# Research Papers — 2026-03 · bundle page 21 of 243

> Full-text markdown of papers #1001–1050 of 12123 published in 2026-03, newest first. AI-readable mirrors of the full 12123-paper 2026-03 archive (50 papers per file).

Canonical page: https://aipod.dev/papers/archive/2026-03/page/21
[← Previous bundle](https://aipod.dev/papers/archive/2026-03/page/20.md)
[Next bundle →](https://aipod.dev/papers/archive/2026-03/page/22.md)

### Can Unsupervised Segmentation Reduce Annotation Costs for Video Semantic Segmentation?

Authors: Samik Some, Vinay P. Namboodiri
Published: 2026-03-29
Categories: cs.CV
arXiv: https://arxiv.org/abs/2603.27697v1

Present-day deep neural networks for video semantic segmentation require a large number of fine-grained pixel-level annotations to achieve the best possible results. Obtaining such annotations, however, is very expensive. On the other hand, raw, unannotated video frames are practically free to obtain. Similarly, coarse annotations, which do not require precise boundaries, are also much cheaper. This paper investigates approaches to reduce the annotation cost required for video segmentation datasets by utilising such resources. We show that using state-of-the-art segmentation foundation models, Segment Anything Model (SAM) and Segment Anything Model 2 (SAM 2), we can utilise both unannotated frames as well as coarse annotations to alleviate the effort required for manual annotation of video segmentation datasets by automating mask generation. Our investigation suggests that if used appropriately, we can reduce the need for annotation by a third with similar performance for video semantic segmentation. More significantly, our analysis suggests that the variety of frames in the dataset is more important than the number of frames for obtaining the best performance.

---

### Optimizing Coverage and Difficulty in Reinforcement Learning for Quiz Composition

Authors: Ricardo Pedro Querido Andrade Silva, Nassim Bouarour, Dina Fettache, Sarab Boussouar, Noha Ibrahim, Sihem Amer-Yahia
Published: 2026-03-29
Categories: cs.LG
arXiv: https://arxiv.org/abs/2603.27695v1

Quiz design is a tedious process that teachers undertake to evaluate the acquisition of knowledge by students. Our goal in this paper is to automate quiz composition from a set of multiple choice questions (MCQs). We formalize a generic sequential decision-making problem with the goal of training an agent to compose a quiz that meets the desired topic coverage and difficulty levels. We investigate DQN, SARSA and A2C/A3C, three reinforcement learning solutions to solve our problem. We run extensive experiments on synthetic and real datasets that study the ability of RL to land on the best quiz. Our results reveal subtle differences in agent behavior and in transfer learning with different data distributions and teacher goals. This was supported by our user study, paving the way for automating various teachers' pedagogical goals.

---

### Can Large Language Models Simulate Human Cognition Beyond Behavioral Imitation?

Authors: Yuxuan Gu, Lunjun Liu, Xiaocheng Feng, Kun Zhu, Weihong Zhong, Lei Huang, Bing Qin
Published: 2026-03-29
Categories: cs.CL
arXiv: https://arxiv.org/abs/2603.27694v1

An essential problem in artificial intelligence is whether LLMs can simulate human cognition or merely imitate surface-level behaviors, while existing datasets suffer from either synthetic reasoning traces or population-level aggregation, failing to capture authentic individual cognitive patterns. We introduce a benchmark grounded in the longitudinal research trajectories of 217 researchers across diverse domains of artificial intelligence, where each author's scientific publications serve as an externalized representation of their cognitive processes. To distinguish whether LLMs transfer cognitive patterns or merely imitate behaviors, our benchmark deliberately employs a cross-domain, temporal-shift generalization setting. A multidimensional cognitive alignment metric is further proposed to assess individual-level cognitive consistency. Through systematic evaluation of state-of-the-art LLMs and various enhancement techniques, we provide a first-stage empirical study on the questions: (1) How well do current LLMs simulate human cognition? and (2) How far can existing techniques enhance these capabilities?

---

### LVRPO: Language-Visual Alignment with GRPO for Multimodal Understanding and Generation

Authors: Shentong Mo, Sukmin Yun
Published: 2026-03-29
Categories: cs.CV, cs.AI, cs.LG, cs.MA, cs.MM
arXiv: https://arxiv.org/abs/2603.27693v1

Unified multimodal pretraining has emerged as a promising paradigm for jointly modeling language and vision within a single foundation model. However, existing approaches largely rely on implicit or indirect alignment signals and remain suboptimal for simultaneously supporting multimodal understanding and generation, particularly in settings that require fine-grained language-visual reasoning and controllable generation. In this work, we propose LVRPO, a language-visual reinforcement-based preference optimization framework that explicitly aligns language and visual representations using Group Relative Policy Optimization (GRPO). Instead of introducing additional alignment losses at the representation level, LVRPO directly optimizes multimodal model behaviors through preference-driven reinforcement signals, encouraging consistent and semantically grounded interactions between language and vision across both understanding and generation tasks. This formulation enables effective alignment without requiring auxiliary encoders or handcrafted cross-modal objectives, and naturally extends to diverse multimodal capabilities. Empirically, LVRPO consistently outperforms strong unified-pretraining baselines on a broad suite of benchmarks spanning multimodal understanding, generation, and reasoning.

---

### Customized Visual Storytelling with Unified Multimodal LLMs

Authors: Wei-Hua Li, Cheng Sun, Chu-Song Chen
Published: 2026-03-29
Categories: cs.CV
arXiv: https://arxiv.org/abs/2603.27690v1

Multimodal story customization aims to generate coherent story flows conditioned on textual descriptions, reference identity images, and shot types. While recent progress in story generation has shown promising results, most approaches rely on text-only inputs. A few studies incorporate character identity cues (e.g., facial ID), but lack broader multimodal conditioning. In this work, we introduce VstoryGen, a multimodal framework that integrates descriptions with character and background references to enable customizable story generation. To enhance cinematic diversity, we introduce shot-type control via parameter-efficient prompt tuning on movie data, enabling the model to generate sequences that more faithfully reflect cinematic grammar. To evaluate our framework, we establish two new benchmarks that assess multimodal story customization from the perspectives of character and scene consistency, text-visual alignment, and shot-type control. Experiments demonstrate that VstoryGen achieves improved consistency and cinematic diversity compared to existing methods.

---

### CrossHGL: A Text-Free Foundation Model for Cross-Domain Heterogeneous Graph Learning

Authors: Xuanze Chen, Jiajun Zhou, Yadong Li, Shanqing Yu, Qi Xuan
Published: 2026-03-29
Categories: cs.LG
arXiv: https://arxiv.org/abs/2603.27685v1

Heterogeneous graph representation learning (HGRL) is essential for modeling complex systems with diverse node and edge types. However, most existing methods are limited to closed-world settings with shared schemas and feature spaces, hindering cross-domain generalization. While recent graph foundation models improve transferability, they often target homogeneous graphs, rely on domain-specific schemas, or require rich textual attributes. Consequently, text-free and few-shot cross-domain HGRL remains underexplored. To address this, we propose CrossHGL, a foundation framework that preserves and transfers multi-relational structural semantics without external textual supervision. Specifically, a semantic-preserving transformation strategy homogenizes heterogeneous graphs while encoding interaction semantics into edge features. Based on this, a prompt-aware multi-domain pre-training framework with a Tri-Prompt mechanism captures transferable knowledge across feature, edge, and structure perspectives via self-supervised contrastive learning. For target-domain adaptation, we develop a parameter-efficient fine-tuning strategy that freezes the pre-trained backbone and performs few-shot classification via prompt composition and prototypical learning. Experiments on node-level and graph-level tasks show that CrossHGL consistently outperforms state-of-the-art baselines, yielding average relative improvements of 25.1% and 7.6% in Micro-F1 for node and graph classification, respectively, while remaining competitive in challenging feature-degenerated settings.

