GenAI4ELab/papercli-papers huggingface dataset:
Prior-aware and Context-guided Group Sampling for Active Probabilistic Subsampling
Authors: Beomgu Kang; Hyunseok Seo
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Subsampling significantly reduces the number of measurements, thereby streamlining data processing and transfer overhead, and shortening acquisition time across diverse real-world applications. The recently introduced Active Deep Probabilistic Subsampling (A-DPS) approach jointly optimizes both the subsampling pattern and the downstream task model, enabling instance- and subject-specific sampling trajectories and effective adaptation to new data at inference time. However, this approach does not fully leverage valuable dataset priors and relies on top-1 sampling, which can impede the optimization process. Herein, we enhance A-DPS by integrating a deterministic (fixed) prior-informed sampling pattern derived from the training dataset, along with group-based sampling via top-k sampling, to achieve more robust optimization—method we call Prior-aware and context-guided Group-based Active DPS (PGA-DPS). We also provide a theoretical analysis supporting improved optimization via group sampling, and validate this with empirical results. We evaluated PGA-DPS on three tasks: classification, image reconstruction, and segmentation, using the MNIST, CIFAR-10, fastMRI knee, and hyperspectral AeroRIT datasets, respectively. In every case, PGA-DPS outperformed A-DPS, DPS, and all other sampling methods.
GIT-BO: High-Dimensional Bayesian Optimization with Tabular Foundation Models
Authors: Rosen Ting-Ying Yu; Cyril Picard; Faez Ahmed
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Bayesian optimization (BO) struggles in high dimensions, where Gaussian-process surrogates demand heavy retraining and brittle assumptions, slowing progress on real engineering and design problems. We introduce GIT-BO, a Gradient-Informed BO framework that couples TabPFN v2, a tabular foundation model that performs zero-shot Bayesian inference in context, with an active-subspace mechanism computed from the model’s own predictive-mean gradients. This aligns exploration to an intrinsic low-dimensional subspace via a Fisher-information estimate and selects queries with a UCB acquisition, requiring no online retraining. Across 60 problem variants spanning 20 benchmarks—nine scalable synthetic families and ten real-world tasks (e.g., power systems, Rover, MOPTA08, Mazda)—up to 500 dimensions, GIT-BO delivers a stronger performance–time trade-off than state-of-the-art GP-based methods (SAASBO, TuRBO, Vanilla BO, BAxUS), ranking highest in performance and with runtime advantages that grow with dimensionality. Limitations include memory footprint and dependence on the capacity of the underlying TFM.
The Geometry of Reasoning: Flowing Logics in Representation Space
Authors: Yufa Zhou; Yixiao Wang; Xunjian Yin; Shuyan Zhou; Anru Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We study how large language models (LLMs) "think" through their representation space. 
We propose a novel geometric framework that models an LLM's reasoning as flows---embedding trajectories evolving where logic goes. 
We disentangle logical structure from semantics by employing the same natural deduction propositions with varied semantic carriers, allowing us to test whether LLMs internalize logic beyond surface form. 
This perspective connects reasoning with geometric quantities such as position, velocity, and curvature, enabling formal analysis in representation and concept spaces. 
Our theory establishes: (1) LLM reasoning corresponds to smooth flows in representation space, and (2) logical statements act as local controllers of these flows' velocities. 
Using learned representation proxies, we design controlled experiments to visualize and quantify reasoning flows, providing empirical validation of our theoretical framework. 
Our findings indicate that training solely via next-token prediction can lead LLMs to internalize logical invariants as higher-order geometry in representation space, challenging the "stochastic parrot" argument.
Experiments across Qwen and LLaMA model families further suggest the presence of a general, possibly universal, representational law underlying machine understanding and human linguistic regularities, largely independent of specific training recipes or model architectures. 
Our work serves as both a conceptual foundation and practical tools for studying reasoning phenomena, offering a new lens for interpretability and formal analysis of LLMs' behavior.
