OpenRaiser/Intern-Atlas huggingface dataset:
Stability-Guided Exploration for Diverse Motion Generation
Venue: arXiv cs.RO 2026
Year: 2026

Scaling up datasets is highly effective in improving the performance of deep learning models, including in the field of robot learning. However, data collection still proves to be a bottleneck. Approaches relying on collecting human demonstrations are labor-intensive and inherently limited: they tend to be narrow, task-specific, and fail to adequately explore the full space of feasible states. Synthetic data generation could remedy this, but current techniques mostly rely on local trajectory optimization and fail to find diverse solutions. In this work, we propose a novel method capable of finding diverse long-horizon manipulations through black-box simulation. We achieve this by combining an RRT-style search with sampling-based MPC, together with a novel sampling scheme that guides the exploration toward stable configurations. Specifically, we sample from a manifold of stable states while growing a search tree directly through simulation, without restricting the planner to purely stable motions. We demonstrate the method's ability to discover diverse manipulation strategies, including pushing, grasping, pivoting, throwing, and tool use, across different robot morphologies, without task-specific guidance.
Optimistic Policy Regularization
Venue: arXiv cs.LG 2026
Year: 2026

Deep reinforcement learning agents frequently suffer from premature convergence, where early entropy collapse causes the policy to discard exploratory behaviors before discovering globally optimal strategies. We introduce Optimistic Policy Regularization (OPR), a lightweight mechanism designed to preserve and reinforce historically successful trajectories during policy optimization. OPR maintains a dynamic buffer of high-performing episodes and biases learning toward these behaviors through directional log-ratio reward shaping and an auxiliary behavioral cloning objective. When instantiated on Proximal Policy Optimization (PPO), OPR substantially improves sample efficiency on the Arcade Learning Environment. Across 49 Atari games evaluated at the 10-million step benchmark, OPR achieves the highest score in 22 environments despite baseline methods being reported at the standard 50-million step horizon. Beyond arcade benchmarks, OPR also generalizes to the CAGE Challenge 2 cyber-defense environment, surpassing the competition-winning Cardiff agent while using the same PPO architecture. These results demonstrate that anchoring policy updates to empirically successful trajectories can improve both sample efficiency and final performance.
Learning-Based Robust Control: Unifying Exploration and Distributional Robustness for Reliable Robotics via Free Energy
Venue: arXiv cs.RO 2026
Year: 2026

A key challenge towards reliable robotic control is devising computational models that can both learn policies and guarantee robustness when deployed in the field. Inspired by the free energy principle in computational neuroscience, to address these challenges, we propose a model for policy computation that jointly learns environment dynamics and rewards, while ensuring robustness to epistemic uncertainties. Expounding a distributionally robust free energy principle, we propose a modification to the maximum diffusion learning framework. After explicitly characterizing robustness of our policies to epistemic uncertainties in both environment and reward, we validate their effectiveness on continuous-control benchmarks, via both simulations and real-world experiments involving manipulation with a Franka Research~3 arm. Across simulation and zero-shot deployment, our approach narrows the sim-to-real gap, and enables repeatable tabletop manipulation without task-specific fine-tuning.
RoboCritics: Enabling Reliable End-to-End LLM Robot Programming through Expert-Informed Critics
Venue: arXiv cs.RO 2026
Year: 2026

End-user robot programming grants users the flexibility to re-task robots in situ, yet it remains challenging for novices due to the need for specialized robotics knowledge. Large Language Models (LLMs) hold the potential to lower the barrier to robot programming by enabling task specification through natural language. However, current LLM-based approaches generate opaque, "black-box" code that is difficult to verify or debug, creating tangible safety and reliability risks in physical systems. We present RoboCritics, an approach that augments LLM-based robot programming with expert-informed motion-level critics. These critics encode robotics expertise to analyze motion-level execution traces for issues such as joint speed violations, collisions, and unsafe end-effector poses. When violations are detected, critics surface transparent feedback and offer one-click fixes that forward structured messages back to the LLM, enabling iterative refinement while keeping users in the loop. We instantiated RoboCritics in a web-based interface connected to a UR3e robot and evaluated it in a between-subjects user study (n=18). Compared to a baseline LLM interface, RoboCritics reduced safety violations, improved execution quality, and shaped how participants verified and refined their programs. Our findings demonstrate that RoboCritics enables more reliable and user-centered end-to-end robot programming with LLMs.
Characterizing Faults in Agentic AI: A Taxonomy of Types, Symptoms, and Root Causes
Venue: arXiv cs.SE 2026
Year: 2026

