OpenRaiser/Intern-Atlas huggingface dataset:
EIDSeg: A Pixel-Level Semantic Segmentation Dataset for Post-Earthquake Damage Assessment from Social Media Images
Venue: arXiv cs.CV 2025
Year: 2025

Rapid post-earthquake damage assessment is crucial for rescue and resource planning. Still, existing remote sensing methods depend on costly aerial images, expert labeling, and produce only binary damage maps for early-stage evaluation. Although ground-level images from social networks provide a valuable source to fill this gap, a large pixel-level annotated dataset for this task is still unavailable. We introduce EIDSeg, the first large-scale semantic segmentation dataset specifically for post-earthquake social media imagery. The dataset comprises 3,266 images from nine major earthquakes (2008-2023), annotated across five classes of infrastructure damage: Undamaged Building, Damaged Building, Destroyed Building, Undamaged Road, and Damaged Road. We propose a practical three-phase cross-disciplinary annotation protocol with labeling guidelines that enables consistent segmentation by non-expert annotators, achieving over 70% inter-annotator agreement. We benchmark several state-of-the-art segmentation models, identifying Encoder-only Mask Transformer (EoMT) as the top-performing method with a Mean Intersection over Union (mIoU) of 80.8%. By unlocking social networks' rich ground-level perspective, our work paves the way for a faster, finer-grained damage assessment in the post-earthquake scenario.
Brain-Inspired Planning for Better Generalization in Reinforcement Learning
Venue: arXiv cs.AI 2025
Year: 2025

Existing Reinforcement Learning (RL) systems encounter significant challenges when applied to real-world scenarios, primarily due to poor generalization across environments that differ from their training conditions. This thesis explores the direction of enhancing agents' zero-shot systematic generalization abilities by granting RL agents reasoning behaviors that are found to help systematic generalization in the human brain. Inspired by human conscious planning behaviors, we first introduced a top-down attention mechanism, which allows a decision-time planning agent to dynamically focus its reasoning on the most relevant aspects of the environmental state given its instantaneous intentions, a process we call "spatial abstraction". This approach significantly improves systematic generalization outside the training tasks. Subsequently, building on spatial abstraction, we developed the Skipper framework to automatically decompose complex tasks into simpler, more manageable sub-tasks. Skipper provides robustness against distributional shifts and efficacy in long-term, compositional planning by focusing on pertinent spatial and temporal elements of the environment. Finally, we identified a common failure mode and safety risk in planning agents that rely on generative models to generate state targets during planning. It is revealed that most agents blindly trust the targets they hallucinate, resulting in delusional planning behaviors. Inspired by how the human brain rejects delusional intentions, we propose learning a feasibility evaluator to enable rejecting hallucinated infeasible targets, which led to significant performance improvements in various kinds of planning agents. Finally, we suggest directions for future research, aimed at achieving general task abstraction and fully enabling abstract planning.
GHOST: Solving the Traveling Salesman Problem on Graphs of Convex Sets
Venue: arXiv cs.AI 2025
Year: 2025

We study GCS-TSP, a new variant of the Traveling Salesman Problem (TSP) defined over a Graph of Convex Sets (GCS) -- a powerful representation for trajectory planning that decomposes the configuration space into convex regions connected by a sparse graph. In this setting, edge costs are not fixed but depend on the specific trajectory selected through each convex region, making classical TSP methods inapplicable. We introduce GHOST, a hierarchical framework that optimally solves the GCS-TSP by combining combinatorial tour search with convex trajectory optimization. GHOST systematically explores tours on a complete graph induced by the GCS, using a novel abstract-path-unfolding algorithm to compute admissible lower bounds that guide best-first search at both the high level (over tours) and the low level (over feasible GCS paths realizing the tour). These bounds provide strong pruning power, enabling efficient search while avoiding unnecessary convex optimization calls. We prove that GHOST guarantees optimality and present a bounded-suboptimal variant for time-critical scenarios. Experiments show that GHOST is orders-of-magnitude faster than unified mixed-integer convex programming baselines for simple cases and uniquely handles complex trajectory planning problems involving high-order continuity constraints and an incomplete GCS.
Rethinking what Matters: Effective and Robust Multilingual Realignment for Low-Resource Languages
Venue: arXiv cs.CL 2025
Year: 2025

