GenAI4ELab/papercli-papers huggingface dataset:
Understanding and Relaxing the Limitations of Transformers for Linear Algebra
Authors: Andres Potapczynski; Alex Ali; Andrew Gordon Wilson
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Matrix operations, such as linear solves, eigendecompositions, and log determinants, are foundational building blocks for any number of downstream applications. Therefore, any broadly capable learning system should be able to effectively approximate these operations in its internal representation. Accordingly, there is great motivation to study transformers for linear algebra --- for if transformers cannot even semi-competently perform matrix operations, then we cannot expect them to form a basis for a generally intelligent system. We demonstrate that current techniques developing transformers for linear algebra have striking failure modes, prohibitive scaling, and particularly poor out-of-distribution generalization to other matrix distributions, and matrices of different sizes. Investigating further, we find that current transformer approaches operate as statistical interpolators, rather than discovering algorithms that will generalize to matrices from other distributions. Based on our understanding of these limitations, we develop a sequence of interventions that substantially improve scaling and performance, including matrix embeddings through a learnable projection, linear attention, looping, and a data pre-training distribution of structured matrices. We term the resulting method the \emph{RangeFormer}, which we show has significantly improved scaling and performance on challenging OOD matrices from the \emph{matrix market}. Moreover, with RangeFormer we show for the first time that transformers can be successfully applied to downstream tasks that involve iterative matrix operations, including Gaussian process learning, and improving the sampling distribution of randomized methods.
KRAMABENCH: A Benchmark for AI Systems on Data-to-Insight Pipelines over Data Lakes
Authors: Eugenie Lai; Gerardo Vitagliano; Ziyu Zhang; Om Chabra; SIVAPRASAD SUDHIR; Anna Zeng; Anton A. Zabreyko; Chenning Li; Ferdi Kossmann; Jialin Ding; Jun Chen; Markos Markakis; Matthew Russo; Weiyang Wang; Ziniu Wu; Mike Cafarella; Lei Cao; Samuel Madden; Tim Kraska
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Discovering insights from a real-world data lake potentially containing unclean, semi-structured, and unstructured data requires a variety of data processing tasks, ranging from
extraction and cleaning to integration, analysis, and modeling. This process often also demands domain knowledge and project-specific insight. While AI models have shown remarkable results in reasoning and code generation, their abilities to design and execute complex pipelines that solve these data-lake-to-insight challenges remain unclear. We introduce KramaBench which consists of 104 manually curated and solved challenges spanning 1700 files, 24 data sources, and 6 domains. KramaBench focuses on testing the end-to-end capabilities of AI systems to solve challenges which require automated orchestration of different data tasks. KramaBench also features a comprehensive evaluation framework assessing the pipeline design and individual data task implementation abilities of AI systems. We evaluate 8 LLMs using our single-agent reference framework DS-Guru, alongside both open- and closed-source single- and multi-agent systems, and find that while current agentic systems may handle isolated data-science tasks and generate plausible draft pipelines, they struggle with producing working end-to-end pipelines. On KramaBench, the best system reaches only 55% end-to-end accuracy in the full data-lake setting. Even with perfect retrieval, the accuracy tops out at 62%. Leading LLMs can identify up to 42% of important data tasks but can only fully implement 20% of individual data tasks.
Influence without Confounding: Causal Discovery from Temporal Data with Long-term Carry-over Effects
Authors: Fan Li; Zixuan Liu; Yi Zhao; Qi Tan; Jinyang Li; Ke Xu; Shuihai Hu; jingbin zhou; TAN Kun
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Learning causal structures from temporal data is fundamental to many practical tasks, such as physical laws discovery and root causes localization.
   Real-world systems often exhibit long-term carry-over effects, where the value of a variable at the current time can be influenced by distant past values of other variables. These effects, due to their large temporal span, are challenging to observe or model. Existing methods typically consider finite lag orders, which may lead to confounding from early historical data. Moreover, incorporating historical information often results in computational scalability issues. 
