GenAI4ELab/papercli-papers huggingface dataset:
Scaling Generalist Data-Analytic Agents
Authors: Shuofei Qiao; Yanqiu Zhao; Zhisong Qiu; Xiaobin Wang; Jintian Zhang; Zhao Bin; Ningyu Zhang; Yong Jiang; Pengjun Xie; Fei Huang; Huajun Chen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Data-analytic agents are emerging as a key catalyst for automated scientific discovery and for the vision of Innovating AI. Current approaches, however, rely heavily on prompt engineering over proprietary models, while open-source models struggle to face diverse-format, large-scale data files and long-horizon, multi-step reasoning that real-world analytics demands. This paper introduces DataMind, a scalable data synthesis and agent training recipe designed to build generalist data-analytic agents. DataMind tackles three key challenges in building open-source data-analytic agents, including insufficient data resources, improper training strategy, and unstable code-based multi-turn rollout. Concretely, DataMind applies 1) a fine-grained task taxonomy and a recursive easy-to-hard task composition mechanism to increase the diversity and difficulty of synthesized queries; 2) a knowledge-augmented trajectory sampling strategy followed by model-based and rule-based filtering; 3) a dynamically adjustable training objective combining both SFT and RL losses; 4) a memory-frugal and stable code-based multi-turn rollout framework. Built on DataMind, we curate DataMind-12K, a high-quality trajectory set spanning diverse domains, task categories, and data file formats for data-analytic tasks. Trained on DataMind-12K, our DataMind-14B achieves state-of-the-art with an average score of 71.16% on multiple data analysis benchmarks, outperforming the strongest proprietary baselines DeepSeek-V3.1 and GPT-5. Our DataMind-7B also performs best among all open-source models with a score of 68.10%. We also incorporate some empirical insights gained from our exploratory trials into the analysis experiments, aiming to provide actionable insights about agentic training for the community. We will release DataMind-12K and DataMind-7B,14B for the community's future research.
Enhancing Trustworthiness of Fine-Tuned LLMs via Regularized Subset Selection
Authors: Kumar Shubham; Nishant Sharma; Karn Tiwari; Prathosh AP
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Supervised fine-tuning (SFT) improves large language model (LLM) perplexity but can also degrade trustworthiness—leading to the generation of untruthful, biased, or unsafe content during user interactions. These issues are often traced back to specific phrases or patterns in the training data. However, correcting them usually requires expensive retraining or new data collection. In this work, we propose a two-stage, compute-efficient repair of the post-SFT models that enhances trustworthiness while preserving the downstream performance. In the first stage, we identify the training samples responsible for failures on trustworthiness metrics like truthfulness, stereotypical bias, and machine ethics—and select a small, diverse subset of these examples using a determinantal point process (DPP)-based regularization. In the second stage, we repair the model under the framework of proximal Bregman response function (PBRF) using a gradient ascent update, which enhances trustworthiness while preserving downstream task performance (perplexity). We evaluate our method on multiple LLMs of varying sizes and demonstrate up to 21\% improvement in trustworthiness metrics with minimal impact ($\leq1$ %) on perplexity. Our method provides a computationally efficient approach to enhance post-SFT models and offers a practical alternative to hours of retraining required for model repair
ICDiffAD: Implicit Conditioning Diffusion Model for Time Series Anomaly Detection
Authors: Fan Zhang; Sinchee Chin; Jing-Hao Xue; Wenming Yang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Time series anomaly detection (TSAD) faces critical challenges from intrinsic data noisiness and temporal heterogeneity, which undermine the reconstruction fidelity of prevailing generative approaches. 
While diffusion models offer theoretical advantages in capturing complex temporal dynamics, their inherent stochasticity introduces irreducible variance in reconstructions.
We present the ICDiffAD, a novel method that synergizes adaptive noise scheduling with semi-deterministic generation to address these limitations. ICDiffAD introduces two key innovations: 
(1) an *SNR Scheduler* that governs training through quantifiable noise scales, enabling robust learning of normative patterns across non-stationary regimes; and 
(2) an *SNR Implicit Conditioning Mechanism* that initializes reverse diffusion from partially corrupted inputs, preserving signal coherence while attenuating anomalous components. 
