GenAI4ELab/papercli-papers huggingface dataset:
Revisting Node Affinity Prediction In Temporal Graphs
Authors: Or Feldman; Krishna Sri Ipsit Mantri; Moshe Eliasof; Chaim Baskin
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Node affinity prediction is a common task that is widely used in temporal graph learning with applications in social and financial networks, recommender systems, and more. Recent works have addressed this task by  adapting state-of-the-art dynamic link property prediction models to node affinity prediction. However, simple heuristics, such as persistent forecast or moving average, outperform these models.
In this work, we analyze the challenges in training current Temporal Graph Neural Networks for node affinity prediction and suggest appropriate solutions. Combining the solutions, we develop NAVIS - Node Affinity prediction model using VIrtual State, by exploiting the equivalence between heuristics and state space models. While promising, training NAVIS is non-trivial. Therefore, we further introduce a novel loss function for node affinity prediction. We evaluate NAVIS  on TGB and show that it outperforms the state of the art, including heuristics.
SARM: Stage-Aware Reward Modeling for Long Horizon Robot Manipulation
Authors: Qianzhong Chen; Justin Yu; Mac Schwager; Pieter Abbeel; Fred Shentu; Philipp Wu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large-scale robot learning has made progress on complex manipulation tasks, yet long-horizon, contact-rich problems—especially those involving deformable objects—remain challenging due to inconsistent demonstration quality. We propose a stage-aware, video-based reward modeling framework that jointly predicts task stage and fine-grained progress, using natural-language subtask annotations to derive consistent labels across variable-length demonstrations. This avoids the brittleness of frame-index-based labeling and provides stable supervision even in tasks like T-shirt folding. Our reward model is robust to demonstration variability, generalizes to out-of-distribution scenarios, and improves downstream policy training. Building on it, we introduce Reward-Aligned Behavior Cloning (RA-BC), which filters and reweights demonstrations based on reward estimates. Experiments show that our method significantly outperforms baselines in both real-world rollouts and human validation. On T-shirt folding, we achieve 83\% success from the flattened state and 67\% from the crumpled state, compared to 8\% and 0\% with vanilla BC. Overall, our results highlight reward modeling as a scalable and annotation-efficient solution for long-horizon robotic manipulation. Project website: https://qianzhong-chen.github.io/sarm.github.io/.
When LLMs get significantly worse: A statistical approach to detect model degradations
Authors: Jonas M. Kübler; Kailash Budhathoki; Matthäus Kleindessner; Xiong Zhou; Junming Yin; Ashish Khetan; George Karypis
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Minimizing the inference cost and latency of foundation models has become a crucial area of research. Optimization approaches include theoretically lossless methods and
others without accuracy guarantees like quantization. In all of these cases it is crucial to ensure that the model quality has not degraded. However, even at temperature
zero, model generations are not necessarily robust even to theoretically lossless model optimizations due to numerical errors. We thus require statistical tools to 
decide whether a finite-sample accuracy deviation is an evidence of a model's degradation or whether it can be attributed to (harmless) noise in the evaluation. We 
propose a statistically sound hypothesis testing framework based on McNemar's test allowing to efficiently detect model degradations, while guaranteeing a controlled rate
of false positives. The crucial insight is that we have to confront the model scores on each sample, rather than aggregated on the task level. Furthermore, we propose 
three approaches to aggregate accuracy estimates across multiple benchmarks into a single decision. We provide an implementation on top of the largely adopted open source
LM Evaluation Harness and provide a case study illustrating that the method correctly flags degraded models, while not flagging model optimizations that are provably 
lossless. We find that with our tests even empirical accuracy degradations of 0.3% can be confidently attributed to actual degradations rather than noise.