   In this paper, we establish a theoretical framework for causal discovery in complex temporal scenarios where observational data exhibit long-term carry-over effect, and propose LEVER, a theoretically guaranteed novel causal discovery method for incomplete temporal data. Specifically, based on the \textit{Limited-history Causal Identifiability Theorem}, we refine the variable values at each time step with data at a few preceding steps to mitigate long-term historical influences. Furthermore, we establish a theoretical connection between QR decomposition and causal discovery, and design an efficient reinforcement learning process to determine the optimal variable ordering. Finally, we recover the causal structure from the R matrix.
   We evaluate LEVER on both synthetic and real-world datasets. In static cases, LEVER reduces SHD by 17.29\%-40.00\% and improves the F1-score by 5.30\%-8.79\% compared to the best baseline. In temporal cases, it achieves a 64\% reduction in SHD and a 45\% improvement in F1-score. Additionally, LEVER demonstrates significantly higher precision on real-world data compared to baseline methods.
Universal Inverse Distillation for Matching Models with Real-Data Supervision (No GANs)
Authors: Nikita Maksimovich Kornilov; David Li; Tikhon Mavrin; Aleksei Leonov; Nikita Gushchin; Evgeny Burnaev; Iaroslav Sergeevich Koshelev; Alexander Korotin
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: While achieving exceptional generative quality, modern diffusion, flow, and other matching models suffer from slow inference, as they require many steps of iterative generation. Recent distillation methods address this problem by training efficient one-step generators under the guidance of a pre-trained teacher model. However, these methods are often constrained to only one specific framework, e.g., only to diffusion or only to flow models. Furthermore, these methods are originally data-free, and to benefit from the usage of real data, it is required to use an additional complex adversarial training with an extra discriminator model. In this paper, we present **RealUID**, a universal distillation framework for all matching models that seamlessly incorporates real data into the distillation procedure without GANs. Our **RealUID** approach offers a simple theoretical foundation that covers previous distillation methods for Flow Matching and Diffusion models, and can be also extended to their modifications, such as Bridge Matching and Stochastic Interpolants. The code can be found in https://github.com/David-cripto/RealUID.
Discrete Diffusion for Reflective Vision-Language-Action Models in Autonomous Driving
Authors: Pengxiang Li; Yinan Zheng; Yue Wang; Huimin Wang; Hang Zhao; Jingjing Liu; Xianyuan Zhan; Kun Zhan; XianPeng Lang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: End-to-End (E2E) solutions have emerged as a mainstream approach for autonomous driving systems, with Vision-Language-Action (VLA) models representing a new paradigm that leverages pre-trained multimodal knowledge from Vision-Language Models (VLMs) to interpret and interact with complex real-world environments. However, these methods remain constrained by the limitations of imitation learning, which struggles to inherently encode physical rules during training. Existing approaches often rely on complex rule-based post-refinement, employ reinforcement learning that remains largely limited to simulation, or utilize diffusion guidance that requires computationally expensive gradient calculations. To address these challenges, we introduce ReflectDrive, a novel learning-based framework that integrates a reflection mechanism for safe trajectory generation via discrete diffusion. We first discretize the two-dimensional driving space to construct an action codebook, enabling the use of pre-trained Diffusion Language Models for planning tasks through fine-tuning. Central to our approach is a safety-aware reflection mechanism that performs iterative self-correction without gradient computation. Our method begins with goal-conditioned trajectory generation to model multi-modal driving behaviors. Based on this, we apply local search methods to identify unsafe tokens and determine feasible solutions, which then serve as safe anchors for inpainting-based regeneration. Evaluated on the NAVSIM benchmark, ReflectDrive demonstrates significant advantages in safety-critical trajectory generation, offering a scalable and reliable solution for autonomous driving systems.
Variational Inference for Cyclic Learning
Authors: Zhuojun Zou; Jie Hao
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Cyclic learning has emerged as a powerful paradigm for weakly-supervised learning. It involves training with pairs of inverse tasks and leverages cycle-consistency in the design of loss functions. However, its potential remains underexplored, as current methods are often narrowly focused on domain-specific implementations.
