GenAI4ELab/papercli-papers huggingface dataset:
Adaptive Data-Knowledge Alignment in Genetic Perturbation Prediction
Authors: Yuanfang Xiang; Lun Ai
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The transcriptional response to genetic perturbation reveals fundamental insights into complex cellular systems. While current approaches have made progress in predicting genetic perturbation responses, they provide limited biological understanding and cannot systematically refine existing knowledge. Overcoming these limitations requires an end-to-end integration of data-driven learning and existing knowledge. However, this integration is challenging due to inconsistencies between data and knowledge bases, such as noise, misannotation, and incompleteness. To address this challenge, we propose ALIGNED (Adaptive aLignment for Inconsistent Genetic kNowledgE and Data), a neuro-symbolic framework based on the Abductive Learning (ABL) paradigm. This end-to-end framework aligns neural and symbolic components and performs systematic knowledge refinement. We introduce a balanced consistency metric to evaluate the predictions' consistency against both data and knowledge. Our results show that ALIGNED outperforms state-of-the-art methods by achieving the highest balanced consistency, while also re-discovering biologically meaningful knowledge. Our work advances beyond existing methods to enable both the transparency and the evolution of mechanistic biological understanding.
CompassNav: Steering From Path Imitation to Decision Understanding In Navigation
Authors: LinFeng Li; Jian Zhao; Yuan Xie; Xin Tan; Xuelong Li
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The dominant paradigm for training Large Vision-Language Models (LVLMs) in navigation relies on imitating expert trajectories. This approach reduces the complex navigation task to a sequence-to-sequence replication of a single correct path, fundamentally limiting the agent's ability to explore and generalize. In this work, we argue for and introduce a new paradigm: a shift from Path Imitation to Decision Understanding. The goal of this paradigm is to build agents that do not just follow, but truly understand how to navigate. We materialize this through two core contributions: first, we introduce Compass-Data-22k, a novel 22k-trajectory dataset.Its Reinforcement Fine-Tuning (RFT) subset provides a panoramic view of the decision landscape by annotating all feasible actions with A* geodesic distances. Second, we design a novel gap-aware hybrid reward function that dynamically adapts its feedback to decision certainty, shifting between decisive signals for optimal actions and nuanced scores to encourage exploration. Integrated into an SFT-then-RFT recipe, our CompassNav agent is trained not to memorize static routes, but to develop an internal ``compass'' that constantly intuits the direction to the goal by evaluating the relative quality of all possible moves. This approach enables our 7B agent to set a new state-of-the-art on Goal navigation benchmarks, outperforming even larger proprietary models, and achieve robust real-world goal navigation on a physical robot.
ARES: Multimodal Adaptive Reasoning via Difficulty-Aware Token-Level Entropy Shaping
Authors: Shuang Chen; Hangyu Guo; Yimeng Ye; Shijue Huang; Wenbo Hu; Jiayu Chen; Manyuan Zhang; Haoxi Li; Song Guo; Nanyun Peng
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent advances in multimodal large reasoning models (MLRMs) have substantially
improved their ability to solve complex textual and visual tasks. However, these
models tend to *overthink* on
simple problems, producing unnecessarily lengthy reasoning traces, while
*under-exploring* on challenging ones, leading to missed solutions. To 
address this imbalance, we propose **ARES**, a unified open-source framework
for *adaptive reasoning* that dynamically allocates exploration effort based
on task difficulty. Our approach is motivated by two key empirical findings:
(i) while single-token entropy is noisy, *high window-entropy (HWE)
tokens* (token-level entropies averaged under a sliding window) can reliably capture reasoning-critical moments; and (ii) reducing HWE usage
benefits easy problems, while increasing it is essential for solving hard ones.
