paper_id string | arxiv_id string | title string | markdown dict | reviews list | scores dict | metadata dict | meta_review dict | decision dict |
|---|---|---|---|---|---|---|---|---|
zpX0teJu9Z | 2402.14009v3 | Geometry-Informed Neural Networks | {
"content": "## Abstract\n\nAbstract Geometry is a ubiquitous tool in computer graphics, design, and engineering.\nHowever, the lack of large shape datasets limits the application of state-of-the-art supervised learning methods and motivates the exploration of alternative learning strategies.\nTo this end, we introd... | [
{
"id": "AZEXhAquVF",
"initial_rating": 6,
"confidence": 3,
"soundness": 2,
"contribution": 3,
"presentation": 3,
"summary": "This work introduces a novel framework for data-free geometry learning. The framework relies on implicit geometry representation - which is a modulated condition... | {
"rating": "3;5;5;6",
"rating_avg": 4.75,
"confidence": "4;2;3;3",
"confidence_avg": 3,
"soundness": "2;2;2;2",
"soundness_avg": 2,
"contribution": "2;2;2;3",
"contribution_avg": 2.25,
"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": "2026-01-14T22:16:04.281193"
} | {
"id": "GRj0dm6BSL",
"metareview": "The paper introduces geometry-informed neural networks (GINNs) for optimizing 3D shapes given user-specified design requirements and constraints. The authors specifically incorporate the diversity constraint to get many different shapes and prevent mode collapse. The authors sh... | {
"decision": "Reject"
} |
zq1zTgSBro | 2406.11006v1 | SPEAR: Receiver-to-Receiver Acoustic Neural Warping Field | {
"content": "## Abstract\n\nAbstract We present SPEAR , a continuous receiver-to-receiver acoustic neural warping field for spatial acoustic effects prediction in an acoustic 3D space with a single stationary audio source. Unlike traditional source-to-receiver modelling methods that require prior space acoustic prop... | [
{
"id": "tuQf1uwlc7",
"initial_rating": 5,
"confidence": 3,
"soundness": 3,
"contribution": 3,
"presentation": 3,
"summary": "The paper introduces SPEAR, a novel neural warping field model designed to predict spatial acoustic effects in a 3D environment with a single stationary audio sou... | {
"rating": "3;3;5;10",
"rating_avg": 5.25,
"confidence": "4;4;3;4",
"confidence_avg": 3.75,
"soundness": "2;3;2;4",
"soundness_avg": 2.75,
"contribution": "1;2;2;4",
"contribution_avg": 2.25,
"presentation": "2;3;3;4",
"presentation_avg": 3
} | {
"primary_area": "",
"track": "main",
"venue": "Submitted to ICLR 2025",
"venueid": "ICLR.cc/2025/Conference/Rejected_Submission",
"processed_at": "2026-01-14T22:16:04.281996"
} | {
"id": "kqz22Z5iei",
"metareview": "This paper received contrasting reviews, indeed, 3 out of 4 reviews leaned more to negative evaluations (rating 5), while 1 was extremely positive (max rating, 10). After rebuttal and extensive discussion, the 2 of the former evaluations downgraded the rating to clearly negative... | {
"decision": "Reject"
} |
zqo2eKjSWH | 2405.07145v1 | Stable Signature is Unstable: Removing Image Watermark from Diffusion Models | {
"content": "## Abstract\n\nAbstract Watermark has been widely deployed by industry to detect AI-generated images. A recent watermarking framework called Stable Signature (proposed by Meta) roots watermark into the parameters of a diffusion model’s decoder such that its generated images are inherently watermarked. S... | [
{
"id": "SNhyNYbcKe",
"initial_rating": 5,
"confidence": 3,
"soundness": 3,
"contribution": 2,
"presentation": 3,
"summary": "The paper introduces a method to remove watermarks added by stable signature [1]. The method achieves high removal effect and good image quality compared to previ... | {
"rating": "3;5;5;5",
"rating_avg": 4.5,
"confidence": "4;4;4;3",
"confidence_avg": 3.75,
"soundness": "2;2;4;3",
"soundness_avg": 2.75,
"contribution": "1;2;3;2",
"contribution_avg": 2,
"presentation": "2;3;4;3",
"presentation_avg": 3
