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01.
arXiv (CS.LG) 2026-06-11

Energy Use of AI Inference, Efficiency Pathways, and Test-Time Scaling

Authors:
Felipe Oviedo↗Fiodar Kazhamiaka↗Esha Choukse↗Allen Kim↗Amy Luers↗Melanie Nakagawa↗Ricardo Bianchini↗Juan M. Lavista Ferres↗

arXiv:2509.20241v2 Announce Type: replace Abstract: As AI inference scales to billions of queries, estimates of per-query energy use are increasingly important for capacity planning, efficiency interventions, and policy. Yet many public estimates assume non-production settings, leading to systematic overestimation. We introduce a bottom-up framework estimating inference energy from token throughput, node power, and overhead under large-scale deployment assumptions. For frontier-scale models (>200B parameters) on H100 nodes, we estimate a median energy of 0.31 Wh/query (IQR 0.16-0.60), indicating widely cited estimates are overstated by 4-20x. In test-time scaling scenarios 15x longer than typical queries, the median energy rises 13x to 3.91 Wh (IQR 2.15-7.05). Across models, serving systems, and hardware, we estimate 8-20x line-of-sight energy reductions. At datacenter scale, serving 1 billion queries/day requires 0.7 GWh; if 10% are long queries, demand rises to 1.7 GWh/day. With efficiency interventions, it falls to 0.8 GWh/day, mitigating the energy impact of test-time scaling.

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02.
medRxiv (Medicine) 2026-06-15

Data-Driven Stochastic Model for Detecting Patientswith Alzheimer's Disease

Authors:
Abeywardana↗G. D↗Tsokos↗

Alzheimer s disease (AD) is a critical neurological disorder that causes the brain to shrink and leads to the eventual death of brain cells, adversely affecting a person s ability to function. AD is a fast-growing disease in the United States and was the fifth leading cause of death among Americans 65 years of age or older in 2023. In the United States 6.9 million people aged 65 or older were diagnosed with AD, along with a high rate of undiagnosed patients. Thus, the objective of our study is to develop a real data-driven predictive model to identify a patient with AD based on eight risk factors: Age, Gender, ADAS-Cog13, Entorhinal, Fusiform, Intracranial Volume (ICV), Amyloid-Beta, and Tau Protein, with a high degree of accuracy. The quality of the model was evaluated using well-established and sophisticated statistical measures: the area under the receiver operating characteristic curve, calibration plot, Hosmer-Lemeshow goodness-of-fit test, and K-fold cross-validation. If a patient is given information on the above risk factors, our proposed binary logistic regression model can classify the patient as having AD or not with at least 98% accuracy.

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03.
arXiv (CS.LG) 2026-06-17

A Bayesian Boolean Matrix Factorization with Application to Copy Number Analysis in Cancer

Authors:
Adolphus Wagala↗Mehmet Samur↗Giovanni Parmigiani↗

arXiv:2606.17491v1 Announce Type: cross Abstract: Binary data factorization is common, but real-valued methods ignore discreteness and yield hard-to-interpret factors. Boolean Matrix Factorization (BooMF) instead decomposes a binary matrix into two lower-rank binary matrices via logical AND and OR, expressing the data as a Boolean disjunction of interpretable patterns. In cancer genomics, BooMF can reveal coordinated feature changes that may drive tumor evolution, unlike rotational or additive decompositions. Most existing BooMF methods are heuristic, greedy, sensitive to initialization, prone to local optima, and do not support principled model selection or uncertainty quantification. We introduce Bayesian Boolean Matrix Factorization (BBMF), a fully conjugate generative model with sparsity-inducing priors. It enforces Boolean constraints, yields interpretable latent factors with coherent uncertainty quantification, and admits Gibbs sampling with closed-form full conditionals. Because cancer evolution often involves widespread, near-simultaneous chromosome-number changes (e.g., whole-genome duplication followed by instability and selection), Boolean factorizations capture these patterns more naturally than additive models. Applied to arm-level copy-number alteration data in multiple myeloma, where entries indicate presence/absence of chromosomal-arm amplifications, BBMF finds a small set of interpretable bicliques linking patient subsets to recurrently co-altered chromosomal arms, providing a compact, biologically meaningful summary of tumor heterogeneity and demonstrating BBMF's utility for uncovering discrete latent structure in complex binary data.

