GenAI4ELab/papercli-papers huggingface dataset:
Error Feedback for Muon and Friends
Authors: Kaja Gruntkowska; Alexander Gaponov; Zhirayr Tovmasyan; Peter Richtárik
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent optimizers like Muon, Scion, and Gluon have pushed the frontier of large-scale deep learning by exploiting layer-wise linear minimization oracles (LMOs) over non-Euclidean norm balls, capturing neural network structure in ways traditional algorithms cannot. Yet, no principled distributed framework exists for these methods, and communication bottlenecks remain unaddressed. The very few distributed variants are heuristic, with no convergence guarantees in sight. We introduce EF21-Muon, the first communication-efficient, non-Euclidean LMO-based optimizer with rigorous convergence guarantees. EF21-Muon supports stochastic gradients, momentum, and bidirectional compression with error feedback–marking the first extension of error feedback beyond the Euclidean setting. It recovers Muon/Scion when compression is off and specific norms are chosen, providing the first efficient distributed implementation of this powerful family. Our theory covers non-Euclidean smooth and the more general $(L_0, L_1)$–smooth setting, matching best-known Euclidean rates and enabling faster convergence under suitable norm choices. We further extend the analysis to layer-wise (generalized) smoothness regimes, capturing the anisotropic structure of deep networks. Experiments on NanoGPT benchmarking EF21-Muon against uncompressed Muon/Scion/Gluon demonstrate up to 7× communication savings with no accuracy degradation.
Bridging Explainability and Embeddings: BEE Aware of Spuriousness
Authors: Cristian Daniel Paduraru; Antonio Barbalau; Radu Filipescu; Andrei Liviu Nicolicioiu; Elena Burceanu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Current methods for detecting spurious correlations rely on data splits or error patterns, leaving many harmful shortcuts invisible when counterexamples are absent. We introduce BEE (Bridging Explainability and Embeddings), a framework that shifts the focus from model predictions to the weight space and embedding geometry underlying decisions. By analyzing how fine-tuning perturbs pretrained representations, BEE uncovers spurious correlations that remain hidden from conventional evaluation pipelines. We use linear probing as a transparent diagnostic lens, revealing spurious features that not only persist after full fine-tuning but also transfer across diverse state-of-the-art models. Our experiments cover numerous datasets and domains: vision (Waterbirds, CelebA, ImageNet-1k), language (CivilComments, MIMIC-CXR medical notes), and multiple embedding families (CLIP, CLIP-DataComp.XL, mGTE, BLIP2, SigLIP2). 
BEE consistently exposes spurious correlations: from concepts that slash the ImageNet accuracy by up to 95\%, to clinical shortcuts in MIMIC-CXR notes that induce dangerous false negatives. Together, these results position BEE as a general and principled tool for diagnosing spurious correlations in weight space, enabling principled dataset auditing and more trustworthy foundation models. The source code is publicly available at https://github.com/bit-ml/bee.
A Bayesian Nonparametric Framework for Private, Fair, and Balanced Tabular Data Synthesis
Authors: Forough Fazeli-Asl; Michael Minyi Zhang; Linglong Kong; Bei Jiang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: A fundamental challenge in data synthesis is protecting the fairness and privacy of
the individual, particularly in data-scarce environments where underrepresented
groups are at risk of further marginalization by reproducing the biases inherent in
the data modeling process. We introduce a privacy- and fairness-aware for a class
of generative models, which fuses the conditional generator within the framework
of Bayesian nonparametric learning (BNPL). This conditional structure imposes
fairness constraints in our generative model by minimizing the mutual information
between generated outcomes and protected attributes. Unlike existing methods
that primarily focus on sensitive binary-valued attributes, our framework extends
seamlessly to non-binary attributes. Moreover, our method provides a systematic
solution to class imbalance, ensuring adequate representation of underrepresented
protected groups. Our proposed approach offers a scalable, privacy-preserving
framework for ethical and equitable data generation, which we demonstrate by
theoretical guarantees and extensive experiments on sensitive empirical examples.