} | {
"primary_area": "",
"track": "main",
"venue": "ICLR 2025 Conference Withdrawn Submission",
"venueid": "ICLR.cc/2025/Conference/Withdrawn_Submission",
"processed_at": "2026-01-14T22:16:04.282598"
} | {
"id": "",
"metareview": "",
"additional_comments": ""
} | {
"decision": ""
} |
zqzsZ5cXbB | 2407.03157v1 | Let the Code LLM Edit Itself When You Edit the Code | {
"content": "## Abstract\n\nAbstract In this work, we investigate a typical scenario in code generation where a developer edits existing code in real time and requests a code assistant, e.g., a large language model, to re-predict the next token or next line on the fly. Naively, the LLM needs to re-encode the entire ... | [
{
"id": "pZQ1uzzNXp",
"initial_rating": 3,
"confidence": 4,
"soundness": 1,
"contribution": 2,
"presentation": 3,
"summary": "This paper introduces a technique to update the rotary positional encoding when a small part of the context tokens are updated. It aims to optimize computational ... | {
"rating": "1;5;6;6",
"rating_avg": 4.5,
"confidence": "4;2;4;3",
"confidence_avg": 3.25,
"soundness": "1;2;4;3",
"soundness_avg": 2.5,
"contribution": "1;2;4;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": "2026-01-14T22:16:04.283247"
} | {
"id": "au44MytjTo",
"metareview": "This paper investigates an interesting question and presents Positional Integrity Encoding (PIE) as an inexpensive alternative to re-encoding the entire KV cache during LLM decoding, specifically for code-related tasks. The reviewers agree that the results are impressive and dem... | {
"decision": "Accept (Poster)"
} |
ztT70ubhsc | 2410.01595v2 | KnobGen: Controlling the Sophistication of Artwork in Sketch-Based Diffusion Models | {
"content": "## Abstract\n\nAbstract Recent advances in diffusion models have significantly improved text-to-image (T2I) generation, but they often struggle to balance fine-grained precision with high-level control. Methods like ControlNet and T2I-Adapter excel at following sketches by seasoned artists but tend to b... | [
{
"id": "aoP8nvAUnH",
"initial_rating": 5,
"confidence": 5,
"soundness": 2,
"contribution": 2,
"presentation": 2,
"summary": "The paper proposes \"KnobGen\", a novel algorithm for sketch+text-based image generation and provides the user control over the balance between fine-grained and c... | {
"rating": "1;5;5;5",
"rating_avg": 4,
"confidence": "5;3;5;5",
"confidence_avg": 4.5,
"soundness": "2;2;2;2",
"soundness_avg": 2,
"contribution": "2;2;2;2",
"contribution_avg": 2,
"presentation": "1;3;2;2",
"presentation_avg": 2
} | {
"primary_area": "",
"track": "main",
"venue": "ICLR 2025 Conference Withdrawn Submission",
"venueid": "ICLR.cc/2025/Conference/Withdrawn_Submission",
"processed_at": "2026-01-14T22:16:04.283878"
} | {
"id": "",
"metareview": "",
"additional_comments": ""
} | {
"decision": ""
} |
ztzZDzgfrh | 2410.11414v1 | ReDeEP: Detecting Hallucination in Retrieval-Augmented Generation via Mechanistic Interpretability | {
"content": "## Abstract\n\nAbstract Retrieval-Augmented Generation (RAG) models are designed to incorporate external knowledge, reducing hallucinations caused by insufficient parametric (internal) knowledge. However, even with accurate and relevant retrieved content, RAG models can still produce hallucinations by g... | [
{
"id": "vROxjzSxZk",
"initial_rating": 5,
"confidence": 4,
"soundness": 3,
"contribution": 3,
"presentation": 2,
"summary": "This paper proposes a method for detecting hallucinations of Retrieval Augmented Generation (RAG) models in the scenario when retrieved context is accurate and re... | {
"rating": "5;6;8",
"rating_avg": 6.333333333333333,
"confidence": "4;3;4",
"confidence_avg": 3.6666666666666665,
"soundness": "3;3;4",
"soundness_avg": 3.3333333333333335,
"contribution": "3;3;4",
"contribution_avg": 3.3333333333333335,
"presentation": "2;3;3",
"presentation_avg": 2.66666666666666... | {
"primary_area": "",