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04.
arXiv (CS.LG) 2026-06-15

Trust but Verify: Mitigating Medical Hallucinations via Post-Hoc Adversarial Auditing and Multi-Agent Feedback Loops

Authors:
Muhammad Osama↗Maheera Amjad↗Zartasha Mustansar↗Arslan Shaukat↗Muhammad U. S. Khan↗

arXiv:2606.14149v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed in healthcare settings, yet their tendency to hallucinate poses risks when clinical decisions are involved. This study examine whether LLMs recommend recently banned or withdrawn pharmaceuticals when answering clinical questions and tests an agent-based method for reducing such errors. We developed a five-agent "Trust but Verify" system using a single LLM backbone. To measure regulatory knowledge obsolescence, we created an adversarial dataset of 103 clinical MCQs where historically correct answers now refer to banned substances. This scale ensures statistical significance across various therapeutic classes. We evaluated three open-access model families (GPT-OSS, Llama-3, Falcon-3) under vanilla and agentic conditions. Performance was measured via pointwise score, label accuracy, Hallucination Error Rate (HER), and Component Fidelity (CF) score. We also observed clinical safety regression in proprietary models. In default configurations, all models showed high hallucination rates, consistently selecting banned drugs that matched training data patterns. Our proposed agentic architecture reduced HER by approximately 53% across models. Pointwise scores shifted from -0.25 (unsafe recommendation) toward 0.0 (appropriate refusal). The safety audit intercepted dangerous outputs even when models' parametric knowledge favored the banned substance. The proposed multi-agent framework offers a model-agnostic method for enforcing regulatory compliance that prioritizes patient safety over fluent text generation. Our work demonstrates a practical approach for deploying autonomous AI systems in safety-critical healthcare settings. It shows how real-time regulatory data can be integrated into LLM pipelines to support clinical decision-making.

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05.
arXiv (CS.AI) 2026-06-16

MADAR: An Address-Free Processor

Authors:
Mohamed Amine Bergach↗

arXiv:2606.15535v1 Announce Type: cross Abstract: In a modern processor, computing is the cheap part. Most of its area and energy go to addressing – moving operands to and from a register file and cache, and running the tags, ports, miss queues, and bypass networks that find a value where it was left. MADAR deletes that machinery by abolishing the address. All state circulates in rings of slots that advance one position per clock; instructions and data ride in the same slots; a value is named by its place in an orbit – a \rp{} coordinate – not by an address; a fixed station computes when a circulating instruction sweeps past its operands, on a schedule set at compile time; and a hierarchy of rings of increasing period replaces the cache hierarchy, movement between them scheduled rather than triggered by a miss. No prior circulating-store, dataflow, or statically scheduled machine combines all four of these. We define the execution model, validate it in a cycle-accurate register-transfer-level implementation, show it compilable – a constructive scheduler emits programs cross-checked against the implementation – and price it with a first-order energy model. The payoff is clearest for AI acceleration: the multiply-accumulate at the heart of every matmul and convolution compiles to a streaming form whose energy per operation stays flat as the reduction grows, and the operand reuse that makes matrix multiplication efficient is carried by the ring-period hierarchy – the memory hierarchy doing by rotation what a cache does by tags. MADAR is a new design point for any computation whose data movement is known before the program runs.

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06.
arXiv (CS.LG) 2026-06-16

Analytic Torsion and Spectral Gap Capture Persistent-Laplacian Performance

Authors:
Jernej Grlj↗Aaron D. Lauda↗

arXiv:2606.16990v1 Announce Type: new Abstract: While persistent Laplacians (PL) offer a richer geometric representation of data than persistent homology, utilizing their full eigenspectrum for learning tasks is often hampered by high dimensionality and the ``varying length'' problem across different filtration scales. We propose a compact spectral representation that distills the persistent Laplacian into three mathematically grounded invariants: Betti numbers, the spectral gap, and analytic torsion. Across benchmark datasets including MNIST, QM-3D, and SKEMPI WT, we demonstrate that this reduced feature space captures the essential predictive signal of the full spectrum, and in some cases outperforms it, while significantly reducing computational overhead and preventing the noise introduced by higher-frequency eigenvalues. Our results suggest that these invariants provide a principled, fixed-length interface between spectral geometry and topological learning.