In this work, we develop generalized solutions for both pairwise cycle-consistent tasks and self-cycle-consistent tasks. By formulating cross-domain mappings as conditional probability functions, we reformulate the cycle-consistency objective as an evidence lower bound optimization problem via variational inference. Based on this formulation, we further propose two training strategies for arbitrary cyclic learning tasks: single-step optimization and alternating optimization.
Our framework demonstrates broad applicability across diverse tasks. In unpaired image translation, it offers a theoretical justification for CycleGAN and yields CycleGN—a competitive GAN-free alternative. 
In unsupervised tracking, following our conceptual design, CycleTrack and CycleTrack-EM achieve state-of-the-art results on multiple benchmarks.
This work establishes the theoretical foundations of cyclic learning and offers a general paradigm for future research.
The source codes for CycleGN and CycleTrack are publicly available.
JavisDiT++: Unified Modeling and Optimization for Joint Audio-Video Generation
Authors: Kai Liu; Yanhao Zheng; Kai Wang; Shengqiong Wu; Rongjunchen Zhang; Jiebo Luo; Dimitrios Hatzinakos; Ziwei Liu; Hao Fei; Tat-Seng Chua
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent AIGC advances have rapidly expanded from text-to-image generation toward high-quality multimodal synthesis across video and audio. Within this context, joint audio-video generation (JAVG) has emerged as a fundamental task that produces synchronized and semantically aligned sound and vision from textual descriptions. However, compared with advanced commercial models such as Veo3, existing open-source methods still suffer from limitations in generation quality, temporal synchrony, and alignment with human preferences. To bridge the gap, this paper presents JavisDiT++, a concise yet powerful framework for efficient and effective JAVG. First, we introduce a modality-specific mixture-of-experts (MS-MoE) design that enables cross-modal interaction efficacy while enhancing single-modal generation quality. Then, we propose a temporal-aligned RoPE (TA-RoPE) strategy to achieve explicit, frame-level synchronization between audio and video tokens. Besides, we develop an audio-video direct preference optimization (AV-DPO) method to align model outputs with human preference across quality, consistency, and synchrony dimensions. Built upon Wan2.1-1.3B-T2V, our model achieves state-of-the-art performance merely with around 1M public training entries, significantly outperforming prior approaches in both qualitative and quantitative evaluations. Comprehensive ablation studies have been conducted to validate the effectiveness of our proposed modules. All the code, model, and dataset are released at https://JavisVerse.github.io/JavisDiT2-page.
LLMs are Greedy Agents: Effects of RL Fine-tuning on Decision-Making Abilities
Authors: Thomas Schmied; Jörg Bornschein; Jordi Grau-Moya; Markus Wulfmeier; Razvan Pascanu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The success of LLMs has sparked interest in various agentic applications. A key hypothesis is that LLMs, leveraging common sense and Chain-of-Thought (CoT) reasoning, can effectively explore and efficiently solve complex domains.  However, LLM agents have been found to suffer from sub-optimal exploration and the knowing-doing gap, the inability to effectively act on knowledge present in the model. In this work, we systematically study why LLMs perform sub-optimally in decision-making scenarios. In particular, we closely examine three prevalent failure modes: greediness, frequency bias, and the knowing-doing gap. We propose mitigation of these shortcomings by fine-tuning via Reinforcement Learning (RL) on self-generated CoT rationales. Our experiments across multi-armed bandits, contextual bandits, and Tic-tac-toe demonstrate that RL fine-tuning enhances the decision-making abilities of LLMs by increasing exploration and narrowing the knowing-doing gap. Finally, we study both classic exploration mechanisms, such as $\epsilon$-greedy, and LLM-specific approaches, such as self-correction and self-consistency, to enable more effective fine-tuning of LLMs for decision-making.