Embodied Agents Meet Personalization: Investigating Challenges and Solutions Through the Lens of Memory Utilization
Authors: Taeyoon Kwon; Dongwook Choi; Hyojun Kim; Sunghwan Kim; Seungjun Moon; Beong-woo Kwak; Kuan-Hao Huang; Jinyoung Yeo
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: LLM-powered embodied agents have shown success on conventional object-rearrangement tasks, but providing personalized assistance that leverages user-specific knowledge from past interactions presents new challenges. We investigate these challenges through the lens of agents' memory utilization along two critical dimensions: object semantics (identifying objects based on personal meaning) and user patterns (recalling sequences from behavioral routines). To assess these capabilities, we construct Memento, an end-to-end two-stage evaluation framework comprising single-memory and joint-memory tasks. Our experiments reveal that current agents can recall simple object semantics but struggle to apply sequential user patterns to planning. Through in-depth analysis, we identify two critical bottlenecks: information overload and coordination failures when handling multiple memories. Based on these findings, we explore memory architectural approaches to address these challenges. Given our observation that episodic memory provides both personalized knowledge and in-context learning benefits, we design a hierarchical knowledge graph-based user-profile memory module that separately manages personalized knowledge, achieving substantial improvements on both single and joint-memory tasks.
MMR-Life: Piecing Together Real-life Scenes for Multimodal Multi-image Reasoning
Authors: Jiachun Li; Shaoping Huang; Zhuoran Jin; Chenlong Zhang; Pengfei Cao; Yubo Chen; Kang Liu; Jun Zhao
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent progress in the reasoning capabilities of multimodal large language models (MLLMs) has empowered them to address more complex tasks such as scientific analysis and mathematical reasoning. Despite their promise, MLLMs' reasoning abilities across different scenarios in real life remain largely unexplored and lack standardized benchmarks for evaluation. To address this gap, we introduce MMR-Life, a comprehensive benchmark designed to evaluate the diverse multimodal multi-image reasoning capabilities of MLLMs across real-life scenarios. MMR-Life consists of 2,646 multiple-choice questions based on 19,108 images primarily sourced from real-world contexts, comprehensively covering seven reasoning types: abductive, analogical, causal, deductive, inductive, spatial, and temporal. Unlike existing reasoning benchmarks, MMR-Life does not rely on domain-specific expertise but instead requires models to integrate information across multiple images and apply diverse reasoning abilities. The evaluation of 37 advanced models highlights the substantial challenge posed by MMR-Life. Even top models like GPT-5 achieve only 58% accuracy and display considerable variance in performance across reasoning types. Moreover, we analyze the reasoning paradigms of existing MLLMs, exploring how factors such as thinking length, reasoning method, and reasoning type affect their performance. In summary, MMR-Life establishes a comprehensive foundation for evaluating, analyzing, and improving the next generation of multimodal reasoning systems.
HiVid: LLM-Guided Video Saliency For Content-Aware VOD And Live Streaming
Authors: Jiahui Chen; Bo Peng; Lianchen Jia; Zeyu Zhang; Tianchi Huang; Lifeng Sun
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Content-aware streaming requires dynamic, chunk-level importance weights to optimize subjective quality of experience (QoE). However, direct human annotation is prohibitively expensive while vision-saliency models generalize poorly. We introduce HiVid, the first framework to leverage Large Language Models (LLMs) as a scalable human proxy to generate high-fidelity weights for both Video-on-Demand (VOD) and live streaming. We address 3 non-trivial challenges: (1) To extend LLMs' limited modality and circumvent token limits, we propose a perception module to assess frames in a local context window, autoregressively building a coherent understanding of the video.
(2) For VOD with rating inconsistency across local windows, we propose a ranking module to perform global re-ranking with a novel LLM-guided merge-sort algorithm.
(3) For live streaming which requires low-latency, online inference without future knowledge, we propose a prediction module to predict future weights with a multi-modal time series model, which comprises a content-aware attention and adaptive horizon to accommodate asynchronous LLM inference. Extensive experiments show HiVid improves weight prediction accuracy by up to 11.5\% for VOD and 26\% for live streaming over SOTA baselines. Real-world user study validates HiVid boosts streaming QoE correlation by 14.7\%.
