OpenRaiser/Intern-Atlas huggingface dataset:
RS2-SAM2: Customized SAM2 for Referring Remote Sensing Image Segmentation
Venue: arXiv cs.CV 2026
Year: 2026

Referring Remote Sensing Image Segmentation (RRSIS) aims to segment target objects in remote sensing (RS) images based on textual descriptions. Although Segment Anything Model 2 (SAM2) has shown remarkable performance in various segmentation tasks, its application to RRSIS presents several challenges, including understanding the text-described RS scenes and generating effective prompts from text. To address these issues, we propose \textbf{RS2-SAM2}, a novel framework that adapts SAM2 to RRSIS by aligning the adapted RS features and textual features while providing pseudo-mask-based dense prompts. Specifically, we employ a union encoder to jointly encode the visual and textual inputs, generating aligned visual and text embeddings as well as multimodal class tokens. A bidirectional hierarchical fusion module is introduced to adapt SAM2 to RS scenes and align adapted visual features with the visually enhanced text embeddings, improving the model's interpretation of text-described RS scenes. To provide precise target cues for SAM2, we design a mask prompt generator, which takes the visual embeddings and class tokens as input and produces a pseudo-mask as the dense prompt of SAM2. Experimental results on several RRSIS benchmarks demonstrate that RS2-SAM2 achieves state-of-the-art performance.
GRITHopper: Decomposition-Free Multi-Hop Dense Retrieval
Venue: arXiv cs.IR 2026
Year: 2026

Decomposition-based multi-hop retrieval methods rely on many autoregressive steps to break down complex queries, which breaks end-to-end differentiability and is computationally expensive. Decomposition-free methods tackle this, but current decomposition-free approaches struggle with longer multi-hop problems and generalization to out-of-distribution data. To address these challenges, we introduce GRITHopper-7B, a novel multi-hop dense retrieval model that achieves state-of-the-art performance on both in-distribution and out-of-distribution benchmarks. GRITHopper combines generative and representational instruction tuning by integrating causal language modeling with dense retrieval training. Through controlled studies, we find that incorporating additional context after the retrieval process, referred to as post-retrieval language modeling, enhances dense retrieval performance. By including elements such as final answers during training, the model learns to better contextualize and retrieve relevant information. GRITHopper-7B offers a robust, scalable, and generalizable solution for multi-hop dense retrieval, and we release it to the community for future research and applications requiring multi-hop reasoning and retrieval capabilities.
NeuroChat: A Neuroadaptive AI Chatbot for Customizing Learning Experiences
Venue: arXiv cs.HC 2026
Year: 2026

Generative AI is transforming education by enabling personalized, on-demand learning experiences. However, current AI systems lack awareness of the learner's cognitive state, limiting their adaptability. Meanwhile, electroencephalography (EEG)-based neuroadaptive systems have shown promise in enhancing engagement through real-time physiological feedback. This paper presents NeuroChat, a neuroadaptive AI tutor that integrates real-time EEG-based engagement tracking with generative AI to adapt its responses. NeuroChat continuously monitors a learner's cognitive engagement and dynamically adjusts content complexity, tone, and response style in a closed-loop interaction. In a within-subjects study (n=24), NeuroChat significantly increased both EEG-measured and self-reported engagement compared to a non-adaptive chatbot. However, no significant differences in short-term learning outcomes were observed. These findings demonstrate the feasibility of real-time cognitive feedback in LLMs, highlighting new directions for adaptive learning, AI tutoring, and deeper personalization in human-AI interaction.
H3PIMAP: A Heterogeneity-Aware Multi-Objective DNN Mapping Framework on Electronic-Photonic Processing-in-Memory Architectures
Venue: arXiv cs.AR 2026
Year: 2026