Code: https://github.com/amazon-science/LLM-Accuracy-Stats
d$^2$Cache: Accelerating Diffusion-Based LLMs via Dual Adaptive Caching
Authors: Yuchu Jiang; Yue Cai; Xiangzhong Luo; Jiale Fu; Jiarui Wang; Chonghan Liu; Xu Yang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Diffusion-based large language models (dLLMs), despite their promising performance, still suffer from inferior inference efficiency. This is because dLLMs rely on bidirectional attention and cannot directly benefit from the standard key-value (KV) cache as autoregressive models (ARMs) do. To tackle this issue, we introduce \textit{Dual aDaptive Cache} (d$^2$Cache), which is a training-free approximate KV cache framework for accelerating dLLM inference. d$^2$Cache features a two-stage fine-grained selection strategy to identify tokens and adaptively update their KV states at each decoding step, while caching the KV states of the remaining tokens for reuse. Furthermore, d$^2$Cache naturally offers a more reliable decoding alternative, which can enable quasi left-to-right generation and mitigate premature overconfidence in tokens at the end of the sequence. Extensive experimental results on two representative dLLMs (\ie, LLaDA and Dream) demonstrate that d$^2$Cache not only achieves substantial inference speedups, but also yields consistent improvements in generation quality. The anonymous evaluation codes are available at \url{https://anonymous.4open.science/r/d2Cache-5538}.
Chart Deep Research in LVLMs via Parallel Relative Policy Optimization
Authors: Jiajin Tang; Gaoyang; Wenjie Wang; Sibei Yang; Xing Chen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: With the rapid advancement of data science, charts have evolved from simple numerical presentation tools to essential instruments for insight discovery and decision-making support. However, current chart data intelligence exhibits significant limitations in deep research capabilities, with existing methods predominantly addressing shallow tasks such as visual recognition or factual question-answering, rather than the complex reasoning and high-level data analysis that deep research requires. This limitation stems from two primary technical bottlenecks: at the training level, existing post-training techniques exhibit deficiencies in handling multi-dimensional reward signal interference and heterogeneous data gradient conflicts, preventing models from achieving balanced development across multiple capability dimensions; at the evaluation level, current methods remain limited to factual retrieval and basic computation, failing to assess end-to-end analytic reasoning and other deep research capabilities. To address the training challenge, we propose PRPO, which performs parallel optimization across reward dimensions and capability partitioning across data types, effectively disentangling conflicts between heterogeneous data and multi-dimensional reward signals while ensuring optimization stability. For the evaluation challenge, we construct MCDR-Bench based on the ``error uniqueness principle," transforming subjective generation assessment into objective error identification through controllable error injection, enabling quantifiable evaluation of deep research capabilities. Experimental validation confirms that the proposed PRPO and MCDR-Bench jointly establish a unified framework that systematically advances chart deep research through enhanced collaborative training and objective evaluation.
RL makes MLLMs see better than SFT
Authors: Junha Song; Sangdoo Yun; Dongyoon Han; Jaegul Choo; Byeongho Heo
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: A dominant assumption in Multimodal Language Model (MLLM) research is that its performance is largely inherited from the LLM backbone, given its immense parameter scale and remarkable capabilities. 
This has created a void in the understanding of the vision encoder, which determines 'how MLLMs perceive images'.
The recent shift in MLLM training paradigms, from Supervised Finetuning (SFT) to Reinforcement Learning (RL), magnifies this oversight—namely, the significant lack of analysis on how such training reshapes the vision encoder as well as the MLLM.
To address this, we first investigate the impact of training strategies on MLLMs, where RL shows a clear advantage in strongly vision-related VQA benchmarks than SFT. 
Motivated by this, we conduct a critical yet under-explored analysis of the vision encoder of MLLMs through diverse and in-depth experiments, ranging from ImageNet classification and segmentation to gradient visualization. 
Our results demonstrate that MLLM's post-training strategy 'i.e, SFT or RL' not only leads to disctinct outcomes on MLLM downstream tasks, but also fundamentally reshapes MLLM's underlying visual representations. 
Specifically, our main finding is that 
RL produces stronger and more localized visual representations compared to SFT, boosting the ability of the vision encoder for MLLM.
We then reframe our findings into a simple recipe for building strong vision encoders for MLLMs, Preference-Instructed Vision OpTimization (PIVOT). 
When integrated into MLLMs, a PIVOT-trained vision encoder outperforms even larger and more heavily-trained counterparts, despite requiring less than 1\% of the computational cost of standard vision pretraining. This result opens an effective and efficient path for advancing the vision backbones of MLLMs.
