GenAI4ELab/papercli-papers huggingface dataset:
Let's Split Up: Zero-Shot Classifier Edits for Fine-Grained Video Understanding
Authors: Kaiting Liu; Hazel Doughty
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Video recognition models are typically trained on fixed taxonomies which are often too coarse, collapsing distinctions in object, manner or outcome under a single label. As tasks and definitions evolve, such models cannot accommodate emerging distinctions and collecting new annotations and retraining to accommodate such changes is costly. To address these challenges, we introduce category splitting, a new task where an existing classifier is edited to refine a coarse category into finer subcategories, while preserving accuracy elsewhere. We propose a zero-shot editing method that leverages the latent compositional structure of video classifiers to expose fine-grained distinctions without additional data. We further show that low-shot fine-tuning, while simple, is highly effective and benefits from our zero-shot initialization. Experiments on our new video benchmarks for category splitting demonstrate that our method substantially outperforms vision-language baselines, improving accuracy on the newly split categories without sacrificing performance on the rest. Project page: https://kaitingliu.github.io/Category-Splitting/.
Target-Aware Video Diffusion Models
Authors: Taeksoo Kim; Hanbyul Joo
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We present a target-aware video diffusion model that generates videos from an input image, in which an actor interacts with a specified target while performing a desired action. The target is defined by a segmentation mask, and the action is described through a text prompt. Our key motivation is to incorporate target awareness into video generation, enabling actors to perform directed actions on designated objects. This enables video diffusion models to act as motion planners, producing plausible predictions of human-object interactions by leveraging the priors of large-scale video generative models. We build our target-aware model by extending a baseline model to incorporate the target mask as an additional input. To enforce target awareness, we introduce a special token that encodes the target's spatial information within the text prompt. We then fine-tune the model with our curated dataset using an additional cross-attention loss that aligns the cross-attention maps associated with this token with the input target mask. To further improve performance, we selectively apply this loss to the most semantically relevant attention regions and transformer blocks. Experimental results show that our target-aware model outperforms existing solutions in generating videos where actors interact accurately with the specified targets. We further demonstrate its efficacy in two downstream applications: zero-shot 3D HOI motion synthesis with physical plausibility and long-term video content creation.
PoliCon: Evaluating LLMs on Achieving Diverse Political Consensus Objectives
Authors: Zhaowei Zhang; Xiaobo Wang; Minghua Yi; Mengmeng Wang; Fengshuo Bai; Zilong Zheng; Yipeng Kang; Yaodong Yang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Achieving political consensus is crucial yet challenging for the effective functioning of social governance. However, although frontier AI systems represented by large language models (LLMs) have developed rapidly in recent years, their capabilities in this scope are still understudied. In this paper, we introduce PoliCon, a novel benchmark constructed from 2,225 high-quality deliberation records of the European Parliament over 13 years, ranging from 2009 to 2022, to evaluate the ability of LLMs to draft consensus resolutions based on divergent party positions under varying collective decision-making contexts and political requirements. Specifically, PoliCon incorporates four factors to build each task environment for finding different political consensus: specific political issues, political goals, participating parties, and power structures based on seat distribution. We also developed an evaluation framework based on social choice theory for PoliCon, which simulates the real voting outcomes of different political parties to assess whether LLM-generated resolutions meet the requirements of the predetermined political consensus. Our experimental results demonstrate that even state-of-the-art models remain undersatisfied with complex tasks like passing resolutions by a two-thirds majority and addressing security issues, while uncovering their inherent partisan biases and revealing some behaviors LLMs show to achieve the consensus, such as prioritizing the stance of the dominant party instead of uniting smaller parties, which highlights PoliCon's promise as an effective platform for studying LLMs' ability to promote political consensus. The code and dataset are released at [PoliCon Website](https://zowiezhang.github.io/projects/PoliCon).