GenDR: Lighten Generative Detail Restoration
Authors: Yan Wang; Shijie Zhao; Kexin Zhang; Junlin Li; Li zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Although recent research applying text-to-image (T2I) diffusion models to real-world super-resolution (SR) has achieved remarkable progress, the misalignment of their targets leads to a suboptimal trade-off between inference speed and detail fidelity. Specifically, the T2I task requires multiple inference steps to synthesize images matching to prompts and reduces the latent dimension to lower generating difficulty. Contrariwise, SR can restore high-frequency details in fewer inference steps, but it necessitates a more reliable variational auto-encoder (VAE) to preserve input information. However, most diffusion-based SRs are multistep and use 4-channel VAEs, while existing models with 16-channel VAEs are overqualified diffusion transformers, e.g., FLUX (12B). To align the target, we present a one-step diffusion model for generative detail restoration, GenDR, distilled from a tailored diffusion model with a larger latent space. In detail, we train a new SD2.1-VAE16 (0.9B) via representation alignment to expand the latent space without increasing the model size. Regarding step distillation, we propose consistent score identity distillation (CiD) that incorporates SR task-specific loss into score distillation to leverage more SR priors and align the training target. Furthermore, we extend CiD with adversarial learning and representation alignment (CiDA) to enhance perceptual quality and accelerate training. We also polish the pipeline to achieve a more efficient inference. Experimental results demonstrate that GenDR achieves state-of-the-art performance in both quantitative metrics and visual fidelity.
ROSETTA: Constructing Code-Based Reward from Unconstrained Language Preference
Authors: Sanjana Srivastava; Kangrui Wang; Yung-Chieh Chan; Tianyuan Dai; Manling Li; Ruohan Zhang; Mengdi Xu; Jiajun Wu; Li Fei-Fei
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Intelligent embodied agents not only need to accomplish preset tasks, but also learn to align with individual human needs and preferences. Extracting reward signals from human language preferences allows an embodied agent to adapt through reinforcement learning. However, human language preferences are unconstrained, diverse, and dynamic, making constructing learnable reward from them a major challenge. We present ROSETTA, a framework that uses foundation models to ground and disambiguate unconstrained natural language preference, construct multi-stage reward functions, and implement them with code generation. Unlike prior works requiring extensive offline training to get general reward models or fine-grained correction on a single task, ROSETTA allows agents to adapt online to preference that evolves and is diverse in language and content. We test ROSETTA on both short-horizon and long-horizon manipulation tasks and conduct extensive human evaluation, finding that ROSETTA outperforms SOTA baselines and achieves 87% average success rate and 86% human satisfaction across 116 preferences.
Contamination Detection for VLMs Using Multi‑Modal Semantic Perturbations
Authors: Jaden Park; Mu Cai; Feng Yao; Jingbo Shang; Soochahn Lee; Yong Jae Lee
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent advances in Vision–Language Models (VLMs) have achieved state-of-the-art performance on numerous benchmark tasks. However, the use of internet-scale, often proprietary, pretraining corpora raises a critical concern for both practitioners and users: inflated performance due to \emph{test-set leakage}. While prior works have proposed mitigation strategies such as decontamination of pretraining data and benchmark redesign for LLMs, the complementary direction of developing detection methods for \emph{contaminated VLMs} remains underexplored. To address this gap, we deliberately contaminate open-source VLMs on popular benchmarks and show that existing detection approaches either fail outright or exhibit inconsistent behavior. We then propose a novel simple yet effective detection method based on \textit{multi-modal semantic perturbation}, demonstrating that contaminated models fail to generalize under controlled perturbations. Finally, we validate our approach across multiple realistic contamination strategies, confirming its robustness and effectiveness. The code and perturbed dataset are released here: \href{https://github.com/jadenpark0/mm-perturb}{https://github.com/jadenpark0/mm-perturb}.
