GenAI4ELab/papercli-papers huggingface dataset:
PEERING INTO THE UNKNOWN: ACTIVE VIEW SELECTION WITH NEURAL UNCERTAINTY MAPS FOR 3D RECONSTRUCTION
Authors: Zhengquan Zhang; Feng Xu; Mengmi Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Imagine trying to understand the shape of a teapot by viewing it from the front—you might see the spout, but completely miss the handle. Some perspectives naturally provide more information than others. How can an AI system determine which viewpoint offers the most valuable insight for accurate and efficient 3D object reconstruction? Active view selection (AVS) for 3D reconstruction remains a fundamental challenge in computer vision. The aim is to identify the minimal set of views that yields the most accurate 3D reconstruction.
Instead of learning radiance fields, like NeRF or 3D Gaussian Splatting, from a current observation and computing uncertainty for each candidate viewpoint, we introduce a novel AVS approach guided by neural uncertainty maps predicted by a lightweight feedforward deep neural network, named UPNet. 
UPNet takes a single input image of a 3D object and outputs a predicted uncertainty map, representing uncertainty values across all possible candidate viewpoints. By leveraging heuristics derived from observing many natural objects and their associated uncertainty patterns, we train UPNet to learn a direct mapping from viewpoint appearance to uncertainty in the underlying volumetric representations. 
Next, our approach aggregates all previously predicted neural uncertainty maps to suppress redundant candidate viewpoints and effectively select the most informative one. Using these selected viewpoints, we train 3D neural rendering models and evaluate the quality of novel view synthesis against other competitive AVS methods. Remarkably, despite using half of the viewpoints than the upper bound, our method achieves comparable reconstruction accuracy. In addition, it significantly reduces computational overhead during AVS, achieving up to a 400 times speedup along with over 50\% reductions in CPU, RAM, and GPU usage compared to baseline methods. Notably, our approach generalizes effectively to AVS tasks involving novel object categories, without requiring any additional training. All code, models, and datasets are available at \url{https://github.com/ZhangLab-DeepNeuroCogLab/PUN}.
DR-Submodular Maximization with Stochastic Biased Gradients: Classical and Quantum Gradient Algorithms
Authors: Shengminjie Chen; Xiaoming Sun; Wenguo Yang; Jialin Zhang; Zihan Zhao
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: In this work, we investigate DR-submodular maximization using stochastic biased gradients, which is a more realistic but challenging setting than stochastic unbiased gradients. We first generalize the Lyapunov framework to incorporate biased stochastic gradients, characterizing the adverse impacts of bias and noise. Leveraging this framework, we consider not only conventional constraints but also a novel constraint class: convex sets with a largest element, which naturally arises in applications such as resource allocations. For this constraint, we propose an $1/e$ approximation algorithm for non-monotone DR-submodular maximization, surpassing the hardness result $1/4$ for general convex constraints. As a direct application of stochastic biased gradients, we consider zero-order DR-submodular maximization and introduce both classical and quantum gradient estimation algorithms. In each constraint we consider, while retaining the same approximation ratio, the iteration complexity of our classical zero-order algorithms is $O(\epsilon^{-3})$, matching that of stochastic unbiased gradients; our quantum zero-order algorithms reach $O(\epsilon^{-1})$ iteration complexity, on par with classical first-order algorithms, demonstrating quantum acceleration and validated in numerical experiments.
Almost Bayesian: Dynamics of SGD Through Singular Learning Theory
Authors: Max Hennick; Stijn De Baerdemacker
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The nature of the relationship between Bayesian sampling and stochastic gradient descent in neural networks has been a long-standing open question in the theory of deep learning. We shed light on this question by modeling the long runtime behaviour of SGD as diffusion on porous media. Using singular learning theory, we show that the late stage dynamics are strongly impacted by the degeneracies of the loss surface. From this we are able to show that under reasonable choices of hyperparameters for vanilla SGD, the local steady state distribution of SGD (if it exists) is effectively a tempered version of the Bayesian posterior over the weights which accounts for local accessibility constraints.