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07.
arXiv (CS.CV) 2026-06-19

Fast Human Attention Prediction for Fixation-guided Active Perception in Autonomous Navigation

Authors:
Fatma Youssef Mohammed↗Grzegorz Malczyk↗Kostas Alexis↗

Human visual attention relies on structured scanpaths to efficiently process scenes, yet instilling this behavior into robot autonomy is in its infancy and hindered by the high,computational costs of existing predictive models. To address this, we introduce GazeLNN, a computationally lightweight,scanpath prediction model that leverages Liquid Neural Networks as its recurrent engine and employs MobileNetV3 for feature extraction. Operating auto-regressively, the architecture predicts sequential fixation heatmaps conditioned on the current visual stimulus and fixation history. Despite requiring only 0.61 GFLOPs, GazeLNN achieves state-of-the-art performance on the MIT Low Resolution dataset achieving 0.47 ScanMatch score. It outperforms existing recurrent baselines across diverse evaluation metrics, while reducing computational costs by 99.40% and accelerating inference by up to six times. To investigate the role of human attention modeling in robot autonomy and demonstrate the practical utility of this highly efficient architecture, we integrate GazeLNN into an active camera-robot control policy trained via Reinforcement Learning. This integration enables human-fixation-guided perception during autonomous navigation, validated through successful real-world deployments on an aerial robot.

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08.
arXiv (CS.AI) 2026-06-16

A comparative and critical study of EEGNet for fNIRS-driven cognitive load classification

Authors:
Mehshan Ahmed Khan↗Houshyar Asadi↗Li Zhang↗Mohammad reza Chalak Qazani↗Ghazal Bargshady↗Stefanos gkikas↗Christian arzate↗Sam Oladazimi↗Zoran Najdovsk↗Lei Wei↗Chee Peng Lim↗

arXiv:2606.16160v1 Announce Type: cross Abstract: Accurately classifying cognitive load from functional near-infrared spectroscopy (fNIRS) signals remains a significant challenge due to temporal variability, inter-subject differences, and sensitivity to preprocessing choices. This study provides a comprehensive evaluation of EEGNet for fNIRS-based cognitive load classification by systematically examining the effects of temporal segmentation strategies (overlapping vs. non-overlapping), window lengths (10s, 20s, 30s), feature extraction methods (Analysis of Variance (ANOVA), Principal Component Analysis (PCA), Fast Independent Component Analysis (FastICA)), learning rate configurations (fixed and adaptive), and evaluation protocols (random split vs. subject-independent (SI)). Results from random-split experiments show that overlapping segmentation, combined with smaller fixed learning rates (0.01-0.001), yields the highest accuracies, due to temporal redundancy and dense sampling of hemodynamic transitions. However, SI evaluation reveals a substantial drop in accuracy, demonstrating limited generalization to unseen participants. Under SI evaluation, non-overlapping segmentation outperformed overlapping windows, with the best accuracy of 56.11% achieved using PCA features with a 20-second window and a 0.1 learning rate. These findings indicate that eliminating temporal redundancy helps the model learn more robust and generalizable representations of cognitive load across individuals. Although adaptive learning rate strategy improved training stability, it did not surpass the performance of optimally selected fixed learning rates. The study highlights the critical role of segmentation strategy and learning rate selection in improving model generalization and identifies methodological considerations essential for developing reliable, real-time, and SI cognitive load classification systems using fNIRS.

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09.
arXiv (CS.LG) 2026-06-16

Phase-Localized Curation Does Not Help: A Negative Result on Per-Phase Metric Selection for Demonstration Filtering

Authors:
Aarav Bedi↗

arXiv:2606.15064v1 Announce Type: new Abstract: Manipulation demonstrations have temporal phase structure, and a natural hypothesis is that demonstration-curation metrics should be applied within phases rather than globally. The idea is to segment each trajectory into phases, score each phase with the metric that is locally most informative, and then aggregate. This follows directly from prior work showing that a single global metric can be the best detector of a defect and yet the worst curator of the resulting policy. We test the per-phase hypothesis on three contact-rich LIBERO pick-and-place tasks with a controlled early-release structural defect, comparing phase-gated curation against the same metrics applied uniformly and against a strong single global metric. Across all three tasks and five random seeds per condition, phase-gated curation is never the best curation strategy, and it is the worst of the three on two of the three tasks (Task 1: 86.0 vs. 92.0 for global; Task 3: 22.7 vs. 48.0 for uniform). We trace the failure to a concrete mechanism. When the defect signal is concentrated in a single phase, rank-aggregating across phases dilutes that signal with uninformative scores from defect-free phases, selecting a worse demonstration subset than simply applying the defect-informative metric everywhere. We further show that the per-phase metric selection does not transfer across tasks, since no phase shares a winning metric between any two tasks, so the selection cannot be reused and must be re-derived per task from a noisy sweep. These results bound a plausible and previously untested method, and they argue that practitioners should prefer identifying a single defect-informative metric over decomposing curation by phase. We release the full pipeline, all metric implementations, and per-seed results.

