GenAI4ELab/papercli-papers huggingface dataset:
RACE Attention: A Strictly Linear-Time Attention Layer for Training on Outrageously Large Contexts
Authors: Sahil Joshi; Agniva Chowdhury; Amar Kanakamedala; Ekam Singh; Evan Tu; Anshumali Shrivastava
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Softmax Attention has a quadratic time complexity in sequence length, which becomes prohibitive to run at long contexts, even with highly optimized GPU kernels. For example, FlashAttention-2/3 (exact, GPU-optimized implementations of Softmax Attention) cannot complete a single forward–backward pass of a single attention layer once the context exceeds $\sim 4$ million tokens on an NVIDIA GH200 (96 GB). We introduce **R**epeated **A**rrays-of-**C**ount **E**stimators (RACE) Attention, a kernel-inspired alternative to Softmax Attention that is strictly linear in sequence length and embedding size. RACE Attention replaces the exponential kernel with a sharpened angular similarity, and approximates attention outputs via Gaussian random projections and \emph{soft} Locality-Sensitive Hashing (LSH), avoiding construction of the full attention matrix. Across language modeling, masked language modeling, and text/image classification, RACE Attention matches or outperforms strong baselines up to $64$K seqeuence length while reducing wall-clock time and memory usage. In addition, we conduct a controlled scaling study on a single attention layer and demonstrate processing of up to 12 million tokens on an NVIDIA GH200 GPU and 75 million tokens on an Intel Xeon® Gold 5220R CPU in a single forward–backward pass, which is well beyond the capabilities of current state-of-the-art attention implementations. RACE Attention thus offers a practical and theoretically grounded mechanism for long-context training on today’s hardware.
Latent Fourier Transform
Authors: Mason Long Wang; Cheng-Zhi Anna Huang
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: We introduce the Latent Fourier Transform (LatentFT), a framework that provides novel frequency-domain controls for generative music models. LatentFT combines a diffusion autoencoder with a latent-space Fourier transform to separate musical patterns by timescale. By masking latents in the frequency domain during training, our method yields representations that can be manipulated coherently at inference. This allows us to generate musical variations and blends from reference examples while preserving characteristics at desired timescales, which are specified as frequencies in the latent space. LatentFT parallels the role of the equalizer in music production: while traditional equalizers operates on audible frequencies to shape timbre, LatentFT operates on latent-space frequencies to shape musical structure. Experiments and listening tests show that LatentFT improves condition adherence and quality compared to baselines. We also present a technique for hearing frequencies in the latent space in isolation, and show different musical attributes reside in different regions of the latent spectrum. Our results show how frequency-domain control in latent space provides an intuitive, continuous frequency axis for conditioning and blending, advancing us toward more interpretable and interactive generative music models.
Graph homophily booster: Reimagining the role of discrete features in heterophilic graph learning
Authors: Ruizhong Qiu; Ting-Wei Li; Gaotang Li; Hanghang Tong
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Graph neural networks (GNNs) have emerged as a powerful tool for modeling graph-structured data, demonstrating remarkable success in many real-world applications such as complex biological network analysis, neuroscientific analysis, and social network analysis. However, existing GNNs often struggle with heterophilic graphs, where connected nodes tend to have dissimilar features or labels. While numerous methods have been proposed to address this challenge, they primarily focus on architectural designs without directly targeting the root cause of the heterophily problem. These approaches still perform even worse than the simplest MLPs on challenging heterophilic datasets. For instance, our experiments show that 23 latest GNNs still fall behind the MLP on the Actor dataset. This critical challenge calls for an innovative approach to addressing graph heterophily beyond architectural designs. To bridge this gap, we propose and study a new and unexplored paradigm: directly increasing the graph homophily via a carefully designed graph transformation. In this work, we present a simple yet effective framework called Graph Homophily Booster (GRAPHITE) to address graph heterophily. To the best of our knowledge, this work is the first method that explicitly transforms the graph to directly improve the graph homophily. Stemmed from the exact definition of homophily, our proposed GRAPHITE creates feature nodes to facilitate homophilic message passing between nodes that share similar features. Furthermore, we both theoretically and empirically show that our proposed GRAPHITE significantly increases the homophily of originally heterophilic graphs, with only a slight increase in the graph size. Extensive experiments on challenging datasets demonstrate that our proposed GRAPHITE significantly outperforms state-of-the-art methods on heterophilic graphs while achieving comparable accuracy with state-of-the-art methods on homophilic graphs. Furthermore, our proposed graph transformation alone can already enhance the performance of homophilic GNNs on heterophilic graphs, even though they were not originally designed for heterophilic graphs. Our code is publicly available at https://github.com/q-rz/ICLR26-GRAPHITE .
