OpenRaiser/Intern-Atlas huggingface dataset:
Strategic Adaptation Under Contextual Change: Insights from a Dyadic Negotiation Testbed for AI Coaching Technologies
Venue: arXiv cs.HC 2026
Year: 2026

Strategic adaptation -- the ability to adjust interaction behavior in response to changing constraints and leverage -- is a central goal of negotiation training and an emerging target for AI coaching systems. However, adaptation is difficult to evaluate because adaptation-relevant moments arise unpredictably in typical tasks. We study a reusable dyadic negotiation testbed that employs a controlled midstream change in one party's outside alternative as a repeatable perturbation to stress-test adaptation. In a six-round chat-based negotiation study (N=100), the perturbation reliably reorganized interaction dynamics: transitions between integrative (cooperative) and distributive (positional) behaviors declined, behavioral diversity narrowed, and interactions drifted toward more distributive tactics. Critically, this distributive drift predicted worse relational experience net of objective outcomes, and adaptation patterns were path dependent on prior behavior. These results establish a methodological bridge for evaluating and comparing AI coaching systems on strategic adaptation as a process and identify failure modes and design targets for adaptive interaction support.
DementiaBank-Emotion: A Multi-Rater Emotion Annotation Corpus for Alzheimer's Disease Speech (Version 1.0)
Venue: arXiv cs.CL 2026
Year: 2026

We present DementiaBank-Emotion, the first multi-rater emotion annotation corpus for Alzheimer's disease (AD) speech. Annotating 1,492 utterances from 108 speakers for Ekman's six basic emotions and neutral, we find that AD patients express significantly more non-neutral emotions (16.9%) than healthy controls (5.7%; p < .001). Exploratory acoustic analysis suggests a possible dissociation: control speakers showed substantial F0 modulation for sadness (Delta = -3.45 semitones from baseline), whereas AD speakers showed minimal change (Delta = +0.11 semitones; interaction p = .023), though this finding is based on limited samples (sadness: n=5 control, n=15 AD) and requires replication. Within AD speech, loudness differentiates emotion categories, indicating partially preserved emotion-prosody mappings. We release the corpus, annotation guidelines, and calibration workshop materials to support research on emotion recognition in clinical populations.
Scaling Agentic Verifier for Competitive Coding
Venue: arXiv cs.CL 2026
Year: 2026

Large language models (LLMs) have demonstrated strong coding capabilities but still struggle to solve competitive programming problems correctly in a single attempt. Execution-based re-ranking offers a promising test-time scaling strategy, yet existing methods are constrained by either difficult test case generation or inefficient random input sampling. To address this limitation, we propose Agentic Verifier, an execution-based agent that actively reasons about program behaviors and searches for highly discriminative test inputs that expose behavioral discrepancies among candidate solutions. Through multi-turn interaction with code execution environments, the verifier iteratively refines the candidate input generator and produces targeted counterexamples rather than blindly sampling inputs. We train the verifier to acquire this discriminative input generation capability via a scalable pipeline combining large-scale data synthesis, rejection fine-tuning, and agentic reinforcement learning. Extensive experiments across five competitive programming benchmarks demonstrate consistent improvements over strong execution-based baselines, achieving up to +10-15% absolute gains in Best@K accuracy. Further analysis reveals clear test-time scaling behavior and highlights the verifier's broader potential beyond reranking.
Multi-Tier UAV Edge Computing Towards Long-Term Energy Stability for Low Altitude Networks
Venue: arXiv cs.NI 2026
Year: 2026

The agile mobility of Unmanned Aerial Vehicles (UAVs) makes them ideal for low-altitude edge computing. This paper proposes a novel multi-tier UAV edge computing system where lightweight Low-Tier UAVs (L-UAVs) function as edge servers for vehicle users, supported by a powerful High-Tier UAV (H-UAV) acting as a backup server. The objective is to minimize task execution delays while ensuring the long-term energy stability of the L-UAVs, despite unknown future system states. To this end, the problem is decoupled using Lyapunov optimization, which adaptively balances the priorities of task delays and L-UAV energy cost based on their real-time energy states. An efficient vehicle to L-UAV matching scheme is designed, and the joint optimization problem for task assignment, computing resource allocation, and trajectory control of L-UAVs and H-UAV is then solved via a Block Coordinate Descent (BCD) algorithm. Simulation results demonstrate a reduction in L-UAV transmission energy of over 26% and superior L-UAV energy stability compared to existing benchmarks.
Data Agents: Levels, State of the Art, and Open Problems
Venue: arXiv cs.DB 2026
Year: 2026

