id": "jIK9O74hBS",
"metareview": "This paper proposes a method to compress rotationally symmetric weight matrices. Due to their rotational symmetry, the encoding of these weight matrices can be redundant. To address this, the authors propose the bits-back coding algorithm for compression.\nMain strengths:\n- The... | {
"decision": "Reject"
} |
B9177IHxCL | 2410.03138v1 | Can LLMs Generate Diverse Molecules? Towards Alignment with Structural Diversity | {
"content": "## Abstract\n\nAbstract Recent advancements in large language models (LLMs) have demonstrated impressive performance in generating molecular structures as drug candidates, which offers significant potential to accelerate drug discovery. However, the current LLMs overlook a critical requirement for drug ... | [
{
"id": "Kdfw21Dvbl",
"initial_rating": 3,
"confidence": 3,
"soundness": 2,
"contribution": 2,
"presentation": 3,
"summary": "This paper proposes a two-stage fine-tuning approach to address the challenge of generating diverse molecules: (1) supervised fine-tuning to enable LLMs to autore... | {
"rating": "3;3;3;8",
"rating_avg": 4.25,
"confidence": "4;3;3;4",
"confidence_avg": 3.5,
"soundness": "2;2;2;4",
"soundness_avg": 2.5,
"contribution": "2;2;2;3",
"contribution_avg": 2.25,
"presentation": "3;2;3;3",
"presentation_avg": 2.75
} | {
"primary_area": "",
"track": "main",
"venue": "Submitted to ICLR 2025",
"venueid": "ICLR.cc/2025/Conference/Rejected_Submission",
"processed_at": "2026-01-14T22:16:01.161207"
} | {
"id": "CAbnxXYG6N",
"metareview": "In this work, authors propose a two-stage fine-tuning approach for large language models (LLMs) to generate diverse molecular structures for drug discovery applications. The method combines supervised fine-tuning to enable autoregressive molecule generation followed by reinforce... | {
"decision": "Reject"
} |
B9XP2R9LtG | 2411.02335v1 | Sparsing Law: Towards Large Language Models with Greater Activation Sparsity | {
"content": "## Abstract\n\nAbstract Activation sparsity denotes the existence of substantial weakly-contributed elements within activation outputs that can be eliminated, benefiting many important applications concerned with large language models (LLMs), such as computation acceleration and model interpretability.\... | [
{
"id": "gTvcHYSQzB",
"initial_rating": 5,
"confidence": 4,
"soundness": 2,
"contribution": 2,
"presentation": 3,
"summary": "This paper presents a study on the activation sparsity in large language models (LLMs), particularly focusing on decoder-only Transformer-based models. The author... | {
"rating": "3;5;5;6",
"rating_avg": 4.75,
"confidence": "4;4;4;5",
"confidence_avg": 4.25,
"soundness": "1;3;2;4",
"soundness_avg": 2.5,
"contribution": "1;2;2;3",
"contribution_avg": 2,
"presentation": "3;3;3;3",
"presentation_avg": 3
} | {
"primary_area": "",
"track": "main",
"venue": "Submitted to ICLR 2025",
"venueid": "ICLR.cc/2025/Conference/Rejected_Submission",
"processed_at": "2026-01-14T22:16:01.161885"
} | {
"id": "2OfjA3CmaQ",
"metareview": "a) Summary:\nThe paper presents a comprehensive study of activation sparsity in large language models (LLMs), focusing on decoder-only Transformer architectures. The key claims include:\n1. Introduction of PPL-p% sparsity as a new performance-aware metric for measuring activatio... | {
"decision": "Reject"
} |
BA1eG7vCNb | 2410.16669v2 | Linear Partial Gromov-Wasserstein Embedding | {
"content": "## Abstract\n\nAbstract The Gromov–Wasserstein (GW) problem, a variant of the classical optimal transport (OT) problem, has attracted growing interest in the machine learning and data science communities due to its ability to quantify similarity between measures in different metric spaces. However, like... | [
{
"id": "Ylm0D0c8cB",
"initial_rating": 8,
"confidence": 1,
"soundness": 4,
"contribution": 3,
"presentation": 4,
"summary": "The authors propose the linear partial Gromov-Wasserstein (LPGW) embedding, a linearization technique for the PGW problem. Theoretically, they prove that LPGW adm... | {
"rating": "3;6;6;8",
"rating_avg": 5.75,
"confidence": "4;3;3;3",
"confidence_avg": 3.25,
"soundness": "3;3;3;3",
"soundness_avg": 3,
"contribution": "2;3;2;4",
"contribution_avg": 2.75,
"presentation": "3;2;2;3",
"presentation_avg": 2.5
} | {
"primary_area": "",
"track": "main",
"venue": "ICLR 2025 Poster",
"venueid": "ICLR.cc/2025/Conference",
"processed_at": "2026-01-14T22:16:01.163104"
} | {
"id": "YY3RezGDwy",
"metareview": "The paper proposes the Linear Partial Gromov-Wasserstein embedding, which significantly improves the computational efficiency of Partial Gromov-Wasserstein distance while preserving its partial matching properties. Reviewers initially raised concerns regarding the novelty of the... | {
"decision": "Accept (Poster)"
} |
BC4lIvfSzv | 2402.09906v2 | Generative Representational Instruction Tuning | {
"content": "## Abstract\n\nAbstract All text-based language problems can be reduced to either generation or embedding. Current models only perform well at one or the other. We introduce generative representational instruction tuning ( GRIT ) whereby a large language model is trained to handle both generative and em... | [
{
"id": "IyvHbxSJN5",
"initial_rating": 8,
"confidence": 4,
"soundness": 3,
"contribution": 3,
"presentation": 4,
"summary": "The paper presents generative representational instruction tuning (GRIT), a unified model for embedding and generative tasks in text. GRIT learns embedding repres... | {
"rating": "6;6;6;8",
"rating_avg": 6.5,
"confidence": "3;3;3;3",
"confidence_avg": 3,
"soundness": "3;3;3;4",
"soundness_avg": 3.25,
"contribution": "3;2;3;4",
"contribution_avg": 3,
"presentation": "3;3;4;4",
"presentation_avg": 3.5
} | {
"primary_area": "",
"track": "main",
"venue": "ICLR 2025 Poster",
"venueid": "ICLR.cc/2025/Conference",
"processed_at": "2026-01-14T22:16:01.164507"
} | {
"id": "FQaHLastHA",
"metareview": "Previous embedding models and generative models are typically learned separately. This paper proposes to learn them together through massive multi-task training and different tasks are separated through instructions. Experimental results are strong, demonstrating the performance... | {
"decision": "Accept (Poster)"
} |
BCP5nAHXqs | 2402.18180v5 | Human Simulacra: Benchmarking the Personification of Large Language Models | {
"content": "## Abstract\n\nAbstract Large language models (LLMs) are recognized as systems that closely mimic aspects of human intelligence. This capability has attracted attention from the social science community, who see the potential in leveraging LLMs to replace human participants in experiments, thereby reduc... | [
{
"id": "KLjZnMSLxm",
"initial_rating": 8,
"confidence": 2,
"soundness": 3,
"contribution": 3,
"presentation": 3,
"summary": "The paper introduces a benchmark to assess the potential of LLMs in simulating human behaviours and personality traits for psychological and sociological applicat... | {
"rating": "3;5;5;6;8",
"rating_avg": 5.4,
"confidence": "4;4;3;3;2",
"confidence_avg": 3.2,
"soundness": "2;3;2;3;3",
"soundness_avg": 2.6,
"contribution": "2;2;2;2;3",
"contribution_avg": 2.2,
"presentation": "1;3;2;3;3",
"presentation_avg": 2.4
} | {
"primary_area": "",
"track": "main",
"venue": "ICLR 2025 Poster",
"venueid": "ICLR.cc/2025/Conference",
"processed_at": "2026-01-14T22:16:01.165428"
} | {
"id": "ZtHixQKePz",
"metareview": "The paper introduces a benchmark to assess the potential of LLMs in simulating human behaviours and personality traits for psychological and sociological applications. This work includes the creation of the \"Human Simulacra\" dataset, which features detailed virtual characters ... | {
"decision": "Accept (Poster)"
} |
BCeock53nt | 2409.10594v1 | Kolmogorov-Arnold Transformer | {
"content": "## Abstract\n\nAbstract Transformers stand as the cornerstone of mordern deep learning. Traditionally, these models rely on multi-layer perceptron (MLP) layers to mix the information between channels. In this paper, we introduce the Kolmogorov–Arnold Transformer (KAT), a novel architecture that replaces... | [
{
"id": "tL6u93km3J",
"initial_rating": 6,
"confidence": 4,
"soundness": 3,
"contribution": 3,
"presentation": 3,
"summary": "There has been a resurgence in Kolmogorov Arnold Networks (KANs) recently as an effective alternative to MLPs. This work carefully studies the major issues with s... | {
"rating": "5;6;6;6;8",
"rating_avg": 6.2,
"confidence": "4;4;4;4;2",
"confidence_avg": 3.6,
"soundness": "3;4;3;3;3",
"soundness_avg": 3.2,
"contribution": "3;4;3;3;3",
"contribution_avg": 3.2,
"presentation": "3;3;4;3;4",
"presentation_avg": 3.4
} | {
"primary_area": "",
"track": "main",
"venue": "ICLR 2025 Poster",
"venueid": "ICLR.cc/2025/Conference",
"processed_at": "2026-01-14T22:16:01.166144"
} | {
"id": "NG27VHP4Ji",
"metareview": "The paper introduces the Kolmogorov–Arnold Transformer (KAT), which replaces traditional MLP layers in transformers with Kolmogorov-Arnold Network (KAN) layers to enhance model performance. The authors address three main challenges: the inefficiency of B-spline functions, the co... | {
"decision": "Accept (Poster)"
} |
BCyAlMoyx5 | 2406.16135v1 | Crosslingual Capabilities and Knowledge Barriers in Multilingual Large Language Models | {
"content": "## Abstract\n\nAbstract Large language models (LLMs) are typically multilingual due to pretraining on diverse multilingual corpora. But can these models relate corresponding concepts across languages, effectively being crosslingual ? This study evaluates six state-of-the-art LLMs on inherently crossling... | [
{
"id": "5N7YwM8fPu",
"initial_rating": 8,
"confidence": 4,
"soundness": 3,
"contribution": 3,
"presentation": 3,
"summary": "This paper investigates how multilingual LLMs perform for actual cross-lingual tasks. This covers a wide range of tasks including machine translation but also sev... | {
"rating": "3;5;8",
"rating_avg": 5.333333333333333,
"confidence": "4;4;4",
"confidence_avg": 4,
"soundness": "2;1;3",
"soundness_avg": 2,
"contribution": "1;1;3",
"contribution_avg": 1.6666666666666667,
"presentation": "2;3;3",
"presentation_avg": 2.6666666666666665
} | {
"primary_area": "",
"track": "main",
"venue": "Submitted to ICLR 2025",
"venueid": "ICLR.cc/2025/Conference/Rejected_Submission",
"processed_at": "2026-01-14T22:16:01.166828"
} | {
"id": "TlKQSvl4YL",
"metareview": "The paper investigates the cross-lingual knowledge barrier for large language models (LLMs) by evaluating their performance on mixed-language tasks, general knowledge benchmarks (MMLU), and domain-specific evaluations (Harry Potter quiz). The paper observes that while LLMs demon... | {
"decision": "Reject"
} |
BDf1IBIuFx | 2406.10225v1 | SatDiffMoE: A Mixture of Estimation Method for Satellite Image Super-resolution with Latent Diffusion Models | {
"content": "## Abstract\n\nAbstract During the acquisition of satellite images, there is generally a trade-off between spatial resolution and temporal resolution (acquisition frequency) due to the onboard sensors of satellite imaging systems. High-resolution satellite images are very important for land crop monitor... | [
{
"id": "4jk5wuhbx7",
"initial_rating": 5,
"confidence": 3,
"soundness": 2,
"contribution": 2,
"presentation": 2,
"summary": "The paper llooks sound based on the idea \"Super-resolution with Latent Diffusion Models for Satellite Image\" This method introduces SatDiffMoE, a novel diffusio... | {
"rating": "3;5;5",
"rating_avg": 4.333333333333333,
"confidence": "4;4;5",
"confidence_avg": 4.333333333333333,
"soundness": "2;3;2",
"soundness_avg": 2.3333333333333335,
"contribution": "2;2;2",
"contribution_avg": 2,
"presentation": "2;2;1",
"presentation_avg": 1.6666666666666667
} | {
"primary_area": "",
"track": "main",
"venue": "ICLR 2025 Conference Withdrawn Submission",
"venueid": "ICLR.cc/2025/Conference/Withdrawn_Submission",
"processed_at": "2026-01-14T22:16:01.167476"
} | {
"id": "",
"metareview": "",
"additional_comments": ""
} | {
"decision": ""
} |
BDisxnHzRL | 2410.08527v1 | Scaling Laws for Predicting Downstream Performance in LLMs | {
"content": "## Abstract\n\nAbstract Precise estimation of downstream performance in large language models (LLMs) prior to training is essential for guiding their development process. Scaling laws analysis utilizes the statistics of a series of significantly smaller sampling language models (LMs) to predict the perf... | [
{
"id": "WFhrxZGJgr",
"initial_rating": 3,
"confidence": 4,
"soundness": 2,
"contribution": 2,
"presentation": 3,
"summary": "This paper introduces FLP (Flops $\\rightarrow$ Loss $\\rightarrow$ Performance), a two stage framework incorporating scaling laws to accurately predict the downs... | {
"rating": "3;3;5;6",
"rating_avg": 4.25,
"confidence": "3;4;5;2",
"confidence_avg": 3.5,
"soundness": "2;2;2;3",
"soundness_avg": 2.25,
"contribution": "2;2;2;3",
"contribution_avg": 2.25,
"presentation": "3;3;3;3",
"presentation_avg": 3
} | {
"primary_area": "",
"track": "main",
"venue": "ICLR 2025 Conference Withdrawn Submission",
"venueid": "ICLR.cc/2025/Conference/Withdrawn_Submission",
"processed_at": "2026-01-14T22:16:01.168127"
} | {
"id": "",
"metareview": "",
"additional_comments": ""
} | {
"decision": ""
} |
BEpaPHDl9r | 2410.22069v1 | Flavors of Margin: Implicit Bias of Steepest Descent in Homogeneous Neural Networks | {
"content": "## Abstract\n\nAbstract We study the implicit bias of the general family of steepest descent algorithms , which includes gradient descent, sign descent and coordinate descent, in deep homogeneous neural networks. We prove that an algorithm-dependent geometric margin starts increasing once the networks r... | [
{
"id": "nbn1nQrDab",
"initial_rating": 6,
"confidence": 3,
"soundness": 4,
"contribution": 3,
"presentation": 3,
"summary": "The paper investigates the implicit bias of the family of steepest descent algorithms, including gradient descent, sign gradient descent, and coordinate descent, ... | {
"rating": "5;6;6;6;8",
"rating_avg": 6.2,
"confidence": "2;4;3;3;5",
"confidence_avg": 3.4,
"soundness": "2;3;3;4;4",
"soundness_avg": 3.2,
"contribution": "2;2;3;3;3",
"contribution_avg": 2.6,
"presentation": "2;3;3;3;4",
"presentation_avg": 3
} | {
"primary_area": "",
"track": "main",
"venue": "ICLR 2025 Poster",
"venueid": "ICLR.cc/2025/Conference",
"processed_at": "2026-01-14T22:16:01.168951"
} | {
"id": "u0NMsOLOEn",
"metareview": "This paper studies the implicit bias of steepest flow (continuous-time version of steepest descent) algorithm in training homogeneous neural networks. The paper extends an existing work Lyu & Li on gradient descent to a general class of algorithms, showing monotonic increase of ... | {
"decision": "Accept (Poster)"
} |
BGZQcyA1GO | 2410.06188v1 | Beyond the Alphabet: Deep Signal Embedding for Enhanced DNA Clustering | {
"content": "## Abstract\n\nAbstract The emerging field of DNA storage employs strands of DNA bases (A/T/C/G) as a storage medium for digital information to enable massive density and durability. The DNA storage pipeline includes: (1) encoding the raw data into sequences of DNA bases; (2) synthesizing the sequences ... | [
{
"id": "2Vu95tloK2",
"initial_rating": 3,
"confidence": 4,
"soundness": 1,
"contribution": 2,
"presentation": 2,
"summary": "This paper tackles the clustering problem of DNA storage by using raw Nanopore signals as opposed to first translating the signals to strings (basecalling) and th... | {
"rating": "3;3;5;8",
"rating_avg": 4.75,
"confidence": "4;4;3;4",
"confidence_avg": 3.75,
"soundness": "2;1;3;4",
"soundness_avg": 2.5,
"contribution": "2;2;3;4",
"contribution_avg": 2.75,
"presentation": "2;2;3;3",
"presentation_avg": 2.5
} | {
"primary_area": "",
"track": "main",
"venue": "ICLR 2025 Conference Withdrawn Submission",
"venueid": "ICLR.cc/2025/Conference/Withdrawn_Submission",
"processed_at": "2026-01-14T22:16:01.169798"
} | {
"id": "",
"metareview": "",
"additional_comments": ""
} | {
"decision": ""
} |
BGppv7fa3K | 2407.18074v2 | Principal-Agent Reinforcement Learning: Orchestrating AI Agents with Contracts | {
"content": "## Abstract\n\nAbstract The increasing deployment of AI is shaping the future landscape of the internet, which is set to become an integrated ecosystem of AI agents. Orchestrating the interaction among AI agents necessitates decentralized, self-sustaining mechanisms that harmonize the tension between in... | [
{
"id": "w8mJsmIpNK",
"initial_rating": 6,
"confidence": 3,
"soundness": 3,
"contribution": 3,
"presentation": 3,
"summary": "The paper proposes to apply principal--agent theory to multi-agent RL with the goal of applying their methods to resolve social dilemma-like situations between AI... | {
