OpenRaiser/Intern-Atlas huggingface dataset:
Iterative Compositional Data Generation for Robot Control
Venue: arXiv cs.RO 2025
Year: 2025

Collecting robotic manipulation data is expensive, making it impractical to acquire demonstrations for the combinatorially large space of tasks that arise in multi-object, multi-robot, and multi-environment settings. While recent generative models can synthesize useful data for individual tasks, they do not exploit the compositional structure of robotic domains and struggle to generalize to unseen task combinations. We propose a semantic compositional diffusion transformer that factorizes transitions into robot-, object-, obstacle-, and objective-specific components and learns their interactions through attention. Once trained on a limited subset of tasks, we show that our model can zero-shot generate high-quality transitions from which we can learn control policies for unseen task combinations. Then, we introduce an iterative self-improvement procedure in which synthetic data is validated via offline reinforcement learning and incorporated into subsequent training rounds. Our approach substantially improves zero-shot performance over monolithic and hard-coded compositional baselines, ultimately solving nearly all held-out tasks and demonstrating the emergence of meaningful compositional structure in the learned representations.
LLMs Can Assist with Proposal Selection at Large User Facilities
Venue: arXiv cs.AI 2025
Year: 2025

We explore how large language models (LLMs) can enhance the proposal selection process at large user facilities, offering a scalable, consistent, and cost-effective alternative to traditional human review. Proposal selection depends on assessing the relative strength among submitted proposals; however, traditional human scoring often suffers from weak inter-proposal correlations and is subject to reviewer bias and inconsistency. A pairwise preference-based approach is logically superior, providing a more rigorous and internally consistent basis for ranking, but its quadratic workload makes it impractical for human reviewers. We address this limitation using LLMs. Leveraging the uniquely well-curated proposals and publication records from three beamlines at the Spallation Neutron Source (SNS), Oak Ridge National Laboratory (ORNL), we show that the LLM rankings correlate strongly with the human rankings (Spearman $ρ\simeq 0.2-0.8$, improving to $\geq 0.5$ after 10\% outlier removal). Moreover, LLM performance is no worse than that of human reviewers in identifying proposals with high publication potential, while costing over two orders of magnitude less. Beyond ranking, LLMs enable advanced analyses that are challenging for humans, such as quantitative assessment of proposal similarity via embedding models, which provides information crucial for review committees.
E-RayZer: Self-supervised 3D Reconstruction as Spatial Visual Pre-training
Venue: arXiv cs.CV 2025
Year: 2025

Self-supervised pre-training has revolutionized foundation models for languages, individual 2D images and videos, but remains largely unexplored for learning 3D-aware representations from multi-view images. In this paper, we present E-RayZer, a self-supervised large 3D Vision model that learns truly 3D-aware representations directly from unlabeled images. Unlike prior self-supervised methods such as RayZer that infer 3D indirectly through latent-space view synthesis, E-RayZer operates directly in 3D space, performing self-supervised 3D reconstruction with Explicit geometry. This formulation eliminates shortcut solutions and yields representations that are geometrically grounded. To ensure convergence and scalability, we introduce a novel fine-grained learning curriculum that organizes training from easy to hard samples and harmonizes heterogeneous data sources in an entirely unsupervised manner. Experiments demonstrate that E-RayZer significantly outperforms RayZer on pose estimation, matches or sometimes surpasses fully supervised reconstruction models such as VGGT. Furthermore, its learned representations outperform leading visual pre-training models (e.g., DINOv3, CroCo v2, VideoMAE V2, and RayZer) when transferring to 3D downstream tasks, establishing E-RayZer as a new paradigm for 3D-aware visual pre-training.
Empowering Dynamic Urban Navigation with Stereo and Mid-Level Vision
Venue: arXiv cs.CV 2025
Year: 2025

The success of foundation models in language and vision motivated research in fully end-to-end robot navigation foundation models (NFMs). NFMs directly map monocular visual input to control actions and ignore mid-level vision modules (tracking, depth estimation, etc) entirely. While the assumption that vision capabilities will emerge implicitly is compelling, it requires large amounts of pixel-to-action supervision that are difficult to obtain. The challenge is especially pronounced in dynamic and unstructured settings, where robust navigation requires precise geometric and dynamic understanding, while the depth-scale ambiguity in monocular views further limits accurate spatial reasoning. In this paper, we show that relying on monocular vision and ignoring mid-level vision priors is inefficient. We present StereoWalker, which augments NFMs with stereo inputs and explicit mid-level vision such as depth estimation and dense pixel tracking. Our intuition is straightforward: stereo inputs resolve the depth-scale ambiguity, and modern mid-level vision models provide reliable geometric and motion structure in dynamic scenes. We also curate a large stereo navigation dataset with automatic action annotation from Internet stereo videos to support training of StereoWalker and to facilitate future research. Through our experiments, we find that mid-level vision enables StereoWalker to achieve a comparable performance as the state-of-the-art using only 1.5% of the training data, and surpasses the state-of-the-art using the full data. We also observe that stereo vision yields higher navigation performance than monocular input.
Benchmarking Automatic Speech Recognition Models for African Languages
Venue: arXiv cs.CL 2025
Year: 2025

