OpenRaiser/Intern-Atlas huggingface dataset:
Can LLM-Simulated Practice and Feedback Upskill Human Counselors? A Randomized Study with 90+ Novice Counselors
Venue: arXiv cs.HC 2026
Year: 2026

The growing demand for accessible mental health support requires training more counselors, yet existing approaches remain resource-intensive and difficult to scale. LLMs can realistically simulate patients and generate actionable feedback for training, but their actual impact on novice counselor skill development remains unknown. We developed an LLM-simulated practice and feedback system and conducted a randomized study with 94 novice counselors, comparing practice alone versus practice with feedback. We evaluated behavioral performance, self-efficacy, and qualitative reflections. Results showed the practice-and-feedback group improved in client-centered microskills (reflections, questions), while the practice-alone group showed no improvements. For empathy, the practice-alone group declined over time and performed significantly worse than the feedback group. Qualitative interviews reinforced these findings: feedback helped participants adopt a client-centered listening approach, while practice-alone participants remained solution-oriented. These results suggest LLM-based training systems can promote effective skill development, and combining simulated practice with structured feedback is critical for meaningful improvement.
Grasp the Graph (GtG) 2.0: Ensemble of Graph Neural Networks for High-Precision Grasp Pose Detection in Clutter
Venue: arXiv cs.RO 2026
Year: 2026

Grasp pose detection in cluttered, real-world environments remains a significant challenge due to noisy and incomplete sensory data combined with complex object geometries. This paper introduces Grasp the Graph 2.0 (GtG 2.0) method, a lightweight yet highly effective hypothesis-and-test robotics grasping framework which leverages an ensemble of Graph Neural Networks for efficient geometric reasoning from point cloud data. Building on the success of GtG 1.0, which demonstrated the potential of Graph Neural Networks for grasp detection but was limited by assumptions of complete, noise-free point clouds and 4-Dof grasping, GtG 2.0 employs a conventional Grasp Pose Generator to efficiently produce 7-Dof grasp candidates. Candidates are assessed with an ensemble Graph Neural Network model which includes points within the gripper jaws (inside points) and surrounding contextual points (outside points). This improved representation boosts grasp detection performance over previous methods using the same generator. GtG 2.0 shows up to a 35% improvement in Average Precision on the GraspNet-1Billion benchmark compared to hypothesis-and-test and Graph Neural Network-based methods, ranking it among the top three frameworks. Experiments with a 3-Dof Delta Parallel robot and Kinect-v1 camera show a success rate of 91% and a clutter completion rate of 100%, demonstrating its flexibility and reliability.
Eliminating Out-of-Domain Recommendations in LLM-based Recommender Systems: A Unified View
Venue: arXiv cs.IR 2026
Year: 2026

Recommender systems based on Large Language Models (LLMs) are often plagued by hallucinations of out-of-domain (OOD) items. To address this, we propose RecLM, a unified framework that bridges the gap between retrieval and generation by instantiating three grounding paradigms under a single architecture: embedding-based retrieval, constrained generation over rewritten item titles, and discrete item-tokenizer generation. Using the same backbone LLM and prompts, we systematically compare these three views on public benchmarks. RecLM strictly eradicates OOD recommendations (OOD@10 = 0) across all variants, and the constrained generation variants RecLM-cgen and RecLM-token achieve overall state-of-the-art accuracy compared to both strong ID-based and LLM-based baselines. Our unified view provides a systematic basis for comparing three distinct paradigms to reduce item hallucinations, offering a practical framework to facilitate the application of LLMs to recommendation tasks. Source code is at https://github.com/microsoft/RecAI.
manvr3d: A Platform for Human-in-the-loop Cell Tracking in Virtual Reality
Venue: arXiv cs.HC 2026
Year: 2026