Data agents are an emerging paradigm that leverages large language models (LLMs) and tool-using agents to automate data management, preparation, and analysis tasks. However, the term "data agent" is currently used inconsistently, conflating simple query responsive assistants with aspirational fully autonomous "data scientists". This ambiguity blurs capability boundaries and accountability, making it difficult for users, system builders, and regulators to reason about what a "data agent" can and cannot do. In this tutorial, we propose the first hierarchical taxonomy of data agents from Level 0 (L0, no autonomy) to Level 5 (L5, full autonomy). Building on this taxonomy, we will introduce a lifecycleand level-driven view of data agents. We will (1) present the L0-L5 taxonomy and the key evolutionary leaps that separate simple assistants from truly autonomous data agents, (2) review representative L0-L2 systems across data management, preparation, and analysis, (3) highlight emerging Proto-L3 systems that strive to autonomously orchestrate end-to-end data workflows to tackle diverse and comprehensive data-related tasks under supervision, and (4) discuss forward-looking research challenges towards proactive (L4) and generative (L5) data agents. We aim to offer both a practical map of today's systems and a research roadmap for the next decade of data-agent development.
ECG-R1: Protocol-Guided and Modality-Agnostic MLLM for Reliable ECG Interpretation
Venue: arXiv cs.CL 2026
Year: 2026

Electrocardiography (ECG) serves as an indispensable diagnostic tool in clinical practice, yet existing multimodal large language models (MLLMs) remain unreliable for ECG interpretation, often producing plausible but clinically incorrect analyses. To address this, we propose ECG-R1, the first reasoning MLLM designed for reliable ECG interpretation via three innovations. First, we construct the interpretation corpus using \textit{Protocol-Guided Instruction Data Generation}, grounding interpretation in measurable ECG features and monograph-defined quantitative thresholds and diagnostic logic. Second, we present a modality-decoupled architecture with \textit{Interleaved Modality Dropout} to improve robustness and cross-modal consistency when either the ECG signal or ECG image is missing. Third, we present \textit{Reinforcement Learning with ECG Diagnostic Evidence Rewards} to strengthen evidence-grounded ECG interpretation. Additionally, we systematically evaluate the ECG interpretation capabilities of proprietary, open-source, and medical MLLMs, and provide the first quantitative evidence that severe hallucinations are widespread, suggesting that the public should not directly trust these outputs without independent verification. Code and data are publicly available at \href{https://github.com/PKUDigitalHealth/ECG-R1}{here}, and an online platform can be accessed at \href{http://ai.heartvoice.com.cn/ECG-R1/}{here}.
How Few-shot Demonstrations Affect Prompt-based Defenses Against LLM Jailbreak Attacks
Venue: arXiv cs.CL 2026
Year: 2026

Large Language Models (LLMs) face increasing threats from jailbreak attacks that bypass safety alignment. While prompt-based defenses such as Role-Oriented Prompts (RoP) and Task-Oriented Prompts (ToP) have shown effectiveness, the role of few-shot demonstrations in these defense strategies remains unclear. Prior work suggests that few-shot examples may compromise safety, but lacks investigation into how few-shot interacts with different system prompt strategies. In this paper, we conduct a comprehensive evaluation on multiple mainstream LLMs across four safety benchmarks (AdvBench, HarmBench, SG-Bench, XSTest) using six jailbreak attack methods. Our key finding reveals that few-shot demonstrations produce opposite effects on RoP and ToP: few-shot enhances RoP's safety rate by up to 4.5% through reinforcing role identity, while it degrades ToP's effectiveness by up to 21.2% through distracting attention from task instructions. Based on these findings, we provide practical recommendations for deploying prompt-based defenses in real-world LLM applications.
Revisiting Prompt Sensitivity in Large Language Models for Text Classification: The Role of Prompt Underspecification
Venue: arXiv cs.CL 2026
Year: 2026