MedAgent-Pro: Towards Evidence-based Multi-modal Medical Diagnosis via Reasoning Agentic Workflow
Authors: Ziyue Wang; Junde Wu; Linghan Cai; Chang Han Low; Xihong Yang; Qiaxuan Li; Yueming Jin
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Modern clinical diagnosis relies on the comprehensive analysis of multi-modal patient data, drawing on medical expertise to ensure systematic and rigorous reasoning. Recent advances in Vision–Language Models (VLMs) and agent-based methods are reshaping medical diagnosis by effectively integrating multi-modal information. However, they often output direct answers and empirical-driven conclusions without clinical evidence supported by quantitative analysis, which compromises their reliability and hinders clinical usability. 
Here we propose MedAgent-Pro, an agentic reasoning paradigm that mirrors modern diagnosis principles via a hierarchical diagnostic workflow, consisting of disease-level standardized plan generation and patient-level personalized step-by-step reasoning. To support disease-level planning, a retrieval-augmented generation agent is designed to access medical guidelines for alignment with clinical standards.  For patient-level reasoning, MedAgent-Pro leverages professional tools such as visual models to take various actions to analyze multi-modal input, and performs evidence-based reflection to iteratively adjust memory, enforcing rigorous reasoning throughout the process. Extensive experiments across a wide range of anatomical regions, imaging modalities, and diseases demonstrate the superiority of MedAgent-Pro over mainstream VLMs, agentic systems and leading expert models. Ablation studies and expert evaluation further confirm its robustness and clinical relevance. Anonymized code link is available in the reproducibility statement.
ARFlow: Auto-regressive Optical Flow Estimation for Arbitrary-Length Videos via Progressive Next-Frame Forecasting
Authors: Jiuming Liu; Mengmeng Liu; Siting Zhu; Yunpeng Zhang; Jiangtao Li; Michael Ying Yang; Francesco Nex; Hao Cheng; Hesheng Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Optical flow estimation is a fundamental computer vision task that predicts per-pixel displacements from consecutive images. Recent works attempt to exploit temporal cues to improve the estimation performance. However, their temporal modeling is restricted to short video sequences due to the unaffordable computational burden, thereby suffering from restricted temporal receptive fields. Moreover, their group-wise paradigm in one forward pass undermines inter-group information exchange, leading to modest performance improvement. To address these problems, we propose a novel multi-frame optical flow network based on an auto-regressive paradigm, named ARFlow. Unlike previous multi-frame methods, our method can be scalable to arbitrary-length videos with marginal computational overhead. Specifically, we design an Auto-regressive Flow Initialization (AFI) module and an Auto-regressive Multi-stride Flow Refinement (AMFR) module to forecast the next-frame flow based on multi-stride history observations. Our ARFlow achieves state-of-the-art performance, ranking 1st on both KITTI-2015 and Spring official benchmarks and 2nd on the MPI-Sintel (Final) benchmark among all open-sourced methods. Furthermore, due to the auto-regressive nature, our method can generalize to arbitrary video length with a constant GPU memory usage of 2.1GB.
Otters: An Energy-Efficient Spiking Transformer via Optical Time-to-First-Spike Encoding
Authors: Zhanglu Yan; Jiayi Mao; Qianhui Liu; Fanfan Li; Tao Luo; Gang Pan; Bowen Zhu; Weng-Fai Wong
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Spiking neural networks (SNNs) promise high energy efficiency, particularly with time-to-first-spike (TTFS) encoding, which maximizes sparsity by emitting at most one spike per neuron. However, this energy advantage is often unrealized because inference requires evaluating a temporal decay function and then multiplying the result by the synaptic weights. 
This paper challenges this costly approach by repurposing a physical hardware `bug', namely, the natural signal decay in optoelectronic devices, as the core computation of TTFS. We fabricated a custom indium oxide optoelectronic synapse that demonstrates how its intrinsic physical decay directly implements the required temporal function. By treating the device's analog output as the fused product of the synaptic weight and temporal decay, optoelectronic synaptic TTFS (named Otters) eliminates these expensive digital operations. 
