OpenRaiser/Intern-Atlas huggingface dataset:
It's Time to Get It Right: Improving Analog Clock Reading and Clock-Hand Spatial Reasoning in Vision-Language Models
Venue: arXiv cs.CV 2026
Year: 2026

Advances in vision-language models (VLMs) have achieved remarkable success on complex multimodal reasoning tasks, leading to the assumption that they should also excel at reading analog clocks. However, contrary to this expectation, our study reveals that reading analog clocks in real-world environments remains a significant challenge for state-of-the-art VLMs. Existing analog clock datasets are largely synthetic or planar with limited stylistic diversity and minimal background context, failing to capture the visual variability of real-world scenes. As a result, VLMs trained on such data exhibit weak spatial-temporal reasoning, frequently confusing the hour and minute hands and struggling under common visual conditions such as occlusion, lighting variation, and cluttered backgrounds. To address this issue, we introduce TickTockVQA, a human-annotated dataset containing analog clocks in diverse real-world scenarios. TickTockVQA provides explicit hour and minute annotations, and includes an AM/PM tag when it is inferable from the visual context. Furthermore, we propose Swap-DPO, a direct preference optimization based fine-tuning framework to align model reasoning toward accurate time interpretation. Experimental results demonstrate that our approach substantially enhances clock reading accuracy and robustness under real-world conditions, establishing a foundation for future research on spatial-temporal reasoning and visual understanding in VLMs.
Alignment-Process-Outcome: Rethinking How AIs and Humans Collaborate
Venue: arXiv cs.HC 2026
Year: 2026

In real-world collaboration, alignment, process structure, and outcome quality do not exhibit a simple linear or one-to-one correspondence: similar alignment may accompany either rapid convergence or extensive multi-branch exploration, and lead to different results. Existing accounts often isolate these dimensions or focus on specific participant types, limiting structural accounts of collaboration. We reconceptualize collaboration through two complementary lenses. The task lens models collaboration as trajectory evolution in a structured task space, revealing patterns such as advancement, branching, and backtracking. The intent lens examines how individual intents are expressed within shared contexts and enter situated decisions. Together, these lenses clarify the structural relationships among alignment, decision-making, and trajectory structure. Rather than reducing collaboration to outcome quality or treating alignment as the sole objective, we propose a unified dynamic view of the relationships among alignment, process, and outcome, and use it to re-examine collaboration structure across Human-Human, AI-AI, and Human-AI settings.
GCGNet: Graph-Consistent Generative Network for Time Series Forecasting with Exogenous Variables
Venue: arXiv cs.LG 2026
Year: 2026

Exogenous variables offer valuable supplementary information for predicting future endogenous variables. Forecasting with exogenous variables needs to consider both past-to-future dependencies (i.e., temporal correlations) and the influence of exogenous variables on endogenous variables (i.e., channel correlations). This is pivotal when future exogenous variables are available, because they may directly affect the future endogenous variables. Many methods have been proposed for time series forecasting with exogenous variables, focusing on modeling temporal and channel correlations. However, most of them use a two-step strategy, modeling temporal and channel correlations separately, which limits their ability to capture joint correlations across time and channels. Furthermore, in real-world scenarios, time series are frequently affected by various forms of noises, underscoring the critical importance of robustness in such correlations modeling. To address these limitations, we propose GCGNet, a Graph-Consistent Generative Network for time series forecasting with exogenous variables. Specifically, GCGNet first employs a Variational Generator to produce coarse predictions. A Graph Structure Aligner then further guides it by evaluating the consistency between the generated and true correlations, where the correlations are represented as graphs, and are robust to noises. Finally, a Graph Refiner is proposed to refine the predictions to prevent degeneration and improve accuracy. Extensive experiments on 12 real-world datasets demonstrate that GCGNet outperforms state-of-the-art baselines.
The Unit Gap: How Sharing Works in Boolean Circuits
Venue: arXiv cs.CC 2026
Year: 2026