"rating": "5;5;6;6",
"rating_avg": 5.5,
"confidence": "4;3;3;3",
"confidence_avg": 3.25,
"soundness": "3;3;3;3",
"soundness_avg": 3,
"contribution": "2;2;3;3",
"contribution_avg": 2.5,
"presentation": "3;3;3;3",
"presentation_avg": 3
} | {
"primary_area": "",
"track": "main",
"venue": "Submitted to ICLR 2025",
"venueid": "ICLR.cc/2025/Conference/Rejected_Submission",
"processed_at": "2026-01-14T22:16:01.170747"
} | {
"id": "HkNQH7fUPq",
"metareview": "This paper reduces mechanism design for coordinating AI agents to solving a turn-based stochastic game. I found the paper to be well-written and easy to follow. However, both the reviewers and I share the concern regarding the limited technical novelty and the significance of th... | {
"decision": "Reject"
} |
BL4WBIfyrz | 2410.17883v1 | Lightweight Neural App Control | {
"content": "## Abstract\n\nAbstract This paper introduces a novel mobile phone control architecture, termed “app agents”, for efficient interactions and controls across various Android apps.\nThe proposed Lightweight Multi-modal App Control ( LiMAC ) takes as input a textual goal and a sequence of past mobile obser... | [
{
"id": "ltYxHJW2O1",
"initial_rating": 6,
"confidence": 4,
"soundness": 3,
"contribution": 3,
"presentation": 3,
"summary": "The paper introduces Lightweight Multi-modal App Control (LiMAC), a framework designed for efficient mobile app control by combining a small Action Transformer (A... | {
"rating": "5;6;6;8",
"rating_avg": 6.25,
"confidence": "4;3;4;3",
"confidence_avg": 3.5,
"soundness": "3;4;3;3",
"soundness_avg": 3.25,
"contribution": "2;3;3;3",
"contribution_avg": 2.75,
"presentation": "3;4;3;3",
"presentation_avg": 3.25
} | {
"primary_area": "",
"track": "main",
"venue": "ICLR 2025 Spotlight",
"venueid": "ICLR.cc/2025/Conference",
"processed_at": "2026-01-14T22:16:01.171880"
} | {
"id": "NioiYjmrUh",
"metareview": "The paper introduces Lightweight Multi-modal App Control (LiMAC), a lightweight and modular to take actions on mobile user interfaces. The framework consists of a small Action Transformer (AcT) which predicts which action (e.g. click, scroll, type) needs to be taken, and, depen... | {
"decision": "Accept (Spotlight)"
} |
BLWaTeucYX | 2410.05340v1 | Generating CAD Code with Vision-Language Models for 3D Designs | {
"content": "## Abstract\n\nAbstract Generative AI has transformed the fields of Design and Manufacturing by providing efficient and automated methods for generating and modifying 3D objects. One approach involves using Large Language Models (LLMs) to generate Computer-Aided Design (CAD) scripting code, which can th... | [
{
"id": "dhe9mMCJ6e",
"initial_rating": 6,
"confidence": 3,
"soundness": 3,
"contribution": 3,
"presentation": 2,
"summary": "The paper entitled “GENERATING CAD CODE WITH VISION-LANGUAGE MODELS FOR 3D DESIGNS” proposes an approach to generate and verify CAD 3D objects from natural langua... | {
"rating": "6;6;6;6",
"rating_avg": 6,
"confidence": "4;3;4;3",
"confidence_avg": 3.5,
"soundness": "4;2;2;3",
"soundness_avg": 2.75,
"contribution": "3;2;3;3",
"contribution_avg": 2.75,
"presentation": "4;3;3;2",
"presentation_avg": 3
} | {
"primary_area": "",
"track": "main",
"venue": "ICLR 2025 Poster",
"venueid": "ICLR.cc/2025/Conference",
"processed_at": "2026-01-14T22:16:01.172534"
} | {
"id": "yin9reVOX2",
"metareview": "In this paper, the authors introduce CADCodeVerify, a novel approach that uses VLMs for iterative verification and refinement of CAD code generation. To evaluate CADCodeVerify, the authors also propose CADPrompt, a benchmark for CAD code generation consisting of 200 natural lang... | {
"decision": "Accept (Poster)"
} |
BLg4PeBqsV | 2406.02180v1 | On The Representation Properties Of The Perturb-Softmax And The Perturb-Argmax Probability Distributions | {
"content": "## Abstract\n\nAbstract The Gumbel-Softmax probability distribution allows learning discrete tokens in generative learning, while the Gumbel-Argmax probability distribution is useful in learning discrete structures in discriminative learning. Despite the efforts invested in optimizing these probability ... | [
{
"id": "Miwmlwg9hB",
"initial_rating": 3,
"confidence": 3,
"soundness": 2,
"contribution": 2,
"presentation": 2,
"summary": "The paper studies representation properties of \"Perturb-Softmax\" and \"Perturb-Argmax\" distributions, generalizations of the Gumbel-Softmax/Gumbel-Max distribu... | {
"rating": "3;3;6",
"rating_avg": 4,
"confidence": "5;3;4",
"confidence_avg": 4,
"soundness": "2;2;3",
"soundness_avg": 2.3333333333333335,
"contribution": "2;2;3",
"contribution_avg": 2.3333333333333335,
"presentation": "2;2;3",
"presentation_avg": 2.3333333333333335
} | {
"primary_area": "",
"track": "main",
"venue": "Submitted to ICLR 2025",
"venueid": "ICLR.cc/2025/Conference/Rejected_Submission",
"processed_at": "2026-01-14T22:16:01.173346"
} | {
"id": "Fw2KJTDSxM",
"metareview": "This work investigates the representation properties of the Gumbel-Softmax and Gumbel-Argmax probability distributions, which are used for learning discrete tokens and structures in generative and discriminative models, respectively. The study identifies the conditions under whi... | {
"decision": "Reject"
} |
BM9qfolt6p | 2405.14331v1 | LucidPPN: Unambiguous Prototypical Parts Network for User-centric Interpretable Computer Vision | {
"content": "## Abstract\n\nAbstract Prototypical parts networks combine the power of deep learning with the explainability of case-based reasoning to make accurate, interpretable decisions. They follow the this looks like that reasoning, representing each prototypical part with patches from training images. However... | [
{
"id": "Kydg8p3P6R",
"initial_rating": 5,
"confidence": 3,
"soundness": 2,
"contribution": 2,
"presentation": 2,
"summary": "Summary Of Contributions:\n1.Introduction of LucidPPN: This novel architecture separates color features from other visual components during inference, enabling cl... | {
"rating": "3;5;5;6",
"rating_avg": 4.75,
"confidence": "4;4;3;5",
"confidence_avg": 4,
"soundness": "2;2;2;4",
"soundness_avg": 2.5,
"contribution": "2;2;2;4",
"contribution_avg": 2.5,
"presentation": "3;3;2;4",
"presentation_avg": 3
} | {
"primary_area": "",
"track": "main",
"venue": "ICLR 2025 Poster",
"venueid": "ICLR.cc/2025/Conference",
"processed_at": "2026-01-14T22:16:01.174080"
} | {
"id": "umztjawYYE",
"metareview": "In this paper, a Lucid Prototypical Parts Network (LucidPPN) prototypical parts network is presented, which has two branches: a ShapeTexNet and a ColorNet. Given an input image, the ShapeTexNet is a convolutional neural network (CNN) that takes a gray-scale version of the image... | {
"decision": "Accept (Poster)"
} |
BOQpRtI4F5 | 2410.10051v1 | Towards Bridging Generalization and Expressivity of Graph Neural Networks | {
"content": "## Abstract\n\nAbstract Expressivity and generalization are two critical aspects of graph neural networks (GNNs). While significant progress has been made in studying the expressivity of GNNs, much less is known about their generalization capabilities, particularly when dealing with the inherent complex... | [
{
"id": "v2S2jx4brD",
"initial_rating": 8,
"confidence": 3,
"soundness": 3,
"contribution": 3,
"presentation": 4,
"summary": "This work present a practical framework for analyzing the generalization property of GNNs via their expressivity. The main result is an extension of previous work... | {
"rating": "5;6;6;8",
"rating_avg": 6.25,
"confidence": "3;3;2;3",
"confidence_avg": 2.75,
"soundness": "2;3;3;3",
"soundness_avg": 2.75,
"contribution": "3;3;3;3",
"contribution_avg": 3,
"presentation": "2;2;3;4",
"presentation_avg": 2.75
} | {
"primary_area": "",
"track": "main",
"venue": "ICLR 2025 Poster",
"venueid": "ICLR.cc/2025/Conference",
"processed_at": "2026-01-14T22:16:01.174968"
} | {
"id": "T7bXrQwLN0",
"metareview": "This paper studies expressivity and generalization of graph neural networks (GNNs). The generalization bound is characterized by the ratio between the concentration of intra-class embeddings and the separation of inter-class embeddings measured by the Wasserstein distance. The t... | {
"decision": "Accept (Poster)"
} |
BPAZ6yW3K7 | 2410.23214v2 | Grounding by Trying: LLMs with Reinforcement Learning-Enhanced Retrieval | {
"content": "## Abstract\n\nAbstract The hallucinations of large language models (LLMs) are increasingly mitigated by allowing LLMs to search for information and to ground their answers in real sources. Unfortunately, LLMs often struggle with posing the right search queries, especially when dealing with complex or o... | [
{
"id": "wFTVJhp0wH",
"initial_rating": 6,
"confidence": 4,
"soundness": 3,
"contribution": 3,
"presentation": 4,
"summary": "In multi-hop question answering, a model needs to perform multiple retrieval steps before arriving at an answer. Since the reward (answer) is not known until the... | {
"rating": "5;5;6;6",
"rating_avg": 5.5,
"confidence": "4;3;3;4",
"confidence_avg": 3.5,
"soundness": "3;2;3;3",
"soundness_avg": 2.75,
"contribution": "3;2;3;3",
"contribution_avg": 2.75,
"presentation": "1;3;3;4",
"presentation_avg": 2.75
} | {
"primary_area": "",
"track": "main",
"venue": "ICLR 2025 Poster",
"venueid": "ICLR.cc/2025/Conference",
"processed_at": "2026-01-14T22:16:01.175941"
} | {
"id": "vmCIzJe2ox",
"metareview": "This submission introduces LeReT, a reinforcement learning framework that helps LLMS improve their search queries. The motivation is that LLMs often struggle with formulating the right queries. LeReT aims to address this by optimizing query quality through trial and error. The e... | {
"decision": "Accept (Poster)"
} |
BPgK5XW1Nb | 2406.04412v1 | Spread Preference Annotation: Direct Preference Judgment for Efficient LLM Alignment | {
"content": "## Abstract\n\nAbstract Aligning large language models (LLMs) with human preferences becomes a key component to obtaining state-of-the-art performance, but it yields a huge cost to construct a large human-annotated preference dataset.\nTo tackle this problem, we propose a new framework that boosts the a... | [
{
"id": "Y1LJ8N9PuS",
"initial_rating": 10,
"confidence": 4,
"soundness": 4,
"contribution": 3,
"presentation": 4,
"summary": "This work proposes SPA, a framework to lower the high costs of collecting large preference datasets for alignment. SPA uses an LLM's logits to generate pairwise ... | {
"rating": "6;8;10",
"rating_avg": 8,
"confidence": "4;3;4",
"confidence_avg": 3.6666666666666665,
"soundness": "3;4;4",
"soundness_avg": 3.6666666666666665,
"contribution": "3;3;3",
"contribution_avg": 3,
"presentation": "3;4;4",
"presentation_avg": 3.6666666666666665
} | {
"primary_area": "",
"track": "main",
"venue": "ICLR 2025 Oral",
"venueid": "ICLR.cc/2025/Conference",
"processed_at": "2026-01-14T22:16:01.176661"
} | {
"id": "iHsqZ9lJaq",
"metareview": "This work presents a novel framework, called Spread Preference Annotation with direct preference judgment (SPA), aimed at reducing the high costs associated with collecting large preference datasets for alignment. Overall, all reviewers agreed that this work is novel and importa... | {
"decision": "Accept (Oral)"
} |
BQgAToASdX | 2410.09940v2 | Generalized Group Data Attribution | {
"content": "## Abstract\n\nAbstract Data Attribution (DA) methods quantify the influence of individual training data points on model outputs and have broad applications such as explainability, data selection, and noisy label identification. However, existing DA methods are often computationally intensive, limiting ... | [
{
"id": "fwXDgMnd4l",
"initial_rating": 3,
"confidence": 3,
"soundness": 4,
"contribution": 1,
"presentation": 3,
"summary": "This paper proposes Generalized Group Data Attribution - a method for combining individual data attribution (scores indicating the influence of single training po... | {
"rating": "3;3;3;3",
"rating_avg": 3,
"confidence": "4;3;4;3",
"confidence_avg": 3.5,
"soundness": "3;3;1;4",
"soundness_avg": 2.75,
"contribution": "2;2;2;1",
"contribution_avg": 1.75,
"presentation": "3;2;2;3",
"presentation_avg": 2.5
} | {
"primary_area": "",
"track": "main",
"venue": "Submitted to ICLR 2025",
"venueid": "ICLR.cc/2025/Conference/Rejected_Submission",
"processed_at": "2026-01-14T22:16:01.177582"
} | {
"id": "wt1pdnmMUy",
"metareview": "**Summary:** The paper proposes group data attribution to replace individual data attribution for more computationally efficient yet robust training. The method uses k-means clustering and other heuristics to group the data.\n\n**Strengths:** The paper is well-written. The probl... | {
"decision": "Reject"
} |
BQwsRy1h3U | 2410.14731v1 | MatryoshkaKV: Adaptive KV Compression via Trainable Orthogonal Projection | {
"content": "## Abstract\n\nAbstract KV cache has become a de facto technique for the inference of large language models (LLMs), where tensors of shape (layer number, head number, sequence length, feature dimension) are introduced to cache historical information for self-attention.\nAs the size of the model and data... | [
{
"id": "Dl21cA2RKT",
"initial_rating": 5,
"confidence": 3,
"soundness": 3,
"contribution": 3,
"presentation": 3,
"summary": "The paper introduces MatryoshkaKV, a method for compressing the Key-Value (KV) cache in large language models (LLMs) to reduce memory during inference. The method... | {
"rating": "3;5;5;6;6",
"rating_avg": 5,
"confidence": "4;3;3;3;2",
"confidence_avg": 3,
"soundness": "2;3;3;3;3",
"soundness_avg": 2.8,
"contribution": "2;2;3;3;3",
"contribution_avg": 2.6,
"presentation": "3;3;3;2;3",
"presentation_avg": 2.8
} | {
"primary_area": "",
"track": "main",
"venue": "ICLR 2025 Poster",
"venueid": "ICLR.cc/2025/Conference",
"processed_at": "2026-01-14T22:16:01.178386"
} | {
"id": "DocN4zQc4U",
"metareview": "Summary:\nMatryoshkaKV introduces a novel method for compressing Key-Value (KV) cache in large language models along the feature dimension using trainable orthogonal projection matrices. The method combines PCA initialization with knowledge distillation and a Matryoshka training... | {
"decision": "Accept (Poster)"
} |
BRdYYyrAOR | 2407.15845v1 | Reconstructing Training Data From Real-World Models Trained with Transfer Learning | {
"content": "## Abstract\n\nAbstract Current methods for reconstructing training data from trained classifiers are restricted to very small models, limited training set sizes, and low-resolution images. Such restrictions hinder their applicability to real-world scenarios. In this paper, we present a novel approach e... | [
{
"id": "AAABDrvaJF",
"initial_rating": 6,
"confidence": 4,
"soundness": 4,
"contribution": 3,
"presentation": 3,
"summary": "This paper explores optimization-based **data inversion** techniques from pre-trained models with transfer learning, in which we could reconstruct the training da... | {
"rating": "3;5;5;6",
"rating_avg": 4.75,
"confidence": "3;5;2;4",
"confidence_avg": 3.5,
"soundness": "2;2;3;4",
"soundness_avg": 2.75,
"contribution": "1;2;2;3",
"contribution_avg": 2,
"presentation": "2;2;3;3",
"presentation_avg": 2.5
} | {
"primary_area": "",
"track": "main",
"venue": "Submitted to ICLR 2025",
"venueid": "ICLR.cc/2025/Conference/Rejected_Submission",
"processed_at": "2026-01-14T22:16:01.179126"
} | {
"id": "FewtO7Ild4",
"metareview": "The paper conducts optimization-based data inversion from pre-trained models with transfer learning. The objective is to reconstruct the training data given the pre-trained encoder and classifiers using clustering techniques. The main contribution is in demonstrating the vulnera... | {
"decision": "Reject"
} |
BSBZCa6N3E | 2410.13852v1 | Retrospective Learning from Interactions | {
"content": "## Abstract\n\nAbstract Multi-turn interactions between large language models (LLMs) and users naturally include implicit feedback signals. If an LLM responds in an unexpected way to an instruction, the user is likely to signal it by rephrasing the request, expressing frustration, or pivoting to an alte... | [
{
"id": "2EzOSFMUGZ",
"initial_rating": 3,
"confidence": 4,
"soundness": 3,
"contribution": 3,
"presentation": 3,
"summary": "The author proposes a novel framework, Retrospective learning from past interactions (RESPECT), for improving the LLMs based on signals from past interactions via... | {
"rating": "3;5;6;6",
"rating_avg": 5,
"confidence": "4;4;5;3",
"confidence_avg": 4,
"soundness": "3;3;3;3",
"soundness_avg": 3,
"contribution": "3;2;3;2",
"contribution_avg": 2.5,
"presentation": "3;3;3;3",
"presentation_avg": 3
} | {
"primary_area": "",
"track": "main",
"venue": "Submitted to ICLR 2025",
