GenAI4ELab/papercli-papers huggingface dataset:
ASSESS: A Semantic and Structural Evaluation Framework for Statement Similarity
Authors: Xiaoyang Liu; Tao Zhu; Zineng Dong; Yuntian Liu; Guo qingfeng; Liu ZhaoXuan; Yu Chen; Tao Luo
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Despite significant strides in statement autoformalization, a critical gap remains in the development of automated evaluation metrics capable of assessing formal translation quality. Existing metrics often fail to balance semantic and structural information: string-based methods neglect semantics, whereas proof-based approaches offer no graded similarity when proofs fail. To address these issues, we introduce ASSESS (A Semantic and Structural Evaluation Framework for Statement Similarity), which captures syntactic structure by transforming formal statements into operator trees and computes a real-valued similarity score using our novel TransTED (Transformation Tree Edit Distance) Similarity metric by incorporating semantic transformations. For rigorous validation, we present EPLA (Evaluating Provability and Likeness for Autoformalization), a benchmark comprising 1,247 expert-annotated formal statement pairs derived from miniF2F and ProofNet, distinctively labeled for both semantic provability and structural likeness. Experiments on the EPLA benchmark demonstrate that TransTED Similarity surpasses existing methods, achieving state-of-the-art accuracy and Kappa score. The benchmark dataset, code, and detailed experimental results are available at https://github.com/XiaoyangLiu-sjtu/ASSESS.
On the Wasserstein Geodesic Principal Component Analysis of probability measures
Authors: Nina Vesseron; Elsa Cazelles; Alice Le Brigant; Klein
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: This paper focuses on Geodesic Principal Component Analysis (GPCA) on a collection of probability distributions using the Otto-Wasserstein geometry. The goal is to identify geodesic curves in the space of probability measures that best capture the modes of variation of the underlying dataset. We first address the case of a collection of Gaussian distributions, and show how to lift the computations in the space of invertible linear maps. For the more general setting of absolutely continuous probability measures, we leverage a novel approach to parameterizing geodesics in Wasserstein space with neural networks. Finally, we compare to classical tangent PCA through various examples and provide illustrations on real-world datasets.
Efficient algorithms for Incremental Metric Bipartite Matching
Authors: Ritesh Seth; Mrinal Garg; Sujoy Bhore; Sharath Raghvendra; Syamantak Das
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The minimum-cost bipartite matching between two sets of points $R$ and $S$ in a metric space has a wide range of applications in machine learning, computer vision, and logistics. For instance, it can be used to estimate the $1$-Wasserstein distance between continuous probability distributions and for efficiently matching requests to servers while minimizing cost.  However, the computational cost of determining the minimum-cost matching for general metrics spaces, poses a significant challenge, particularly in dynamic settings where points arrive over time and each update requires re-executing the algorithm. In this paper, given a fixed set $S$, we describe a deterministic algorithm that maintains, after $i$ additions to $R$, an $O(1/\delta^{0.631})$-approximate minimum-cost matching of cardinality $i$ between sets $R$ and $S$ in any metric space, with an amortized insertion time of $\widetilde{O}(n^{1+\delta})$ for adding points in $R$. To the best of our knowledge, this is the first algorithm for incremental minimum-cost matching that applies to arbitrary metric spaces.

Interestingly, an important subroutine of our algorithm lends itself to efficient parallelization. We provide both a CPU implementation and a GPU implementation that leverages parallelism. Extensive experiments on both synthetic and real world datasets showcase that our algorithm either matches or outperforms all benchmarks in terms of speed while significantly improving upon the accuracy.
