GenAI4ELab/papercli-papers huggingface dataset:
Multi-Synaptic Cooperation: A Bio-Inspired Framework for Robust and Scalable  Continual Learning
Authors: Penghui Li; Zhuang Ma; Yunliang Zang; Qiang Yu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Continual learning aims to acquire new knowledge incrementally while retaining prior information, with catastrophic forgetting (CF) being a central challenge. Existing methods can mitigate CF to some extent but are constrained by limited capacity, which often requires dynamic expansion for long task sequences and makes performance sensitive to task order. Inspired by the richness and plasticity of synaptic connections in biological nervous systems, we propose the Multi-Synaptic Cooperative Network (MSCN), a generalized framework that models cooperative interactions among multiple synapses through multi-synaptic connections modulated by local synaptic activity. This design enhances model representational capacity and enables task-adaptive plasticity by means of multi-synaptic cooperation, providing a new avenue for expanding model capacity while improving robustness to task order. During learning, our MSCN dynamically activates task-relevant synapses while suppressing irrelevant ones, enabling targeted retrieval and minimizing interference. Extensive experiments across four benchmark datasets, involving both spiking and non-spiking neural networks, demonstrate that our method consistently outperforms state-of-the-art continual learning methods with significantly improved robustness to task-order variation. Furthermore, our analysis reveals an optimal trade-off between synaptic richness and learning efficiency, where excessive connectivity can impair circuit performance. These findings highlight the importance of the multi-synaptic cooperation mechanism for achieving efficient continual learning and provide new insights into biologically inspired, robust, and scalable continual learning.
MnemoDyn: Learning Resting State Dynamics from  $40$K FMRI sequences
Authors: Sourav Pal; Viet Luong; Hoseok Lee; Tingting Dan; Guorong Wu; Richard Davidson; Won Hwa Kim; Vikas Singh
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We present a dynamical-systems based model for resting-state functional magnetic resonance imaging (rs-fMRI), trained on a dataset of roughly $40$K rs-fMRI sequences covering a wide variety of public and  available-by-permission datasets. While most existing proposals use transformer backbones, we utilize multi-resolution temporal modeling of the dynamics across parcellated brain regions. We show that MnemoDyn is compute efficient and generalizes very well across diverse populations and scanning protocols. When benchmarked against current state-of-the-art transformer-based approaches, MnemoDyn consistently delivers superior reconstruction quality.
Overall, we find that with such large-scale pre-training on (non-proprietary) rs-fMRI datasets, we get a highly performant model for various downstream tasks. Our results also provide evidence of the efficacy of the model on small sample size studies which has implications for neuroimaging studies at large where resting state fMRI is a commonly acquired imaging modality.
Salient Object Ranking via Cyclical Perception-Viewing Interaction Modeling
Authors: Rongjin Guo; Ke Xu; Rynson W. H. Lau
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Salient Object Ranking (SOR) aims to predict human attention shifts across different salient objects in a scene. Although a number of methods have been proposed for the task, they typically rely on modeling the bottom-up influences of image features on attention shifts. In this work, we observe that when free-viewing an image, humans instinctively browse the objects in such a way as to maximize contextual understanding of the image. This implies a cyclical interaction between content (or story) understanding of the image and attention shift over it. Based on this observation, we propose a novel SOR approach that models this explicit top-down cognitive pathway with two novel modules: a story prediction (SP) module and a guided ranking (GR) module. By formulating content understanding as the image caption generation task, the SP module learns to generate and complete the image captions conditioned on the salient object queries of the GR module, while the GR module learns to detect salient objects and their viewing orders guided by the SP module. Extensive experiments on SOR benchmarks demonstrate that our approach outperforms state-of-the-art SOR methods.
OFMU: OPTIMIZATION-DRIVEN FRAMEWORK FOR MACHINE UNLEARNING
Authors: Sadia Asif; Mohammad Mohammadi Amiri
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large language models deployed in sensitive applications increasingly require the
ability to unlearn specific knowledge, such as user requests, copyrighted materi-
als, or outdated information, without retraining from scratch to ensure regulatory
compliance, user privacy, and safety. This task, known as machine unlearning,
aims to remove the influence of targeted data (forgetting) while maintaining per-
formance on the remaining data (retention). A common approach is to formu-
late this as a multi-objective problem and reduce it to a single-objective prob-
lem via scalarization, where forgetting and retention losses are combined using
a weighted sum. However, this often results in unstable training dynamics and
degraded model utility due to conflicting gradient directions. To address these
challenges, we propose OFMU, a penalty-based bi-level optimization framework
that explicitly prioritizes forgetting while preserving retention through a hierar-
chical structure. Our method enforces forgetting via an inner maximization step
that incorporates a similarity-aware penalty to decorrelate the gradients of the for-
get and retention objectives, and restores utility through an outer minimization
step. To ensure scalability, we develop a two-loop algorithm with provable conver-
gence guarantees under both convex and non-convex regimes. We further provide
a rigorous theoretical analysis of convergence rates and show that our approach
achieves better trade-offs between forgetting efficacy and model utility compared
to prior methods. Extensive experiments across vision and language benchmarks
demonstrate that OFMU consistently outperforms existing unlearning methods in
both forgetting efficacy and retained utility.