"venueid": "ICLR.cc/2025/Conference/Rejected_Submission",
"processed_at": "2026-01-14T22:16:01.180000"
} | {
"id": "HynNm57Sos",
"metareview": "The authors present a way to farm supervision signals from LMs in multiturn interactions and use this as training data to improve the model. The novelty of this approach beyond reward-modeling / RLHF / RLAIF is not very clear. While authors present this as continual learning (wh... | {
"decision": "Reject"
} |
BTk1hNuIPq | 2411.01173v1 | Reasoning Limitations of Multimodal Large Language Models. A case study of Bongard Problems | {
"content": "## Abstract\n\nAbstract Abstract visual reasoning (AVR) encompasses a suite of tasks whose solving requires the ability to discover common concepts underlying the set of pictures through an analogy-making process, similarly to human IQ tests.\nBongard Problems (BPs), proposed in 1968, constitute a funda... | [
{
"id": "ZUKMMzpCMB",
"initial_rating": 5,
"confidence": 4,
"soundness": 3,
"contribution": 2,
"presentation": 3,
"summary": "The paper focuses on the capability of multimodal large language models (MLLM) to solve Bongard Problems (BP). The authors propose a new BP dataset, where the two... | {
"rating": "3;5;6;8",
"rating_avg": 5.5,
"confidence": "4;4;4;3",
"confidence_avg": 3.75,
"soundness": "3;2;3;3",
"soundness_avg": 2.75,
"contribution": "2;2;3;3",
"contribution_avg": 2.5,
"presentation": "2;3;3;3",
"presentation_avg": 2.75
} | {
"primary_area": "",
"track": "main",
"venue": "Submitted to ICLR 2025",
"venueid": "ICLR.cc/2025/Conference/Rejected_Submission",
"processed_at": "2026-01-14T22:16:01.180777"
} | {
"id": "mTW3O5wjC0",
"metareview": "The AC acknowledges the authors’ efforts in exploring a potential direction for this field and providing dataset statistics. However, the paper's primary contribution, the dataset, lacks sufficient novelty, after careful checking and syncing with reviwers' commets, as it appears... | {
"decision": "Reject"
} |
BTr3PSlT0T | 2405.03690v2 | How Good is my Video LMM? Complex Video Reasoning and Robustness Evaluation Suite for Video-LMMs | {
"content": "## Abstract\n\nAbstract Recent advancements in Large Language Models (LLMs) have led to the development of Video Large Multi-modal Models (Video-LMMs) that can handle a wide range of video understanding tasks. These models have the potential to be deployed in real-world applications such as robotics, AI... | [
{
"id": "dYWqTjazeE",
"initial_rating": 6,
"confidence": 4,
"soundness": 3,
"contribution": 2,
"presentation": 2,
"summary": "In this paper, the authors present a Complex Video Reasoning and Robustness Evaluation Suite (CVRR-ES) for assessing the performance of Video-LMMs on 11 diverse r... | {
"rating": "3;3;3;6",
"rating_avg": 3.75,
"confidence": "4;5;4;4",
"confidence_avg": 4.25,
"soundness": "2;3;3;3",
"soundness_avg": 2.75,
"contribution": "1;1;1;2",
"contribution_avg": 1.25,
"presentation": "2;3;2;2",
"presentation_avg": 2.25
} | {
"primary_area": "",
"track": "main",
"venue": "ICLR 2025 Conference Withdrawn Submission",
"venueid": "ICLR.cc/2025/Conference/Withdrawn_Submission",
"processed_at": "2026-01-14T22:16:01.181480"
} | {
"id": "",
"metareview": "",
"additional_comments": ""
} | {
"decision": ""
} |
BUQLiu4VA8 | 2407.15238v1 | Variational Potential Flow: A Novel Probabilistic Framework for Energy-Based Generative Modelling | {
"content": "## Abstract\n\nAbstract Energy based models (EBMs) are appealing for their generality and simplicity in data likelihood modeling, but have conventionally been difficult to train due to the unstable and time-consuming implicit MCMC sampling during contrastive divergence training.\nIn this paper, we prese... | [
{
"id": "MRS0mlDRR8",
"initial_rating": 3,
"confidence": 3,
"soundness": 1,
"contribution": 2,
"presentation": 1,
"summary": "The paper introduces a new framework for energy-based generative models called Variational Potential Flow (VAPO). This approach eliminates the need for implicit M... | {
"rating": "3;3;6;6",
"rating_avg": 4.5,
"confidence": "4;3;4;4",
"confidence_avg": 3.75,
"soundness": "3;1;3;3",
"soundness_avg": 2.5,
"contribution": "2;2;3;3",
"contribution_avg": 2.5,
"presentation": "2;1;3;3",
"presentation_avg": 2.25
} | {
"primary_area": "",
"track": "main",
"venue": "ICLR 2025 Conference Withdrawn Submission",
"venueid": "ICLR.cc/2025/Conference/Withdrawn_Submission",
"processed_at": "2026-01-14T22:16:01.182323"
} | {
"id": "",
"metareview": "",
"additional_comments": ""
} | {
"decision": ""
} |
BUj9VSCoET | 2410.02475v1 | Efficient Residual Learning with Mixture-of-Experts for Universal Dexterous Grasping | {
"content": "## Abstract\n\nAbstract Universal dexterous grasping across diverse objects presents a fundamental yet formidable challenge in robot learning. Existing approaches using reinforcement learning (RL) to develop policies on extensive object datasets face critical limitations, including complex curriculum de... | [
{
"id": "eD0fIfWGa1",
"initial_rating": 8,
"confidence": 5,
"soundness": 4,
"contribution": 3,
"presentation": 4,
"summary": "This work combines concepts from residual policy learning, mixture of experts and student-teacher distillation to train generalizable grasping policies with a dex... | {
"rating": "3;6;8;8",
"rating_avg": 6.25,
"confidence": "4;3;4;5",
"confidence_avg": 4,
"soundness": "2;3;3;4",
"soundness_avg": 3,
"contribution": "3;3;4;3",
"contribution_avg": 3.25,
"presentation": "3;4;4;4",
"presentation_avg": 3.75
} | {
"primary_area": "",
"track": "main",
"venue": "ICLR 2025 Poster",
"venueid": "ICLR.cc/2025/Conference",
"processed_at": "2026-01-14T22:16:01.183066"
} | {
"id": "iiUIV4cIKc",
"metareview": "ResDex is a framework for dexterous grasping that combines residual policy learning and a mixture-of-experts (MoE) approach. Each base policy is trained on clusters of objects using only proprioceptive data, while a hyper policy fuses these base policies with a small residual ad... | {
"decision": "Accept (Poster)"
} |
BVACdtrPsh | 2410.11538v1 | MCTBench: Multimodal Cognition towards Text-Rich Visual Scenes Benchmark | {
"content": "## Abstract\n\nAbstract The comprehension of text-rich visual scenes has become a focal point for evaluating Multi-modal Large Language Models (MLLMs) due to their widespread applications. Current benchmarks tailored to the scenario emphasize perceptual capabilities, while overlooking the assessment of ... | [
{
"id": "jMuEiUqEWt",
"initial_rating": 3,
"confidence": 4,
"soundness": 2,
"contribution": 2,
"presentation": 2,
"summary": "The paper introduces MCTBench, a benchmark for evaluating the cognitive abilities of multimodal large language models (MLLMs) in text-rich visual scenes. MCTBench... | {
"rating": "3;3;3;3",
"rating_avg": 3,
"confidence": "4;4;3;4",
"confidence_avg": 3.75,
"soundness": "3;2;2;2",
"soundness_avg": 2.25,
"contribution": "3;3;2;2",
"contribution_avg": 2.5,
"presentation": "1;1;2;2",
"presentation_avg": 1.5
} | {
"primary_area": "",
"track": "main",
"venue": "Submitted to ICLR 2025",
"venueid": "ICLR.cc/2025/Conference/Rejected_Submission",
"processed_at": "2026-01-14T22:16:01.183657"
} | {
"id": "bq8XkeGl0R",
"metareview": "This paper presents MCTBench, a new multimodal benchmark designed to evaluate the cognitive abilities of MLLM through visual reasoning and content generation tasks.\n\nThe strengths of this paper include the introduction of an automatic evaluation pipeline to improve the efficie... | {
"decision": "Reject"
} |
BW8O4wHgbo | 2401.03630v2 | Why Solving Multi-agent Path Finding with Large Language Models has not Succeeded Yet | {
"content": "## Abstract\n\nAbstract With the explosive influence caused by the success of large language models (LLM) like ChatGPT and GPT-4, there has been an extensive amount of recent work showing that foundation models can be used to solve a large variety of tasks. However, there is very limited work that share... | [
{
"id": "KLPOm6DzvZ",
"initial_rating": 3,
"confidence": 4,
"soundness": 2,
"contribution": 1,
"presentation": 3,
"summary": "The paper investigates the use of LLMs for solving multi-agent path-finding problems(MAPF), focusing on moving multiple agents from start to goal locations withou... | {
"rating": "1;3;3;3",
"rating_avg": 2.5,
"confidence": "4;4;5;4",
"confidence_avg": 4.25,
"soundness": "1;2;2;2",
"soundness_avg": 1.75,
"contribution": "1;2;2;1",
"contribution_avg": 1.5,
"presentation": "2;2;2;3",
"presentation_avg": 2.25
} | {
"primary_area": "",
"track": "main",
"venue": "Submitted to ICLR 2025",
"venueid": "ICLR.cc/2025/Conference/Rejected_Submission",
"processed_at": "2026-01-14T22:16:01.184234"
} | {
"id": "SipFgZnJzW",
"metareview": "The paper explores the use of large language models (LLMs) for multi-agent path-finding (MAPF) problems. The study investigates whether LLMs are able to generate valid paths for agents in different MAPF scenarios without heuristic guidance or additional training. Experiments sho... | {
"decision": "Reject"
} |
BWS5gVjgeY | 2411.03766v1 | Number Cookbook: Number Understanding of Language Models and How to Improve It | {
"content": "## Abstract\n\nAbstract Large language models (LLMs) can solve an increasing number of complex reasoning tasks while making surprising mistakes in basic numerical understanding and processing (such as 9.11 > 9.9 9.11 9.9 9.11>9.9 9.11 > 9.9 ). The latter ability is essential for tackling complex arithme... | [
{
"id": "4uChDzva78",
"initial_rating": 5,
"confidence": 3,
"soundness": 3,
"contribution": 2,
"presentation": 2,
"summary": "This paper investigates the empirical abilities of LLMs to solve numerical reasoning tasks of varying complexity. It proposes a benchmark, called NUPA, incorporat... | {
"rating": "3;3;5;5",
"rating_avg": 4,
"confidence": "4;4;4;3",
"confidence_avg": 3.75,
"soundness": "2;2;2;3",
"soundness_avg": 2.25,
"contribution": "3;2;2;2",
"contribution_avg": 2.25,
"presentation": "1;2;2;1",
"presentation_avg": 1.5
} | {
"primary_area": "",
"track": "main",
"venue": "ICLR 2025 Poster",
"venueid": "ICLR.cc/2025/Conference",
"processed_at": "2026-01-14T22:16:01.184945"
} | {
"id": "BPxnBIvgRh",
"metareview": "The authors present a new benchmark for numerical reasoning and several experiments studying the effect of various manipulations on benchmark performance. This addresses an important problem, broadens the scope of previous benchmarks, and raises insights about model failures -- ... | {
"decision": "Accept (Poster)"
} |
BWuBDdXVnH | 2410.02705v1 | ControlAR: Controllable Image Generation with Autoregressive Models | {
"content": "## Abstract\n\nAbstract Autoregressive (AR) models have reformulated image generation as next-token prediction , demonstrating remarkable potential and emerging as strong competitors to diffusion models.\nHowever, control-to-image generation, akin to ControlNet, remains largely unexplored within AR mode... | [
{
"id": "GTN7mRDO4w",
"initial_rating": 6,
"confidence": 4,
"soundness": 3,
"contribution": 3,
"presentation": 3,
"summary": "This paper introduces ControlAR, a method to efficiently enable controllability in autoregressive (AR) image generation models. ControlAR proposes a control encod... | {
"rating": "5;5;6;8",
"rating_avg": 6,
"confidence": "4;4;4;4",
"confidence_avg": 4,
"soundness": "3;2;3;4",
"soundness_avg": 3,
"contribution": "3;2;3;3",
"contribution_avg": 2.75,
"presentation": "3;3;3;4",
"presentation_avg": 3.25
} | {
"primary_area": "",
"track": "main",
"venue": "ICLR 2025 Poster",
"venueid": "ICLR.cc/2025/Conference",
"processed_at": "2026-01-14T22:16:01.185706"
} | {
"id": "12Xtq3OwPC",
"metareview": "This paper introduces ControlAR, a framework to integrate spatial controls into autoregressive image generation models. It enables AR-based controllable image generation by introducing a lightweight control encoder and a conditional decoding strategy. This approach generates eac... | {
"decision": "Accept (Poster)"
} |
BXMoS69LLR | 2406.16201v1 | Blind Baselines Beat Membership Inference Attacks for Foundation Models | {
"content": "## Abstract\n\nAbstract Membership inference (MI) attacks try to determine\nif a data sample was used to train\na machine learning model.\nFor foundation models trained on unknown Web data,\nMI attacks can be used to detect copyrighted training materials,\nmeasure test set contamination, or audit machin... | [
{
"id": "NQQi64XmZs",
"initial_rating": 5,
"confidence": 5,
"soundness": 4,
"contribution": 1,
"presentation": 4,
"summary": "This paper demonstrates that existing MI evaluations for foundation models perform poorly due to distribution shifts between member and non-member data. The autho... | {
"rating": "3;5;5;5",
"rating_avg": 4.5,
"confidence": "5;4;4;5",
"confidence_avg": 4.5,
"soundness": "2;3;2;4",
"soundness_avg": 2.75,
"contribution": "2;2;2;1",
"contribution_avg": 1.75,
"presentation": "3;3;2;4",
"presentation_avg": 3
} | {
"primary_area": "",
"track": "main",
"venue": "Submitted to ICLR 2025",
"venueid": "ICLR.cc/2025/Conference/Rejected_Submission",
"processed_at": "2026-01-14T22:16:01.186270"
} | {
"id": "AMF0hUQG43",
"metareview": "The authors evaluate membership inference attacks against foundation models, and find that existing attacks are ineffective for determining the membership of a given sample. In particular, the author find that a blind baseline that distinguishes between member and non-member dis... | {
"decision": "Reject"
} |
BbZy8nI1si | 2406.12056v3 | Learning Molecular Representation in a Cell | {
"content": "## Abstract\n\nAbstract Predicting drug efficacy and safety in vivo requires information on biological responses (e.g., cell morphology and gene expression) to small molecule perturbations. However, current molecular representation learning methods do not provide a comprehensive view of cell states unde... | [
{
"id": "oT9Qo4UF1b",
"initial_rating": 6,
"confidence": 3,
"soundness": 3,
"contribution": 3,
"presentation": 3,
"summary": "The paper introduces a novel approach called Information Alignment (InfoAlign) for learning molecular representations by integrating molecular structure, cell mor... | {
"rating": "5;5;6;6",
"rating_avg": 5.5,
"confidence": "4;3;4;3",
"confidence_avg": 3.5,
"soundness": "3;2;3;3",
"soundness_avg": 2.75,
"contribution": "3;2;3;3",
"contribution_avg": 2.75,
"presentation": "2;2;4;3",
"presentation_avg": 2.75
} | {
"primary_area": "",
"track": "main",
"venue": "ICLR 2025 Poster",
"venueid": "ICLR.cc/2025/Conference",
"processed_at": "2026-01-14T22:16:01.186921"
} | {
"id": "9suPocIruS",
"metareview": "This paper proposes a new approach to learn molecular representations through incorporating the information of cellular responses. This is done by (1) constructing a context graph for cellular response data and (2) information bottleneck training on the extracted random walk. \n... | {
"decision": "Accept (Poster)"
} |
BdPbmgJ2jo | 2309.07663v1 | High-dimensional Asymptotics of VAEs: Threshold of Posterior Collapse and Dataset-Size Dependence of Rate-Distortion Curve | {
"content": "## Abstract\n\nAbstract In the Variational Autoencoder (VAE), the variational posterior often aligns closely with the prior, which is known as posterior collapse and hinders the quality of representation learning.\nTo mitigate this problem, an adjustable hyperparameter beta has been introduced in the VA... | [
{
"id": "tnZSWD95Xi",
"initial_rating": 5,
"confidence": 4,
"soundness": 2,
"contribution": 1,
"presentation": 3,
"summary": "This paper studies the RD curves in VAEs from a function of dataset size and dimensionality. The authors suggest that the RD curves as a function of data complexi... | {
"rating": "3;5;6;6",
"rating_avg": 5,
"confidence": "5;4;4;3",
"confidence_avg": 4,
"soundness": "2;2;3;3",
"soundness_avg": 2.5,
"contribution": "2;1;3;3",
"contribution_avg": 2.25,
"presentation": "3;3;3;2",
"presentation_avg": 2.75
} | {
"primary_area": "",
"track": "main",
"venue": "Submitted to ICLR 2025",
"venueid": "ICLR.cc/2025/Conference/Rejected_Submission",
"processed_at": "2026-01-14T22:16:01.187936"
} | {
"id": "3yW78egrpQ",
"metareview": "In the paper, the authors rigorously examine the factors contributing to posterior collapse in variational autoencoders (VAEs), focusing on the influence of the hyperparameter beta and data size in VAEs. \n\nWhile there is a consensus among the reviewers that the theories are so... | {
"decision": "Reject"
} |
BfI0D1ci9r | 2410.04818v1 | Physics-informed GNN for non-linear constrained optimization: PINCO, a solver for the AC-optimal power flow | {