We propose manvr3d, a novel VR-ready platform for interactive human-in-the-loop cell tracking. We utilize VR controllers and eye-tracking hardware to facilitate rapid ground truth generation and proofreading for deep learning-based cell tracking models. Life scientists reconstruct the developmental history of organisms on the cellular level by analyzing 3D time-lapse microscopy images acquired at high spatio-temporal resolution. The reconstruction of such cell lineage trees traditionally involves tracking individual cells through all recorded time points, manually annotating their positions, and then linking them over time to create complete trajectories. Deep learning-based algorithms accelerate this process, yet depend heavily on manually-annotated high-quality ground truth data and curation. Visual representation of the image data in this process still relies primarily on 2D renderings, which greatly limits spatial understanding and navigation. In this work, we bridge the gap between deep learning-based cell tracking software and 3D/VR visualization to create a human-in-the-loop cell tracking system. We lift the incremental annotation, training and proofreading loop of the deep learning model into the 3rd dimension and apply natural user interfaces like hand gestures and eye tracking to accelerate the cell tracking workflow for life scientists.
Satellite Autonomous Clock Fault Monitoring with Inter-Satellite Ranges Using Euclidean Distance Matrices
Venue: arXiv cs.RO 2026
Year: 2026

To address the need for robust positioning, navigation, and timing services in lunar environments, this paper proposes a novel onboard clock phase jump detection framework for satellite constellations using range measurements obtained from dual one-way inter-satellite links. Our approach leverages vertex redundantly rigid graphs to detect faults without relying on prior knowledge of satellite positions or clock biases, providing flexibility for lunar satellite networks with diverse satellite types and operators. We model satellite constellations as graphs, where satellites are vertices and inter-satellite links are edges. The proposed algorithm detects and identifies satellites with clock jumps by monitoring the singular values of the geometric-centered Euclidean distance matrix (GCEDM) of 5-clique sub-graphs. The proposed method is validated through simulations of a GPS constellation and a notional constellation around the Moon, demonstrating its effectiveness in various configurations.
Comparing statistical and deep learning techniques for parameter estimation of continuous-time stochastic differentiable equations
Venue: arXiv cs.LG 2026
Year: 2026

Stochastic differential equations such as the Ornstein-Uhlenbeck process have long been used to model realworld probablistic events such as stock prices and temperature fluctuations. While statistical methods such as Maximum Likelihood Estimation (MLE), Kalman Filtering, Inverse Variable Method, and more have historically been used to estimate the parameters of stochastic differential equations, the recent explosion of deep learning technology suggests that models such as a Recurrent Neural Network (RNN) could produce more precise estimators. We present a series of experiments that compare the estimation accuracy and computational expensiveness of a statistical method (MLE) with a deep learning model (RNN) for the parameters of the Ornstein-Uhlenbeck process.
Reputation as a Solution to Cooperation Collapse in LLM-based MASs
Venue: arXiv cs.AI 2026
Year: 2026

Cooperation has long been a fundamental topic in both human society and AI systems. However, recent studies indicate that the collapse of cooperation may emerge in multi-agent systems (MASs) driven by large language models (LLMs). To address this challenge, we explore reputation systems as a remedy. We propose RepuNet, a dynamic, dual-level reputation framework that models both agent-level reputation dynamics and system-level network evolution. Specifically, driven by direct interactions and indirect gossip, agents form reputations for both themselves and their peers, and decide whether to connect or disconnect other agents for future interactions. Through three distinct scenarios, we show that RepuNet effectively avoids cooperation collapse, promoting and sustaining cooperation in LLM-based MASs. Moreover, we find that reputation systems can give rise to rich emergent behaviors in LLM-based MASs, such as the formation of cooperative clusters, the social isolation of exploitative agents, and the preference for sharing positive gossip rather than negative ones. The GitHub repository for our project can be accessed via the following link: https://github.com/RGB-0000FF/RepuNet.
ItDPDM: Information-Theoretic Discrete Poisson Diffusion Model
Venue: arXiv cs.LG 2026
Year: 2026

