# Research Papers — 2026-03 · bundle page 105 of 243

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### FMMC: Harnessing the Power of Foundation Models for Accurate Material Classification

Authors: Qingran Lin, Fengwei Yang, Chaolun Zhu
Published: 2026-03-18
Categories: cs.CV
arXiv: https://arxiv.org/abs/2603.17390v2

Material classification has emerged as a critical task in computer vision and graphics, supporting the assignment of accurate material properties to a wide range of digital and real-world applications. While traditionally framed as an image classification task, this domain faces significant challenges due to the scarcity of annotated data, limiting the accuracy and generalizability of trained models. Recent advances in vision-language foundation models (VLMs) offer promising avenues to address these issues, yet existing solutions leveraging these models still exhibit unsatisfying results in material recognition tasks. In this work, we propose a novel framework that effectively harnesses foundation models to overcome data limitations and enhance classification accuracy. Our method integrates two key innovations: (a) a robust image generation and auto-labeling pipeline that creates a diverse and high-quality training dataset with material-centric images, and automatically assigns labels by fusing object semantics and material attributes in text prompts; (b) a prior incorporation strategy to distill information from VLMs, combined with a joint fine-tuning method that optimizes a pre-trained vision foundation model alongside VLM-derived priors, preserving broad generalizability while adapting to material-specific features. Extensive experiments demonstrate significant improvements on multiple datasets. We show that our synthetic dataset effectively captures the characteristics of real world materials, and the integration of priors from vision-language models significantly enhances the final performance. The source code and dataset will be released.

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### Toward Phonology-Guided Sign Language Motion Generation: A Diffusion Baseline and Conditioning Analysis

Authors: Rui Hong, Jana Kosecka
Published: 2026-03-18
Categories: cs.CV
arXiv: https://arxiv.org/abs/2603.17388v2

Generating natural, correct, and visually smooth 3D avatar sign language motion conditioned on the text inputs continues to be very challenging. In this work, we train a generative model of 3D body motion and explore the role of phonological attribute conditioning for sign language motion generation, using ASL-LEX 2.0 annotations such as hand shape, hand location and movement. We first establish a strong diffusion baseline using an Human Motion MDM-style diffusion model with SMPL-X representation, which outperforms SignAvatar, a state-of-the-art CVAE method, on gloss discriminability metrics. We then systematically study the role of text conditioning using different text encoders (CLIP vs. T5), conditioning modes (gloss-only vs. gloss+phonological attributes), and attribute notation format (symbolic vs. natural language). Our analysis reveals that translating symbolic ASL-LEX notations to natural language is a necessary condition for effective CLIP-based attribute conditioning, while T5 is largely unaffected by this translation. Furthermore, our best-performing variant (CLIP with mapped attributes) outperforms SignAvatar across all metrics. These findings highlight input representation as a critical factor for text-encoder-based attribute conditioning, and motivate structured conditioning approaches where gloss and phonological attributes are encoded through independent pathways.

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### CRE-T1 Preview Technical Report: Beyond Contrastive Learning for Reasoning-Intensive Retrieval

Authors: Guangzhi Wang, Yinghao Jiao, Zhi Liu
Published: 2026-03-18
Categories: cs.IR, cs.AI
arXiv: https://arxiv.org/abs/2603.17387v1

The central challenge of reasoning-intensive retrieval lies in identifying implicitreasoning relationships between queries and documents, rather than superficial se-mantic or lexical similarity. The contrastive learning paradigm is fundamentallya static representation consolidation technique: during training, it encodes hier-archical relevance concepts into fixed geometric structures in the vector space,and at inference time it cannot dynamically adjust relevance judgments accord-ing to the specific reasoning demands of each query. Consequently, performancedegrades noticeably when vocabulary mismatch exists between queries and doc-uments or when implicit reasoning is required to establish relevance. This pa-per proposes Thought 1 (T1), a generative retrieval model that shifts relevancemodeling from static alignment to dynamic reasoning. On the query side, T1 dy-namically generates intermediate reasoning trajectories for each query to bridgeimplicit reasoning relationships and uses <embtoken> as a semantic aggregationpoint for the reasoning output. On the document side, it employs an instruction+ text + <embtoken> encoding format to support high-throughput indexing. Tointernalize dynamic reasoning capabilities into vector representations, we adopt athree-stage training curriculum and introduce GRPO in the third stage, enablingthe model to learn optimal derivation strategies for different queries through trial-and-error reinforcement learning. On the BRIGHT benchmark, T1-4B exhibitsstrong performance under the original query setting, outperforming larger modelstrained with contrastive learning overall, and achieving performance comparableto multi-stage retrieval pipelines. The results demonstrate that replacing static rep-resentation alignment with dynamic reasoning generation can effectively improvereasoning-intensive retrieval performance.

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### PJB: A Reasoning-Aware Benchmark for Person-Job Retrieval

Authors: Guangzhi Wang, Xiaohui Yang, Kai Li, Jiawen He, Kai Yang, Ruixuan Zhang, Zhi Liu
Published: 2026-03-18
Categories: cs.IR, cs.CL
arXiv: https://arxiv.org/abs/2603.17386v1

As retrieval models converge on generic benchmarks, the pressing question is no longer "who scores higher" but rather "where do systems fail, and why?" Person-job matching is a domain that urgently demands such diagnostic capability -- it requires systems not only to verify explicit constraints but also to perform skill-transfer inference and job-competency reasoning, yet existing benchmarks provide no systematic diagnostic support for this task. We introduce PJB (Person-Job Benchmark), a reasoning-aware retrieval evaluation dataset that uses complete job descriptions as queries and complete resumes as documents, defines relevance through job-competency judgment, is grounded in real-world recruitment data spanning six industry domains and nearly 200,000 resumes, and upgrades evaluation from "who scores higher" to "where do systems differ, and why" through domain-family and reasoning-type diagnostic labels. Diagnostic experiments using dense retrieval reveal that performance heterogeneity across industry domains far exceeds the gains from module upgrades for the same model, indicating that aggregate scores alone can severely mislead optimization decisions. At the module level, reranking yields stable improvements while query understanding not only fails to help but actually degrades overall performance when combined with reranking -- the two modules face fundamentally different improvement bottlenecks. The value of PJB lies not in yet another leaderboard of average scores, but in providing recruitment retrieval systems with a capability map that pinpoints where to invest.

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### The Causal Uncertainty Principle: Manifold Tearing and the Topological Limits of Counterfactual Interventions

Authors: Rui Wu, Hong Xie, Yongjun Li
Published: 2026-03-18
Categories: cs.LG
arXiv: https://arxiv.org/abs/2603.17385v1

Judea Pearl's do-calculus provides a foundation for causal inference, but its translation to continuous generative models remains fraught with geometric challenges. We establish the fundamental limits of such interventions. We define the Counterfactual Event Horizon and prove the Manifold Tearing Theorem: deterministic flows inevitably develop finite-time singularities under extreme interventions. We establish the Causal Uncertainty Principle for the trade-off between intervention extremity and identity preservation. Finally, we introduce Geometry-Aware Causal Flow (GACF), a scalable algorithm that utilizes a topological radar to bypass manifold tearing, validated on high-dimensional scRNA-seq data.

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### Cohomological Obstructions to Global Counterfactuals: A Sheaf-Theoretic Foundation for Generative Causal Models

Authors: Rui Wu, Hong Xie, Yongjun Li
Published: 2026-03-18
Categories: cs.LG
arXiv: https://arxiv.org/abs/2603.17384v1

Current continuous generative models (e.g., Diffusion Models, Flow Matching) implicitly assume that locally consistent causal mechanisms naturally yield globally coherent counterfactuals. In this paper, we prove that this assumption fails fundamentally when the causal graph exhibits non-trivial homology (e.g., structural conflicts or hidden confounders). We formalize structural causal models as cellular sheaves over Wasserstein spaces, providing a strict algebraic topological definition of cohomological obstructions in measure spaces. To ensure computational tractability and avoid deterministic singularities (which we define as manifold tearing), we introduce entropic regularization and derive the Entropic Wasserstein Causal Sheaf Laplacian, a novel system of coupled non-linear Fokker-Planck equations. Crucially, we prove an entropic pullback lemma for the first variation of pushforward measures. By integrating this with the Implicit Function Theorem (IFT) on Sinkhorn optimality conditions, we establish a direct algorithmic bridge to automatic differentiation (VJP), achieving O(1)-memory reverse-mode gradients strictly independent of the iteration horizon. Empirically, our framework successfully leverages thermodynamic noise to navigate topological barriers ("entropic tunneling") in high-dimensional scRNA-seq counterfactuals. Finally, we invert this theoretical framework to introduce the Topological Causal Score, demonstrating that our Sheaf Laplacian acts as a highly sensitive algebraic detector for topology-aware causal discovery.

