GenAI4ELab/papercli-papers huggingface dataset:
LipNeXt: Scaling up Lipschitz-based Certified Robustness to Billion-parameter Models
Authors: Kai Hu; Haoqi Hu; Matt Fredrikson
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Lipschitz-based certification offers efficient, deterministic robustness guarantees but has struggled to scale in model size, training efficiency, and ImageNet performance. We introduce \emph{LipNeXt}, the first \emph{constraint-free} and \emph{convolution-free} 1-Lipschitz architecture for certified robustness. LipNeXt is built using two techniques: (1) a manifold optimization procedure that updates parameters directly on the orthogonal manifold and (2) a \emph{Spatial Shift Module} to model spatial pattern without convolutions. The full network uses orthogonal projections, spatial shifts, a simple 1-Lipschitz $\beta$-Abs nonlinearity, and $L_2$ spatial pooling to maintain tight Lipschitz control while enabling expressive feature mixing. Across CIFAR-10/100 and Tiny-ImageNet, LipNeXt achieves state-of-the-art clean and certified robust accuracy (CRA), and on ImageNet it scales to 1–2B large models, improving CRA over prior Lipschitz models (e.g., up to $+8\%$ at $\varepsilon{=}1$) while retaining efficient, stable low-precision training. These results demonstrate that Lipschitz-based certification can benefit from modern scaling trends without sacrificing determinism or efficiency.
Human Behavior Atlas: Benchmarking Unified Psychological And Social Behavior Understanding
Authors: Keane Ong; Wei Dai; Carol Li; Dewei Feng; Hengzhi Li; Jingyao Wu; Jiaee Cheong; Rui Mao; Gianmarco Mengaldo; Erik Cambria; Paul Pu Liang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Using intelligent systems to perceive psychological and social behaviors, that is, the underlying affective, cognitive, and pathological states that are manifested through observable behaviors and social interactions, remains a challenge due to their complex, multifaceted, and personalized nature. Existing work tackling these dimensions through specialized datasets and single-task systems often miss opportunities for scalability, cross-task transfer, and broader generalization. To address this gap, we curate Human Behavior Atlas, a unified benchmark of diverse behavioral tasks designed to support the development of foundation models for understanding psychological and social behaviors. Human Behavior Atlas comprises over 100,000 samples spanning text, audio, and visual modalities, covering tasks on *affective states*, *cognitive states*, *pathologies*, and *social processes*. Our unification efforts can reduce redundancy and cost, enable training to scale efficiently across tasks, and enhance generalization of behavioral features across domains. On Human Behavior Atlas, we train three models: Omnisapiens-7B SFT, Omnisapiens-7B BAM, and Omnisapiens-7B RL. We show that training on Human Behavior Atlas enables models to consistently outperform existing multimodal LLMs across diverse behavioral tasks. Pretraining on Human Behavior Atlas also improves transfer to novel behavioral datasets; with the targeted use of behavioral descriptors yielding meaningful performance gains. The benchmark, models, and codes can be found at: https://github.com/MIT-MI/human_behavior_atlas.
Can Language Models Discover Scaling Laws?
Authors: Haowei Lin; Haotian Ye; Wenzheng Feng; Quzhe Huang; Yujun Li; Hubert Lim; Zhengrui Li; Xiangyu Wang; Jianzhu Ma; Yitao Liang; James Zou
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Discovering scaling laws for predicting model performance at scale is a fundamental and open-ended challenge, mostly reliant on slow, case specific human experimentation. To investigate the potential for LLMs to automate this process, we collect over 5,000 experiments from existing literature and curate seven diverse scaling law discovery tasks. While existing agents struggle to produce accurate law formulas, this paper introduces SLDAgent, an evolution-based agent that co-optimize the scaling law model and the parameters, enabling it to autonomously explore complex relationships between variables. For the first time, we demonstrates that SLDAgent can automatically discover laws that exhibit consistently more accurate extrapolation than their established, human-derived counterparts across all tasks.
Through comprehensive analysis, we elucidate why these discovered laws are superior and verify their practical utility in both pretraining and finetuning applications. This work establishes a new paradigm for agentic scientific discovery, showing that AI systems can understand their own scaling behavior, and can contribute novel and practical knowledge back to the research community.
