GenAI4ELab/papercli-papers huggingface dataset:
P2P: Automated Paper-to-Poster Generation and Fine-Grained Benchmark
Authors: Tao Sun; Enhao Pan; Zhengkai Yang; Kaixin Sui; Jiajun Shi; Xianfu Cheng; Tongliang Li; Ge Zhang; Wenhao Huang; Jian Yang; Zhoujun Li
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Academic posters are vital for scholarly communication, yet their manual creation is time-consuming. However, automated academic poster generation faces significant challenges in preserving intricate scientific details and achieving effective visual-textual integration.
Existing approaches often struggle with semantic richness, structural nuances, and lack standardized benchmarks for evaluating generated academic posters comprehensively. 
To address these limitations, we introduce P2P, the first flexible, LLM-based multi-agent framework that generates high-quality, HTML-rendered academic posters directly from research papers. P2P employs three specialized agents—for visual element processing, content generation, and final poster assembly—each integrated with dedicated checker modules to enable iterative refinement and ensure output quality. 
To foster advancements and rigorous evaluation in this domain, we argue that generated posters must be assessed from two complementary perspectives: objective fidelity and subjective quality. 
So we establish P2Peval, a comprehensive benchmark featuring 1738 checklist items and a dual evaluation methodology (Fine-Grained and Universal). 
Our Fine-Grained Evaluation uses human-annotated checklists to objectively measure the faithful preservation of verifiable content from the source paper. Concurrently, our Universal Evaluation captures subjective, holistic quality by training a model to align with human aesthetic preferences across key design principles. We evaluate a total of 35 models.
To power these advancements, we also release P2Pinstruct, the first large-scale instruction dataset comprising over 30,000 high-quality examples tailored for the academic paper-to-poster generation task. Furthermore,  our contributions aim to streamline research dissemination while offering a principled blueprint for evaluating complex, creative AI-generated artifacts. 
The code is on the GitHub, https://github.com/multimodal-art-projection/P2P.
Debugging Concept Bottleneck Models through Removal and Retraining
Authors: Eric Enouen; sainyam galhotra
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Concept Bottleneck Models (CBMs) use a set of human-interpretable concepts to predict the final task label, enabling domain experts to not only validate the CBM's predictions, but also intervene on incorrect concepts at test time. However, these interventions fail to address systemic misalignment between the CBM and the expert's reasoning, such as when the model learns shortcuts from biased data. To address this, we present a general interpretable debugging framework for CBMs that follows a two-step process of *Removal* and *Retraining*. In the *Removal* step, experts use concept explanations to identify and remove any undesired concepts. In the *Retraining* step, we introduce **CBDebug**, a novel method that leverages the interpretability of CBMs as a bridge for converting concept-level user feedback into sample-level auxiliary labels. These labels are then used to apply supervised bias mitigation and targeted augmentation, reducing the model’s reliance on undesired concepts. We evaluate our framework with both real and automated expert feedback, and find that **CBDebug** significantly outperforms prior retraining methods across multiple CBM architectures (PIP-Net, Post-hoc CBM) and benchmarks with known spurious correlations.
From What to Why: A Multi-Agent System for Evidence-based Chemical Reaction Condition Reasoning
Authors: Cheng Yang; Jiaxuan Lu; Haiyuan Wan; Junchi Yu; Feiwei Qin
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The chemical reaction recommendation is to select proper reaction condition parameters for chemical reactions, which is pivotal to accelerating chemical science.With the rapid development of large language models (LLMs), there is growing interest in leveraging their reasoning and planning capabilities for reaction condition recommendation.Despite their success, existing methods rarely explain the rationale behind the recommended reaction conditions, limiting their utility in high-stakes scientific workflows. In this work, we propose ChemMAS, a multi-agent system that reframes condition prediction as an evidence-based reasoning task. ChemMAS decomposes the task into mechanistic grounding, multi-channel recall, constraint-aware agentic debate, and rationale aggregation. Each decision is backed by interpretable justifications grounded in chemical knowledge and retrieved precedents. Experiments show that ChemMAS achieves 20–35\% gains over domain-specific baselines and outperforms general-purpose LLMs by 10–15\% in Top-1 similarity, while offering falsifiable, human-trustable rationales, which establishes a new paradigm for explainable AI in scientific discovery.