ChronoPlay: A Framework for Modeling Dual Dynamics and Authenticity in Game RAG Benchmarks
Authors: Liyang He; Yuren Zhang; Ziwei Zhu; lizhenghui; Shiwei Tong
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Retrieval Augmented Generation (RAG) systems are increasingly vital in dynamic domains like online gaming, yet the lack of a dedicated benchmark has impeded standardized evaluation in this area. The core difficulty lies in Dual Dynamics: the constant interplay between game content updates and the shifting focus of the player community. Furthermore, the necessity of automating such a benchmark introduces a critical requirement for player-centric authenticity to ensure generated questions are realistic. To address this integrated challenge, we introduce ChronoPlay, a novel framework for the automated and continuous generation of game RAG benchmarks. ChronoPlay utilizes a dual-dynamic update mechanism to track both forms of change, and a dual-source synthesis engine that draws from official sources and player community to ensure both factual correctness and authentic query patterns. We instantiate our framework on three distinct games to create the first dynamic RAG benchmark for the gaming domain, offering new insights into model performance under these complex and realistic conditions. Our code is available at: https://github.com/hly1998/ChronoPlay.
Active Learning of 3D Gaussian Splatting with Consistent Region Partition and Robust Pose Estimation
Authors: Ruiqi Li; Yiu-ming Cheung
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Radiance fields have been successful in reconstructing 3D assets for scenes presented in Virtual Reality and Augmented Reality (VR/AR). The general workflow of scanning objects with radiance field representation involves a heavy workload of capturing images depicting the object empirically by the user, and lacks feedback for the image collection stage. This would lead to potential repeated or deficient gathering of information, affecting the efficiency of the reconstruction workflow. In this paper, we therefore present an active learning algorithm for 3D Gaussian Splatting that guides the image capturing by estimating the pose of the most informative image. Specifically, our method first partitions the consistent regions in the model by analyzing the Gaussian attributes and visibility features. Then, we determine the informative region to explore by estimating the semantic feature variance of each Gaussian, which evaluates the quality of the Gaussian cloud from the semantic level features. Furthermore, we tackle the practical problem of noise in the pose of the collected image via a robust pose optimization method. Extensive experimental results on both synthetic and real-world scenes demonstrate the remarkable performance of our algorithm in active learning of the radiance field under both accurate and noisy pose conditions.
DualToken: Towards Unifying Visual Understanding and Generation with Dual Visual Vocabularies
Authors: Wei Song; Yuran Wang; Zijia Song; Yadong Li; Zenan Zhou; Long Chen; Xu Jhua; Jiaqi Wang; Kaicheng Yu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The differing representation spaces required for visual understanding and generation pose a challenge in unifying them within the autoregressive paradigm of large language models. A vision tokenizer trained for reconstruction excels at capturing low-level visual appearance, making it well-suited for visual generation but lacking high-level semantic representations for understanding tasks. Conversely, a vision encoder trained via contrastive learning aligns well with language but struggles to decode back into the pixel space for generation tasks. To bridge this gap, we propose DualToken, a method that unifies representations for both understanding and generation within a single tokenizer. However, directly integrating reconstruction and semantic objectives creates conflicts, leading to degraded performance in both reconstruction fidelity and semantic accuracy. Instead of forcing a single codebook to capture both visual appearance and semantics, DualToken disentangles them by introducing separate codebooks for high-level semantics and low-level visual details. As a result, DualToken achieves 0.25 rFID and 82.0% zero-shot accuracy on ImageNet, and demonstrates strong effectiveness in downstream MLLM tasks for both understanding and generation. Specifically, our method surpasses VILA-U by 5.8 points on average across ten visual understanding benchmarks and delivers a 13% improvement on GenAI-Bench. Notably, incorporating dual visual tokens outperforms using a single token type on both understanding and generation tasks. We hope our research offers a new perspective on leveraging dual visual vocabularies for building unified vision–language models. Project page is available [here](https://songweii.github.io/dualtoken-project-page/).