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### VisionNVS: Self-Supervised Inpainting for Novel View Synthesis under the Virtual-Shift Paradigm

Authors: Hongbo Lu, Liang Yao, Chenghao He, Fan Liu, Wenlong Liao, Tao He, Pai Peng
Published: 2026-03-18
Categories: cs.CV
arXiv: https://arxiv.org/abs/2603.17382v1

A fundamental bottleneck in Novel View Synthesis (NVS) for autonomous driving is the inherent supervision gap on novel trajectories: models are tasked with synthesizing unseen views during inference, yet lack ground truth images for these shifted poses during training. In this paper, we propose VisionNVS, a camera-only framework that fundamentally reformulates view synthesis from an ill-posed extrapolation problem into a self-supervised inpainting task. By introducing a ``Virtual-Shift'' strategy, we use monocular depth proxies to simulate occlusion patterns and map them onto the original view. This paradigm shift allows the use of raw, recorded images as pixel-perfect supervision, effectively eliminating the domain gap inherent in previous approaches. Furthermore, we address spatial consistency through a Pseudo-3D Seam Synthesis strategy, which integrates visual data from adjacent cameras during training to explicitly model real-world photometric discrepancies and calibration errors. Experiments demonstrate that VisionNVS achieves superior geometric fidelity and visual quality compared to LiDAR-dependent baselines, offering a robust solution for scalable driving simulation.

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### An Auditable AI Agent Loop for Empirical Economics: A Case Study in Forecast Combination

Authors: Minchul Shin
Published: 2026-03-18
Categories: econ.EM, stat.ML
arXiv: https://arxiv.org/abs/2603.17381v4

AI coding agents, general purpose assistants that write and execute code, make empirical specification search fast and cheap, but they also widen hidden researcher degrees of freedom. This paper adapts an open-source agent-loop architecture to an empirical economics workflow and adds a post-search holdout evaluation. In a forecast-combination illustration, independent agent searches find methods that improve on benchmarks from the original study. Logged search and holdout evaluation together make adaptive specification search more transparent and help distinguish robust improvements from sample-specific discoveries.

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### SCALE:Scalable Conditional Atlas-Level Endpoint transport for virtual cell perturbation prediction

Authors: Shuizhou Chen, Lang Yu, Xueqin Lin, Xinjie Mao, Songming Zhang, Xinyu Gu, Hao Wu, Sheng Xu, Kedu Jin, Lei Bai, Quan Qian, Qin Chen et al.
Published: 2026-03-18
Categories: cs.LG, cs.AI, q-bio.QM
arXiv: https://arxiv.org/abs/2603.17380v3

Virtual-cell models aim to predict how cell populations respond to perturbations, but control and treated cells are measured as unpaired populations, complicating the learning of perturbation-specific effects. We present SCALE, a conditional transport model that represents cells as unordered sets and predicts treated populations without cell-level matching. A shared set-aware encoder and conditional DiT backbone learn latent transport, making endpoint supervision directly delta-aligned without an auxiliary delta objective. Across genetic, chemical, developmental and immune perturbations, SCALE recovered gene-expression changes, response directions and population structure. In CRISPR data with dominant cell-line effects, SCALE outperformed competing methods across seven metrics and maintained separation among gene-target representations rather than collapsing them into a shared region. SCALE further prioritized cytokines predicted to produce distinct immune activation and inflammatory responses. Experiments using matched PBMC samples from three donors confirmed these predicted differences. Together, SCALE enables perturbation-specific prediction from unpaired populations and supports experimental prioritization.

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### SCALE:Scalable Conditional Atlas-Level Endpoint transport for virtual cell perturbation prediction

Authors: Shuizhou Chen, Lang Yu, Kedu Jin, Songming Zhang, Hao Wu, Wenxuan Huang, Sheng Xu, Quan Qian, Qin Chen, Lei Bai, Siqi Sun, Zhangyang Gao
Published: 2026-03-18
Categories: cs.LG, cs.AI, q-bio.QM
arXiv: https://arxiv.org/abs/2603.17380v2

Virtual cell models aim to enable in silico experimentation by predicting how cells respond to genetic, chemical, or cytokine perturbations from single-cell measurements. In practice, however, large-scale perturbation prediction remains constrained by three coupled bottlenecks: inefficient training and inference pipelines, unstable modeling in high-dimensional sparse expression space, and evaluation protocols that overemphasize reconstruction-like accuracy while underestimating biological fidelity. In this work we present a specialized large-scale foundation model SCALE for virtual cell perturbation prediction that addresses the above limitations jointly. First, we build a BioNeMo-based training and inference framework that substantially improves data throughput, distributed scalability, and deployment efficiency, yielding 12.51* speedup on pretrain and 1.29* on inference over the prior SOTA pipeline under matched system settings. Second, we formulate perturbation prediction as conditional transport and implement it with a set-aware flow architecture that couples LLaMA-based cellular encoding with endpoint-oriented supervision. This design yields more stable training and stronger recovery of perturbation effects. Third, we evaluate the model on Tahoe-100M using a rigorous cell-level protocol centered on biologically meaningful metrics rather than reconstruction alone. On this benchmark, our model improves PDCorr by 12.02% and DE Overlap by 10.66% over STATE. Together, these results suggest that advancing virtual cells requires not only better generative objectives, but also the co-design of scalable infrastructure, stable transport modeling, and biologically faithful evaluation.

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### Efficient Exploration at Scale

Authors: Seyed Mohammad Asghari, Chris Chute, Vikranth Dwaracherla, Xiuyuan Lu, Mehdi Jafarnia, Victor Minden, Zheng Wen, Benjamin Van Roy
Published: 2026-03-18
Categories: cs.LG, cs.AI
arXiv: https://arxiv.org/abs/2603.17378v2

We develop an online learning algorithm that dramatically improves the data efficiency of reinforcement learning from human feedback (RLHF). Our algorithm incrementally updates reward and language models as choice data is received. The reward model is fit to the choice data, while the language model is updated by a variation of reinforce, with reinforcement signals provided by the reward model. Several features enable the efficiency gains: a small affirmative nudge added to each reinforcement signal, an epistemic neural network that models reward uncertainty, and information-directed exploration. With Gemma large language models (LLMs), our algorithm matches the performance of offline RLHF trained on 200K labels using fewer than 20K labels, representing more than a 10x gain in data efficiency. Extrapolating from our results, we expect our algorithm trained on 1M labels to match offline RLHF trained on 1B labels. This represents a 1,000x gain. To our knowledge, these are the first results to demonstrate that such large improvements are possible.

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### Efficient Exploration at Scale

Authors: Seyed Mohammad Asghari, Chris Chute, Vikranth Dwaracherla, Xiuyuan Lu, Mehdi Jafarnia, Victor Minden, Zheng Wen, Benjamin Van Roy
Published: 2026-03-18
Categories: cs.LG, cs.AI
arXiv: https://arxiv.org/abs/2603.17378v1

We develop an online learning algorithm that dramatically improves the data efficiency of reinforcement learning from human feedback (RLHF). Our algorithm incrementally updates reward and language models as choice data is received. The reward model is fit to the choice data, while the language model is updated by a variation of reinforce, with reinforcement signals provided by the reward model. Several features enable the efficiency gains: a small affirmative nudge added to each reinforcement signal, an epistemic neural network that models reward uncertainty, and information-directed exploration. With Gemma large language models (LLMs), our algorithm matches the performance of offline RLHF trained on 200K labels using fewer than 20K labels, representing more than a 10x gain in data efficiency. Extrapolating from our results, we expect our algorithm trained on 1M labels to match offline RLHF trained on 1B labels. This represents a 1,000x gain. To our knowledge, these are the first results to demonstrate that such large improvements are possible.

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### Stereo World Model: Camera-Guided Stereo Video Generation

Authors: Yang-Tian Sun, Zehuan Huang, Yifan Niu, Lin Ma, Yan-Pei Cao, Yuewen Ma, Xiaojuan Qi
Published: 2026-03-18
Categories: cs.CV
arXiv: https://arxiv.org/abs/2603.17375v1

We present StereoWorld, a camera-conditioned stereo world model that jointly learns appearance and binocular geometry for end-to-end stereo video generation.Unlike monocular RGB or RGBD approaches, StereoWorld operates exclusively within the RGB modality, while simultaneously grounding geometry directly from disparity. To efficiently achieve consistent stereo generation, our approach introduces two key designs: (1) a unified camera-frame RoPE that augments latent tokens with camera-aware rotary positional encoding, enabling relative, view- and time-consistent conditioning while preserving pretrained video priors via a stable attention initialization; and (2) a stereo-aware attention decomposition that factors full 4D attention into 3D intra-view attention plus horizontal row attention, leveraging the epipolar prior to capture disparity-aligned correspondences with substantially lower compute. Across benchmarks, StereoWorld improves stereo consistency, disparity accuracy, and camera-motion fidelity over strong monocular-then-convert pipelines, achieving more than 3x faster generation with an additional 5% gain in viewpoint consistency. Beyond benchmarks, StereoWorld enables end-to-end binocular VR rendering without depth estimation or inpainting, enhances embodied policy learning through metric-scale depth grounding, and is compatible with long-video distillation for extended interactive stereo synthesis.