The future of artificial intelligence (AI) acceleration demands a paradigm shift beyond the limitations of purely electronic or photonic architectures. Photonic analog computing delivers unmatched speed and parallelism but struggles with data movement, robustness, and precision, while electronic processing-in-memory (PIM) enables energy-efficient computing by co-locating storage and computation but suffers from endurance and reconfiguration constraints, limiting it to static weight mapping. Neither approach alone achieves the balance needed for adaptive, efficient AI. To break this impasse, we study a hybrid electronic-photonic-PIM computing architecture and introduce H3PIMAP, a heterogeneity-aware mapping framework that seamlessly orchestrates workloads across electronic and optical tiers. By optimizing workload partitioning through a two-stage multi-objective exploration method, H3PIMAP harnesses light speed for high-throughput operations and PIM efficiency for memory-bound tasks. In system-level evaluations, H3PIMAP delivers a 3.32x latency reduction across language and vision models and, on large language models, achieves 77.0% lower latency with 14.6% lower energy at matched quality, outperforming homogeneous and naive mapping strategies. This proposed framework lays the foundation for hybrid AI accelerators, bridging the gap between electronic and photonic computation for next-generation efficiency and scalability.
Helios 2.0: A Robust, Ultra-Low Power Gesture Recognition System Optimised for Event-Sensor based Wearables
Venue: arXiv cs.HC 2026
Year: 2026

We present an advance in wearable technology: a mobile-optimized, real-time, ultra-low-power event camera system that enables natural hand gesture control for smart glasses, dramatically improving user experience. While hand gesture recognition in computer vision has advanced significantly, critical challenges remain in creating systems that are intuitive, adaptable across diverse users and environments, and energy-efficient enough for practical wearable applications. Our approach tackles these challenges through carefully selected microgestures: lateral thumb swipes across the index finger (in both directions) and a double pinch between thumb and index fingertips. These human-centered interactions leverage natural hand movements, ensuring intuitive usability without requiring users to learn complex command sequences. To overcome variability in users and environments, we developed a novel simulation methodology that enables comprehensive domain sampling without extensive real-world data collection. Our power-optimised architecture maintains exceptional performance, achieving F1 scores above 80\% on benchmark datasets featuring diverse users and environments. The resulting models operate at just 6-8 mW when exploiting the Qualcomm Snapdragon Hexagon DSP, with our 2-channel implementation exceeding 70\% F1 accuracy and our 6-channel model surpassing 80\% F1 accuracy across all gesture classes in user studies. These results were achieved using only synthetic training data. This improves on the state-of-the-art for F1 accuracy by 20\% with a power reduction 25x when using DSP. This advancement brings deploying ultra-low-power vision systems in wearable devices closer and opens new possibilities for seamless human-computer interaction.
Higher-Dimensional Automata : Extension to Infinite Tracks
Venue: arXiv cs.FL 2026
Year: 2026

We introduce higher-dimensional automata for infinite interval ipomsets ($ω$-HDAs). We define key concepts from different points of view, inspired from their finite counterparts. Then we explore languages recognized by $ω$-HDAs under Büchi and Muller semantics. We show that Muller acceptance is more expressive than Büchi acceptance and, in contrast to the finite case, both semantics do not yield languages closed under subsumption. Then, we adapt the original rational operations to deal with $ω$-HDAs and show that while languages of $ω$-HDAs are $ω$-rational, not all $ω$-rational languages can be expressed by $ω$-HDAs.
Safety Guardrails for LLM-Enabled Robots
Venue: arXiv cs.RO 2026
Year: 2026