SketchEvo: Leveraging Drawing Dynamics for Enhanced Image Synthesis
Authors: Zhixin Feng; Runan Yin; LAN YANG; Kaiyue Pang; Ke Li; Honggang Zhang; Yi-Zhe Song
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Sketching represents humanity's most intuitive form of visual expression -- a universal language that transcends barriers. Although recent diffusion models integrate sketches with text, they often regard the complete sketch merely as a static visual constraint, neglecting the human preference information inherently conveyed during the dynamic sketching process.This oversight leads to images that, despite technical adherence to sketches, fail to align with human aesthetic expectations. Our framework, SketchEvo, harnesses the dynamic evolution of sketches by capturing the progression from initial strokes to completed drawing. Current preference alignment techniques struggle with sketch-guided generation because the dual constraints of text and sketch create insufficiently different latent samples when using noise perturbations alone. SketchEvo addresses this through two complementary innovations: first, by leveraging sketches at different completion stages to create meaningfully divergent samples for effective aesthetic learning during training; second, through a sequence-guided rollback mechanism that applies these learned preferences during inference by balancing textual semantics with structural guidance. Extensive experiments demonstrate that these complementary approaches enable SketchEvo to deliver improved aesthetic quality while maintaining sketch fidelity, successfully generalizing to incomplete and abstract sketches throughout the drawing process.
Distilling Causal Signals for One-Shot Directed Evolution of Antibodies
Authors: Sai Pooja Mahajan; Natasa Tagasovska; Stefania Vasilaki; Arian Rokkum Jamasb; Andrew Martin Watkins; Rajesh Ranganath
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Improving antibody binding to an antigen without antibody–antigen complex structures or antigen-specific training data is a central challenge in therapeutic protein design. We introduce **AffinityEnhancer**, a framework for one-shot antibody affinity improvement with strong generalization: given a single lead sequence, we propose variants that increase affinity without fine-tuning on the lead and without using antigen information, epitope/paratope labels, or the lead’s structure in complex with the antigen. During training, AffinityEnhancer leverages a pan-antigen dataset of diverse binding environments (antigens) and constructs paired examples of related sequences with higher vs. lower measured binding. A shared, structure-aware module learns to transform low-affinity sequences toward high-affinity ones, distilling consistent, causal features associated with improved binding across environments. By combining pretrained sequence–structure embeddings with a sequence decoder, AffinityEnhancer generalizes to entirely unseen antibody seeds. Across multiple held-out internal and public leads, AffinityEnhancer concentrates mutations on the rim of the paratope, outperforms existing structure-conditioned and inpainting baselines, and achieves substantial in silico affinity gains in true one-shot experiments, despite never observing antigen-specific data at test time.[https://github.com/prescient-design/AffinityEnhancer]
Robust Fine-Tuning from Non-Robust Pretrained Models: Mitigating Suboptimal Transfer With Epsilon-Scheduling
Authors: Jonas Ngnawe; Maxime Heuillet; Sabyasachi Sahoo; Yann Pequignot; Ola Ahmad; Audrey Durand; Frederic Precioso; Christian Gagné
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Fine-tuning pretrained models is a standard and effective workflow in modern machine learning. However, robust fine-tuning (RFT), which aims to simultaneously achieve adaptation to a downstream task and robustness to adversarial examples, remains challenging. Despite the abundance of non-robust pretrained models in open-source repositories, their potential for RFT is less understood. We address this knowledge gap by systematically examining RFT from such non-robust models. Our experiments reveal that fine-tuning non-robust models with a robust objective, even under small perturbations, can lead to poor performance, a phenomenon that we dub _suboptimal transfer_. In challenging scenarios (eg, difficult tasks, high perturbation), the resulting performance can be so low that it may be considered a transfer failure. We find that fine-tuning using a robust objective impedes task adaptation at the beginning of training and eventually prevents optimal transfer. However, we propose a novel heuristic, _Epsilon-Scheduling_, a schedule over perturbation strength used during training that promotes optimal transfer. Additionally, we introduce _expected robustness_, a metric that captures performance across a range of perturbations, providing a more comprehensive evaluation of the accuracy-robustness trade-off of diverse models at test-time. Extensive experiments on wide range of configurations (six pretrained models and five datasets) show that _Epsilon-Scheduling_ successfully prevents _suboptimal transfer_ and consistently improves expected robustness.