MVR: Multi-view Video Reward Shaping for Reinforcement Learning
Authors: Lirui Luo; Guoxi Zhang; Hongming Xu; Yaodong Yang; Cong Fang; Qing Li
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Reward design is of great importance for solving complex tasks with reinforcement learning. Recent studies have explored using image-text similarity produced by vision-language models (VLMs) to augment rewards of a task with visual feedback. A common practice linearly adds VLM scores to task or success rewards without explicit shaping, potentially altering the optimal policy. Moreover, such approaches, often relying on single static images, struggle with tasks whose desired behavior involves complex, dynamic motions spanning multiple visually different states. Furthermore, single viewpoints can occlude critical aspects of an agent's behavior. To address these issues, this paper presents Multi-View Video Reward Shaping (MVR), a framework that models the relevance of states regarding the target task using videos captured from multiple viewpoints. MVR leverages video-text similarity from a frozen pre-trained VLM to learn a state relevance function that mitigates the bias towards specific static poses inherent in image-based methods. Additionally, we introduce a state-dependent reward shaping formulation that integrates task-specific rewards and VLM-based guidance, automatically reducing the influence of VLM guidance once the desired motion pattern is achieved. We confirm the efficacy of the proposed framework with extensive experiments on challenging humanoid locomotion tasks from HumanoidBench and manipulation tasks from MetaWorld, verifying the design choices through ablation studies.
Back to Square Roots: An Optimal Bound on the Matrix Factorization Error for Multi-Epoch Differentially Private SGD
Authors: Nikita Kalinin; Ryan McKenna; Jalaj Upadhyay; Christoph H. Lampert
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Matrix factorization mechanisms for differentially private training have emerged as a promising approach to improve model utility under privacy constraints. In practical settings, models are typically trained over multiple epochs, requiring matrix factorizations that account for repeated participation. Existing theoretical upper and lower bounds on multi-epoch factorization error leave a significant gap. In this work, we introduce a new explicit factorization method, Banded Inverse Square Root (BISR), which imposes a banded structure on the inverse correlation matrix. This factorization enables us to derive an explicit and tight characterization of the multi-epoch error. We further prove that BISR achieves asymptotically optimal error by matching the upper and lower bounds. Empirically, BISR performs on par with the state of the art factorization methods, while being simpler to implement, computationally efficient, and easier to analyze.
Bridging Piano Transcription and Rendering via Disentangled Score Content and Style
Authors: Wei Zeng; JUNCHUAN ZHAO; Ye Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Expressive performance rendering (EPR) and automatic piano transcription (APT) are fundamental yet inverse tasks in music information retrieval: EPR generates expressive performances from symbolic scores, while APT recovers scores from performances. Despite their dual nature, prior work has addressed them independently. In this paper, we propose a unified framework that jointly models EPR and APT by disentangling note-level score content and global performance style representations from both paired and unpaired data. Our framework is built on a transformer-based sequence-to-sequence (Seq2Seq) architecture and is trained using only sequence-aligned data, without requiring fine-grained note-level alignment. To automate the rendering process while ensuring stylistic compatibility with the score, we introduce an independent diffusion-based performance style recommendation (PSR) module that generates style embeddings directly from score content. This modular component supports both style transfer and flexible rendering across a range of expressive styles. Experimental results from both objective and subjective evaluations demonstrate that our framework achieves competitive performance on EPR and APT tasks, while enabling effective content–style disentanglement, reliable style transfer, and stylistically appropriate rendering. Demos are available at https://wei-zeng98.github.io/joint-apt-epr/.
Using Reinforcement Learning to Train Large Language Models to Explain Human Decisions
Authors: Jian-Qiao Zhu; Hanbo Xie; Dilip Arumugam; Robert Wilson; Thomas L. Griffiths
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: A central goal of cognitive modeling is to develop models that not only predict human behavior but also provide insight into the underlying cognitive mechanisms. While neural network models trained on large-scale behavioral data often achieve strong predictive performance, they typically fall short in offering interpretable explanations of the cognitive processes they capture. In this work, we explore the potential of pretrained large language models (LLMs) to serve as dual-purpose cognitive models--capable of both accurate prediction and interpretable explanation in natural language. Specifically, we employ reinforcement learning with outcome-based rewards to guide LLMs toward generating explicit reasoning traces for explaining human risky choices. Our findings demonstrate that this approach produces high-quality explanations alongside strong quantitative predictions of human decisions.