Building on these insights, ARES introduces a two-stage training pipeline. In the
*Adaptive Cold-Start* stage, we curate multimodal and textual data paired
with reasoning traces of length proportional to problem difficulty, equipping the
model with initial difficulty awareness. In the second stage, we develop
*Adaptive Entropy Policy Optimization (AEPO)*, which uses HWE tokens as
exploration triggers to decide *when to explore*, and a hierarchical entropy
reward with dynamic KL control to decide *how much to explore*. Extensive
experiments demonstrate that ARES achieves state-of-the-art performance and
reasoning efficiency across diverse mathematical, logical, and multimodal
benchmarks, while closing the gap to leading commercial systems under
significantly lower inference costs. The anonymous code repository is available at https://anonymous.4open.science/r/ARES-60728M.
Fine-Grained Class-Conditional Distribution Balancing for Debiased Learning
Authors: Miaoyun Zhao; Qiang Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Achieving group-robust generalization in the presence of spurious correlations remains a significant challenge, particularly when bias annotations are unavailable.
Recent studies on Class-Conditional Distribution Balancing (CCDB) reveal that spurious correlations often stem from mismatches between the class-conditional and marginal distributions of bias attributes. They achieve promising results by addressing this issue through simple distribution matching in a bias-agnostic manner. 
However, CCDB approximates each distribution using a single Gaussian, which is overly simplistic and rarely holds in real-world applications. 
To address this limitation, we propose a novel Multi-stage data-Selective reTraining strategy (MST), which describes each distribution in greater detail using the hard confusion matrix.
Building on these finer descriptions, we propose a fine-grained variant of CCDB, termed FG-CCDB, which enhances distribution matching through more precise confusion-cell-wise reweighting. FG-CCDB learns sample weights from a global perspective, effectively mitigating spurious correlations without incurring substantial storage or computational overhead.
Extensive experiments demonstrate that MST serves as a reliable proxy for ground-truth bias annotations and can be seamlessly integrated with bias-supervised methods.
Moreover, when combined with FG-CCDB, our method performs on par with bias-supervised approaches on binary classification tasks and significantly outperforms them in highly biased multi-class and multi-shortcut scenarios.
TTOM: Test-Time Optimization and Memorization for Compositional Video Generation
Authors: Leigang Qu; Ziyang Wang; Na Zheng; Wenjie Wang; Liqiang Nie; Tat-Seng Chua
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Video Foundation Models (VFMs) exhibit remarkable visual generation performance, but struggle in compositional scenarios (\eg, motion, numeracy, and spatial relation). 
In this work, we introduce **Test-Time Optimization and Memorization (TTOM)**, a training-free framework that aligns VFM outputs with spatiotemporal layouts during inference for better text-image alignment.
Rather than direct intervention to latents or attention per-sample in existing work, we integrate and optimize new parameters guided by a general layout-attention objective. 
Furthermore, we formulate
video generation within a streaming setting, and maintain historical optimization contexts with a parametric memory mechanism that supports flexible operations, such as insert, read, update, and delete. 
Notably, we found that TTOM disentangles compositional world knowledge, showing powerful transferability and generalization. 
Experimental results on the T2V-CompBench and Vbench benchmarks establish TTOM as an effective, practical, scalable, and efficient framework to achieve cross-modal alignment for compositional video generation on the fly.
Human-MME: A Holistic Evaluation Benchmark for Human-Centric Multimodal Large Language Models
Authors: Yuansen Liu; Haiming Tang; Jinlong Peng; Jiangning Zhang; Xiaozhong Ji; Qingdong He; Donghao Luo; Zhenye Gan; Junwei Zhu; Yunhang Shen; Chaoyou Fu; Chengjie Wang; Xiaobin Hu; Shuicheng YAN
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Multimodal Large Language Models (MLLMs) have demonstrated significant advances in visual understanding tasks. 
However, their capacity to comprehend human-centric scenes has rarely been explored, primarily due to the absence of comprehensive evaluation benchmarks that take into account both the human-oriented granular level and higher-dimensional causal reasoning ability. 
Such high-quality evaluation benchmarks face tough obstacles, given the physical complexity of the human body and the difficulty of annotating granular structures.
In this paper, we propose Human-MME, a rigorously curated benchmark designed to provide a more holistic evaluation of MLLMs in human-centric scene understanding. Compared with other existing benchmarks, our work provides three key features:
**(1) Diversity in human scene**, spanning 4 primary
visual domains with 15 secondary domains and 43 sub-fields to ensure broad scenario 
coverage.