MAPSS: Manifold-based Assessment of Perceptual Source Separation
Authors: Amir Ivry; Samuele Cornell; Shinji Watanabe
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Objective assessment of audio source‑separation systems still mismatches subjective human perception, especially when interference from competing talkers and distortion of the target signal interact. We introduce Perceptual Separation (PS) and Perceptual Match (PM), a complementary pair of measures that, by design, isolate these leakage and distortion factors.
Our intrusive approach generates a set of fundamental distortions, e.g., clipping, notch filter, and pitch shift from each reference waveform signal in the mixture. Distortions, references, and system outputs from all sources are independently encoded by a pre-trained self-supervised model, then aggregated and embedded with a manifold learning technique called diffusion maps, which aligns Euclidean distances on the manifold with dissimilarities of the encoded waveform representations.
On this manifold, PM captures the self‑distortion of a source by measuring distances from its output to its reference and associated distortions, while PS captures leakage by also accounting for distances from the output to non‑attributed references and distortions.
Both measures are differentiable and operate at a resolution as high as 75 frames per second, allowing granular optimization and analysis.
We further derive, for both measures, frame-level deterministic error radius and non-asymptotic, high-probability confidence intervals.
Experiments on English, Spanish, and music mixtures show that, against 18 widely used measures, the PS and PM are almost always placed first or second in linear and rank correlations with subjective human mean-opinion scores.
InnovatorBench: Evaluating Agents’ Ability to Conduct Innovative AI Research
Authors: Yunze Wu; Dayuan Fu; Weiye Si; Zhen Huang; Mohan Jiang; Keyu Li; Shijie Xia; Jie Sun; Tianze Xu; Yang Xiao; Pengrui Lu; Xiaojie Cai; Lyumanshan Ye; Wenhong Zhu; Xiangkun Hu; Pengfei Liu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: AI agents could accelerate scientific discovery by automating hypothesis formation, experiment design, coding, execution, and analysis, yet existing benchmarks probe narrow skills in simplified settings. To address this gap, we introduce InnovatorBench, a benchmark-platform pair for realistic, end-to-end assessment of agents performing Large Language Model (LLM) research. It comprises 20 tasks spanning Data Construction, Filtering, Augmentation, Loss Design, Reward Design, and Scaffold Construction, which require runnable artifacts and assessment of correctness, performance, output quality, and uncertainty. To support agent operation, we develop ResearchGym, a research environment offering rich action spaces, distributed and long-horizon execution, asynchronous monitoring, and snapshot saving. We also implement a lightweight agent that couples explicit reasoning with executable planning using frontier models such as Claude-4, GPT-5, GLM-4.5, and Kimi-K2. Our experiments demonstrate that while frontier models show promise in code-driven research tasks, they struggle with fragile algorithm-related tasks and long-horizon decision making, such as impatience, poor resource management, and overreliance on template-based reasoning. Furthermore, agents require over 11 hours to achieve their best performance on InnovatorBench, underscoring the benchmark's difficulty and showing the potential of InnovatorBench to be the next generation of code-based research benchmark.
Reliable Probabilistic Forecasting of Irregular Time Series through Marginalization-Consistent Flows
Authors: Vijaya Krishna Yalavarthi; Randolf Scholz; Christian Klötergens; Kiran Madhusudhanan; Stefan Born; Lars Schmidt-Thieme
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Probabilistic forecasting of joint distributions for irregular time series with missing values is an underexplored area in machine learning. Existing models, such as Gaussian Process Regression and ProFITi, are limited: while ProFITi is highly expressive due to its use of normalizing flows, it often produces unrealistic predictions because it lacks marginalization consistency—marginal distributions of subsets of variables may not match those predicted directly, leading to inaccurate marginal forecasts when trained on joints.
We propose MOSES (Mixtures of Separable Flows), a novel model that parametrizes a stochastic process via a mixture of normalizing flows, where each component combines a latent multivariate Gaussian with separable univariate transformations. This design allows MOSES to be analytically marginalized, enabling accurate and reliable predictions for various probabilistic queries.