Token-Efficient Item Representation via Images for LLM Recommender Systems
Authors: Kibum Kim; Sein Kim; HongSeok Kang; Jiwan Kim; Heewoong Noh; Yeonjun In; Kanghoon Yoon; Jinoh Oh; Julian McAuley; Chanyoung Park
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large Language Models (LLMs) have recently emerged as a powerful backbone for recommender systems. Existing LLM-based recommender systems take two different approaches for representing items in natural language, i.e., Attribute-based Representation and Description-based Representation. In this work, we aim to address the trade-off between efficiency and effectiveness that these two approaches encounter, when representing items consumed by users. Based on our observation that there is a significant information overlap between images and descriptions associated with items, we propose a novel method, **I**tem representation for **LLM**-based **Rec**ommender system (I-LLMRec). Our main idea is to leverage images as an alternative to lengthy textual descriptions for representing items, aiming at reducing token usage while preserving the rich semantic information of item descriptions.
Through extensive experiments on real-world Amazon datasets, we demonstrate that I-LLMRec outperforms existing methods that leverage textual descriptions for representing items in both efficiency and effectiveness by leveraging images. Moreover, a further appeal of I-LLMRec is its ability to reduce sensitivity to noise in descriptions, leading to more robust recommendations.
RedSage: A Cybersecurity Generalist LLM
Authors: Naufal Suryanto; Muzammal Naseer; Pengfei Li; Syed Talal Wasim; Jinhui Yi; Juergen Gall; Paolo Ceravolo; Ernesto Damiani
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Cybersecurity operations demand assistant LLMs that support diverse workflows without exposing sensitive data. Existing solutions either rely on proprietary APIs with privacy risks or on open models lacking domain adaptation. To bridge this gap, we curate 11.8B tokens of cybersecurity-focused continual pretraining data via large-scale web filtering and manual collection of high-quality resources, spanning 28.6K documents across frameworks, offensive techniques, and security tools.
Building on this, we design an agentic augmentation pipeline that simulates expert workflows to generate 266K multi-turn cybersecurity samples for supervised fine-tuning. Combined with general open-source LLM data, these resources enable the training of RedSage, an open-source, locally deployable cybersecurity assistant with domain-aware pretraining and post-training.
To rigorously evaluate the models, we introduce RedSage-Bench, a benchmark with 30K multiple-choice and 240 open-ended Q\&A items covering cybersecurity knowledge, skills, and tool expertise. RedSage is further evaluated on established cybersecurity benchmarks (e.g., CTI-Bench, CyberMetric, SECURE) and general LLM benchmarks to assess broader generalization. At the 8B scale, RedSage achieves consistently better results, surpassing the baseline models by up to +5.59 points on cybersecurity benchmarks and +5.05 points on Open LLM Leaderboard tasks. These findings demonstrate that domain-aware agentic augmentation and pre/post-training can not only enhance cybersecurity-specific expertise but also help to improve general reasoning and instruction-following. Project page: https://risys-lab.github.io/RedSage/
ForestPersons: A Large-Scale Dataset for Under-Canopy Missing Person Detection
Authors: Deokyun Kim; Jeongjun Lee; Jungwon Choi; Jonggeon Park; Giyoung Lee; Yookyung Kim; Myungseok Ki; Juho Lee; Jihun Cha
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Detecting missing persons in forest environments remains a challenge, as dense canopy cover often conceals individuals from detection in top-down or oblique aerial imagery typically captured by Unmanned Aerial Vehicles (UAVs). While UAVs are effective for covering large, inaccessible areas, their aerial perspectives often miss critical visual cues beneath the forest canopy. This limitation underscores the need for under-canopy perspectives better suited for detecting missing persons in such environments. To address this gap, we introduce ForestPersons, a novel large-scale dataset specifically designed for under-canopy person detection. ForestPersons contains 96,482 images and 204,078 annotations collected under diverse environmental and temporal conditions. Each annotation includes a bounding box, pose, and visibility label for occlusion-aware analysis. ForestPersons provides ground-level and low-altitude perspectives that closely reflect the visual conditions encountered by Micro Aerial Vehicles (MAVs) during forest Search and Rescue (SAR) missions. Our baseline evaluations reveal that standard object detection models, trained on prior large-scale object detection datasets or SAR-oriented datasets, show limited performance on ForestPersons. This indicates that prior benchmarks are not well aligned with the challenges of missing person detection under the forest canopy. We offer this benchmark to support advanced person detection capabilities in real-world SAR scenarios. The dataset is publicly available at https://huggingface.co/datasets/etri/ForestPersons.