GPG: A Simple and Strong Reinforcement Learning Baseline for Model Reasoning
Authors: Xiangxiang Chu; Hailang Huang; Xiao Zhang; Fei Wei; Yong Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Reinforcement Learning (RL) can directly enhance the reasoning capabilities of large language models without extensive reliance on Supervised Fine-Tuning (SFT). In this work, we revisit the traditional Policy Gradient (PG) mechanism and propose a minimalist RL approach termed Group Policy Gradient (GPG). Unlike conventional methods, GPG directly optimizes the original RL objective, thus obviating the need for surrogate loss functions. By eliminating the critic and reference models, avoiding KL divergence constraints, and addressing the advantage and gradient estimation bias, our approach significantly simplifies the training process compared to Group Relative Policy Optimization (GRPO). Our approach achieves superior performance without relying on auxiliary techniques or adjustments. As illustrated in Figure 1, extensive experiments demonstrate that our method not only reduces computational costs but also consistently outperforms GRPO across various unimodal and multimodal tasks.
PTQ4ARVG: Post-Training Quantization for AutoRegressive Visual Generation Models
Authors: Xuewen Liu; Zhikai Li; Jing Zhang; Mengjuan Chen; Jianquan Li; Qingyi Gu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: AutoRegressive Visual Generation (ARVG) models retain an architecture compatible with language models, while achieving performance comparable to diffusion-based models. Quantization is commonly employed in neural networks to reduce model size and computational latency. However, applying quantization to ARVG remains largely underexplored, and existing quantization methods fail to generalize effectively to ARVG models. In this paper, we explore this issue and identify three key challenges: (1) severe outliers at channel-wise level, (2) highly dynamic activations at token-wise level, and (3) mismatched distribution information at sample-wise level. To these ends, we propose PTQ4ARVG, a training-free post-training quantization (PTQ) framework consisting of: (1) Gain-Projected Scaling (GPS) mitigates the channel-wise outliers, which expands the quantization loss via a Taylor series to quantify the gain of scaling for activation-weight quantization, and derives the optimal scaling factor through differentiation. (2) Static Token-Wise Quantization (STWQ) leverages the inherent properties of ARVG, fixed token length and position-invariant distribution across samples, to address token-wise variance without incurring dynamic calibration overhead. (3) Distribution-Guided Calibration (DGC) selects samples that contribute most to distributional entropy, eliminating the sample-wise distribution mismatch. Extensive experiments show that PTQ4ARVG can effectively quantize the ARVG family models to 8-bit and 6-bit while maintaining competitive performance.
ReDDiT: Rehashing Noise for Discrete Visual Generation
Authors: Tianren Ma; Xiaosong Zhang; Boyu Yang; Junlan Feng; Qixiang Ye
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: In the visual generative area, discrete diffusion models are gaining traction for their efficiency and compatibility. However, pioneered attempts still fall behind their continuous counterparts, which we attribute to noise (absorbing state) design
and sampling heuristics. In this study, we propose a rehashing noise approach for discrete diffusion transformer (termed **ReDDiT**), with the aim to extend absorbing states and improve expressive capacity of discrete diffusion models. ReDDiT enriches the potential paths that latent variables traverse during training with randomized multi-index corruption. The derived rehash sampler, which reverses the randomized absorbing paths, guarantees high diversity and low discrepancy of the generation process. These reformulations lead to more consistent and competitive generation quality, mitigating the need for heavily tuned randomness. Experiments show that ReDDiT significantly outperforms the baseline model (reducing gFID from 6.18 to **1.61**) and is on par with the continuous counterparts. The code and models will be publicly available.