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### Shot-Aware Frame Sampling for Video Understanding

Authors: Mengyu Zhao, Di Fu, Yongyu Xie, Jiaxing Zhang, Zhigang Yuan, Shirin Jalali, Yong Cao
Published: 2026-03-18
Categories: cs.CV
arXiv: https://arxiv.org/abs/2603.17374v1

Video frame sampling is essential for efficient long-video understanding with Vision-Language Models (VLMs), since dense inputs are costly and often exceed context limits. Yet when only a small number of frames can be retained, existing samplers often fail to balance broad video coverage with brief but critical events, which can lead to unreliable downstream predictions. To address this issue, we present InfoShot, a task-agnostic, shot-aware frame sampler for long-video understanding. InfoShot first partitions a video into semantically consistent shots, and then selects two complementary keyframes from each shot: one to represent the main content and one to capture unusual within-shot changes. This design is guided by an information-theoretic objective that encourages the sampled set to retain high information about both shot structure and sparse within-shot deviations. In this way, it improves the chance of preserving both overall video context and short decision-critical moments without requiring any retraining. To better evaluate such short-lived events, we further introduce SynFlash, a synthetic benchmark with controllable sub-second anomaly patterns and frame-level ground truth, and we also evaluate InfoShot on existing anomaly datasets and general video understanding tasks. Experiments show that InfoShot improves anomaly hit rate and downstream Video-QA accuracy under frame number constraints, while matching or outperforming strong baselines on standard video understanding benchmarks.

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### SafeTutors: Benchmarking Pedagogical Safety in AI Tutoring Systems

Authors: Rima Hazra, Bikram Ghuku, Ilona Marchenko, Yaroslava Tokarieva, Sayan Layek, Somnath Banerjee, Julia Stoyanovich, Mykola Pechenizkiy
Published: 2026-03-18
Categories: cs.CL
arXiv: https://arxiv.org/abs/2603.17373v2

Large language models are rapidly being deployed as AI tutors, yet current evaluation paradigms assess problem-solving accuracy and generic safety in isolation, failing to capture whether a model is simultaneously pedagogically effective and safe across student-tutor interaction. We argue that tutoring safety is fundamentally different from conventional LLM safety: the primary risk is not toxic content but the quiet erosion of learning through answer over-disclosure, misconception reinforcement, and the abdication of scaffolding. To systematically study this failure mode, we introduce SafeTutors, a benchmark that jointly evaluates safety and pedagogy across mathematics, physics, and chemistry. SafeTutors is organized around a theoretically grounded risk taxonomy comprising 11 harm dimensions and 48 sub-risks drawn from learning-science literature. We uncover that all models show broad harm; scale doesn't reliably help; and multi-turn dialogue worsens behavior, with pedagogical failures rising from 17.7% to 77.8%. Harms also vary by subject, so mitigations must be discipline-aware, and single-turn "safe/helpful" results can mask systematic tutor failure over extended interaction.

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### SafeTutors: Benchmarking Pedagogical Safety in AI Tutoring Systems

Authors: Rima Hazra, Bikram Ghuku, Ilona Marchenko, Yaroslava Tokarieva, Sayan Layek, Somnath Banerjee, Julia Stoyanovich, Mykola Pechenizkiy
Published: 2026-03-18
Categories: cs.CL
arXiv: https://arxiv.org/abs/2603.17373v1

Large language models are rapidly being deployed as AI tutors, yet current evaluation paradigms assess problem-solving accuracy and generic safety in isolation, failing to capture whether a model is simultaneously pedagogically effective and safe across student-tutor interaction. We argue that tutoring safety is fundamentally different from conventional LLM safety: the primary risk is not toxic content but the quiet erosion of learning through answer over-disclosure, misconception reinforcement, and the abdication of scaffolding. To systematically study this failure mode, we introduce SafeTutors, a benchmark that jointly evaluates safety and pedagogy across mathematics, physics, and chemistry. SafeTutors is organized around a theoretically grounded risk taxonomy comprising 11 harm dimensions and 48 sub-risks drawn from learning-science literature. We uncover that all models show broad harm; scale doesn't reliably help; and multi-turn dialogue worsens behavior, with pedagogical failures rising from 17.7% to 77.8%. Harms also vary by subject, so mitigations must be discipline-aware, and single-turn "safe/helpful" results can mask systematic tutor failure over extended interaction.

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### Understanding and Defending VLM Jailbreaks via Jailbreak-Related Representation Shift

Authors: Zhihua Wei, Qiang Li, Jian Ruan, Zhenxin Qin, Leilei Wen, Dongrui Liu, Wen Shen
Published: 2026-03-18
Categories: cs.CV, cs.AI
arXiv: https://arxiv.org/abs/2603.17372v1

Large vision-language models (VLMs) often exhibit weakened safety alignment with the integration of the visual modality. Even when text prompts contain explicit harmful intent, adding an image can substantially increase jailbreak success rates. In this paper, we observe that VLMs can clearly distinguish benign inputs from harmful ones in their representation space. Moreover, even among harmful inputs, jailbreak samples form a distinct internal state that is separable from refusal samples. These observations suggest that jailbreaks do not arise from a failure to recognize harmful intent. Instead, the visual modality shifts representations toward a specific jailbreak state, thereby leading to a failure to trigger refusal. To quantify this transition, we identify a jailbreak direction and define the jailbreak-related shift as the component of the image-induced representation shift along this direction. Our analysis shows that the jailbreak-related shift reliably characterizes jailbreak behavior, providing a unified explanation for diverse jailbreak scenarios. Finally, we propose a defense method that enhances VLM safety by removing the jailbreak-related shift (JRS-Rem) at inference time. Experiments show that JRS-Rem provides strong defense across multiple scenarios while preserving performance on benign tasks.

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### Material Magic Wand: Material-Aware Grouping of 3D Parts in Untextured Meshes

Authors: Umangi Jain, Vladimir Kim, Matheus Gadelha, Igor Gilitschenski, Zhiqin Chen
Published: 2026-03-18
Categories: cs.CV
arXiv: https://arxiv.org/abs/2603.17370v1

We introduce the problem of material-aware part grouping in untextured meshes. Many real-world shapes, such as scales of pinecones or windows of buildings, contain repeated structures that share the same material but exhibit geometric variations. When assigning materials to such meshes, these repeated parts often require piece-by-piece manual identification and selection, which is tedious and time-consuming. To address this, we propose Material Magic Wand, a tool that allows artists to select part groups based on their estimated material properties -- when one part is selected, our algorithm automatically retrieves all other parts likely to share the same material. The key component of our approach is a part encoder that generates a material-aware embedding for each 3D part, accounting for both local geometry and global context. We train our model with a supervised contrastive loss that brings embeddings of material-consistent parts closer while separating those of different materials; therefore, part grouping can be achieved by retrieving embeddings that are close to the embedding of the selected part. To benchmark this task, we introduce a curated dataset of 100 shapes with 241 part-level queries. We verify the effectiveness of our method through extensive experiments and demonstrate its practical value in an interactive material assignment application.

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### Towards Safer Large Reasoning Models by Promoting Safety Decision-Making before Chain-of-Thought Generation

Authors: Jianan Chen, Zhifang Zhang, Shuo He, Linan Yue, Lei Feng, Minling Zhang
Published: 2026-03-18
Categories: cs.AI
arXiv: https://arxiv.org/abs/2603.17368v2

Large reasoning models (LRMs) achieved remarkable performance via chain-of-thought (CoT), but recent studies showed that such enhanced reasoning capabilities are at the expense of significantly degraded safety capabilities. In this paper, we reveal that LRMs' safety degradation occurs only after CoT is enabled, and this degradation is not observed when CoT is disabled. This observation motivates us to consider encouraging LRMs to make safety decisions before CoT generation. To this end, we propose a novel safety alignment method that promotes the safety decision-making of LRMs before starting CoT generation. Specifically, we first utilize a Bert-based classifier to extract safety decision signals from a safe model (e.g., a CoT-disabled LRM) and then integrate these signals into LRMs' safety alignment as auxiliary supervision. In this way, the safety gradients can be backpropagated to the LRMs' latent representations, effectively strengthening the LRMs' safety decision-making abilities against CoT generation. Extensive experiments demonstrate that our method substantially improves the safety capabilities of LRMs while effectively maintaining LRMs' general reasoning performance.