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10.
arXiv (CS.AI) 2026-06-16

GAS-Leak-LLM: Genetic Algorithm-Based Suffix Optimization for Black-Box LLM Jailbreaking

Authors:
Aman Anifer↗Vignesh Kumar Kembu↗Vishnu M↗Antonino Nocera↗Vinod P↗Amal Murali PK↗Akshay S Rajan↗

arXiv:2606.15788v1 Announce Type: cross Abstract: Large Language Models (LLMs) constitute pivotal components within the AI-dominated information technology ecosystem. To mitigate risks associated with harmful or policy-violating outputs, commercial systems employ advanced alignment strategies and multi-layered content moderation mechanisms. Despite these safeguards, recent research has demonstrated that LLMs remain vulnerable to adversarial manipulation, particularly through jailbreaking and prompt injection techniques. In this work, we propose GAS-Leak-LLM a novel jailbreaking attack based on a genetic algorithm that systematically evolves adversarial suffix to bypass safety constraints. Operating in a strict black-box setting, our method requires no access to model parameters or internals, thereby reflecting realistic threat scenarios in deployed systems. Through the iterative application of selection, mutation, and crossover heuristics, the framework systematically explores the discrete prompt space to identify high-fitness adversarial suffixes. Empirical findings reveal critical shortcomings in existing safety enforcement mechanisms and confirm the effectiveness and practical viability of the proposed attack.

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11.
arXiv (CS.CL) 2026-06-18

UniECG: Understanding and Generating ECG in One Unified Model

Authors:
Jiarui Jin↗Haoyu Wang↗Xiang Lan↗Jun Li↗Hongyan Li↗Shenda Hong↗

Electrocardiogram (ECG) interpretation is a fundamental skill in medical education, yet students often need more than static examples to connect waveform evidence with diagnostic reasoning. This paper presents UniECG as a step toward interactive ECG education. UniECG supports two complementary learning interactions: given an ECG signal or image, it generates an evidence-based explanation; given a textual learning objective, it generates a corresponding ECG signal example for case-based learning. The model follows a two-stage design. First, it learns grounded ECG explanation from ECG signal–image–text data. Second, it introduces special ECG generation tokens and aligns their hidden representations with a pretrained text-conditioned ECG diffusion model, enabling controllable signal-level ECG generation. We evaluate UniECG through grounded ECG explanation and generation-oriented qualitative analysis, examining its potential to support explanation and case-based learning. UniECG is intended as an educational aid and a research step toward interactive AI-assisted ECG learning, rather than a clinically validated diagnostic system.

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12.
arXiv (quant-ph) 2026-06-12

Schrödinger Symmetry in Spherically-symmetric Static Mini-superspaces with Matter Fields

Authors:
Taishi Sano↗Yuki Yokokura↗

arXiv:2512.13651v3 Announce Type: replace-cross Abstract: Schr\"{o}dinger symmetry has been shown to emerge in a ``fluid limit" from the full superspace to several mini-superspace models. To investigate one aspect of the robustness of this emergent symmetry, we consider two spherically-symmetric static mini-superspace models with matter fields at the classical level: (i) a Maxwell field with a cosmological constant and (ii) $n$ massless scalar fields. By developing a method based on canonical transformations, we demonstrate that for model (i), 3D Schrödinger symmetry emerges, and the solution is the (anti-)de Sitter Reissner-Nordström spacetime, and for model (ii), $(2+n)$D Schrödinger symmetry appears, and the solution is a generalized Janis-Newman-Winicour spacetime and its ``interior", a Kantowski-Sachs type closed universe. Furthermore, for the vacuum model, we find that 2D Schrödinger symmetry holds with different lapse functions and mini-superspace coordinates, suggesting the potential, yet unconfirmed, covariance of the symmetry. Finally, we propose a physical interpretation of the symmetry under the Hamiltonian constraint $H$: symmetry generators commuting with $H$ map a solution to another one, while those non-commuting with $H$ generate a new theory with the Schrödinger symmetry and the transformed configuration is a solution to the new theory. These results reinforce the robustness of the emergent Schrödinger symmetry and open new frontiers for exploring dynamics of matter and gravity.