We study the gap between the minimum size of a Boolean circuit (DAG) and the minimum size of a formula (tree circuit) over the And-Inverter Graph (AIG) basis {AND, NOT} with free inversions. We prove that this gap is always 0 or 1 (Unit Gap Theorem), that sharing requires opt(f) >= n essential variables (Threshold Theorem), and that no sharing is needed when opt(f) <= 3 (Tree Theorem). Gate counts in optimal circuits satisfy an exact decomposition formula with a binary sharing term. When the gap equals 1, it arises from exactly one gate with fan-out 2, employing either dual-polarity or same-polarity reuse; we prove that no other sharing structure can produce a unit gap.
Samyama: A Unified Graph-Vector Database with In-Database Optimization, Agentic Enrichment, and Hardware Acceleration
Venue: arXiv cs.DB 2026
Year: 2026

Modern data architectures are fragmented across graph databases, vector stores, analytics engines, and optimization solvers, resulting in complex ETL pipelines and synchronization overhead. We present Samyama, a high-performance graph-vector database written in Rust that unifies these workloads into a single engine. Samyama combines a RocksDB-backed persistent store with a versioned-arena MVCC model, a vectorized query executor with 35 physical operators, a cost-based query planner with plan enumeration and predicate pushdown, a dedicated CSR-based analytics engine, and native RDF/SPARQL support. The system integrates 22 metaheuristic optimization solvers directly into its query language, implements HNSW vector indexing with Graph RAG capabilities, and introduces Agentic Enrichment for autonomous graph expansion via LLMs. The Enterprise Edition adds GPU acceleration via wgpu, production-grade observability, point-in-time recovery, and hardened high availability with HTTP/2 Raft transport. Our evaluation on commodity hardware (Mac Mini M4, 16 GB RAM) demonstrates: ingestion at 255K nodes/s (CPU) and 412K nodes/s (GPU-accelerated); 115K Cypher queries/sec at 1M nodes; 4.0-4.7x latency reduction from late materialization on multi-hop traversals; 8.2x GPU PageRank speedup at 1M nodes; and 100% LDBC Graphalytics validation (28/28 tests). These results demonstrate that a unified graph-vector-optimization engine can achieve competitive performance on commodity hardware while maintaining Rust's memory safety guarantees.
CEMR: An Effective Subgraph Matching Algorithm with Redundant Extension Elimination
Venue: arXiv cs.DB 2026
Year: 2026

Subgraph matching is a fundamental problem in graph analysis with a wide range of applications. However, due to its inherent NP-hardness, enumerating subgraph matches efficiently on large real-world graphs remains highly challenging. Most existing works adopt a depth-first search (DFS) backtracking strategy, where a partial embedding is gradually extended in a DFS manner along a branch of the search trees until either a full embedding is found or no further extension is possible. A major limitation of this paradigm is the significant amount of duplicate computation that occurs during enumeration, which increases the overall runtime. To overcome this limitation, we propose a novel subgraph matching algorithm, CEMR. It incorporates two techniques to reduce duplicate extensions: common extension merging, which leverages a black-white vertex encoding, and common extension reusing, which employs common extension buffers. In addition, we design two pruning techniques to discard unpromising search branches. Extensive experiments on real-world datasets and diverse query workloads demonstrate that CEMR outperforms state-of-the-art subgraph matching methods.
Examining the Role of YouTube Production and Consumption Dynamics on the Formation of Extreme Ideologies
Venue: arXiv cs.CL 2026
Year: 2026

The relationship between content production and consumption on algorithm-driven platforms like YouTube plays a critical role in shaping ideological behaviors. While prior work has largely focused on user behavior and algorithmic recommendations, the interplay between what is produced and what gets consumed, and its role in ideological shifts remains understudied. In this paper, we present a longitudinal, mixed-methods analysis combining one year of YouTube watch history with two waves of ideological surveys from 1,100 U.S. participants. We identify users who exhibited significant shifts toward more extreme ideologies and compare their content consumption and the production patterns of YouTube channels they engaged with to ideologically stable users. Our findings show that users who became more extreme consumed have different consumption habits from those who do not. This gets amplified by the fact that channels favored by users with extreme ideologies also have a higher affinity to produce content with a higher anger, grievance and other such markers. Lastly, using time series analysis, we examine whether content producers are the primary drivers of consumption behavior or merely responding to user demand.
Coupling-Aware RHS Beamforming for Wideband Multi-User Sum Rate Maximization
Venue: arXiv cs.IT 2026
Year: 2026