This dual strategy ensures high-fidelity reconstructions aligned with the input’s manifold, reconciling generative flexibility with detection accuracy. 
Across five multivariate benchmarks, ICDiffAD improves the F1 score by 19.57\% and reduces false positives by 60.23\% compared to existing diffusion model-based TSAD methods.
ImageRAG: Dynamic Image Retrieval for Reference-Guided Image Generation
Authors: Rotem Shalev Arkushin; Rinon Gal; Amit H. Bermano; Ohad Fried
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: While recent generative models synthesize high-quality visual content, they still struggle with generating rare or fine-grained concepts.
To address this challenge, we explore the usage of Retrieval-Augmented Generation (RAG) for image generation, and introduce ImageRAG, a training-free method for rare concept generation. 
Using a Vision Language Model (VLM), ImageRAG identifies generation gaps between an input prompt and a generated image dynamically, retrieves relevant images, and uses them as context to guide the generation process. 
Prior approaches that use retrieved images require training models specifically for retrieval-based generation. In contrast, ImageRAG leverages existing image conditioning models, and does not require RAG-specific training.
We demonstrate our approach is highly adaptable through evaluation over different backbones, including models trained to receive image inputs and models augmented with a post-training image-prompt adapter. 
Through extensive quantitative, qualitative, and subjective evaluation, we show that incorporating retrieved references consistently improves the generation abilities of rare and fine-grained concepts across three datasets and three generative models.
FAPO: Flawed-Aware Policy Optimization for Efficient and Reliable Reasoning
Authors: Yuyang Ding; Chi Zhang; Juntao Li; Haibin Lin; Xin Liu; Min Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Reinforcement learning with verifiable rewards (RLVR) has emerged as a promising paradigm for enhancing the reasoning capabilities of 
large language models (LLMs).
In this context, models explore reasoning trajectories and exploit rollouts with correct answers as positive signals for policy optimization.
However, these rollouts might involve flawed patterns such as answer-guessing and jump-in-reasoning.
Such flawed-positive rollouts are rewarded identically to fully correct ones, causing policy models to internalize these unreliable reasoning patterns.
In this work, we first conduct a systematic study of flawed-positive rollouts in RL and find that they enable rapid capability gains during the early optimization stage, while constraining reasoning capability later by reinforcing unreliable patterns.
Building on these insights, we propose **F**lawed-**A**ware **P**olicy **O**ptimization (**FAPO**), which presents a parameter-free reward penalty for flawed-positive rollouts, enabling the policy to leverage them as useful shortcuts in the warm-up stage, securing stable early gains, while gradually shifting optimization toward reliable reasoning in the later refinement stage.
To accurately and comprehensively detect flawed-positive rollouts, we introduce a generative reward model (GenRM) with a process-level reward that precisely localizes reasoning errors.
Experiments show that FAPO is effective in broad domains, improving outcome correctness, process reliability, and training stability without increasing the token budget.
Long-Context Generalization with Sparse Attention
Authors: Pavlo Vasylenko; Hugo Pitorro; Andre Martins; Marcos Vinicius Treviso
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Transformer-based architectures traditionally employ softmax to compute attention weights, which produces dense distributions over all tokens in a sequence. 
    While effective in many settings, this density has been shown to be detrimental for tasks that demand precise focus on fixed-size patterns: as sequence length increases, non-informative tokens accumulate  attention probability mass, leading to dispersion and representational collapse.
    We show in this paper that dynamically sparse attention mechanisms using $\alpha$-entmax can avoid these issues, due to their ability to assign exact zeros to irrelevant tokens. Furthermore, we introduce Adaptive-Scalable Entmax (ASEntmax), which endows $\alpha$-entmax with a learnable temperature parameter, allowing the attention distribution to interpolate between sparse (pattern-focused) and dense (softmax-like) regimes.