Although the integration of large language models (LLMs) into robotics has unlocked transformative capabilities, it has also introduced significant safety concerns, ranging from average-case LLM errors (e.g., hallucinations) to adversarial jailbreaking attacks, which can produce harmful robot behavior in real-world settings. Traditional robot safety approaches do not address the contextual vulnerabilities of LLMs, and current LLM safety approaches overlook the physical risks posed by robots operating in real-world environments. To ensure the safety of LLM-enabled robots, we propose RoboGuard, a two-stage guardrail architecture. RoboGuard first contextualizes pre-defined safety rules by grounding them in the robot's environment using a root-of-trust LLM. This LLM is shielded from malicious prompts and employs chain-of-thought (CoT) reasoning to generate context-dependent safety specifications, such as temporal logic constraints. RoboGuard then resolves conflicts between these contextual safety specifications and potentially unsafe plans using temporal logic control synthesis, ensuring compliance while minimally violating user preferences. In simulation and real-world experiments that consider worst-case jailbreaking attacks, RoboGuard reduces the execution of unsafe plans from over 92% to below 3% without compromising performance on safe plans. We also demonstrate that RoboGuard is resource-efficient, robust against adaptive attacks, and enhanced by its root-of-trust LLM's CoT reasoning. These results demonstrate the potential of RoboGuard to mitigate the safety risks and enhance the reliability of LLM-enabled robots. We provide additional resources at https://robo-guard.github.io/.
PLK-Calib: Single-shot and Target-less LiDAR-Camera Extrinsic Calibration using Plücker Lines
Venue: arXiv cs.RO 2026
Year: 2026

Accurate LiDAR-Camera (LC) calibration is challenging but crucial for autonomous systems and robotics. In this paper, we propose two single-shot and target-less algorithms to estimate the calibration parameters between LiDAR and camera using line features. The first algorithm constructs line-to-line constraints by defining points-to-line projection errors and minimizes the projection error. The second algorithm (PLK-Calib) utilizes the co-perpendicular and co-parallel geometric properties of lines in Plücker (PLK) coordinate, and decouples the rotation and translation into two constraints, enabling more accurate estimates. Our degenerate analysis and Monte Carlo simulation indicate that three nonparallel line pairs are the minimal requirements to estimate the extrinsic parameters. Furthermore, we collect an LC calibration dataset with varying extrinsic under three different scenarios and use it to evaluate the performance of our proposed algorithms.
Code Digital Twin: A Knowledge Infrastructure for AI-Assisted Complex Software Development
Venue: arXiv cs.SE 2026
Year: 2026

Recent advances in AI coding tools powered by large language models (LLMs) have shown strong capabilities in software engineering tasks, raising expectations of major productivity gains. Tools such as Cursor and Claude Code have popularized "vibe coding" (where developers steer development through high-level intent), commonly relying on context engineering and Retrieval-Augmented Generation (RAG) to ground generation in a codebase. However, these paradigms struggle in ultra-complex enterprise systems, where software evolves incrementally under pervasive design constraints and depends on tacit knowledge such as responsibilities, intent, and decision rationales distributed across code, configurations, discussions, and version history. In this environment, context engineering faces a fundamental barrier: the required context is scattered across artifacts and entangled across time, beyond the capacity of LLMs to reliably capture, prioritize, and fuse evidence into correct and trustworthy decisions, even as context windows grow. To bridge this gap, we propose the Code Digital Twin, a persistent and evolving knowledge infrastructure built on the codebase. It separates long-term knowledge engineering from task-time context engineering and serves as a backend "context engine" for AI coding assistants. The Code Digital Twin models both the physical and conceptual layers of software and co-evolves with the system. By integrating hybrid knowledge representations, multi-stage extraction pipelines, incremental updates, AI-empowered applications, and human-in-the-loop feedback, it transforms fragmented knowledge into explicit and actionable representations, providing a roadmap toward sustainable and resilient development and evolution of ultra-complex systems.
Bio-Skin: A Cost-Effective Thermostatic Tactile Sensor with Multi-Modal Force and Temperature Detection
Venue: arXiv cs.RO 2026
Year: 2026