Realignment is a promising strategy to improve cross-lingual transfer in multilingual language models. However, empirical results are mixed and often unreliable, particularly for typologically distant or low-resource languages (LRLs) compared to English. Moreover, word realignment tools often rely on high-quality parallel data, which can be scarce or noisy for many LRLs. In this work, we conduct an extensive empirical study to investigate whether realignment truly benefits from using all available languages, or if strategically selected subsets can offer comparable or even improved cross-lingual transfer, and study the impact on LRLs. Our controlled experiments show that realignment can be particularly effective for LRLs and that using carefully selected, linguistically diverse subsets can match full multilingual alignment, and even outperform it for unseen LRLs. This indicates that effective realignment does not require exhaustive language coverage and can reduce data collection overhead, while remaining both efficient and robust when guided by informed language selection.
Stronger Hardness for Maximum Robust Flow and Randomized Network Interdiction
Venue: arXiv cs.DM 2025
Year: 2025

We study the following fundamental network optimization problem known as Maximum Robust Flow (MRF): A planner determines a flow on $s$-$t$-paths in a given capacitated network. Then, an adversary removes $k$ arcs from the network, interrupting all flow on paths containing a removed arc. The planner's goal is to maximize the value of the surviving flow, anticipating the adversary's response (i.e., a worst-case failure of $k$ arcs). It has long been known that MRF can be solved in polynomial time when $k = 1$ (Aneja et al., 2001), whereas it is $N\!P$-hard when $k$ is part of the input (Disser and Matuschke, 2020). However, the complexity of the problem for constant values of $k > 1$ has remained elusive, in part due to structure of the natural LP description preventing the use of the equivalence of optimization and separation. This paper introduces a reduction showing that the basic version of MRF described above encapsulates the seemingly much more general variant where the adversary's choices are constrained to $k$-cliques in a compatibility graph on the arcs of the network. As a consequence of this reduction, we are able to prove the following results: (1) MRF is $N\!P$-hard for any constant number $k > 1$ of failing arcs. (2) When $k$ is part of the input, MRF is $P^{N\!P[\log]}$-hard. (3) The integer version of MRF is $Σ_2^P$-hard.
Video Dataset for Surgical Phase, Keypoint, and Instrument Recognition in Laparoscopic Surgery (PhaKIR)
Venue: arXiv cs.CV 2025
Year: 2025

Robotic- and computer-assisted minimally invasive surgery (RAMIS) is increasingly relying on computer vision methods for reliable instrument recognition and surgical workflow understanding. Developing such systems often requires large, well-annotated datasets, but existing resources often address isolated tasks, neglect temporal dependencies, or lack multi-center variability. We present the Surgical Procedure Phase, Keypoint, and Instrument Recognition (PhaKIR) dataset, comprising eight complete laparoscopic cholecystectomy videos recorded at three medical centers. The dataset provides frame-level annotations for three interconnected tasks: surgical phase recognition (485,875 frames), instrument keypoint estimation (19,435 frames), and instrument instance segmentation (19,435 frames). PhaKIR is, to our knowledge, the first multi-institutional dataset to jointly provide phase labels, instrument pose information, and pixel-accurate instrument segmentations, while also enabling the exploitation of temporal context since full surgical procedure sequences are available. It served as the basis for the PhaKIR Challenge as part of the Endoscopic Vision (EndoVis) Challenge at MICCAI 2024 to benchmark methods in surgical scene understanding, thereby further validating the dataset's quality and relevance. The dataset is publicly available upon request via the Zenodo platform.
Offloading Data Center Tax
Venue: arXiv cs.AR 2025
Year: 2025

