GenAI4ELab/papercli-papers huggingface dataset:
RegionReasoner: Region-Grounded Multi-Round Visual Reasoning
Authors: Wenfang Sun; Hao Chen; Yingjun Du; Yefeng Zheng; Cees G. M. Snoek
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large vision-language models have achieved remarkable progress in visual reasoning, yet most existing systems rely on single-step or text-only reasoning, limiting their ability to iteratively refine understanding across multiple visual contexts.
To address this limitation, we introduce a new multi-round visual reasoning benchmark with training and test sets spanning both detection and segmentation tasks, enabling systematic evaluation under iterative reasoning scenarios.
We further propose RegionReasoner, a reinforcement learning framework that enforces grounded reasoning by requiring each reasoning trace to explicitly cite the corresponding reference bounding boxes, while maintaining semantic coherence via a global–local consistency reward.
This reward extracts key objects and nouns from both global scene captions and region-level captions, aligning them with the reasoning trace to ensure consistency across reasoning steps.
RegionReasoner is optimized with structured rewards combining grounding fidelity and global–local semantic alignment.
Experiments on detection and segmentation tasks show that RegionReasoner-7B, together with our newly introduced benchmark RegionDial-Bench, considerably improves multi-round reasoning accuracy, spatial grounding precision, and global–local consistency, establishing a strong baseline for this emerging research direction.
WSM: Decay-Free Learning Rate Schedule via Checkpoint Merging for LLM Pre-training
Authors: Changxin Tian; jiapeng wang; Qian Zhao; Kunlong Chen; Jia Liu; Ziqi Liu; Jiaxin Mao; Xin Zhao; Zhiqiang Zhang; JUN ZHOU
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: Recent advances in learning rate~(LR) scheduling have demonstrated the effectiveness of decay-free approaches that eliminate the traditional decay phase while maintaining competitive performance. Model merging techniques have emerged as particularly promising solutions in this domain. We present Warmup-Stable and Merge (WSM), a general framework that establishes a formal connection between learning rate decay and model merging. WSM provides a unified theoretical foundation for emulating various decay strategies—including cosine decay, linear decay and inverse square root decay—as principled model averaging schemes, while remaining fully compatible with diverse optimization methods. Through extensive experiments, we identify merge duration—the training window for checkpoint aggregation—as the most critical factor influencing model performance, surpassing the importance of both checkpoint interval and merge quantity. With the high-quality annealing data, our framework consistently outperforms the widely-adopted Warmup-Stable-Decay (WSD) approach across multiple benchmarks, achieving significant improvements of +3.5\% on MATH, +2.9\% on HumanEval, and +5.5\% on MMLU-Pro. The performance advantages extend to supervised fine-tuning scenarios, highlighting WSM's potential for long-term model refinement.
Can LLMs Refuse Questions They Do Not Know? Measuring Knowledge-Aware Refusal in Factual Tasks
Authors: Wenbo Pan; Jie Xu; Qiguang Chen; Junhao Dong; Libo Qin; Xinfeng Li; Yu Haining; Xiaohua Jia
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large Language Models (LLMs) should refuse to answer questions beyond their knowledge. This capability, which we term knowledge-aware refusal, is crucial for factual reliability, while existing metrics fail to capture this ability. In this work, we propose the Refusal Index (RI), a novel and principled metric that measures how accurately LLMs refuse questions they do not know. We define RI as Spearman's rank correlation between refusal probability and error probability. RI is practically measurable with a lightweight two-pass evaluation method which only require observed refusal rates across two standard evaluation runs. Extensive experiments across 16 models and 5 datasets demonstrate that RI accurately quantifies a model's knowledge-aware refusal capability. Notably, RI remains stable across different refusal rates and provides consistent model rankings independent of a model's overall accuracy and refusal rates. These properties suggest RI captures a stable, intrinsic aspect of model knowledge calibration. More importantly, RI provides insight into an important but previously overlooked aspect of LLM factuality: while LLMs achieve high accuracy on factual tasks, their refusal behavior can be unreliable and fragile.