Agentic AI systems combine large language model (LLM) reasoning with external tool invocation and long-horizon task execution. Although these systems are increasingly deployed in practice, their architectural composition introduces reliability challenges that differ from those in traditional software systems and standalone LLM applications. However, there is limited empirical understanding of how faults originate, manifest, and propagate in real-world agentic AI systems. To address this gap, we conduct a large-scale empirical study of faults in agentic AI systems. We collect 13,602 issues and pull requests from 40 open-source agentic AI repositories and apply stratified sampling to select 385 faults for in-depth qualitative analysis. Using grounded theory, we derive taxonomies of fault types, observable symptoms, and root causes. We further apply Apriori-based association rule mining to identify statistically significant relationships among faults, symptoms, and root causes, revealing common fault propagation patterns. Finally, we validate the taxonomy through a developer study with 145 practitioners. Our analysis identifies 37 distinct fault types grouped into 13 higher-level fault categories, along with 13 classes of observable symptoms and 12 categories of root causes. The results show that many failures originate from mismatches between probabilistically generated artifacts and deterministic interface constraints, frequently involving dependency integration, data validation, and runtime environment handling. Association rule mining further reveals recurring propagation pathways across system components, such as token management faults leading to authentication failures and datetime handling defects causing scheduling anomalies. Practitioners rated the taxonomy as representative of real-world failures (mean = 3.97/5), and 83.8% reported that it covered faults they had encountered.
An Extended Topological Model For High-Contrast Optical Flow
Venue: arXiv cs.CV 2026
Year: 2026

In this paper, we identify low-dimensional models for dense core subsets in the space of $3\times 3$ high-contrast optical flow patches sampled from the Sintel dataset. In particular, we leverage the theory of approximate and discrete circle bundles to identify a 3-manifold whose boundary is a previously proposed optical flow torus, together with disjoint circles corresponding to pairs of binary step-edge range image patches. The 3-manifold model we introduce provides an explanation for why the previously-proposed torus model could not be verified with direct methods (e.g., a straightforward persistent homology computation). We also demonstrate that nearly all optical flow patches in the top 1 percent by contrast norm are found near the family of binary step-edge circles described above, rather than the optical flow torus, and that these frequently occurring patches are concentrated near motion boundaries (which are of particular importance for computer vision tasks such as object segmentation and tracking). Our findings offer insights on the subtle interplay between topology and geometry in inference for visual data.
Are Audio-Language Models Listening? Audio-Specialist Heads for Adaptive Audio Steering
Venue: arXiv cs.SD 2026
Year: 2026

Multimodal large language models can exhibit text dominance, over-relying on linguistic priors instead of grounding predictions in non-text inputs. One example is large audio-language models (LALMs) where decisive audio evidence can be under-utilized even when it contains important information. To address this issue we use mechanistic interpretability to identify a small set of audio-specialist attention heads whose audio attention yields a ``listening'' signal. We show that this signal increases when audio evidence affects the model's output, providing an indicator of audio engagement under standard prompting. Leveraging this localization, we construct an audio--silence steering direction and apply an inference-time activation intervention to the final representation, amplifying the model's audio effect. To demonstrate the utility of this intervention, we show on MMAU that this improves accuracy by up to +8.0 percentage points on two Qwen-based LALMs, without any parameter updates.
Patch Validation in Automated Vulnerability Repair
Venue: arXiv cs.SE 2026
Year: 2026