Latent Diffusion Model without Variational Autoencoder
Authors: Minglei Shi; Haolin Wang; Wenzhao Zheng; Ziyang Yuan; Xiaoshi Wu; Xintao Wang; Pengfei Wan; Jie Zhou; Jiwen Lu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent progress in diffusion-based visual generation has largely relied on latent diffusion models with Variational Autoencoders (VAEs). While effective for high-fidelity synthesis, this VAE+Diffusion paradigm still suffers from limited training and inference efficiency, along with poor transferability to broader vision tasks. These issues stem from a key limitation of VAE latent spaces: the lack of clear semantic separation and strong discriminative structure. Our analysis confirms that these properties are not only crucial for perception and understanding tasks, but also equally essential for the stable and efficient training of latent diffusion models. Motivated by this insight, we introduce **SVG**—a novel latent diffusion model without variational autoencoders, which unleashes **S**elf-supervised representations for **V**isual **G**eneration. SVG constructs a feature space with clear semantic discriminability by leveraging frozen DINO features, while a lightweight residual branch captures fine-grained details for high-fidelity reconstruction. Diffusion models are trained directly on this semantically structured latent space to facilitate more efficient learning. As a result, SVG enables accelerated diffusion training, supports few-step sampling, and improves generative quality. Experimental results further show that SVG preserves the semantic and discriminative capabilities of the underlying self-supervised representations, providing a principled pathway toward task-general, high-quality visual representations.
SpatialViz-Bench: A Cognitively-Grounded Benchmark for Diagnosing Spatial Visualization in MLLMs
Authors: Siting Wang; Minnan Pei; Luoyang Sun; Cheng Deng; Yuchen Li; Kun Shao; Zheng Tian; Haifeng Zhang; Jun Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Humans can imagine and manipulate visual images mentally, a capability known as \textit{spatial visualization}. 
While many multi-modal benchmarks assess reasoning on visible visual information, the ability to infer unseen relationships through spatial visualization remains insufficiently evaluated as a spatial skill. 
This reliance on publicly sourced problems from IQ tests or math competitions risks data contamination and compromises assessment reliability.
To this end, we introduce \textbf{\textit{SpatialViz-Bench}}, a comprehensive multi-modal benchmark for \textit{spatial visualization} with \emph{12} tasks across \emph{4} sub-abilities, comprising \emph{1,180} programmatically generated problems, a scalable framework that allows for expansion to ensure fair and continuously reliable evaluations. 
Our evaluation of \emph{27} Multi-modal Large Language Models (MLLMs) reveals wide performance variations, demonstrates the benchmark's strong discriminative power, and uncovers counter-intuitive findings: Chain-of-Thought (CoT) prompting paradoxically degrades accuracy on open-source models.
Through statistical and qualitative analysis of error types, SpatialViz-Bench demonstrates that state-of-the-art MLLMs exhibit deficiencies in \textit{spatial visualization} tasks, thereby addressing a significant lacuna in the field.
Generative Adversarial Reasoner: Enhancing LLM Reasoning with Adversarial Reinforcement Learning
Authors: Qihao Liu; Luoxin Ye; Wufei Ma; Yu-Cheng Chou; Alan Yuille
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large language models (LLMs) with explicit reasoning capabilities excel at mathematical reasoning yet still commit process errors, such as incorrect calculations, brittle logic, and superficially plausible but invalid steps. In this paper, we introduce Generative Adversarial Reasoner, an on-policy joint training framework designed to enhance reasoning by co-evolving an LLM reasoner and an LLM-based discriminator through adversarial reinforcement learning. A compute-efficient review schedule partitions each reasoning chain into logically complete slices of comparable length, and the discriminator evaluates each slice’s soundness with concise, structured justifications. Learning couples complementary signals: the LLM reasoner is rewarded for logically consistent steps that yield correct answers, while the discriminator earns rewards for correctly detecting errors or distinguishing traces in the reasoning process. This produces dense, well-calibrated, on-policy step-level rewards that supplement sparse exact-match signals, improving credit assignment, increasing sample efficiency, and enhancing overall reasoning quality of LLMs. Across various mathematical benchmarks, the method delivers consistent gains over strong baselines with standard RL post-training. Specifically, on AIME24, we improve DeepSeek-R1-Distill-Qwen-7B from 54.0 to 61.3 (+7.3) and DeepSeek-R1-Distill-Llama-8B from 43.7 to 53.7 (+10.0). The modular discriminator also enables flexible reward shaping for objectives such as teacher distillation, preference alignment, and mathematical proof-based reasoning.