Scenethesis: A Language and Vision Agentic Framework for 3D Scene Generation
Authors: Lu Ling; Chen-Hsuan Lin; Tsung-Yi Lin; Yifan Ding; Yu Zeng; Yichen Sheng; Yunhao Ge; Ming-Yu Liu; Aniket Bera; Max Li
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Generating interactive 3D scenes from text requires not only synthesizing assets but arranging them with spatial intelligence—support, affordances, and plausibility. However, training data for interactive scenes is dominated by a few indoor datasets, so learning-based methods overfit to in-distribution layouts and struggle to compose diverse arrangements (e.g., outdoor settings and small-on-large relations). Meanwhile, LLM-based layout planners can propose diverse arrangements, but the lack of visual grounding often yields implausible placements that violate commonsense physics. We propose Scenethesis, a training-free, agentic framework that couples LLM-based scene planning with vision-guided layout refinement. Given a text prompt, Scenethesis first drafts a coarse layout with an LLM; a vision module refines the layout and extracts scene structure to capture inter-object relations. A novel optimization stage enforces pose alignment and physical plausibility, and a final judge verifies spatial coherence and triggers targeted repair when needed. Across indoor and outdoor prompts,  Scenethesis produces  realistic, relation-rich, and physically plausible 3D interactive scenes, reducing collisions and stability failures compared to SOTA methods, making it practical for virtual content creation, simulation, and embodied AI.
PAGE-4D: VGGT-4D Perception via Disentangled Pose and Geometry Estimation
Authors: Kaichen Zhou; Yuhan Wang; Grace Chen; Gaspard Beaudouin; Fangneng Zhan; Paul Pu Liang; Mengyu Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent 3D feed-forward models, such as the Visual Geometry Grounded Transformer (VGGT), have shown strong capability in inferring 3D attributes of static scenes. However, since they are typically trained on static datasets, these models often struggle in real-world scenarios involving complex dynamic elements, such as moving humans or deformable objects like umbrellas. To address this limitation, we introduce PAGE-4D, a feedforward model that extends VGGT to dynamic scenes, enabling camera pose estimation, depth prediction, point cloud reconstruction, and point tracking—all without post-processing. Training a geometry transformer for dynamic scenes from scratch, however, demands large-scale dynamic datasets and substantial computational resources, which are often impractical. To overcome this, we propose an efficient fine-tuning strategy that allows PAGE-4D to generalize to dynamic scenarios using only limited dynamic data and compute. In particular, we design a dynamics-aware aggregator that disentangles dynamic from static content for downstream scene understanding tasks: it first predicts a dynamics-aware mask, which then guides a dynamics-aware global attention mechanism. Extensive experiments show that PAGE-4D consistently outperforms the original VGGT in dynamic scenarios, achieving superior results in camera pose estimation, monocular and video depth estimation, and dense point map reconstruction. The source code and pretrained model weights are provided in the https://page4d.github.io.
Unfolding Spatial Cognition: Evaluating Multimodal Models on Visual Simulations
Authors: Linjie Li; Mahtab Bigverdi; Jiawei Gu; Zixian Ma; Yinuo Yang; Ziang Li; Yejin Choi; Ranjay Krishna
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Spatial cognition is essential for human intelligence, enabling problem-solving through visual simulations rather than relying solely on verbal reasoning. However, existing AI benchmarks primarily
   assess verbal reasoning, neglecting the complexities of non-verbal, multi-step visual simulation. We introduce STARE (Spatial Transformations and Reasoning Evaluation), a benchmark designed to
  evaluate multimodal large language models on tasks better solved through multi-step visual simulation. STARE features ~4K tasks spanning foundational geometric transformations (2D and 3D),
  integrated spatial reasoning (cube net folding and tangram puzzles), and real-world spatial reasoning (perspective and temporal reasoning), reflecting practical cognitive challenges like object
  assembly, mechanical diagram interpretation, and everyday spatial navigation. Our evaluations show that models excel at reasoning over simpler 2D transformations, but perform close to random
  chance on more complex tasks like 3D cube net folding and tangram puzzles that require multi-step visual simulations. Humans achieve near-perfect accuracy but take considerable time (up to 28.0s)
  on complex tasks, reducing response time by 7.5 seconds on average with intermediate visual simulations. In contrast, models exhibit inconsistent performance gains from visual simulations,
  improving on most tasks but declining in specific cases like tangram puzzles (GPT-4o, o1) and cube net folding (Claude-3.5, Gemini-2.0 Flash), indicating that models cannot consistently leverage
  intermediate visual information. Even o3, a strong reasoning model, lags significantly behind human performance across tasks. By evaluating non-verbal visual reasoning beyond conventional
  text-based benchmarks, STARE highlights critical gaps in current AI spatial capabilities and sets a new standard for assessing spatial intelligence in multimodal models.
