GenAI4ELab/papercli-papers huggingface dataset:
MindMix: A Multimodal Foundation Model for Auditory Perception Decoding via Deep Neural-Acoustic Alignment
Authors: RUI LIU; Zhige Chen; Pengshu; Wenlong You; Zhi-An Huang; Jibin Wu; KC Tan
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Decoding complex auditory experiences from non-invasive EEG is a rapidly emerging field that holds significant promise for advancing both fundamental neuroscience and human-machine interaction technologies. Recent developments in EEG foundation models have yielded powerful neural representations that are promising for auditory decoding. However, the effectiveness of these models remains fundamentally constrained by their limited integration with acoustic stimulus information. Specifically, the lack of deep coupling between neural signals and auditory inputs hampers the models’ ability to generalize effectively across diverse auditory tasks. To bridge this gap, we introduce MindMix, a multimodal foundation model designed to bridge the gap between unimodal EEG foundations and task-specific auditory decoders. MindMix employs a two-stage training strategy: first, a high-capacity EEG encoder is pre-trained on over 3,000 hours of EEG data to learn generalized EEG features that can transfer across tasks and subjects. Second, the model learns the neural-acoustic mapping using over 100 hours of paired data, facilitated by our novel Cross-Attention Low-Rank Alignment module, which facilitates fine-grained, cross-modal information integration. Experimental results demonstrate that MindMix substantially surpassing existing baselines across a range of auditory decoding tasks, including auditory attention decoding, auditory emotion recognition, and cross-modal retrieval. This work thus establishes a foundation for future research in multimodal brain decoding and auditory brain-computer interfaces. Our code is available at https://github.com/CookieMikeLiu/MindMix.
Enhancing Communication Compression via Discrepancy-aware Calibration for Federated Learning
Authors: Zhiyi Wan; Yijia Chi; Liang Li; Wanrou Du; Miao Pan; Xiaoqi Qin
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Federated Learning (FL) offers a privacy-preserving paradigm for distributed model training by enabling clients to collaboratively learn a shared model without exchanging their raw data. However, the communication overhead associated with exchanging model updates remains a critical challenge, particularly for devices with limited bandwidth and battery resources. 
Existing communication compression methods largely rely on simple heuristics based on magnitude or randomness. 
For example, Top-k drops the elements with small magnitude, while low-rank methods such as ATOMO and PowerSGD truncate singular values with small magnitude. 
However, these rules do not account for the discrepancy between the compressed and the original outputs, which can lead to the loss of important information. 
To address this issue, we propose a novel discrepancy-aware communication compression method that enhances performance under severely constrained communication conditions.
Each client uses a small subset of its local data as calibration data to directly measure the output discrepancy induced by dropping candidate compression units and uses it as a compression metric to guide the selection. 
By integrating this strategy, we can enhance existing mainstream compression schemes, enabling more efficient communication.
Empirical results across multiple datasets and models show that our method achieves a significant improvement in accuracy under stringent communication constraints, notably an $18.9\\%$ relative accuracy improvement at a compression ratio of $0.1$, validating its efficacy for scalable and communication-efficient FL.
Our code is available at https://github.com/wzy1026wzy/Discrepancy-aware-Compression-for-FL.
Repurposing Synthetic Data for Fine-grained Search Agent Supervision
Authors: Yida Zhao; Kuan Li; Xixi Wu; Liwen Zhang; Ding-Chu Zhang; Baixuan Li; Maojia Song; Zhuo Chen; Chenxi Wang; Xinyu Wang; Kewei Tu; Pengjun Xie; Fei Huang; Jingren Zhou; Yong Jiang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: LLM-based search agents are increasingly trained on entity-centric synthetic data to solve complex, knowledge-intensive tasks. However, prevailing training methods like Group Relative Policy Optimization (GRPO) discard this rich entity information, relying instead on sparse, outcome-based rewards. This critical limitation renders them unable to distinguish informative "near-miss" samples—those with substantially correct reasoning but a flawed final answer—from complete failures, thus discarding valuable learning signals. We address this by leveraging the very entities discarded during training. Our empirical analysis reveals a strong positive correlation between the number of ground-truth entities identified during an agent's reasoning process and final answer accuracy. Building on this insight, we introduce Entity-aware Group Relative Policy Optimization (E-GRPO), a novel framework that formulates a dense entity-aware reward function. E-GRPO assigns partial rewards to incorrect samples proportional to their entity match rate, enabling the model to effectively learn from these ''near-misses''. Experiments on diverse question-answering (QA) and deep research benchmarks show that E-GRPO consistently and significantly outperforms the GRPO baseline. Furthermore, our analysis reveals that E-GRPO not only achieves superior accuracy but also induces more efficient reasoning policies that require fewer tool calls, demonstrating a more effective and sample-efficient approach to aligning search agents.