Automated Vulnerability Repair (AVR) systems, especially those leveraging large language models (LLMs), have demonstrated promising results in patching vulnerabilities -- that is, if we trust their patch validation methodology. Ground-truth patches from human developers often come with new tests that not only ensure mitigation of the vulnerability but also encode extra semantics such as root cause location, optimal fix strategy, or subtle coding styles or conventions. And yet, none of the recent AVR systems verify that the auto-generated patches additionally pass these new tests (termed as $\text{PoC}^+$ tests). This is a subtle yet critical omission. To fill this gap, we constructed a benchmark, $\textrm{PVBench}$, with 209 cases spanning 20 projects. Each case includes basic tests (functional tests before the patch and the PoC exploit) as well as the associated $\text{PoC}^+$ tests. Evaluated on three state-of-the-art AVR systems, we find that over 40\% of patches validated as correct by basic tests fail under $\text{PoC}^+$ testing, revealing substantial overestimation on patch success rates. Analyzing these patches that are falsely labeled as correct, we suggest that AVR tools should improve in three critical areas: root cause analysis, adherence to program specifications, and capturing developer intention.
Contextual Counterfactual Credit Assignment for Multi-Agent Reinforcement Learning in LLM Collaboration
Venue: arXiv cs.LG 2026
Year: 2026

Cooperative multi-agent reinforcement learning (MARL) systems powered by large language models (LLMs) are frequently optimized via sparse terminal-only feedback. This shared signal entangles upstream decisions, obstructing accurate decision-level credit assignment. To address this trajectory-level diffusion, we introduce Contextual Counterfactual Credit Assignment (\textbf{\texttt{C3}}). Instead of distributing rewards across an entire episode, \textbf{\texttt{C3}} isolates the causal impact of individual messages by freezing the exact transcript-derived context, evaluating context-matched alternatives via fixed-continuation replay, and applying a leave-one-out (LOO) baseline. This localized intervention extracts unbiased, low-variance marginal advantages for standard policy-gradient optimization. Evaluated across five mathematical and coding benchmarks under matched budgets, \textbf{\texttt{C3}} improves terminal performance over established baselines. Mechanistic diagnostics further show that these gains are accompanied by higher credit fidelity, lower contextual variance, and stronger inter-agent causal dependence. Our code is available at https://github.com/EIT-EAST-Lab/C3.
IGLU: The Integrated Gaussian Linear Unit Activation Function
Venue: arXiv cs.LG 2026
Year: 2026

Activation functions are fundamental to deep neural networks, governing gradient flow, optimization stability, and representational capacity. Within historic deep architectures, while ReLU has been the dominant choice for the activation function, modern transformer-based models increasingly are adopting smoother alternatives such as GELU and other self-gated alternatives. Despite their empirical success, the mathematical relationships among these functions and the principles underlying their effectiveness remains only partially understood. We introduce IGLU, a parametric activation function derived as a scale mixture of GELU gates under a half-normal mixing distribution. This derivation yields a closed-form expression whose gating component is exactly the Cauchy CDF, providing a principled one-parameter family that continuously interpolates between identity-like and ReLU-like behavior via a single sharpness parameter $σ$. Unlike GELU's Gaussian gate, IGLU's heavy-tailed Cauchy gate decays polynomially in the negative tail, guaranteeing non-zero gradients for all finite inputs and offering greater robustness to vanishing gradients. We further introduce IGLU-Approx, a computationally efficient rational approximation of IGLU expressed entirely in terms of ReLU operations that eliminates transcendental function evaluation. Through evaluations on CIFAR-10, CIFAR-100, and WikiText-103 across ResNet-20, ViT-Tiny, and GPT-2 Small, IGLU achieves competitive or superior performance on both vision and language datasets against ReLU and GELU baselines, with IGLU-Approx recovering this performance at substantially reduced computational cost. In particular, we show that employing a heavy-tailed gate leads to considerable performance gains in heavily imbalanced classification datasets.
Counting on Consensus: Selecting the Right Inter-annotator Agreement Metric for NLP Annotation and Evaluation
Venue: arXiv cs.CL 2026
Year: 2026