To use the Otters' paradigm in complex architectures such as the transformer, which are challenging to train directly due to sparsity, we introduce a novel quantized neural network-to-SNN conversion algorithm. 
This complete hardware-software co-design enables our model to achieve state-of-the-art accuracy across seven GLUE benchmark datasets and demonstrates a 1.77$\times$ improvement in energy efficiency over previous leading SNNs, based on a comprehensive analysis of compute, data movement, and memory access costs using energy measurements from a commercial 22nm process. 
Our work thus establishes a new paradigm for energy-efficient SNNs that translates fundamental device physics directly into powerful computational primitives. All codes and data are open source.
The Less You Depend, The More You Learn: Synthesizing Novel Views from Sparse, Unposed Images without Any 3D Knowledge
Authors: Haoru Wang; Kai Ye; Minghan Qin; Yangyan Li; Wenzheng Chen; Baoquan Chen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent advances in feed-forward Novel View Synthesis (NVS) have led to a divergence between two design philosophies: bias-driven methods, which rely on explicit 3D knowledge, such as handcrafted 3D representations (e.g., NeRF and 3DGS) and camera poses annotated by Structure-from-Motion algorithms, and data-centric methods, which learn to understand 3D structure implicitly from large-scale imagery data. This raises a fundamental question: which paradigm is more scalable in an era of ever-increasing data availability? In this work, we conduct a comprehensive analysis of existing methods and uncover a critical trend that the performance of methods requiring less 3D knowledge accelerates more as training data increases, eventually outperforming their 3D knowledge-driven counterparts, which we term “the less you depend, the more you learn.” Guided by this finding, we design a feed-forward NVS framework that removes both explicit scene structure and pose annotation reliance. By eliminating these dependencies, our method leverages great scalability, learning implicit 3D awareness directly from vast quantities of 2D images, without any pose information for training or inference. Extensive experiments demonstrate that our model achieves state-of-the-art NVS performance, even outperforming methods relying on posed training data. The results validate not only the effectiveness of our data-centric paradigm but also the power of our scalability finding as a guiding principle.
SpineBench: A Clinically Salient, Level-Aware Benchmark Powered by the SpineMed-450k Corpus
Authors: Ming Zhao; Wenhui Dong; Yang Zhang; XiangZheng; Zhonghao Zhang; Zian Zhou; YUNZHI GUAN; Liukun Xu; Wei Peng; Zhaoyang Gong; Zhicheng Zhang; Dachuan li; Xiaosheng Ma; Yuli Ma; Jianing Ni; Changjiang Jiang; Lixia Tian; Chen Qixin; Xia Kaishun; Pingping Liu; Tongshun Zhang; ZhiqiangLiu; Zhongan Bi; Chenyang Si; Tiansheng Sun; Caifeng Shan
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Spine disorders affect 619 million people globally and are a leading cause of disability, yet AI-assisted diagnosis remains limited by the lack of level-aware, multimodal datasets. Clinical decision-making for spine disorders requires sophisticated reasoning across X-ray, CT, and MRI at specific vertebral levels. However, progress has been constrained by the absence of traceable, clinically-grounded instruction data and standardized, spine-specific benchmarks. To address this, we introduce SpineMed, an ecosystem co-designed with practicing spine surgeons. It features SpineMed-450k, the first large-scale dataset explicitly designed for vertebral-level reasoning across imaging modalities with over 450,000 instruction instances, and SpineBench, a clinically-grounded evaluation framework. SpineMed-450k is curated from diverse sources, including textbooks, guidelines, open datasets, and $\sim$1,000 de-identified hospital cases, using a clinician-in-the-loop pipeline with a two-stage LLM generation method (draft and revision) to ensure high-quality, traceable data for question-answering, multi-turn consultations, and report generation. SpineBench evaluates models on clinically salient axes, including level identification, pathology assessment, and surgical planning. Our comprehensive evaluation of several recently advanced large vision-language models (LVLMs) on SpineBench reveals systematic weaknesses in fine-grained, level-specific reasoning. In contrast, our model fine-tuned on SpineMed-450k demonstrates consistent and significant improvements across all tasks. Clinician assessments confirm the diagnostic clarity and practical utility of our model's outputs.