Wideband multi-user transmission assisted by reconfigurable holographic surfaces (RHSs) is fundamentally limited by mutual coupling effect among densely packed sub-wavelength radiation elements. This paper develops a coupling-aware wideband RHS model and an efficient joint beamforming framework to maximize the multi-user sum rate under practical feeder power and RHS excitation power constraints. We establish an electromagnetic equivalent model based on magnetic-dipole elements and a physically interpretable coupling decomposition into free space near field coupling and guided surface wave coupling. For optimization, we employ a weighted minimum mean square error (WMMSE)-based block coordinate method with a closed-form digital precoder update and introduce a Jacobian-aided coupling consistent hologram update that preserves coupling sensitivity via a first-order surrogate while keeping the hologram subproblem convex and efficiently solvable by projected first-order methods. Meep experiments verify the correctness of the proposed coupling model, and the simulations for a 28~GHz, 1~GHz-bandwidth RHS downlink prove the effectiveness of Jacobian-aided WMMSE-based method.
ImageEdit-R1: Boosting Multi-Agent Image Editing via Reinforcement Learning
Venue: arXiv cs.CV 2026
Year: 2026

With the rapid advancement of commercial multi-modal models, image editing has garnered significant attention due to its widespread applicability in daily life. Despite impressive progress, existing image editing systems, particularly closed-source or proprietary models, often struggle with complex, indirect, or multi-step user instructions. These limitations hinder their ability to perform nuanced, context-aware edits that align with human intent. In this work, we propose ImageEdit-R1, a multi-agent framework for intelligent image editing that leverages reinforcement learning to coordinate high-level decision-making across a set of specialized, pretrained vision-language and generative agents. Each agent is responsible for distinct capabilities--such as understanding user intent, identifying regions of interest, selecting appropriate editing actions, and synthesizing visual content--while reinforcement learning governs their collaboration to ensure coherent and goal-directed behavior. Unlike existing approaches that rely on monolithic models or hand-crafted pipelines, our method treats image editing as a sequential decision-making problem, enabling dynamic and context-aware editing strategies. Experimental results demonstrate that ImageEdit-R1 consistently outperforms both individual closed-source diffusion models and alternative multi-agent framework baselines across multiple image editing datasets.
EAGLE-Pangu: Accelerator-Safe Tree Speculative Decoding on Ascend NPUs
Venue: arXiv cs.LG 2026
Year: 2026

Autoregressive decoding remains a primary bottleneck in large language model (LLM) serving, motivating speculative decoding methods that reduce expensive teacher-model invocations by verifying multiple candidate tokens per step. Tree-structured speculation further increases parallelism, but is often brittle when ported across heterogeneous backends and accelerator stacks, where attention masking, KV-cache layouts, and indexing semantics are not interchangeable. We present EAGLE-Pangu, a reproducible system that ports EAGLE-3-style tree speculative decoding to a Pangu teacher backend on Ascend NPUs. EAGLE-Pangu contributes (i) an explicit branch/commit cache manager built on the Cache API, (ii) accelerator-safe tree tensorization that removes undefined negative indices by construction and validates structural invariants, and (iii) a fused-kernel-compatible teacher verification path with a debuggable eager fallback. On 240 turns from MT-Bench and HumanEval-style prompts, EAGLE-Pangu improves end-to-end decoding throughput by 1.27x on average, up to 2.46x at p99, over teacher-only greedy decoding in the fused-kernel performance path. We also provide a fused-kernel-free reference path with structured traces and invariant checks to support reproducible debugging and ablation across execution modes and tree budgets.
Adaptive Vision-Based Control of Redundant Robots with Null-Space Interaction for Human-Robot Collaboration
Venue: arXiv cs.RO 2026
Year: 2026