---

### Prototype-Aligned Federated Soft-Prompts for Continual Web Personalization

Authors: Canran Xiao, Liwei Hou
Published: 2026-03-29
Categories: cs.LG
arXiv: https://arxiv.org/abs/2603.27678v1

Continual web personalization is essential for engagement, yet real-world non-stationarity and privacy constraints make it hard to adapt quickly without forgetting long-term preferences. We target this gap by seeking a privacy-conscious, parameter-efficient interface that controls stability-plasticity at the user/session level while tying user memory to a shared semantic prior. We propose ProtoFed-SP, a prompt-based framework that injects dual-timescale soft prompts into a frozen backbone: a fast, sparse short-term prompt tracks session intent, while a slow long-term prompt is anchored to a small server-side prototype library that is continually refreshed via differentially private federated aggregation. Queries are routed to Top-M prototypes to compose a personalized prompt. Across eight benchmarks, ProtoFed-SP improves NDCG@10 by +2.9% and HR@10 by +2.0% over the strongest baselines, with notable gains on Amazon-Books (+5.0% NDCG vs. INFER), H&M (+2.5% vs. Dual-LoRA), and Taobao (+2.2% vs. FedRAP). It also lowers forgetting (AF) and Steps-to-95% and preserves accuracy under practical DP budgets. Our contribution is a unifying, privacy-aware prompting interface with prototype anchoring that delivers robust continual personalization and offers a transparent, controllable mechanism to balance stability and plasticity in deployment.

---

### Energy Score-Guided Neural Gaussian Mixture Model for Predictive Uncertainty Quantification

Authors: Yang Yang, Chunlin Ji, Haoyang Li, Ke Deng
Published: 2026-03-29
Categories: stat.ML, cs.LG
arXiv: https://arxiv.org/abs/2603.27672v1

Quantifying predictive uncertainty is essential for real world machine learning applications, especially in scenarios requiring reliable and interpretable predictions. Many common parametric approaches rely on neural networks to estimate distribution parameters by optimizing the negative log likelihood. However, these methods often encounter challenges like training instability and mode collapse, leading to poor estimates of the mean and variance of the target output distribution. In this work, we propose the Neural Energy Gaussian Mixture Model (NE-GMM), a novel framework that integrates Gaussian Mixture Model (GMM) with Energy Score (ES) to enhance predictive uncertainty quantification. NE-GMM leverages the flexibility of GMM to capture complex multimodal distributions and leverages the robustness of ES to ensure well calibrated predictions in diverse scenarios. We theoretically prove that the hybrid loss function satisfies the properties of a strictly proper scoring rule, ensuring alignment with the true data distribution, and establish generalization error bounds, demonstrating that the model's empirical performance closely aligns with its expected performance on unseen data. Extensive experiments on both synthetic and real world datasets demonstrate the superiority of NE-GMM in terms of both predictive accuracy and uncertainty quantification.

---

### ProgressVLA: Progress-Guided Diffusion Policy for Vision-Language Robotic Manipulation

Authors: Hongyu Yan, Qiwei Li, Jiaolong Yang, Yadong Mu
Published: 2026-03-29
Categories: cs.RO, cs.AI
arXiv: https://arxiv.org/abs/2603.27670v1

Most existing vision-language-action (VLA) models for robotic manipulation lack progress awareness, typically relying on hand-crafted heuristics for task termination. This limitation is particularly severe in long-horizon tasks involving cascaded sub-goals. In this work, we investigate the estimation and integration of task progress, proposing a novel model named {\textbf \vla}. Our technical contributions are twofold: (1) \emph{robust progress estimation}: We pre-train a progress estimator on large-scale, unsupervised video-text robotic datasets. This estimator achieves a low prediction residual (0.07 on a scale of $(0, 1)$) in simulation and demonstrates zero-shot generalization to unseen real-world samples, and (2) \emph{differentiable progress guidance}: We introduce an inverse dynamics world model that maps predicted action tokens into future latent visual states. These latents are then processed by the progress estimator; by applying a maximal progress regularization, we establish a differentiable pipeline that provides progress-piloted guidance to refine action tokens. Extensive experiments on the CALVIN and LIBERO benchmarks, alongside real-world robot deployment, consistently demonstrate substantial improvements in success rates and generalization over strong baselines.

---

### EvA: An Evidence-First Audio Understanding Paradigm for LALMs

Authors: Xinyuan Xie, Shunian Chen, Zhiheng Liu, Yuhao Zhang, Zhiqiang Lv, Liyin Liang, Benyou Wang
Published: 2026-03-29
Categories: cs.SD, cs.AI
arXiv: https://arxiv.org/abs/2603.27667v2

Large Audio Language Models (LALMs) still struggle in complex acoustic scenes because they often fail to preserve task-relevant acoustic evidence before reasoning begins. We identify this error pattern as the evidence bottleneck: state-of-the-art systems show larger deficits in acoustic evidence extraction than in downstream reasoning, suggesting that upstream perception is often the limiting factor. To address this problem, we propose EvA (Evidence-First Audio), a dual-path architecture that enhances acoustic evidence preservation through hierarchical aggregation and non-compressive, time-aligned fusion. We also build EvA-Perception, a large-scale training set with about 54K event-ordered captions and 500K evidence-grounded QA pairs. Under a unified zero-shot protocol, EvA achieves the best open-source \emph{Perception} results on MMAU, MMAR, and MMSU, with the largest gains on perception-heavy splits. Human evaluation on open-ended captioning further shows improved fine-grained acoustic coverage and caption quality. These results support the evidence-first hypothesis: stronger audio understanding depends on preserving acoustic evidence before reasoning. Project can be found at https://satsuki2486441738.github.io/EvA/.

---

### Gated Condition Injection without Multimodal Attention: Towards Controllable Linear-Attention Transformers

Authors: Yuhe Liu, Zhenxiong Tan, Yujia Hu, Songhua Liu, Xinchao Wang
Published: 2026-03-29
Categories: cs.CV
arXiv: https://arxiv.org/abs/2603.27666v1

Recent advances in diffusion-based controllable visual generation have led to remarkable improvements in image quality. However, these powerful models are typically deployed on cloud servers due to their large computational demands, raising serious concerns about user data privacy. To enable secure and efficient on-device generation, we explore in this paper controllable diffusion models built upon linear attention architectures, which offer superior scalability and efficiency, even on edge devices. Yet, our experiments reveal that existing controllable generation frameworks, such as ControlNet and OminiControl, either lack the flexibility to support multiple heterogeneous condition types or suffer from slow convergence on such linear-attention models. To address these limitations, we propose a novel controllable diffusion framework tailored for linear attention backbones like SANA. The core of our method lies in a unified gated conditioning module working in a dual-path pipeline, which effectively integrates multi-type conditional inputs, such as spatially aligned and non-aligned cues. Extensive experiments on multiple tasks and benchmarks demonstrate that our approach achieves state-of-the-art controllable generation performance based on linear-attention models, surpassing existing methods in terms of fidelity and controllability.

---

### Test-Time Instance-Specific Parameter Composition: A New Paradigm for Adaptive Generative Modeling

Authors: Minh-Tuan Tran, Xuan-May Le, Quan Hung Tran, Mehrtash Harandi, Dinh Phung, Trung Le
Published: 2026-03-29
Categories: cs.CV, cs.LG
arXiv: https://arxiv.org/abs/2603.27665v1

Existing generative models, such as diffusion and auto-regressive networks, are inherently static, relying on a fixed set of pretrained parameters to handle all inputs. In contrast, humans flexibly adapt their internal generative representations to each perceptual or imaginative context. Inspired by this capability, we introduce Composer, a new paradigm for adaptive generative modeling based on test-time instance-specific parameter composition. Composer generates input-conditioned parameter adaptations at inference time, which are injected into the pretrained model's weights, enabling per-input specialization without fine-tuning or retraining. Adaptation occurs once prior to multi-step generation, yielding higher-quality, context-aware outputs with minimal computational and memory overhead. Experiments show that Composer substantially improves performance across diverse generative models and use cases, including lightweight/quantized models and test-time scaling. By leveraging input-aware parameter composition, Composer establishes a new paradigm for designing generative models that dynamically adapt to each input, moving beyond static parameterization.

---

### Investigating the Influence of Language on Sycophantic Behavior of Multilingual LLMs

Authors: Bayan Abdullah Aldahlawi, A. B. M. Ashikur Rahman, Irfan Ahmad
Published: 2026-03-29
Categories: cs.CL
arXiv: https://arxiv.org/abs/2603.27664v1

Large language models (LLMs) have achieved strong performance across a wide range of tasks, but they are also prone to sycophancy, the tendency to agree with user statements regardless of validity. Previous research has outlined both the extent and the underlying causes of sycophancy in earlier models, such as ChatGPT-3.5 and Davinci. Newer models have since undergone multiple mitigation strategies, yet there remains a critical need to systematically test their behavior. In particular, the effect of language on sycophancy has not been explored. In this work, we investigate how the language influences sycophantic responses. We evaluate three state-of-the-art models, GPT-4o mini, Gemini 1.5 Flash, and Claude 3.5 Haiku, using a set of tweet-like opinion prompts translated into five additional languages: Arabic, Chinese, French, Spanish, and Portuguese. Our results show that although newer models exhibit significantly less sycophancy overall compared to earlier generations, the extent of sycophancy is still influenced by the language. We further provide a granular analysis of how language shapes model agreeableness across sensitive topics, revealing systematic cultural and linguistic patterns. These findings highlight both the progress of mitigation efforts and the need for broader multilingual audits to ensure trustworthy and bias-aware deployment of LLMs.