NExT-OMNI: Towards Any-to-Any Omnimodal Foundation Models with Discrete Flow Matching
Authors: Run Luo; Xiaobo Xia; Lu Wang; Longze Chen; Renke Shan; Jing Luo; Min Yang; Tat-Seng Chua
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Next-generation multimodal foundation models capable of any-to-any cross-modal generation and multi-turn interaction will serve as core components of artificial general intelligence systems, playing a pivotal role in human-machine interaction. However, most existing multimodal models remain constrained by autoregressive architectures, whose inherent limitations prevent a balanced integration of understanding and generation capabilities. Although hybrid and decoupling strategies have been explored to address these tasks within unified frameworks separately, their redundant, non-integrated designs limit their applicability to broader scenarios, such as cross-modal retrieval. In this work, we introduce NExT-OMNI, an open-source omnimodal foundation model that achieves unified modeling through discrete flow paradigms. By leveraging metric-induced probability paths and kinetic optimal velocities, NExT-OMNI natively supports any-to-any understanding and generation with enhanced response efficiency, while enabling broader application scenarios through concise unified representations rather than task-decoupled designs. Trained on large-scale interleaved text, image, video, and audio data, NExT-OMNI delivers competitive performance on multimodal understanding and generation benchmarks, while outperforming prior unified models in multi-turn multimodal interaction and cross-modal retrieval, highlighting its architectural advantages as a next-generation multimodal foundation model. The code is available at https://github.com/ritzz-ai/Next-OMNI.
Synthetic Bootstrapped Pretraining
Authors: Zitong Yang; Aonan Zhang; Hong Liu; Tatsunori Hashimoto; Emmanuel Candes; Chong Wang; Ruoming Pang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We introduce Synthetic Bootstrapped Pretraining (SBP), a language model (LM) pretraining procedure that first learns a model of relations between documents from the pretraining dataset and then leverages it to synthesize a vast new corpus for joint training.
While the standard pretraining teaches LMs to learn causal correlations among tokens within a single document, it is not designed to efficiently model the rich, learnable inter-document correlations that can potentially lead to better performance.
We validate SBP by designing a compute-matched pretraining setup and pretrain a 3B-parameter and a 6B-parameter model on up to 1T tokens from scratch.
We find SBP consistently improves upon a strong repetition baseline and delivers up to 60% of performance improvement attainable by an oracle upper bound with access to 20x more unique data.
Qualitative analysis reveals that the synthesized documents go beyond mere paraphrases -- SBP first abstracts a core concept from the seed material and then crafts a new narration on top of it.
Besides strong empirical performance, SBP admits a natural Bayesian interpretation: the synthesizer implicitly learns to abstract the latent concepts shared between related documents.
Detect, Decide, Unlearn: A Transfer-Aware Framework for Continual Learning
Authors: Yiwen Wang; Diana Benavides-Prado; Yun Sing Koh
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Continual learning (CL) aims to continuously learn new tasks from data streams. While most CL research focuses on mitigating catastrophic forgetting, memorizing outdated knowledge can cause negative transfer, where irrelevant prior knowledge interferes with new task learning and impairs adaptability. Inspired by how the human brain selectively unlearns unimportant information to prioritize learning and to recall relevant knowledge, we explore the intuition that effective CL should not only preserve but also selectively unlearn prior knowledge that hinders adaptation. We introduce DEtect, Decide, Unlearn in Continual lEarning (DEDUCE), a novel CL framework that dynamically detects negative transfer and mitigates it by a hybrid unlearning mechanism. Specifically, we investigate two complementary negative transfer detection strategies: transferability bound and gradient conflict analysis. Based on this detection, the model decides whether to activate a Local Unlearning Module (LUM) to filter outdated knowledge before learning new task. Additionally, a Global Unlearning Module (GUM) periodically reclaims model capacity to enhance plasticity. Our experiments demonstrate that DEDUCE effectively mitigates task interference and improves overall accuracy with an average gain of up to 4.55\% over state-of-the-art baselines.