Tactile sensors can significantly enhance the perception of humanoid robotics systems by providing contact information that facilitates human-like interactions. However, existing commercial tactile sensors focus on improving the resolution and sensitivity of single-modal detection with high-cost components and densely integrated design, incurring complex manufacturing processes and unaffordable prices. In this work, we present Bio-Skin, a cost-effective multi-modal tactile sensor that utilizes single-axis Hall-effect sensors for planar normal force measurement and bar-shape piezo resistors for 2D shear force measurement. A thermistor coupling with a heating wire is integrated into a silicone body to achieve temperature sensation and thermostatic function analogous to human skin. We also present a cross-reference framework to validate the two modalities of the force sensing signal, improving the sensing fidelity in a complex electromagnetic environment. Bio-Skin has a multi-layer design, and each layer is manufactured sequentially and subsequently integrated, thereby offering a fast production pathway. After calibration, Bio-Skin demonstrates performance metrics-including signal-to-range ratio, sampling rate, and measurement range-comparable to current commercial products, with one-tenth of the cost. The sensor's real-world performance is evaluated using an Allegro hand in object grasping tasks, while its temperature regulation functionality was assessed in a material detection task.
Large Language Model as Meta-Surrogate for Data-Driven Many-Task Optimization: A Proof-of-Principle Study
Venue: arXiv cs.LG 2026
Year: 2026

In many-task optimization scenarios, surrogate models are valuable for mitigating the computational burden of repeated fitness evaluations across tasks. This study proposes a novel meta-surrogate framework to assist many-task optimization, by leveraging the knowledge transfer strengths and emergent capabilities of large language models (LLMs). We formulate a unified framework for many-task fitness prediction, by defining a universal model with metadata to fit a group of problems. Fitness prediction is performed on metadata and decision variables, enabling efficient knowledge sharing across tasks and adaptability to new tasks. The LLM-based meta-surrogate treats fitness prediction as conditional probability estimation, employing a unified token sequence representation for task metadata, inputs, and outputs. This approach facilitates efficient inter-task knowledge sharing through shared token embeddings and captures complex task dependencies via multi-task model training. Experimental results demonstrate the model's emergent generalization ability, including zero-shot performance on problems with unseen dimensions. When integrated into evolutionary transfer optimization (ETO), our framework supports dual-level knowledge transfer -- at both the surrogate and individual levels -- enhancing optimization efficiency and robustness. This work establishes a novel foundation for applying LLMs in surrogate modeling, offering a versatile solution for many-task optimization.
CellStyle: Improved Zero-Shot Cell Segmentation via Style Transfer
Venue: arXiv cs.LG 2026
Year: 2026

Cell microscopy data are abundant; however, corresponding segmentation annotations remain scarce. Moreover, variations in cell types, imaging devices, and staining techniques introduce significant domain gaps between datasets. As a result, even large, pretrained segmentation models trained on diverse datasets (source datasets) struggle to generalize to unseen datasets (target datasets). To overcome this generalization problem, we propose CellStyle, which improves the segmentation quality of such models without requiring labels for the target dataset, thereby enabling zero-shot adaptation. CellStyle transfers the attributes of an unannotated target dataset, such as texture, color, and noise, to the annotated source dataset. This transfer is performed while preserving the cell shapes of the source images, ensuring that the existing source annotations can still be used while maintaining the visual characteristics of the target dataset. The styled synthetic images with the existing annotations enable the finetuning of a generalist segmentation model for application to the unannotated target data. We demonstrate that CellStyle significantly improves zero-shot cell segmentation performance across diverse datasets by finetuning multiple segmentation models on the style-transferred data. The code will be made publicly available.
Is CLIP ideal? No. Can we fix it? Yes!
Venue: arXiv cs.LG 2026
Year: 2026

Contrastive Language-Image Pre-Training (CLIP) is a popular method for learning multimodal latent spaces with well-organized semantics. Despite its wide range of applications, CLIP's latent space is known to fail at handling complex visual-textual interactions. Recent works attempt to address its shortcomings with data-centric or algorithmic approaches. But what if the problem is more fundamental, and lies in the geometry of CLIP? Toward this end, we rigorously analyze CLIP's latent space properties, and prove that no CLIP-like joint embedding space exists which can correctly do any two of the following at the same time: 1. represent basic descriptions and image content, 2. represent attribute binding, 3. represent spatial location and relationships, 4. represent negation. Informed by this analysis, we propose Dense Cosine Similarity Maps (DCSMs) as a principled and interpretable scoring method for CLIP-like models, which solves the fundamental limitations of CLIP by retaining the semantic topology of the image patches and text tokens. This method improves upon the performance of classical CLIP-like joint encoder models on a wide array of benchmarks. We share our code and data here for reproducibility: https://github.com/Raphoo/DCSM_Ideal_CLIP
PlainQAFact: Retrieval-augmented Factual Consistency Evaluation Metric for Biomedical Plain Language Summarization
Venue: arXiv cs.CL 2026
Year: 2026