Thompson Sampling via Fine-Tuning of LLMs
Authors: Nicolas Menet; Aleksandar Terzic; Michael Hersche; Andreas Krause; Abbas Rahimi
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Bayesian optimization in large unstructured discrete spaces is often hindered by the computational cost of maximizing acquisition functions due to the absence of gradients. We propose a scalable alternative based on Thompson sampling that eliminates the need for acquisition function maximization by directly parameterizing the probability that a candidate yields the maximum reward. Our approach, *Thompson Sampling via Fine-Tuning* (ToSFiT) leverages the prior knowledge embedded in prompt-conditioned large language models, and incrementally adapts them toward the posterior. Theoretically, we derive a novel regret bound for a variational formulation of Thompson Sampling that matches the strong guarantees of its standard counterpart. Our analysis reveals the critical role of careful adaptation to the posterior probability of maximality—a principle that underpins our ToSFiT algorithm. Empirically, we validate our method on three diverse tasks: FAQ response refinement, thermally stable protein search, and quantum circuit design. Within a collection of methods covering in-context Bayesian optimization, reinforcement learning, and evolutionary search, ToSFiT exhibits both state-of-the-art sample efficiency and computational efficiency.
To Sink or Not to Sink: Visual Information Pathways in Large Vision-Language Models
Authors: Jiayun Luo; Wan-Cyuan Fan; Lyuyang Wang; Xiangteng He; Tanzila Rahman; Purang Abolmaesumi; Leonid Sigal
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large Vision Language Models (LVLMs) have recently emerged as powerful architectures capable of understanding and reasoning over both visual and textual information. These models typically rely on two key components: a Vision Transformer (ViT) and a Large Language Model (LLM). ViT encodes visual content into a sequence of image tokens and serves as the perceptual front-end -- the eyes of the model. In contrast, the LLM interprets these tokens to perform high-level reasoning, generates responses, and functions as the cognitive core -- the brain of the model. However, it remains unclear which visual tokens contribute most significantly to understanding and reasoning, and how effectively these signals are propagated from ViT to the LLM. While most existing works have focused on identifying attention sinks, low-semantic tokens receiving disproportionately high attention, within the LLM, we shift the focus to the vision encoder by identifying a class of high-norm visual tokens from ViT, referred to as ViT attention sinks -- a problem that has been rarely studied but is indeed very important for LVLMs. Our findings show that these ViT sinks encapsulate high-level semantic concepts from images, allowing the LLM to perform more effective understanding and reasoning. Despite their importance, these sink tokens are often overlooked in existing LVLM architectures. To explore their contribution, we present both qualitative and quantitative analyses of the information embedded in these sink tokens. We also propose both training-free and training-based approaches to better leverage how this information is interpreted by the LLM, and to what extent. By explicitly utilizing these tokens, we demonstrate substantial improvements across a range of LVLMs and visual reasoning tasks, including but not limited to mathematical problem solving, logical inference, and geometric understanding, highlighting the untapped potential of ViT attention sinks in enhancing visual reasoning.
SenseFlow: Scaling Distribution Matching for Flow-based Text-to-Image Distillation
Authors: Xingtong Ge; Xin Zhang; Tongda Xu; Yi Zhang; Xinjie Zhang; Yan Wang; Jun Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The Distribution Matching Distillation (DMD) has been successfully applied to text-to-image diffusion models such as Stable Diffusion (SD) 1.5. However, vanilla DMD suffers from convergence difficulties on large-scale flow-based text-to-image models, such as SD 3.5 and FLUX. In this paper, we first analyze the issues when applying vanilla DMD on large-scale models. Then, to overcome the scalability challenge, we propose implicit distribution alignment (IDA) to constrain the divergence between the generator and the fake distribution. Furthermore, we propose intra-segment guidance (ISG) to relocate the timestep denoising importance from the teacher model. With IDA alone, DMD converges for SD 3.5; employing both IDA and ISG, DMD converges for SD 3.5 and FLUX.1 dev. Together with a scaled VFM-based discriminator, our final model, dubbed **SenseFlow**, achieves superior performance in distillation for both diffusion based text-to-image models such as SDXL, and flow-matching models such as SD 3.5 Large and FLUX.1 dev. The source code is available at https://github.com/XingtongGe/SenseFlow.