The data centers of today are running diverse workloads sharing many common lower level functions called tax components. Any optimization to any tax component will lead to performance improvements across the data center fleet. Typically, performance enhancements in tax components are achieved by offloading them to accelerators, however, it is not practical to offload every tax component. The goal of this paper is to identify opportunities to offload more than one tax component together. We focus on MongoDB which is a common microservice used in a large number of applications in the datacenter. We profile MongoDB running as part of the DeathStarBench benchmark suite, identifying its tax components and their microarchitectural implications. We make observations and suggestions based on the inferences made to offload a few of the tax components in this application.
Rep2Text: Decoding Full Text from a Single LLM Token Representation
Venue: arXiv cs.CL 2025
Year: 2025

Large language models (LLMs) have achieved remarkable progress across diverse tasks, yet their internal mechanisms remain largely opaque. In this work, we address a fundamental question: to what extent can the original input text be recovered from a single last-token representation within an LLM? We propose Rep2Text, a novel framework for decoding full text from last-token representations. Rep2Text employs a trainable adapter that projects a target model's internal representations into the embedding space of a decoding language model, which then autoregressively reconstructs the input text. Experiments on various model combinations (Llama-3.1-8B, Gemma-7B, Mistral-7B-v0.1, Llama-3.2-3B) demonstrate that, on average, over half of the information in 16-token sequences can be recovered from this compressed representation while maintaining strong semantic integrity and coherence. Furthermore, our analysis reveals an information bottleneck effect: longer sequences exhibit decreased token-level recovery while preserving strong semantic integrity. Besides, our framework also demonstrates robust generalization to out-of-distribution medical data.
Adaptive Initial Residual Connections for GNNs with Theoretical Guarantees
Venue: arXiv cs.LG 2025
Year: 2025

Message passing is the core operation in graph neural networks, where each node updates its embeddings by aggregating information from its neighbors. However, in deep architectures, this process often leads to diminished expressiveness. A popular solution is to use residual connections, where the input from the current (or initial) layer is added to aggregated neighbor information to preserve embeddings across layers. Following a recent line of research, we investigate an adaptive residual scheme in which different nodes have varying residual strengths. We prove that this approach prevents oversmoothing; particularly, we show that the Dirichlet energy of the embeddings remains bounded away from zero. This is the first theoretical guarantee not only for the adaptive setting, but also for static residual connections (where residual strengths are shared across nodes) with activation functions. Furthermore, extensive experiments show that this adaptive approach outperforms standard and state-of-the-art message passing mechanisms, especially on heterophilic graphs. To improve the time complexity of our approach, we introduce a variant in which residual strengths are not learned but instead set heuristically, a choice that performs as well as the learnable version.
Saarthi: An End-to-End Intelligent Platform for Optimising Distributed Serverless Workloads
Venue: arXiv cs.DC 2025
Year: 2025

FaaS offers significant advantages with its infrastructure abstraction, on-demand execution, and attractive no idle resource pricing for modern cloud applications. Despite these benefits, challenges such as startup latencies, static configurations, sub-optimal resource allocation and scheduling still exist due to coupled resource offering and workload-agnostic generic scheduling behaviour. These issues often lead to inconsistent function performance and unexpected operational costs for users and service providers. This paper introduces Saarthi, a novel, end-to-end serverless framework that intelligently manages the dynamic resource needs of function workloads, representing a significant step toward self-driving serverless platforms. Unlike platforms that rely on static resource configurations, Saarthi is input-aware, allowing it to intelligently anticipate resource requirements based on the characteristics of an incoming request payload. This input-driven approach reinforces function right-sizing and enables smart request orchestration across available function configurations. Saarthi further integrates a proactive fault-tolerant redundancy mechanism and employs a multi-objective Integer Linear Programming (ILP) model to maintain an optimal function quantity. This optimisation aims to maximise system throughput while simultaneously reducing overall operational costs. We validate the effectiveness of Saarthi by implementing it as a framework atop OpenFaaS. Our results demonstrate Saarthi's ability to achieve up to 1.45x better throughput, 1.84x reduced costs, while maintaining up to 98.3% service level targets with an overhead of up to 0.2 seconds as compared to the baseline OpenFaaS.
Duality-based Mode Operations and Pyramid Multilayer Mapping for Rhetorical Modes
Venue: arXiv cs.CL 2025
Year: 2025