**(2) Progressive and diverse evaluation dimensions**, evaluating the human-based activities progressively from the human-oriented granular perception to the higher-dimensional multi-target and causal reasoning, consisting of eight dimensions with 19,945 real-world image question pairs and an evaluation suite.
**(3) High-quality annotations with rich data paradigms**, constructing the automated annotation pipeline and human-annotation platform, supporting rigorous manual labeling by expert annotators to facilitate precise and reliable model assessment. Our benchmark extends the single-person and single-image understanding to the multi-person and multi-image mutual understanding by constructing the choice, short-answer, grounding, ranking and judgment question components, and complex question-answer pairs of their combination. The extensive experiments on 20 state-of-the-art MLLMs effectively expose the limitations and guide future MLLMs research toward better human-centric image understanding and reasoning. Data and code are available at [https://github.com/Yuan-Hou/Human-MME](https://github.com/Yuan-Hou/Human-MME).
Texture Vector-Quantization and Reconstruction Aware Prediction for Generative Super-Resolution
Authors: Qifan Li; Jiale Zou; Jinhua Zhang; Wei Long; Xingyu Zhou; Shuhang Gu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Vector-quantized based models have recently demonstrated strong potential for visual prior modeling. However, existing VQ-based methods simply encode visual features with nearest codebook items and train index predictor with code-level supervision. Due to the richness of visual signal, VQ encoding often leads to large quantization error. Furthermore, training predictor with code-level supervision can not take the final reconstruction errors into consideration, result in sub-optimal prior modeling accuracy. 
 In this paper we address the above two issues and propose a Texture Vector-Quantization and a Reconstruction Aware Prediction strategy. The texture vector-quantization strategy  leverages the task character of super-resolution and only introduce codebook to model the prior of missing textures. While the reconstruction aware prediction strategy makes use of the straight-through estimator to directly train index predictor with image-level supervision. Our proposed generative SR model TVQ&RAP is able to deliver photo-realistic SR results with small computational cost.
Revisual-R1: Advancing Multimodal Reasoning From Optimized Cold Start to Staged Reinforcement Learning
Authors: Shuang Chen; Hangyu Guo; Zhaochen Su; Yafu Li; Jiacheng Chen; Yulun Wu; Weijie Wang; ZhiYuan Feng; Xiaoye Qu; Yu Cheng
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Inspired by the remarkable reasoning capabilities of Deepseek-R1 in complex textual tasks, many works attempt to incentivize similar capabilities in Multimodal Large Language Models (MLLMs) by directly applying reinforcement learning (RL). However, they still struggle to activate complex reasoning. 
In this paper, rather than examining multimodal RL in isolation, we delve into current training pipelines and identify three crucial phenomena: 1) Effective cold start initialization is critical for enhancing MLLM reasoning. Intriguingly, we find that initializing with carefully selected text data alone can lead to performance surpassing many recent multimodal reasoning models, even before multimodal RL.2) Standard GRPO applied to multimodal RL suffers from gradient stagnation, which degrades training stability and performance. 3) Subsequent text-only RL training, following the multimodal RL phase, further enhances multimodal reasoning.
This staged training approach effectively balances perceptual grounding and cognitive reasoning development. 
By incorporating the above insights and addressing multimodal RL issues, we introduce \textbf{ReVisual-R1}, achieving a new state-of-the-art among open-source 7B MLLMs on challenging benchmarks including MathVerse, MathVision, WeMath, LogicVista, DynaMath, and challenging AIME2024 and AIME2025.
DenseGRPO: From Sparse to Dense Reward for Flow Matching Model Alignment
Authors: Haoyou Deng; Keyu Yan; Chaojie Mao; Xiang Wang; Yu Liu; Changxin Gao; Nong Sang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent GRPO-based approaches built on flow matching models have shown remarkable improvements in human preference alignment for text-to-image generation.