CaReBench: A Fine-grained Benchmark for Video Captioning and Retrieval
Authors: Yifan Xu; Xinhao Li; Yichun Yang; Desen Meng; Rui Huang; Limin Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Video understanding, including video captioning and retrieval, is still a great challenge for video-language models (VLMs). The existing video retrieval and caption benchmarks only include short descriptions, limits their ability of detailed video understanding evaluation. To address this problem, we present CaReBench, a testing benchmark for fine-grained video Captioning and Retrieval with 1,000 high-quality pairs of videos and human-annotated detailed captions. Uniquely, it provides manually separated spatial annotations and temporal annotations for each video. Based on this design, we introduce two evaluation metrics, ReBias and CapST, specifically tailored for video retrieval and video captioning tasks, respectively. These metrics enable a comprehensive investigation into the spatial and temporal biases inherent in VLMs. In addition, to handle both video retrieval and video captioning tasks in a unified framework, we develop a simple baseline based on a Multimodal Language Model (MLLM). By implementing a two-stage Supervised Fine-Tuning (SFT), we fully unlock the potential of MLLM, enabling it not only to generate detailed video descriptions but also to extract video features. Surprisingly, experimental results demonstrate that, compared to the CLIP-based models designed for retrieval and the popular MLLMs skilled in video captioning, our baseline shows competitive performance in both fine-grained video retrieval and video detailed captioning.
Interference-Isolated Elastic Weight Consolidation and Knowledge Calibration for Incremental Object Detection
Authors: De Cheng; Mingyue Zeng; Zhipeng Xu; Di Xu; Nannan Wang; Xinbo Gao
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Incremental Object Detection (IOD) enables AI systems to continuously learn new object classes over time while retaining knowledge of previously learned categories. This capability is essential for adapting to dynamic environments without forgetting prior information. Although existing IOD methods have made progress in mitigating catastrophic forgetting, they usually lack explicit and quantitative modeling of information conflicts during knowledge preservation, making task boundaries ambiguous. Such conflicts often stem from the fact that a single image can contain objects belonging to previous, present, and future tasks, where unlabeled past and future objects are often mistakenly treated as background. In this paper, we propose a novel approach grounded in Elastic Weight Consolidation (EWC) to alleviate conflict knowledge preservation caused by task interference. Specifically, we introduce the Interference Knowledge Isolated Elastic Weight Consolidation (IKI-EWC) framework for IOD, which leverages the mispredictions of the old detector on new task data to estimate task conflicts and suppresses them at the parameter level. By reformulating the Bayesian posterior of model parameters, we derive a mathematical relationship between previously learned knowledge and interference knowledge, enabling targeted elimination of conflicts during model weight updates. In addition, we also propose a prototype-based knowledge calibration (PKC) mechanism to further preserve old knowledge during the training of the objector's classification head. This method employs a learnable projection layer to compensate semantic drift in old class prototypes, and then jointly trains the classification head using both calibrated prototypes and current task features, thereby mitigating forgetting caused by classifier updates. Extensive experiments on PASCAL VOC and MS-COCO benchmarks demonstrate the effectiveness of the proposed method, outperforming state-of-the-art approaches in various settings.
Dual Perspectives on Non-Contrastive Self-Supervised Learning
Authors: Jean Ponce; Basile Terver; Martial Hebert; Michael Arbel
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The {\em stop gradient} and {\em exponential moving average} iterative procedures are commonly used in non-contrastive approaches to self-supervised learning to avoid representation collapse, with excellent performance in downstream applications in practice. This presentation investigates these procedures from the dual viewpoints of optimization and dynamical systems. We show that, in general, although they {\em do not} optimize the original objective, or {\em any} other smooth function, they {\em do} avoid collapse. Following [Tian et al. 2021], but without any of the extra assumptions used in their proofs, we then show using a dynamical system perspective that, in the linear case, minimizing the original objective function without the use of a stop gradient or exponential moving average {\em always} leads to collapse. Conversely, we characterize explicitly the equilibria of the dynamical systems associated with these two procedures in this linear setting as algebraic varieties in their parameter space, and show that they are, in general, {\em asymptotically stable}. Our theoretical findings are illustrated by empirical experiments with real and synthetic data.
Oversmoothing, "Oversquashing'', Heterophily, Long-Range, and more: Demystifying Common Beliefs in Graph Machine Learning
Authors: Adrian Arnaiz-Rodriguez; Federico Errica
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: After a renaissance phase in which researchers revisited the message-passing paradigm through the lens of deep learning, the graph machine learning community shifted its attention towards a deeper and practical understanding of message-passing's benefits and limitations. In this paper, we notice how the fast pace of progress around the topics of oversmoothing and oversquashing, the homophily-heterophily dichotomy, and long-range tasks, came with the consolidation of commonly accepted beliefs and assumptions -- under the form of universal statements -- that are not always true nor easy to distinguish from each other. We argue that this has led to ambiguities around the investigated problems, preventing researchers from focusing on and addressing precise research questions while causing a good amount of misunderstandings. Our contribution is to make such common beliefs explicit and encourage critical thinking around these topics, refuting universal statements via simple yet formally sufficient counterexamples. The end goal is to clarify conceptual differences, helping researchers address more clearly defined and targeted problems. The hope is to clarify the distinction between the different issues and promote separate but intertwined research directions to address them.