Scaling Laws of SignSGD in Linear Regression: When Does It Outperform SGD?
Authors: Jihwan Kim; Dogyoon Song; Chulhee Yun
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We study scaling laws of signSGD under a power-law random features (PLRF) model that accounts for both feature and target decay. We analyze the population risk of a linear model trained with one-pass signSGD on Gaussian-sketched features. We express the risk as a function of model size, training steps, learning rate, and the feature and target decay parameters. Comparing against the SGD risk analyzed by Paquette et al. (2024), we identify a drift-normalization effect and a noise-reshaping effect unique to signSGD. We then obtain compute-optimal scaling laws under the optimal choice of learning rate. Our analysis shows that the noise-reshaping effect can make the compute-optimal slope of signSGD steeper than that of SGD in regimes where noise is dominant. Finally, we observe that the widely used warmup-stable-decay (WSD) schedule further reduces the noise term and sharpens the compute-optimal slope, when feature decay is fast but target decay is slow.
TVTSyn: Content-Synchronous Time-Varying Timbre for Streaming Voice Conversion and Anonymization
Authors: Waris Quamer; Mu-Ruei Tseng; Ghady Nasrallah; Ricardo Gutierrez-Osuna
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Real-time voice conversion and speaker anonymization require causal, low-latency synthesis without sacrificing intelligibility or naturalness. Current systems have a core representational mismatch: content is time-varying, while speaker identity is injected as a static global embedding. We introduce a streamable speech synthesizer that aligns the temporal granularity of identity and content via a content-synchronous, time-varying timbre (TVT) representation. A Global Timbre Memory expands a global timbre instance into multiple compact facets; frame-level content attends to this memory, a gate regulates variation, and spherical interpolation preserves identity geometry while enabling smooth local changes. In addition, a factorized vector-quantized bottleneck regularizes content to reduce residual speaker leakage. The resulting system is streamable end-to-end, with <80 ms GPU latency. Experiments show improvements in naturalness, speaker transfer, and anonymization  compared to SOTA streaming baselines, establishing TVT as a scalable approach for privacy-preserving and expressive speech synthesis under strict latency budgets.
GLASS Flows: Efficient Inference for Reward Alignment of Flow and Diffusion Models
Authors: Peter Holderrieth; Uriel Singer; Tommi Jaakkola; Ricky T. Q. Chen; Yaron Lipman; Brian Karrer
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: The performance of flow matching and diffusion models can be greatly improved at inference time using reward adaptation algorithms, yet efficiency remains a major limitation. While several algorithms were proposed, we demonstrate that a common bottleneck is the *sampling* method these algorithms rely on: many algorithms require to sample Markov transitions via SDE sampling, which is significantly less efficient and often less performant than ODE sampling. To remove this bottleneck, we introduce GLASS Flows, a new sampling paradigm that simulates a ''flow matching model within a flow matching model'' to sample Markov transitions. As we show in this work, this ''inner'' flow matching model can be retrieved from any pre-trained model without any re-training, effectively combining the efficiency of ODEs with the stochastic evolution of SDEs. On large-scale text-to-image models, we show that GLASS Flows eliminate the trade-off between stochastic evolution and efficiency. GLASS Flows improve state-of-the-art performance in text-to-image generation, making it a simple, drop-in solution for inference-time scaling of flow and diffusion models.