Multi-Condition Conformal Selection
Authors: Qingyang Hao; Wenbo Liao; Bingyi Jing; Hongxin Wei
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Selecting high-quality candidates from large-scale datasets is critically important in resource-constrained applications such as drug discovery, precision medicine, and the alignment of large language models. While conformal selection methods offer a rigorous solution with False Discovery Rate (FDR) control, their applicability is confined to single-threshold scenarios (i.e., y > c) and overlooks practical needs for multi-condition selection, such as conjunctive or disjunctive conditions.  In this work, we propose the Multi-Condition Conformal Selection (MCCS) algorithm, which extends conformal selection to scenarios with multiple conditions. In particular, we introduce a novel nonconformity score with regional monotonicity for conjunctive conditions and a global Benjamini–Hochberg (BH) procedure for disjunctive conditions, thereby establishing finite-sample FDR control with theoretical guarantees. The integration of these components enables the proposed method to achieve rigorous FDR-controlled selection in various multi-condition environments. Extensive experiments validate the superiority of MCCS over baselines, its generalizability across diverse condition combinations, different real-world modalities, and multi-task scalability.
Neural Optimal Transport Meets Multivariate Conformal Prediction
Authors: Vladimir Kondratyev; Alexander Fishkov; Mahmoud Hegazy; Nikita Kotelevskii; Rémi Flamary; Maxim Panov; Eric Moulines
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We propose a framework for conditional vector quantile regression (CVQR) that combines neural optimal transport with amortized optimization, and apply it to multivariate conformal prediction. Classical quantile regression does not extend naturally to multivariate responses, while existing approaches often ignore the geometry of joint distributions. Our method parameterizes the conditional vector quantile function as the gradient of a convex potential implemented by an input-convex neural network, ensuring monotonicity and uniform ranks. To reduce the cost of solving high-dimensional variational problems, we introduce amortized optimization of the dual potentials, yielding efficient training and faster inference.  

  We then exploit the induced multivariate ranks for conformal prediction, constructing distribution-free predictive regions with finite-sample validity. Unlike coordinatewise methods, our approach adapts to the geometry of the conditional distribution, producing tighter and more informative regions. Experiments on benchmark datasets show improved coverage–efficiency trade-offs compared to baselines, highlighting the benefits of integrating neural optimal transport with conformal prediction.
DeLiVR: Differential Spatiotemporal Lie Bias for Efficient Video Deraining
Authors: Shuning Sun; Jialang Lu; Xiang Chen; Jichao Wang; Dianjie Lu; Guijuan Zhang; Guangwei Gao; Zhuoran Zheng
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Videos captured in the wild often suffer from rain streaks, blur, and noise. In addition, even slight changes in camera pose can amplify cross-frame mismatches and temporal artifacts. Existing methods rely on optical flow or heuristic alignment, which are computationally expensive and less robust. To address these challenges, Lie groups provide a principled way to represent continuous geometric transformations, making them well-suited for enforcing spatial and temporal consistency in video modeling. Building on this insight, we propose DeLiVR, an efficient video deraining method that injects spatiotemporal Lie-group differential biases directly into attention scores of the network. Specifically, the method introduces two complementary components. First, a rotation-bounded Lie relative bias predicts the in-plane  angle of each frame using a compact prediction module, which normalized coordinates are rotated and compared with base coordinates to achieve geometry-consistent alignment before feature aggregation. Second, a differential group displacement computes angular differences between adjacent frames to estimate a velocity. These biases are combined with temporal decay and a banded attention mask to emphasize short-range reliable relations while suppressing long-range noise. DeLiVR achieves sharper details, fewer rain remnants, and stronger temporal coherence on both synthetic and real rainy benchmarks. The code is publicly available at https://github.com/Shuning0312/ICLR-DeLiVR.
Align Once, Benefit Multilingually: Enforcing Multilingual Consistency for LLM Safety Alignment
Authors: Yuyan Bu; Xiaohao Liu; ZhaoXing Ren; Yaodong Yang; Juntao Dai
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The widespread deployment of large language models (LLMs) across linguistic communities necessitates reliable multilingual safety alignment. However, recent efforts to extend alignment to other languages often require substantial resources, either through large-scale, high-quality supervision in the target language or through pairwise alignment with high-resource languages, which limits scalability.
In this work, we propose a resource-efficient method for improving multilingual safety alignment. 
We introduce a plug-and-play Multi-Lingual Consistency (MLC) loss that can be integrated into existing monolingual alignment pipelines. 
By improving collinearity between multilingual representation vectors, our method encourages directional consistency at the multilingual semantic level in a single update. This allows simultaneous alignment across multiple languages using only multilingual prompt variants without requiring additional response-level supervision in low-resource languages. We validate the proposed method across different model architectures and alignment paradigms, and demonstrate its effectiveness in enhancing multilingual safety with limited impact on general model utility. Further evaluation across languages and tasks indicates improved cross-lingual generalization, suggesting the proposed approach as a practical solution for multilingual consistency alignment under limited supervision.