Let's Explore Step by Step: Generating Provable Formal Statements with Deductive Exploration
Authors: Qi Liu; Kangjie Bao; Yue Yang; Xinhao Zheng; Renqiu Xia; Qinxiang Cao; Junchi Yan
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Mathematical problem synthesis shows promise in resolving data exhaustion, contamination, and leakage for AI training and evaluation. Despite enormous efforts,
an **expressiveness-validity-complexity trilemma** remains an open question. Existing methods either lack whole-process verifiability, are constrained to a particular domain, or are bounded by external models.
This paper breaks the trilemma by proposing the framework of **DExploration** _(**D**eductive **Exploration**)_, which formulates problem synthesis as a step-by-step exploration process instead of one-shot generation. Agents are equipped with three simple yet powerful atomic actions: _introducing_ variables/hypotheses, _deducing_ new facts, and _submitting_ derived facts. The entire exploration process is formally verified by Lean 4, which encompasses most mathematical domains up to the research level.
Once a conclusion is submitted, the framework outputs a formal statement with guaranteed provability, reducing the need for external models.
To bootstrap training data for DExploration, we propose **Exploratory Transformation** to distill exploration trajectories from existing large-scale theorem-proving data. It rewrites formal proofs into a deductive style, parses dependencies among variables, hypotheses, and proof steps, then reassembles them into exploration trajectories by a topological order.
Experiments validate the effectiveness and efficiency of our methods, achieving an improved success rate ($40.70\\% \mapsto 54.52\\%$), reduced token cost ($52.9\text{K} \mapsto 8.8\text{K}, 83\\%\downarrow$), broader complexity and difficulty distributions, and Pareto optimality.
In $2726$ valid generations, three state-of-the-art provers fail on $60$ (Pass@4) and $8$ (Pass@64).
Your Agent May Misevolve: Emergent Risks in Self-evolving LLM Agents
Authors: Shuai Shao; Qihan Ren; Dongrui Liu; Chen Qian; Boyi Wei; Dadi Guo; Yang JingYi; Xinhao Song; Linfeng Zhang; Weinan Zhang; Jing Shao
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Advances in Large Language Models (LLMs) have enabled a new class of \textbf{\textit{self-evolving agents}} that autonomously improve through environmental interaction, demonstrating strong capabilities. However, self-evolution also introduces novel risks overlooked by current safety research. In this work, we study case where an agent's self-evolution deviates in unintended ways, leading to undesirable or even harmful outcomes. We refer to this as \textit{\textbf{Misevolution}}. We evaluate misevolution along four key evolutionary pathways: model, memory, tool, and workflow. Our empirical findings reveal that misevolution is a widespread risk, affecting agents built even on top-tier LLMs (\textit{e.g.}, Gemini-2.5-Pro). Different emergent risks are observed, such as degradation of safety alignment after memory accumulation, or unintended introduction of vulnerabilities in tool creation and reuse.  To our knowledge, this is the first study to systematically conceptualize misevolution and provide empirical evidence of its occurrence, highlighting an urgent need for new safety paradigms for self-evolving agents. Finally, we discuss potential mitigation strategies to inspire further research on building safer and more trustworthy self-evolving agents.