    Our empirical evaluation on synthetic tasks and language modeling demonstrates that ASEntmax substantially outperforms softmax, scalable softmax, and fixed-temperature $\alpha$-entmax baselines, achieving up to 1000$\times$ length extrapolation on synthetic benchmarks and superior long-context generalization on language modeling while preserving short-context performance, including better perplexity trends and higher retrieval accuracies at 8$\times$ training length.
Efficient Offline Reinforcement Learning via Peer-Influenced Constraint
Authors: Yujia Zhang; Lin Li; Wei Wei; Jianguo Wu; Yi Ma; Jiye Liang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Offline reinforcement learning (RL) seeks to learn an optimal policy from a fixed dataset, but distributional shift between the dataset and the learned policy often leads to suboptimal real-world performance. Existing methods typically use behavior policy regularization to constrain the learned policy, but these conservative approaches can limit performance and generalization, especially when the behavior policy is suboptimal. We propose a Peer-Influenced Constraint (PIC) framework with a ``peer review" mechanism. Specifically, we construct a set of similar states and use the corresponding actions as candidates, from which we select the optimal action to constrain the policy. This method helps the policy escape local optima while approximately ensuring the staying within the in-distribution space, boosting both performance and generalization. We also introduce an improved version, Ensemble Peer-Influenced Constraint (EPIC), which combines ensemble methods to achieve strong performance while maintaining high efficiency. Additionally, we uncover the Coupling Effect between PIC and uncertainty estimation, providing valuable insights for offline RL. We evaluate our methods on classic continuous control tasks from the D4RL benchmark, with both PIC and EPIC achieving competitive performance compared to state-of-the-art approaches.
Token-Based Audio Inpainting via Discrete Diffusion
Authors: Tali Dror; Iftach Shoham; Moshe Buchris; Oren Gal; Haim H. Permuter; Gilad Katz; Eliya Nachmani
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Audio inpainting seeks to restore missing segments in degraded recordings. Previous diffusion-based methods exhibit impaired performance when the missing region is large. We introduce the first approach that applies discrete diffusion over tokenized music representations from a pre-trained audio tokenizer, enabling stable and semantically coherent restoration of long gaps. Our method further incorporates two training approaches: a derivative-based regularization loss that enforces smooth temporal dynamics, and a span-based absorbing transition that provides structured corruption during diffusion. Experiments on the MusicNet
and MAESTRO datasets with gaps up to 750ms show that our approach consistently outperforms strong baselines across range of gap lengths, for gaps of 150ms and above. This work advances musical audio restoration and introduces new directions for discrete diffusion model training. Visit our project page for examples and code.
Continual Unlearning for Text-to-Image Diffusion Models: A Regularization Perspective
Authors: Justin Lee; Zheda Mai; Jinsu Yoo; Chongyu Fan; Cheng Zhang; Wei-Lun Chao
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Machine unlearning—the ability to remove designated concepts from a pre-trained
model—has advanced rapidly, particularly for text-to-image diffusion models.
However, existing methods typically assume that unlearning requests arrive all
at once, whereas in practice they often arrive sequentially. We present the first
systematic study of continual unlearning in text-to-image diffusion models and
show that popular unlearning methods suffer from rapid utility collapse: after only
a few requests, models forget retained knowledge and generate degraded images.
We trace this failure to cumulative parameter drift from the pre-training weights
and argue that regularization is crucial to addressing it. To this end, we study a
suite of add-on regularizers that (1) mitigate drift and (2) remain compatible with
existing unlearning methods. Beyond generic regularizers, we show that semantic
awareness is essential for preserving concepts close to the unlearning target, and
propose a gradient-projection method that constrains parameter drift orthogonal
to their subspace. This substantially improves continual unlearning performance
and is complementary to other regularizers for further gains. Taken together, our
study establishes continual unlearning as a fundamental challenge in text-to-image
generation and provides insights, baselines, and open directions for advancing safe
and accountable generative AI.