Revisiting Confidence Calibration for Misclassification Detection in VLMs
Authors: Jincheng Huang; Jie Xu; Xiaoshuang Shi; Ping Hu; Lei Feng; Xiaofeng Zhu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Confidence calibration has been widely studied to improve the trustworthiness of predictions in vision-language models (VLMs). However, we theoretically reveal that standard confidence calibration inherently _impairs_ the ability to distinguish between correct and incorrect predictions (i.e., Misclassification Detection, MisD), which is crucial for reliable deployment of VLMs in high-risk applications. In this paper, we investigate MisD in VLMs and propose confidence recalibration to enhance MisD. Specifically, we design a new confidence calibration objective to replace the standard one. This modification theoretically achieves higher precision in the MisD task and reduces the mixing of correct and incorrect predictions at every confidence level, thereby overcoming the limitations of standard calibration for MisD. As the calibration objective is not differentiable, we introduce a differentiable surrogate loss to enable better optimization. Moreover, to preserve the predictions and zero-shot ability of the original VLM, we develop a post-hoc framework, which employs a lightweight meta network to predict sample-specific temperature factors, trained with the surrogate loss. Extensive experiments across multiple metrics validate the effectiveness of our approach on MisD.
Samples Are Not Equal: A Sample Selection Approach for Deep Clustering
Authors: Zhengxing Jiao; Yaxin Hou; Jun Ma; Yuhang Li; Ding Ding; Yuheng Jia; Hui LIU; Junhui Hou
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Deep clustering has recently achieved remarkable progress across various domains. However, existing clustering methods typically treat all samples equally, neglecting the inherent differences in their feature patterns and learning states. Such redundant learning often drives models to overemphasize simple feature patterns in high-density regions, weakening their ability to capture complex yet diverse ones in low-density regions. To address this issue, we propose a novel plug-in designed to mitigate overfitting to simple and redundant feature patterns while encouraging the learning of more complex yet diverse ones. Specifically, we introduce a density-aware clustering head initialization strategy that adaptively adjusts each sample's contribution to cluster prototypes according to its local density in the feature space. This strategy mitigates the bias towards high-density regions and encourages a more comprehensive attention on medium- and low-density ones. Furthermore, we design a dynamic sample selection strategy that evaluates the learning state of samples based on the feature consistency and pseudo-label stability. By removing sufficiently learned samples and prioritizing unstable ones, this strategy adaptively reallocates training resources, enabling the model to consistently focus on samples that remain under-learned throughout training. Our method can be integrated as a plug-in into a wide range of deep clustering architectures. Extensive experiments on multiple benchmark datasets demonstrate that our method improves clustering accuracy by up to $\textbf{6.1}$\% and enhances training efficiency by up to $\textbf{1.3$\times$}$. Code is available at [https://github.com/notoaudrey/Samples-Are-Not-Equal](https://github.com/notoaudrey/Samples-Are-Not-Equal).
Mango-GS: Enhancing Spatio-Temporal Consistency in Dynamic Scenes Reconstruction using Multi-Frame Node-Guided 4D Gaussian Splatting
Authors: Tingxuan Huang; Haowei Zhu; Jun-Hai Yong; Hao Pan; Bin Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Reconstructing dynamic 3D scenes with photorealistic detail and temporal coherence remains a significant challenge. Existing Gaussian splatting approaches modeling scenes rely on per-frame optimization, causing them to overfit to instantaneous states rather than learning true motion dynamics. To address this, we present Mango-GS, a multi-frame, node-guided framework for high-fidelity 4D reconstruction. Our approach leverages a temporal Transformer to learn complex motion dependencies across a window of frames, ensuring the generation of plausible trajectories. For efficiency, this temporal modeling is confined to a sparse set of control nodes. These nodes are uniquely designed with  decoupled position and latent codes, which provide a stable semantic anchor for motion influence and prevents correspondence errors for large movements. Our framework is trained end-to-end, enhanced by a input masking strategy and two multi-frame loss to ensure robustness. Extensive experiments demonstrate that Mango-GS achieves state-of-the-art quality and fast rendering speed, enabling high-fidelity reconstruction and real-time rendering of dynamic scenes.