Human annotation remains the foundation of reliable and interpretable data in Natural Language Processing (NLP). As annotation and evaluation tasks continue to expand, from categorical labelling to segmentation, subjective judgment, and continuous rating, measuring agreement between annotators has become increasingly more complex. This paper outlines how inter-annotator agreement (IAA) has been conceptualised and applied across NLP and related disciplines, describing the assumptions and limitations of common approaches. We organise agreement measures by task type and discuss how factors such as label imbalance and missing data influence reliability estimates. In addition, we highlight best practices for clear and transparent reporting, including the use of confidence intervals and the analysis of disagreement patterns. The paper aims to serve as a guide for selecting and interpreting agreement measures, promoting more consistent and reproducible human annotation and evaluation in NLP.
Stochastic Attention via Langevin Dynamics on the Modern Hopfield Energy
Venue: arXiv cs.LG 2026
Year: 2026

Attention heads retrieve: given a query, they return a softmax-weighted average of stored values. We show that this computation is one step of gradient descent on a classical energy function, and that Langevin sampling from the corresponding distribution yields stochastic attention: a training-free sampler controlled by a single temperature. Lowering the temperature gives exact retrieval; raising it gives open-ended generation. Because the energy gradient equals the attention map, no score network, training loop, or learned model is required. We derive a closed-form entropy inflection condition that identifies the retrieval-to-generation transition temperature for any memory geometry, with a scaling law $β^*\!\sim\!\sqrt{d}$ for random patterns. We validate on five domains (64 to 4,096 dimensions). On MNIST digit images, stochastic attention is $2.6{\times}$ more novel and $2.0{\times}$ more diverse than the best learned baseline (a VAE trained on the same patterns), while matching a Metropolis-corrected gold standard. On protein sequences from the Pfam RRM family, the generation regime achieves $6.9{\times}$ lower amino acid composition divergence than the VAE (KL $= 0.060$ vs.\ $0.416$) at matched novelty, demonstrating that the training-free score function preserves family-level fidelity that learned models lose. A denoising diffusion baseline (DDPM) fails across all memory sizes tested ($K = 100$ to $3{,}500$), producing samples indistinguishable from isotropic noise. The approach requires no architectural changes to the underlying attention mechanism.
Material Driven HRI Design: Aesthetics as Explainability
Venue: arXiv cs.RO 2026
Year: 2026

Aesthetics - often treated as secondary to function-guides how people interpret robots' roles. A great deal of robot designs - both real and fictitious - use sleek industrial aesthetics. These feature hard glossy plastics, hiding as much of the underlying mechanical and electrical components as possible, resembling something akin to a nude humanoid figure. This leaves robots as something of a blank slate to which end-users apply coverings to, often based on media of fiction and non-fiction alike. We argue that designers can take cues from fashion to design interaction and set appropriate expectations. Rather than viewing appearance as decoration, we propose that color, texture, and material choices function as interaction signals. These signals can invite or discourage touch, clarify a robot's role, and help align user expectations with a robot's actual capabilities. When done thoughtfully, such cues can create familiarity and legibility; when done poorly, they can lead to wrong expectations. This preliminary paper proposes a framework describing how materials can create explainability by signaling expectations for interaction, task, and environment. We use this framework to do a content analysis of 6 robots.
Notational Animating: An Interactive Approach to Creating and Editing Animation Keyframes
Venue: arXiv cs.HC 2026
Year: 2026

We introduce the concept of notational animating, an interaction paradigm for animation authoring where users sketch high-level notations over static drawings to indicate intended motions, which are then interpreted by automatic methods (e.g., GenAI models) to generate animation keyframes. Sketched notations have long served as cognitive instruments for animators, capturing forces, poses, dynamics, paths, and other animation features. However, such notations are often context-dependent, non-categorical, ambiguous, and composable based on our analysis of real-world animator-produced sketches. To facilitate interpretation, we first formalize these notations into a structured animation representation (i.e., source, path, and target). We then built an animation authoring system that translates high-level notations into the formalized intended animation, provides dynamic UI widgets for fine-grained parameter control, and establishes a closed feedback loop to resolve ambiguity. Finally, through a preliminary study with animators, we assess the usability of notational animating, reflect its affordance, and identify its contexts of use.
Physics-informed AI Accelerated Retention Analysis of Ferroelectric Vertical NAND: From Day-Scale TCAD to Second-Scale Surrogate Model
Venue: arXiv cs.LG 2026
Year: 2026