LikePhys: Evaluating Intuitive Physics Understanding in Video Diffusion Models via Likelihood Preference
Authors: Jianhao Yuan; Fabio Pizzati; Francesco Pinto; Lars Kunze; Ivan Laptev; Paul Newman; Philip Torr; Daniele De Martini
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Intuitive physics understanding in video diffusion models plays an essential role in building general-purpose physically plausible world simulators, yet accurately evaluating such capacity remains a challenging task due to the difficulty in disentangling physics correctness from visual appearance in generation. To the end, we introduce LikePhys, a training-free method that evaluates intuitive physics in video diffusion models by distinguishing physically valid and impossible videos using the denoising objective as an ELBO-based likelihood surrogate on a curated dataset of valid-invalid pairs. By testing on our constructed benchmark of twelve scenarios spanning over four physics domains, we show that our evaluation metric, Plausibility Preference Error (PPE), demonstrates strong alignment with human
preference, outperforming state-of-the-art evaluator baselines. We then systematically benchmark intuitive physics understanding in current video diffusion models. Our study further analyses how model design and inference settings affect intuitive physics understanding and highlights domain-specific capacity variations across physical laws. Empirical results show that, despite current models struggling with complex and chaotic dynamics, there is a clear trend of improvement in physics understanding as model capacity and inference settings scale up.
Revisiting [CLS] and Patch Token Interaction in  Vision Transformers
Authors: Alexis Marouani; Oriane Siméoni; Herve Jegou; Piotr Bojanowski; Huy V. Vo
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Vision Transformers have emerged as powerful, scalable and versatile representation learners. To capture both global and local features, a learnable [CLS] class token is typically prepended to the input sequence of patch tokens. Despite their distinct nature, both token types are processed identically throughout the model.
In this work, we investigate the friction between global and local feature learning under different pre-training strategies by analyzing the interactions between class and patch tokens.
Our analysis reveals that standard normalization layers introduce an implicit differentiation between these token types. Building on this insight, we propose specialized processing paths that selectively disentangle the computational flow of class and patch tokens, particularly within normalization layers and early query-key-value projections.
This targeted specialization leads to significantly improved patch representation quality for dense prediction tasks. Our experiments demonstrate segmentation performance gains of over 2 mIoU points on standard benchmarks, while maintaining strong classification accuracy. The proposed modifications introduce only an 8\% increase in parameters, with no additional computational overhead.
Through comprehensive ablations, we provide insights into which architectural components benefit most from specialization and how our approach generalizes across model scales and learning frameworks.
SketchThinker-R1: Towards Efficient Sketch-Style Reasoning in Large Multimodal Models
Authors: Ruiyang Zhang; Dongzhan Zhou; Zhedong Zheng
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Despite the empirical success of extensive, step-by-step reasoning in large multimodal models, long reasoning processes inevitably incur substantial computational overhead, i.e., in terms of higher token costs and increased response time, which undermines inference efficiency. In contrast, humans often employ sketch-style reasoning: a concise, goal-directed cognitive process that prioritizes salient
information and enables efficient problem-solving. Inspired by this cognitive efficiency, we propose SketchThinker-R1, which incentivizes sketch-style reasoning ability in large multimodal models. Our method consists of three primary stages. In the Sketch-Mode Cold Start stage, we convert standard long reasoning process into sketch-style reasoning and finetune base multimodal model, instilling initial sketch-style reasoning capability. Next, we train SketchJudge Reward Model, which explicitly evaluates thinking process of model and assigns higher scores to sketch-style reasoning. Finally, we conduct Sketch-Thinking Reinforcement Learning under supervision of SketchJudge to further generalize sketch-style reasoning ability. Experimental evaluation on four benchmarks reveals that our SketchThinker-R1 achieves over 64% reduction in reasoning token cost without compromising final answer accuracy. Qualitative analysis further shows that sketch-style reasoning focuses more on key cues during problem solving.