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### Variational Kernel Design for Internal Noise: Gaussian Chaos Noise, Representation Compatibility, and Reliable Deep Learning

Authors: Ziran Liu
Published: 2026-03-18
Categories: cs.LG, math.PR
arXiv: https://arxiv.org/abs/2603.17365v1

Internal noise in deep networks is usually inherited from heuristics such as dropout, hard masking, or additive perturbation. We ask two questions: what correlation geometry should internal noise have, and is the implemented perturbation compatible with the representations it acts on? We answer these questions through Variational Kernel Design (VKD), a framework in which a noise mechanism is specified by a law family, a correlation kernel, and an injection operator, and is derived from learning desiderata. In a solved spatial subfamily, a quadratic maximum-entropy principle over latent log-fields yields a Gaussian optimizer with precision given by the Dirichlet Laplacian, so the induced geometry is the Dirichlet Green kernel. Wick normalization then gives a canonical positive mean-one gate, Gaussian Chaos Noise (GCh). For the sample-wise gate used in practice, we prove exact Gaussian control of pairwise log-ratio deformation, margin-sensitive ranking stability, and an exact expected intrinsic roughness budget; hard binary masks instead induce singular or coherence-amplified distortions on positive coherent representations. On ImageNet and ImageNet-C, GCh consistently improves calibration and under shift also improves NLL at competitive accuracy.

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### Public Profile Matters: A Scalable Integrated Approach to Recommend Citations in the Wild

Authors: Karan Goyal, Dikshant Kukreja, Vikram Goyal, Mukesh Mohania
Published: 2026-03-18
Categories: cs.IR, cs.AI, cs.CL, cs.SI
arXiv: https://arxiv.org/abs/2603.17361v2

Proper citation of relevant literature is essential for contextualising and validating scientific contributions. While current citation recommendation systems leverage local and global textual information, they often overlook the nuances of the human citation behaviour. Recent methods that incorporate such patterns improve performance but incur high computational costs and introduce systematic biases into downstream rerankers. To address this, we propose Profiler, a lightweight, non-learnable module that captures human citation patterns efficiently and without bias, significantly enhancing candidate retrieval. Furthermore, we identify a critical limitation in current evaluation protocol: the systems are assessed in a transductive setting, which fails to reflect real-world scenarios. We introduce a rigorous Inductive evaluation setting that enforces strict temporal constraints, simulating the recommendation of citations for newly authored papers in the wild. Finally, we present DAVINCI, a novel reranking model that integrates profiler-derived confidence priors with semantic information via an adaptive vector-gating mechanism. Our system achieves new state-of-the-art results across multiple benchmark datasets, demonstrating superior efficiency and generalisability.

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### MCoT-MVS: Multi-level Vision Selection by Multi-modal Chain-of-Thought Reasoning for Composed Image Retrieval

Authors: Xuri Ge, Chunhao Wang, Xindi Wang, Zheyun Qin, Zhumin Chen, Xin Xin
Published: 2026-03-18
Categories: cs.CV
arXiv: https://arxiv.org/abs/2603.17360v1

Composed Image Retrieval (CIR) aims to retrieve target images based on a reference image and modified texts. However, existing methods often struggle to extract the correct semantic cues from the reference image that best reflect the user's intent under textual modification prompts, resulting in interference from irrelevant visual noise. In this paper, we propose a novel Multi-level Vision Selection by Multi-modal Chain-of-Thought Reasoning (MCoT-MVS) for CIR, integrating attention-aware multi-level vision features guided by reasoning cues from a multi-modal large language model (MLLM). Specifically, we leverage an MLLM to perform chain-of-thought reasoning on the multimodal composed input, generating the retained, removed, and target-inferred texts. These textual cues subsequently guide two reference visual attention selection modules to selectively extract discriminative patch-level and instance-level semantics from the reference image. Finally, to effectively fuse these multi-granular visual cues with the modified text and the imagined target description, we design a weighted hierarchical combination module to align the composed query with target images in a unified embedding space. Extensive experiments on two CIR benchmarks, namely CIRR and FashionIQ, demonstrate that our approach consistently outperforms existing methods and achieves new state-of-the-art performance. Code and trained models are publicly released.

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### A 3D Reconstruction Benchmark for Asset Inspection

Authors: James L. Gray, Nikolai Goncharov, Alexandre Cardaillac, Ryan Griffiths, Jack Naylor, Donald G. Dansereau
Published: 2026-03-18
Categories: cs.CV, eess.IV
arXiv: https://arxiv.org/abs/2603.17358v1

Asset management requires accurate 3D models to inform the maintenance, repair, and assessment of buildings, maritime vessels, and other key structures as they age. These downstream applications rely on high-fidelity models produced from aerial surveys in close proximity to the asset, enabling operators to locate and characterise deterioration or damage and plan repairs. Captured images typically have high overlap between adjacent camera poses, sufficient detail at millimetre scale, and challenging visual appearances such as reflections and transparency. However, existing 3D reconstruction datasets lack examples of these conditions, making it difficult to benchmark methods for this task. We present a new dataset with ground truth depth maps, camera poses, and mesh models of three synthetic scenes with simulated inspection trajectories and varying levels of surface condition on non-Lambertian scene content. We evaluate state-of-the-art reconstruction methods on this dataset. Our results demonstrate that current approaches struggle significantly with the dense capture trajectories and complex surface conditions inherent to this domain, exposing a critical scalability gap and pointing toward new research directions for deployable 3D reconstruction in asset inspection. Project page: https://roboticimaging.org/Projects/asset-inspection-dataset/

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### WebPII: Benchmarking Visual PII Detection for Computer-Use Agents

Authors: Nathan Zhao
Published: 2026-03-18
Categories: cs.CR, cs.AI
arXiv: https://arxiv.org/abs/2603.17357v1

Computer use agents create new privacy risks: training data collected from real websites inevitably contains sensitive information, and cloud-hosted inference exposes user screenshots. Detecting personally identifiable information in web screenshots is critical for privacy-preserving deployment, but no public benchmark exists for this task. We introduce WebPII, a fine-grained synthetic benchmark of 44,865 annotated e-commerce UI images designed with three key properties: extended PII taxonomy including transaction-level identifiers that enable reidentification, anticipatory detection for partially-filled forms where users are actively entering data, and scalable generation through VLM-based UI reproduction. Experiments validate that these design choices improve layout-invariant detection across diverse interfaces and generalization to held-out page types. We train WebRedact to demonstrate practical utility, more than doubling text-extraction baseline accuracy (0.753 vs 0.357 mAP@50) at real-time CPU latency (20ms). We release the dataset and model to support privacy-preserving computer use research.

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### PACE-RAG: Patient-Aware Contextual and Evidence-Constrained RAG for Clinical Drug Recommendation

Authors: Chaeyoung Huh, Hyunmin Hwang, Jung Hwan Shin, Sungyang Jo, Jinse Park, Jong Chul Ye
Published: 2026-03-18
Categories: cs.CL
arXiv: https://arxiv.org/abs/2603.17356v3

Drug recommendation requires a deep understanding of individual patient context, especially for complex conditions like Parkinson's disease. While LLMs possess broad medical knowledge, they fail to capture the subtle nuances of actual prescribing patterns. Existing RAG methods also struggle with these complexities because guideline-based retrieval remains too generic and similar-patient retrieval often replicates majority patterns without accounting for the unique clinical nuances of individual patients. To bridge this gap, we propose PACE-RAG (Patient-Aware Contextual and Evidence-Constrained RAG). Rather than directly copying frequent medications from retrieved patients, PACE-RAG personalizes recommendations by first extracting patient-specific clinical features, retrieving cases around these features, and then refining the final prescription using the patient's current symptoms, active medication history, and focus-specific prescribing tendencies. By analyzing treatment patterns tailored to specific clinical features, PACE-RAG generates patient-specific medication recommendations along with an explainable clinical summary. PACE-RAG achieved the strongest performance among the evaluated inference-only LLM-based methods, reaching F1 scores of 80.84% and 47.22% on the Parkinson's disease and MIMIC-IV cohorts, respectively. Our code is available at: https://github.com/ChaeYoungHuh/PACE-RAG.

---

### PACE-RAG: Patient-Aware Contextual and Evidence-Constrained RAG for Clinical Drug Recommendation

Authors: Chaeyoung Huh, Hyunmin Hwang, Jung Hwan Shin, Sungyang Jo, Jinse Park, Jong Chul Ye
Published: 2026-03-18
Categories: cs.CL
arXiv: https://arxiv.org/abs/2603.17356v2

Drug recommendation requires a deep understanding of individual patient context, especially for complex conditions like Parkinson's disease. While LLMs possess broad medical knowledge, they fail to capture the subtle nuances of actual prescribing patterns. Existing RAG methods also struggle with these complexities because guideline-based retrieval remains too generic and similar-patient retrieval often replicates majority patterns without accounting for the unique clinical nuances of individual patients. To bridge this gap, we propose PACE-RAG (Patient-Aware Contextual and Evidence-Constrained RAG). Rather than directly copying frequent medications from retrieved patients, PACE-RAG personalizes recommendations by first extracting patient-specific clinical features, retrieving cases around these features, and then refining the final prescription using the patient's current symptoms, active medication history, and focus-specific prescribing tendencies. By analyzing treatment patterns tailored to specific clinical features, PACE-RAG generates patient-specific medication recommendations along with an explainable clinical summary. Evaluated on a Parkinson's cohort and the MIMIC-IV benchmark using Llama-3.1-8B and Qwen3-8B, PACE-RAG achieved state-of-the-art performance, reaching F1 scores of 80.84% and 47.22%, respectively. These results suggest that PACE-RAG is a robust and clinically grounded framework for personalized decision support. Our code is available at: https://github.com/ChaeYoungHuh/PACE-RAG.