DeepRAG: Thinking to Retrieve Step by Step for Large Language Models
Authors: Xinyan Guan; Jiali Zeng; Fandong Meng; Chunlei Xin; Yaojie Lu; Hongyu Lin; Xianpei Han; Le Sun; Jie Zhou
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large Language Models (LLMs) have shown remarkable reasoning capabilities, while their practical applications are limited by severe factual hallucinations due to limitations in the timeliness, accuracy, and comprehensiveness of their parametric knowledge. Meanwhile, enhancing retrieval-augmented generation (RAG) with reasoning remains challenging due to ineffective task decomposition and redundant retrieval, which can introduce noise and degrade response quality. In this paper, we propose DeepRAG, a framework that models retrieval-augmented reasoning as a Markov Decision Process (MDP), enabling reasonable and adaptive retrieval. By iteratively decomposing queries, DeepRAG dynamically determines whether to retrieve external knowledge or rely on parametric reasoning at each step. Experiments show that DeepRAG improves retrieval efficiency and boosts answer accuracy by 25.41%, demonstrating its effectiveness in enhancing retrieval-augmented reasoning.
ReactDance: Hierarchical Representation for High-Fidelity and Coherent Long-Form Reactive Dance Generation
Authors: Jingzhong Lin; Xinru Li; Yuanyuan Qi; Bohao Zhang; Wenxiang Liu; Kecheng Tang; Wenxuan Huang; Xiangfeng Xu; Bangyan Li; Changbo Wang; Gaoqi He
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Reactive dance generation (RDG), the task of generating a dance conditioned on a lead dancer's motion, holds significant promise for enhancing human-robot interaction and immersive digital entertainment. Despite progress in duet synchronization and motion-music alignment, two key challenges remain: generating fine-grained spatial interactions and ensuring long-term temporal coherence. In this work, we introduce $\textbf{ReactDance}$, a diffusion framework that operates on a novel hierarchical latent space to address these spatiotemporal challenges in RDG. First, for fine-grained spatial control and artistic expression, we propose Hierarchical Finite Scalar Quantization ($\textbf{HFSQ}$). This multi-scale motion representation effectively disentangles coarse body posture from high-frequency dynamics, enabling independent and detailed control over both aspects through a layered guidance mechanism. Second, to efficiently generate long sequences with high temporal coherence, we propose Blockwise Local Context ($\textbf{BLC}$), a non-autoregressive sampling strategy. Departing from slow, frame-by-frame generation, BLC partitions the sequence into blocks and synthesizes them in parallel via periodic causal masking and positional encodings. Coherence across these blocks is ensured by a dense sliding-window training approach that enriches the representation with local temporal context. Extensive experiments show that ReactDance substantially outperforms state-of-the-art methods in motion quality, long-term coherence, and sampling efficiency. Project page: https://ripemangobox.github.io/ReactDance.
Diagnosing and Remedying Knowledge Deficiencies in LLMs via Label-free Curricular Meaningful Learning
Authors: Kai Xiong; Xiao Ding; Yixin Cao; Li Du; Jiahao Ying; Yang Zhao; Bing Qin; Ting Liu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large Language Models (LLMs) have demonstrated impressive generalization ability by learning from extensive unlabeled text. However, they still exhibit reasoning mistakes, which can affect their trustworthiness and reliability. Although users can interact with LLMs and provide diverse and comprehensive queries to expose the flaws of LLMs, obtaining sufficient and effective feedback is demanding. Furthermore, comprehensively evaluating LLMs with limited labeled samples is difficult. These make it a challenge to diagnose and remedy the deficiencies in LLMs through rich label-free user queries. To tackle this challenge and considersing that LLMs' reasoning mistakes often stem from knowledge deficiencies, we propose label-free curricular meaningful learning (LaMer), which first employs relative entropy to diagnose and quantify knowledge deficiencies of LLMs in a label-free setting. Then, LaMer adaptively synthesizes augmentation data based on deficiency severity and progressively remedies them with a curricular remedy strategy. Experiments show that LaMer effectively diagnoses and remedies knowledge deficiencies in LLMs, improving various LLMs across seven out-of-distribution (OOD) reasoning benchmarks, achieving comparable results to baselines with only 40% training data. LaMer even surpasses methods that rely on labeled data for deficiency diagnosis. In application, LaMer offers a diagnostic tool for efficient LLM development.