Nevertheless, they still suffer from the sparse reward problem: the terminal reward of the entire denoising trajectory is applied to all intermediate steps, resulting in a mismatch between the global feedback signals and the exact fine-grained contributions at intermediate denoising steps.
To address this issue, we introduce \textbf{DenseGRPO}, a novel framework that aligns human preference with dense rewards, which evaluates the fine-grained contribution of each denoising step.
Specifically, our approach includes two key components: (1) we propose to predict the step-wise reward gain as dense reward of each denoising step, which applies a reward model on the intermediate clean images via an ODE-based approach. This manner ensures an alignment between feedback signals and the contributions of individual steps, facilitating effective training;
and (2) based on the estimated dense rewards, a mismatch drawback between the uniform exploration setting and the time-varying noise intensity in existing GRPO-based methods is revealed, leading to an inappropriate exploration space.
Thus, we propose a reward-aware scheme to calibrate the exploration space by adaptively adjusting a timestep-specific stochasticity injection in the SDE sampler, ensuring a suitable exploration space at all timesteps.
Extensive experiments on multiple standard benchmarks demonstrate the effectiveness of the proposed DenseGRPO and highlight the critical role of the valid dense rewards in flow matching model alignment.
MCIF: Multimodal Crosslingual Instruction-Following Benchmark from Scientific Talks
Authors: Sara Papi; Maike Züfle; Marco Gaido; Beatrice Savoldi; Danni Liu; Ioannis Douros; Luisa Bentivogli; Jan Niehues
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent advances in large language models have laid the foundation for multimodal LLMs (MLLMs), which unify text, speech, and vision within a single framework. As these models are rapidly evolving toward general-purpose instruction following across diverse and complex tasks, a key frontier is evaluating their crosslingual and multimodal capabilities over both short- and long-form inputs.
However, existing benchmarks fall short in evaluating these dimensions jointly: they are often limited to English, mostly focus on a single modality at a time, rely on short-form inputs, or lack human annotations--hindering comprehensive assessment of model performance across languages, modalities, and task complexity. To address these gaps, we introduce MCIF (Multimodal Crosslingual Instruction Following), the first crosslingual human-annotated benchmark based on scientific talks on NLP and beyond. MCIF evaluates instruction following in crosslingual, multimodal settings over different input lengths and spans four macro-tasks: recognition, translation, question answering, and summarization. It covers three core modalities (speech, vision, and text) and four diverse languages (English, German, Italian, and Chinese), fully aligned across all dimensions. This parallel design enables a systematic evaluation of MLLMs' abilities to interpret instructions across languages and effectively integrate multimodal contextual information. Our benchmarking and analysis of 23 models highlight universal challenges across modalities and tasks, indicating substantial room for improvement in future MLLMs development. MCIF is released under CC-BY 4.0 license to promote open research.
Empowering Multi-Robot Cooperation via Sequential World Models
Authors: Zijie Zhao; Honglei Guo; Shengqian Chen; Kaixuan Xu; Bo Jiang; Yuanheng Zhu; Dongbin Zhao
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Model-based reinforcement learning (MBRL) has achieved remarkable success in robotics due to its high sample efficiency and planning capability.
However, extending MBRL to physical multi-robot cooperation remains challenging due to the complexity of joint dynamics.
To address this challenge, we propose the Sequential World Model (**SeqWM**), a novel framework that integrates the sequential paradigm into multi-robot MBRL.
SeqWM employs independent, autoregressive agent-wise world models to represent joint dynamics, where each agent generates its future trajectory and plans its actions based on the predictions of its predecessors.
This design lowers modeling complexity and enables the emergence of advanced cooperative behaviors through explicit intention sharing.
Experiments on Bi-DexHands and Multi-Quadruped demonstrate that SeqWM outperforms existing state-of-the-art model-based and model-free baselines in both overall performance and sample efficiency, while exhibiting advanced cooperative behaviors such as predictive adaptation, temporal alignment, and role division.
Furthermore, SeqWM has been successfully deployed on physical quadruped robots, validating its effectiveness in real-world multi-robot systems.