---

### OnlineHMR: Video-based Online World-Grounded Human Mesh Recovery

Authors: Yiwen Zhao, Ce Zheng, Yufu Wang, Hsueh-Han Daniel Yang, Liting Wen, Laszlo A. Jeni
Published: 2026-03-18
Categories: cs.CV
arXiv: https://arxiv.org/abs/2603.17355v1

Human mesh recovery (HMR) models 3D human body from monocular videos, with recent works extending it to world-coordinate human trajectory and motion reconstruction. However, most existing methods remain offline, relying on future frames or global optimization, which limits their applicability in interactive feedback and perception-action loop scenarios such as AR/VR and telepresence. To address this, we propose OnlineHMR, a fully online framework that jointly satisfies four essential criteria of online processing, including system-level causality, faithfulness, temporal consistency, and efficiency. Built upon a two-branch architecture, OnlineHMR enables streaming inference via a causal key-value cache design and a curated sliding-window learning strategy. Meanwhile, a human-centric incremental SLAM provides online world-grounded alignment under physically plausible trajectory correction. Experimental results show that our method achieves performance comparable to existing chunk-based approaches on the standard EMDB benchmark and highly dynamic custom videos, while uniquely supporting online processing. Page and code are available at https://tsukasane.github.io/Video-OnlineHMR/.

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### Beyond Outliers: A Data-Free Layer-wise Mixed-Precision Quantization Approach Driven by Numerical and Structural Dual-Sensitivity

Authors: Hengyuan Zhang, Xinrong Chen, Zunhai Su, Xiao Liang, Jing Xiong, Wendong Xu, He Xiao, Chaofan Tao, Wei Zhang, Ruobing Xie, Lei Jiang, Hayden Kwok-Hay So et al.
Published: 2026-03-18
Categories: cs.LG, cs.CL
arXiv: https://arxiv.org/abs/2603.17354v1

Layer-wise mixed-precision quantization (LMPQ) enables effective compression under extreme low-bit settings by allocating higher precision to sensitive layers. However, existing methods typically treat all intra-layer weight modules uniformly and rely on a single numerical property when estimating sensitivity, overlooking their distinct operational roles and structural characteristics. To address this, we propose NSDS, a novel calibration-free LMPQ framework driven by Numerical and Structural Dual-Sensitivity. Specifically, it first mechanistically decomposes each layer into distinct operational roles and quantifies their sensitivity from both numerical and structural perspectives. These dual-aspect scores are then aggregated into a unified layer-wise metric through a robust aggregation scheme based on MAD-Sigmoid and Soft-OR to guide bit allocation. Extensive experiments demonstrate that NSDS consistently achieves superior performance compared to various baselines across diverse models and downstream tasks, without relying on any calibration data.

---

### Learning Permutation Distributions via Reflected Diffusion on Ranks

Authors: Sizhuang He, Yangtian Zhang, Shiyang Zhang, David van Dijk
Published: 2026-03-18
Categories: cs.LG, cs.AI
arXiv: https://arxiv.org/abs/2603.17353v2

The finite symmetric group S_n provides a natural domain for permutations, yet learning probability distributions on S_n is challenging due to its factorially growing size and discrete, non-Euclidean structure. Recent permutation diffusion methods define forward noising via shuffle-based random walks (e.g., riffle shuffles) and learn reverse transitions with Plackett-Luce (PL) variants, but the resulting trajectories can be abrupt and increasingly hard to denoise as n grows. We propose Soft-Rank Diffusion, a discrete diffusion framework that replaces shuffle-based corruption with a structured soft-rank forward process: we lift permutations to a continuous latent representation of order by relaxing discrete ranks into soft ranks, yielding smoother and more tractable trajectories. For the reverse process, we introduce contextualized generalized Plackett-Luce (cGPL) denoisers that generalize prior PL-style parameterizations and improve expressivity for sequential decision structures. Experiments on sorting and combinatorial optimization benchmarks show that Soft-Rank Diffusion consistently outperforms prior diffusion baselines, with particularly strong gains in long-sequence and intrinsically sequential settings.

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### EvoGuard: An Extensible Agentic RL-based Framework for Practical and Evolving AI-Generated Image Detection

Authors: Chenyang Zhu, Maorong Wang, Jun Liu, Ching-Chun Chang, Isao Echizen
Published: 2026-03-18
Categories: cs.CV
arXiv: https://arxiv.org/abs/2603.17343v2

The rapid proliferation of AI-Generated Images (AIGIs) poses severe misinformation risks, making AIGI detection critical yet challenging. Traditional detection paradigms mainly rely on low-level features, whereas recent research increasingly focuses on leveraging the general understanding ability of Multimodal Large Language Models (MLLMs) to achieve better generalization, yet it still suffers from limited extensibility and expensive data annotations. Instead of building yet another detector, we recast AIGI detection as learned, reasoning-based evidence synthesis over a pool of heterogeneous off-the-shelf detectors, realized through EvoGuard, a novel agentic framework. A capability-aware selection mechanism profiles each detector and gathers complementary evidence per sample; a dynamic orchestration mechanism then reasons over heterogeneous outputs across multiple rounds, cross-validating conflicting or low-confidence signals before concluding. This design exploits the complementary strengths among heterogeneous detectors, transcending the limits of any single model. Furthermore, optimized by a GRPO-based Agentic Reinforcement Learning algorithm using only low-cost binary labels, it eliminates the reliance on fine-grained annotations. Extensive experiments demonstrate that this learned reasoning paradigm outperforms single-detector and static ensembling, achieving SOTA accuracy while mitigating the bias between positive and negative samples. More importantly, it allows the plug-and-play integration of new detectors to boost overall performance in a train-free manner, offering a highly practical, long-term solution to ever-evolving AIGI threats. Source code will be publicly available upon acceptance.

---

### Grid Spatial Understanding: A Dataset for Textual Spatial Reasoning over Grids, Embodied Settings, and Coordinate Structures

Authors: Risham Sidhu, Julia Hockenmaier
Published: 2026-03-18
Categories: cs.CL
arXiv: https://arxiv.org/abs/2603.17333v1

We introduce GSU, a text-only grid dataset to evaluate the spatial reasoning capabilities of LLMs over 3 core tasks: navigation, object localization, and structure composition. By forgoing visual inputs, isolating spatial reasoning from perception, we show that while most models grasp basic grid concepts, they struggle with frames of reference relative to an embodied agent and identifying 3D shapes from coordinate lists. We also find that exposure to a visual modality does not provide a generalizable understanding of 3D space that VLMs are able to utilize for these tasks. Finally, we show that while the very latest frontier models can solve the provided tasks (though harder variants may still stump them), fully fine-tuning a small LM or LORA fine-tuning a small LLM show potential to match frontier model performance, suggesting an avenue for specialized embodied agents.

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### A Progressive Visual-Logic-Aligned Framework for Ride-Hailing Adjudication

Authors: Weiming Wu, Zi-Jian Cheng, Jie Meng, Peng Zhen, Shan Huang, Qun Li, Guobin Wu, Lan-Zhe Guo
Published: 2026-03-18
Categories: cs.AI, cs.LG
arXiv: https://arxiv.org/abs/2603.17328v1

The efficient adjudication of responsibility disputes is pivotal for maintaining marketplace fairness. However, the exponential surge in ride-hailing volume renders manual review intractable, while conventional automated methods lack the reasoning transparency required for quasi-judicial decisions. Although Multimodal LLMs offer a promising paradigm, they fundamentally struggle to bridge the gap between general visual semantics and rigorous evidentiary protocols, often leading to perceptual hallucinations and logical looseness. To address these systemic misalignments, we introduce RideJudge, a Progressive Visual-Logic-Aligned Framework. Instead of relying on generic pre-training, we bridge the semantic gap via SynTraj, a synthesis engine that grounds abstract liability concepts into concrete trajectory patterns. To resolve the conflict between massive regulation volume and limited context windows, we propose an Adaptive Context Optimization strategy that distills expert knowledge, coupled with a Chain-of-Adjudication mechanism to enforce active evidentiary inquiry. Furthermore, addressing the inadequacy of sparse binary feedback for complex liability assessment, we implement a novel Ordinal-Sensitive Reinforcement Learning mechanism that calibrates decision boundaries against hierarchical severity. Extensive experiments show that our RideJudge-8B achieves 88.41\% accuracy, surpassing 32B-scale baselines and establishing a new standard for interpretable adjudication.