Rhetorical modes are useful in both academic and non-academic writing, and can be subjects to be studied within linguistic research and computational modeling. Establishing a conceptual bridge among these domains could enable each to benefit from the others. This paper proposes duality-based mode operations (split-unite, forward-backward, expansion-reduction and orthogonal dualities) to expand the set of rhetorical modes, introducing generated modes like combination and generalization, thereby enhancing epistemic diversity across multiple applications. It further presents a pyramid multilayer mapping framework (e.g., three layers from the rhetorical model layer, to cognitive layer, and to epistemic layers) that reduces the resulting cognitive complexity. The degrees of expressive diversity and complexity reduction are quantified through binomial combinatorics and Shannon entropy analysis. A Marginal Rhetorical Bit (MRB) is identified, permitting the definition of a rhetorical-scalable parameter that measures expressive growth speed in bits per stage. A direct entropy measure shows that hierarchical selection over smaller subsets markedly reduces choice uncertainty compared with flat selection across all modes. These considerations appear to transform static and non-measurable rhetorical taxonomies into more dynamic and more measurable systems for discourse design. From this work, it would be possible to identify a pathway for future AI systems to operate not only on language tokens but on layered rhetorical reasoning structures, bridging linguistic, pedagogical, academic, and computational research
DMA Collectives for Efficient ML Communication Offloads
Venue: arXiv cs.DC 2025
Year: 2025

Offloading machine learning (ML) communication collectives to direct memory access (DMA) engines has emerged as an interesting and low-cost solution to efficiently overlap computation and communication in inference and training. Doing so delivers superior concurrent performance by freeing up all GPU cores for computation and also lowers interference in the memory sub-system (caches). While DMA collectives show strong promise, prior works have only studied them in limited context (bandwidth-bound transfer sizes only, performance-only). To address this, we provide a comprehensive performance, power/energy and synchronization costs analysis of offloading ML communication collectives (all-gather, all-to-all) to DMA engines on state-of-the-art AMD Instinct MI300X GPUs. Our analysis reveals that, compared to the state-of-the-art RCCL communication collectives library, DMA collectives are at-par or better for large sizes (10s of MB to GB) in terms of both performance (16% better) and power (32% better). However, they significantly lag for latency-bound small sizes; 4.5X and 2.5X slower for all-gather and all-to-all, respectively. We provide a detailed latency breakdown of a DMA transfer and identify that DMA command scheduling and synchronization costs can limit DMA collective performance. To tackle this, we harness existing DMA architecture innovations, hitherto untapped, to build optimized DMA collectives and demonstrate their efficacy on real hardware. Our optimized implementations considerably close the performance gap for DMA collectives at smaller sizes (30% slower and 20% faster all-gather and all-to-all, respectively) and further improves performance (by 7%) and power savings at larger sizes (3-10%). Overall, this work represents a significant step toward making DMA collectives suitable for adoption in mainstream collective libraries.
How Do VLAs Effectively Inherit from VLMs?
Venue: arXiv cs.RO 2025
Year: 2025

Vision-language-action (VLA) models hold the promise to attain generalizable embodied control. To achieve this, a pervasive paradigm is to leverage the rich vision-semantic priors of large vision-language models (VLMs). However, the fundamental question persists: How do VLAs effectively inherit the prior knowledge from VLMs? To address this critical question, we introduce a diagnostic benchmark, GrinningFace, an emoji tabletop manipulation task where the robot arm is asked to place objects onto printed emojis corresponding to language instructions. This task design is particularly revealing -- knowledge associated with emojis is ubiquitous in Internet-scale datasets used for VLM pre-training, yet emojis themselves are largely absent from standard robotics datasets. Consequently, they provide a clean proxy: successful task completion indicates effective transfer of VLM priors to embodied control. We implement this diagnostic task in both simulated environment and a real robot, and compare various promising techniques for knowledge transfer. Specifically, we investigate the effects of parameter-efficient fine-tuning, VLM freezing, co-training, predicting discretized actions, and predicting latent actions. Through systematic evaluation, our work not only demonstrates the critical importance of preserving VLM priors for the generalization of VLA but also establishes guidelines for future research in developing truly generalizable embodied AI systems.
Dual-branch Spatial-Temporal Self-supervised Representation for Enhanced Road Network Learning
Venue: arXiv cs.LG 2025
Year: 2025