Automatic speech recognition (ASR) for African languages remains constrained by limited labeled data and the lack of systematic guidance on model selection, data scaling, and decoding strategies. Large pre-trained systems such as Whisper, XLS-R, MMS, and W2v-BERT have expanded access to ASR technology, but their comparative behavior in African low-resource contexts has not been studied in a unified and systematic way. In this work, we benchmark four state-of-the-art ASR models across 13 African languages, fine-tuning them on progressively larger subsets of transcribed data ranging from 1 to 400 hours. Beyond reporting error rates, we provide new insights into why models behave differently under varying conditions. We show that MMS and W2v-BERT are more data efficient in very low-resource regimes, XLS-R scales more effectively as additional data becomes available, and Whisper demonstrates advantages in mid-resource conditions. We also analyze where external language model decoding yields improvements and identify cases where it plateaus or introduces additional errors, depending on the alignment between acoustic and text resources. By highlighting the interaction between pre-training coverage, model architecture, dataset domain, and resource availability, this study offers practical and insights into the design of ASR systems for underrepresented languages.
Seamless Transitions: A Comprehensive Review of Live Migration Technologies
Venue: arXiv cs.DC 2025
Year: 2025

Live migration, a technology enabling seamless transition of operational computational entities between various hosts while preserving continuous functionality and client connectivity, has been the subject of extensive research. However, existing reviews often overlook critical technical aspects and practical challenges integral to the usage of live migration techniques in real-world scenarios. This work bridges this gap by integrating the aspects explored in existing reviews together with a comprehensive analysis of live migration technologies across multiple dimensions, with focus on migration techniques, migration units, and infrastructure characteristics. Despite efforts to make live migration widely accessible, its reliance on multiple system factors can create challenges. In certain cases, the complexities and resource demands outweigh the benefits, making its implementation hard to justify. The focus of this work is mainly on container based and virtual machine-based migration technologies, examining the current state of the art and the disparity in adoption between these two approaches. Furthermore, this work explores the impact of migration objectives and operational constraints on the usability and efficacy of existing technologies. By outlining current technical challenges and providing guidelines for future research and development directions, this work serves a dual purpose: first, to equip enthusiasts with a valuable resource on live migration, and second, to contribute to the advancement of live migration technologies and their practical implementation across diverse computing environments.
The TCG CREST -- RKMVERI Submission for the NCIIPC Startup India AI Grand Challenge
Venue: arXiv cs.SD 2025
Year: 2025

In this report, we summarize the integrated multilingual audio processing pipeline developed by our team for the inaugural NCIIPC Startup India AI GRAND CHALLENGE, addressing Problem Statement 06: Language-Agnostic Speaker Identification and Diarisation, and subsequent Transcription and Translation System. Our primary focus was on advancing speaker diarization, a critical component for multilingual and code-mixed scenarios. The main intent of this work was to study the real-world applicability of our in-house speaker diarization (SD) systems. To this end, we investigated a robust voice activity detection (VAD) technique and fine-tuned speaker embedding models for improved speaker identification in low-resource settings. We leveraged our own recently proposed multi-kernel consensus spectral clustering framework, which substantially improved the diarization performance across all recordings in the training corpus provided by the organizers. Complementary modules for speaker and language identification, automatic speech recognition (ASR), and neural machine translation were integrated in the pipeline. Post-processing refinements further improved system robustness.
Memoryless Policy Iteration for Episodic POMDPs
Venue: arXiv cs.LG 2025
Year: 2025

Memoryless and finite-memory policies offer a practical alternative for solving partially observable Markov decision processes (POMDPs), as they operate directly in the output space rather than in the high-dimensional belief space. However, extending classical methods such as policy iteration to this setting remains difficult; the output process is non-Markovian, making policy-improvement steps interdependent across stages. We introduce a new family of monotonically improving policy-iteration algorithms that alternate between single-stage output-based policy improvements and policy evaluations according to a prescribed periodic pattern. We show that this family admits optimal patterns that maximize a natural computational-efficiency index, and we identify the simplest pattern with minimal period. Building on this structure, we further develop a model-free variant that estimates values from data and learns memoryless policies directly. Across several POMDPs examples, our method achieves significant computational speedups over policy-gradient baselines and recent specialized algorithms in both model-based and model-free settings.
Your plan may succeed, but what about failure? Investigating how people use ChatGPT for long-term life task planning
Venue: arXiv cs.HC 2025
Year: 2025