ACADREASON: Exploring the Limits of Reasoning Models with Academic Research Problems
Authors: Xin Gui; King Zhu; JinCheng Ren; Qianben Chen; Zekun Moore Wang; Yizhi LI; Xinpeng Liu; REN WENLI; Linyu Miao; Tianrui Qin; Ziqi Shu; He Zhu; Dingfeng Shi; Jiaheng Liu; Yuchen Eleanor Jiang; Minghao Liu; Ge Zhang; Wangchunshu Zhou
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: In recent years, the research focus of large language models (LLMs) and agents has shifted increasingly from demonstrating novel capabilities to complex reasoning and tackling challenging tasks. However, existing evaluations focus mainly on math/code contests or general tasks, while existing multi-domain academic benchmarks lack sufficient reasoning depth, leaving the field without a rigorous benchmark for high-level reasoning. To fill this gap, we introduce the ACADREASON benchmark, designed to evaluate the ability of LLMs and agents to acquire and reason over academic knowledge. 
It consists of 50 expert-annotated academic problems across five high-reasoning domains, including computer science, economics, law, mathematics, and philosophy. All questions are sourced from top-tier publications in recent years and undergo rigorous annotation and quality control to ensure they are both challenging and answerable. We conduct systematic evaluations over 10 mainstream LLMs and agents. The results show that most LLMs scored below 20 points, with even the cutting-edge GPT-5 achieving only 16 points. While agents achieved higher scores, none exceeded 40 points. This demonstrates the current capability gap between LLMs and agents in super-intelligent academic research tasks and highlights the challenges of ACADREASON. The code and data for the ACADREASON benchmark are available at https://github.com/OPPO-PersonalAI/Acadreason-benchmark.
Entering the Era of Discrete Diffusion Models: A Benchmark for Schrödinger Bridges and Entropic Optimal Transport
Authors: Xavier Aramayo Carrasco; Grigoriy Ksenofontov; Aleksei Leonov; Iaroslav Sergeevich Koshelev; Alexander Korotin
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The Entropic Optimal Transport (EOT) problem and its dynamic counterpart, the Schrödinger bridge (SB) problem, play an important role in modern machine learning, linking generative modeling with optimal transport theory. While recent advances in discrete diffusion and flow models have sparked growing interest in applying SB methods to discrete domains, there remains no reliable way to assess how well these methods actually solve the underlying problem. We address this challenge by introducing a benchmark for SB on discrete spaces. Our construction yields pairs of probability distributions with analytically known SB solutions, enabling rigorous evaluation. As a byproduct of building this benchmark, we obtain two new SB algorithms, DLightSB and DLightSB-M, and additionally extend prior related work to construct the $\alpha$-CSBM algorithm. We demonstrate the utility of our benchmark by evaluating both existing and new solvers in high-dimensional discrete settings. This work provides the first step toward proper evaluation of SB methods on discrete spaces, paving the way for more reproducible future studies. The code for the benchmark and all associated experiments is available at [this repository](https://github.com/gregkseno/catsbench).
Replicable Reinforcement Learning with Linear Function Approximation
Authors: Eric Eaton; Marcel Hussing; Michael Kearns; Aaron Roth; Sikata Bela Sengupta; Jessica Sorrell
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Replication of experimental results has been a challenge faced by many scientific disciplines, including the field of machine learning. Recent work on the theory of machine learning has formalized replicability as the demand that an algorithm produce identical outcomes when executed twice on different samples from the same distribution. Provably replicable algorithms are especially interesting for reinforcement learning (RL), where algorithms are known to be unstable in practice. While replicable algorithms exist for tabular RL settings, extending these guarantees to more practical function approximation settings has remained an open problem. In this work, we make progress by developing replicable methods for linear function approximation in RL. We first introduce two efficient algorithms for replicable random design regression and uncentered covariance estimation, each of independent interest. We then leverage these tools to provide the first provably efficient replicable RL algorithms for linear Markov decision processes in both the generative model and episodic settings. Finally, we evaluate our algorithms experimentally and show how they can inspire more consistent neural policies.