ReLi3D: Relightable Multi-view 3D Reconstruction with Disentangled Illumination
Authors: Jan-Niklas Dihlmann; Mark Boss; Simon Donné; Andreas Engelhardt; Hendrik Lensch; Varun Jampani
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Reconstructing 3D assets from images has long required separate pipelines for geometry reconstruction, material estimation, and illumination recovery, each with distinct limitations and computational overhead. We present MIDR-3D, the first unified end-to-end pipeline that simultaneously reconstructs complete 3D geometry, spatially-varying physically-based materials, and environment illumination
from sparse multi-view images in under one second. Our key insight is that multi-view constraints can dramatically improve material and illumination disentanglement, a problem that remains fundamentally ill-posed for single image methods. Key to our approach is the fusion of the multi-view input via a transformer cross-conditioning architecture, followed by a novel unified two path prediction strategy. The first path predicts the object’s structure and appearance, while the second path predicts the environment illumination from image background or object reflections. This combined with a differentiable Monte Carlo multiple importance sampling renderer, creates an optimal illumination disentanglement training pipeline. Further with our mixed-domain training protocol, combining synthetic PBR datasets with real-world RGB captures, we establish generalizable results across geometry, material accuracy, and illumination quality. By unifying previously separate reconstruction tasks into a single feed-forward pass, we enable near-instantaneous generation of complete, relightable 3D assets.
FreeViS: Training-free Video Stylization with Inconsistent References
Authors: Jiacong Xu; Yiqun Mei; Ke Zhang; Vishal M. Patel
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Video stylization plays a key role in content creation, but it remains a challenging problem. Naïvely applying image stylization frame-by-frame hurts temporal consistency and reduces style richness. Alternatively, training a dedicated video stylization model typically requires paired video data and is computationally expensive. In this paper, we propose FreeViS, a training-free video stylization framework that generates stylized videos with rich style details and strong temporal coherence. Our method integrates multiple stylized references to a pretrained image-to-video (I2V) model, effectively mitigating the propagation errors observed in prior works, without introducing flickers and stutters. In addition, it leverages high-frequency compensation to constrain the content layout and motion, together with flow-based motion cues to preserve style textures in low-saliency regions. Through extensive evaluations, FreeViS delivers higher stylization fidelity and superior temporal consistency, outperforming recent baselines and achieving strong human preference. Our training-free pipeline offers a practical and economic solution for high-quality, temporally coherent video stylization.
Boosting Medical Visual Understanding From Multi-Granular Language Learning
Authors: Zihan Li; Yiqing Wang; Sina Farsiu; Paul Kinahan
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent advances in image-text pretraining have significantly enhanced visual understanding by aligning visual and textual representations. Contrastive Language-Image Pretraining (CLIP) has played a pivotal role in multimodal learning. However, its focus on single-label, single-granularity alignment limits its effectiveness in complex domains such as medical imaging, where images often correspond to multiple labels across different levels of granularity. To address this, we propose Multi-Granular Language Learning (MGLL), a contrastive learning framework designed to improve both multi-label and cross-granularity alignment. MGLL leverages structured multi-label supervision, integrates textual descriptions across granularities, and introduces soft-label supervision with point-wise constraints to enhance alignment. MGLL employs smooth Kullback–Leibler (KL) divergence to ensure cross-granularity consistency while maintaining computational efficiency as a plug-and-play module for vision-language models. Pretrained on our constructed large-scale multi-granular datasets and evaluated across multiple datasets, MGLL outperforms other state-of-the-art methods in downstream tasks. The code is available at https://github.com/HUANGLIZI/MGLL.