ComPhy: Composing Physical Models with end-to-end Alignment
Authors: Alessandro Trenta; Andrea Cossu; Davide Bacciu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Real-world phenomena typically involve multiple, interwoven dynamics that can be elegantly captured by systems of Partial Differential Equations (PDEs).   However, accurately solving such systems remains a challenge. In this paper, we introduce ComPhy (CP), a novel modular framework designed to leverage the inherent physical structure of the problem to solve systems of PDEs. CP assigns each PDE to a dedicated learning module,  each capable of incorporating state-of-the-art methodologies such as Physics-Informed Neural Networks or Neural Conservation Laws. 
  Crucially, CP introduces an end-to-end alignment mechanism, explicitly designed around the physical interplay of shared variables, enabling knowledge transfer between modules, and promoting solutions that are the result of the collective effort of all modules. 
  CP is the first approach specifically designed to tackle systems of PDEs, and our results show that it outperforms state-of-the-art approaches where a single model is trained on all PDEs at once.
Interpolation-Based Conditioning of Flow Matching Models for Bioisosteric Ligand Design
Authors: Yael Ziv; Martin Buttenschoen; Lukas Scheibelberger; Brian Marsden; Charlotte Deane
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Fast, unconditional 3D generative models can now produce high-quality molecules, but adapting them for specific design tasks often requires costly retraining. To address this, we introduce Interpolate-Integrate and Replacement Guidance, two training-free, inference-time conditioning strategies that provide control over E(3)-equivariant flow-matching models. 
Our methods generate bioisosteric 3D molecules by conditioning on seed ligands or fragment sets to preserve key determinants like shape and pharmacophore patterns, without requiring the original fragment atoms to be present. We demonstrate their effectiveness on three drug-relevant tasks: natural product ligand hopping, bioisosteric fragment merging, and pharmacophore merging.
Speculative Speculative Decoding
Authors: Tanishq Kumar; Tri Dao; Avner May
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Autoregressive decoding is bottlenecked by its sequential nature. Speculative decoding has become a standard way to accelerate inference by using a fast draft model to predict upcoming tokens from a slower target model, and then verifying them in parallel with a single target model forward pass. However, speculative decoding itself relies on a sequential dependence between speculation and verification. We introduce speculative speculative decoding (SSD) to parallelize these operations. While a verification is ongoing, the draft model predicts likely verification outcomes and prepares speculations pre-emptively for them. If the actual verification outcome is then in the predicted set, a speculation can be returned immediately, eliminating drafting overhead entirely. We identify three key challenges presented by speculative speculative decoding, and suggest principled methods to solve each. The result is Saguaro, an optimized SSD algorithm. Our implementation is on average 30% faster than optimized speculative decoding baselines and up to 5x faster than autoregressive decoding with open source inference engines.
Reasoning-Driven Multimodal LLM for Domain Generalization
Authors: Zhipeng Xu; Zilong Wang; XINYANG JIANG; Dongsheng Li; De Cheng; Nannan Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: This paper addresses the domain generalization (DG) problem in deep learning. 
While most DG methods focus on enforcing visual feature invariance, we leverage the reasoning capability of multimodal large language models (MLLMs) and explore the potential of constructing reasoning chains that derives image categories to achieve more robust predictions under domain shift.
To this end, we systematically study the role of reasoning in DG using DomainBed-Reasoning, a newly constructed extension of DomainBed dataset, in which each sample is paired with class-relevant reasoning chains.
Our analysis reveals two key challenges: (i) fine-tuning MLLMs with reasoning chains for classification is more challenging than direct label supervision, since the model must optimize complex reasoning sequences before label prediction; and (ii) mismatches in reasoning patterns between supervision signals and fine-tuned MLLMs lead to a trade-off between semantic richness (informative but harder to optimize) and optimization efficiency (easier to optimize but less informative).
To address these issues, we propose RD-MLDG (Reasoning-Driven Multimodal LLM for Domain Generalization), a framework with two components: (i) MTCT (Multi-Task Cross-Training), which introduces an additional direct classification pathway to guide reasoning supervision; and (ii) SARR (Self-Aligned Reasoning Regularization), which preserves the semantic richness of reasoning chains while mitigating reasoning-pattern mismatches via iterative self-labeling.
Experiments on standard DomainBed datasets (PACS, VLCS, OfficeHome, TerraIncognita) demonstrate that RD-MLDG achieves state-of-the-art performances, highlighting reasoning as a promising complementary signal for robust out-of-domain generalization.