HumanPCR: Probing MLLM Capabilities in Diverse Human-Centric Scenes
Authors: Keliang Li; Hongze Shen; Hao Shi; RuiBing Hou; Hong Chang; Jie Huang; Chenghao Jia; Wen Wang; Yiling Wu; Dongmei Jiang; Shiguang Shan; Xilin Chen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The aspiration for artificial general intelligence, fueled by the rapid progress of multimodal understanding, demands models to understand humans in diverse and complex scenarios, as humans manifests intelligence and embody the world. We propose HumanPCR, an evaluation suite for probing MLLMs’ capacity in human-centric visual contexts across three hierarchical levels: Perception, Comprehension, and Reasoning (denoted by Human-P, Human-C, and Human-R, respectively). Human-P and Human-C consist of over 6,000 multiple-choice questions evaluating 34 fine-grained tasks covering 9 essential dimensions. Human-R presents a manually curated challenging video reasoning test that requires integrating multiple visual evidence, proactively extracting implicit context beyond question cues, and applying human-like expertise. Each question includes human-annotated Chain-of-Thought (CoT) rationales with key visual evidence to support further research. Extensive evaluations on over 30 state-of-the-art models exhibit significant challenges in human-centric visual understanding, particularly in tasks involving detailed space perception, temporal understanding, and mind modeling. The analysis of Human-R further exposes a critical failure in reasoning: models struggle to proactively gather necessary visual evidence, instead showing a faulty reliance on query-prompted cues, with advanced techniques offering only marginal gains. We hope HumanPCR and our findings will advance the development, evaluation, and human-centric applications of multimodal models.
Query-Specific Causal Graph Pruning Under Tiered Knowledge
Authors: Yizuo Chen; Jane E. Barker
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We present a systematic method for pruning edges from causal graphs by leveraging tiered knowledge. We characterize conditions under which edges can be removed from a causal graph while preserving the identifiability of (conditional) causal effects. This result enables causal identification on simplified graphs that are substantially smaller than the original graphs. The approach is particularly valuable when researchers are interested in causal relationships within specific tiers while accounting for broader influences from other tiers without fully specifying them. Building on this, we introduce a query-specific causal discovery algorithm that takes a causal query and observational data as input and returns a graph tailored specifically to that query. Through both theoretical analysis and empirical studies, we demonstrate that our discovery algorithm can achieve exponential speedups compared to the existing method when tiered knowledge is available.
TrojanTO: Action-Level Backdoor Attacks Against Trajectory Optimization Models
Authors: Yang Dai; Oubo Ma; Xingxing Liang; Longfei Zhang; Xiaochun Cao; Shouling Ji; Jiaheng Zhang; Jincai Huang; Li Shen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Trajectory Optimization (TO) models have achieved remarkable success in offline reinforcement learning (offline RL). However, their vulnerability to backdoor attacks remains largely unexplored. We find that existing backdoor attacks in RL, which typically rely on reward manipulation throughout training, are largely ineffective against TO models due to their inherent sequence modeling nature and large network size. Moreover, the complexities introduced by high-dimensional continuous action further compound the challenge of injecting effective backdoors. To address these gaps, we propose TrojanTO, the first action-level backdoor attack against TO models. TrojanTO is a post-training attack and employs alternating training to forge a strong connection between triggers and target actions, ensuring high attack effectiveness. To maintain attack stealthiness, it utilizes trajectory filtering to preserve the benign performance and batch poisoning for trigger consistency. Extensive evaluations demonstrate that TrojanTO effectively implants backdoors across diverse tasks and attack objectives with a low attack budget (0.3\% of trajectories). Furthermore, TrojanTO exhibits broad applicability to DT, GDT, and DC, underscoring its scalability across diverse TO model architectures.
Graph Signal Processing Meets Mamba2: Adaptive Filter Bank via Delta Modulation
Authors: Yehjin Shin; Seojin Kim; Noseong Park
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: State-space models (SSMs) offer efficient alternatives to attention with linear-time recurrence. Mamba2, a recent SSM-based language model, uses selective input gating and a multi-head structure, enabling parallel computation and strong benchmark performance. However, its multi-head recurrence operates independently without structured utilization or analysis. In this work, we propose a novel method called **H**ierarchical **AD**aptive filter bank for **E**fficient **S**SMs (*HADES*), a Graph Signal Processing (GSP)-inspired framework that reinterprets Mamba2 as an adaptive filter bank on a line graph. Our hierarchical architecture introduces two filter types: shared filters for global low-pass behavior and expert filters for local high-pass behavior, achieved through structured bias on the parameter $\Delta$. *HADES* achieves comparable performance to baseline models including Mamba2 across various benchmarks in language modeling, commonsense reasoning, and long-context retrieval, while using only **58.9%** of the original parameters. In this regard, *HADES* bridges GSP and neural sequence modeling, enabling efficient, hierarchical, and interpretable filtering within state-space models.