Bures-Wasserstein Flow Matching for Graph Generation
Authors: Keyue Jiang; Jiahao Cui; Xiaowen Dong; Laura Toni
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Graph generation has emerged as a critical task in fields ranging from drug discovery to circuit design. Contemporary approaches, notably diffusion and flow-based models, have achieved solid graph generative performance through constructing a probability path that interpolates between reference and data distributions. However, these methods typically model the evolution of individual nodes and edges independently and use linear interpolations in the disjoint space of nodes/edges to build the path. This disentangled interpolation breaks the interconnected patterns of graphs, making the constructed probability path irregular and non-smooth, which causes poor training dynamics and faulty sampling convergence. To address the limitation, this paper first presents a theoretically grounded framework for probability path construction in graph generative models. Specifically, we model the joint evolution of the nodes and edges by representing graphs as connected systems parameterized by Markov random fields (MRF). We then leverage the optimal transport displacement between MRF objects to design a smooth probability path that ensures the co-evolution of graph components. Based on this, we introduce BWFlow, a flow-matching framework for graph generation that utilizes the derived optimal probability path to benefit the training and sampling algorithm design. Experimental evaluations in plain graph generation and molecule generation validate the effectiveness of BWFlow with competitive performance, better training convergence, and efficient sampling.
VINCIE: Unlocking In-context Image Editing from Video
Authors: Leigang Qu; Feng Cheng; Ziyan Yang; Qi Zhao; Shanchuan Lin; Yichun Shi; Yicong Li; Wenjie Wang; Tat-Seng Chua; Lu Jiang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: In-context image editing aims to modify images based on a contextual sequence comprising text and previously generated images. Existing methods typically depend on task-specific pipelines and expert models (e.g., segmentation and inpainting) to curate training data. In this work, we explore whether an in-context image editing model can be learned directly from videos. We introduce a scalable approach to annotate videos as interleaved multimodal sequences. 
To effectively learn from this data, we design three proxy tasks: next-image prediction, current segmentation prediction, and next-segmentation prediction. 
Additionally, we propose a novel multi-turn image editing benchmark to advance research in this area.
Extensive experiments demonstrate that our model exhibits strong in-context image editing capabilities and achieves state-of-the-art results on two multi-turn image editing benchmarks. Despite being trained exclusively on videos, our model also shows promising abilities in multi-concept composition, story generation, and chain-of-editing applications.
Improving Autoregressive Video Modeling with History Understanding
Authors: Wenyang Luo; Haina Qin; Bing Li; Jiwen Lu; Xin Tao; Pengfei Wan; Kun Gai
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Video autoregressive generation (VideoAR) sequentially predicts future frames conditioned on history frames. Despite the advance of recent diffusion-based VideoAR, the role of conditioning signal—internal representations of history frames—remains underexplored. Inspired by the success of strong condition representations in text-conditioned generation, we investigate: \textit{Can better internal representations of history frames improve VideoAR performance?} Through systematic analysis, we show that history representation quality positively correlates with VideoAR, and that enhancing these representations provides gains that cannot be achieved by refining future frames representations alone. Based on these insights, we propose \textbf{MiMo} (Masked History Modeling), a novel framework that seamlessly integrates representation learning into diffusion-based VideoAR. MiMo applies masks to history frame tokens and trains the model to predict masked tokens of current and future frames alongside the diffusion objective, yielding predictive and robust history representations without relying on vision foundation models (VFMs) or heavy architectural changes. Extensive experiments demonstrate that MiMo achieves competitive performance in video prediction and generation tasks while substantially improving training efficiency. Our work underscores the importance of history representations in VideoAR.
Expertise Can Be Helpful for Reinforcement Learning-based Macro Placement
Authors: Chengrui Gao; Yunqi Shi; Ke Xue; Ruo-Tong Chen; Siyuan Xu; Mingxuan Yuan; Chao Qian; Zhi-Hua Zhou
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Chip placement determines the locations of electronic components on a chip layout, which directly impacts performance, power, and area (PPA) metrics, and thus is a critical step in electronic design automation (EDA). As modern chips scale to accommodate millions of components, manual placement by human experts becomes infeasible, necessitating the use of automated algorithms. 