MCP Security Bench (MSB): Benchmarking Attacks Against Model Context Protocol in LLM Agents
Authors: Dongsen Zhang; Zekun Li; Xu Luo; Xuannan Liu; Pei Pei Li; Wenjun Xu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The Model Context Protocol (MCP) standardizes how large language model (LLM) agents discover, describe, and call external tools. While MCP unlocks broad interoperability, it also enlarges the attack surface by making tools first-class, composable objects with natural-language metadata, and standardized I/O. We present MSB (MCP Security Benchmark), the first end-to-end evaluation suite that systematically measures how well LLM agents resist MCP-specific attacks throughout the full tool-use pipeline: task planning, tool invocation, and response handling. MSB contributes: (1) a taxonomy of 12 attacks including name-collision, preference manipulation, prompt injections embedded in tool descriptions, out-of-scope parameter requests, user-impersonating responses, false-error escalation, tool-transfer, retrieval injection, and mixed attacks; (2) an evaluation harness that executes attacks by running real tools (both benign and malicious) via MCP rather than simulation; and (3) a robustness metric that quantifies the trade-off between security and performance: Net Resilient Performance (NRP). We evaluate nine popular LLM agents across 10 domains and 405 tools, producing 2,000 attack instances. Results reveal the effectiveness of attacks against each stage of MCP. Models with stronger performance are more vulnerable to attacks due to their outstanding tool calling and instruction following capabilities. MSB provides a practical baseline for researchers and practitioners to study, compare, and harden MCP agents. Code: https://github.com/dongsenzhang/MSB
AQER: A Scalable and Efficient Data Loader for Digital Quantum Computers
Authors: Kaining Zhang; Xinbiao Wang; Yuxuan Du; Min-Hsiu Hsieh; Dacheng Tao
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Digital quantum computing promises to offer computational capabilities beyond the reach of classical systems, yet its capabilities are often challenged by scarce quantum resources. A critical bottleneck in this context is how to load classical or quantum data into quantum circuits efficiently. Approximate quantum loaders (AQLs) provide a viable solution to this problem by balancing fidelity and circuit complexity. However, most existing AQL methods are either heuristic or provide guarantees only for specific input types, and a general theoretical framework is still lacking. To address this gap, here we reformulate most AQL methods into a unified framework and establish information-theoretic bounds on their approximation error. Our analysis reveals that the achievable infidelity between the prepared state and target state scales linearly with the total entanglement entropy across subsystems when the loading circuit is applied to the target state. In light of this, we develop AQER, a scalable AQL method that constructs the loading circuit by systematically reducing entanglement in target states. We conduct systematic experiments to evaluate the effectiveness of AQER, using synthetic datasets, classical image and language datasets, and a quantum many-body state datasets with up to 50 qubits. The results show that AQER consistently outperforms existing methods in both accuracy and gate efficiency. Our work paves the way for scalable quantum data processing and real-world quantum computing applications.
MiSS: Revisiting the Trade-off in LoRA with an Efficient Shard-Sharing Structure
Authors: Jiale Kang; Qingyu Yin
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Low-Rank Adaptation (LoRA) is a widely adopted technique for parameter-efficient fine-tuning, but its slow convergence has spurred the development of numerous variants. Nevertheless, current approaches struggle to achieve simultaneous improvements in performance, memory footprint, and computational efficiency. To address this challenge, we revisit the causes of LoRA’s slow convergence and, based on these insights, propose \textbf{M}atr\textbf{i}x \textbf{S}hard \textbf{S}haring (MiSS) that shards the original weight matrix and updates by sharing a single trainable matrix $\boldsymbol{D}$ initialized to zero. To simultaneously ensure computational efficiency, low memory footprint, and scalable serving, we introduce MiSS$^e$. Through theoretical analyses and empirical results, our method reduces optimization complexity while maintaining strong performance, striking a favorable balance between performance, memory, and efficiency. Furthermore, we provide a comprehensive analysis of different PEFT methods with respect to memory usage, initialization time, and computational efficiency. By mapping the Pareto frontier, we show that MiSS achieves a favorable balance across these dimensions, integrating the strengths of prior approaches.