Demos and code are available at:  https://github.com/zhaozijie2022/seqwm
VLM-SubtleBench: How Far Are VLMs from Human-Level Subtle Comparative Reasoning?
Authors: Minkyu Kim; Sangheon Lee; Dongmin Park
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The ability to distinguish subtle differences between visually similar images is essential for diverse domains such as industrial anomaly detection, medical imaging, and aerial surveillance. While comparative reasoning benchmarks for vision-language models (VLMs) have recently emerged, they primarily focus on images with large, salient differences and fail to capture the nuanced reasoning required for real-world applications. In this work, we introduce **VLM-SubtleBench**, a benchmark designed to evaluate VLMs on *subtle comparative reasoning*. Our benchmark covers ten difference types—Attribute, State, Emotion, Temporal, Spatial, Existence, Quantity, Quality, Viewpoint, and Action—and curate paired question–image sets reflecting these fine-grained variations. Unlike prior benchmarks restricted to natural image datasets, our benchmark spans diverse domains, including industrial, aerial, and medical imagery. Through extensive evaluation of both proprietary and open-source VLMs, we reveal systematic gaps between model and human performance across difference types and domains, and provide controlled analyses highlighting where VLMs’ reasoning sharply deteriorates. Together, our benchmark and findings establish a foundation for advancing VLMs toward human-level comparative reasoning.
AutoDrive-R²: Incentivizing Reasoning and Self-Reflection Capacity for VLA Model in Autonomous Driving
Authors: Zhenlong Yuan; Chengxuan Qian; Jing Tang; Rui Chen; Zijian Song; Lei Sun; Xiangxiang Chu; Yujun Cai; Dapeng Zhang; Shuo Li
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Vision–Language–Action (VLA) models in autonomous driving systems have recently demonstrated transformative potential by integrating multimodal perception with decision-making capabilities. However, the interpretability and coherence of the decision process and the plausibility of action sequences remain largely underexplored. To address these issues, we propose AutoDrive-R², a novel VLA framework that enhances both reasoning and self-reflection capabilities of autonomous driving systems through chain-of-thought (CoT) processing and reinforcement learning (RL). Specifically, we first propose an innovative CoT dataset named nuScenesR²-6K for supervised fine-tuning, which effectively builds cognitive bridges between input information and output trajectories through a four-step logical chain with self-reflection for validation. Moreover, to maximize both reasoning and self-reflection during the RL stage, we further employ the Group Relative Policy Optimization (GRPO) algorithm within a physics-grounded reward framework that incorporates spatial alignment, vehicle dynamic, and temporal smoothness criteria to ensure reliable and realistic trajectory planning. Extensive evaluation results across both nuScenes and Waymo datasets demonstrates the state-of-the-art performance and robust generalization capacity of our proposed method.
Synergizing Understanding and Generation with Interleaved Analyzing-Drafting Thinking
Authors: Shengqiong Wu; Bobo Li; Xinkai Wang; Xiangtai Li; Lei Cui; Furu Wei; Shuicheng YAN; Hao Fei; Tat-Seng Chua
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Unified Vision–Language Models (UVLMs) aim to advance multimodal learning by supporting both understanding and generation within a single framework. However, existing approaches largely focus on architectural unification while overlooking the need for explicit interaction between the two capabilities during task solving. As a result, current models treat understanding and generation as parallel skills rather than synergistic processes. To achieve real synergy, we introduce the interleaved Analyzing–Drafting problem-solving loop (AD-Loop), a new think paradigm that dynamically alternates between analytic and drafting operations. By interleaving textual thoughts with visual thoughts, AD-Loop enables models to iteratively refine both comprehension and outputs, fostering genuine synergy. To train this mechanism, we design a two-stage strategy: supervised learning on interleaved thought data to initialize alternation, followed by reinforcement learning to promote adaptive and autonomous control. Extensive experiments demonstrate that AD-Loop consistently improves performance across standard benchmarks for both understanding and generation, with strong transferability to various UVLMs architectures. Visual analyses further validate the effectiveness of implicit visual thoughts. These results highlight AD-Loop as a principled and broadly applicable strategy for synergizing comprehension and creation.