Beyond Binary Rewards: Training LMs to Reason About Their Uncertainty
Authors: Mehul Damani; Isha Puri; Stewart Slocum; Idan Shenfeld; Leshem Choshen; Yoon Kim; Jacob Andreas
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: When language models (LMs) are trained via reinforcement learning (RL) to generate natural language “reasoning chains”, their performance improves on a variety of difficult question answering tasks. Today, almost all successful applications of RL for reasoning use binary reward functions that evaluate the correctness of LM outputs. Because such reward functions do not penalize guessing or low-confidence outputs, they often have the unintended side-effect of degrading calibration and increasing the rate at which LMs generate incorrect responses (or “hallucinate”) in other problem domains. This paper describes RLCR (Reinforcement Learning with Calibration Rewards), an approach to training reasoning models that jointly improves accuracy and calibrated confidence estimation. During RLCR, LMs generate both predictions and numerical confidence estimates after reasoning. They are trained to optimize a reward function that augments a binary correctness score with a Brier score—a scoring rule for confidence estimates that incentivizes calibrated prediction. We first prove that this reward function (or any analogous reward function that uses a bounded, proper scoring rule) yields models whose predictions are both accurate and well-calibrated. We next show that across diverse datasets, RLCR substantially improves calibration with no loss in accuracy, on both in-domain and out-of-domain evaluations—outperforming both ordinary RL training and classifiers trained to assign post-hoc confidence scores. While ordinary RL hurts calibration, RLCR improves it. Finally, we demonstrate that verbalized confidence can be leveraged at test time to improve accuracy and calibration via confidence-weighted scaling methods. Our results show that explicitly optimizing for calibration can produce more generally reliable reasoning models.
Stochastic Self-Organization in Multi-Agent Systems
Authors: Nurbek Tastan; Samuel Horváth; Karthik Nandakumar
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large Language Models (LLMs) have enabled multi-agent systems (MAS) where agents collaborate to solve tasks beyond the reach of a single model. Yet most existing approaches rely on fixed topologies, pretrained graph generators, optimization over edges, or external LLM judges, thereby adding complexity. We introduce a response-conditioned framework that adapts communication on the fly. Agents independently generate answers and assess peer contributions using a Shapley~value-inspired approximation. A directed acyclic graph (DAG) is then constructed to route information from high-contribution agents to others, ensuring stable and efficient message passing without the need for additional supervision or training. We provide a theoretical analysis showing that multiple agents increase the chance of correctness and that the correct answers naturally dominate information flow. Experiments with both strong and weak LLM backends demonstrate robust performance, with significant gains in the weak regime where prior methods collapse.
A.I.R.: Enabling Adaptive, Iterative, and Reasoning-based Frame Selection For Video Question Answering
Authors: Yuanhao Zou; Shengji Jin; Andong Deng; Youpeng Zhao; Jun Wang; Chen Chen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Effectively applying Vision-Language Models (VLMs) to Video Question Answering (VideoQA) hinges on selecting a concise yet comprehensive set of frames, as processing entire videos is computationally infeasible. However, current frame selection methods face a critical trade-off: approaches relying on lightweight similarity models, such as CLIP, often fail to capture the nuances of complex queries, resulting in inaccurate similarity scores that cannot reflect the authentic query-frame relevance, which further undermines frame selection. Meanwhile, methods that leverage a VLM for deeper analysis achieve higher accuracy but incur prohibitive computational costs. To address these limitations, we propose A.I.R., a training-free approach for Adaptive, Iterative, and Reasoning-based frame selection. We leverage a powerful VLM to perform deep, semantic analysis on complex queries, and this analysis is deployed within a cost-effective iterative loop that processes only a small batch of the most high-potential frames at a time. Extensive experiments on various VideoQA benchmarks demonstrate that our approach outperforms existing frame selection methods, significantly boosts the performance of the foundation VLM, and achieves substantial gains in computational efficiency over other VLM-based techniques.