Efficient Agent Training for Computer Use
Authors: Yanheng He; Jiahe Jin; Pengfei Liu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Scaling up high-quality trajectory data has long been a critical bottleneck for developing human-like computer use agents. We introduce PC Agent-E, an efficient agent training framework that significantly reduces reliance on large-scale human demonstrations. Starting with just 312 human-annotated computer use trajectories, we further augment them by synthesizing diverse alternative action decisions with Claude 3.7 Sonnet. Trained on these enriched trajectories, our PC Agent-E model achieved a remarkable 141% relative improvement, and even surpassed the Claude 3.7 Sonnet by 10% in relative terms on WindowsAgentArena-V2, an improved benchmark we also released. By integrating robust human computer use skills with automated AI data synthesis capabilities, our method not only brought substantial improvements over training on human trajectories alone, but also significantly surpassed direct distillation from Claude 3.7 Sonnet.
MoRA: Mobility as the Backbone for Geospatial Representation Learning at Scale
Authors: Ya Wen; Jixuan Cai; Qiyao Ma; Linyan Li; Xinhuan Chen; Chris Webster; Yulun Zhou
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Representation learning of geospatial locations remains a core challenge in achieving general geospatial intelligence, with increasingly diverging philosophies and techniques. While Earth observation paradigms excel at depicting locations in their physical states, we propose that a location’s full characterization requires grounding in both its physical attributes and its internal human activity pattern, the latter being particularly crucial for understanding its human-centric functions. We present MoRA, a human-centric geospatial framework that leverages a mobility graph as its core backbone to fuse various data modalities, aiming to learn embeddings that represent the socio-economic context and functional role of a location. MoRA achieves this through the integration of spatial tokenization, GNNs, and asymmetric contrastive learning to align 100M+ POIs, massive remote sensing imagery, and structured demographic statistics with a billion-edge mobility graph, ensuring the three auxiliary modalities are interpreted through the lens of fundamental human dynamics. To rigorously evaluate the effectiveness of MoRA, we construct a benchmark dataset composed of 9 downstream prediction tasks across social and economic domains. Experiments show that MoRA, with four input modalities and a compact 128-dimensional representation space, achieves superior predictive performances than state-of-the-art models by an average of 12.9\%. Echoing LLM scaling laws, we further demonstrate the scaling behavior in geospatial representation learning. We open-source code and pretrained models at: https://github.com/ylzhouchris/MoRA.
RL Squeezes, SFT Expands: A Comparative Study of Reasoning LLMs
Authors: Kohsei Matsutani; Shota Takashiro; Gouki Minegishi; Takeshi Kojima; Yusuke Iwasawa; Yutaka Matsuo
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large language models (LLMs) are typically trained by reinforcement learning (RL) with verifiable rewards (RLVR) and supervised fine-tuning (SFT) on reasoning traces to improve their reasoning abilities. However, how these methods shape reasoning capabilities remains largely elusive. Going beyond an accuracy-based investigation of how these two components sculpt the reasoning process, this paper introduces a novel analysis framework that quantifies reasoning paths and captures their qualitative changes under each training process (with models of 1.5B, 7B, and 14B parameters on mathematical and code domains). Specifically, we investigate the reasoning process at two levels of granularity: the trajectory-level, which examines complete reasoning outputs, and the step-level, which analyzes reasoning graphs whose nodes correspond to individual reasoning steps. Notably, clustering of unique reasoning trajectories shows complementary effects: RL compresses incorrect trajectories, whereas SFT expands correct ones. Step-level analysis reveals that RL steepens (about 2.5 times), while SFT flattens (reduced to about one-third), the decay rates of node visitation frequency, degree, and betweenness centrality distributions in the reasoning graph. This indicates that RL concentrates reasoning functionality into a small subset of steps, while SFT homogenizes it across many steps. 