"content": "## Abstract\n\nAbstract The energy transition is driving the integration of large shares of intermittent power sources in the electric power grid. Therefore, addressing the AC optimal power flow (AC-OPF) effectively becomes increasingly essential.\nThe AC-OPF, which is a fundamental optimization problem... | [
{
"id": "lpQ0HBmVRa",
"initial_rating": 3,
"confidence": 4,
"soundness": 1,
"contribution": 1,
"presentation": 1,
"summary": "The paper presents a GNN-based approach to address the ACOPF problem, with the primary aim of reducing the computation time required by traditional interior-point... | {
"rating": "1;1;3;3;5",
"rating_avg": 2.6,
"confidence": "5;5;4;4;4",
"confidence_avg": 4.4,
"soundness": "2;2;3;1;3",
"soundness_avg": 2.2,
"contribution": "1;2;2;1;2",
"contribution_avg": 1.6,
"presentation": "1;3;3;1;4",
"presentation_avg": 2.4
} | {
"primary_area": "",
"track": "main",
"venue": "Submitted to ICLR 2025",
"venueid": "ICLR.cc/2025/Conference/Rejected_Submission",
"processed_at": "2026-01-14T22:16:01.189052"
} | {
"id": "ipmpADDFWJ",
"metareview": "This paper addresses the challenges of integrating intermittent renewable energy sources, by enhancing the Alternating Current Optimal Power Flow (AC-OPF) process. AC-OPF is a fundamental optimization problem in power systems that ensures safe and cost-effective grid operation. ... | {
"decision": "Reject"
} |
BfQNrKJMXq | 2406.08184v1 | MobileAgentBench: An Efficient and User-Friendly Benchmark for Mobile LLM Agents | {
"content": "## Abstract\n\nAbstract Large language model (LLM)-based mobile agents are increasingly popular due to their capability to interact directly with mobile phone Graphic User Interfaces (GUIs) and their potential to autonomously manage daily tasks. Despite their promising prospects in both academic and ind... | [
{
"id": "dYTHbNav58",
"initial_rating": 5,
"confidence": 4,
"soundness": 2,
"contribution": 2,
"presentation": 2,
"summary": "This paper introduces MobileAgentBench, a new benchmark for evaluating Large Language Model (LLM)-based mobile agents on the Android platform. The authors argue t... | {
"rating": "3;5;5;6",
"rating_avg": 4.75,
"confidence": "3;4;4;4",
"confidence_avg": 3.75,
"soundness": "3;3;2;3",
"soundness_avg": 2.75,
"contribution": "3;2;2;2",
"contribution_avg": 2.25,
"presentation": "2;2;2;3",
"presentation_avg": 2.25
} | {
"primary_area": "",
"track": "main",
"venue": "ICLR 2025 Conference Withdrawn Submission",
"venueid": "ICLR.cc/2025/Conference/Withdrawn_Submission",
"processed_at": "2026-01-14T22:16:01.189805"
} | {
"id": "",
"metareview": "",
"additional_comments": ""
} | {
"decision": ""
} |
Bff9RniI03 | 2410.18076v1 | Leveraging Skills from Unlabeled Prior Data for Efficient Online Exploration | {
"content": "## Abstract\n\nAbstract Unsupervised pretraining has been transformative in many supervised domains. However, applying such ideas to reinforcement learning (RL) presents a unique challenge in that fine-tuning does not involve mimicking task-specific data, but rather exploring and locating the solution t... | [
{
"id": "2bEyTMUlT5",
"initial_rating": 5,
"confidence": 4,
"soundness": 3,
"contribution": 2,
"presentation": 3,
"summary": "The paper proposes a hierarchical policy for leveraging unlabeled offline data for exploration. In the offline stage, low-level skills are extracted, and in the o... | {
"rating": "3;5;5;5;8",
"rating_avg": 5.2,
"confidence": "4;3;4;4;2",
"confidence_avg": 3.4,
"soundness": "2;2;2;3;3",
"soundness_avg": 2.4,
"contribution": "2;2;2;2;3",
"contribution_avg": 2.2,
"presentation": "3;3;3;3;4",
"presentation_avg": 3.2
} | {
"primary_area": "",
"track": "main",
"venue": "Submitted to ICLR 2025",
"venueid": "ICLR.cc/2025/Conference/Rejected_Submission",
"processed_at": "2026-01-14T22:16:01.190514"
} | {
"id": "QZrqEscDLX",
"metareview": "This paper proposes using offline data to first extract a low-level skill policy $\\pi(a|s, z)$, and then learn a high-level policy $\\psi(a | s, z)$ that combines them during the online phase.\n\nStrengths\n1. Impressive gains over several baselines in Figure 3.\n2. Authors add... | {
"decision": "Reject"
} |
BgYbk6ZmeX | 2403.06090v3 | What Matters When Repurposing Diffusion Models for General Dense Perception Tasks? | {
"content": "## Abstract\n\nAbstract Extensive pre-training with large data is indispensable for downstream geometry and semantic visual perception tasks. Thanks to large-scale text-to-image (T2I) pretraining, recent works show promising results by simply fine-tuning T2I diffusion models for dense perception tasks. ... | [
{
"id": "1ailxNLGYH",
"initial_rating": 6,
"confidence": 4,
"soundness": 3,
"contribution": 3,
"presentation": 4,
"summary": "This paper investigates key factors affecting the transfer performance of pretrained diffusion models repurposed for dense visual perception tasks, emphasizing th... | {
"rating": "5;6;6;6",
"rating_avg": 5.75,
"confidence": "3;4;3;4",
"confidence_avg": 3.5,
"soundness": "2;3;3;3",
"soundness_avg": 2.75,
"contribution": "3;4;3;3",
"contribution_avg": 3.25,
"presentation": "2;4;3;4",
"presentation_avg": 3.25
} | {
"primary_area": "",
"track": "main",
"venue": "ICLR 2025 Poster",
"venueid": "ICLR.cc/2025/Conference",
"processed_at": "2026-01-14T22:16:01.191255"
} | {
"id": "ux6cg4IwfY",
"metareview": "The paper introduces GenPercept, a novel approach leveraging diffusion models to enhance dense visual perception tasks. The work claims improvements in inference speed and detail of predictions, substantiated through a series of experiments across multiple tasks.\n\nThe paper st... | {
"decision": "Accept (Poster)"
} |
BgxsmpVoOX | 2410.22376v1 | Rare-to-Frequent: Unlocking Compositional Generation Power of Diffusion Models on Rare Concepts with LLM Guidance | {
"content": "## Abstract\n\nAbstract State-of-the-art text-to-image (T2I) diffusion models often struggle to generate rare compositions of concepts, e.g., objects with unusual attributes. In this paper, we show that the compositional generation power of diffusion models on such rare concepts can be significantly enh... | [
{
"id": "xzuXWPfp5z",
"initial_rating": 6,
"confidence": 4,
"soundness": 3,
"contribution": 3,
"presentation": 3,
"summary": "This paper studies how to perform rare concept image generation with current pre-trained diffusion models. The authors leverage the LLMs to extract the rare conce... | {
"rating": "6;6;8;8",
"rating_avg": 7,
"confidence": "4;4;4;4",
"confidence_avg": 4,
"soundness": "3;3;3;4",
"soundness_avg": 3.25,
"contribution": "3;3;3;4",
"contribution_avg": 3.25,
"presentation": "3;3;4;4",
"presentation_avg": 3.5
} | {
"primary_area": "",
"track": "main",
"venue": "ICLR 2025 Spotlight",
"venueid": "ICLR.cc/2025/Conference",
"processed_at": "2026-01-14T22:16:01.191994"
} | {
"id": "FqZWk86rZA",
"metareview": "This paper proposes an interesting approach for compositional image generation with rare concepts. Given the text prompt with rare concepts, the LLM first decomposes the prompt into regions and then maps rare concepts into frequent ones, which further guides the diffusion sampli... | {
"decision": "Accept (Spotlight)"
} |
BhBVAC5i2T | 2207.08012v5 | Meta-Referential Games to Learn Compositional Learning Behaviours | {
"content": "## Abstract\n\nAbstract Human beings use compositionality to generalise from past experiences to novel experiences. We assume a separation of our experiences into fundamental atomic components that can be recombined in novel ways to support our ability to engage with novel experiences.\nWe frame this as... | [
{
"id": "1zJ7BE9ygZ",
"initial_rating": 5,
"confidence": 2,
"soundness": 3,
"contribution": 2,
"presentation": 1,
"summary": "This paper introduce a novel benchmark aiming to assess the ability of artificial agents to meta-learn behaviors that leverage the compositional nature of their s... | {
"rating": "5;5;6;6",
"rating_avg": 5.5,
"confidence": "3;2;3;2",
"confidence_avg": 2.5,
"soundness": "3;3;3;2",
"soundness_avg": 2.75,
"contribution": "2;2;3;3",
"contribution_avg": 2.5,
"presentation": "2;1;2;2",
"presentation_avg": 1.75
} | {
"primary_area": "",
"track": "main",
"venue": "Submitted to ICLR 2025",
"venueid": "ICLR.cc/2025/Conference/Rejected_Submission",
"processed_at": "2026-01-14T22:16:01.192782"
} | {
"id": "Gbw5J57M0T",
"metareview": "This paper presents a benchmark to assess the ability of artificial agents to meta-learn behaviors that leverage the compositional nature of their sensory inputs. In this benchmark, two collaborative agent strive to meta-learn to solve referential games. In each episode, the tw... | {
"decision": "Reject"
} |
BjZP3fTlVg | 2410.02173v1 | Efficiently Deploying LLMs with Controlled Risk | {
"content": "## Abstract\n\nAbstract Deploying large language models in production requires simultaneous attention to efficiency and risk control. Prior work has shown the possibility to cut costs while maintaining similar accuracy, but has neglected to focus on risk control. By contrast, here we present hierarchica... | [
{
"id": "C9Eg6yNpOB",
"initial_rating": 3,
"confidence": 2,
"soundness": 1,
"contribution": 1,
"presentation": 1,
"summary": "The paper introduces Hierarchical Chains with Multi-Level Abstention (HCMA), a framework aimed at improving both efficiency and risk control in deploying LLMs.",
... | {
"rating": "3;3;3",
"rating_avg": 3,
"confidence": "4;4;2",
"confidence_avg": 3.3333333333333335,
"soundness": "2;3;1",
"soundness_avg": 2,
"contribution": "2;2;1",
"contribution_avg": 1.6666666666666667,
"presentation": "1;3;1",
"presentation_avg": 1.6666666666666667
} | {
"primary_area": "",
"track": "main",
"venue": "ICLR 2025 Conference Withdrawn Submission",
"venueid": "ICLR.cc/2025/Conference/Withdrawn_Submission",
"processed_at": "2026-01-14T22:16:01.193560"
} | {
"id": "",
"metareview": "",
"additional_comments": ""
} | {
"decision": ""
} |
BkJrXT3e5T | 2406.02509v1 | CamCo: Camera-Controllable 3D-Consistent Image-to-Video Generation | {
"content": "## Abstract\n\nAbstract Recently video diffusion models have emerged as expressive generative tools for high-quality video content creation readily available to general users. However, these models often do not offer precise control over camera poses for video generation, limiting the expression of cine... | [
{
"id": "C5tktFZTX0",
"initial_rating": 5,
"confidence": 4,
"soundness": 3,
"contribution": 2,
"presentation": 3,
"summary": "The paper proposes to address the image-to-video generation with precise camera control. The author parameterizes the camera pose using Plücker coordinates and a... | {
"rating": "3;5;5;5;6",
"rating_avg": 4.8,
"confidence": "4;4;5;4;4",
"confidence_avg": 4.2,
"soundness": "3;3;3;3;3",
"soundness_avg": 3,
"contribution": "1;2;2;2;3",
"contribution_avg": 2,
"presentation": "4;3;3;3;3",
"presentation_avg": 3.2
} | {
"primary_area": "",
"track": "main",
"venue": "Submitted to ICLR 2025",
"venueid": "ICLR.cc/2025/Conference/Rejected_Submission",
"processed_at": "2026-01-14T22:16:01.194173"
} | {
"id": "upMklf0NZF",
"metareview": "Summary:\nThe paper tackles the important problem of camera-controlled image-to-video generation. It achieves this by using Plücker coordinates to parameterize camera pose input and an epipolar attention module to enforce epipolar constraints. With fine-tuning on real videos and... | {
"decision": "Reject"
} |
BksqWM8737 | 2409.06744v2 | ProteinBench: A Holistic Evaluation of Protein Foundation Models | {
"content": "## Abstract\n\nAbstract Recent years have witnessed a surge in the development of protein foundation models, significantly improving performance in protein prediction and generative tasks ranging from 3D structure prediction and protein design to conformational dynamics. However, the capabilities and li... | [
{
"id": "5Ip40otUwC",
"initial_rating": 6,
"confidence": 4,
"soundness": 2,
"contribution": 3,
"presentation": 3,
"summary": "The authors introduce a standardized benchmarking framework to evaluate the performance of protein foundation models. The framework includes 1) task taxonomy, cat... | {
"rating": "3;5;6;6",
"rating_avg": 5,
"confidence": "4;2;2;4",
"confidence_avg": 3,
"soundness": "2;3;3;2",
"soundness_avg": 2.5,
"contribution": "2;2;3;3",
"contribution_avg": 2.5,
"presentation": "2;2;2;3",
"presentation_avg": 2.25
} | {
"primary_area": "",
"track": "main",
"venue": "ICLR 2025 Poster",
"venueid": "ICLR.cc/2025/Conference",
"processed_at": "2026-01-14T22:16:01.194955"
} | {
"id": "GWoQLhKyB0",
"metareview": "This paper claims that so far, there have been numerous trained foundation models for protein prediction, but there haven't been standardized benchmarks to evaluate their performance on downstream tasks. This paper proposes a comprehensive list of over 9 benchmarks to assess not... | {
"decision": "Accept (Poster)"
} |
BlSIKSPhfz | 2410.22625v1 | Non-Equilibrium Dynamics of Hybrid Continuous-Discrete Ground-State Sampling | {
"content": "## Abstract\n\nAbstract We propose a general framework for a hybrid continuous-discrete algorithm that integrates continuous-time deterministic dynamics with Metropolis-Hastings steps to combine search dynamics with and without detailed balance. Our purpose is to study the non-equilibrium dynamics that ... | [
{
"id": "ddwyrKo0YU",
"initial_rating": 5,
"confidence": 3,
"soundness": 3,
"contribution": 2,
"presentation": 2,
"summary": "This paper proposes a new algorithm that combines chaotic search and Metropolis-Hastings. The goal seems to solve optimization problems in discrete non-convex ene... | {
"rating": "3;5;5;6;6",
"rating_avg": 5,
"confidence": "4;3;3;4;2",
"confidence_avg": 3.2,
"soundness": "2;3;3;3;3",
"soundness_avg": 2.8,
"contribution": "2;3;2;3;3",
"contribution_avg": 2.6,
"presentation": "1;3;2;3;3",
"presentation_avg": 2.4
} | {
"primary_area": "",
"track": "main",
"venue": "ICLR 2025 Poster",
"venueid": "ICLR.cc/2025/Conference",
"processed_at": "2026-01-14T22:16:01.195891"
} | {
"id": "OquXWbu6d0",
"metareview": "This paper considers designing non-equilibrium dynamics for computing the ground state of rugged energy landscapes. Two variants of the CAC (chaotic amplitude control) algorithm, namely CAC with momentum (CACm) and Metropolis-Hastings CAC with momentum (HMCACm) were proposed. Em... | {
"decision": "Accept (Poster)"
} |
BmG88rONaU | 2410.15624v1 | Test-time Adaptation for Cross-modal Retrieval with Query Shift | {
"content": "## Abstract\n\nAbstract The success of most existing cross-modal retrieval methods heavily relies on the assumption that the given queries follow the same distribution of the source domain.\nHowever, such an assumption is easily violated in real-world scenarios due to the complexity and diversity of que... | [
{
"id": "S2zcNIO9fu",
"initial_rating": 6,
"confidence": 3,
"soundness": 3,
"contribution": 3,
"presentation": 3,
"summary": "The paper addresses the challenge of cross-modal retrieval in scenarios where the query data distribution deviates from the source domain, a phenomenon known as \... | {
"rating": "5;6;8;8",
"rating_avg": 6.75,
"confidence": "3;3;4;3",
"confidence_avg": 3.25,
"soundness": "3;3;3;3",
"soundness_avg": 3,
"contribution": "3;3;2;3",
"contribution_avg": 2.75,
"presentation": "3;3;3;4",
"presentation_avg": 3.25
} | {
"primary_area": "",
"track": "main",
"venue": "ICLR 2025 Spotlight",
"venueid": "ICLR.cc/2025/Conference",
"processed_at": "2026-01-14T22:16:01.196640"
} | {
"id": "FnUP37m4nn",
"metareview": "This paper tackles the challenging 'query shift' problem in cross-modal retrieval, in which the distribution of query data deviates from the source domain. They formulate their algorithm as a test-time adaptation for cross-modal retrieval, which includes a query prediction refin... | {
"decision": "Accept (Spotlight)"
} |
BmYzoPppij | 2410.02950v1 | LLMCO2: Advancing Accurate Carbon Footprint Prediction for LLM Inferences | {
"content": "## Abstract\n\nAbstract Throughout its lifecycle, a large language model (LLM) generates a substantially larger carbon footprint during inference than training. LLM inference requests vary in batch size, prompt length, and token generation number, while cloud providers employ different GPU types and qua... | [
{
"id": "SKgH8Tq7W9",
"initial_rating": 6,
"confidence": 1,
"soundness": 3,
"contribution": 2,
"presentation": 3,
"summary": "This paper introduces LLMCO$_2$, a GNN-based pipeline to estimate the carbon footprint of LLM inference.\nLLMCO$_2$, separate LLM's prefill and decode stage durin... | {
"rating": "1;3;6",
"rating_avg": 3.3333333333333335,
"confidence": "4;4;1",
"confidence_avg": 3,
"soundness": "2;2;3",
"soundness_avg": 2.3333333333333335,
"contribution": "1;2;2",
"contribution_avg": 1.6666666666666667,
"presentation": "2;2;3",