---

### LiDAR for Crowd Management: Applications, Benefits, and Future Directions

Authors: Abdullah Khanfor, Chaima Zaghouani, Hakim Ghazzai, Ahmad Alsharoa, Gianluca Setti
Published: 2026-03-29
Categories: cs.CV
arXiv: https://arxiv.org/abs/2603.27663v1

Light Detection and Ranging (LiDAR) technology offers significant advantages for effective crowd management. This article presents LiDAR technology and highlights its primary advantages over other monitoring technologies, including enhanced privacy, performance in various weather conditions, and precise 3D mapping. We present a general taxonomy of four key tasks in crowd management: crowd detection, counting, tracking, and behavior classification, with illustrative examples of LiDAR applications for each task. We identify challenges and open research directions, including the scarcity of dedicated datasets, sensor fusion requirements, artificial intelligence integration, and processing needs for LiDAR point clouds. This article offers actionable insights for developing crowd management solutions tailored to public safety applications.

---

### A Benchmarking Methodology to Assess Open-Source Video Large Language Models in Automatic Captioning of News Videos

Authors: David Miranda Paredes, Jose M. Saavedra, Marcelo Pizarro
Published: 2026-03-29
Categories: cs.CV
arXiv: https://arxiv.org/abs/2603.27662v1

News videos are among the most prevalent content types produced by television stations and online streaming platforms, yet generating textual descriptions to facilitate indexing and retrieval largely remains a manual process. Video Large Language Models (VidLLMs) offer significant potential to automate this task, but a comprehensive evaluation in the news domain is still lacking. This work presents a comparative study of eight state-of-the-art open-source VidLLMs for automatic news video captioning, evaluated on two complementary benchmark datasets: a Chilean TV news corpus (approximately 1,345 clips) and a BBC News corpus (9,838 clips). We employ lexical metrics (METEOR, ROUGE-L), semantic metrics (BERTScore, CLIPScore, Text Similarity, Mean Reciprocal Rank), and two novel fidelity metrics proposed in this work: the Thematic Fidelity Score (TFS) and Entity Fidelity Score (EFS). Our analysis reveals that standard metrics exhibit limited discriminative power for news video captioning due to surface-form dependence, static-frame insensitivity, and function-word inflation. TFS and EFS address these gaps by directly assessing thematic structure preservation and named-entity coverage in the generated captions. Results show that Gemma~3 achieves the highest overall performance across both datasets and most evaluation dimensions, with Qwen-VL as a consistent runner-up.

---

### Amped: Adaptive Multi-stage Non-edge Pruning for Edge Detection

Authors: Yuhan Gao, Xinqing Li, Xin He, Bing Li, Xinzhong Zhu, Ming-Ming Cheng, Yun Liu
Published: 2026-03-29
Categories: cs.CV
arXiv: https://arxiv.org/abs/2603.27661v1

Edge detection is a fundamental image analysis task that underpins numerous high-level vision applications. Recent advances in Transformer architectures have significantly improved edge quality by capturing long-range dependencies, but this often comes with computational overhead. Achieving higher pixel-level accuracy requires increased input resolution, further escalating computational cost and limiting practical deployment. Building on the strong representational capacity of recent Transformer-based edge detectors, we propose an Adaptive Multi-stage non-edge Pruning framework for Edge Detection(Amped). Amped identifies high-confidence non-edge tokens and removes them as early as possible to substantially reduce computation, thus retaining high accuracy while cutting GFLOPs and accelerating inference with minimal performance loss. Moreover, to mitigate the structural complexity of existing edge detection networks and facilitate their integration into real-world systems, we introduce a simple yet high-performance Transformer-based model, termed Streamline Edge Detector(SED). Applied to both existing detectors and our SED, the proposed pruning strategy provides a favorable balance between accuracy and efficiency-reducing GFLOPs by up to 40% with only a 0.4% drop in ODS F-measure. In addition, despite its simplicity, SED achieves a state-of-the-art ODS F-measure of 86.5%. The code will be released.

---

### The Degree of Language Diacriticity and Its Effect on Tasks

Authors: Adi Cohen, Yuval Pinter
Published: 2026-03-29
Categories: cs.CL
arXiv: https://arxiv.org/abs/2603.27653v1

Diacritics are orthographic marks that clarify pronunciation, distinguish similar words, or alter meaning. They play a central role in many writing systems, yet their impact on language technology has not been systematically quantified across scripts. While prior work has examined diacritics in individual languages, there's no cross-linguistic, data-driven framework for measuring the degree to which writing systems rely on them and how this affects downstream tasks. We propose a data-driven framework for quantifying diacritic complexity using corpus-level, information-theoretic metrics that capture the frequency, ambiguity, and structural diversity of character-diacritic combinations. We compute these metrics over 24 corpora in 15 languages, spanning both single- and multi-diacritic scripts. We then examine how diacritic complexity correlates with performance on the task of diacritics restoration, evaluating BERT- and RNN-based models. We find that across languages, higher diacritic complexity is strongly associated with lower restoration accuracy. In single-diacritic scripts, where character-diacritic combinations are more predictable, frequency-based and structural measures largely align. In multi-diacritic scripts, however, structural complexity exhibits the strongest association with performance, surpassing frequency-based measures. These findings show that measurable properties of diacritic usage influence the performance of diacritic restoration models, demonstrating that orthographic complexity is not only descriptive but functionally relevant for modeling.

---

### Budget-Xfer: Budget-Constrained Source Language Selection for Cross-Lingual Transfer to African Languages

Authors: Tewodros Kederalah Idris, Roald Eiselen, Prasenjit Mitra
Published: 2026-03-29
Categories: cs.CL
arXiv: https://arxiv.org/abs/2603.27651v1

Cross-lingual transfer learning enables NLP for low-resource languages by leveraging labeled data from higher-resource sources, yet existing comparisons of source language selection strategies do not control for total training data, confounding language selection effects with data quantity effects. We introduce Budget-Xfer, a framework that formulates multi-source cross-lingual transfer as a budget-constrained resource allocation problem. Given a fixed annotation budget B, our framework jointly optimizes which source languages to include and how much data to allocate from each. We evaluate four allocation strategies across named entity recognition and sentiment analysis for three African target languages (Hausa, Yoruba, Swahili) using two multilingual models, conducting 288 experiments. Our results show that (1) multi-source transfer significantly outperforms single-source transfer (Cohen's d = 0.80 to 1.98), driven by a structural budget underutilization bottleneck; (2) among multi-source strategies, differences are modest and non-significant; and (3) the value of embedding similarity as a selection proxy is task-dependent, with random selection outperforming similarity-based selection for NER but not sentiment analysis.

---

### V-CAST: Video Curvature-Aware Spatio-Temporal Pruning for Efficient Video Large Language Models

Authors: Xinying Lin, Xuyang Liu, Yiyu Wang, Teng Ma, Wenqi Ren
Published: 2026-03-29
Categories: cs.CV
arXiv: https://arxiv.org/abs/2603.27650v1

Video large language models (VideoLLMs) show strong capability in video understanding, yet long-context inference is still dominated by massive redundant visual tokens in the prefill stage. We revisit token compression for VideoLLMs under a tight budget and identify a key bottleneck, namely insufficient spatio-temporal information coverage. Existing methods often introduce discontinuous coverage through coarse per-frame allocation or scene segmentation, and token merging can further misalign spatio-temporal coordinates under MRoPE-style discrete (t,h,w) bindings. To address these issues, we propose V-CAST (Video Curvature-Aware Spatio-Temporal Pruning), a training-free, plug-and-play pruning policy for long-context video inference. V-CAST casts token compression as a trajectory approximation problem and introduces a curvature-guided temporal allocation module that routes per-frame token budgets to semantic turns and event boundaries. It further adopts a dual-anchor spatial selection mechanism that preserves high-entropy visual evidence without attention intervention, while keeping retained tokens at their original coordinates to maintain positional alignment. Extensive experiments across multiple VideoLLMs of different architectures and scales demonstrate that V-CAST achieves 98.6% of the original performance, outperforms the second-best method by +1.1% on average, and reduces peak memory and total latency to 86.7% and 86.4% of vanilla Qwen3-VL-8B-Instruct.