Toward Faithful Retrieval-Augmented Generation with Sparse Autoencoders
Authors: Guangzhi Xiong; Zhenghao He; Bohan Liu; Sanchit Sinha; Aidong Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Retrieval-Augmented Generation (RAG) improves the factuality of large language models (LLMs) by grounding outputs in retrieved evidence, but faithfulness failures, where generations contradict or extend beyond the provided sources, remain a critical challenge. Existing hallucination detection methods for RAG often rely either on large-scale detector training, which requires substantial annotated data, or on querying external LLM judges, which leads to high inference costs. Although some approaches attempt to leverage internal representations of LLMs for hallucination detection, their accuracy remains limited. Motivated by recent advances in mechanistic interpretability, we employ sparse autoencoders (SAEs) to disentangle internal activations, successfully identifying features that are specifically triggered during RAG hallucinations. Building on a systematic pipeline of information-based feature selection and additive feature modeling, we introduce RAGLens, a lightweight hallucination detector that accurately flags unfaithful RAG outputs using LLM internal representations. RAGLens not only achieves superior detection performance compared to existing methods, but also provides interpretable rationales for its decisions, enabling effective post-hoc mitigation of unfaithful RAG. Finally, we justify our design choices and reveal new insights into the distribution of hallucination-related signals within LLMs. The code is available at https://github.com/Teddy-XiongGZ/RAGLens.
Adaptive Regularization for Large-Scale Sparse Feature Embedding Models
Authors: Mang Li; Wei Lyu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The one-epoch overfitting problem has drawn widespread attention, especially in CTR and CVR estimation models in search, advertising, and recommendation domains. These models which rely heavily on large-scale sparse categorical features, often suffer a significant decline in performance when trained for multiple epochs. Although recent studies have proposed heuristic solutions, the fundamental cause of this phenomenon remains unclear. In this work, we present a theoretical explanation grounded in Rademacher complexity, supported by empirical experiments, to explain why overfitting occurs in models with large-scale sparse categorical features. Based on this analysis, we propose a  regularization method that constrains the norm budget of embedding layers adaptively. Our approach not only prevents the severe performance degradation observed during multi-epoch training, but also improves model performance within a single epoch. This method has already been deployed in online production systems.
Graph Tokenization for Bridging Graphs and Transformers
Authors: Zeyuan Guo; Enmao Diao; Cheng Yang; Chuan Shi
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The success of large pretrained Transformers is closely tied to tokenizers, which convert raw input into discrete symbols. Extending these models to graph-structured data remains a significant challenge. 
In this work, we introduce a graph tokenization framework that generates sequential representations of graphs by combining reversible graph serialization, which preserves graph information, with Byte Pair Encoding (BPE), a widely adopted tokenizer in large language models (LLMs).
To better capture structural information, the graph serialization process is guided by global statistics of graph substructures, ensuring that frequently occurring substructures appear more often in the sequence and can be merged by BPE into meaningful tokens.
Empirical results demonstrate that the proposed tokenizer enables Transformers such as BERT to be directly applied to graph benchmarks without architectural modifications.
The proposed approach achieves state-of-the-art results on 14 benchmark datasets and frequently outperforms both graph neural networks and specialized graph transformers. This work bridges the gap between graph-structured data and the ecosystem of sequence models.
Our code is available at \href{ https://github.com/BUPT-GAMMA/Graph-Tokenization-for-Bridging-Graphs-and-Transformers }{\color{blue}here}.
On The Expressive Power of GNN Derivatives
Authors: Yam Eitan; Moshe Eliasof; Yoav Gelberg; Fabrizio Frasca; Guy Bar-Shalom; Haggai Maron
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Despite significant advances in Graph Neural Networks (GNNs), their limited expressivity remains a fundamental challenge. Research on GNN expressivity has produced many expressive architectures, leading to architecture hierarchies with models of increasing expressive power. Separately, derivatives of GNNs with respect to node features have been widely studied in the context of the oversquashing and over-smoothing phenomena, GNN explainability, and more. To date, these derivatives remain unexplored as a means to enhance GNN expressivity. In this paper, we show that these derivatives provide a natural way to enhance the expressivity of GNNs. We introduce High-Order Derivative GNN (HOD-GNN), a novel method that enhances the expressivity of Message Passing Neural Networks (MPNNs) by leveraging high-order node derivatives of the base model. These derivatives generate expressive structure-aware node embeddings processed by a second GNN in an end-to-end trainable architecture. Theoretically, we show that the resulting architecture family's expressive power aligns with the WL hierarchy. We also draw deep connections between HOD-GNN, Subgraph GNNs, and popular structural encoding schemes. For computational efficiency, we develop a message-passing algorithm for computing high-order derivatives of MPNNs that exploits graph sparsity and parallelism. Evaluations on popular graph learning benchmarks demonstrate HOD-GNN’s strong performance on popular graph learning tasks.