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### FineViT: Progressively Unlocking Fine-Grained Perception with Dense Recaptions

Authors: Peisen Zhao, Xiaopeng Zhang, Mingxing Xu, Ruoyu Sun, Zewei Du, Dunzheng Wang, Guanghao Zheng, Haohang Xu, Zhibo Zhang, Yuhang Zhang, Yi Ai, Lin Liu et al.
Published: 2026-03-18
Categories: cs.CV
arXiv: https://arxiv.org/abs/2603.17326v1

While Multimodal Large Language Models (MLLMs) have experienced rapid advancements, their visual encoders frequently remain a performance bottleneck. Conventional CLIP-based encoders struggle with dense spatial tasks due to the loss of visual details caused by low-resolution pretraining and the reliance on noisy, coarse web-crawled image-text pairs. To overcome these limitations, we introduce FineViT, a novel vision encoder specifically designed to unlock fine-grained perception. By replacing coarse web data with dense recaptions, we systematically mitigate information loss through a progressive training paradigm.: first, the encoder is trained from scratch at a high native resolution on billions of global recaptioned image-text pairs, establishing a robust, detail rich semantic foundation. Subsequently, we further enhance its local perception through LLM alignment, utilizing our curated FineCap-450M dataset that comprises over $450$ million high quality local captions. Extensive experiments validate the effectiveness of the progressive strategy. FineViT achieves state-of-the-art zero-shot recognition and retrieval performance, especially in long-context retrieval, and consistently outperforms multimodal visual encoders such as SigLIP2 and Qwen-ViT when integrated into MLLMs. We hope FineViT could serve as a powerful new baseline for fine-grained visual perception.

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### MedSAD-CLIP: Supervised CLIP with Token-Patch Cross-Attention for Medical Anomaly Detection and Segmentation

Authors: Thuy Truong Tran, Minh Kha Do, Phuc Nguyen Duy, Min Hun Lee
Published: 2026-03-18
Categories: cs.CV
arXiv: https://arxiv.org/abs/2603.17325v1

Medical anomaly detection (MAD) and segmentation play a critical role in assisting clinical diagnosis by identifying abnormal regions in medical images and localizing pathological regions. Recent CLIP-based studies are promising for anomaly detection in zero-/few-shot settings, and typically rely on global representations and weak supervision, often producing coarse localization and limited segmentation quality. In this work, we study supervised adaptation of CLIP for MAD under a realistic clinical setting where a limited yet meaningful amount of labeled abnormal data is available. Our model MedSAD-CLIP leverages fine-grained text-visual cues via the Token-Patch Cross-Attention(TPCA) to improve lesion localization while preserving the generalization capability of CLIP representations. Lightweight image adapters and learnable prompt tokens efficiently adapt the pretrained CLIP encoder to the medical domain while preserving its rich semantic alignment. Furthermore, a Margin-based image-text Contrastive Loss is designed to enhance global feature discrimination between normal and abnormal representations. Extensive experiments on four diverse benchmarks-Brain, Retina, Lung, and Breast datasets-demonstrate the effectiveness of our approach, achieving superior performance in both pixel-level segmentation and image-level classification over state-of-the-art methods. Our results highlight the potential of supervised CLIP adaptation as a unified and scalable paradigm for medical anomaly understanding. Code will be made available at https://github.com/thuy4tbn99/MedSAD-CLIP

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### ShuttleEnv: An Interactive Data-Driven RL Environment for Badminton Strategy Modeling

Authors: Ang Li, Xinyang Gong, Bozhou Chen, Yunlong Lu, Jiaming Ji, Yongyi Wang, Yaodong Yang, Wenxin Li
Published: 2026-03-18
Categories: cs.AI, cs.LG
arXiv: https://arxiv.org/abs/2603.17324v1

We present ShuttleEnv, an interactive and data-driven simulation environment for badminton, designed to support reinforcement learning and strategic behavior analysis in fast-paced adversarial sports. The environment is grounded in elite-player match data and employs explicit probabilistic models to simulate rally-level dynamics, enabling realistic and interpretable agent-opponent interactions without relying on physics-based simulation. In this demonstration, we showcase multiple trained agents within ShuttleEnv and provide live, step-by-step visualization of badminton rallies, allowing attendees to explore different play styles, observe emergent strategies, and interactively analyze decision-making behaviors. ShuttleEnv serves as a reusable platform for research, visualization, and demonstration of intelligent agents in sports AI. Our ShuttleEnv demo video URL: https://drive.google.com/file/d/1hTR4P16U27H2O0-w316bR73pxE2ucczX/view

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### Physics-informed offline reinforcement learning eliminates catastrophic fuel waste in maritime routing

Authors: Aniruddha Bora, Julie Chalfant, Chryssostomos Chryssostomidis
Published: 2026-03-18
Categories: cs.AI, cs.LG, cs.RO
arXiv: https://arxiv.org/abs/2603.17319v1

International shipping produces approximately 3% of global greenhouse gas emissions, yet voyage routing remains dominated by heuristic methods. We present PIER (Physics-Informed, Energy-efficient, Risk-aware routing), an offline reinforcement learning framework that learns fuel-efficient, safety-aware routing policies from physics-calibrated environments grounded in historical vessel tracking data and ocean reanalysis products, requiring no online simulator. Validated on one full year (2023) of AIS data across seven Gulf of Mexico routes (840 episodes per method), PIER reduces mean CO2 emissions by 10% relative to great-circle routing. However, PIER's primary contribution is eliminating catastrophic fuel waste: great-circle routing incurs extreme fuel consumption (>1.5x median) in 4.8% of voyages; PIER reduces this to 0.5%, a 9-fold reduction. Per-voyage fuel variance is 3.5x lower (p<0.001), with bootstrap 95% CI for mean savings (2.9%, 15.7%). Partial validation against observed AIS vessel behavior confirms consistency with the fastest real transits while exhibiting 23.1x lower variance. Crucially, PIER is forecast-independent: unlike A* path optimization whose wave protection degrades 4.5x under realistic forecast uncertainty, PIER maintains constant performance using only local observations. The framework combines physics-informed state construction, demonstration-augmented offline data, and a decoupled post-hoc safety shield, an architecture that transfers to wildfire evacuation, aircraft trajectory optimization, and autonomous navigation in unmapped terrain.

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### A Proposal-Free Query-Guided Network for Grounded Multimodal Named Entity Recognition

Authors: Hongbing Li, Jiamin Liu, Shuo Zhang, Bo Xiao
Published: 2026-03-18
Categories: cs.CV
arXiv: https://arxiv.org/abs/2603.17314v2

Grounded Multimodal Named Entity Recognition (GMNER) identifies named entities, including their spans and types, in natural language text and grounds them to the corresponding regions in associated images. Most existing approaches split this task into two steps: they first detect objects using a pre-trained general-purpose detector and then match named entities to the detected objects. However, these methods face a major limitation. Because pre-trained general-purpose object detectors operate independently of textual entities, they tend to detect common objects and frequently overlook specific fine-grained regions required by named entities. This misalignment between object detectors and entities introduces imprecision and can impair overall system performance. In this paper, we propose a proposal-free Query-Guided Network (QGN) that unifies multimodal reasoning and decoding through text guidance and cross- modal interaction. QGN enables accurate grounding and robust performance in open-domain scenarios. Extensive experiments demonstrate that QGN achieves top performance among compared GMNER models on widely used benchmarks.

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### Recurrent Reasoning with Vision-Language Models for Estimating Long-Horizon Embodied Task Progress

Authors: Yuelin Zhang, Sijie Cheng, Chen Li, Zongzhao Li, Yuxin Huang, Yang Liu, Wenbing Huang
Published: 2026-03-18
Categories: cs.CV, cs.AI
arXiv: https://arxiv.org/abs/2603.17312v1

Accurately estimating task progress is critical for embodied agents to plan and execute long-horizon, multi-step tasks. Despite promising advances, existing Vision-Language Models (VLMs) based methods primarily leverage their video understanding capabilities, while neglecting their complex reasoning potential. Furthermore, processing long video trajectories with VLMs is computationally prohibitive for real-world deployment. To address these challenges, we propose the Recurrent Reasoning Vision-Language Model ($\text{R}^2$VLM). Our model features a recurrent reasoning framework that processes local video snippets iteratively, maintaining a global context through an evolving Chain of Thought (CoT). This CoT explicitly records task decomposition, key steps, and their completion status, enabling the model to reason about complex temporal dependencies. This design avoids the high cost of processing long videos while preserving essential reasoning capabilities. We train $\text{R}^2$VLM on large-scale, automatically generated datasets from ALFRED and Ego4D. Extensive experiments on progress estimation and downstream applications, including progress-enhanced policy learning, reward modeling for reinforcement learning, and proactive assistance, demonstrate that $\text{R}^2$VLM achieves strong performance and generalization, achieving a new state-of-the-art in long-horizon task progress estimation. The models and benchmarks are publicly available at \href{https://huggingface.co/collections/zhangyuelin/r2vlm}{huggingface}.