"presentation_avg": 2.3333333333333335
} | {
"primary_area": "",
"track": "main",
"venue": "ICLR 2025 Conference Withdrawn Submission",
"venueid": "ICLR.cc/2025/Conference/Withdrawn_Submission",
"processed_at": "2026-01-14T22:16:01.197283"
} | {
"id": "",
"metareview": "",
"additional_comments": ""
} | {
"decision": ""
} |
Bmzv2Gch9v | 2410.08669v1 | SmartPretrain: Model-Agnostic and Dataset-Agnostic Representation Learning for Motion Prediction | {
"content": "## Abstract\n\nAbstract Predicting the future motion of surrounding agents is essential for autonomous vehicles (AVs) to operate safely in dynamic, human-robot-mixed environments. However, the scarcity of large-scale driving datasets has hindered the development of robust and generalizable motion predic... | [
{
"id": "p9l61Blu3L",
"initial_rating": 6,
"confidence": 4,
"soundness": 3,
"contribution": 3,
"presentation": 3,
"summary": "The work proposes a pretraining self-supervised learning framework that can be applied to many models, and trained on different datasets for motion prediction. Th... | {
"rating": "5;6;6;6",
"rating_avg": 5.75,
"confidence": "4;5;4;4",
"confidence_avg": 4.25,
"soundness": "3;3;3;3",
"soundness_avg": 3,
"contribution": "2;2;3;3",
"contribution_avg": 2.5,
"presentation": "3;3;3;3",
"presentation_avg": 3
} | {
"primary_area": "",
"track": "main",
"venue": "ICLR 2025 Poster",
"venueid": "ICLR.cc/2025/Conference",
"processed_at": "2026-01-14T22:16:01.197906"
} | {
"id": "G0a0psBP0C",
"metareview": "This paper proposes a pretraining framework for trajectory prediction tasks using real-world datasets. Key ideas include self-supervised pre-training methods (contrastive and reconstruction learning). Experiments is performed on multiple datasets including Argoverse 1/2, Waymo O... | {
"decision": "Accept (Poster)"
} |
Bo62NeU6VF | 2409.14586v1 | Backtracking Improves Generation Safety | {
"content": "## Abstract\n\nAbstract Text generation has a fundamental limitation almost by definition: there is no taking back tokens that have been generated, even when they are clearly problematic .\nIn the context of language model safety, when a partial unsafe generation is produced, language models by their na... | [
{
"id": "uOAxtHEcgc",
"initial_rating": 8,
"confidence": 2,
"soundness": 4,
"contribution": 4,
"presentation": 4,
"summary": "The paper proposes a novel technique called Backtracking, to enhance the safety of large language models by allowing them to undo unsafe responses. Traditional s... | {
"rating": "6;6;8;8",
"rating_avg": 7,
"confidence": "4;4;4;2",
"confidence_avg": 3.5,
"soundness": "3;4;3;4",
"soundness_avg": 3.5,
"contribution": "3;3;3;4",
"contribution_avg": 3.25,
"presentation": "3;4;4;4",
"presentation_avg": 3.75
} | {
"primary_area": "",
"track": "main",
"venue": "ICLR 2025 Oral",
"venueid": "ICLR.cc/2025/Conference",
"processed_at": "2026-01-14T22:16:01.198662"
} | {
"id": "Hk79XbuCpA",
"metareview": "The paper presents \"backtracking,\" a novel method to improve language model safety by enabling recovery from unsafe partial generations using a [RESET] token. Unlike traditional prevention-based approaches, backtracking trains models to recognize and reset unsafe outputs mid-g... | {
"decision": "Accept (Oral)"
} |
BomQa84efw | 2407.15835v2 | dMel: Speech Tokenization Made Simple | {
"content": "## Abstract\n\nAbstract Large language models have revolutionized natural language processing by leveraging self-supervised pretraining on vast textual data.\nInspired by this success, researchers have investigated complicated speech tokenization methods to discretize continuous speech signals so that l... | [
{
"id": "7zcpYFANwE",
"initial_rating": 8,
"confidence": 4,
"soundness": 3,
"contribution": 3,
"presentation": 3,
"summary": "The paper introduces a novel approach to speech tokenization by discretizing mel-filterbank channels. This method effectively preserves both semantic and acoustic... | {
"rating": "3;5;5;6;8",
"rating_avg": 5.4,
"confidence": "5;4;4;4;4",
"confidence_avg": 4.2,
"soundness": "2;3;3;3;3",
"soundness_avg": 2.8,
"contribution": "2;3;2;3;3",
"contribution_avg": 2.6,
"presentation": "2;2;3;3;3",
"presentation_avg": 2.6
} | {
"primary_area": "",
"track": "main",
"venue": "Submitted to ICLR 2025",
"venueid": "ICLR.cc/2025/Conference/Rejected_Submission",
"processed_at": "2026-01-14T22:16:01.199401"
} | {
"id": "WUaAiklzIp",
"metareview": "This paper proposes dMel, a simple method for quantizing Mel spectrograms into discrete units. Unlike self-supervised semantic tokens and neural codecs, dMel is model-free. The authors train a transformer-based language model for speech-text modelling and evaluate their proposed... | {
"decision": "Reject"
} |
Bon3TPZOG0 | 2409.02426v1 | Diffusion Models Learn Low-Dimensional Distributions via Subspace Clustering | {
"content": "## Abstract\n\nAbstract Recent empirical studies have demonstrated that diffusion models can effectively learn the image distribution and generate new samples. Remarkably, these models can achieve this even with a small number of training samples despite a large image dimension, circumventing the curse ... | [
{
"id": "96ko1n5ToO",
"initial_rating": 3,
"confidence": 3,
"soundness": 3,
"contribution": 2,
"presentation": 3,
"summary": "The paper suggests that diffusion models can circumvent curse of dimensionality by clustering to fit the intrinsic dimension which is in general much lower than a... | {
"rating": "3;3;6",
"rating_avg": 4,
"confidence": "2;3;4",
"confidence_avg": 3,
"soundness": "2;3;3",
"soundness_avg": 2.6666666666666665,
"contribution": "1;2;2",
"contribution_avg": 1.6666666666666667,
"presentation": "3;3;3",
"presentation_avg": 3
} | {
"primary_area": "",
"track": "main",
"venue": "ICLR 2025 Conference Withdrawn Submission",
"venueid": "ICLR.cc/2025/Conference/Withdrawn_Submission",
"processed_at": "2026-01-14T22:16:01.200325"
} | {
"id": "",
"metareview": "",
"additional_comments": ""
} | {
"decision": ""
} |
BpIbnXWfhL | 2411.03349v1 | RuAG: Learned-rule-augmented Generation for Large Language Models | {
"content": "## Abstract\n\nAbstract In-context learning (ICL) and Retrieval-Augmented Generation (RAG) have gained attention for their ability to enhance LLMs’ reasoning by incorporating external knowledge but suffer from limited contextual window size, leading to insufficient information injection. To this end, we... | [
{
"id": "a2afUTIsMw",
"initial_rating": 5,
"confidence": 4,
"soundness": 2,
"contribution": 2,
"presentation": 2,
"summary": "The paper presents a rule-augmented generation approach, where rules are learned from the training dataset using Monte Carlo Tree Search (MCTS).\nIt shows that le... | {
"rating": "5;5;8",
"rating_avg": 6,
"confidence": "3;4;3",
"confidence_avg": 3.3333333333333335,
"soundness": "3;2;3",
"soundness_avg": 2.6666666666666665,
"contribution": "2;2;3",
"contribution_avg": 2.3333333333333335,
"presentation": "2;2;3",
"presentation_avg": 2.3333333333333335
} | {
"primary_area": "",
"track": "main",
"venue": "ICLR 2025 Poster",
"venueid": "ICLR.cc/2025/Conference",
"processed_at": "2026-01-14T22:16:01.201345"
} | {
"id": "oUSmvnshIv",
"metareview": "The paper proposes a novel framework that distills offline data into logical rules using Monte Carlo Tree Search (MCTS), integrating these rules with LLMs to enhance reasoning across tasks. Strengths include its scalability, computational efficiency compared to methods like RAG,... | {
"decision": "Accept (Poster)"
} |
BpKbKeY0La | 2404.01717v3 | AddSR: Accelerating Diffusion-based Blind Super-Resolution with Adversarial Diffusion Distillation | {
"content": "## Abstract\n\nAbstract Blind super-resolution methods based on stable diffusion showcase formidable generative capabilities in reconstructing clear high-resolution images with intricate details from low-resolution inputs. However, their practical applicability is often hampered by poor efficiency, stem... | [
{
"id": "XQ0Ma4gC42",
"initial_rating": 6,
"confidence": 1,
"soundness": 3,
"contribution": 3,
"presentation": 2,
"summary": "This paper presents AddSR, an efficient blind image super-resolution method based on Adversarial Diffusion Distillation (ADD). It introduces a timestep-adaptive a... | {
"rating": "3;5;5;6",
"rating_avg": 4.75,
"confidence": "4;4;4;5",
"confidence_avg": 4.25,
"soundness": "2;3;3;3",
"soundness_avg": 2.75,
"contribution": "1;2;2;3",
"contribution_avg": 2,
"presentation": "2;4;3;3",
"presentation_avg": 3
} | {
"primary_area": "",
"track": "main",
"venue": "ICLR 2025 Conference Withdrawn Submission",
"venueid": "ICLR.cc/2025/Conference/Withdrawn_Submission",
"processed_at": "2026-01-14T22:16:01.201965"
} | {
"id": "",
"metareview": "",
"additional_comments": ""
} | {
"decision": ""
} |
Bpn8q40n1n | 2410.00086v2 | ACE: All-round Creator and Editor Following Instructions via Diffusion Transformer | {
"content": "## Abstract\n\nAbstract Diffusion models have emerged as a powerful generative technology and have been found to be applicable in various scenarios.\nMost existing foundational diffusion models are primarily designed for text-guided visual generation and do not support multi-modal conditions, which are ... | [
{
"id": "Ut7V2TYefS",
"initial_rating": 6,
"confidence": 4,
"soundness": 3,
"contribution": 3,
"presentation": 2,
"summary": "The paper presents a method to train a unified model for 8 different tasks: Text-guided Generation, Low-level Visual Analysis, Controllable Generation, Semantic E... | {
"rating": "5;6;6;6;8",
"rating_avg": 6.2,
"confidence": "3;4;4;4;4",
"confidence_avg": 3.8,
"soundness": "3;3;3;3;3",
"soundness_avg": 3,
"contribution": "3;3;3;3;4",
"contribution_avg": 3.2,
"presentation": "2;4;3;2;3",
"presentation_avg": 2.8
} | {
"primary_area": "",
"track": "main",
"venue": "ICLR 2025 Poster",
"venueid": "ICLR.cc/2025/Conference",
"processed_at": "2026-01-14T22:16:01.202737"
} | {
"id": "i4IG5EMJVd",
"metareview": "This paper proposes an all-in-one model that supports a wide range of visual generation and editing tasks. Reviewers recognize the contribution of the unified framework and extensive evaluations. Questions are raised regarding design choices, more analysis, and experiments. The ... | {
"decision": "Accept (Poster)"
} |
Bq3fEAGXUL | 2409.18314v1 | Realistic Evaluation of Model Merging for Compositional Generalization | {
"content": "## Abstract\n\nAbstract Merging has become a widespread way to cheaply combine individual models into a single model that inherits their capabilities and attains better performance.\nThis popularity has spurred rapid development of many new merging methods, which are typically validated in disparate exp... | [
{
"id": "4K1sSslfQX",
"initial_rating": 5,
"confidence": 2,
"soundness": 3,
"contribution": 2,
"presentation": 3,
"summary": "This paper provides an empirical study of model merging. Specifically, it focuses on the compositional generalization of capabilities, with control of many experi... | {
"rating": "5;5;6",
"rating_avg": 5.333333333333333,
"confidence": "4;2;2",
"confidence_avg": 2.6666666666666665,
"soundness": "2;3;3",
"soundness_avg": 2.6666666666666665,
"contribution": "2;2;2",
"contribution_avg": 2,
"presentation": "4;3;3",
"presentation_avg": 3.3333333333333335
} | {
"primary_area": "",
"track": "main",
"venue": "Submitted to ICLR 2025",
"venueid": "ICLR.cc/2025/Conference/Rejected_Submission",
"processed_at": "2026-01-14T22:16:01.203795"
} | {
"id": "1hYE4SgMHw",
"metareview": "(a) summary\n\nThis paper investigates how to evaluate the compositional generalization capability of model merging methods. It proposes a shared experimental setting for conducting empirical studies of the performance, computational cost, and scalability of merging methods. Exp... | {
"decision": "Reject"
} |
BqbeJzN9Ie | 2403.11273v1 | BrightDreamer: Generic 3D Gaussian Generative Framework for Fast Text-to-3D Synthesis | {
"content": "## Abstract\n\nAbstract Text-to-3D synthesis has recently seen intriguing advances by combining the text-to-image models with 3D representation methods, e.g . , Gaussian Splatting (GS), via Score Distillation Sampling (SDS). However, a hurdle of existing methods is the low efficiency, per-prompt optimiz... | [
{
"id": "6iMef9mV2s",
"initial_rating": 5,
"confidence": 4,
"soundness": 2,
"contribution": 2,
"presentation": 3,
"summary": "The paper presents an amortized text-to-3D Gaussian generator trained with SDS loss. The framework consists of two modules: TSD, responsible for center deformatio... | {
"rating": "3;3;5;5",
"rating_avg": 4,
"confidence": "2;4;5;4",
"confidence_avg": 3.75,
"soundness": "2;2;2;2",
"soundness_avg": 2,
"contribution": "2;1;2;2",
"contribution_avg": 1.75,
"presentation": "2;2;2;3",
"presentation_avg": 2.25
} | {
"primary_area": "",
"track": "main",
"venue": "ICLR 2025 Conference Withdrawn Submission",
"venueid": "ICLR.cc/2025/Conference/Withdrawn_Submission",
"processed_at": "2026-01-14T22:16:01.204542"
} | {
"id": "",
"metareview": "",
"additional_comments": ""
} | {
"decision": ""
} |
BuBBRn0zFD | 2409.07594v1 | Automated Discovery of Pairwise Interactions from Unstructured Data | {
"content": "## Abstract\n\nAbstract Pairwise interactions between perturbations to a system can provide evidence for the causal dependencies of the underlying underlying mechanisms of a system. When observations are low dimensional, hand crafted measurements, detecting interactions amounts to simple statistical tes... | [
{
"id": "KPXs5QVOpg",
"initial_rating": 5,
"confidence": 3,
"soundness": 3,
"contribution": 3,
"presentation": 3,
"summary": "This paper presents a novel active learning method to discover pairs of highly correlated variables. Concretely, we imagine that an experimentalist has access to ... | {
"rating": "3;5;5;6",
"rating_avg": 4.75,
"confidence": "5;3;3;2",
"confidence_avg": 3.25,
"soundness": "3;3;3;3",
"soundness_avg": 3,
"contribution": "3;3;3;3",
"contribution_avg": 3,
"presentation": "4;3;3;3",
"presentation_avg": 3.25
} | {
"primary_area": "",
"track": "main",
"venue": "Submitted to ICLR 2025",
"venueid": "ICLR.cc/2025/Conference/Rejected_Submission",
"processed_at": "2026-01-14T22:16:01.205359"
} | {
"id": "uEPYvzZwCb",
"metareview": "An interesting paper, which unfortunately does not pass the bar for acceptance at ICLR. Substantial points of disagreements still coexisted with 1aqQ at the end of the review process, and while I can buy the authors' argument for point (1), I believe a bit more could have been d... | {
"decision": "Reject"
} |
BvMuyqPvk1 | 2405.11907v4 | Ensemble and Mixture-of-Experts DeepONets For Operator Learning | {
"content": "## Abstract\n\nAbstract We present a novel deep operator network (DeepONet) architecture for operator learning, the ensemble DeepONet, that allows for enriching the trunk network of a single DeepONet with multiple distinct trunk networks. This trunk enrichment allows for greater expressivity and general... | [
{
"id": "idualNO6bA",
"initial_rating": 5,
"confidence": 4,
"soundness": 3,
"contribution": 2,
"presentation": 3,
"summary": "This paper applies classical mixture-of-expert paradigm to learn mathematical operators. By incorporating different experts, and therefore enhancing basis represe... | {
"rating": "3;5;5",
"rating_avg": 4.333333333333333,
"confidence": "3;4;4",
"confidence_avg": 3.6666666666666665,
"soundness": "3;3;3",
"soundness_avg": 3,
"contribution": "2;3;2",
"contribution_avg": 2.3333333333333335,
"presentation": "2;3;3",
"presentation_avg": 2.6666666666666665
} | {
"primary_area": "",
"track": "main",
"venue": "ICLR 2025 Conference Withdrawn Submission",
"venueid": "ICLR.cc/2025/Conference/Withdrawn_Submission",
"processed_at": "2026-01-14T22:16:01.206332"
} | {
"id": "",
"metareview": "",
"additional_comments": ""
} | {
"decision": ""
} |
Bvqsas4TYX | 2408.13518v1 | Selective Preference Optimization via Token-Level Reward Function Estimation | {
"content": "## Abstract\n\nAbstract Recent advancements in large language model alignment leverage token-level supervisions to perform fine-grained preference optimization. However, existing token-level alignment methods either optimize on all available tokens, which can be noisy and inefficient, or perform selecti... | [
{
"id": "LVWsJts1yd",
"initial_rating": 3,
"confidence": 4,
"soundness": 2,
"contribution": 2,
"presentation": 3,
"summary": "The paper introduces Selective Preference Optimization (SePO), a novel strategy for aligning large language models (LLMs) at the token level by selectively optimi... | {
"rating": "3;3;3;5",
"rating_avg": 3.5,
"confidence": "4;4;4;3",
"confidence_avg": 3.75,
"soundness": "2;2;2;3",
"soundness_avg": 2.25,