Furthermore, by evaluating the reasoning graph topologies from multiple perspectives, we delineate the shared and distinct characteristics of RL and SFT. Our work presents a novel reasoning path perspective that explains why the current best practice of two-stage training, with SFT followed by RL, is successful, and offers practical implications for data construction and more efficient learning approaches.
Vision-SR1: Self-Rewarding Vision-Language Model via Reasoning Decomposition and Multi-Reward Policy Optimization
Authors: Zongxia Li; Wenhao Yu; Chengsong Huang; Zhenwen Liang; Rui Liu; Fuxiao Liu; Jingxi Chen; Dian Yu; Jordan Lee Boyd-Graber; Haitao Mi; Dong Yu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Vision-Language Models (VLMs) often suffer from visual hallucinations -- generating things that are not consistent with visual inputs -- and language shortcuts, where they skip the visual part and just rely on text priors. These issues arise because most post-training methods for VLMs rely on simple verifiable answer matching and supervise only final outputs, leaving intermediate visual reasoning without explicit guidance. As a result, VLMs receive sparse visual signals and often learn to prioritize language-based reasoning over visual perception. To mitigate this, some existing methods add visual supervision using human annotations or distilled labels from external large models. However, human annotations are labor-intensive and costly, and external signals can introduce high latency cost.

In this paper, we introduce Vision-SR1, a three-stage self-rewarding reinforcement learning method that improves visual reasoning without relying on external visual supervision. Vision-SR1 decomposes VLM reasoning into two components: visual reasoning and language reasoning, where the model is first prompted to produce self-contained visual descriptions sufficient to answer the question without referring back to the input image, before jointly optimizing both visual and language reasoning through our multi-reward loss objective. To validate this self-containment, the same VLM model is re-prompted to perform language reasoning using only the generated visual reasoning as input to compute visual reward.  The final reward is computed through a decoupled reward-advantage framework, where visual reward and language reasoning reward each have their advantages, log probabilities, and KL divergence calculated separately. This decoupling enables more fine-grained reward computation by preventing the entanglement of heterogeneous reward signals. Our experiments show that Vision-SR1 improves visual reasoning, mitigates visual hallucinations, and reduces reliance on language shortcuts across diverse vision-language tasks, while being more efficient than methods that rely on external visual reward models, which require additional GPUs to host. In contrast, Vision-SR1 introduces no extra GPU overhead beyond that of standard training.
ReTabAD: A Benchmark for Restoring Semantic Context in Tabular Anomaly Detection
Authors: Sanghyu Yoon; Dongmin Kim; Suhee Yoon; Ye Seul Sim; Seungdong Yoa; Hye-Seung Cho; Soonyoung Lee; Hankook Lee; Woohyung Lim
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: In tabular anomaly detection (AD), textual semantic context often carries critical signals, as the definition of an anomaly is closely tied to domain-specific context. However, existing benchmarks provide only raw data points without semantic context, overlooking rich textual metadata such as feature descriptions and domain knowledge that experts rely on in practice. This limitation restricts research flexibility and prevents models from fully leveraging domain knowledge for detection. ReTabAD addresses this gap by Restoring textual semantics to enable context-aware Tabular AD research. We provide (1) 20 carefully curated tabular datasets enriched with structured textual metadata, together with implementations of state-of-the-art AD algorithms—including classical, deep learning, and LLM-based approaches—and (2) a zero-shot LLM framework that leverages semantic context without task-specific training, establishing a strong baseline for future research. Furthermore, this work provides insights into the role and utility of textual metadata in AD through experiments and analysis. Results show that semantic context improves detection performance and enhances interpretability by supporting domain-aware reasoning. These findings establish ReTabAD as a benchmark for systematic exploration of context-aware AD.