FastVGGT: Fast Visual Geometry Transformer
Authors: You Shen; Zhipeng Zhang; Yansong Qu; Xiawu Zheng; Jiayi Ji; Shengchuan Zhang; Liujuan Cao
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Scaling visual geometry transformers for long image sequences poses a significant computational and memory challenge. In this work, we diagnose this issue in the state-of-the-art model VGGT, and trace the primary bottleneck to its Global Attention layer. Our analysis reveals a ``token collapse'' phenomenon, where many tokens attend to nearly identical regions, resulting in redundant computation and inefficiency. Motivated by this finding, we propose FastVGGT, a training-free framework that strategically prunes these redundant tokens. Instead of uniform merging, FastVGGT employs a tailored, three-part token partitioning strategy. It preserves initial-frame tokens as a stable global reference, retains salient tokens to maintain fine details, and utilizes region-based random sampling to ensure spatially balanced coverage. Extensive experiments on multiple 3D geometry benchmarks validate our approach's effectiveness. Notably, on sequences of 1000 images, FastVGGT achieves a 4$\times$ speedup over the original VGGT while simultaneously mitigating error accumulation, demonstrating its efficiency and robustness for long-sequence scenarios. For further details, please visit our project page: https://mystorm16.github.io/fastvggt/.
Temporal Geometry of Deep Networks: Hyperbolic Representations of Training Dynamics for Intrinsic Explainability
Authors: Ambarish Moharil
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: This paper investigates how multilayer perceptrons (MLPs) can be represented in non-Euclidean spaces, with emphasis on the Poincaré model of hyperbolic geometry. We aim to capture the geometric evolution of their weighted topology and self-organization over time. Instead of restricting analysis to single checkpoints, we construct temporal parameter-graphs across $T$ snapshots of the optimization process. This reflects the view that neural networks encode information not only in their weights but also in the trajectory traced during training. Drawing on the idea that many complex networks admit embeddings in hidden metric spaces where distances correspond to connection likelihood, we present a geometric and temporal graph-based meta learning framework for obtaining dynamic hyperbolic representations of the underlying neural parameter graphs. Our model embeds temporal parameter-graphs in the Poincaré ball and learns from them while maintaining equivariance to within-snapshot neuron permutations and invariance to permutations of past snapshots. In doing so, it preserves functional equivalence across time and recovers the network’s latent geometry. Experiments on regression and classification tasks with trained MLPs show that hyperbolic temporal representations expose how structure emerges during training, offering intrinsic explanations of self-organisation in a given model training environment.
PixelCraft: A Multi-Agent system for High-Fidelity Visual Reasoning on Structured Images
Authors: Shuoshuo Zhang; Zijian Li; Yizhen Zhang; Jingjing Fu; Lei Song; Jiang Bian; Jun Zhang; Yujiu Yang; Rui Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Structured images (e.g., charts and geometric diagrams) remain challenging for multimodal large language models (MLLMs), as perceptual slips can cascade into erroneous conclusions. Intermediate visual cues can steer reasoning; however, existing cue-based methods are constrained with low-fidelity image processing and linear, rigid reasoning patterns, limiting their effectiveness on complex structured-image tasks. In this paper, we propose PixelCraft, a novel multi-agent system for high-fidelity image processing and flexible visual reasoning on structured images. The system comprises a dispatcher, a planner, a reasoner, critics, and a set of visual tool agents. To achieve high-fidelity processing, we construct a high-quality corpus and fine-tune an MLLM into a grounding model, whose pixel-level localizations are integrated with traditional computer vision (CV) algorithms in tool agents. 
Building on this foundation, PixelCraft facilitates flexible visual reasoning through a dynamic three-stage workflow of tool selection, agent discussion, and self-criticism. 
Moreover, unlike prior linear reasoning patterns that simply append historical images, PixelCraft maintains an image memory to allow the planner to adaptively revisit earlier visual steps, explore alternative reasoning branches, and dynamically adjust the reasoning trajectory during discussion. Extensive experiments on challenging chart and geometry benchmarks demonstrate that PixelCraft significantly improves visual reasoning performance for advanced MLLMs, setting a new standard for structured image reasoning.