Road network representation learning (RNRL) has attracted increasing attention from both researchers and practitioners as various spatiotemporal tasks are emerging. Recent advanced methods leverage Graph Neural Networks (GNNs) and contrastive learning to characterize the spatial structure of road segments in a self-supervised paradigm. However, spatial heterogeneity and temporal dynamics of road networks raise severe challenges to the neighborhood smoothing mechanism of self-supervised GNNs. To address these issues, we propose a $\textbf{D}$ual-branch $\textbf{S}$patial-$\textbf{T}$emporal self-supervised representation framework for enhanced road representations, termed as DST. On one hand, DST designs a mix-hop transition matrix for graph convolution to incorporate dynamic relations of roads from trajectories. Besides, DST contrasts road representations of the vanilla road network against that of the hypergraph in a spatial self-supervised way. The hypergraph is newly built based on three types of hyperedges to capture long-range relations. On the other hand, DST performs next token prediction as the temporal self-supervised task on the sequences of traffic dynamics based on a causal Transformer, which is further regularized by differentiating traffic modes of weekdays from those of weekends. Extensive experiments against state-of-the-art methods verify the superiority of our proposed framework. Moreover, the comprehensive spatiotemporal modeling facilitates DST to excel in zero-shot learning scenarios.
Modeling and Topology Estimation of Low Rank Dynamical Networks
Venue: arXiv cs.GR 2025
Year: 2025

Conventional topology learning methods for dynamical networks become inapplicable to processes exhibiting low-rank characteristics. To address this, we propose the low rank dynamical network model which ensures identifiability. By employing causal Wiener filtering, we establish a necessary and sufficient condition that links the sparsity pattern of the filter to conditional Granger causality. Building on this theoretical result, we develop a consistent method for estimating all network edges. Simulation results demonstrate the parsimony of the proposed framework and consistency of the topology estimation approach.
Accessibility Gaps in U.S. Government Dashboards for Blind and Low-Vision Residents
Venue: arXiv cs.HC 2025
Year: 2025

Public dashboards are now a common way for US government agencies to share high stakes information with residents. We audited six live systems at federal, state, and city levels: CDC respiratory illness, HUD homelessness PIT and HIC, California HCD Annual Progress Report, New York City Mayor's Management Report, Houston Permitting, and Chicago public health and budget dashboards. Using a rubric based on screen reader needs and WCAG, we checked five items: (1) discoverability of key metrics by assistive tech, (2) keyboard access without mouse hover, (3) clear semantic labels for axes, series, and categories, (4) short plain language status and trend notes, and (5) machine readable tables or CSVs that mirror what sighted users see. Findings are mixed. Many charts fail basic discoverability or depend on hover, which blocks keyboard and screen reader use. Plain language summaries are common in CDC and Chicago, but rare in HUD and Houston. Machine readable data is strong for NYC, California, and HUD; it is weaker or unclear for Houston. Several sites promise service for the public or for customers yet do not name accessibility in their descriptions. Across systems we also observe urgency inversion: faster, operational dashboards tend to provide fewer accessible affordances than slower accountability dashboards. These patterns matter for equal participation and for ADA Title II compliance that references WCAG 2.1 AA. We propose three steps for any public dashboard: add a brief status and trend text at the same update cadence, publish a matching table or CSV of the visual metrics, and state an explicit accessibility commitment.