HSIC Bottleneck for Cross-Generator and Domain-Incremental Synthetic Image Detection
Authors: Chin-Chia Yang; Yung-Yu Chuang; Hwann-Tzong Chen; Tyng-Luh Liu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Synthetic image generators evolve rapidly, challenging detectors to generalize across current methods and adapt to new ones. We study domain-incremental synthetic image detection with a two-phase evaluation. Phase I trains on either diffusion- or GAN-based data and tests on the combined group to quantify bidirectional cross-generator transfer. Phase II sequentially introduces renders from 3D Gaussian Splatting (3DGS) head avatar pipelines, requiring adaptation while preserving earlier performance. We observe that CLIP-based detectors inherit text-image alignment semantics that are irrelevant to authenticity and hinder generalization. We introduce a Hilbert-Schmidt Independence Criterion (HSIC) bottleneck loss on intermediate CLIP ViT features, encouraging representations predictive of real versus synthetic while independent of generator identity and caption alignment. For domain-incremental learning, we propose HSIC-Guided Replay (HGR), which selects per-class exemplars via a hybrid score combining HSIC relevance with k-center coverage, yielding compact memories that mitigate forgetting. Empirically, the HSIC bottleneck improves transfer between diffusion and GAN families, and HGR sustains prior accuracy while adapting to 3DGS renders. These results underscore the value of information-theoretic feature shaping and principled replay for resilient detection under shifting generative regimes.
Co-occurring Associated REtained concepts in Diffusion Unlearning
Authors: Miso Kim; Georu Lee; Yunji Kim; Hoki Kim; Jinseong Park; Woojin Lee
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Unlearning has emerged as a key technique to mitigate harmful content generation in diffusion models. However, existing methods often remove not only the target concept, but also benign co-occurring concepts. Unlearning nudity can unintentionally suppress the concept of person, preventing a model from generating images with person. We define these undesirably suppressed co-occurring concepts that must be preserved $\textbf{CARE}$ ($\textbf{C}$o-occurring $\textbf{A}$ssociated $\textbf{RE}$tained concepts). Then, we introduce the  $\textbf{CARE score}$, a general metric that directly quantifies their preservation across unlearning tasks. With this foundation, we propose $\textbf{ReCARE}$ ($\textbf{R}$obust $\textbf{e}$rasure for $\textbf{CARE}$), a framework that explicitly safeguards CARE while erasing only the target concept. ReCARE automatically constructs the CARE-set, a curated vocabulary of benign co-occurring tokens extracted from target images, and leverages this vocabulary during training for stable unlearning. Extensive experiments across various target concepts ($\textit{Nudity}$, $\textit{Van Gogh}$ style, and $\textit{Tench}$ object) demonstrate that ReCARE achieves overall state-of-the-art performance in balancing robust concept erasure, overall utility, and CARE preservation.
Predictive CVaR Q-learning
Authors: Ju-Hyun Kim; Seungki Min
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We propose a sample-efficient Q-learning algorithm for reinforcement learning with the Conditional Value-at-Risk (CVaR) objective. Our algorithm is built upon predictive tail value function, a novel formulation of risk-sensitive action value, that admits a recursive structure as in the conventional risk-neutral Bellman equation. This structure enables the Q-learning algorithm to utilize the entire set of sample trajectories rather than relying only on worst-case outcomes, enhancing the sample efficiency. We further derive a Bellman optimality equation and a policy improvement theorem, which provide theoretical foundations of our algorithm and remedy inconsistencies that have existed in the literature. Empirical results demonstrate that our method consistently improves CVaR performance while maintaining stable and interpretable learning dynamics.