Reconstructing KV Caches with Cross-Layer Fusion for Enhanced Transformers
Authors: Hongzhan Lin; ZhiqiBai; Xinmiao Zhang; Siran Yang; Jiamang Wang; Yunlong Xu; Jiaheng Liu; Yongchi Zhao; Xiang Li; Yuchi Xu; Wenbo Su; Bo Zheng
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Transformer decoders have achieved strong results across tasks, but the memory required for the KV cache becomes prohibitive at long sequence lengths.
Although Cross-layer KV Cache sharing (e.g., YOCO, CLA) offers a path to mitigate KV Cache bottleneck, it typically underperforms within-layer methods like GQA.
To understand the root cause, we investigate the information flow of keys and values of the top-layers.
Our preliminary reveals a clear distribution: values are predominantly derived from the bottom layer, while keys draw more information from both bottom and middle layers.
Building upon this, we propose FusedKV, whose top-layer KV caches are a learnable fusion of the most informative ones from the bottom and middle layers. 
This fusion operates directly on post-RoPE keys, preserving relative positional information without the computational cost of re-applying rotary embeddings.
To further improve efficiency, we propose FusedKV-Lite, an cross-layer sharing approach, where top-layer KV caches are directly derived from the bottom-layer values and the middle-layer keys.
Compared to FusedKV, FusedKV-Lite reduces I/O overhead at the cost of a slight increase in perplexity.
In experiments on LLMs ranging from 332M to 4B parameters, our proposed method reduce 50\% cache memory while achieving lower validation perplexity than the standard Transformer decoder, establishing it as a memory-efficient, high-performance architectural alternative. We have made our Triton implementation available.
MT-DAO: Multi-Timescale Distributed Adaptive Optimizers with Local Updates
Authors: Alex Iacob; Andrej Jovanovic; Mher Safaryan; Meghdad Kurmanji; Lorenzo Sani; Samuel Horváth; William F. Shen; Xinchi Qiu; Nicholas D. Lane
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Training large models with distributed data parallelism (DDP) requires frequent communication of gradients across workers, which can saturate bandwidth. Infrequent communication strategies (e.g., Local SGD) reduce this overhead but, when applied to adaptive optimizers, often suffer a performance gap relative to fully synchronous DDP.
We trace this gap to a time-scale mismatch: the optimizer's fast-moving momentum, tuned for frequent updates, decays too quickly to smooth gradients over long intervals, leading to noise-dominated optimization. To address this, we propose MT-DAO, a family of optimizers that employs multiple slow- and fast-moving first momenta or the gradient to track update dynamics across different time scales, for which we provide the first convergence guarantees. 
Empirically, for language-model pre-training, this eliminates the performance gap with DDP, outperforming infrequent-communication baselines in perplexity and reducing iso-token wall-clock time by 6-27% on Ethernet interconnects. At the 720M scale, MT-DAO reaches a target perplexity in 24% fewer steps and 35% less time than the single-momentum DDP baseline.  MT-DAO enables effective cross-datacenter training and training over wide geographic areas.
Fisher-Rao Sensitivity for Out-of-Distribution Detection in Deep Neural Networks
Authors: Anthony Nguyen; Antonin Bertrand; Sylvie Le Hégarat-Mascle; Emanuel Aldea; Mohammed Nabil EL-KORSO; Renaud LUSTRAT; Franck FLORIN
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Deep neural networks often remain overconfident on Out-of-Distribution (OoD) inputs. We revisit this problem through Riemannian information geometry. We model the network's predictions as a statistical manifold and find that OoD inputs exhibit higher local Fisher-Rao sensitivity. By quantifying this sensitivity with the trace of the Fisher Information Matrix (FIM), we derive a unifying geometric connection between two common OoD signals: feature magnitude and output uncertainty. Analyzing the limitations of this multiplicative form, we extend our analysis using a product manifold construction. This provides a theoretical framework for the robust additive scores used in state-of-the-art (SOTA) detectors and motivates our final, competitive method.