Generative modeling of non-negative, discrete data, such as symbolic music, remains challenging due to two persistent limitations in existing methods. Firstly, many approaches rely on modeling continuous embeddings, which is suboptimal for inherently discrete data distributions. Secondly, most models optimize variational bounds rather than exact data likelihood, resulting in inaccurate likelihood estimates and degraded sampling quality. While recent diffusion-based models have addressed these issues separately, we tackle them jointly. In this work, we introduce the Information-Theoretic Discrete Poisson Diffusion Model (ItDPDM), inspired by photon arrival process, which combines exact likelihood estimation with fully discrete-state modeling. Central to our approach is an information-theoretic Poisson Reconstruction Loss (PRL) that has a provable exact relationship with the true data likelihood. ItDPDM achieves improved likelihood and sampling performance over prior discrete and continuous diffusion models on a variety of synthetic discrete datasets. Furthermore, on real-world datasets such as symbolic music and images, ItDPDM attains superior likelihood estimates and competitive generation quality-demonstrating a proof of concept for distribution-robust discrete generative modeling.
Walrus: An Efficient Decentralized Storage Network
Venue: arXiv cs.DC 2026
Year: 2026

Decentralized storage systems face a fundamental trade-off between replication overhead, recovery efficiency, and security guarantees. Current approaches either rely on full replication, incurring substantial storage costs, or employ trivial erasure coding schemes that struggle with efficient recovery especially under high storage-node churn. We present Walrus, a novel decentralized blob storage system that addresses these limitations through multiple technical innovations. At the core of Walrus is RedStuff, a two-dimensional erasure coding protocol that achieves high security with only 4.5x replication factor, while enabling self-healing recovery that requires bandwidth proportional to only the lost data $(O(|blob|/n)$ versus $O(|blob|)$ in traditional systems). Crucially, RedStuff is the first protocol to support storage challenges in asynchronous networks, preventing adversaries from exploiting network delays to pass verification without actually storing data. Walrus also introduces a novel multi-stage epoch change protocol that efficiently handles storage node churn while maintaining uninterrupted availability during committee transitions. Our system incorporates authenticated data structures to defend against malicious clients and ensures data consistency throughout storage and retrieval processes. Experimental evaluation demonstrates that Walrus achieves practical performance at scale, making it suitable for a wide range of decentralized applications requiring high-integrity, available blob storage with reasonable overhead.
Fast and low energy approximate full adder based on FELIX logic
Venue: arXiv cs.AR 2026
Year: 2026

In the "Big Data" era, a lot of data must be processed and moved between processing and memory units. New technologies and architectures have emerged to improve system performance and overcome the memory bottleneck. The memristor is a technology with both computing and memory capabilities. In-Memory Computing (IMC) can be performed by applying memristors to stateful design methods. The Fast and Energy-Efficient Logic in Memory (FELIX) logic is one of the stateful implementation logics compatible with memristive crossbar arrays. The way computations are performed can be changed to improve performance. Approximate design methods can be applied in error-resilient applications. In error-resilient applications, an acceptable amount of precision is lost while features such as hardware complexity, latency, and energy are improved. In this paper, using these two concepts, an approximate full adder circuit with exact Cout and approximate Sum outputs has been proposed using the FELIX design method for IMC in two different implementation approaches. The applied memristor count in the proposed FELIX-based Approximate Full Adder (FAFA) in the two proposed implementation approaches (FAFA1 and FAFA2) is improved by 14.28% and 28.57%, energy consumption is improved by 73.735% and 81.754%, respectively. The number of computational steps in both approaches is improved by 66.66% compared to the exact FELIX-based full adder. In this paper, two different scenarios are considered for evaluating the FAFA. In the 1st and 2nd scenarios, respectively, for the three and four Most Significant Bits (MSBs), the exact full adder is used, and for the five and four Least Significant Bits (LSBs), the FAFA is used. The results of error analysis and evaluations of the FAFA in three different image processing applications confirmed that FAFA has high accuracy and acceptable performance.
VALISENS: A Validated Innovative Multi-Sensor System for Cooperative Automated Driving
Venue: arXiv cs.RO 2026
Year: 2026