Steering Autoregressive Music Generation with Recursive Feature Machines
Authors: Daniel Zhao; Daniel Beaglehole; Julian McAuley; Taylor Berg-Kirkpatrick; Zachary Novack
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Controllable music generation remains a significant challenge, with existing methods often requiring model retraining or introducing audible artifacts. We introduce MusicRFM, a framework that adapts Recursive Feature Machines (RFMs) to enable fine-grained, interpretable control over frozen, pre-trained music models by directly steering their internal activations. RFMs analyze a model's internal gradients to produce interpretable "concept directions", or specific axes in the activation space that correspond to musical attributes like notes or chords. We first train lightweight RFM probes to discover these directions within MusicGen's hidden states; then, during inference, we inject them back into the model to guide the generation process in real-time without per-step optimization. We present advanced mechanisms for this control, including dynamic, time-varying schedules and methods for the simultaneous enforcement of multiple musical properties. Our method successfully navigates the trade-off between control and generation quality: we can increase the accuracy of generating a target musical note from 0.23 to 0.82, while text prompt adherence remains within approximately 0.02 of the unsteered baseline, demonstrating effective control with minimal impact on prompt fidelity.
Post-training Large Language Models for Diverse High-Quality Responses
Authors: Yilei Chen; Souradip Chakraborty; Lorenz Wolf; Ioannis Paschalidis; Aldo Pacchiano
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Reinforcement learning has emerged as a popular method for post-training large language models (LLMs). While improving the model's performance on downstream tasks, it often reduces the model's output diversity, leading to narrow, canonical responses. Existing methods to enhance diversity are limited, either by operating at inference time or by focusing on lexical differences. We propose a novel training method named DQO (Diversity Quality Optimization) based on determinantal point processes (DPPs) to jointly optimize LLMs for quality and semantic diversity. Our approach samples and embeds a group of responses for each prompt, then uses the determinant of a kernel-based similarity matrix to measure diversity as the volume spanned by the embeddings of these responses. Experiments across instruction-following, summarization, story generation, and reasoning tasks demonstrate that our method substantially improves semantic diversity without sacrificing model quality.
RESFL: An Uncertainty-Aware Framework for Responsible Federated Learning by Balancing Privacy, Fairness and Utility
Authors: Dawood Wasif; Terrence J Moore; Jin-Hee Cho
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Federated Learning (FL) has gained prominence in machine learning applications across critical domains, offering collaborative model training without centralized data aggregation. However, FL frameworks that protect privacy often sacrifice fairness and reliability; differential privacy reduces data leakage but hides sensitive attributes needed for bias correction, worsening performance gaps across demographic groups. This work explores the trade-off between privacy and fairness in FL-based object detection and introduces RESFL, an integrated solution optimizing both. RESFL incorporates adversarial privacy disentanglement and uncertainty-guided fairness-aware aggregation. The adversarial component uses a gradient reversal layer to remove sensitive attributes, reducing privacy risks while maintaining fairness. The uncertainty-aware aggregation employs an evidential neural network to weight client updates adaptively, prioritizing contributions with lower fairness disparities and higher confidence. This ensures robust and equitable FL model updates. We demonstrate the effectiveness of RESFL in high-stakes autonomous vehicle scenarios, where it achieves high mAP on FACET and CARLA, reduces membership-inference attack success by 37%, reduces equality-of-opportunity gap by 17% relative to the FedAvg baseline, and maintains superior adversarial robustness. However, RESFL is inherently domain-agnostic and thus applicable to a broad range of application domains beyond autonomous driving.