---

### PRBench: End-to-end Paper Reproduction in Physics Research

Authors: Shi Qiu, Junyi Deng, Yiwei Deng, Haoran Dong, Jieyu Fu, Mao Li, Zeyu Li, Zhaolong Zhang, Huiwen Zheng, Leidong Bao, Anqi Lv, Zihan Mo et al.
Published: 2026-03-29
Categories: cs.CL, hep-lat, hep-ph, physics.comp-ph, physics.optics
arXiv: https://arxiv.org/abs/2603.27646v1

AI agents powered by large language models exhibit strong reasoning and problem-solving capabilities, enabling them to assist scientific research tasks such as formula derivation and code generation. However, whether these agents can reliably perform end-to-end reproduction from real scientific papers remains an open question. We introduce PRBench, a benchmark of 30 expert-curated tasks spanning 11 subfields of physics. Each task requires an agent to comprehend the methodology of a published paper, implement the corresponding algorithms from scratch, and produce quantitative results matching the original publication. Agents are provided only with the task instruction and paper content, and operate in a sandboxed execution environment. All tasks are contributed by domain experts from over 20 research groups at the School of Physics, Peking University, each grounded in a real published paper and validated through end-to-end reproduction with verified ground-truth results and detailed scoring rubrics. Using an agentified assessment pipeline, we evaluate a set of coding agents on PRBench and analyze their capabilities across key dimensions of scientific reasoning and execution. The best-performing agent, OpenAI Codex powered by GPT-5.3-Codex, achieves a mean overall score of 34%. All agents exhibit a zero end-to-end callback success rate, with particularly poor performance in data accuracy and code correctness. We further identify systematic failure modes, including errors in formula implementation, inability to debug numerical simulations, and fabrication of output data. Overall, PRBench provides a rigorous benchmark for evaluating progress toward autonomous scientific research.

---

### OpenDPR: Open-Vocabulary Change Detection via Vision-Centric Diffusion-Guided Prototype Retrieval for Remote Sensing Imagery

Authors: Qi Guo, Jue Wang, Yinhe Liu, Yanfei Zhong
Published: 2026-03-29
Categories: cs.CV
arXiv: https://arxiv.org/abs/2603.27645v2

Open-vocabulary change detection (OVCD) seeks to recognize arbitrary changes of interest by enabling generalization beyond a fixed set of predefined classes. We reformulate OVCD as a two-stage pipeline: first generate class-agnostic change proposals using visual foundation models (VFMs) such as SAM and DINOv2, and then perform category identification with vision-language models (VLMs) such as CLIP. We reveal that category identification errors are the primary bottleneck of OVCD, mainly due to the limited ability of VLMs based on image-text matching to represent fine-grained land-cover categories. To address this, we propose OpenDPR, a training-free vision-centric diffusion-guided prototype retrieval framework. OpenDPR leverages diffusion models to construct diverse prototypes for target categories offline, and to perform similarity retrieval with change proposals in the visual space during inference. The secondary bottleneck lies in change localization, due to the inherent lack of change priors in VFMs. To bridge this gap, we design a spatial-to-change weakly supervised change detection module named S2C to adapt their strong spatial modeling capabilities for change localization. Integrating the pretrained S2C into OpenDPR leads to an optional weakly supervised variant named OpenDPR-W, which further improves OVCD with minimal supervision. Experimental results on four benchmark datasets demonstrate that the proposed methods achieve state-of-the-art performance under both supervision modes. Code is available at https://github.com/guoqi2002/OpenDPR.

---

### OPRO: Orthogonal Panel-Relative Operators for Panel-Aware In-Context Image Generation

Authors: Sanghyeon Lee, Minwoo Lee, Euijin Shin, Kangyeol Kim, Seunghwan Choi, Jaegul Choo
Published: 2026-03-29
Categories: cs.CV
arXiv: https://arxiv.org/abs/2603.27637v1

We introduce a parameter-efficient adaptation method for panel-aware in-context image generation with pre-trained diffusion transformers. The key idea is to compose learnable, panel-specific orthogonal operators onto the backbone's frozen positional encodings. This design provides two desirable properties: (1) isometry, which preserves the geometry of internal features, and (2) same-panel invariance, which maintains the model's pre-trained intra-panel synthesis behavior. Through controlled experiments, we demonstrate that the effectiveness of our adaptation method is not tied to a specific positional encoding design but generalizes across diverse positional encoding regimes. By enabling effective panel-relative conditioning, the proposed method consistently improves in-context image-based instructional editing pipelines, including state-of-the-art approaches.

---

### ContraMap: Contrastive Uncertainty Mapping for Robot Environment Representation

Authors: Chi Cuong Le, Weiming Zhi
Published: 2026-03-29
Categories: cs.RO, cs.AI, cs.CV
arXiv: https://arxiv.org/abs/2603.27632v1

Reliable robot perception requires not only predicting scene structure, but also identifying where predictions should be treated as unreliable due to sparse or missing observations. We present ContraMap, a contrastive continuous mapping method that augments kernel-based discriminative maps with an explicit uncertainty class trained using synthetic noise samples. This formulation treats unobserved regions as a contrastive class, enabling joint environment prediction and spatial uncertainty estimation in real time without Bayesian inference. Under a simple mixture-model view, we show that the probability assigned to the uncertainty class is a monotonic function of a distance-aware uncertainty surrogate. Experiments in 2D occupancy mapping, 3D semantic mapping, and tabletop scene reconstruction show that ContraMap preserves mapping quality, produces spatially coherent uncertainty estimates, and is substantially more efficient than Bayesian kernelmap baselines.

---

### On the Asymptotics of Self-Supervised Pre-training: Two-Stage M-Estimation and Representation Symmetry

Authors: Mohammad Tinati, Stephen Tu
Published: 2026-03-29
Categories: cs.LG, stat.ML
arXiv: https://arxiv.org/abs/2603.27631v2

Self-supervised pre-training, where large corpora of unlabeled data are used to learn representations for downstream fine-tuning, has become a cornerstone of modern machine learning. While a growing body of theoretical work has begun to analyze this paradigm, existing bounds leave open the question of how sharp the current rates are, and whether they accurately capture the complex interaction between pre-training and fine-tuning. In this paper, we address this gap by developing an asymptotic theory of pre-training via two-stage M-estimation. A key challenge is that the pre-training estimator is often identifiable only up to a group symmetry, a feature common in representation learning that requires careful treatment. We address this issue using tools from Riemannian geometry to study the intrinsic parameters of the pre-training representation, which we link with the downstream predictor through a notion of orbit-invariance, precisely characterizing the limiting distribution of the downstream test risk. We apply our main result to several case studies, including spectral pre-training, factor models, and Gaussian mixture models, and obtain substantial improvements in problem-specific factors over prior art when applicable.

---

### RTLSeek: Boosting the LLM-Based RTL Generation with Multi-Stage Diversity-Oriented Reinforcement Learning

Authors: Xinyu Zhang, Zhiteng Chao, Yonghao Wang, Bin Sun, Tianyun Ma, Tianmeng Yang, Jianan Mu, Jing Justin Ye, Huawei Li
Published: 2026-03-29
Categories: cs.AR, cs.LG
arXiv: https://arxiv.org/abs/2603.27630v1

Register Transfer Level (RTL) design translates high-level specifications into hardware using HDLs such as Verilog. Although LLM-based RTL generation is promising, the scarcity of functionally verifiable high-quality data limits both accuracy and diversity. Existing post-training typically produces a single HDL implementation per specification, lacking awareness of RTL variations needed for different design goals. We propose RTLSeek, a post-training paradigm that applies rule-based Diversity-Oriented Reinforcement Learning to improve RTL correctness and diversity. Our Diversity-Centric Multi-Objective Reward Scheduling integrates expert knowledge with EDA feedback, and a three-stage framework maximizes the utility of limited data. Experiments on the RTLLM benchmark show that RTLSeek surpasses prior methods, with ablation results confirming that encouraging broader design-space exploration improves RTL quality and achieves the principle of "the more generated, the better results." Implementation framework, including the dataset, source code, and model weights, is shown at https://anonymous.4open.science/r/DAC2026ID71-ACB4/.