"contribution": "2;2;2;3",
"contribution_avg": 2.25,
"presentation": "3;2;3;3",
"presentation_avg": 2.75
} | {
"primary_area": "",
"track": "main",
"venue": "ICLR 2025 Conference Withdrawn Submission",
"venueid": "ICLR.cc/2025/Conference/Withdrawn_Submission",
"processed_at": "2026-01-14T22:16:01.207058"
} | {
"id": "",
"metareview": "",
"additional_comments": ""
} | {
"decision": ""
} |
BwR8t91yqh | 2410.00079v1 | Interactive Speculative Planning: Enhance Agent Efficiency through Co-design of System and User Interface | {
"content": "## Abstract\n\nAbstract Agents, as user-centric tools, are increasingly deployed for human task delegation, assisting with a broad spectrum of requests by generating thoughts, engaging with user proxies, and producing action plans. However, agents based on large language models (LLMs) often face substan... | [
{
"id": "0bnqtLUIpM",
"initial_rating": 8,
"confidence": 4,
"soundness": 3,
"contribution": 3,
"presentation": 3,
"summary": "Describes a speculative planning algorithm for LLM agents that assumes an approximate model and a target model. It is assumed that the target model is more capabl... | {
"rating": "5;5;8",
"rating_avg": 6,
"confidence": "4;3;4",
"confidence_avg": 3.6666666666666665,
"soundness": "3;3;3",
"soundness_avg": 3,
"contribution": "2;2;3",
"contribution_avg": 2.3333333333333335,
"presentation": "2;2;3",
"presentation_avg": 2.3333333333333335
} | {
"primary_area": "",
"track": "main",
"venue": "ICLR 2025 Poster",
"venueid": "ICLR.cc/2025/Conference",
"processed_at": "2026-01-14T22:16:01.207737"
} | {
"id": "nBsyudXzSa",
"metareview": "The paper focuses on proposing a new method for designing an agent system as well as a user interface together - placing user interactions in the center. As agents begin to automate more and more tasks, such methods will become very relevant. All reviewers are also unanimous in ... | {
"decision": "Accept (Poster)"
} |
BydkbNH0gj | 2403.09559v4 | LESS IS MORE: HIGH-VALUE DATA SELECTION FOR VISUAL INSTRUCTION TUNING | {
"content": "## Abstract\n\nAbstract Visual instruction tuning is the key to building large vision language models (LVLMs), which can greatly improve the task generalization and solving capabilities by learning a mixture of instruction data from diverse visual tasks.\nPrevious work mostly collects multiple existing ... | [
{
"id": "3MagLEdQJ0",
"initial_rating": 5,
"confidence": 4,
"soundness": 2,
"contribution": 3,
"presentation": 3,
"summary": "The paper concentrates on reducing the data redundancy of instruction-following MLLMs. The authors show that pruning a certain ratio of specific training data has... | {
"rating": "3;5;5;6",
"rating_avg": 4.75,
"confidence": "5;4;3;4",
"confidence_avg": 4,
"soundness": "2;2;2;3",
"soundness_avg": 2.25,
"contribution": "2;3;2;3",
"contribution_avg": 2.5,
"presentation": "3;3;2;3",
"presentation_avg": 2.75
} | {
"primary_area": "",
"track": "main",
"venue": "Submitted to ICLR 2025",
"venueid": "ICLR.cc/2025/Conference/Rejected_Submission",
"processed_at": "2026-01-14T22:16:01.208452"
} | {
"id": "074JXKKUh9",
"metareview": "### **Summary**\nThis paper introduces **TIVE**, a method designed to reduce redundancy in visual instruction datasets by leveraging **task difficulty** and **instance influence** scores calculated via gradient-based techniques. The approach is evaluated on multiple vision-langu... | {
"decision": "Reject"
} |
C0Boqhem9u | 2410.20053v1 | LinBridge: A Learnable Framework for Interpreting Nonlinear Neural Encoding Models | {
"content": "## Abstract\n\nAbstract Neural encoding of artificial neural networks (ANNs) aligns the computational representations of ANNs with brain responses, providing profound insights into the neural basis underpinning information processing in the human brain. Current neural encoding studies primarily employ l... | [
{
"id": "GQoX6XjpmO",
"initial_rating": 3,
"confidence": 4,
"soundness": 3,
"contribution": 2,
"presentation": 2,
"summary": "The authors introduce LinBridge, a framework for non-linear mapping between neural network activations and fMRI data (here, the Natural Scenes Dataset), with the ... | {
"rating": "3;3;3;5;8",
"rating_avg": 4.4,
"confidence": "3;4;4;2;4",
"confidence_avg": 3.4,
"soundness": "2;2;3;2;3",
"soundness_avg": 2.4,
"contribution": "2;2;2;2;3",
"contribution_avg": 2.2,
"presentation": "2;2;2;1;2",
"presentation_avg": 1.8
} | {
"primary_area": "",
"track": "main",
"venue": "Submitted to ICLR 2025",
"venueid": "ICLR.cc/2025/Conference/Rejected_Submission",
"processed_at": "2026-01-14T22:16:01.209408"
} | {
"id": "Sy9DXZkx33",
"metareview": "This paper introduces a novel framework for characterizing and interpreting the relationships between artificial neural networks and fMRI imaging data. The reviewers praised the paper for proposing a conceptually simple and effective solution to a timely and important problem. ... | {
"decision": "Reject"
} |
C0HDYvGwol | 2410.18974v1 | 3D-Adapter: Geometry-Consistent Multi-View Diffusion for High-Quality 3D Generation | {
"content": "## Abstract\n\nAbstract Multi-view image diffusion models have significantly advanced open-domain 3D object generation. However, most existing models rely on 2D network architectures that lack inherent 3D biases, resulting in compromised geometric consistency. To address this challenge, we introduce 3D-... | [
{
"id": "I1TTWWZAfA",
"initial_rating": 5,
"confidence": 4,
"soundness": 2,
"contribution": 3,
"presentation": 2,
"summary": "The paper introduces 3D-Adapter, a plug-in module aimed at improving 3D consistency in multi-view diffusion models for 3D generation. By integrating a combination... | {
"rating": "3;5;6;6;8",
"rating_avg": 5.6,
"confidence": "5;4;5;4;5",
"confidence_avg": 4.6,
"soundness": "2;2;3;4;3",
"soundness_avg": 2.8,
"contribution": "1;3;3;4;3",
"contribution_avg": 2.8,
"presentation": "2;2;2;4;3",
"presentation_avg": 2.6
} | {
"primary_area": "",
"track": "main",
"venue": "Submitted to ICLR 2025",
"venueid": "ICLR.cc/2025/Conference/Rejected_Submission",
"processed_at": "2026-01-14T22:16:01.210229"
} | {
"id": "TuaDS6ZyfM",
"metareview": "The paper presents a method to incorporate depth prior into the framework of 3D generation. The paper receives mixed ratings from the reviewers. The reviewers have some concerns in terms of several perspectives. First, the reviewer argues that the novelty of the presented work i... | {
"decision": "Reject"
} |
C1E0Oo5qgK | 2408.11194v2 | Compress Guidance in Conditional Diffusion Sampling | {
"content": "## Abstract\n\nAbstract We found that enforcing guidance throughout the sampling process is often counterproductive due to the model-fitting issue, where samples are ‘tuned’ to match the classifier’s parameters rather than generalizing the expected condition. This work identifies and quantifies the prob... | [
{
"id": "JwXN5occn6",
"initial_rating": 6,
"confidence": 4,
"soundness": 3,
"contribution": 2,
"presentation": 2,
"summary": "This paper identifies a model fitting problem in classifier guidance used in conventional diffusion models and proposes a solution called compress guidance. The m... | {
"rating": "3;3;3;6;6",
"rating_avg": 4.2,
"confidence": "5;2;4;4;4",
"confidence_avg": 3.8,
"soundness": "1;3;2;3;3",
"soundness_avg": 2.4,
"contribution": "1;2;2;3;2",
"contribution_avg": 2,
"presentation": "2;3;1;2;2",
"presentation_avg": 2
} | {
"primary_area": "",
"track": "main",
"venue": "ICLR 2025 Conference Withdrawn Submission",
"venueid": "ICLR.cc/2025/Conference/Withdrawn_Submission",
"processed_at": "2026-01-14T22:16:01.211099"
} | {
"id": "",
"metareview": "",
"additional_comments": ""
} | {
"decision": ""
} |
C1Wp4ubvXZ | 2410.02005v1 | FairlyUncertain: A Comprehensive Benchmark of Uncertainty in Algorithmic Fairness | {
"content": "## Abstract\n\nAbstract Fair predictive algorithms hinge on both equality and trust, yet inherent uncertainty in real-world data challenges our ability to make consistent, fair, and calibrated decisions. While fairly managing predictive error has been extensively explored, some recent work has begun to ... | [
{
"id": "IqBhO2b6TD",
"initial_rating": 5,
"confidence": 5,
"soundness": 3,
"contribution": 3,
"presentation": 3,
"summary": "The paper introduces FairlyUncertain, a Python package designed to evaluate uncertainty in fairness for machine learning algorithms. The package proposes methods ... | {
"rating": "5;5;5;5;6",
"rating_avg": 5.2,
"confidence": "4;3;3;5;3",
"confidence_avg": 3.6,
"soundness": "2;3;3;3;3",
"soundness_avg": 2.8,
"contribution": "3;3;2;3;3",
"contribution_avg": 2.8,
"presentation": "3;3;3;3;3",
"presentation_avg": 3
} | {
"primary_area": "",
"track": "main",
"venue": "Submitted to ICLR 2025",
"venueid": "ICLR.cc/2025/Conference/Rejected_Submission",
"processed_at": "2026-01-14T22:16:01.211834"
} | {
"id": "p2Dpy8YyCP",
"metareview": "This paper introduces FairlyUncertain, a tool that evaluate uncertainty in fairness for machine learning algorithms. This framework emphasizes consistent uncertainty estimation and be calibrated to observed randomness. \n\nThe paper falls a bit short in its contribution. The mai... | {
"decision": "Reject"
} |
C1wSR50nYf | 2410.01635v1 | Does Graph Prompt Work? A Data Operation Perspective with Theoretical Analysis | {
"content": "## Abstract\n\nAbstract In recent years, graph prompting has emerged as a promising research direction, enabling the learning of additional tokens or subgraphs appended to the original graphs without requiring retraining of pre-trained graph models across various applications. This novel paradigm, shift... | [
{
"id": "Zg9fpuh1ef",
"initial_rating": 6,
"confidence": 3,
"soundness": 3,
"contribution": 3,
"presentation": 3,
"summary": "The paper presents a theoretical framework for understanding graph prompting, a method of incorporating additional tokens or subgraphs without requiring retrainin... | {
"rating": "3;6;8",
"rating_avg": 5.666666666666667,
"confidence": "3;3;3",
"confidence_avg": 3,
"soundness": "2;3;3",
"soundness_avg": 2.6666666666666665,
"contribution": "2;3;3",
"contribution_avg": 2.6666666666666665,
"presentation": "2;3;2",
"presentation_avg": 2.3333333333333335
} | {
"primary_area": "",
"track": "main",
"venue": "ICLR 2025 Conference Withdrawn Submission",
"venueid": "ICLR.cc/2025/Conference/Withdrawn_Submission",
"processed_at": "2026-01-14T22:16:01.213056"
} | {
"id": "",
"metareview": "",
"additional_comments": ""
} | {
"decision": ""
} |
C33p2CNOQ8 | 2410.20035v1 | Training the Untrainable: Introducing Inductive Bias via Representational Alignment | {
"content": "## Abstract\n\nAbstract We demonstrate that architectures which traditionally are considered to be ill-suited for a task can be trained using inductive biases from another architecture. Networks are considered untrainable when they overfit, underfit, or converge to poor results even when tuning their hy... | [
{
"id": "XlsKxPG1VE",
"initial_rating": 3,
"confidence": 5,
"soundness": 2,
"contribution": 2,
"presentation": 4,
"summary": "This paper proposes guidance, a method where a well-performing guide network directs the layer-wise representations of a target network, transferring inductive bi... | {
"rating": "3;3;6;8",
"rating_avg": 5,
"confidence": "4;4;4;4",
"confidence_avg": 4,
"soundness": "2;2;3;3",
"soundness_avg": 2.5,
"contribution": "2;2;3;3",
"contribution_avg": 2.5,
"presentation": "2;4;4;3",
"presentation_avg": 3.25
} | {
"primary_area": "",
"track": "main",
"venue": "Submitted to ICLR 2025",
"venueid": "ICLR.cc/2025/Conference/Rejected_Submission",
"processed_at": "2026-01-14T22:16:01.214101"
} | {
"id": "um4G8Om0hZ",
"metareview": "(a) summary\n\nThis paper investigates how to use a guide network to train a target one traditionally overfitting(FCN) or underfitting(CNN). It performs representation alignment between the guide and target DNN by introducing additional loss terms. The initial results suggest th... | {
"decision": "Reject"
} |
C5w86qtcgY | 2402.02490v2 | Decentralized Finite-Sum Optimization over Time-Varying Networks | {
"content": "## Abstract\n\nAbstract We consider decentralized time-varying stochastic optimization problems where each of the functions held by the nodes has a finite sum structure. Such problems can be efficiently solved using variance reduction techniques. Our aim is to explore the lower complexity bounds (for co... | [
{
"id": "luh5eYD39N",
"initial_rating": 8,
"confidence": 2,
"soundness": 3,
"contribution": 3,
"presentation": 3,
"summary": "This paper studies decentralized finite-sum optimization problem over time-varying graphs. Theorem 4.3 and Theorem 4.5 establish lower bounds on computational and... | {
"rating": "3;5;5;8",
"rating_avg": 5.25,
"confidence": "4;4;4;2",
"confidence_avg": 3.5,
"soundness": "3;3;3;3",
"soundness_avg": 3,
"contribution": "2;2;2;3",
"contribution_avg": 2.25,
"presentation": "2;3;2;3",
"presentation_avg": 2.5
} | {
"primary_area": "",
"track": "main",
"venue": "Submitted to ICLR 2025",
"venueid": "ICLR.cc/2025/Conference/Rejected_Submission",
"processed_at": "2026-01-14T22:16:01.215206"
} | {
"id": "4MR8xxIco2",
"metareview": "While the authors see some possible merit in this work, after the rebuttal and discussion most of the reviewers still view the paper as being below the acceptance threshold (two weakly and one strongly). There most common recurring concern was the limited technical novelty, and... | {
"decision": "Reject"
} |
C6hUK6Q1Pi | 2408.00203v1 | OmniParser for Pure Vision Based GUI Agent | {
"content": "## Abstract\n\nAbstract The recent success of large vision language models shows great potential in driving the agent system operating on user interfaces. However, we argue that the power multimodal models like GPT-4V as a general agent on multiple operating systems across different applications is larg... | [
{
"id": "Vm0WzWyhej",
"initial_rating": 6,
"confidence": 5,
"soundness": 3,
"contribution": 3,
"presentation": 3,
"summary": "This paper introduces OMNIPARSER, a method designed to enhance the action-generating capabilities of multimodal models like GPT-4V when interacting with user inte... | {
"rating": "3;5;6;6",
"rating_avg": 5,
"confidence": "5;4;5;5",
"confidence_avg": 4.75,
"soundness": "3;3;3;3",
"soundness_avg": 3,
"contribution": "3;3;3;3",
"contribution_avg": 3,
"presentation": "3;3;3;3",
"presentation_avg": 3
} | {
"primary_area": "",
"track": "main",
"venue": "Submitted to ICLR 2025",
"venueid": "ICLR.cc/2025/Conference/Rejected_Submission",
"processed_at": "2026-01-14T22:16:01.216169"
} | {
"id": "0ZRIzZwWoQ",
"metareview": "This paper is borderline with two reviewers slightly positive, and two reviewers on the negative side. Overall, the reviewers generally agree that the paper is well-written and the proposed method shows promising results on various benchmarks. However, they raise concerns about ... | {
"decision": "Reject"
} |
C8jXEugWkq | 2408.06321v3 | EqNIO: Subequivariant Neural Inertial Odometry | {
"content": "## Abstract\n\nAbstract Neural network-based odometry using accelerometer and gyroscope readings from a single IMU can achieve robust, and low-drift localization capabilities, through the use of neural displacement priors . These priors learn to produce denoised displacement measurements but need to ign... | [
{
"id": "Ff3WU4OYpV",
"initial_rating": 5,
"confidence": 4,
"soundness": 3,
"contribution": 2,
"presentation": 3,
"summary": "The authors propose a method to adapt existing inertial odometry (IO) architectures to be invariant to the IMU orientation. This is done by making use of an $O_g(... | {
"rating": "5;5;8;8",
"rating_avg": 6.5,
"confidence": "4;4;4;2",
"confidence_avg": 3.5,
"soundness": "2;3;3;3",
"soundness_avg": 2.75,
"contribution": "3;2;3;3",
"contribution_avg": 2.75,
"presentation": "2;3;3;3",
"presentation_avg": 2.75
} | {
"primary_area": "",
"track": "main",
"venue": "ICLR 2025 Poster",
"venueid": "ICLR.cc/2025/Conference",
"processed_at": "2026-01-14T22:16:01.216884"
} | {
"id": "ROECalCK5O",
"metareview": "This paper tackles neural inertial odometry estimation, and seeks to regularize training by developing a network that is equivariant to rotations over x,y in a gravity-aligned frame. Theoretically, this is a more principle approach that the earlier methods of regularization via ... | {
"decision": "Accept (Poster)"
} |
CA06Nqa7CG | 2405.18246v2 | Utilitarian Algorithm Configuration for Infinite Parameter Spaces | {
"content": "## Abstract\n\nAbstract Utilitarian algorithm configuration is a general-purpose technique for automatically searching the parameter space of a given algorithm to optimize its performance, as measured by a given utility function, on a given set of inputs. Recently introduced utilitarian configuration pr... | [