StreamSplat: Towards Online Dynamic 3D Reconstruction from Uncalibrated Video Streams
Authors: Zike Wu; Qi Yan; Xuanyu Yi; Lele Wang; Renjie Liao
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Real-time reconstruction of dynamic 3D scenes from uncalibrated video streams demands robust online methods that recover scene dynamics from sparse observations under strict latency and memory constraints. Yet most dynamic reconstruction methods rely on hours of per-scene optimization under full-sequence access, limiting practical deployment.
In this work, we introduce **StreamSplat**, a fully feed-forward framework that instantly transforms uncalibrated video streams of arbitrary length into dynamic 3D Gaussian Splatting (3DGS) representations in an online manner. 
It is achieved via three key technical innovations: 1) a probabilistic sampling mechanism that robustly predicts 3D Gaussians from uncalibrated inputs; 2) a bidirectional deformation field that yields reliable associations across frames and mitigates long-term error accumulation; 3) an adaptive Gaussian fusion operation that propagates persistent Gaussians while handling emerging and vanishing ones.
Extensive experiments on standard dynamic and static benchmarks demonstrate that StreamSplat achieves state-of-the-art reconstruction quality and dynamic scene modeling. Uniquely, our method supports the online reconstruction of arbitrarily long video streams with a $1200\times$ speedup over optimization-based methods. 
Our code and models are available at https://streamsplat3d.github.io/.
Combination-of-Experts with Knowledge Sharing for Cross-Task Vehicle Routing Problems
Authors: Zikang Yu; Jinbiao Chen; Jiahai Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent neural methods have shown promise in generalizing across various vehicle routing problems (VRPs). These methods adopt either a fully-shared dense model across all VRP tasks (i.e., variants) or a mixture-of-experts model that assigns node embeddings within each task instance to different experts. However, they both struggle to generalize from training tasks with basic constraints to out-of-distribution (OOD) tasks involving unseen constraint combinations and new basic constraints, as they overlook the fact that each VRP task is defined by a combination of multiple basic constraints. To address this, this paper proposes a novel model, combination-of-experts with knowledge sharing (CoEKS), which leverages the structural characteristic of VRP tasks. CoEKS enhances generalization to constraint combinations via two complementary components: a combination-of-experts architecture enabling flexible combinations via prior assignment of constraint-specific experts, and a knowledge sharing strategy strengthening generalization via automatic learning of transferable general knowledge across constraints. Moreover, CoEKS allows new experts to be plugged into the trained model for rapid adaptation to new constraints. Experiments demonstrate that CoEKS outperforms state-of-the-art methods on in-distribution tasks and delivers greater gains on OOD tasks, including unseen constraint combinations (relative improvement of 12\% over SOTA) and new constraints (25\% improvement).
Demystifying Supervision Data Generalization in Multimodal LMs
Authors: Xuan Qi; Luxi He; Dan Roth; Xingyu Fu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Conventional wisdom in selecting supervision data for multimodal large language models (MLLMs) is to prioritize datasets that are intuitively similar to the target task (e.g. text-rich v.s. vision-centric). However, it remains unclear how reliably such similarity translates into improved performance on the test benchmarks. 
In this paper, we take the first step to study the problem in MLLMs: can we predict a training data's influence on a target benchmark even before any training takes place?
To answer this question, we first conduct an in-depth analysis using 14 vision-language datasets covering 7 diverse tasks. Our analysis shows that intuitive task
similarity is unreliable in predicting task generalizability, and that transfer depends on the specific dataset rather than the broader task category. 
We propose DATAPROPHET, a training-free, simple yet effective metric based on multimodal perplexity, similarity, and data diversity. Our experiments demonstrate that the influence rankings for different supervision datasets derived from DATAPROPHET is strongly-correlated with rankings based on the actual performance increase after training, with a Kendall’s $\tau$ correlation coefficient of 86.0\%. Moreover, we show that DATAPROPHET can help select better supervision data, achieving up to 6.9\% improvement in average over uniform selection, 1.4\% over SoTA training-based baseline, and 0.2\% higher than oracle experiment performance-based selection. Our code and data will be released.