Long-term life task planning is inherently complex and uncertain, yet little is known about how emerging AI systems support this process. This study investigates how people use ChatGPT for such planning tasks, focusing on user practices, uncertainties, and perceptions of AI assistance. We conducted an interview study with 14 participants who engaged in long-term planning activities using ChatGPT, combining analysis of their prompts and interview responses. The task topics across diverse domains, including personal well-being, event planning, and professional learning, along with prompts to initiate, refine, and contextualize plans. ChatGPT helped structure complex goals into manageable steps, generate ideas, and sustain motivation, serving as a reflective partner. Yet its outputs were often generic or idealized, lacking personalization, contextual realism, and adaptability, requiring users to actively adapt and verify results. Participants expressed a need for AI systems that provide adaptive and trustworthy guidance while acknowledging uncertainty and potential failure in long-term planning. Our findings show how AI supports long-term life task planning under evolving uncertainty and highlight design implications for systems that are adaptive, uncertainty-aware, and capable of supporting long-term planning as an evolving human-AI collaboration.
Information-driven Fusion of Pathology Foundation Models for Enhanced Disease Characterization
Venue: arXiv cs.CV 2025
Year: 2025

Foundation models (FMs) have demonstrated strong performance across diverse pathology tasks. While there are similarities in the pre-training objectives of FMs, there is still limited understanding of their complementarity, redundancy in embedding spaces, or biological interpretation of features. In this study, we propose an information-driven, intelligent fusion strategy for integrating multiple pathology FMs into a unified representation and systematically evaluate its performance for cancer grading and staging across three distinct diseases. Diagnostic H&E whole-slide images from kidney (519 slides), prostate (490 slides), and rectal (200 slides) cancers were dichotomized into low versus high grade or stage. Both tile-level FMs (Conch v1.5, MUSK, Virchow2, H-Optimus1, Prov-Gigapath) and slide-level FMs (TITAN, CHIEF, MADELEINE) were considered to train downstream classifiers. We then evaluated three FM fusion schemes at both tile and slide levels: majority-vote ensembling, naive feature concatenation, and intelligent fusion based on correlation-guided pruning of redundant features. Under patient-stratified cross-validation with hold-out testing, intelligent fusion of tile-level embeddings yielded consistent gains in classification performance across all three cancers compared with the best single FMs and naive fusion. Global similarity metrics revealed substantial alignment of FM embedding spaces, contrasted by lower local neighborhood agreement, indicating complementary fine-grained information across FMs. Attention maps showed that intelligent fusion yielded concentrated attention on tumor regions while reducing spurious focus on benign regions. Our findings suggest that intelligent, correlation-guided fusion of pathology FMs can yield compact, task-tailored representations that enhance both predictive performance and interpretability in downstream computational pathology tasks.
FIBER: A Multilingual Evaluation Resource for Factual Inference Bias
Venue: arXiv cs.CL 2025
Year: 2025

Large language models are widely used across domains, yet there are concerns about their factual reliability and biases. Factual knowledge probing offers a systematic means to evaluate these aspects. Most existing benchmarks focus on single-entity facts and monolingual data. We therefore present FIBER, a multilingual benchmark for evaluating factual knowledge in single- and multi-entity settings. The dataset includes sentence completion, question-answering, and object-count prediction tasks in English, Italian, and Turkish. Using FIBER, we examine whether the prompt language induces inference bias in entity selection and how large language models perform on multi-entity versus single-entity questions. The results indicate that the language of the prompt can influence the model's generated output, particularly for entities associated with the country corresponding to that language. However, this effect varies across different topics such that 31% of the topics exhibit factual inference bias score greater than 0.5. Moreover, the level of bias differs across languages such that Turkish prompts show higher bias compared to Italian in 83% of the topics, suggesting a language-dependent pattern. Our findings also show that models face greater difficulty when handling multi-entity questions than the single-entity questions. Model performance differs across both languages and model sizes. The highest mean average precision is achieved in English, while Turkish and Italian lead to noticeably lower scores. Larger models, including Llama-3.1-8B and Qwen-2.5-7B, show consistently better performance than smaller 3B-4B models.
Stabilising Learner Trajectories: A Doubly Robust Evaluation of AI-Guided Student Support using Activity Theory
Venue: arXiv cs.CY 2025
Year: 2025