Beyond Outliers: A Study of Optimizers Under Quantization
Authors: Georgios Vlassis; Saleh Ashkboos; Alexandra Volkova; Torsten Hoefler; Dan Alistarh
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: As new optimizers gain traction and model quantization becomes standard for efficient deployment, a key question arises: how does the choice of optimizer affect model performance in the presence of quantization? Despite progress in both areas, systematic evidence on optimizer–quantization interactions remains limited. To fill this gap, we study the impact of optimizer choice on model robustness under quantization, considering both post-training quantization (PTQ), and quantization-aware training (QAT). We first train full-precision models, ranging from 50M to 1.5B parameters, with six optimizers, to explore the hyperparameter landscape, and establish well-tuned baselines.
We then apply PTQ to evaluate how model performance degrades when trained with different optimizers. We find that outlier-related metrics, such as the max-to-mean ratio (MMR) and Kurtosis, fail to predict the PTQ performance across different optimizers. We show analytically that this is due to the MMR capturing only isolated layer errors, while ignoring how quantization errors accumulate and propagate through the network. To study the QAT degradation, we train quantized models from scratch and compare them to our original-precision baselines. We find that optimizers performing well in the original pretraining setup may not remain optimal under QAT, and that models trained with Shampoo show the lowest accuracy degradation. Finally, we derive scaling laws for quantization-aware training under different optimizers, showing that Shampoo achieves the highest parameter efficiency of all tested optimizers.
Pareto Variational Autoencoder
Authors: Mincheol Cho; Yedarm Seong; Joong-Ho Won
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: This paper introduces a new class of multivariate power-law distributions---the symmetric Pareto (symPareto) distribution---which can be viewed as an $\ell_1$-norm-based counterpart of the multivariate $t$ distribution, with the motivation of capturing the heavy tail of the target distribution in generative modeling and bringing robustness to noise in downstream tasks such as image denoising.  The symPareto distribution possesses many attractive information-geometric properties with respect to the $\gamma$-power divergence that is a natural alternative to the Kullback-Leibler divergence, the core of the conventional variational autoencoder (VAE) models, for power families.
Leveraging on the joint minimization view of variational inference, this paper proposes the ParetoVAE, a probabilistic autoencoder that minimizes the $\gamma$-power divergence between two statistical manifolds. ParetoVAE employs the symPareto distribution for both prior and encoder, with flexible decoder options including multivariate $t$ and symPareto distributions.  Empirical evidences demonstrate the effectiveness of ParetoVAE across multiple domains through varying the types of the decoder. The $t$ decoder achieves superior performance in sparse, heavy-tailed data reconstruction and word frequency analysis; the symPareto decoder enables robust high-dimensional denoising.
ConfHit: Conformal Generative Design with Oracle-Free Guarantees
Authors: Siddhartha Laghuvarapu; Ying Jin; Jimeng Sun
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The success of deep generative models in scientific discovery requires not only the ability to generate novel candidates but also reliable guarantees that these candidates indeed satisfy desired properties.  Recent conformal-prediction methods offer a path to such guarantees, but its application to generative modeling in drug discovery is limited by budget constraints, lack of oracle access, and distribution shift. To this end, we introduce ConfHit, a distribution-free framework that provides validity guarantees under these conditions. ConfHit formalizes two central questions: (i) Certification: whether a generated batch can be guaranteed to contain at least one hit with a user-specified confidence level, and (ii) Design: whether the generation can be refined to a compact set without weakening this guarantee. ConfHit leverages weighted exchangeability between historical and generated samples to eliminate the need for an experimental oracle, constructs multiple-sample density-ratio weighted conformal p-value to quantify statistical confidence in hits, and proposes a nested testing procedure to certify and refine candidate sets of multiple generated samples while maintaining statistical guarantees. Across representative generative molecule design tasks and a broad range of methods, ConfHit consistently delivers valid coverage guarantees at multiple confidence levels while maintaining compact certified sets, establishing a principled and reliable framework for generative modeling.