---

### DSevolve: Enabling Real-Time Adaptive Scheduling on Dynamic Flexible Job Shop with LLM-Evolved Heuristic Portfolios

Authors: XinLei Zhou, Jin Huang, Jie Yang, Xinyu Li, Liang Gao
Published: 2026-03-29
Categories: cs.AI
arXiv: https://arxiv.org/abs/2603.27628v2

In dynamic flexible job shops, order arrivals, machine breakdowns, and processing-time deviations continually reshape the scheduling state and the priority trade-offs behind dispatching decisions. Dispatching rules are well suited to this setting because they are fast, interpretable, and easy to deploy, and recent LLM-assisted automatic heuristic design further expands their expressiveness by evolving composite priority functions. The key challenge is to make these evolved rule behaviors state-adaptive without losing the rapid response needed for online rescheduling. This paper proposes a dynamic self-evolutionary framework DSevolve, which separates offline rule-library construction from online state-conditioned rule selection. Offline, an LLM-guided quality-diversity search combines multi-persona seeding, a MAP-Elites behavioral archive, and behavior-guided variation to evolve a library of complementary rules rather than a single elite, and event-level simulation then trains a neural selector to rank the rules by state. Online, after each rescheduling event, a neural selector maps a 22-dimensional state fingerprint to rule scores and dispatches the top-ranked rule within about a second, meeting the response-time requirement after each disruption. Experiments on dynamic instances derived from standard flexible job shop benchmarks show that DSevolve achieves lower mean makespan than individual LLM-evolved rules, classical dispatching rules, and learning-based baselines under a one-active-rule deployment protocol. Trained only on small instances, the selector transfers zero-shot to substantially larger dynamic shops. These results show that state-conditioned selection preserves the speed and interpretability of dispatching rules while improving adaptability.

---

### Umwelt Engineering: Designing the Cognitive Worlds of Linguistic Agents

Authors: Rodney Jehu-Appiah
Published: 2026-03-29
Categories: cs.CL, cs.AI
arXiv: https://arxiv.org/abs/2603.27626v1

I propose Umwelt engineering -- the deliberate design of the linguistic cognitive environment -- as a third layer in the agent design stack, upstream of both prompt and context engineering. Two experiments test the thesis that altering the medium of reasoning alters cognition itself. In Experiment 1, three language models reason under two vocabulary constraints -- No-Have (eliminating possessive "to have") and E-Prime (eliminating "to be") -- across seven tasks (N=4,470 trials). No-Have improves ethical reasoning by 19.1 pp (p < 0.001), classification by 6.5 pp (p < 0.001), and epistemic calibration by 7.4 pp, while achieving 92.8% constraint compliance. E-Prime shows dramatic but model-dependent effects: cross-model correlations reach r = -0.75. In Experiment 2, 16 linguistically constrained agents tackle 17 debugging problems. No constrained agent outperforms the control individually, yet a 3-agent ensemble achieves 100% ground-truth coverage versus 88.2% for the control. A permutation test confirms only 8% of random 3-agent subsets achieve full coverage, and every successful subset contains the counterfactual agent. Two mechanisms emerge: cognitive restructuring and cognitive diversification. The primary limitation is the absence of an active control matching constraint prompt elaborateness.

---

### Clore: Interactive Pathology Image Segmentation with Click-based Local Refinement

Authors: Tiantong Wang, Minfan Zhao, Jun Shi, Hannan Wang, Yue Dai
Published: 2026-03-29
Categories: cs.CV
arXiv: https://arxiv.org/abs/2603.27625v1

Recent advancements in deep learning-based interactive segmentation methods have significantly improved pathology image segmentation. Most existing approaches utilize user-provided positive and negative clicks to guide the segmentation process. However, these methods primarily rely on iterative global updates for refinement, which lead to redundant re-prediction and often fail to capture fine-grained structures or correct subtle errors during localized adjustments. To address this limitation, we propose the Click-based Local Refinement (Clore) pipeline, a simple yet efficient method designed to enhance interactive segmentation. The key innovation of Clore lies in its hierarchical interaction paradigm: the initial clicks drive global segmentation to rapidly outline large target regions, while subsequent clicks progressively refine local details to achieve precise boundaries. This approach not only improves the ability to handle fine-grained segmentation tasks but also achieves high-quality results with fewer interactions. Experimental results on four datasets demonstrate that Clore achieves the best balance between segmentation accuracy and interaction cost, making it an effective solution for efficient and accurate interactive pathology image segmentation.

---

### Expert Streaming: Accelerating Low-Batch MoE Inference via Multi-chiplet Architecture and Dynamic Expert Trajectory Scheduling

Authors: Songchen Ma, Hongyi Li, Weihao Zhang, Yonghao Tan, Pingcheng Dong, Yu Liu, Lan Liu, Yuzhong Jiao, Xuejiao Liu, Luhong Liang, Kwang-Ting Cheng
Published: 2026-03-29
Categories: cs.AR, cs.AI
arXiv: https://arxiv.org/abs/2603.27624v1

Mixture-of-Experts is a promising approach for edge AI with low-batch inference. Yet, on-device deployments often face limited on-chip memory and severe workload imbalance; the prevalent use of offloading further incurs off-chip memory access bottlenecks. Moreover, MoE sparsity and dynamic gating shift distributed strategies toward much finer granularity and introduce runtime scheduling considerations. Recently, high die-to-die bandwidth chiplet interconnects have created new opportunities for multi-chiplet systems to address workload imbalance and offloading bottlenecks with fine-grained scheduling. In this paper, we propose Fully Sharded Expert Data Parallelism, a parallelization paradigm specifically architected for low-batch MoE inference on multi-chiplet accelerators. FSE-DP attains adaptive computation-communication overlap and balanced load by orchestrating fine-grained, complementary expert streams along dynamic trajectories across high-bandwidth D2D links. The attendant dataflow complexity is tamed by a minimal, hardware-amenable set of virtualization rules and a lightweight scheduling algorithm. Our approach achieves 1.22 to 2.00 times speedup over state-of-the-art baselines and saves up to 78.8 percent on-chip memory.

---

### What Does a System Modify When It Modifies Itself?

Authors: Florentin Koch
Published: 2026-03-29
Categories: cs.AI, q-bio.NC
arXiv: https://arxiv.org/abs/2603.27611v2

When a cognitive system modifies its own functioning, what exactly does it modify: a low-level rule, a control rule, or the criterion that evaluates its revisions? Cognitive science describes executive control, metacognition, and hierarchical learning, while artificial intelligence modifies policies, parameters, and learning mechanisms, but the two fields lack common criteria for comparing these transformations. We propose a minimal analytical model distinguishing functional rules Phi_t = {R0, ..., Rk}, modification mechanisms Mt, and evaluation criteria Nt. It separates two independent dimensions: the depth of the modified target and whether the modification is blind or reflexive. Internal representational access AtR and endogenous causal control AtC are further distinguished from external inspectability and modifiability by a designer. Four regimes are defined by the target of modification: action without organizational modification, modification of low-level rules, modification of control rules or mechanisms, and revision of the evaluation criterion. Each is related to cognitive phenomena and artificial systems while making explicit the system boundary on which attribution of endogenous self-modification depends. The comparison yields a conditional crossed-opacities hypothesis: humans often have richer endogenous self-description at abstract and strategic levels than at implementation levels, whereas current artificial systems may be externally inspectable and modifiable at operational levels without corresponding self-representation or endogenous control. The framework also identifies three difficulties - viability, evaluation of revisions to evaluation criteria, and continuity of identity - and proposes two empirical discriminants for identifying the target of a modification and the causal role of self-representation.