Large language models (LLMs) are widely used as zero-shot and few-shot classifiers, where task behaviour is largely controlled through prompting. A growing number of works have observed that LLMs are sensitive to prompt variations, with small changes leading to large changes in performance. However, in many cases, the investigation of sensitivity is performed using underspecified prompts that provide minimal task instructions and weakly constrain the model's output space. In this work, we argue that a significant portion of the observed prompt sensitivity can be attributed to prompt underspecification. We systematically study and compare the sensitivity of underspecified prompts and prompts that provide specific instructions. Utilising performance analysis, logit analysis, and linear probing, we find that underspecified prompts exhibit higher performance variance and lower logit values for relevant tokens, while instruction-prompts suffer less from such problems. However, linear probing analysis suggests that the effects of prompt underspecification have only a marginal impact on the internal LLM representations, instead emerging in the final layers. Overall, our findings highlight the need for more rigour when investigating and mitigating prompt sensitivity.
GeneralVLA: Generalizable Vision-Language-Action Models with Knowledge-Guided Trajectory Planning
Venue: arXiv cs.RO 2026
Year: 2026

Large foundation models have shown strong open-world generalization to complex problems in vision and language, but similar levels of generalization have yet to be achieved in robotics. One fundamental challenge is that the models exhibit limited zero-shot capability, which hampers their ability to generalize effectively to unseen scenarios. In this work, we propose GeneralVLA (Generalizable Vision-Language-Action Models with Knowledge-Guided Trajectory Planning), a hierarchical vision-language-action (VLA) model that can be more effective in utilizing the generalization of foundation models, enabling zero-shot manipulation and automatically generating data for robotics. In particular, we study a class of hierarchical VLA model where the high-level ASM (Affordance Segmentation Module) is finetuned to perceive image keypoint affordances of the scene; the mid-level 3DAgent carries out task understanding, skill knowledge, and trajectory planning to produce a 3D path indicating the desired robot end-effector trajectory. The intermediate 3D path prediction is then served as guidance to the low-level, 3D-aware control policy capable of precise manipulation. Compared to alternative approaches, our method requires no real-world robotic data collection or human demonstration, making it much more scalable to diverse tasks and viewpoints. Empirically, GeneralVLA successfully generates trajectories for 14 tasks, significantly outperforming state-of-the-art methods such as VoxPoser. The generated demonstrations can train more robust behavior cloning policies than training with human demonstrations or from data generated by VoxPoser, Scaling-up, and Code-As-Policies. We believe GeneralVLA can be the scalable method for both generating data for robotics and solving novel tasks in a zero-shot setting. Code: https://github.com/AIGeeksGroup/GeneralVLA. Website: https://aigeeksgroup.github.io/GeneralVLA.
Fine-tuning Pre-trained Vision-Language Models in a Human-Annotation-Free Manner
Venue: arXiv cs.CV 2026
Year: 2026

Large-scale vision-language models (VLMs) such as CLIP exhibit strong zero-shot generalization, but adapting them to downstream tasks typically requires costly labeled data. Existing unsupervised self-training methods rely on pseudo-labeling, yet often suffer from unreliable confidence filtering, confirmation bias, and underutilization of low-confidence samples. We propose Collaborative Fine-Tuning (CoFT), an unsupervised adaptation framework that leverages unlabeled data through a dual-model, cross-modal collaboration mechanism. CoFT introduces a dual-prompt learning strategy with positive and negative textual prompts to explicitly model pseudo-label cleanliness in a sample-dependent manner, removing the need for hand-crafted thresholds or noise assumptions. The negative prompt also regularizes lightweight visual adaptation modules, improving robustness under noisy supervision. CoFT employs a two-phase training scheme, transitioning from parameter-efficient fine-tuning on high-confidence samples to full fine-tuning guided by collaboratively filtered pseudo-labels. Building on CoFT, CoFT+ further enhances adaptation via iterative fine-tuning, momentum contrastive learning, and LLM-generated prompts. Extensive experiments demonstrate consistent gains over existing unsupervised methods and even few-shot supervised baselines.
Finding NeMO: A Geometry-Aware Representation of Template Views for Few-Shot Perception
Venue: arXiv cs.CV 2026
Year: 2026