Causal Interpretation of Neural Network Computations with Contribution Decomposition
Authors: Joshua Brendan Melander; Zaki Alaoui; Shenghua Liu; Surya Ganguli; Stephen Baccus
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Understanding how neural networks transform inputs into outputs is crucial for interpreting and manipulating their behavior. Most existing approaches analyze internal representations by identifying hidden-layer activation patterns correlated with human-interpretable concepts. Here we take a direct approach to examine how hidden neurons act to drive network outputs. We introduce CODEC ($\textbf{Co}$ntribution $\textbf{Dec}$omposition), a method that uses sparse autoencoders to decompose network behavior into sparse motifs of hidden-neuron contributions, revealing causal processes that cannot be determined by analyzing activations alone. Applying CODEC to benchmark image-classification networks, we find that contributions grow in sparsity and dimensionality across layers and, unexpectedly, that they progressively decorrelate positive and negative effects on network outputs. We further show that decomposing contributions into sparse modes enables greater control and interpretation of intermediate layers, supporting both causal manipulations of network output and human-interpretable visualizations of distinct image components that combine to drive that output. Finally, by analyzing state-of-the-art models of neural activity in the vertebrate retina, we demonstrate that CODEC uncovers combinatorial actions of model interneurons and identifies the sources of dynamic receptive fields. Overall, CODEC provides a rich and interpretable framework for understanding how nonlinear computations evolve across hierarchical layers, establishing contribution modes as an informative unit of analysis for mechanistic insights into artificial neural networks.
HEIST: A Graph Foundation Model for Spatial Transcriptomics and Proteomics Data
Authors: Hiren Madhu; João Felipe Rocha; Tinglin Huang; Siddharth Viswanath; Smita Krishnaswamy; Rex Ying
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Single-cell transcriptomics and proteomics have become a great source for data-driven insights into biology, enabling the use of advanced deep learning methods to understand cellular heterogeneity and gene expression at the single-cell level. With the advent of spatial-omics data, we have the promise of characterizing cells within their tissue context as it provides both spatial coordinates and intra-cellular transcriptional or protein counts. Beyond transcriptomics, proteomics offers a complementary view by directly measuring proteins, which are the primary effectors of cellular function and key therapeutic targets. However, existing models either ignore the spatial information or the complex genetic and proteomic programs within cells. Thus they cannot infer how cell internal regulation adapts to microenvironmental cues. Furthermore, these models often utilize fixed gene vocabularies, hindering their generalizability to datasets with different genes than pretraining. In this paper, we introduce HEIST, a hierarchical graph transformer foundation model for spatial transcriptomics and proteomics. HEIST models tissues as hierarchical graphs. The higher level graph is a spatial cell graph, and each cell in turn, is represented by its lower level gene co-expression network graph. Rather than using a fixed gene vocabulary, HEIST computes gene embeddings from its co-expression network and cellular context. HEIST  achieves this by performing both intra-level and cross-level message passing to utilize the hierarchy in its embeddings and can thus generalize to novel datatypes including spatial proteomics without retraining. HEIST is pretrained on 22.3M cells from 124 tissues across 15 organs using spatially-aware contrastive and masked autoencoding objectives. Unsupervised analysis of HEIST embeddings reveals spatially informed subpopulations missed by prior models. Downstream evaluations demonstrate generalizability to proteomics data and state-of-the-art performance in clinical outcome prediction, cell type annotation, and gene imputation across multiple technologies.
Trajectory Generation with Conservative Value Guidance for Offline Reinforcement Learning
Authors: Tieru Wang; Kunbao Wu; Guoshun Nan
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent advances in offline reinforcement learning (RL) have led to the development of high-performing algorithms that achieve impressive results across standard benchmarks. However, many of these methods depend on increasingly complex planning architectures, which hinder their deployment in real-world settings due to high inference costs. To overcome this limitation, recent research has explored data augmentation techniques that offload computation from online decision-making to offline data preparation. Among these, diffusion-based generative models have shown potential in synthesizing diverse trajectories but incur significant overhead in training and data generation.
In this work, we propose Trajectory Generation with Conservative Value Guidance (TGCVG), a novel trajectory-level data augmentation framework that integrates a high-performing offline policy with a learned dynamics model. To ensure that the synthesized trajectories are both high-quality and close to the original dataset distribution, we introduce a value-guided regularization during the training of the offline policy. This regularization encourages conservative action selection, effectively mitigating distributional shift during trajectory synthesis.