---

### What does a system modify when it modifies itself?

Authors: Florentin Koch
Published: 2026-03-29
Categories: cs.AI, q-bio.NC
arXiv: https://arxiv.org/abs/2603.27611v1

When a cognitive system modifies its own functioning, what exactly does it modify: a low-level rule, a control rule, or the norm that evaluates its own revisions? Cognitive science describes executive control, metacognition, and hierarchical learning with precision, but lacks a formal framework distinguishing these targets of transformation. Contemporary artificial intelligence likewise exhibits self-modification without common criteria for comparison with biological cognition. We show that the question of what counts as a self-modifying system entails a minimal structure: a hierarchy of rules, a fixed core, and a distinction between effective rules, represented rules, and causally accessible rules. Four regimes are identified: (1) action without modification, (2) low-level modification, (3) structural modification, and (4) teleological revision. Each regime is anchored in a cognitive phenomenon and a corresponding artificial system. Applied to humans, the framework yields a central result: a crossing of opacities. Humans have self-representation and causal power concentrated at upper hierarchical levels, while operational levels remain largely opaque. Reflexive artificial systems display the inverse profile: rich representation and causal access at operational levels, but none at the highest evaluative level. This crossed asymmetry provides a structural signature for human-AI comparison. The framework also offers insight into artificial consciousness, with higher-order theories and Attention Schema Theory as special cases. We derive four testable predictions and identify four open problems: the independence of transformativity and autonomy, the viability of self-modification, the teleological lock, and identity under transformation.

---

### You Only Erase Once: Erasing Anything without Bringing Unexpected Content

Authors: Yixing Zhu, Qing Zhang, Wenju Xu, Wei-Shi Zheng
Published: 2026-03-29
Categories: cs.CV
arXiv: https://arxiv.org/abs/2603.27599v1

We present YOEO, an approach for object erasure. Unlike recent diffusion-based methods which struggle to erase target objects without generating unexpected content within the masked regions due to lack of sufficient paired training data and explicit constraint on content generation, our method allows to produce high-quality object erasure results free of unwanted objects or artifacts while faithfully preserving the overall context coherence to the surrounding content. We achieve this goal by training an object erasure diffusion model on unpaired data containing only large-scale real-world images, under the supervision of a sundries detector and a context coherence loss that are built upon an entity segmentation model. To enable more efficient training and inference, a diffusion distillation strategy is employed to train for a few-step erasure diffusion model. Extensive experiments show that our method outperforms the state-of-the-art object erasure methods. Code will be available at https://zyxunh.github.io/YOEO-ProjectPage/.

---

### Calibration and transfer in indicator-based assessments of artificial consciousness

Authors: Florentin Koch
Published: 2026-03-29
Categories: cs.AI, q-bio.NC
arXiv: https://arxiv.org/abs/2603.27597v2

Research on artificial consciousness increasingly shifts evaluation from behaviour to internal architecture. Theory-based indicators are used to update probability assignments. This improves on behavioural tests but raises two distinct problems. First, these assignments cannot currently be calibrated against independently established artificial consciousness outcomes. Second, their evidential relevance is transferred from biological cases without independent support that indicator-consciousness relations remain stable across substrates. This commentary distinguishes calibration from transfer and adapts the iterative natural-kind strategy by proposing a preliminary, theory-relative comparative space for cross-substrate assessment.

---

### From indicators to biology: the calibration problem in artificial consciousness

Authors: Florentin Koch
Published: 2026-03-29
Categories: cs.AI, q-bio.NC
arXiv: https://arxiv.org/abs/2603.27597v1

Recent work on artificial consciousness shifts evaluation from behaviour to internal architecture, deriving indicators from theories of consciousness and updating credences accordingly. This is progress beyond naive Turing-style tests. But the indicator-based programme remains epistemically under-calibrated: consciousness science is theoretically fragmented, indicators lack independent validation, and no ground truth of artificial phenomenality exists. Under these conditions, probabilistic consciousness attribution to current AI systems is premature. A more defensible near-term strategy is to redirect effort toward biologically grounded engineering -- biohybrid, neuromorphic, and connectome-scale systems -- that reduces the gap with the only domain where consciousness is empirically anchored: living systems.

---

### STRIDE: When to Speak Meets Sequence Denoising for Streaming Video Understanding

Authors: Junho Kim, Hosu Lee, James M. Rehg, Minsu Kim, Yong Man Ro
Published: 2026-03-29
Categories: cs.CV, cs.AI
arXiv: https://arxiv.org/abs/2603.27593v1

Recent progress in video large language models (Video-LLMs) has enabled strong offline reasoning over long and complex videos. However, real-world deployments increasingly require streaming perception and proactive interaction, where video frames arrive online and the system must decide not only what to respond, but also when to respond. In this work, we revisit proactive activation in streaming video as a structured sequence modeling problem, motivated by the observation that temporal transitions in streaming video naturally form span-structured activation patterns. To capture this span-level structure, we model activation signals jointly over a sliding temporal window and update them iteratively as new frames arrive. We propose STRIDE (Structured Temporal Refinement with Iterative DEnoising), which employs a lightweight masked diffusion module at the activation interface to jointly predict and progressively refine activation signals across the window. Extensive experiments on diverse streaming benchmarks and downstream models demonstrate that STRIDE shows more reliable and temporally coherent proactive responses, significantly improving when-to-speak decision quality in online streaming scenarios.

---

### Fundamental Limits of Man-in-the-Middle Attack Detection in Model-Free Reinforcement Learning

Authors: Rishi Rani, Massimo Franceschetti
Published: 2026-03-29
Categories: eess.SY, cs.LG
arXiv: https://arxiv.org/abs/2603.27592v2

We consider the problem of learning-based man-in-the-middle (MITM) attacks in cyber-physical systems (CPS), and extend our previously proposed Bellman Deviation Detection (BDD) framework for model-free reinforcement learning (RL). We refine the standard MDP attack model by allowing the reward function to depend on both the current and subsequent states, thereby capturing reward variations induced by errors in the adversary's transition estimate. We also derive an optimal system-identification strategy for the adversary that minimizes detectable value deviations. Further, we prove that the agent's asymptotic learning time required to secure the system scales linearly with the adversary's learning time, and that this matches the optimal lower bound. Hence, the proposed detection scheme is order-optimal in detection efficiency. Finally, we extend the framework to asynchronous and intermittent attack scenarios, where reliable detection is preserved.

---

### An Energy-Efficient Spiking Neural Network Architecture for Predictive Insulin Delivery

Authors: Sahil Shrivastava
Published: 2026-03-29
Categories: cs.LG
arXiv: https://arxiv.org/abs/2603.27589v1

Diabetes mellitus affects over 537 million adults worldwide. Insulin-dependent patients require continuous glucose monitoring and precise dose calculation while operating under strict power budgets on wearable devices. This paper presents PDDS - an in-silico, software-complete research prototype of an event-driven computational pipeline for predictive insulin dose calculation. Motivated by neuromorphic computing principles for ultra-low-power wearable edge devices, the core contribution is a three-layer Leaky Integrate-and-Fire (LIF) Spiking Neural Network trained on 128,025 windows from OhioT1DM (66.5% real patients) and the FDA-accepted UVa/Padova physiological simulator (33.5%), achieving 85.90% validation accuracy. We present three rigorously honest evaluations: (1) a standard test-set comparison against ADA threshold rules, bidirectional LSTM (99.06% accuracy), and MLP (99.00%), where the SNN achieves 85.24% - we demonstrate this gap reflects the stochastic encoding trade-off, not architectural failure; (2) a temporal benchmark on 426 non-obvious clinician-annotated hypoglycemia windows where neither the SNN (9.2% recall) nor the ADA rule (16.7% recall) performs adequately, identifying the system's key limitation and the primary direction for future work; (3) a power-efficiency analysis showing the SNN requires 79,267x less energy per inference than the LSTM (1,551 Femtojoules vs. 122.9 nanojoules), justifying the SNN architecture for continuous wearable deployment. The system is not yet connected to physical hardware; it constitutes the computational middle layer of a five phase roadmap toward clinical validation. Keywords: spiking neural network, glucose severity classification, edge computing, hypoglycemia detection, event-driven architecture, LIF neuron, Poisson encoding, OhioT1DM, in-silico, neuromorphic, power efficiency.

---

### A Robust Low-Rank Prior Model for Structured Cartoon-Texture Image Decomposition with Heavy-Tailed Noise

Authors: Weihao Tang, Hongjin He
Published: 2026-03-29
Categories: cs.CV, math.OC
arXiv: https://arxiv.org/abs/2603.27579v1

Cartoon-texture image decomposition is a fundamental yet challenging problem in image processing. A significant hurdle in achieving accurate decomposition is the pervasive presence of noise in the observed images, which severely impedes robust results. To address the challenging problem of cartoon-texture decomposition in the presence of heavy-tailed noise, we in this paper propose a robust low-rank prior model. Our approach departs from conventional models by adopting the Huber loss function as the data-fidelity term, rather than the traditional $\ell_2$-norm, while retaining the total variation norm and nuclear norm to characterize the cartoon and texture components, respectively. Given the inherent structure, we employ two implementable operator splitting algorithms, tailored to different degradation operators. Extensive numerical experiments, particularly on image restoration tasks under high-intensity heavy-tailed noise, efficiently demonstrate the superior performance of our model.