Recently, reinforcement learning (RL) has emerged as a promising approach for automating macro placement, owing to its high optimization efficiency and potential for generalization. 
Despite their promise, existing RL-based methods often neglect the value of expert knowledge accumulated through years of engineering practice. They tend to optimize oversimplified proxy objectives, resulting in suboptimal placements that deviate significantly from expert-designed solutions. 
To bridge this gap, we propose a novel RL-based placement framework that integrates EDA domain expertise from two complementary perspectives: (1) Expert Knowledge Injection: Incorporating well-established placement knowledge, such as dataflow guidance, periphery bias, macro grouping, and I/O keepout constraints, to guide the learning process toward human-level solutions. (2) Expert Workflow Imitation: Emulating the post-refinement process of human experts (i.e., updating the design iteratively based on backend PPA feedback) to progressively optimize timing metrics by employing preference optimization.
Experiments on the ICCAD 2015 and OpenROAD benchmarks demonstrate that our method achieves substantial improvements in PPA metrics (e.g., 32.53\% in total negative slack and 7.74\% in worst negative slack compared to the runner-up method on average), outperforming advanced analytical, black-box optimization, and RL-based methods.
A Rich Knowledge Space for Scalable Deepfake Detection
Authors: Inho Jung; Hyeongjun Choi; Binh M. Le; Hohyun Na; Simon S. Woo
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The proliferation of realistic deepfakes has driven the development of numerous benchmark datasets to support detection research. Despite their increasing volume and diversity, no prior effort has systematically consolidated these resources into a unified framework for large-scale model training, nor has there been a massively pre-trained model tailored to deepfake detection. In this work, we introduce Multi-modal Multi-type Integrated Deepfake Dataset (MMI-DD), a large-scale resource containing 3.6 million facial images, the largest collection to date. It unifies diverse benchmarks with uniform preprocessing, and further provides fine-grained annotations across four deepfake types, as well as VLM-generated descriptions capturing both facial and environmental attributes for each image. By leveraging this comprehensive multi-modal dataset, we construct a foundational deepfake knowledge space that empowers our model to discern a broad spectrum of synthetic media. Our method, $SD^2$ (Scalable Deepfake Detection), refines CLIP for deepfake detection, optimizing image-text classification with rich, type-specific labels. We enhance this with intermediate visual features capturing low-level cues and text label separation loss for stability. We further leverage VLM-generated descriptions and contrastive learning to expand the scope of forgery knowledge, reducing overfitting and enhancing generalization. Extensive experiments on challenging deepfake datasets and AIGC benchmark demonstrate the effectiveness, scalability, and real-world applicability of our approach.
Don't Throw Away Your Beams: Improving Consistency-based Uncertainties in LLMs via Beam Search
Authors: Ekaterina Fadeeva; Maiya Goloburda; Aleksandr Rubashevskii; Roman Vashurin; Artem Shelmanov; Preslav Nakov; Mrinmaya Sachan; Maxim Panov
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Consistency-based methods have emerged as an effective approach to uncertainty quantification (UQ) in large language models. These methods typically rely on several generations obtained via multinomial sampling, measuring their agreement level. However, in short-form QA, multinomial sampling is prone to producing duplicates due to peaked distributions, and its stochasticity introduces considerable variance in uncertainty estimates across runs. We introduce a new family of methods that employ beam search to generate candidates for consistency-based UQ, yielding improved performance and reduced variance compared to multinomial sampling. We also provide a theoretical lower bound on the beam set probability mass under which beam search achieves a smaller error than multinomial sampling. We empirically evaluate our approach on six QA datasets and find that its consistent improvements over multinomial sampling lead to state-of-the-art UQ performance.