Learning from the Electronic Structure of Molecules across the Periodic Table
Authors: Manasa Kaniselvan; Benjamin Kurt Miller; Meng Gao; Juno Nam; Daniel S. Levine
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Machine-Learned Interatomic Potentials (MLIPs) require vast amounts of atomic structure data to learn forces and energies, and their performance continues to improve with training set size. Meanwhile, the even greater quantities of accompanying data in the Hamiltonian matrix $\mathbf{H}$ behind these datasets has so far gone unused for this purpose. Here, we provide a recipe for integrating the orbital interaction data within $\mathbf{H}$ towards training pipelines for atomic-level properties. We first introduce HELM ('Hamiltonian-trained Electronic-structure Learning for Molecules'), a state-of-the-art Hamiltonian prediction model which bridges the gap between Hamiltonian prediction and universal MLIPs by scaling to $\mathbf{H}$ of structures with 100+ atoms, high elemental diversity, and large basis sets including diffuse functions. To accompany HELM, we release a curated Hamiltonian matrix dataset, 'OMol\_CSH\_58k', with unprecedented elemental diversity (58 elements), molecular size (up to 150 atoms), and basis set (def2-TZVPD). Finally, we introduce 'Hamiltonian pretraining' as a method to extract meaningful descriptors of atomic environments even from a limited number atomic structures, and repurpose this shared embedding space to improve performance on energy-prediction in low-data regimes. Our results highlight the use of electronic interactions as a rich and transferable data source for representing chemical space.
WideSearch: Benchmarking Agentic Broad Info-Seeking
Authors: Ryan Wong; Jiawei Wang; Junjie zhao; Li Chen; Yan Gao; Zhanglong; Xuan Zhou; Zuo Wang; Kai Xiang; Ge Zhang; Wenhao Huang; Yang Wang; WANG KE
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: From professional research to everyday planning, many tasks are bottlenecked by wide-scale information seeking, which is more repetitive than cognitively complex. With the rapid development of Large Language Models (LLMs), automated search agents powered by LLMs offer a promising solution to liberate humans from this tedious work. However, the capability of these agents to perform such "wide-context" collection reliably and completely remains largely unevaluated due to a lack of suitable benchmarks. To bridge this gap, we introduce WideSearch, a new benchmark engineered to evaluate agent reliability on these large-scale collection tasks. The benchmark features 200 manually curated questions (100 in English, 100 in Chinese) from over 15 diverse domains, grounded in real user queries. Each task requires agents to collect large-scale atomic information, which could be verified one by one objectively, and arrange it into a well-organized output. A rigorous five-stage quality control pipeline ensures the difficulty, completeness, and verifiability of the dataset. We benchmark over 10 state-of-the-art agentic search systems, including single-agent, multi-agent frameworks, and end-to-end commercial systems. Most systems achieve overall success rates near 0\%, with the best performer reaching just 7\%. However, given sufficient time, cross-validation by multiple human testers can achieve a near 100\% success rate. These results demonstrate that present search agents have critical deficiencies in large-scale information seeking, underscoring urgent areas for future research and development in agentic search.
Talk, Evaluate, Diagnose: User-aware Agent Evaluation with Automated Error Analysis
Authors: Penny Chong; Harshavardhan Abichandani; Jiyuan SHEN; Atin Ghosh; Min Pyae Moe; Yifan Mai; Daniel Dahlmeier
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Agent applications are increasingly adopted to automate workflows across diverse tasks. However, due to the heterogeneous domains they operate in, it is challenging to create a scalable evaluation framework. Prior works each employ their own methods to determine task success, such as database lookups, regex match, etc., adding complexity to the development of a unified agent evaluation approach. Moreover, they do not systematically account for the user’s role nor expertise in the interaction, providing incomplete insights into the agent’s performance. We argue that effective agent evaluation goes beyond correctness alone, incorporating conversation quality, efficiency and systematic diagnosis of agent errors. To address this, we introduce the TED framework (Talk, Evaluate, Diagnose). (1) Talk: We leverage reusable, generic expert and non-expert user persona templates for user-agent interaction. (2) Evaluate: We adapt existing datasets by representing subgoals—such as tool signatures, and responses—as natural language grading notes, evaluated automatically with LLM-as-a-judge. We propose new metrics that capture both turn efficiency and intermediate progress of the agent complementing the user-aware setup. (3) Diagnose: We introduce an automated error analysis tool that analyzes the inconsistencies of the judge and agents, uncovering common errors, and providing actionable feedback for agent improvement. We show that our TED framework reveals new insights regarding agent performance across models and user expertise levels. We also demonstrate potential gains in agent
performance with peaks of 8-10% on our proposed metrics after incorporating the identified error remedies into the agent’s design.