Ferroelectric field-effect transistors (FeFET)-based vertical NAND (Fe-VNAND) has emerged as a promising candidate to overcome z-scaling limitations with lower programming voltages. However, the data retention of 3D Fe-VNAND is hindered by the complex interaction between charge detrapping and ferroelectric depolarization. Developing optimized device designs requires exploring an extensive parameter space, but the high computational cost of conventional Technology Computer-Aided Design (TCAD) tools makes such wide-scale optimization impractical. To overcome these simulation barriers, we present a Physics-Informed Neural Operator (PINO)-based AI surrogate model designed for high-efficiency prediction of threshold voltage (Vth) shifts and retention behavior. By embedding fundamental physical principles into the learning architecture, our PINO framework achieves a speedup exceeding 10000x compared to TCAD while maintaining physical accuracy. This study demonstrates the model's effectiveness on a single FeFET configuration, serving as a pathway toward modeling the retention loss mechanisms.
VertiAdaptor: Online Kinodynamics Adaptation for Vertically Challenging Terrain
Venue: arXiv cs.RO 2026
Year: 2026

Autonomous driving in off-road environments presents significant challenges due to the dynamic and unpredictable nature of unstructured terrain. Traditional kinodynamic models often struggle to generalize across diverse geometric and semantic terrain types, underscoring the need for real-time adaptation to ensure safe and reliable navigation. We propose VertiAdaptor (VA), a novel online adaptation framework that efficiently integrates elevation with semantic embeddings to enable terrain-aware kinodynamic modeling and planning via function encoders. VA learns a kinodynamic space spanned by a set of neural ordinary differential equation basis functions, capturing complex vehicle-terrain interactions across varied environments. After offline training, the proposed approach can rapidly adapt to new, unseen environments by identifying kinodynamics in the learned space through a computationally efficient least-squares calculation. We evaluate VA within the Verti-Bench simulator, built on the Chrono multi-physics engine, and validate its performance both in simulation and on a physical Verti-4-Wheeler platform. Our results demonstrate that VA improves prediction accuracy by up to 23.9% and achieves a 5X faster adaptation time, advancing the robustness and reliability of autonomous robots in complex and evolving off-road environments.
SoK: Self-Sovereign Digital Identities
Venue: arXiv cs.CR 2026
Year: 2026

Self-Sovereign Digital Identity (SSDI) enables individuals to control their own identity assertions and data, rather than relying on centralized or federated systems prone to large-scale data breaches. By eliminating centralized databases maintained by service providers and identity brokers, SSDIs offer enhanced security and privacy. However, adoption remains slow, and research in this area lacks systematization and uniformity. To address these gaps, we present a comprehensive systematization of knowledge on self-sovereign digital identities, with a primary focus on identifying the challenges that impede real-world adoption. We survey 80 academic and non-academic sources and identify six major challenges: (i) binding a single identity to one individual or organization, (ii) the absence of mature cryptographic and communication protocols, (iii) significant usability barriers, (iv) regulatory and oversight gaps, (v) bootstrapping to critical-mass adoption, and (vi) dependence on a permissionless, decentralized, yet singular infrastructure that may expose unforeseen vulnerabilities over time. We then analyze 47 scientific publications and find that the vast majority focus on blockchain-based solutions rather than generalized SSDI architectures. Additionally, we catalog 12 real-world, production-grade SSDI applications. Our evaluation of these solutions reveals that self-sovereignty is, in practice, a spectrum rather than a binary property. Finally, we explore the frontiers of SSDI by identifying major trends, open problems, and opportunities for future research. We hope this systematization will help advance the shift from centralized to self-sovereign digital identities in a disciplined and impactful way.