{
"id": "2nrVQ9J0eD",
"initial_rating": 5,
"confidence": 2,
"soundness": 3,
"contribution": 3,
"presentation": 2,
"summary": "This paper studies utilitarian algorithm configuration, which is about automatically searching the parameter space of a given algorithm to optimise for its perfor... | {
"rating": "5;6;6;8;8;8",
"rating_avg": 6.833333333333333,
"confidence": "2;3;4;2;3;3",
"confidence_avg": 2.8333333333333335,
"soundness": "3;3;3;3;4;4",
"soundness_avg": 3.3333333333333335,
"contribution": "3;2;3;3;3;4",
"contribution_avg": 3,
"presentation": "2;4;3;3;3;3",
"presentation_avg": 3
} | {
"primary_area": "",
"track": "main",
"venue": "ICLR 2025 Poster",
"venueid": "ICLR.cc/2025/Conference",
"processed_at": "2026-01-14T22:16:01.217749"
} | {
"id": "gwySUE8S25",
"metareview": "Core technical content is bleeding onto the 11th page, for which current guidance is desk rejection. Moreover, none of the reviewers have thoroughly evaluated the main theoretical claims in terms of best arm identification with respect to the state of the art, and are overly pos... | {
"decision": "Accept (Poster)"
} |
CAssIgPN4I | 2406.08474v2 | Real2Code: Reconstruct Articulated Objects via Code Generation | {
"content": "## Abstract\n\nAbstract We present Real2Code, a novel approach to reconstructing articulated objects via code generation. Given visual observations of an object, we first reconstruct its part geometry using an image segmentation model and a shape completion model. We then represent the object parts with... | [
{
"id": "0kWfHPKY49",
"initial_rating": 6,
"confidence": 4,
"soundness": 3,
"contribution": 3,
"presentation": 3,
"summary": "The paper reconstructs articulated objects from visual observations. The approach utilizes a modular pipeline which first reconstructs part level geometry from se... | {
"rating": "5;6;6;6;8",
"rating_avg": 6.2,
"confidence": "3;3;4;4;4",
"confidence_avg": 3.6,
"soundness": "3;3;2;3;4",
"soundness_avg": 3,
"contribution": "2;3;2;3;4",
"contribution_avg": 2.8,
"presentation": "3;3;2;3;4",
"presentation_avg": 3
} | {
"primary_area": "",
"track": "main",
"venue": "ICLR 2025 Poster",
"venueid": "ICLR.cc/2025/Conference",
"processed_at": "2026-01-14T22:16:01.218595"
} | {
"id": "XLfkyorEpK",
"metareview": "This paper presents Real2Code, a novel approach for reconstructing articulated objects from visual observations by generating code, using fine-tuned LLMs specialized for this task. The writing is clear, the method is innovative, and the results are convincing and demonstrate the... | {
"decision": "Accept (Poster)"
} |
CFKZKjrQ5r | 2402.02611v2 | FCoReBench: Can Large Language Models Solve Challenging First-Order Combinatorial Reasoning Problems? | {
"content": "## Abstract\n\nAbstract Recent works show that the largest of the large language models (LLMs) can solve many simple reasoning tasks expressed in natural language, without any/much supervision. But, can they also solve challenging first-order combinatorial reasoning problems, such as graph coloring, kna... | [
{
"id": "qYupyjHyg2",
"initial_rating": 3,
"confidence": 2,
"soundness": 2,
"contribution": 2,
"presentation": 1,
"summary": "This paper focuses on the problem-solving ability of LLM on first-order combinatorial problems in natural language form, arguing that no existing benchmark could ... | {
"rating": "3;3;3;5",
"rating_avg": 3.5,
"confidence": "4;3;2;3",
"confidence_avg": 3,
"soundness": "2;2;2;3",
"soundness_avg": 2.25,
"contribution": "2;2;2;3",
"contribution_avg": 2.25,
"presentation": "2;1;1;3",
"presentation_avg": 1.75
} | {
"primary_area": "",
"track": "main",
"venue": "Submitted to ICLR 2025",
"venueid": "ICLR.cc/2025/Conference/Rejected_Submission",
"processed_at": "2026-01-14T22:16:01.219489"
} | {
"id": "GmM8D55hI0",
"metareview": "This paper introduces a new benchmark, FCoReBench, consisting of 40 combinatorial optimization problems whose constraints, inputs, outputs, and examples are all stated in natural languages. Additionally, this paper also proposes a new framework, SymPro-LM, which outperforms exis... | {
"decision": "Reject"
} |
CFLEIeX7iK | 2410.09693v1 | Neural Solver Selection for Combinatorial Optimization | {
"content": "## Abstract\n\nAbstract Machine learning has increasingly been employed to solve NP-hard combinatorial optimization problems, resulting in the emergence of neural solvers that demonstrate remarkable performance, even with minimal domain-specific knowledge. To date, the community has created numerous ope... | [
{
"id": "KYclmo2YQS",
"initial_rating": 6,
"confidence": 4,
"soundness": 3,
"contribution": 3,
"presentation": 3,
"summary": "This paper considers a new perspective on solving the Combinatorial Optimization (CO) problem using deep learning. Given an instance, a deep learning framework is... | {
"rating": "3;5;5;6",
"rating_avg": 4.75,
"confidence": "4;4;3;4",
"confidence_avg": 3.75,
"soundness": "2;2;2;3",
"soundness_avg": 2.25,
"contribution": "2;2;3;3",
"contribution_avg": 2.5,
"presentation": "2;4;3;3",
"presentation_avg": 3
} | {
"primary_area": "",
"track": "main",
"venue": "Submitted to ICLR 2025",
"venueid": "ICLR.cc/2025/Conference/Rejected_Submission",
"processed_at": "2026-01-14T22:16:01.220330"
} | {
"id": "wHv2LFKJTz",
"metareview": "This paper proposed a learning based solver selection method for neural vehicle routing models. It involves feature extraction, selection model, and selection strategy, aiming to allocate each instance to the most suitable solver from a pool of neural VRP models. Reviewers agree... | {
"decision": "Reject"
} |
CFOQd4tqn1 | 2403.10953v2 | Ctrl123: Consistent Novel View Synthesis via Closed-Loop Transcription | {
"content": "## Abstract\n\nAbstract Large image diffusion models have demonstrated zero-shot capability in novel view synthesis (NVS). However, existing diffusion-based NVS methods struggle to generate novel views that are accurately consistent with the corresponding ground truth poses and appearances, even on the ... | [
{
"id": "ThZ3xlg32f",
"initial_rating": 3,
"confidence": 4,
"soundness": 3,
"contribution": 2,
"presentation": 1,
"summary": "The paper proposes a method labelled Ctrl123 that is an extension of the Zero123 model for new view synthesis. The motivation is to improve the accuracy with whi... | {
"rating": "3;3;3;5;6",
"rating_avg": 4,
"confidence": "5;4;4;4;4",
"confidence_avg": 4.2,
"soundness": "1;3;3;3;3",
"soundness_avg": 2.6,
"contribution": "1;2;2;2;3",
"contribution_avg": 2,
"presentation": "1;1;1;2;3",
"presentation_avg": 1.6
} | {
"primary_area": "",
"track": "main",
"venue": "Submitted to ICLR 2025",
"venueid": "ICLR.cc/2025/Conference/Rejected_Submission",
"processed_at": "2026-01-14T22:16:01.221249"
} | {
"id": "p5i4jt8ifi",
"metareview": "The paper introduces Ctrl123, which aims at improving multi-view consistency in novel view synthesis by aligning generated views with ground truth in the CLIP patch feature space. It extends Zero123 by fine-tuning with modified loss functions, including a closed-loop transcripti... | {
"decision": "Reject"
} |
CGbfokGFP7 | 2409.12957v1 | 3DTopia-XL: Scaling High-quality 3D Asset Generation via Primitive Diffusion | {
"content": "## Abstract\n\nAbstract The increasing demand for high-quality 3D assets across various industries necessitates efficient and automated 3D content creation.\nDespite recent advancements in 3D generative models, existing methods still face challenges with optimization speed, geometric fidelity, and the l... | [
{
"id": "wV4tYIzAJt",
"initial_rating": 5,
"confidence": 4,
"soundness": 2,
"contribution": 2,
"presentation": 3,
"summary": "This paper introduces 3DTopia-XL, a 3D generative model designed to create 3D assets from both images and text. It extends 3D representation from M-SDF to support... | {
"rating": "3;5;5;6",
"rating_avg": 4.75,
"confidence": "4;4;4;3",
"confidence_avg": 3.75,
"soundness": "2;3;2;3",
"soundness_avg": 2.5,
"contribution": "2;2;2;3",
"contribution_avg": 2.25,
"presentation": "3;3;3;2",
"presentation_avg": 2.75
} | {
"primary_area": "",
"track": "main",
"venue": "ICLR 2025 Conference Withdrawn Submission",
"venueid": "ICLR.cc/2025/Conference/Withdrawn_Submission",
"processed_at": "2026-01-14T22:16:01.221967"
} | {
"id": "",
"metareview": "",
"additional_comments": ""
} | {
"decision": ""
} |
CGhgB8Kz8i | 2410.10370v1 | Innovative Thinking, Infinite Humor: Humor Research of Large Language Models through Structured Thought Leaps | {
"content": "## Abstract\n\nAbstract Humor is a culturally nuanced aspect of human language that presents challenges for understanding and generation, requiring participants to possess good creativity and strong associative thinking. Similar to reasoning tasks like solving math problems, humor generation requires co... | [
{
"id": "coOLgBEVW4",
"initial_rating": 6,
"confidence": 3,
"soundness": 3,
"contribution": 3,
"presentation": 3,
"summary": "This paper introduces the Creative Leap of Structured Thought (CLoST) framework, which enhances large language models' ability to generate and recognize humor thr... | {
"rating": "3;3;6;6",
"rating_avg": 4.5,
"confidence": "4;4;3;3",
"confidence_avg": 3.5,
"soundness": "2;2;4;3",
"soundness_avg": 2.75,
"contribution": "2;1;3;3",
"contribution_avg": 2.25,
"presentation": "2;1;3;3",
"presentation_avg": 2.25
} | {
"primary_area": "",
"track": "main",
"venue": "ICLR 2025 Poster",
"venueid": "ICLR.cc/2025/Conference",
"processed_at": "2026-01-14T22:16:01.222700"
} | {
"id": "wQGK97DsAb",
"metareview": "The paper proposes a new framework called Creative Leap of Structured Thought (CLoST) to reinforce humor understanding by LLMs. The authors propose a systematic method inspired by KGs and causal relationships. The framework consists of two stages: Associative Automatic Instructi... | {
"decision": "Accept (Poster)"
} |
CI4sCBMXjP | 2410.09343v1 | ELICIT: LLM Augmentation Via External In-context Capability | {
"content": "## Abstract\n\nAbstract Enhancing the adaptive capabilities of large language models is a critical pursuit in both research and application.\nTraditional fine-tuning methods require substantial data and computational resources, especially for enhancing specific capabilities, while in-context learning is... | [
{
"id": "9Jaqt2jx2N",
"initial_rating": 8,
"confidence": 3,
"soundness": 3,
"contribution": 3,
"presentation": 3,
"summary": "This paper proposes ELICIT, a framework that stores the task vectors corresponding to different in-context-learning (ICL) prompts and dynamically augments the giv... | {
"rating": "3;5;8;8",
"rating_avg": 6,
"confidence": "4;3;3;3",
"confidence_avg": 3.25,
"soundness": "3;3;4;3",
"soundness_avg": 3.25,
"contribution": "2;3;3;3",
"contribution_avg": 2.75,
"presentation": "3;3;3;3",
"presentation_avg": 3
} | {
"primary_area": "",
"track": "main",
"venue": "ICLR 2025 Poster",
"venueid": "ICLR.cc/2025/Conference",
"processed_at": "2026-01-14T22:16:01.223425"
} | {
"id": "YYiQuMLUSE",
"metareview": "This paper proposes a new approach that improves LLMs capabilities by introducing an external ICL capacity library.\n\nThree reviewers support the contributions of this paper with clear cceptance scores while one reviewer gave clear rejection.\n\nAC carefully read the paper, rev... | {
"decision": "Accept (Poster)"
} |
CI5Cj0vktS | 2410.03974v1 | Robust Barycenter Estimation using Semi-Unbalanced Neural Optimal Transport | {
"content": "## Abstract\n\nAbstract A common challenge in aggregating data from multiple sources can be formalized as an Optimal Transport (OT) barycenter problem, which seeks to compute the average of probability distributions with respect to OT discrepancies. However, the presence of outliers and noise in the dat... | [
{
"id": "Ji13YodqdH",
"initial_rating": 5,
"confidence": 3,
"soundness": 2,
"contribution": 1,
"presentation": 2,
"summary": "This paper proposes a neural network-based method to estimate continuous barycenter via the dual formulation of the semi-unbalanced OT.",
"strengths": "1. The... | {
"rating": "5;5;5;6",
"rating_avg": 5.25,
"confidence": "4;4;3;4",
"confidence_avg": 3.75,
"soundness": "3;3;2;4",
"soundness_avg": 3,
"contribution": "2;2;1;3",
"contribution_avg": 2,
"presentation": "3;3;2;4",
"presentation_avg": 3
} | {
"primary_area": "",
"track": "main",
"venue": "ICLR 2025 Poster",
"venueid": "ICLR.cc/2025/Conference",
"processed_at": "2026-01-14T22:16:01.224242"
} | {
"id": "BJvTWnp6Kl",
"metareview": "In the paper, the authors proposed a new approach to estimate the (semi)-unbalanced barycenter of continuous distributions. It is done via formulated the problem as a min-max optimization problem and the method can be used for any general cost function. All the reviewers agree t... | {
"decision": "Accept (Poster)"
} |
CIN2VRxPKU | 2410.15153v3 | Evaluating Deep Unlearning in Large Language Models | {
"content": "## Abstract\n\nAbstract Machine unlearning is a key requirement of many data protection regulations such as GDPR. Prior work on unlearning has mostly considered superficial unlearning tasks where a single or a few related pieces of information are required to be removed.\nHowever, the task of unlearning... | [
{
"id": "PNGhOaehPq",
"initial_rating": 5,
"confidence": 4,
"soundness": 2,
"contribution": 2,
"presentation": 3,
"summary": "This paper presents a critique of current unlearning methods through the lens of fact unlearning - it shows that while current unlearning methods can unlearn the ... | {
"rating": "5;5;5",
"rating_avg": 5,
"confidence": "3;5;4",
"confidence_avg": 4,
"soundness": "3;2;2",
"soundness_avg": 2.3333333333333335,
"contribution": "2;2;2",
"contribution_avg": 2,
"presentation": "3;1;3",
"presentation_avg": 2.3333333333333335
} | {
"primary_area": "",
"track": "main",
"venue": "Submitted to ICLR 2025",
"venueid": "ICLR.cc/2025/Conference/Rejected_Submission",
"processed_at": "2026-01-14T22:16:01.224944"
} | {
"id": "6aPsRi4HVK",
"metareview": "This paper introduces \"deep unlearning\" as a novel approach in the domain of large language models (LLMs), emphasizing its importance in effectively erasing certain facts. As the target fact can be deduced from logical rules, superficial unlearning methods, which solely unlear... | {
"decision": "Reject"
} |
CIs9x2ZRgh | 2410.05101v2 | CR-CTC: Consistency regularization on CTC for improved speech recognition | {
"content": "## Abstract\n\nAbstract Connectionist Temporal Classification (CTC) is a widely used method for automatic speech recognition (ASR), renowned for its simplicity and computational efficiency. However, it often falls short in recognition performance compared to transducer or systems combining CTC and atten... | [
{
"id": "mWNjKAYuiO",
"initial_rating": 8,
"confidence": 5,
"soundness": 3,
"contribution": 3,
"presentation": 3,
"summary": "This paper applies a new type of self-consistency loss on different augmented view for CTC based ASR model. The new consistency regularized loss is doing KL over ... | {
"rating": "3;5;8;8",
"rating_avg": 6,
"confidence": "5;4;4;5",
"confidence_avg": 4.5,
"soundness": "3;3;3;3",
"soundness_avg": 3,
"contribution": "2;2;3;3",
"contribution_avg": 2.5,
"presentation": "2;3;3;3",
"presentation_avg": 2.75
} | {
"primary_area": "",
"track": "main",
"venue": "ICLR 2025 Poster",
"venueid": "ICLR.cc/2025/Conference",
"processed_at": "2026-01-14T22:16:01.225542"
} | {
"id": "h6hEIY0wem",
"metareview": "This paper proposed to use consistency regulation to improve CTC training by enforcing constancy between two CTC distributions obtained from different augmented views of the input speech Mel-spectrogram. The proposed method is simple but effective, showing impressive improvemen... | {
"decision": "Accept (Poster)"
} |
CKXul9iX77 | 2409.03731v2 | A Deep Generative Learning Approach for Two-stage Adaptive Robust Optimization | {
"content": "## Abstract\n\nTwo-stage adaptive robust optimization (ARO) is a powerful approach for planning under uncertainty, balancing first-stage decisions with recourse decisions made after uncertainty is realized. To account for uncertainty, modelers typically define a simple uncertainty set over which potenti... | [
{
"id": "ytsLmZTiij",
"initial_rating": 5,
"confidence": 3,
"soundness": 3,
"contribution": 2,
"presentation": 3,
"summary": "This paper introduces AGRO, a novel method for two-stage adaptive robust optimization (ARO) using a variational autoencoder (VAE) to generate adversarial and real... | {
"rating": "5;6;6;6",
"rating_avg": 5.75,
"confidence": "3;4;3;2",
"confidence_avg": 3,
"soundness": "3;2;3;3",