V2P-Bench: Evaluating Video-Language Understanding with Visual Prompts for Better Human-Model Interaction
Authors: Yiming Zhao; Yu Zeng; Yukun Qi; YaoYang Liu; Xikun Bao; Lin Chen; Zehui Chen; Qing Miao; Chenxi Liu; Jie Zhao; Feng Zhao
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large Vision-Language Models (LVLMs) have made significant strides in the field of video understanding in recent times. Nevertheless, existing video benchmarks predominantly rely on text prompts for evaluation, which often require complex referential language. To address this limitation, we propose V2P-Bench, a robust and comprehensive benchmark for evaluating the ability of LVLMs to understand Video Visual Prompts in human–model interaction scenarios. V2P-Bench consists of 980 videos and 1172 well-structured high-quality QA pairs, each paired with manually annotated visual prompt frames. The benchmark spans three main tasks and twelve categories, thereby enabling fine-grained, instance-level evaluation. Through an in-depth analysis of current LVLMs, we identify several key findings: 1) Visual prompts are both more model-friendly and user-friendly in interactive scenarios than text prompts, leading to significantly improved model performance and enhanced user experience. 2) Models are reasonably capable of zero-shot understanding of visual prompts, but struggle with spatiotemporal understanding. Even o1 achieves only 71.8%, far below the human expert score of 88.3%, while most open-source models perform below 60%. 3) LVLMs exhibit pervasive hack phenomena in video question answering, which intensify with longer videos and lower frame sampling density, artificially inflating performance scores. We anticipate that V2P-Bench will not only shed light on these challenges but also serve as a foundational tool for advancing human–model interaction. The code and datasets are available at https://github.com/gaotiexinqu/v2p-bench.
Beyond Prompt-Induced Lies: Investigating LLM Deception on Benign Prompts
Authors: Zhaomin Wu; Mingzhe Du; See-Kiong Ng; Bingsheng He
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: Large Language Models (LLMs) are widely deployed in reasoning, planning, and decision-making tasks, making their trustworthiness critical. A significant and underexplored risk is intentional deception, where an LLM deliberately fabricates or conceals information to serve a hidden objective. Existing studies typically induce deception by explicitly setting a hidden objective through prompting or fine-tuning, which may not reflect real-world human-LLM interactions. Moving beyond such human-induced deception, we investigate LLMs' self-initiated deception on benign prompts. To address the absence of ground truth, we propose a framework based on Contact Searching Questions~(CSQ). This framework introduces two statistical metrics derived from psychological principles to quantify the likelihood of deception. The first, the *Deceptive Intention Score*, measures the model's bias toward a hidden objective. The second, the *Deceptive Behavior Score*, measures the inconsistency between the LLM's internal belief and its expressed output. Evaluating 16 leading LLMs, we find that both metrics rise in parallel and escalate with task difficulty for most models. Moreover, increasing model capacity does not always reduce deception, posing a significant challenge for future LLM development.
LLM DNA: Tracing Model Evolution via Functional Representations
Authors: Zhaomin Wu; Haodong Zhao; Ziyang Wang; Jizhou Guo; Qian Wang; Bingsheng He
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: The explosive growth of large language models (LLMs) has created a vast but opaque landscape: millions of models exist, yet their evolutionary relationships through fine-tuning, distillation, or adaptation are often undocumented or unclear, complicating LLM management. Existing methods are limited by task specificity, fixed model sets, or strict assumptions about tokenizers or architectures. Inspired by biological DNA, we address these limitations by mathematically defining *LLM DNA* as a low-dimensional, bi-Lipschitz representation of functional behavior. We prove that LLM DNA satisfies *inheritance* and *genetic determinism* and establish its existence. Building on this theory, we derive a general, scalable, training-free pipeline for DNA extraction. In experiments across 305 LLMs, DNA aligns with prior studies on limited subsets and achieves superior or competitive performance on various tasks. Beyond these tasks, DNA comparisons uncover previously undocumented relationships among LLMs. We further construct the evolutionary tree of LLMs using phylogenetic algorithms, which align with shifts from encoder-decoder to decoder-only architectures, reflect temporal progression, and reveal distinct evolutionary speeds across LLM families.