While predictive models are increasingly common in higher education, causal evidence regarding the interventions they trigger remains rare. This study evaluates an AI-guided student support system at a large university using doubly robust propensity score matching. We advance the methodology for learning analytics evaluation by leveraging time-aligned, dynamic AI probability of success scores to match 1,859 treated students to controls, thereby mitigating the selection and immortal time biases often overlooked in observational studies. Results indicate that the intervention effectively stabilised precarious trajectories, and compared to the control group, supported students significantly reduced their course failure rates and achieved higher cumulative grades. However, effects on the speed of qualification completion were positive but statistically constrained. We interpreted these findings through Activity Theory, framing the intervention as a socio-technical brake that interrupts and slows the accumulation of academic failure among at-risk students. The student support-AI configuration successfully resolved the primary contradiction of immediate academic risk, but secondary contradictions within institutional structures limited the acceleration of degree completion. We conclude that while AI-enabled support effectively arrests decline, translating this stability into faster progression requires aligning intervention strategies with broader institutional governance.
Benchmarking RL-Enhanced Spatial Indices Against Traditional, Advanced, and Learned Counterparts
Venue: arXiv cs.DB 2025
Year: 2025

Reinforcement learning has recently been used to enhance index structures, giving rise to reinforcement learning-enhanced spatial indices (RLESIs) that aim to improve query efficiency during index construction. However, their practical benefits remain unclear due to the lack of unified implementations and comprehensive evaluations, especially in disk-based settings. We present the first modular and extensible benchmark for RLESIs. Built on top of an existing spatial index library, our framework decouples index training from building, supports parameter tuning, and enables consistent comparison with traditional, advanced, and learned spatial indices. We evaluate 12 representative spatial indices across six datasets and diverse workloads, including point, range, kNN, spatial join, and mixed read/write queries. Using latency, I/O, and index statistics as metrics, we find that while RLESIs can reduce query latency with tuning, they consistently underperform learned spatial indices and advanced variants in both query efficiency and index build cost. These findings highlight that although RLESIs offer promising architectural compatibility, their high tuning costs and limited generalization hinder practical adoption.
Sphere Decoding Revisited
Venue: arXiv cs.IT 2025
Year: 2025

In this paper, the paradigm of sphere decoding (SD) for solving the integer least square problem (ILS) is revisited, where extra degrees of freedom are introduced to exploit the decoding potential. Firstly, the equivalent sphere decoding (ESD) is proposed, which is essentially the same with the classic Fincke-Pohst sphere decoding but characterizes the sphere radius $D>0$ with two new parameters named as initial searching size $K>1$ and deviation factor $σ>0$. By fixing $σ$ properly, we show that given the sphere radius $D\triangleqσ\sqrt{2\ln K}$, the complexity of ESD in terms of the number of visited nodes is upper bounded by $|S|<nK$, thus resulting in an explicit and tractable decoding trade-off solely controlled by $K$. To the best of our knowledge, this is the first time that the complexity of sphere decoding is exactly specified, where considerable decoding potential can be explored from it. After that, two enhancement mechanisms named as normalized weighting and candidate protection are proposed to further upgrade the ESD algorithm. On one hand, given the same setups of $K$ and $σ$, a larger sphere radius is achieved, indicating a better decoding trade-off. On the other hand, the proposed ESD algorithm is generalized, which bridges suboptimal and optimal decoding performance through the flexible choice of $K$. Finally, further performance optimization and complexity reduction with respect to ESD are also derived, and the introduced tractable and flexible decoding trade-off is verified through large-scale MIMO detection.
PersonaLive! Expressive Portrait Image Animation for Live Streaming
Venue: arXiv cs.CV 2025
Year: 2025

Current diffusion-based portrait animation models predominantly focus on enhancing visual quality and expression realism, while overlooking generation latency and real-time performance, which restricts their application range in the live streaming scenario. We propose PersonaLive, a novel diffusion-based framework towards streaming real-time portrait animation with multi-stage training recipes. Specifically, we first adopt hybrid implicit signals, namely implicit facial representations and 3D implicit keypoints, to achieve expressive image-level motion control. Then, a fewer-step appearance distillation strategy is proposed to eliminate appearance redundancy in the denoising process, greatly improving inference efficiency. Finally, we introduce an autoregressive micro-chunk streaming generation paradigm equipped with a sliding training strategy and a historical keyframe mechanism to enable low-latency and stable long-term video generation. Extensive experiments demonstrate that PersonaLive achieves state-of-the-art performance with up to 7-22x speedup over prior diffusion-based portrait animation models.