---

### Structured Observation Language for Efficient and Generalizable Vision-Language Navigation

Authors: Daojie Peng, Fulong Ma, Jun Ma
Published: 2026-03-29
Categories: cs.CV, cs.RO
arXiv: https://arxiv.org/abs/2603.27577v3

Vision-Language Navigation (VLN) requires an embodied agent to navigate complex environments by following natural language instructions, which typically demands tight fusion of visual and language modalities. Existing VLN methods often convert raw images into visual tokens or implicit features, requiring large-scale visual pre-training and suffering from poor generalization under environmental variations (e.g., lighting, texture). To address these issues, we propose SOL-Nav (Structured Observation Language for Navigation), a novel framework that translates egocentric visual observations into compact structured language descriptions for efficient and generalizable navigation. Specifically, we divide RGB-D images into a NxN grid, extract representative semantic, color, and depth information for each grid cell to form structured text, and concatenate this with the language instruction as pure language input to a pre-trained language model (PLM). Experimental results on standard VLN benchmarks (R2R, RxR) and real-world deployments demonstrate that SOL-Nav significantly reduces the model size and training data dependency, fully leverages the reasoning and representation capabilities of PLMs, and achieves strong generalization to unseen environments.

---

### InnerPond: Fostering Inter-Self Dialogue with a Multi-Agent Approach for Introspection

Authors: Hayeon Jeon, Dakyeom Ahn, Sunyu Pang, Yunseo Choi, Suhwoo Yoon, Joonhwan Lee, Eun-mee Kim, Hajin Lim
Published: 2026-03-29
Categories: cs.HC, cs.AI
arXiv: https://arxiv.org/abs/2603.27563v1

Introspection is central to identity construction and future planning, yet most digital tools approach the self as a unified entity. In contrast, Dialogical Self Theory (DST) views the self as composed of multiple internal perspectives, such as values, concerns, and aspirations, that can come into tension or dialogue with one another. Building on this view, we designed InnerPond, a research probe in the form of a multi-agent system that represents these internal perspectives as distinct LLM-based agents for introspection. Its design was shaped through iterative explorations of spatial metaphors, interaction scaffolding, and conversational orchestration, culminating in a shared spatial environment for organizing and relating multiple inner perspectives. In a user study with 17 young adults navigating career choices, participants engaged with the probe by co-creating inner voices with AI, composing relational inner landscapes, and orchestrating dialogue as observers and mediators, offering insight into how such systems could support introspection. Overall, this work offers design implications for AI-supported introspection tools that enable exploration of the self's multiplicity.

---

### Learning to See through Illumination Extremes with Event Streaming in Multimodal Large Language Models

Authors: Baoheng Zhang, Jiahui Liu, Gui Zhao, Weizhou Zhang, Yixuan Ma, Jun Jiang, Yingxian Chen, Wilton W. T. Fok, Xiaojuan Qi, Hayden Kwok-Hay So
Published: 2026-03-29
Categories: cs.CV
arXiv: https://arxiv.org/abs/2603.27558v1

Multimodal Large Language Models (MLLMs) perform strong vision-language reasoning under standard conditions but fail in extreme illumination, where RGB inputs lose irrevocable structure and semantics. We propose Event-MLLM, an event-enhanced model that performs all-light visual reasoning by dynamically fusing event streams with RGB frames. Two key components drive our approach: an Illumination Indicator - a learnable signal derived from a DINOv2 branch that represents exposure degradation and adaptively modulates event-RGB fusion - and an Illumination Correction Loss that aligns fused features with non-degraded (normal-light) semantics in the latent space, compensating for information lost in extreme lighting. We curate the first multi-illumination event-instruction corpus for MLLMs, with 2,241 event-RGB samples (around 6 QA pairs each) across diverse scenes and 17 brightness rates (0.05x - 20x), plus an instruct-following benchmark for reasoning, counting, and fine-grained recognition under extreme lighting. Experiments show that Event-MLLM markedly outperforms general-purpose, illumination-adaptive, and event-only baselines, setting a new state of the art in robust multimodal perception and reasoning under challenging illumination.

---

### A General Model for Deepfake Speech Detection: Diverse Bonafide Resources or Diverse AI-Based Generators

Authors: Lam Pham, Khoi Vu, Dat Tran, David Fischinger, Alexander Schindler, Martin Boyer, Ian McLoughlin
Published: 2026-03-29
Categories: cs.SD, cs.AI
arXiv: https://arxiv.org/abs/2603.27557v2

In this paper, we analyze two main factors of Bonafide Resource (BR) or AI-based Generator (AG) which affect the performance and the generality of a Deepfake Speech Detection (DSD) model. To this end, we first propose a deep-learning based model, referred to as the baseline. Then, we conducted experiments on the baseline by which we indicate how Bonafide Resource (BR) and AI-based Generator (AG) factors affect the threshold score used to detect fake or bonafide input audio in the inference process. Given the experimental results, a dataset, which re-uses public Deepfake Speech Detection (DSD) datasets and shows a balance between Bonafide Resource (BR) or AI-based Generator (AG), is proposed. We then train various deep-learning based models on the proposed dataset and conduct cross-dataset evaluation on different benchmark datasets. The cross-dataset evaluation results prove that the balance of Bonafide Resources (BR) and AI-based Generators (AG) is the key factor to train and achieve a general Deepfake Speech Detection (DSD) model.

---

### Towards Domain-Generalized Open-Vocabulary Object Detection: A Progressive Domain-invariant Cross-modal Alignment Method

Authors: Xiaoran Xu, Xiaoshan Yang, Jiangang Yang, Yifan Xu, Jian Liu, Changsheng Xu
Published: 2026-03-29
Categories: cs.CV
arXiv: https://arxiv.org/abs/2603.27556v2

Open-Vocabulary Object Detection (OVOD) has achieved remarkable success in generalizing to novel categories. However, this success often rests on the implicit assumption of domain stationarity. In this work, we revisit the OVOD paradigm and study a key vulnerability: the fragile coupling between visual manifolds and textual embeddings under distribution shifts. We first formulate Domain-Generalized Open-Vocabulary Object Detection (DG-OVOD) as an evaluation protocol for open-vocabulary recognition under visual shifts. Through empirical analysis, we observe that visual shifts can destabilize the latent cross-modal space, causing novel-category visual signals to drift away from their semantic anchors. Motivated by these observations, we propose Progressive Domain-invariant Cross-modal Alignment (PICA). PICA departs from uniform training by introducing a multi-level curriculum based on ambiguity and signal strength. It constructs a quality-adjusted curriculum over pseudo-word prototypes, refined by sample reliability and visual consistency, to encourage more stable cross-domain modality alignment. Our findings suggest that OVOD robustness under domain shifts is closely linked to the stability of the latent cross-modal alignment space. Our work provides a DG-OVOD evaluation protocol and a practical perspective on building more generalizable open-vocabulary systems beyond static laboratory conditions.

---

### PANDORA: Pixel-wise Attention Dissolution and Latent Guidance for Zero-Shot Object Removal

Authors: Dinh-Khoi Vo, Van-Loc Nguyen, Tam V. Nguyen, Minh-Triet Tran, Trung-Nghia Le
Published: 2026-03-29
Categories: cs.CV
arXiv: https://arxiv.org/abs/2603.27555v1

Removing objects from natural images is challenging due to difficulty of synthesizing semantically coherent content while preserving background integrity. Existing methods often rely on fine-tuning, prompt engineering, or inference-time optimization, yet still suffer from texture inconsistency, rigid artifacts, weak foreground-background disentanglement, and poor scalability for multi-object removal. We propose a novel zero-shot object removal framework, namely PANDORA, that operates directly on pre-trained text-to-image diffusion models, requiring no fine-tuning, prompts, or optimization. We propose Pixel-wise Attention Dissolution to remove object by nullifying the most correlated attention keys for masked pixels, effectively eliminating the object from self-attention flow and allowing background context to dominate reconstruction. We further introduce Localized Attentional Disentanglement Guidance to steer denoising toward latent manifolds favorable to clean object removal. Together, these components enable precise, non-rigid, prompt-free, and scalable multi-object erasure in a single pass. Experiments demonstrate superior visual fidelity and semantic plausibility compared to state-of-the-art methods. The project page is available at https://vdkhoi20.github.io/PANDORA.