G4Splat: Geometry-Guided Gaussian Splatting with Generative Prior
Authors: Junfeng Ni; Yixin Chen; Zhifei Yang; Yu Liu; Ruijie Lu; Song-Chun Zhu; Siyuan Huang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Despite recent advances in leveraging generative prior from pre-trained diffusion models for 3D scene reconstruction, existing methods still face two critical limitations. First, due to the lack of reliable geometric supervision, they struggle to produce high-quality reconstructions even in observed regions, let alone in unobserved areas. Second, they lack effective mechanisms to mitigate multi-view inconsistencies in the generated images, leading to severe shape–appearance ambiguities and degraded scene geometry.
In this paper, we identify accurate geometry as the fundamental prerequisite for effectively exploiting generative models to enhance 3D scene reconstruction. 
We first propose to leverage the prevalence of planar structures to derive accurate metric-scale depth maps, providing reliable supervision in both observed and unobserved regions. 
Furthermore, we incorporate this geometry guidance throughout the generative pipeline to improve visibility mask estimation, guide novel view selection, and enhance multi-view consistency when inpainting with video diffusion models, resulting in accurate and consistent scene completion.
Extensive experiments on Replica, ScanNet++, DeepBlending and Mip-NeRF 360 show that our method consistently outperforms existing baselines in both geometry and appearance reconstruction, particularly for unobserved regions.
Moreover, our method naturally supports single-view inputs and unposed videos, with strong generalizability in both indoor and outdoor scenarios with practical real-world applicability. Project page: https://dali-jack.github.io/g4splat-web/.
Aligned Agents, Biased Swarm: Measuring Bias Amplification in Multi-Agent Systems
Authors: Keyu Li; Jin Gao; Dequan Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: While Multi-Agent Systems (MAS) are increasingly deployed for complex workflows, their emergent properties—particularly the accumulation of bias—remain poorly understood. Because real-world MAS are too complex to analyze entirely, evaluating their ethical robustness requires first isolating their foundational mechanics. In this work, we conduct a baseline empirical study investigating how basic MAS topologies and feedback loops influence prejudice. Contrary to the assumption that multi-agent collaboration naturally dilutes bias, we hypothesize that structured workflows act as echo chambers, amplifying minor stochastic biases into systemic polarization. To evaluate this, we introduce Discrim-Eval-Open, an open-ended benchmark that bypasses individual model neutrality through forced comparative judgments across demographic groups. Analyzing bias cascades across various structures reveals that architectural sophistication frequently exacerbates bias rather than mitigating it. We observe systemic amplification even when isolated agents operate neutrally, and identify a 'Trigger Vulnerability' where injecting purely objective context drastically accelerates polarization. By stripping away advanced swarm complexity to study foundational dynamics, we establish a crucial baseline: structural complexity does not guarantee ethical robustness. Our code is available at https://github.com/weizhihao1/MAS-Bias.
Training Deep Normalization-Free Spiking Neural Networks with Lateral Inhibition
Authors: Peiyu Liu; Jianhao Ding; Zhaofei Yu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Spiking Neural Networks (SNNs) have garnered significant attention as a central paradigm in neuromorphic computing, owing to their energy efficiency and biological plausibility. However, training deep SNNs has critically depended on explicit normalization schemes, leading to a trade-off between performance and biological realism. To resolve this conflict, we propose a normalization-free learning framework that incorporates lateral inhibition inspired by cortical circuits. Our framework replaces the traditional feedforward SNN layer with distinct excitatory (E) and inhibitory (I) neuronal populations that capture the key features of the cortical E-I interaction. The E-I circuit dynamically regulates neuronal activity through subtractive and divisive inhibition, which respectively control the excitability and gain of neurons. To stabilize end-to-end training of the biologically constrained SNNs, we propose two key techniques: E-I Init and E-I Prop. E-I Init is a dynamic parameter initialization scheme that balances excitatory and inhibitory inputs while performing gain control. E-I Prop decouples the backpropagation of the circuit from the forward pass, regulating gradient flow. Experiments across multiple datasets and network architectures demonstrate that our framework enables stable training of deep normalization-free SNNs with biological realism, achieving competitive performance. Therefore, our work not only provides a solution to training deep SNNs but also serves as a computational platform for further exploring the functions of E-I interaction in large-scale cortical computation. Code is available at https://github.com/vwOvOwv/DeepEISNN.