Hallucinated outputs from large language models (LLMs) pose risks in the medical domain, especially for lay audiences making health-related decisions. Existing automatic factual consistency evaluation methods, such as entailment- and question-answering (QA)- based, struggle with plain language summarization (PLS) due to elaborative explanation phenomenon, which introduces external content (e.g., definitions, background, examples) absent from the scientific abstract to enhance comprehension. To address this, we introduce PlainQAFact, an automatic factual consistency evaluation metric trained on a fine-grained, human-annotated dataset PlainFact, for evaluating factual consistency of both source-simplified and elaborately explained sentences. PlainQAFact first classifies sentence type, then applies a retrieval-augmented QA scoring method. Empirical results show that existing evaluation metrics fail to evaluate the factual consistency in PLS, especially for elaborative explanations, whereas PlainQAFact consistently outperforms them across all evaluation settings. We further analyze PlainQAFact's effectiveness across external knowledge sources, answer extraction strategies, answer overlap measures, and document granularity levels, refining its overall factual consistency assessment. Taken together, our work presents a sentence-aware, retrieval-augmented metric targeted at elaborative explanations in biomedical PLS tasks, providing the community with both a robust benchmark and a practical tool to advance reliable and safe plain language communication in the medical domain. PlainQAFact and PlainFact are available at: https://github.com/zhiwenyou103/PlainQAFact
I Predict Therefore I Am: Is Next Token Prediction Enough to Learn Human-Interpretable Concepts from Data?
Venue: arXiv cs.LG 2026
Year: 2026

Recent empirical evidence shows that LLM representations encode human-interpretable concepts. Nevertheless, the mechanisms by which these representations emerge remain largely unexplored. To shed further light on this, we introduce a novel generative model that generates tokens on the basis of such concepts formulated as latent discrete variables. Under mild conditions, even when the mapping from the latent space to the observed space is non-invertible, we establish rigorous identifiability result: the representations learned by LLMs through next-token prediction can be approximately modeled as the logarithm of the posterior probabilities of these latent discrete concepts given input context, up to an linear transformation. This theoretical finding: 1) provides evidence that LLMs capture essential underlying generative factors, 2) offers a unified and principled perspective for understanding the linear representation hypothesis, and 3) motivates a theoretically grounded approach for evaluating sparse autoencoders. Empirically, we validate our theoretical results through evaluations on both simulation data and the Pythia, Llama, and DeepSeek model families.
Learning Rate Annealing Improves Tuning Robustness in Stochastic Optimization
Venue: arXiv cs.LG 2026
Year: 2026

The learning rate in stochastic gradient methods is a critical hyperparameter that is notoriously costly to tune via standard grid search, especially for training modern large-scale models with billions of parameters. We identify a theoretical advantage of learning rate annealing schemes that decay the learning rate to zero at a polynomial rate, such as the widely-used cosine schedule, by demonstrating their increased robustness to initial parameter misspecification due to a coarse grid search. We present an analysis in a stochastic convex optimization setup demonstrating that the convergence rate of stochastic gradient descent with annealed schedules depends sublinearly on the multiplicative misspecification factor $ρ$ (i.e., the grid resolution), achieving a rate of $O(ρ^{1/(2p+1)}/\sqrt{T})$ where $p$ is the degree of polynomial decay and $T$ is the number of steps. This is in contrast to the $O(ρ/\sqrt{T})$ rate obtained under the inverse-square-root and fixed stepsize schedules, which depend linearly on $ρ$. Experiments confirm the increased robustness compared to tuning with a fixed stepsize, that has significant implications for the computational overhead of hyperparameter search in practical training scenarios.