Empirical results on standard benchmarks demonstrate that TGCVG not only improves the performance of state-of-the-art offline RL algorithms but also significantly reduces training and trajectory synthesis time. These findings highlight the effectiveness of value-aware data generation in improving both efficiency and policy performance.
How Dark Patterns Manipulate Web Agents
Authors: Phil Cuvin; Hao Zhu; Diyi Yang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Deceptive UI designs, widely instantiated across the web and commonly known
as dark patterns, manipulate users into performing actions misaligned with their
goals. In this paper, we show that dark patterns are highly effective in steer-
ing agent trajectories, posing a significant risk to agent robustness. To quan-
tify this risk, we introduce DECEPTICON, an environment for testing individual
dark patterns in isolation. DECEPTICON includes 700 web navigation tasks with
dark patterns—600 generated tasks and 100 real-world tasks, designed to measure
instruction-following success and dark pattern effectiveness. Across state-of-the-
art agents, we find dark patterns successfully steer agent trajectories towards mali-
cious outcomes in over 70% of tested generated and real-world tasks—compared
to a human average of 31%. Moreover, we find that dark pattern effectiveness
correlates positively with model size and test-time reasoning, making larger, more
capable models more susceptible. Leading countermeasures against adversarial
attacks, including in-context prompting and guardrail models, fail to consistently
reduce the success rate of dark pattern interventions. Our findings reveal dark pat-
terns as a latent and unmitigated risk to web agents, highlighting the urgent need
for robust defenses against manipulative designs.
Prompt-MII: Meta-Learning Instruction Induction for LLMs
Authors: Emily Xiao; Yixiao Zeng; Ada Chen; Chin-Jou Li; Amanda Bertsch; Graham Neubig
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: A popular method to adapt large language models (LLMs) to new tasks is in-context learning (ICL), which is effective but incurs high inference costs as context length grows. In this paper we propose a method to perform instruction induction, where we take training examples and reduce them to a compact but descriptive prompt that can achieve performance comparable to ICL over the full training set. Specifically, we propose Prompt-MII, a reinforcement learning (RL) based framework to meta-learn an instruction induction model that can generate compact instructions on the fly for an arbitrary new dataset. We train on over 3,000 diverse classification datasets from the HuggingFace hub, and evaluate on 90 unseen tasks. Prompt-MII improves downstream model quality by 4-9 F1 points (10-20\% relative), matching ICL performance while requiring 3-13x fewer tokens.
IncVGGT: Incremental VGGT for Memory-Bounded Long-Range 3D Reconstruction
Authors: Keyu Fang; Changchun Zhou; Yuzhe Fu; Hai Helen Li; Yiran Chen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We present IncVGGT, a training-free incremental variant of VGGT that makes transformer-based 3D reconstruction feasible for long sequences in real-world applications. Vanilla VGGT relies on dense global attention, which causes memory to grow quadratically and requires excessive computation, making it impractical for long-sequence scenarios. Even evolved streaming variants, such as StreamVGGT, still suffer from rapidly growing cache and latency. IncVGGT addresses these challenges from two orthogonal directions: (1) register and fuse overlapping frames into composite views, reducing duplicate tokens, and (2) history-side pruning retains only the top-$k$ most relevant/maximum slots together with the most recent one, bounding cache growth. This incremental and memory-efficient design minimizes computation and memory occupation across arbitrarily long sequences. Compared to StreamVGGT, IncVGGT sustains arbitrarily long sequences with large efficiency gains (e.g., on 500-frame sequences, 58.5$\times$ fewer operators, 9$\times$ lower memory, 25.7$\times$ less energy, and 4.9$\times$ faster inference) while maintaining comparable accuracy. More importantly, unlike existing baselines that directly run out of memory beyond 300 (VGGT)–500 (StreamVGGT) frames, IncVGGT continues to operate smoothly even on 10k-frame inputs under an 80GB GPU, showing that our design truly scales to ultra-long sequences without hitting memory limits. These results highlight IncVGGT’s potential for deployment in resource-constrained edge devices for long-range 3D scenarios.