---

### Ruyi2.5 Technical Report

Authors: Huan Song, Shuyu Tian, Qingfei Zhao, Wenhao Hong, Jiang Liu, Ting Long, Jiawei Shao, Xuelong Li
Published: 2026-03-18
Categories: cs.CL
arXiv: https://arxiv.org/abs/2603.17311v1

We present Ruyi2.5, a multimodal familial model built on the AI Flow framework. Extending Ruyi2's "Train Once, Deploy Many" paradigm to the multimodal domain, Ruyi2.5 constructs a shared-backbone architecture that co-trains models of varying scales within a single unified pipeline, ensuring semantic consistency across all deployment tiers. Built upon Ruyi2.5, Ruyi2.5-Camera model is developed as a privacy-preserving camera service system, which instantiates Ruyi2.5-Camera into a two-stage recognition pipeline: an edge model applies information-bottleneck-guided irreversible feature mapping to de-identify raw frames at the source, while a cloud model performs deep behavior reasoning. To accelerate reinforcement learning fine-tuning, we further propose Binary Prefix Policy Optimization (BPPO), which reduces sample redundancy via binary response selection and focuses gradient updates on response prefixes, achieving a 2 to 3 times training speedup over GRPO. Experiments show Ruyi2.5 matches Qwen3-VL on the general multimodal benchmarks, while Ruyi2.5-Camera substantially outperforms Qwen3-VL on privacy-constrained surveillance tasks.

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### InfoDensity: Rewarding Information-Dense Traces for Efficient Reasoning

Authors: Chengwei Wei, Jung-jae Kim, Longyin Zhang, Shengkai Chen, Nancy F. Chen
Published: 2026-03-18
Categories: cs.AI, cs.CL
arXiv: https://arxiv.org/abs/2603.17310v2

Large Language Models (LLMs) with extended reasoning capabilities often generate verbose and redundant reasoning traces, incurring unnecessary computational cost. While existing reinforcement learning approaches address this by optimizing final response length, they neglect the quality of intermediate reasoning steps, leaving models vulnerable to reward hacking. We argue that verbosity is not merely a length problem, but a symptom of poor intermediate reasoning quality. To investigate this, we conduct an empirical study tracking the per-token predictive entropy of large reasoning models across reasoning trajectories. We find that high-quality reasoning traces exhibit two consistent properties: low uncertainty convergence and fast uncertainty descent. These findings suggest that high-quality reasoning traces are informationally dense, that is, reasoning steps contribute to reaching a low uncertainty level relative to the total reasoning length. Motivated by this, we propose InfoDensity, a reward framework for RL training that captures both properties through a single suffix-max envelope of the entropy trajectory, weighted by a length scaling term that favors achieving equivalent quality more concisely. Experiments on mathematical and general reasoning benchmarks demonstrate that InfoDensity outperforms state-of-the-art baselines on the accuracy-efficiency trade-off.

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### ReLMXEL: Adaptive RL-Based Memory Controller with Explainable Energy and Latency Optimization

Authors: Panuganti Chirag Sai, Gandholi Sarat, R. Raghunatha Sarma, Venkata Kalyan Tavva, Naveen M
Published: 2026-03-18
Categories: cs.AR, cs.AI, cs.LG, cs.MA, eess.SY
arXiv: https://arxiv.org/abs/2603.17309v1

Reducing latency and energy consumption is critical to improving the efficiency of memory systems in modern computing. This work introduces ReLMXEL (Reinforcement Learning for Memory Controller with Explainable Energy and Latency Optimization), a explainable multi-agent online reinforcement learning framework that dynamically optimizes memory controller parameters using reward decomposition. ReLMXEL operates within the memory controller, leveraging detailed memory behavior metrics to guide decision-making. Experimental evaluations across diverse workloads demonstrate consistent performance gains over baseline configurations, with refinements driven by workload-specific memory access behaviour. By incorporating explainability into the learning process, ReLMXEL not only enhances performance but also increases the transparency of control decisions, paving the way for more accountable and adaptive memory system designs.

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### Symphony: A Cognitively-Inspired Multi-Agent System for Long-Video Understanding

Authors: Haiyang Yan, Hongyun Zhou, Peng Xu, Xiaoxue Feng, Mengyi Liu
Published: 2026-03-18
Categories: cs.CV, cs.AI
arXiv: https://arxiv.org/abs/2603.17307v1

Despite rapid developments and widespread applications of MLLM agents, they still struggle with long-form video understanding (LVU) tasks, which are characterized by high information density and extended temporal spans. Recent research on LVU agents demonstrates that simple task decomposition and collaboration mechanisms are insufficient for long-chain reasoning tasks. Moreover, directly reducing the time context through embedding-based retrieval may lose key information of complex problems. In this paper, we propose Symphony, a multi-agent system, to alleviate these limitations. By emulating human cognition patterns, Symphony decomposes LVU into fine-grained subtasks and incorporates a deep reasoning collaboration mechanism enhanced by reflection, effectively improving the reasoning capability. Additionally, Symphony provides a VLM-based grounding approach to analyze LVU tasks and assess the relevance of video segments, which significantly enhances the ability to locate complex problems with implicit intentions and large temporal spans. Experimental results show that Symphony achieves state-of-the-art performance on LVBench, LongVideoBench, VideoMME, and MLVU, with a 5.0% improvement over the prior state-of-the-art method on LVBench. Code is available at https://github.com/Haiyang0226/Symphony.

---

### Evidence for systematic semantic structure in individual letters

Authors: Gexin Zhao
Published: 2026-03-18
Categories: cs.CL, q-bio.NC
arXiv: https://arxiv.org/abs/2603.17306v4

Associations between speech sounds and meaning are well documented but have not been systematically mapped over a whole alphabet. Here we map them across the 26 English letters and find that each carries a structured, multidimensional semantic profile that is recoverable from text, perceived across languages, and predicted by articulatory features. Three large language models independently detected consistent semantic structure across nine perceptual dimensions in 220 pairwise letter contrasts, and the profiles they recovered were then tested in preregistered experiments with 1,388 human participants. Native English readers chose the predicted word above chance (85.3%, selected items; 65.7%, all contrasts), and the preference followed the letter rather than the words that carried it. Listeners of five typologically diverse languages showed the same preference (76.7%, selected pairs; 68.4%, randomly drawn contrasts), regardless of their English proficiency. Articulatory features assigned to each letter predicted both the model profiles and the human judgments. Letter-meaning association is thus a systematic, multidimensional property of the alphabet rather than a set of isolated effects.

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### Evidence for systematic semantic structure in individual phonemes

Authors: Gexin Zhao
Published: 2026-03-18
Categories: cs.CL, q-bio.NC
arXiv: https://arxiv.org/abs/2603.17306v3

A foundational assumption in linguistics holds that sound-meaning relations are largely arbitrary. Here we show that this assumption fails at the level of individual phonemes: each English phoneme carries a structured, multidimensional semantic profile that is recoverable from text, perceived across languages, and grounded in articulation. Three large language models independently detected consistent semantic structure across nine perceptual dimensions in 220 pairwise letter contrasts. Native English speakers (N = 93) confirmed these associations in a preregistered forced-choice task (85.3% agreement with model predictions), and listeners of five typologically diverse languages (N = 155) replicated the effect under audio presentation (73.2%-81.9% accuracy). Articulatory features predicted the structure with cross-validated R^2 of 0.56-0.98, indicating that the bodily act of producing a sound systematically shapes the meaning it conveys. These findings reframe phoneme-level iconicity as a pervasive, embodied property of the phonological system.

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### Contrastive Reasoning Alignment: Reinforcement Learning from Hidden Representations

Authors: Haozheng Luo, Yimin Wang, Jiahao Yu, Binghui Wang, Yan Chen
Published: 2026-03-18
Categories: cs.AI, cs.CL, cs.LG
arXiv: https://arxiv.org/abs/2603.17305v2

We propose CRAFT, a red-teaming alignment framework that leverages model reasoning capabilities and hidden representations to improve robustness against jailbreak attacks. Unlike prior defenses that operate primarily at the output level, CRAFT aligns large reasoning models to generate safety-aware reasoning traces by explicitly optimizing objectives defined over the hidden state space. Methodologically, CRAFT integrates contrastive representation learning with reinforcement learning to separate safe and unsafe reasoning trajectories, yielding a latent-space geometry that supports robust, reasoning-level safety alignment. Theoretically, we show that incorporating latent-textual consistency into GRPO eliminates superficially aligned policies by ruling them out as local optima. Empirically, we evaluate CRAFT on multiple safety benchmarks using two strong reasoning models, Qwen3-4B-Thinking and R1-Distill-Llama-8B, where it consistently outperforms state-of-the-art defenses such as IPO and SafeKey. Notably, CRAFT delivers an average 79.0% improvement in reasoning safety and 87.7% improvement in final-response safety over the base models, demonstrating the effectiveness of hidden-space reasoning alignment.