Human-robot collaboration aims to extend human ability through cooperation with robots. This technology is currently helping people with physical disabilities, has transformed the manufacturing process of companies, improved surgical performance, and will likely revolutionize the daily lives of everyone in the future. Being able to enhance the performance of both sides, such that human-robot collaboration outperforms a single robot/human, remains an open issue. For safer and more effective collaboration, a new control scheme has been proposed for redundant robots in this paper, consisting of an adaptive vision-based control term in task space and an interactive control term in null space. Such a formulation allows the robot to autonomously carry out tasks in an unknown environment without prior calibration while also interacting with humans to deal with unforeseen changes (e.g., potential collision, temporary needs) under the redundant configuration. The decoupling between task space and null space helps to explore the collaboration safely and effectively without affecting the main task of the robot end-effector. The stability of the closed-loop system has been rigorously proved with Lyapunov methods, and both the convergence of the position error in task space and that of the damping model in null space are guaranteed. The experimental results of a robot manipulator guided with the technology of augmented reality (AR) are presented to illustrate the performance of the control scheme.
Toward Robust LLM-Based Judges: Taxonomic Bias Evaluation and Debiasing Optimization
Venue: arXiv cs.CL 2026
Year: 2026

Large language model (LLM)-based judges are widely adopted for automated evaluation and reward modeling, yet their judgments are often affected by judgment biases. Accurately evaluating these biases is essential for ensuring the reliability of LLM-based judges. However, existing studies typically investigate limited biases under a single judge formulation, either generative or discriminative, lacking a comprehensive evaluation. To bridge this gap, we propose JudgeBiasBench, a benchmark for systematically quantifying biases in LLM-based judges. JudgeBiasBench defines a taxonomy of judgment biases across 4 dimensions, and constructs bias-augmented evaluation instances through a controlled bias injection pipeline, covering 12 representative bias types. We conduct extensive experiments across both generative and discriminative judges, revealing that current judges exhibit significant and diverse bias patterns that often compromise the reliability of automated evaluation. To mitigate judgment bias, we propose bias-aware training that explicitly incorporates bias-related attributes into the training process, encouraging judges to disentangle task-relevant quality from bias-correlated cues. By adopting reinforcement learning for generative judges and contrastive learning for discriminative judges, our methods effectively reduce judgment biases while largely preserving general evaluation capability.
''I don't want to break it'': An Exploration of Perceived Fragility in Shape-Changing Interfaces
Venue: arXiv cs.HC 2026
Year: 2026

Shape-Changing Interfaces (SCIs) dynamically alter their form, an inherent characteristic that introduces fragility into their design. As a result, users' perceptions of an interface's fragility or its potential to move or break may influence their interaction, however the extent of this effect is unclear. To address this gap, we conducted a qualitative study (N = 18) using video stimuli showcasing 20 existing SCIs. Through thematic analysis, we identified key factors impacting perceived fragility and formalized these into a framework. We then conducted a second study (N = 36) for which we fabricated SCIs that varied across selected fragility-related dimensions. We recorded user interactions and compared how the selected dimensions shaped manipulation of the objects and how they were considered by users. Together, these studies provide a structured foundational understanding of perceived fragility in SCIs and offer insights to enhance perceived robustness and inform future SCI development.
DeReCo: Decoupling Representation and Coordination Learning for Object-Adaptive Decentralized Multi-Robot Cooperative Transport
Venue: arXiv cs.RO 2026
Year: 2026