Dual-Branch Representations with Dynamic Gated Fusion and Triple-Granularity Alignment for Deep Multi-View Clustering
Authors: Wenyuan Kong; Zhibin Gu; Bing Li
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Multi-view clustering seeks to exploit complementary information across different views to enhance clustering performance, where both semantic and structural information are crucial. However, existing approaches often bias toward one type of information while treating the other as auxiliary, overlooking that the reliability of these signals may vary across datasets and that semantic and structural cues can provide complementary and parallel guidance. As a result, such methods may face limitations in generalization and suboptimal clustering performance. To address these issues, we propose a novel method, Dual-branch Representations with dynamic gatEd fusion and triple-grAnularity alignMent (DREAM), for deep multi-view clustering. Specifically, DREAM disentangles semantic information via a Variational Autoencoder (VAE) branch, while simultaneously captures structure-aware features through a Graph Convolutional Network (GCN) branch. The resulting representations are dynamically integrated using a gated fusion module that leverages structural cues as complementary guidance, adaptively balancing semantic and structural contributions to produce clustering-oriented latent embeddings. To further improve robustness and discriminability, we introduce a triple-granularity feature alignment mechanism that enforces consistency across views, within individual samples, and intra-cluster, thereby preserving semantic-structural coherence while enhancing inter-cluster separability. Extensive experiments on benchmark datasets demonstrate that DREAM significantly outperforms SOTA approaches, highlighting the effectiveness of disentangled dual-branch encoding, adaptive gated fusion, and triple-granularity feature alignment.
AdaRank: Adaptive Rank Pruning for Enhanced Model Merging
Authors: Chanhyuk Lee; Jiho Choi; Chanryeol Lee; Donggyun Kim; Seunghoon Hong
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Model merging has emerged as a promising approach for unifying independently fine-tuned models into an integrated framework, significantly enhancing computational efficiency in multi-task learning. 
Recently, several SVD-based techniques have been introduced to exploit low-rank structures for enhanced merging, but their reliance on heuristically designed rank selection often leads to inter-task interference and suboptimal performance.
In this paper, we propose **AdaRank**, a model merging framework that replaces this heuristic selection by adaptively selecting the beneficial singular components of task vectors to merge multiple models. 
We first show empirically that **(i)** selecting only the top singular components of task vectors can cause critical interference with other tasks, and **(ii)** assigning fixed ranks does not align with the varying complexity of tasks and layers. 
AdaRank addresses both issues by adapting per-component masks, indicating the selection of the component, to the unlabeled test data with entropy minimization. 
Our experimental findings show that AdaRank consistently improves existing merging methods across diverse backbones from different modalities, largely narrowing the performance gap against individually fine-tuned models.
Training-Free Reward-Guided Image Editing via Trajectory Optimal Control
Authors: Jinho Chang; Jaemin Kim; Jong Chul Ye
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent advancements in diffusion and flow-matching models have demonstrated remarkable capabilities in high-fidelity image synthesis. A prominent line of research involves reward-guided guidance, which steers the generation process during inference to align with specific objectives. However, leveraging this reward-guided approach to the task of image editing, which requires preserving the semantic content of the source image while enhancing a target reward, is largely unexplored.
In this work, we introduce a novel framework for training-free, reward-guided image editing. We formulate the editing process as a trajectory optimal control problem where the reverse process of a diffusion model is treated as a controllable trajectory originating from the source image, and the adjoint states are iteratively updated to steer the editing process. Through extensive experiments across distinct editing tasks, we demonstrate that our approach significantly outperforms existing inversion-based training-free guidance baselines, achieving a superior balance between reward maximization and fidelity to the source image without reward hacking.
Johnson-Lindenstrauss Lemma Guided Network for Efficient 3D Medical Segmentation
Authors: Jinpeng Lu; Linghan Cai; Yinda Chen; Guo Tang; Songhan Jiang; Haoyuan Shi; Zhiwei Xiong
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Lightweight 3D medical image segmentation remains constrained by a fundamental "efficiency / robustness conflict", particularly when processing complex anatomical structures and heterogeneous modalities. In this paper, we study how to redesign the framework based on the characteristics of high-dimensional 3D images, and explore data synergy to overcome the fragile representation of lightweight methods. Our approach, VeloxSeg, begins with a deployable and extensible dual-stream CNN-Transformer architecture composed of Paired Window Attention (PWA) and Johnson-Lindenstrauss lemma-guided convolution (JLC). For each 3D image, we invoke a "glance-and-focus" principle, where PWA rapidly retrieves multi-scale information, and JLC ensures robust local feature extraction with minimal parameters, significantly enhancing the model's ability to operate with low computational budget. Followed by an extension of the dual-stream architecture that incorporates modal interaction into the multi-scale image-retrieval process, VeloxSeg efficiently models heterogeneous modalities. Finally, Spatially Decoupled Knowledge Transfer (SDKT) via Gram matrices injects the texture prior extracted by a self-supervised network into the segmentation network, yielding stronger representations than baselines at no extra inference cost. Experimental results on multimodal benchmarks show that VeloxSeg achieves a 26\% Dice improvement, alongside increasing GPU throughput by 11x, CPU by 48x, and reducing training peak GPU memory usage by 1/20, inference by 1/24. Code is available at https://github.com/JinPLu/VeloxSeg.