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### 3D MRI-Based Alzheimer's Disease Classification Using Multi-Modal 3D CNN with Leakage-Aware Subject-Level Evaluation

Authors: Md Sifat, Sania Akter, Akif Islam, Md. Ekramul Hamid, Abu Saleh Musa Miah, Najmul Hassan, Md Abdur Rahim, Jungpil Shin
Published: 2026-03-18
Categories: cs.CV
arXiv: https://arxiv.org/abs/2603.17304v2

Deep learning has become an important tool for Alzheimer's disease (AD) classification from structural MRI. Many existing studies analyze individual 2D slices extracted from MRI volumes, while clinical neuroimaging practice typically relies on the full three dimensional structure of the brain. From this perspective, volumetric analysis may better capture spatial relationships among brain regions that are relevant to disease progression. Motivated by this idea, this work proposes a multimodal 3D convolutional neural network for AD classification using raw OASIS 1 MRI volumes. The model combines structural T1 information with gray matter, white matter, and cerebrospinal fluid probability maps obtained through FSL FAST segmentation in order to capture complementary neuroanatomical information. The proposed approach is evaluated on the clinically labelled OASIS 1 cohort using 5 fold subject level cross validation, achieving a mean accuracy of 72.34% plus or minus 4.66% and a ROC AUC of 0.7781 plus or minus 0.0365. GradCAM visualizations further indicate that the model focuses on anatomically meaningful regions, including the medial temporal lobe and ventricular areas that are known to be associated with Alzheimer's related structural changes. To better understand how data representation and evaluation strategies may influence reported performance, additional diagnostic experiments were conducted on a slice based version of the dataset under both slice level and subject level protocols. These observations help provide context for the volumetric results. Overall, the proposed multimodal 3D framework establishes a reproducible subject level benchmark and highlights the potential benefits of volumetric MRI analysis for Alzheimer's disease classification.

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### 3D MRI-Based Alzheimer's Disease Classification Using Multi-Modal 3D CNN with Leakage-Aware Subject-Level Evaluation

Authors: Md Sifat, Sania Akter, Akif Islam, Md. Ekramul Hamid, Abu Saleh Musa Miah, Najmul Hassan, Md Abdur Rahim, Jungpil Shin
Published: 2026-03-18
Categories: cs.CV
arXiv: https://arxiv.org/abs/2603.17304v1

Deep learning has become an important tool for Alzheimer's disease (AD) classification from structural MRI. Many existing studies analyze individual 2D slices extracted from MRI volumes, while clinical neuroimaging practice typically relies on the full three dimensional structure of the brain. From this perspective, volumetric analysis may better capture spatial relationships among brain regions that are relevant to disease progression. Motivated by this idea, this work proposes a multimodal 3D convolutional neural network for AD classification using raw OASIS 1 MRI volumes. The model combines structural T1 information with gray matter, white matter, and cerebrospinal fluid probability maps obtained through FSL FAST segmentation in order to capture complementary neuroanatomical information. The proposed approach is evaluated on the clinically labelled OASIS 1 cohort using 5 fold subject level cross validation, achieving a mean accuracy of 72.34% plus or minus 4.66% and a ROC AUC of 0.7781 plus or minus 0.0365. GradCAM visualizations further indicate that the model focuses on anatomically meaningful regions, including the medial temporal lobe and ventricular areas that are known to be associated with Alzheimer's related structural changes. To better understand how data representation and evaluation strategies may influence reported performance, additional diagnostic experiments were conducted on a slice based version of the dataset under both slice level and subject level protocols. These observations help provide context for the volumetric results. Overall, the proposed multimodal 3D framework establishes a reproducible subject level benchmark and highlights the potential benefits of volumetric MRI analysis for Alzheimer's disease classification.

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### From Words to Worlds: Benchmarking Cross-Cultural Cultural Understanding in Machine Translation

Authors: Bangju Han, Yingqi Wang, Huang Qing, Tiyuan Li, Fengyi Yang, Ahtamjan Ahmat, Abibulla Atawulla, Yating Yang, Xi Zhou
Published: 2026-03-18
Categories: cs.CL, cs.AI
arXiv: https://arxiv.org/abs/2603.17303v1

Culture-expressions, such as idioms, slang, and culture-specific items (CSIs), are pervasive in natural language and encode meanings that go beyond literal linguistic form. Accurately translating such expressions remains challenging for machine translation systems. Despite this, existing benchmarks remain fragmented and do not provide a systematic framework for evaluating translation performance on culture-loaded expressions. To address this gap, we introduce CulT-Eval, a benchmark designed to evaluate how models handle different types of culturally grounded expressions. CulT-Eval comprises over 7,959 carefully curated instances spanning multiple types of culturally grounded expressions, with a comprehensive error taxonomy covering culturally grounded expressions. Through extensive evaluation of large language models and detailed analysis, we identify recurring and systematic failure modes that are not adequately captured by existing automatic metrics. Accordingly, we propose a complementary evaluation metric that targets culturally induced meaning deviations overlooked by standard MT metrics. The results indicate that current models struggle to preserve culturally grounded meaning and to capture the cultural and contextual nuances essential for accurate translation. Our benchmark and code are available at https://anonymous.4open.science/r/CulT-Eval-E75D/.

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### WINFlowNets: Warm-up Integrated Networks Training of Generative Flow Networks for Robotics and Machine Fault Adaptation

Authors: Zahin Sufiyan, Shadan Golestan, Yoshihiro Mitsuka, Shotaro Miwa, Osmar Zaiane
Published: 2026-03-18
Categories: cs.LG
arXiv: https://arxiv.org/abs/2603.17301v1

Generative Flow Networks for continuous scenarios (CFlowNets) have shown promise in solving sequential decision-making tasks by learning stochastic policies using a flow and a retrieval network. Despite their demonstrated efficiency compared to state-of-the-art Reinforcement Learning (RL) algorithms, their practical application in robotic control tasks is constrained by the reliance on pre-training the retrieval network. This dependency poses challenges in dynamic robotic environments, where pre-training data may not be readily available or representative of the current environment. This paper introduces WINFlowNets, a novel CFlowNets framework that enables the co-training of flow and retrieval networks. WINFlowNets begins with a warm-up phase for the retrieval network to bootstrap its policy, followed by a shared training architecture and a shared replay buffer for co-training both networks. Experiments in simulated robotic environments demonstrate that WINFlowNets surpasses CFlowNets and state-of-the-art RL algorithms in terms of average reward and training stability. Furthermore, WINFlowNets exhibits strong adaptive capability in fault environments, making it suitable for tasks that demand quick adaptation with limited sample data. These findings highlight WINFlowNets' potential for deployment in dynamic and malfunction-prone robotic systems, where traditional pre-training or sample inefficient data collection may be impractical.

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### GUIDE: GenAI Units In Digital Design Education

Authors: Weihua Xiao, Jason Blocklove, Matthew DeLorenzo, Johann Knechtel, Ozgur Sinanoglu, Kanad Basu, Jeyavijayan Rajendran, Siddharth Garg, Ramesh Karri
Published: 2026-03-18
Categories: cs.CY, cs.AI
arXiv: https://arxiv.org/abs/2603.17296v1

GenAI Units In Digital Design Education (GUIDE) is an open courseware repository with runnable Google Colab labs and other materials. We describe the repository's architecture and educational approach based on standardized teaching units comprising slides, short videos, runnable labs, and related papers. This organization enables consistency for both the students' learning experience and the reuse and grading by instructors. We demonstrate GUIDE in practice with three representative units: VeriThoughts for reasoning and formal-verification-backed RTL generation, enhanced LLM-aided testbench generation, and LLMPirate for IP Piracy. We also provide details for four example course instances (GUIDE4ChipDesign, Build your ASIC, GUIDE4HardwareSecurity, and Hardware Design) that assemble GUIDE units into full semester offerings, learning outcomes, and capstone projects, all based on proven materials. For example, the GUIDE4HardwareSecurity course includes a project on LLM-aided hardware Trojan insertion that has been successfully deployed in the classroom and in Cybersecurity Games and Conference (CSAW), a student competition and academic conference for cybersecurity. We also organized an NYU Cognichip Hackathon, engaging students across 24 international teams in AI-assisted RTL design workflows. The GUIDE repository is open for contributions and available at: https://github.com/FCHXWH823/LLM4ChipDesign.

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