"track": "main",
"venue": "ICLR 2025 Spotlight",
"venueid": "ICLR.cc/2025/Conference",
"processed_at": "2026-01-14T22:16:04.284622"
} | {
"id": "4VGwuPCskt",
"metareview": "This paper studies the potential hallucinations issue of Retrieval Augmented Generation (RAG) models in practice and proposes a hallucination detection method, ReDeEP, and a RAG truthfulness improvement method, AARF. Reviewers agreed that the paper is clearly written, and the id... | {
"decision": "Accept (Spotlight)"
} |
zuKrRYM3Tg | 2402.04012v2 | Quantized Approximately Orthogonal Recurrent Neural Networks | {
"content": "## Abstract\n\nAbstract Orthogonal recurrent neural networks (ORNNs) are an appealing option for learning tasks involving time series with long-term dependencies, thanks to their simplicity and computational stability. However, these networks often require a substantial number of parameters to perform w... | [
{
"id": "olrHGyXtee",
"initial_rating": 3,
"confidence": 4,
"soundness": 2,
"contribution": 1,
"presentation": 3,
"summary": "In this paper, the authors study the quantization of orthogonal recurrent neural networks (ORNNs). They first investigate the impact of quantization on the orthog... | {
"rating": "1;3;3;5",
"rating_avg": 3,
"confidence": "5;3;4;3",
"confidence_avg": 3.75,
"soundness": "3;2;2;2",
"soundness_avg": 2.25,
"contribution": "1;2;1;2",
"contribution_avg": 1.5,
"presentation": "4;2;3;2",
"presentation_avg": 2.75
} | {
"primary_area": "",
"track": "main",
"venue": "Submitted to ICLR 2025",
"venueid": "ICLR.cc/2025/Conference/Rejected_Submission",
"processed_at": "2026-01-14T22:16:04.285456"
} | {
"id": "mO3MBKYGOz",
"metareview": "There were serious concerns raised by the reviewers related to the lack of novelty, poor writing, and experimental evaluations. \n\nThe authors did not reply to those concerns during the rebuttal period. Since the paper did not receive promising reviews to begin with and the aut... | {
"decision": "Reject"
} |
zvaiz3FjA9 | 2410.04089v1 | Designing Concise ConvNets with Columnar Stages | {
"content": "## Abstract\n\nAbstract In the era of vision Transformers, the recent success of VanillaNet shows the huge potential of simple and concise convolutional neural networks (ConvNets). Where such models mainly focus on runtime, it is also crucial to simultaneously focus on other aspects, e.g., FLOPs, parame... | [
{
"id": "uyE6SFkeLt",
"initial_rating": 6,
"confidence": 4,
"soundness": 3,
"contribution": 3,
"presentation": 3,
"summary": "This paper introduces Columnar Stage Network (CoSNet) to deploy parallel conv units with fewer kernels, and reduce the 1x1 conv layers. To optimize the model effi... | {
"rating": "3;6;6",
"rating_avg": 5,
"confidence": "5;3;4",
"confidence_avg": 4,
"soundness": "3;3;3",
"soundness_avg": 3,
"contribution": "3;3;3",
"contribution_avg": 3,
"presentation": "3;3;3",
"presentation_avg": 3
} | {
"primary_area": "",
"track": "main",
"venue": "ICLR 2025 Poster",
"venueid": "ICLR.cc/2025/Conference",
"processed_at": "2026-01-14T22:16:04.286172"
} | {
"id": "s0HDSAm4qL",
"metareview": "This paper presents CoSNet, a series of network architectures of convolutional architectures to improve the efficiency of deep neural networks. At the core of this work lies a lot of model designs and adjustments; finally, an efficient architecture is obtained and works well in ... | {
"decision": "Accept (Poster)"
} |
zxO4WuVGns | 2409.03710v1 | Inverse decision-making using neural amortized Bayesian actors | {
"content": "## Abstract\n\nAbstract Bayesian observer and actor models have provided normative explanations for many behavioral phenomena in perception, sensorimotor control, and other areas of cognitive science and neuroscience. They attribute behavioral variability and biases to different interpretable entities s... | [
{
"id": "CHrpkDBoiw",
"initial_rating": 3,
"confidence": 3,
"soundness": 2,
"contribution": 3,
"presentation": 2,