We present Neural Memory Object (NeMO), a novel object-centric representation that can be used to detect, segment and estimate the 6DoF pose of objects unseen during training using RGB images. Our method consists of an encoder that requires only a few RGB template views depicting an object to generate a sparse object-like point cloud using a learned UDF containing semantic and geometric information. Next, a decoder takes the object encoding together with a query image to generate a variety of dense predictions. Through extensive experiments, we show that our method can be used for few-shot object perception without requiring any camera-specific parameters or retraining on target data. Our proposed concept of outsourcing object information in a NeMO and using a single network for multiple perception tasks enhances interaction with novel objects, improving scalability and efficiency by enabling quick object onboarding without retraining or extensive pre-processing. We report competitive and state-of-the-art results on various datasets and perception tasks of the BOP benchmark, demonstrating the versatility of our approach. https://github.com/DLR-RM/nemo
UnMaskFork: Test-Time Scaling for Masked Diffusion via Deterministic Action Branching
Venue: arXiv cs.LG 2026
Year: 2026

Test-time scaling strategies have effectively leveraged inference-time compute to enhance the reasoning abilities of Autoregressive Large Language Models. In this work, we demonstrate that Masked Diffusion Language Models (MDLMs) are inherently amenable to advanced search strategies, owing to their iterative and non-autoregressive generation process. To leverage this, we propose UnMaskFork (UMF), a framework that formulates the unmasking trajectory as a search tree and employs Monte Carlo Tree Search to optimize the generation path. In contrast to standard scaling methods relying on stochastic sampling, UMF explores the search space through deterministic partial unmasking actions performed by multiple MDLMs. Our empirical evaluation demonstrates that UMF consistently outperforms existing test-time scaling baselines on complex coding benchmarks, while also exhibiting strong scalability on mathematical reasoning tasks.
SparVAR: Exploring Sparsity in Visual AutoRegressive Modeling for Training-Free Acceleration
Venue: arXiv cs.CV 2026
Year: 2026

Visual AutoRegressive (VAR) modeling has garnered significant attention for its innovative next-scale prediction paradigm. However, mainstream VAR paradigms attend to all tokens across historical scales at each autoregressive step. As the next scale resolution grows, the computational complexity of attention increases quartically with resolution, causing substantial latency. Prior accelerations often skip high-resolution scales, which speeds up inference but discards high-frequency details and harms image quality. To address these problems, we present SparVAR, a training-free acceleration framework that exploits three properties of VAR attention: (i) strong attention sinks, (ii) cross-scale activation similarity, and (iii) pronounced locality. Specifically, we dynamically predict the sparse attention pattern of later high-resolution scales from a sparse decision scale, and construct scale self-similar sparse attention via an efficient index-mapping mechanism, enabling high-efficiency sparse attention computation at large scales. Furthermore, we propose cross-scale local sparse attention and implement an efficient block-wise sparse kernel, which achieves $\mathbf{> 5\times}$ faster forward speed than FlashAttention. Extensive experiments demonstrate that the proposed SparseVAR can reduce the generation time of an 8B model producing $1024\times1024$ high-resolution images to the 1s, without skipping the last scales. Compared with the VAR baseline accelerated by FlashAttention, our method achieves a $\mathbf{1.57\times}$ speed-up while preserving almost all high-frequency details. When combined with existing scale-skipping strategies, SparseVAR attains up to a $\mathbf{2.28\times}$ acceleration, while maintaining competitive visual generation quality. Code is available at https://github.com/CAS-CLab/SparVAR.
Beyond Rejection Sampling: Trajectory Fusion for Scaling Mathematical Reasoning
Venue: arXiv cs.CL 2026
Year: 2026