Sample Reward Soups: Query-efficient Multi-Reward Guidance for Text-to-Image Diffusion Models
Authors: Yinghua Yao; Yuangang Pan; Guoji Fu; Ivor Tsang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent advances in inference-time alignment of diffusion models have shown reduced susceptibility to reward over-optimization. However, when aligning with multiple black-box reward functions, the number of required queries grows exponentially with the number of reward functions, making the alignment process highly inefficient. To address the challenge, we propose the first inference-time soup strategy, named Sample Reward Soups (SRSoup), for Pareto-optimal sampling across the entire space of preferences. Specifically, at each denoising step, we independently steer multiple denoising distributions using reward-guided search gradients (one for each reward function) and then linearly interpolate their search gradients. This design is effective because sample rewards can be shared when two denoising distributions are close, particularly during the early stages of the denoising process. As a result, SRSoup significantly reduces the number of queries required in the early stages without sacrificing performance. Extensive experiments demonstrate the effectiveness of SRSoup in aligning T2I models with diverse reward functions, establishing a practical and scalable solution. The code is available at https://github.com/EvaFlower/Sample-Reward-Soups-ICLR26.
Align-Then-stEer: Adapting the Vision-Language Action Models through Unified Latent Guidance
Authors: Yang Zhang; Chenwei Wang; ouyang lu; Yuan Zhao; Yunfei Ge; Zhenglong Sun; Xiu Li; Chi Zhang; Chenjia Bai; Xuelong Li
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Vision-Language-Action (VLA) models pre-trained on large, diverse datasets show remarkable potential for general-purpose robotic manipulation. However, a primary bottleneck remains in adapting these models to downstream tasks, especially when the robot's embodiment or the task itself differs from the pre-training data. This discrepancy leads to a significant mismatch in action distributions, demanding extensive data and compute for effective fine-tuning. To address this challenge, we introduce Align-Then-stEer (ATE), a novel, data-efficient, and plug-and-play adaptation framework. ATE first aligns disparate action spaces by constructing a unified latent space, where a variational autoencoder constrained by reverse KL divergence embeds adaptation actions into modes of the pre-training action latent distribution. Subsequently, it steers the diffusion- or flow-based VLA's generation process during fine-tuning via a guidance mechanism that pushes the model's output distribution towards the target domain. We conduct extensive experiments on cross-embodiment and cross-task manipulation in both simulation and real world. Compared to direct fine-tuning of representative VLAs, our method improves the average multi-task success rate by up to 9.8% in simulation and achieves a striking 32% success rate gain in a real-world cross-embodiment setting. Our work presents a general and lightweight solution that greatly enhances the practicality of deploying  VLA models to new robotic platforms and tasks. Our code is released at \url{https://github.com/TeleHuman/Align-Then-Steer}.
Loc$^{2}$: Interpretable Cross-View Localization via Depth-Lifted Local Feature Matching
Authors: Zimin Xia; Chenghao Xu; Alexandre Alahi
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We propose an accurate and interpretable fine-grained cross-view localization method that estimates the 3 Degrees of Freedom (DoF) pose of a ground-level image by matching its local features with a reference aerial image. Unlike prior approaches that rely on global descriptors or bird’s-eye-view (BEV) transformations, our method directly learns ground–aerial image-plane correspondences using weak supervision from camera poses. The matched ground points are lifted into BEV space with monocular depth predictions, and scale-aware Procrustes alignment is then applied to estimate camera rotation, translation, and optionally the scale between relative depth and the aerial metric space. This formulation is lightweight, end-to-end trainable, and requires no pixel-level annotations. Experiments show state-of-the-art accuracy in challenging scenarios such as cross-area testing and unknown orientation. Furthermore, our method offers strong interpretability: correspondence quality directly reflects localization accuracy and enables outlier rejection via RANSAC, while overlaying the re-scaled ground layout on the aerial image provides an intuitive visual cue of localization performance.