Generalizing decentralized multi-robot cooperative transport across objects with diverse shapes and physical properties remains a fundamental challenge. Under decentralized execution, two key challenges arise: object-dependent representation learning under partial observability and coordination learning in multi-agent reinforcement learning (MARL) under non-stationarity. A typical approach jointly optimizes object-dependent representations and coordinated policies in an end-to-end manner while randomizing object shapes and physical properties during training. However, this joint optimization tightly couples representation and coordination learning, introducing bidirectional interference: inaccurate representations under partial observability destabilize coordination learning, while non-stationarity in MARL further degrades representation learning, resulting in sample-inefficient training. To address this structural coupling, we propose DeReCo, a novel MARL framework that decouples representation and coordination learning for object-adaptive multi-robot cooperative transport, improving sample efficiency and generalization across objects and transport scenarios. DeReCo adopts a three-stage training strategy: (1) centralized coordination learning with privileged object information, (2) reconstruction of object-dependent representations from local observations, and (3) progressive removal of privileged information for decentralized execution. This decoupling mitigates interference between representation and coordination learning and enables stable and sample-efficient training. Experimental results show that DeReCo outperforms baselines in simulation on three training objects, generalizes to six unseen objects with varying masses and friction coefficients, and achieves superior performance on two unseen objects in real-robot experiments.
SaiVLA-0: Cerebrum--Pons--Cerebellum Tripartite Architecture for Compute-Aware Vision-Language-Action
Venue: arXiv cs.RO 2026
Year: 2026

We revisit Vision-Language-Action through a neuroscience-inspired triad. Biologically, the Cerebrum provides stable high-level multimodal priors and remains frozen; the Pons Adapter integrates these cortical features with real-time proprioceptive inputs and compiles intent into execution-ready tokens; and the Cerebellum (ParaCAT) performs fast, parallel categorical decoding for online control, with hysteresis/EMA/temperature/entropy for stability. A fixed-ratio schedule and two-stage feature caching make the system compute-aware and reproducible. Inspired by active, foveated vision, our wrist ROIs are geometrically tied to the end-effector via calibrated projection, providing a movement-stabilized, high-resolution view that is sensitive to fine-grained pose changes and complements the global context of the main view. The design is modular: upgrading the Cerebrum only retrains the Pons; changing robots only trains the Cerebellum; cerebellum-only RL can further refine control without touching high-level semantics. As a concept-and-protocol paper with preliminary evidence, we outline a timing protocol under matched conditions (GPU, resolution, batch) to verify anticipated efficiency gains. We also report preliminary LIBERO evidence showing that split feature caching reduces training time (7.5h to 4.5h) and improves average success (86.5% to 92.5%) under official N1.5 head-only training, and that SaiVLA0 reaches 99.0% mean success.
TRIAGE: Type-Routed Interventions via Aleatoric-Epistemic Gated Estimation in Robotic Manipulation and Adaptive Perception -- Don't Treat All Uncertainty the Same
Venue: arXiv cs.RO 2026
Year: 2026

Most uncertainty-aware robotic systems collapse prediction uncertainty into a single scalar score and use it to trigger uniform corrective responses. This aggregation obscures whether uncertainty arises from corrupted observations or from mismatch between the learned model and the true system dynamics. As a result, corrective actions may be applied to the wrong component of the closed loop, degrading performance relative to leaving the policy unchanged. We introduce a lightweight post hoc framework that decomposes uncertainty into aleatoric and epistemic components and uses these signals to regulate system responses at inference time. Aleatoric uncertainty is estimated from deviations in the observation distribution using a Mahalanobis density model, while epistemic uncertainty is detected using a noise robust forward dynamics ensemble that isolates model mismatch from measurement corruption. The two signals remain empirically near orthogonal during closed loop execution and enable type specific responses. High aleatoric uncertainty triggers observation recovery, while high epistemic uncertainty moderates control actions. The same signals also regulate adaptive perception by guiding model capacity selection during tracking inference. Experiments demonstrate consistent improvements across both control and perception tasks. In robotic manipulation, the decomposed controller improves task success from 59.4% to 80.4% under compound perturbations and outperforms a combined uncertainty baseline by up to 21.0%. In adaptive tracking inference on MOT17, uncertainty-guided model selection reduces average compute by 58.2% relative to a fixed high capacity detector while preserving detection quality within 0.4%. Code and demo videos are available at https://divake.github.io/uncertainty-decomposition/.