Reliable perception remains a key challenge for Connected Automated Vehicles (CAVs) in complex real-world environments, where varying lighting conditions and adverse weather degrade sensing performance. While existing multi-sensor solutions improve local robustness, they remain constrained by limited sensing range, line-of-sight occlusions, and sensor failures on individual vehicles. This paper introduces VALISENS, a validated cooperative perception system that extends multi-sensor fusion beyond a single vehicle through Vehicle-to-Everything (V2X)-enabled collaboration between Connected Automated Vehicles (CAVs) and intelligent infrastructure. VALISENS integrates onboard and roadside LiDARs, radars, RGB cameras, and thermal cameras within a unified multi-agent perception framework. Thermal cameras enhances the detection of Vulnerable Road Users (VRUs) under challenging lighting conditions, while roadside sensors reduce occlusions and expand the effective perception range. In addition, an integrated sensor monitoring module continuously assesses sensor health and detects anomalies before system degradation occurs. The proposed system is implemented and evaluated in a dedicated real-world testbed. Experimental results show that VALISENS improves pedestrian situational awareness by up to 18% compared with vehicle-only sensing, while the sensor monitoring module achieves over 97% accuracy, demonstrating its effectiveness and its potential to support future Cooperative Intelligent Transport Systems (C-ITS) applications.
Time Perception in Virtual Reality: Effects of Emotional Valence and Stress Level
Venue: arXiv cs.HC 2026
Year: 2026

Immersive virtual reality (VR) enables controlled tests of how emotional context distorts subjective time, yet findings remain mixed. Fifty-seven adults explored three five-minute VR environments (tranquil garden, neutral room, threatening sewer) while eye tracking recorded pupil diameter. Baseline stress was assessed before VR exposure. After each environment, participants estimated elapsed time and rated mood, calmness, and arousal. Repeated-measures ANCOVA (baseline stress as covariate) and linear mixed-effects models converged on a valence gradient: garden intervals were overestimated, sewer intervals underestimated, and neutral estimates were intermediate. Pupil diameter showed the complementary pattern (sewer > garden > neutral) and covaried with the magnitude of temporal distortion. Baseline stress did not predict time estimates and did not moderate valence effects. Results indicate that affectively distinct VR contexts reliably shift perceived time and elicit convergent physiological and self-report responses. These findings inform emotion-aware design of immersive experiences and digital-wellbeing safeguards that support safe, sustained immersion.
Multi-Domain Audio Question Answering Benchmark Toward Acoustic Content Reasoning
Venue: arXiv cs.SD 2026
Year: 2026

We present Task 5 of the DCASE 2025 Challenge: an Audio Question Answering (AQA) benchmark spanning multiple domains of sound understanding. This task defines three QA subsets (Bioacoustics, Temporal Soundscapes, and Complex QA) to test audio-language models on interactive question-answering over diverse acoustic scenes. We describe the dataset composition (from marine mammal calls to soundscapes and complex real-world clips), the evaluation protocol (top-1 accuracy with answer-shuffling robustness), and baseline systems (Qwen2-Audio-7B, AudioFlamingo 2, Gemini-2-Flash). Preliminary results on the development set are compared, showing strong variation across models and subsets. This challenge aims to advance the audio understanding and reasoning capabilities of audio-language models toward human-level acuity, which are crucial for enabling AI agents to perceive and interact about the world effectively.
Verified Purely Functional Catenable Real-Time Deques
Venue: arXiv cs.PL 2026
Year: 2026

We present OCaml and Rocq implementations of Kaplan and Tarjan's purely functional, real-time catenable deques. The correctness of our Rocq implementation is machine-checked.
Scalable LLM Reasoning Acceleration with Low-rank Distillation
Venue: arXiv cs.CL 2026
Year: 2026

Due to long generations, large language model (LLM) math reasoning demands significant computational resources and time. While many existing efficient inference methods have been developed with excellent performance preservation on language tasks, they often severely degrade math performance. In this paper, we propose Caprese, a resource-efficient distillation method to recover lost capabilities from deploying efficient inference methods, focused primarily in feedforward blocks. With original weights unperturbed, roughly 1% of additional parameters, and only 20K synthetic training samples, we are able to recover much if not all of the reasoning capabilities lost from efficient inference for thinking LLMs and without harm to language tasks for instruct LLMs. Moreover, Caprese slashes the number of active parameters (~2B cut for Gemma 2 9B and Llama 3.1 8B) and integrates cleanly into existing model layers to reduce latency (>16% time-to-next-token reduction) while encouraging response brevity (up to 8.5% fewer tokens).
Efficient and Reproducible Biomedical Question Answering using Retrieval Augmented Generation
Venue: arXiv cs.IR 2026
Year: 2026