DiffSDA: Unsupervised Diffusion Sequential Disentanglement Across Modalities
Authors: Hedi Zisling; Ilan Naiman; Nimrod Berman; Supasorn Suwajanakorn; Omri Azencot
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Unsupervised representation learning, particularly sequential disentanglement, aims to separate static and dynamic factors of variation in data without relying on labels. This remains a challenging problem, as existing approaches based on variational autoencoders and generative adversarial networks often rely on multiple loss terms, complicating the optimization process. Furthermore, sequential disentanglement methods face challenges when applied to real-world data, and there is currently no established evaluation protocol for assessing their performance in such settings. Recently, diffusion models have emerged as state-of-the-art generative models, but no theoretical formalization exists for their application to sequential disentanglement. In this work, we introduce the Diffusion Sequential Disentanglement Autoencoder (DiffSDA), a novel, modal-agnostic framework effective across diverse real-world data modalities, including time series, video, and audio. DiffSDA leverages a new probabilistic modeling, latent diffusion, and efficient samplers, while incorporating a challenging evaluation protocol for rigorous testing. Our experiments on diverse real-world benchmarks demonstrate that DiffSDA outperforms recent state-of-the-art methods in sequential disentanglement.
Multi-Subspace Multi-Modal Modeling for Diffusion Models: Estimation, Convergence and Mixture of Experts
Authors: Ruofeng Yang; Yongcan Li; Bo Jiang; Cheng Chen; Shuai Li
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent diffusion models demonstrate remarkable sample efficiency and fast optimization, contradicting standard estimation bounds that suffer from the curse of dimensionality $n^{-1/D}$ with the data dimension $D$. Since images are usually a union of low-dimensional manifolds, current works model the data as a union of linear subspaces with Gaussian latent and achieve a $1/\sqrt{n}$ bound. Though this modeling reflects the multi-manifold property, the Gaussian latent can not capture the multi-modal property of the latent manifold. To bridge this gap, we propose the mixture subspace of low-rank mixture of Gaussian (MoLR-MoG) modeling, which models the target data as a union of $K$ linear subspaces, and each subspace admits a mixture of Gaussian latent ($n_k$ modals with dimension $d_k$). With this modeling, the corresponding score function naturally has a mixture of expert (MoE) structure, captures the multi-modal information, and contains nonlinear property. Empirically, our MoE-latent MoG network significantly outperforms MoLRG Gaussian baselines and matches MoE-latent U-Net performance with $10\times$ fewer parameters, validating its practical suitability. Theoretically, we provide provable convergence guarantees for the optimization process and establish an estimation error bound of $R^4\sqrt{\sum_{k=1}^K n_k}\sqrt{\sum_{k=1}^K n_k d_k}/\sqrt{n}$, successfully escaping the dimensionality curse.  Collectively, with MoLR-MoG modeling, this work explains why diffusion models only require a small training sample and enjoy a fast optimization process. Furthermore, we also show the potential of MoE structure for diffusion models from the manifold perspective.
Discount Model Search for Quality Diversity Optimization in High-Dimensional Measure Spaces
Authors: Bryon Tjanaka; Henry Chen; Matthew Christopher Fontaine; Stefanos Nikolaidis
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: Quality diversity (QD) optimization searches for a collection of solutions that optimize an objective while attaining diverse outputs of a user-specified, vector-valued measure function. Contemporary QD algorithms are typically limited to low-dimensional measures because high-dimensional measures are prone to distortion, where many solutions found by the QD algorithm map to similar measures. For example, the state-of-the-art CMA-MAE algorithm guides measure space exploration with a histogram in measure space that records so-called discount values. However, CMA-MAE stagnates in domains with high-dimensional measure spaces because solutions with similar measures fall into the same histogram cell and hence receive the same discount value. To address these limitations, we propose Discount Model Search (DMS), which guides exploration with a model that provides a smooth, continuous representation of discount values. In high-dimensional measure spaces, this model enables DMS to distinguish between solutions with similar measures and thus continue exploration. We show that DMS facilitates new capabilities for QD algorithms by introducing two new domains where the measure space is the high-dimensional space of images, which enables users to specify their desired measures by providing a dataset of images rather than hand-designing the measure function. Results in these domains and on high-dimensional benchmarks show that DMS outperforms CMA-MAE and other existing black-box QD algorithms.