Thanks to its inherent marginalization consistency, MOSES significantly outperforms all baselines—including ProFITi—on marginal predictions.
For joint predictions, it beats all other consistent models and performs close to or slightly worse than ProFITi. Implementation details:~\url{https://github.com/yalavarthivk/separable_flows}
Structure Learning from Time-Series Data with Lag-Agnostic Structural Prior
Authors: Taiyu Ban; Changxin Rong; Xiangyu Wang; Lyuzhou Chen; Yanze Gao; Xin Wang; Huanhuan Chen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Learning instantaneous and time-lagged causal relationships from time-series data is essential for uncovering fine-grained, temporally-aware interactions. Although this problem has been formulated as a continuous optimization task amenable to modern machine learning methods, existing approaches largely neglect the use of coarse-grained, lag-agnostic causal priors, an important form of prior knowledge that is often available in practice. To address this gap, we propose a novel framework for structure learning from time series to integrate lag-agnostic priors, enabling the discovery of lag-specific causal links without requiring precise temporal annotations. We introduce formulations to precisely characterize the lag-agnostic prior, and demonstrate their consequential and process-equivalence to priors, maintaining consistency with the intended semantics of the priors throughout optimization. We further analyze the challenge for optimization due to the increased non-convexity by lag-agnostic prior constraints, and introduce a data-driven initialization to mitigate this issue. Experiments on both synthetic and real-world datasets show that our method effectively incorporates lag-agnostic prior knowledge to enhance the recovery of fine-grained, lag-aware structures.
Threading Keyframe with Narratives: MLLMs as Strong Long Video Comprehenders
Authors: Bo Fang; YuXin Song; Haoyuan Sun; Qiangqiang Wu; Wenhao Wu; Antoni B. Chan
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Employing Multimodal Large Language Models (MLLMs) for long video understanding remains a challenging problem due to the dilemma between the substantial number of video frames (i.e., visual tokens) versus the limited context length of language models. Traditional uniform sampling often leads to selection of irrelevant content,  while post-training MLLMs on thousands of frames imposes a substantial computational burden. In this paper, we propose _Narrating KeyFrames Capturing_ (Nar-KFC), a plug-and-play module to facilitate effective and efficient long video perception. Nar-KFC generally involves two collaborative steps. First, we formulate the _keyframe_ selection process as an integer quadratic programming problem, jointly optimizing query-relevance and frame-diversity. To avoid its computational complexity, a customized greedy search strategy is designed as an efficient alternative. Second, to mitigate the temporal discontinuity caused by sparse keyframe sampling, we further introduce interleaved textual _narratives_ generated from non-keyframes using off-the-shelf captioners. These narratives are inserted between keyframes based on their true temporal order, forming a coherent and compact representation. Nar-KFC thus serves as a temporal- and content-aware compression strategy that complements visual and textual modalities. Experimental results on multiple long-video benchmarks demonstrate that Nar-KFC significantly improves the performance of popular MLLMs. Codes are publicly available at https://github.com/bofang98/Nar-KFC.
What Layers When: Learning to Skip Compute in LLMs with Residual Gates
Authors: Filipe Laitenberger; Dawid Jan Kopiczko; Cees G. M. Snoek; Yuki M Asano
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We introduce GateSkip, a simple residual-stream gating mechanism that enables token-wise layer skipping in decoder-only LMs. Each Attention/MLP branch is equipped with a sigmoid-linear gate that compresses the branch’s output before it re-enters the residual stream. During inference we rank tokens by the gate and skip low-importance ones using a per-layer budget. While early-exit or router-based Mixture-of-Depths models are known to be unstable and need extensive retraining, our smooth, differentiable gates fine-tune stably on top of pretrained models. On long-form reasoning, we save up to 15% compute while retaining >90% of baseline accuracy. On instruction-tuned models we see accuracy gains at full compute and match baseline quality near 50% savings. The learned gates give insight into transformer information flow (e.g., BOS tokens act as anchors), and the method combines easily with quantization, pruning, and self-speculative decoding.