PartSAM: A Scalable Promptable Part Segmentation Model Trained on Native 3D Data
Authors: Zhe Zhu; Le Wan; Rui Xu; Yiheng Zhang; Honghua Chen; Zhiyang Dou; Cheng Lin; Yuan Liu; Mingqiang Wei
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Segmenting 3D objects into parts is a long-standing challenge in computer vision. To overcome taxonomy constraints and generalize to unseen 3D objects, recent works turn to open-world part segmentation. These approaches typically transfer supervision from 2D foundation models, such as SAM, by lifting multi-view masks into 3D. However, this indirect paradigm fails to capture intrinsic geometry, leading to surface-only understanding, uncontrolled decomposition, and limited generalization. We present PartSAM, the first promptable part segmentation model trained natively on large-scale 3D data. Following the design philosophy of SAM, PartSAM employs an encoder–decoder architecture in which a triplane-based dual-branch encoder produces spatially structured tokens for scalable part-aware representation learning. To enable large-scale supervision, we further introduce a model-in-the-loop annotation pipeline that curates over five million 3D shape–part pairs from online assets, providing diverse and fine-grained labels. This combination of scalable architecture and diverse 3D data yields emergent open-world capabilities: with a single prompt, PartSAM achieves highly accurate part identification, and in a “Segment-Every-Part” mode, it automatically decomposes shapes into both surface and internal structures. Extensive experiments show that PartSAM outperforms state-of-the-art methods by large margins across multiple benchmarks, marking a decisive step toward foundation models for 3D part understanding.
Adaptive Social Learning via Mode Policy Optimization for Language Agents
Authors: Minzheng Wang; Yongbin Li; Haobo Wang; Xinghua Zhang; Nan Xu; Bingli Wu; Fei Huang; Haiyang Yu; Wenji Mao
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Effective social intelligence simulation requires language agents to dynamically adjust reasoning depth, a capability notably absent in current studies. Existing methods either lack explicit reasoning or employ lengthy Chain-of-Thought reasoning uniformly across all scenarios, resulting in excessive token usage and inflexible social behaviors in tasks such as negotiation or collaboration. 
To address this, we propose an $\textbf{A}$daptive $\textbf{S}$ocial $\textbf{L}$earning ($\textbf{ASL}$) framework in this paper, aiming to improve the adaptive reasoning ability of language agents in dynamic social interactions. To this end, we first identify the hierarchical reasoning modes under such context, ranging from intuitive response to deep deliberation based on the cognitive control theory. We then develop the $\textbf{A}$daptive $\textbf{M}$ode $\textbf{P}$olicy $\textbf{O}$ptimization ($\textbf{AMPO}$) algorithm to learn the context-aware mode adaptation and reasoning. Our framework advances existing research in three key aspects: (1) Multi-granular reasoning mode design, (2) Context-aware mode switching in rich social interaction, and (3) Token-efficient reasoning with depth adaptation. Extensive experiments on the benchmark social intelligence environment verify that ASL achieves 15.6\% higher task performance than GPT-4o. Notably, our AMPO outperforms GRPO by 7.0\% with 32.8\% shorter thinking chains, demonstrating the advantages of our AMPO and the learned adaptive reasoning ability over GRPO's solution.