"summary": "This paper introduces a method for performing Bayesian inference on the parameters of Bayesian observer-actor models, particularly suited for scenarios where Baye... | {
"rating": "3;3;6",
"rating_avg": 4,
"confidence": "2;3;2",
"confidence_avg": 2.3333333333333335,
"soundness": "3;2;3",
"soundness_avg": 2.6666666666666665,
"contribution": "2;2;4",
"contribution_avg": 2.6666666666666665,
"presentation": "3;2;4",
"presentation_avg": 3
} | {
"primary_area": "",
"track": "main",
"venue": "ICLR 2025 Poster",
"venueid": "ICLR.cc/2025/Conference",
"processed_at": "2026-01-14T22:16:04.286824"
} | {
"id": "uomLCNCiOy",
"metareview": "This paper investigate a new method for inverse decision-making in sensorimotor tasks with continuous actions.\nThe proposed approach aims to infer what are a subject’s optimal decisions, with the assumption of optimal human behavior. \nAs mentioned by reviewers, the parameteriz... | {
"decision": "Accept (Poster)"
} |
zxqdVo9FjY | 2410.13991v1 | Generalization for Least Squares Regression with Simple Spiked Covariances | {
"content": "## Abstract\n\nAbstract Random matrix theory has proven to be a valuable tool in analyzing the generalization of linear models. However, the generalization properties of even two-layer neural networks trained by gradient descent remain poorly understood. To understand the generalization performance of s... | [
{
"id": "WqthOZZiOY",
"initial_rating": 5,
"confidence": 3,
"soundness": 2,
"contribution": 1,
"presentation": 3,
"summary": "The authors analyze the generalization properties of spiked covariate models. The theoretical analysis is motivated by recent works on two-layer networks trained ... | {
"rating": "3;3;3;5;5",
"rating_avg": 3.8,
"confidence": "3;4;4;3;3",
"confidence_avg": 3.4,
"soundness": "2;3;2;3;2",
"soundness_avg": 2.4,
"contribution": "2;2;1;3;1",
"contribution_avg": 1.8,
"presentation": "2;3;1;4;3",
"presentation_avg": 2.6
} | {
"primary_area": "",
"track": "main",
"venue": "Submitted to ICLR 2025",
"venueid": "ICLR.cc/2025/Conference/Rejected_Submission",
"processed_at": "2026-01-14T22:16:04.287802"
} | {
"id": "GzIdpBrTa1",
"metareview": "Summary of Scientific Claims and Findings:\nThe paper investigates the generalization properties of least squares regression with spiked covariance matrices, motivated by neural network training dynamics after one gradient step. The authors provide asymptotic analyses and derive... | {
"decision": "Reject"
} |
zzR1Uskhj0 | 2410.04080v1 | High Probability Bounds for Cross-Learning Contextual Bandits with Unknown Context Distributions | {
"content": "## Abstract\n\nAbstract Motivated by applications in online bidding and sleeping bandits, we examine the problem of contextual bandits with cross learning, where the learner observes the loss associated with the action across all possible contexts, not just the current round’s context. Our focus is on a... | [
{
"id": "NLrlOlSugS",
"initial_rating": 3,
"confidence": 3,
"soundness": 2,
"contribution": 2,
"presentation": 1,
"summary": "The paper proposes an algorithm that achieves high probability regret bound (which is stronger than the expected regret bound) for the cross-learning contextual b... | {
"rating": "3;5;6;6;8",
"rating_avg": 5.6,
"confidence": "3;3;4;3;3",
"confidence_avg": 3.2,
"soundness": "2;3;3;4;4",
"soundness_avg": 3.2,
"contribution": "2;3;3;3;3",
"contribution_avg": 2.8,
"presentation": "1;3;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": "2026-01-14T22:16:04.288938"
} | {
"id": "QJB25lfeno",
"metareview": "This paper explores adversarial context bandits, specifically focusing on a scenario where the losses of each arm are observable under all contexts when the algorithm selects that arm. The objective is to minimize regret by comparing the algorithm's performance to the best arm i... | {
"decision": "Reject"
} |
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