"soundness_avg": 2.75,
"contribution": "2;3;2;3",
"contribution_avg": 2.5,
"presentation": "3;3;3;3",
"presentation_avg": 3
} | {
"primary_area": "",
"track": "main",
"venue": "ICLR 2025 Poster",
"venueid": "ICLR.cc/2025/Conference",
"processed_at": "2026-01-14T22:16:01.226165"
} | {
"id": "0owS9u5bHF",
"metareview": "This paper introduces AGRO, a two-stage adaptive robust optimization (ARO) method that combines variational autoencoder (VAE) within a column-and-constraint generation for adversarial uncertainty sets. The authors show that AGRO reduces planning costs compared to classical ARO a... | {
"decision": "Accept (Poster)"
} |
CKYsXi0dOV | 2410.16267v1 | BLIP-3-Video: You Only Need 32 Tokens to Represent a Video Even in VLMs | {
"content": "## Abstract\n\nAbstract We present xGen-MM-Vid (BLIP-3-Video): a multimodal language model for videos, particularly designed to efficiently capture temporal information over multiple frames. BLIP-3-Video takes advantage of the ‘temporal encoder’ in addition to the conventional visual tokenizer, which ma... | [
{
"id": "j9GN5KOdCv",
"initial_rating": 3,
"confidence": 4,
"soundness": 2,
"contribution": 2,
"presentation": 2,
"summary": "This paper presents BLIP-3-Video, which introduces a \"temporal encoder\" alongside a conventional visual tokenizer, allowing it to significantly reduce visual to... | {
"rating": "3;5;6;6",
"rating_avg": 5,
"confidence": "4;4;4;3",
"confidence_avg": 3.75,
"soundness": "2;2;3;3",
"soundness_avg": 2.5,
"contribution": "2;1;3;4",
"contribution_avg": 2.5,
"presentation": "2;2;2;3",
"presentation_avg": 2.25
} | {
"primary_area": "",
"track": "main",
"venue": "Submitted to ICLR 2025",
"venueid": "ICLR.cc/2025/Conference/Rejected_Submission",
"processed_at": "2026-01-14T22:16:01.226873"
} | {
"id": "aMhWC9yGvh",
"metareview": "This paper provides BLIP-3-Video, a Video LLM that build on the previous BLIP-3 architecture but focuses on incorporating temporality into the architecture by learning spatio-temporal pooling to obtain video representations in only 32 tokens. The paper is clearly written, provid... | {
"decision": "Reject"
} |
CKdlPUWDEE | 2406.09041v2 | ME-Switch: A Memory-Efficient Expert Switching Framework for Large Language Models | {
"content": "## Abstract\n\nAbstract The typical process for LLM’s development involves pre-training a general foundation model on massive data, followed by fine-tuning on task-specific data to obtain a series of specialized experts. Serving these experts can pose significant memory challenges, as loading all expert... | [
{
"id": "FqNZT1V8eL",
"initial_rating": 3,
"confidence": 4,
"soundness": 2,
"contribution": 2,
"presentation": 2,
"summary": "The authors introduce ME-Switch, a memory-efficient framework for serving MoE-based LLMs. Given a set of MoE models across specified domains, they present a quant... | {
"rating": "3;5;6;6",
"rating_avg": 5,
"confidence": "4;4;4;4",
"confidence_avg": 4,
"soundness": "2;3;3;3",
"soundness_avg": 2.75,
"contribution": "2;3;3;3",
"contribution_avg": 2.75,
"presentation": "2;2;4;3",
"presentation_avg": 2.75
} | {
"primary_area": "",
"track": "main",
"venue": "Submitted to ICLR 2025",
"venueid": "ICLR.cc/2025/Conference/Rejected_Submission",
"processed_at": "2026-01-14T22:16:01.227710"
} | {
"id": "NJEaRfxFn4",
"metareview": "This paper tackles the growing challenge of serving multiple fine-tuned LLMs while keeping memory usage in check. ME-Switch’s approach—using salient-aware delta compression to carefully quantize non-salient channels and preserve the important ones—feels genuinely promising. The ... | {
"decision": "Reject"
} |
CKqiQosLKc | 2410.01312v1 | Sampling from Energy-based Policies using Diffusion | {
"content": "## Abstract\n\nAbstract Energy-based policies offer a flexible framework for modeling complex, multimodal behaviors in reinforcement learning (RL). In maximum entropy RL, the optimal policy is a Boltzmann distribution derived from the soft Q-function, but direct sampling from this distribution in contin... | [
{
"id": "ctCqx6Z1hy",
"initial_rating": 6,
"confidence": 3,
"soundness": 3,
"contribution": 3,
"presentation": 3,
"summary": "The authors have developed a new actor-critic algorithm called Diffusion Q-Sampling (DQS), which uses a diffusion-based model to sample from energy-based policies... | {
"rating": "3;3;3;6",
"rating_avg": 3.75,
"confidence": "4;4;3;3",
"confidence_avg": 3.5,
"soundness": "1;2;2;3",
"soundness_avg": 2,
"contribution": "2;2;2;3",
"contribution_avg": 2.25,
"presentation": "3;2;2;3",
"presentation_avg": 2.5
} | {
"primary_area": "",
"track": "main",
"venue": "ICLR 2025 Conference Withdrawn Submission",
"venueid": "ICLR.cc/2025/Conference/Withdrawn_Submission",
"processed_at": "2026-01-14T22:16:01.228376"
} | {
"id": "",
"metareview": "",
"additional_comments": ""
} | {
"decision": ""
} |
CN2bmVVpOh | 2402.08211v1 | Transformer Mechanisms Mimic Frontostriatal Gating Operations When Trained on Human Working Memory Tasks | {
"content": "## Abstract\n\nAbstract Models based on the Transformer neural network architecture have seen success on a wide variety of tasks that appear to require complex “cognitive branching”– or the ability to maintain pursuit of one goal while accomplishing others. In cognitive neuroscience, success on such tas... | [
{
"id": "M1RwWCVU7B",
"initial_rating": 5,
"confidence": 4,
"soundness": 3,
"contribution": 2,
"presentation": 3,
"summary": "This paper explores how Transformers, when trained on human working memory tasks, develop mechanisms that relate to frontostriatal gating operations observed in h... | {
"rating": "3;5;5",
"rating_avg": 4.333333333333333,
"confidence": "3;4;4",
"confidence_avg": 3.6666666666666665,
"soundness": "1;3;3",
"soundness_avg": 2.3333333333333335,
"contribution": "2;1;2",
"contribution_avg": 1.6666666666666667,
"presentation": "2;1;3",
"presentation_avg": 2
} | {
"primary_area": "",
"track": "main",
"venue": "Submitted to ICLR 2025",
"venueid": "ICLR.cc/2025/Conference/Rejected_Submission",
"processed_at": "2026-01-14T22:16:01.229216"
} | {
"id": "8Z96c5P38f",
"metareview": "This work shows that Transformers, when trained on tasks requiring working memory gating, develop input and output gating mechanisms that resemble those in human frontostriatal systems.\n\nThe paper is generally well-written and aims to bridge the gap between gating mechanisms i... | {
"decision": "Reject"
} |
CO4wKfSyhb | 2405.14446v2 | Worldwide Federated Training of Language Models | {
"content": "## Abstract\n\nAbstract The reliance of language model training on massive amounts of computation and vast datasets scraped from potentially low-quality, copyrighted, or sensitive data has come into question practically, legally, and ethically. Federated learning provides a plausible alternative by enab... | [
{
"id": "UYKDVR4wNi",
"initial_rating": 3,
"confidence": 2,
"soundness": 1,
"contribution": 2,
"presentation": 2,
"summary": "This paper proposes that WorldLM addresses hierarchical federated learning and creates federations of federations, where different federations enforce various reg... | {
"rating": "3;3;3;5;5",
"rating_avg": 3.8,
"confidence": "4;4;2;3;2",
"confidence_avg": 3,
"soundness": "2;2;1;2;3",
"soundness_avg": 2,
"contribution": "2;2;2;2;3",
"contribution_avg": 2.2,
"presentation": "2;2;2;3;2",
"presentation_avg": 2.2
} | {
"primary_area": "",
"track": "main",
"venue": "ICLR 2025 Conference Withdrawn Submission",
"venueid": "ICLR.cc/2025/Conference/Withdrawn_Submission",
"processed_at": "2026-01-14T22:16:01.229911"
} | {
"id": "",
"metareview": "",
"additional_comments": ""
} | {
"decision": ""
} |
COdUNtjMEp | 2410.11778v1 | On the Training Convergence of Transformers for In-Context Classification | {
"content": "## Abstract\n\nAbstract While transformers have demonstrated impressive capacities for in-context learning (ICL) in practice, theoretical understanding of the underlying mechanism enabling transformers to perform ICL is still in its infant stage. This work aims to theoretically study the training dynami... | [
{
"id": "YQkYwQzCdT",
"initial_rating": 8,
"confidence": 5,
"soundness": 4,
"contribution": 3,
"presentation": 3,
"summary": "The paper investigates the training convergence of transformers for in-context classification tasks. It demonstrates that a single-layer transformer trained by gr... | {
"rating": "3;5;6;6",
"rating_avg": 5,
"confidence": "4;4;4;4",
"confidence_avg": 4,
"soundness": "3;3;3;4",
"soundness_avg": 3.25,
"contribution": "1;2;2;3",
"contribution_avg": 2,
"presentation": "2;3;3;3",
"presentation_avg": 2.75
} | {
"primary_area": "",
"track": "main",
"venue": "Submitted to ICLR 2025",
"venueid": "ICLR.cc/2025/Conference/Rejected_Submission",
"processed_at": "2026-01-14T22:16:01.230886"
} | {
"id": "sTLVwnS8BU",
"metareview": "This paper provides a theoretical analysis of in-context learning for classification tasks. The authors use a fairly standard setting of 1-layer linear attention. However the distinction of the work arises from its focus on binary and multiclass classification tasks and the asso... | {
"decision": "Reject"
} |
CRmiX0v16e | 2406.02548v2 | Open-YOLO 3D: Towards Fast and Accurate Open-Vocabulary 3D Instance Segmentation | {
"content": "## Abstract\n\nAbstract Recent works on open-vocabulary 3D instance segmentation show strong promise, but at the cost of slow inference speed and high computation requirements. This high computation cost is typically due to their heavy reliance on 3D clip features, which require computationally expensiv... | [
{
"id": "Qfehz0Hdsj",
"initial_rating": 6,
"confidence": 3,
"soundness": 3,
"contribution": 3,
"presentation": 3,
"summary": "This paper introduces an efficient 3D mask labeling method that leverages multi-view 2D label maps, referred to as Low Granularity (LG) Label Maps, created from 2... | {
"rating": "5;6;6;8;10",
"rating_avg": 7,
"confidence": "4;3;3;4;5",
"confidence_avg": 3.8,
"soundness": "2;4;3;3;3",
"soundness_avg": 3,
"contribution": "2;3;3;3;3",
"contribution_avg": 2.8,
"presentation": "3;3;3;3;4",
"presentation_avg": 3.2
} | {
"primary_area": "",
"track": "main",
"venue": "ICLR 2025 Oral",
"venueid": "ICLR.cc/2025/Conference",
"processed_at": "2026-01-14T22:16:01.231801"
} | {
"id": "T8IoyF93zz",
"metareview": "This paper presents an efficient approach to open-vocabulary 3D instance segmentation by leveraging 2D bounding box priors from a pre-trained open-vocabulary 2D object detector. The authors propose the Multi-View Prompt Distribution (MVPDist) method, which effectively utilizes m... | {
"decision": "Accept (Oral)"
} |
CS2JWaziYr | 2408.11049v3 | MagicDec: Breaking the Latency-Throughput Tradeoff for Long Context Generation with Speculative Decoding | {
"content": "## Abstract\n\nAbstract Large Language Models (LLMs) have become more prevalent in long-context applications such as interactive chatbots, document analysis, and agent workflows, but it is challenging to serve long-context requests with low latency and high throughput. Speculative decoding (SD) is a wid... | [
{
"id": "4avedGW11A",
"initial_rating": 5,
"confidence": 3,
"soundness": 2,
"contribution": 3,
"presentation": 4,
"summary": "This paper introduces a speculative decoding technique designed to enhance throughput and reduce latency in long-context Large Language Models (LLMs), addressing ... | {
"rating": "5;5;6;8",
"rating_avg": 6,
"confidence": "3;3;2;4",
"confidence_avg": 3,
"soundness": "3;2;3;3",
"soundness_avg": 2.75,
"contribution": "2;2;2;4",
"contribution_avg": 2.5,
"presentation": "2;4;3;3",
"presentation_avg": 3
} | {
"primary_area": "",
"track": "main",
"venue": "ICLR 2025 Poster",
"venueid": "ICLR.cc/2025/Conference",
"processed_at": "2026-01-14T22:16:01.232379"
} | {
"id": "n8aBehwieg",
"metareview": "(a) Summary of Scientific Claims and Findings\n\nThe paper presents MagicDec, a speculative decoding technique aimed at improving throughput and reducing latency for long-context Large Language Models (LLMs). It challenges the conventional understanding by demonstrating that spe... | {
"decision": "Accept (Poster)"
} |
CSZKElOtG5 | 2406.05927v2 | MeanSparse: Post-Training Robustness Enhancement Through Mean-Centered Feature Sparsification | {
"content": "## Abstract\n\nAbstract We present a simple yet effective method to improve the robustness of both Convolutional and attention-based Neural Networks against adversarial examples by post-processing an adversarially trained model.\nOur technique, MeanSparse , cascades the activation functions of a trained... | [
{
"id": "CvlyXY766u",
"initial_rating": 5,
"confidence": 3,
"soundness": 3,
"contribution": 2,
"presentation": 2,
"summary": "This paper introduces a method called MEANSPARSE, which enhances the adversarial robustness of trained neural networks in a post-processing manner without comprom... | {
"rating": "3;3;5;6",
"rating_avg": 4.25,
"confidence": "5;5;3;3",
"confidence_avg": 4,
"soundness": "3;2;3;3",
"soundness_avg": 2.75,
"contribution": "3;2;2;3",
"contribution_avg": 2.5,
"presentation": "3;3;2;3",
"presentation_avg": 2.75
} | {
"primary_area": "",
"track": "main",
"venue": "Submitted to ICLR 2025",
"venueid": "ICLR.cc/2025/Conference/Rejected_Submission",
"processed_at": "2026-01-14T22:16:01.233274"
} | {
"id": "KtEAUHrV3D",
"metareview": "**Summary** This work explores sparsity for adversarial robustness by proposing and evaluating a particular sparsity transform on deep representations. The MeanSparse operation is similar to (soft-)thresholding, in reducing differences about the center to zero, but distinct in m... | {
"decision": "Reject"
} |
CTC7CmirNr | 2409.02908v3 | Masked Diffusion Models are Secretly Time-Agnostic Masked Models and Exploit Inaccurate Categorical Sampling | {
"content": "## Abstract\n\nAbstract Masked diffusion models (MDMs) have emerged as a popular research topic for generative modeling of discrete data, thanks to their superior performance over other discrete diffusion models, and are rivaling the auto-regressive models (ARMs) for language modeling tasks. The recent ... | [
{
"id": "KvWZIGZf6l",
"initial_rating": 5,
"confidence": 2,
"soundness": 3,
"contribution": 3,
"presentation": 2,
"summary": "This paper has two primary objectives. First, it draws a connection between masked diffusion models and time-agnostic; second, the paper examines various strategi... | {
"rating": "5;6;6;8",
"rating_avg": 6.25,
"confidence": "2;4;3;3",
"confidence_avg": 3,
"soundness": "3;4;3;4",
"soundness_avg": 3.5,
"contribution": "3;3;3;3",
"contribution_avg": 3,
"presentation": "2;4;2;4",
"presentation_avg": 3
} | {
"primary_area": "",
"track": "main",
"venue": "ICLR 2025 Poster",
"venueid": "ICLR.cc/2025/Conference",
"processed_at": "2026-01-14T22:16:01.234168"
} | {
"id": "lGGbs1K9NA",
"metareview": "The paper provide a theoretical analysis of the recently proposed masked diffusion models (MDMs) for discrete generation, showing that MDMs are essentially time-agnostic masked models. Further, the paper also introduces a novel sampling method that is theoretically equivalent to... | {
"decision": "Accept (Poster)"
} |
CUABD2qIB4 | 2406.04508v1 | OCCAM: Towards Cost-Efficient and Accuracy-Aware Classification Inference | {
"content": "## Abstract\n\nAbstract Image classification is a fundamental building block for a majority of computer vision applications.\nWith the growing popularity and capacity of machine learning models, people can easily access trained image classifiers as a service online or offline. However, model use comes w... | [
{
"id": "r4RBO2o5Zz",
"initial_rating": 6,
"confidence": 3,
"soundness": 3,
"contribution": 2,
"presentation": 3,
"summary": "This paper addresses the trade-off between classification accuracy and inference cost by proposing a framework that combines small and large models. The authors i... | {
"rating": "5;5;6;6",
"rating_avg": 5.5,
"confidence": "4;2;3;4",
"confidence_avg": 3.25,
"soundness": "2;3;3;3",
"soundness_avg": 2.75,
"contribution": "2;2;2;3",
"contribution_avg": 2.25,
"presentation": "3;3;3;3",
"presentation_avg": 3
} | {
"primary_area": "",
"track": "main",
"venue": "ICLR 2025 Poster",
"venueid": "ICLR.cc/2025/Conference",
"processed_at": "2026-01-14T22:16:01.235297"
} | {
"id": "U3Cfy5LXuS",
"metareview": "This paper proposes OCCAM, a principled approach to optimize classifier assignments across queries, maximizing accuracy under user-specified cost budgets by solving an integer linear programming problem. Experiments demonstrate that OCCAM reduces inference costs by up to 40% wit... | {
"decision": "Accept (Poster)"
} |
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