---

### Annotation-Free Detection of Drivable Areas and Curbs Leveraging LiDAR Point Cloud Maps

Authors: Fulong Ma, Daojie Peng, Jun Ma
Published: 2026-03-29
Categories: cs.CV
arXiv: https://arxiv.org/abs/2603.27553v1

Drivable areas and curbs are critical traffic elements for autonomous driving, forming essential components of the vehicle visual perception system and ensuring driving safety. Deep neural networks (DNNs) have significantly improved perception performance for drivable area and curb detection, but most DNN-based methods rely on large manually labeled datasets, which are costly, time-consuming, and expert-dependent, limiting their real-world application. Thus, we developed an automated training data generation module. Our previous work generated training labels using single-frame LiDAR and RGB data, suffering from occlusion and distant point cloud sparsity. In this paper, we propose a novel map-based automatic data labeler (MADL) module, combining LiDAR mapping/localization with curb detection to automatically generate training data for both tasks. MADL avoids occlusion and point cloud sparsity issues via LiDAR mapping, creating accurate large-scale datasets for DNN training. In addition, we construct a data review agent to filter the data generated by the MADL module, eliminating low-quality samples. Experiments on the KITTI, KITTI-CARLA and 3D-Curb datasets show that MADL achieves impressive performance compared to manual labeling, and outperforms traditional and state-of-the-art self-supervised methods in robustness and accuracy.

---

### BLOSSOM: Block-wise Federated Learning Over Shared and Sparse Observed Modalities

Authors: Pranav M R, Jayant Chandwani, Ahmed M. Abdelmoniem, Arnab K. Paul
Published: 2026-03-29
Categories: cs.LG, cs.DC
arXiv: https://arxiv.org/abs/2603.27552v2

Multimodal federated learning (FL) is essential for real-world applications such as autonomous systems and healthcare, where data is distributed across heterogeneous clients with varying and often missing modalities. However, most existing FL approaches assume uniform modality availability, limiting their applicability in practice. We introduce BLOSSOM, a task-agnostic framework for multimodal FL designed to operate under shared and sparsely observed modality conditions. BLOSSOM supports clients with arbitrary modality subsets and enables flexible sharing of model components. To address client and task heterogeneity, we propose a block-wise aggregation strategy that selectively aggregates shared components while keeping task-specific blocks private, enabling partial personalization. We evaluate BLOSSOM on multiple diverse multimodal datasets and analyse the effects of missing modalities and personalization. Our results show that block-wise personalization significantly improves performance, particularly in settings with severe modality sparsity. In modality-incomplete scenarios, BLOSSOM achieves an average performance gain of 18.7% over full-model aggregation, while in modality-exclusive settings the gain increases to 37.7%, highlighting the importance of block-wise learning for practical multimodal FL systems.

---

### Drag or Traction: Understanding How Designers Appropriate Friction in AI Ideation Outputs

Authors: A. Baki Kocaballi, Joseph Kizana, Sharon Stein, Simon Buckingham Shum
Published: 2026-03-29
Categories: cs.HC, cs.AI
arXiv: https://arxiv.org/abs/2603.27550v1

Seamless AI presents output as a finished, polished product that users consume rather than shape. This risks design fixation: users anchor on AI suggestions rather than generating their own ideas. We propose Generative Friction, which introduces intentional disruptions to AI output (fragmentation, delay, ambiguity) designed to transform it from finished product into semi-finished material, inviting human contribution rather than passive acceptance. In a qualitative study with six designers, we identified the different ways in which designers appropriated the different types of friction: users mined keywords from broken text, used delays as workspace for independent thought, and solved metaphors as creative puzzles. However, this transformation was not universal, motivating the concept of Friction Disposition, a user's propensity to interpret resistance as invitation rather than obstruction. Grounded in tolerance for ambiguity and pre-existing workflow orientation, Friction Disposition emerged as a potential moderator: high-disposition users treated friction as "liberating," while low-disposition users experienced drag. We contribute the concept of Generative Friction as distinct from Protective Friction, with design implications for AI tools that counter fixation while preserving agency.

---

### MV-RoMa: From Pairwise Matching into Multi-View Track Reconstruction

Authors: Jongmin Lee, Seungyeop Kang, Sungjoo Yoo
Published: 2026-03-29
Categories: cs.CV
arXiv: https://arxiv.org/abs/2603.27542v1

Establishing consistent correspondences across images is essential for 3D vision tasks such as structure-from-motion (SfM), yet most existing matchers operate in a pairwise manner, often producing fragmented and geometrically inconsistent tracks when their predictions are chained across views. We propose MV-RoMa, a multi-view dense matching model that jointly estimates dense correspondences from a source image to multiple co-visible targets. Specifically, we design an efficient model architecture which avoids high computational cost of full cross-attention for multi-view feature interaction: (i) multi-view encoder that leverages pair-wise matching results as a geometric prior, and (ii) multi-view matching refiner that refines correspondences using pixel-wise attention. Additionally, we propose a post-processing strategy that integrates our model's consistent multi-view correspondences as high-quality tracks for SfM. Across diverse and challenging benchmarks, MV-RoMa produces more reliable correspondences and substantially denser, more accurate 3D reconstructions than existing sparse and dense matching methods. Project page: https://icetea-cv.github.io/mv-roma/.

---

### A Novel Immune Algorithm for Multiparty Multiobjective Optimization

Authors: Kesheng Chen, Wenjian Luo, Qi Zhou, Yujiang liu, Peilan Xu, Yuhui Shi
Published: 2026-03-29
Categories: cs.NE, cs.AI
arXiv: https://arxiv.org/abs/2603.27541v1

Traditional multiobjective optimization problems (MOPs) are insufficiently equipped for scenarios involving multiple decision makers (DMs), which are prevalent in many practical applications. These scenarios are categorized as multiparty multiobjective optimization problems (MPMOPs). For MPMOPs, the goal is to find a solution set that is as close to the Pareto front of each DM as much as possible. This poses challenges for evolutionary algorithms in terms of searching and selecting. To better solve MPMOPs, this paper proposes a novel approach called the multiparty immune algorithm (MPIA). The MPIA incorporates an inter-party guided crossover strategy based on the individual's non-dominated sorting ranks from different DM perspectives and an adaptive activation strategy based on the proposed multiparty cover metric (MCM). These strategies enable MPIA to activate suitable individuals for the next operations, maintain population diversity from different DM perspectives, and enhance the algorithm's search capability. To evaluate the performance of MPIA, we compare it with ordinary multiobjective evolutionary algorithms (MOEAs) and state-of-the-art multiparty multiobjective optimization evolutionary algorithms (MPMOEAs) by solving synthetic multiparty multiobjective problems and real-world biparty multiobjective unmanned aerial vehicle path planning (BPUAV-PP) problems involving multiple DMs. Experimental results demonstrate that MPIA outperforms other algorithms.

---

### Toward Reliable Evaluation of LLM-Based Financial Multi-Agent Systems: Taxonomy, Coordination Primacy, and Cost Awareness

Authors: Phat Nguyen, Thang Pham
Published: 2026-03-29
Categories: cs.MA, cs.AI, cs.CE
arXiv: https://arxiv.org/abs/2603.27539v1

Multi-agent systems based on large language models (LLMs) for financial trading have grown rapidly since 2023, yet the field lacks a shared framework for understanding what drives performance or for evaluating claims credibly. This survey makes three contributions. First, we introduce a four-dimensional taxonomy, covering architecture pattern, coordination mechanism, memory architecture, and tool integration; applied to 12 multi-agent systems and two single-agent baselines. Second, we formulate the Coordination Primacy Hypothesis (CPH): inter-agent coordination protocol design is a primary driver of trading decision quality, often exerting greater influence than model scaling. CPH is presented as a falsifiable research hypothesis supported by tiered structural evidence rather than as an empirically validated conclusion; its definitive validation requires evaluation infrastructure that does not yet exist in the field. Third, we document five pervasive evaluation failures (look-ahead bias, survivorship bias, backtesting overfitting, transaction cost neglect, and regime-shift blindness) and show that these can reverse the sign of reported returns. Building on the CPH and the evaluation critique, we introduce the Coordination Breakeven Spread (CBS), a metric for determining whether multi-agent coordination adds genuine value net of transaction costs, and propose minimum evaluation standards as prerequisites for validating the CPH.

---