Point-MoE: Large-Scale Multi-Dataset Training with Mixture-of-Experts for 3D Semantic Segmentation
Authors: Xuweiyi Chen; Wentao Zhou; Aruni RoyChowdhury; Zezhou Cheng
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: While massively both scaling data and models have become central in NLP and 2D vision, their benefits for 3D point cloud understanding remain limited. 
We study the initial step of 3D point cloud scaling under a realistic regime: large-scale multi-dataset joint training for 3D semantic segmentation, with no dataset labels available at inference time. 
Point clouds arise from a wide range of sensors (e.g., depth cameras, LiDAR) and scenes (e.g., indoor, outdoor), yielding heterogeneous scanning patterns, sampling densities, and semantic biases; naively mixing such datasets degrades standard models. 
We introduce **Point-MoE**, a Mixture-of-Experts design that expands capacity through sparsely activated expert MLPs and a lightweight top-$k$ router, allowing tokens to select specialized experts without requiring dataset supervision. 
Trained jointly on a diverse mix of indoor and outdoor datasets and evaluated on seen datasets and in zero-shot settings, Point-MoE outperforms prior methods without using dataset labels for either training or inference.
This outlines a scalable path for 3D perception: letting the model discover structure in heterogeneous 3D data rather than imposing it via manual curation or dataset-specific heuristics.
Exploratory Memory-Augmented LLM Agent via Hybrid On- and Off-Policy Optimization
Authors: Zeyuan Liu; Jeonghye Kim; Xufang Luo; Dongsheng Li; Yuqing Yang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Exploration remains the key bottleneck for large language model agents trained with reinforcement learning. While prior methods exploit pretrained knowledge, they fail in environments requiring the discovery of novel states. We propose EMPO$^2$, a hybrid RL framework that leverages memory for exploration and combines on- and off-policy updates to make LLMs perform well with memory while also ensuring robustness without it. On ScienceWorld and WebShop, EMPO$^2$ achieves 128.6% and 11.3% improvements over GRPO, respectively. Moreover, in out-of-distribution tests, EMPO$^2$ demonstrates superior adaptability to new tasks, requiring only a few trials with memory and no parameter updates. These results highlight EMPO$^2$ as a promising framework for building more exploratory and generalizable LLM-based agents.
Rex-Thinker: Grounded Object Referring via Chain-of-Thought Reasoning
Authors: Qing Jiang; Xingyu Chen; Zhaoyang Zeng; Junzhi Yu; Lei Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Object referring aims to detect all objects in an image that match a given natural language description. We argue that a robust object referring model should be grounded, meaning its predictions should be both explainable and faithful to the visual content. Specifically, it should satisfy two key properties: 1) Verifiable, by producing interpretable reasoning that justifies its predictions and clearly links them to visual evidence; and 2) Trustworthy, by learning to abstain when no object in the image satisfies the given expression. However, most methods treat referring as a direct bounding box prediction task, offering limited interpretability and struggling to reject expressions with no matching object. In this work, we propose Rex-Thinker, a model that formulates object referring as an explicit CoT reasoning task. Given a referring expression, we first identify all candidate object instances corresponding to the referred object category. Rex-Thinker then performs step-by-step reasoning over each candidate to assess whether it matches the given expression, before making a final prediction. To support this paradigm, we construct a large-scale CoT-style referring dataset named HumanRef-CoT by prompting GPT-4o on the HumanRef dataset. Each reasoning trace follows a structured planning, action, and summarization format, enabling the model to learn decomposed, interpretable reasoning over object candidates. We then train Rex-Thinker in two stages: a cold-start supervised fine-tuning phase to teach the model how to perform structured reasoning, followed by GRPO-based RL learning to improve accuracy and generalization. Experiments show that our approach outperforms standard baselines in both precision and interpretability on in-domain evaluation, while also demonstrating improved ability to reject hallucinated outputs and strong generalization in out-of-domain settings. Code is available at https://github.com/IDEA-Research/Rex-Thinker