Large language models (LLMs) have made impressive strides in mathematical reasoning, often fine-tuned using rejection sampling that retains only correct reasoning trajectories. While effective, this paradigm treats supervision as a binary filter that systematically excludes teacher-generated errors, leaving a gap in how reasoning failures are modeled during training. In this paper, we propose TrajFusion, a fine-tuning strategy that reframes rejection sampling as a structured supervision construction process. Specifically, TrajFusion forms fused trajectories that explicitly model trial-and-error reasoning by interleaving selected incorrect trajectories with reflection prompts and correct trajectories. The length of each fused sample is adaptively controlled based on the frequency and diversity of teacher errors, providing richer supervision for challenging problems while safely reducing to vanilla rejection sampling fine-tuning (RFT) when error signals are uninformative. TrajFusion requires no changes to the architecture or training objective. Extensive experiments across multiple math benchmarks demonstrate that TrajFusion consistently outperforms RFT, particularly on challenging and long-form reasoning problems.
Convivial Fabrication: Towards Relational Computational Tools For and From Craft Practices
Venue: arXiv cs.HC 2026
Year: 2026

Computational tools for fabrication often treat materials as passive rather than active participants in design, abstracting away relationships between craftspeople and materials. For craft communities that value relational practices, abstractions limit the adoption and creative uptake of computational tools which might otherwise be beneficial. To understand how better tool design could support richer relations between individuals, tools, and materials, we interviewed expert woodworkers, fiber artists, and metalworkers. We identify three orders of convivial relations central to craft: immediate relations between individuals, tools, and materials; mid-range relations between communities, platforms, and shared materials; and extended relations between institutions, infrastructures, and ecologies. Our analysis shows how craftspeople engage and struggle with convivial relations across all three orders, creating workflows that learn from materials while supporting autonomy. We conclude with design principles for computational tools and infrastructures to better support material dialogue, collective knowledge, and accountability, along with richer and more convivial relations between craftspeople, tools, and the material worlds around them.
LCUDiff: Latent Capacity Upgrade Diffusion for Faithful Human Body Restoration
Venue: arXiv cs.CV 2026
Year: 2026

Existing methods for restoring degraded human-centric images often struggle with insufficient fidelity, particularly in human body restoration (HBR). Recent diffusion-based restoration methods commonly adapt pre-trained text-to-image diffusion models, where the variational autoencoder (VAE) can significantly bottleneck restoration fidelity. We propose LCUDiff, a stable one-step framework that upgrades a pre-trained latent diffusion model from the 4-channel latent space to the 16-channel latent space. For VAE fine-tuning, channel splitting distillation (CSD) is used to keep the first four channels aligned with pre-trained priors while allocating the additional channels to effectively encode high-frequency details. We further design prior-preserving adaptation (PPA) to smoothly bridge the mismatch between 4-channel diffusion backbones and the higher-dimensional 16-channel latent. In addition, we propose a decoder router (DeR) for per-sample decoder routing using restoration-quality score annotations, which improves visual quality across diverse conditions. Experiments on synthetic and real-world datasets show competitive results with higher fidelity and fewer artifacts under mild degradations, while preserving one-step efficiency. The code and model will be at https://github.com/gobunu/LCUDiff.
Crypto-RV: High-Efficiency FPGA-Based RISC-V Cryptographic Co-Processor for IoT Security
Venue: arXiv cs.AR 2026
Year: 2026

Cryptographic operations are critical for securing IoT, edge computing, and autonomous systems. However, current RISC-V platforms lack efficient hardware support for comprehensive cryptographic algorithm families and post-quantum cryptography. This paper presents Crypto-RV, a RISC-V co-processor architecture that unifies support for SHA-256, SHA-512, SM3, SHA3-256, SHAKE-128, SHAKE-256 AES-128, HARAKA-256, and HARAKA-512 within a single 64-bit datapath. Crypto-RV introduces three key architectural innovations: a high-bandwidth internal buffer (128x64-bit), cryptography-specialized execution units with four-stage pipelined datapaths, and a double-buffering mechanism with adaptive scheduling optimized for large-hash. Implemented on Xilinx ZCU102 FPGA at 160 MHz with 0.851 W dynamic power, Crypto-RV achieves 165 times to 1,061 times speedup over baseline RISC-V cores, 5.8 times to 17.4 times better energy efficiency compared to powerful CPUs. The design occupies only 34,704 LUTs, 37,329 FFs, and 22 BRAMs demonstrating viability for high-performance, energy-efficient cryptographic processing in resource-constrained IoT environments.