Biomedical question-answering (QA) systems require effective retrieval and generation components to ensure accuracy, efficiency, and scalability. This study systematically examines a Retrieval-Augmented Generation (RAG) system for biomedical QA, evaluating retrieval strategies and response time trade-offs. We first assess state-of-the-art retrieval methods, including BM25, BioBERT, MedCPT, and a hybrid approach, alongside common data stores such as Elasticsearch, MongoDB, and FAISS, on a ~10% subset of PubMed (2.4M documents) to measure indexing efficiency, retrieval latency, and retriever performance in the end-to-end RAG system. Based on these insights, we deploy the final RAG system on the full 24M PubMed corpus, comparing different retrievers' impact on overall performance. Evaluations of the retrieval depth show that retrieving 50 documents with BM25 before reranking with MedCPT optimally balances accuracy (0.90), recall (0.90), and response time (1.91s). BM25 retrieval time remains stable (82ms), while MedCPT incurs the main computational cost. These results highlight previously not well-known trade-offs in retrieval depth, efficiency, and scalability for biomedical QA. With open-source code, the system is fully reproducible and extensible.
An Exploration of Default Images in Text-to-Image Generation
Venue: arXiv cs.HC 2026
Year: 2026

In the creative practice of text-to-image (TTI) generation, images are synthesized from textual prompts. By design, TTI models always yield an output, even if the prompt contains unknown terms. In this case, the model may generate default images: images that closely resemble each other across many unrelated prompts. Studying default images is valuable for designing better solutions for prompt engineering and TTI generation. We present the first investigation into default images on Midjourney. We describe an initial study in which we manually created input prompts triggering default images, and several ablation studies. Building on these, we conduct a computational analysis of over 750,000 images, revealing consistent default images across unrelated prompts. We also conduct an online user study investigating how default images may affect user satisfaction. Our work lays the foundation for understanding default images in TTI generation, highlighting their practical relevance as well as challenges and future research directions.
Towards Fair In-Context Learning with Tabular Foundation Models
Venue: arXiv cs.LG 2026
Year: 2026

Transformer-based tabular foundation models have recently demonstrated promising in-context learning (ICL) performance on structured data, emerging as competitive alternatives to gradient-boosted trees. However, the fairness implications of this new paradigm remain largely unexplored. We present the first investigation of fairness in tabular ICL, evaluating three recently proposed foundation models--TabPFNv2, TabICL, and TabDPT--on multiple benchmark datasets. To mitigate biases, we explore three pre-processing fairness-enhancing methods: correlation removal (decorrelating input features from the sensitive attribute), group-balanced sample selection (ensuring equal representation of protected groups in context examples), and uncertainty-based sample selection (prioritizing context examples with high sensitive-attribute prediction uncertainty). Our experiments show that the uncertainty-based strategy consistently improves group fairness metrics (e.g., demographic parity, equalized odds, and equal opportunity) with minimal impact on predictive accuracy. We release our code to facilitate reproducibility https://github.com/patrikken/Fair-TabICL.
SAD Neural Networks: Divergent Gradient Flows and Asymptotic Optimality via o-minimal Structures
Venue: arXiv cs.LG 2026
Year: 2026

We study gradient flows for loss landscapes of fully connected feedforward neural networks with commonly used continuously differentiable activation functions such as the logistic, hyperbolic tangent, softplus or GELU function. We prove that the gradient flow either converges to a critical point or diverges to infinity while the loss converges to an asymptotic critical value. Moreover, we prove the existence of a threshold $\varepsilon>0$ such that the loss value of any gradient flow initialized at most $\varepsilon$ above the optimal level converges to it. For polynomial target functions and sufficiently big architecture and data set, we prove that the optimal loss value is zero and can only be realized asymptotically. From this setting, we deduce our main result that any gradient flow with sufficiently good initialization diverges to infinity. Our proof heavily relies on the geometry of o-minimal structures. We confirm these theoretical findings with numerical experiments and extend our investigation to more realistic scenarios, where we observe an analogous behavior.