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13.
arXiv (CS.AI) 2026-06-15

Beyond LoRA: Is Sparsity-Induced Adaptation Better?

Authors:
Elijah Cadenhead↗Cristian McGee↗Xin Li↗El Houcine Bergou↗Aritra Dutta↗

arXiv:2606.13767v1 Announce Type: cross Abstract: Low-rank adaptation (LoRA) and its variants provide a memory- and compute-efficient alternative to full fine-tuning of pre-trained models. However, questions remain about the comparative generalizability of these approaches and how the structural restrictions on low-rank updates preserve effective adaptation performance. We present a historical framing, covering the past (full fine-tuning and original LoRA), the present (different variants of LoRA), and propose simpler, cheaper, parameter-efficient extensions by inducing sparsity within existing LoRA variants: Cheap LoRA (cLA), training a single low-rank factor with the other fixed (deterministically or, in its randomized variant, stochastically), and the chained circulant variant, ${c}^3$LA. We frame cLA as a structured instance of asymmetric LoRA, serving as a controlled column-subspace restriction of full fine-tuning. We derive information-theoretic generalization error bounds for these variants, marking one of the first endeavors in this area. Empirically, we evaluate 11 fine-tuning methods across 10 pre-trained models and 14 datasets, analyzing the fine-tuned models' performance and generalization using tools such as loss landscapes and spectral analysis. Despite the sensitivity of fine-tuned models to the pre-trained model, datasets, and other factors, our study suggests that restricting LoRA-based PEFT methods' adaptation to a sparse, structured column space remains competitive across tasks with their parameter-matched baselines while reducing up to 10% training time and peak GPU memory up to 15%, even with a naïve, non-optimized, sparse implementation. Our theoretical and empirical generalization measures provide a more consistent and principled approach to their cost-effective adaptation than commonly used analytical tools. Overview and code are available at: https://elicaden.github.io/Beyond_LoRA/.

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14.
medRxiv (Medicine) 2026-06-22

Histologically validated diffusion MRI signatures of neuroinflammation and neurodegeneration in Alzheimer disease

Authors:
Wang↗Jiang↗Luna↗Niu↗Nan↗Sun↗Perrin↗R. J↗Franklin↗Cruchaga↗Flores↗Benzinger↗…

Noninvasive neuroinflammation measurement remains a major barrier for Alzheimer disease (AD) therapeutics. We present generalized diffusion basis spectrum imaging (g-DBSI), a diffusion MRI framework that decomposes the tissue signal into biologically interpretable microstructural compartments. In postmortem Knight ADRC brains, g-DBSI-derived restricted isotropic fraction (RIF) and restricted anisotropic fraction (RAF) mapped cellularity and neurofilament density, while their ratio (RIF/RAF) tracked inflammatory cell density and peri-plaque amyloid-beta with higher specificity and regional consistency than RIF alone. In 112 living Knight ADRC participants stratified by PET amyloid, g-DBSI metrics showed amyloid-dependent trajectories: in low-amyloid individuals, RIF and RAF rose together with amyloid, consistent with early neuropil expansion and glial elaboration, whereas in high-amyloid individuals, RIF/RAF increased, and RAF declined, indicating established neuroinflammatory remodeling and neurofilament loss. CSF proteomics linked RIF/RAF to glia-enriched immune and vascular pathways, supporting g-DBSI as a clinically compatible MRI biomarker of neuroinflammation and neurodegeneration in AD.

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15.
arXiv (CS.CL) 2026-06-17

EComAgentBench: Benchmarking Shopping Agents on Long-Horizon Tasks with Distributed Hidden Intent

Authors:
Zeyao Du↗Tong Li↗Haibo Zhang↗

As LLM-based shopping agents enter production, existing benchmarks fail to capture how a shopper's requirements arrive: stated implicitly in the query, recorded in a profile, or revealed only when the right question is asked. Benchmarks that expose full intent upfront and grade only the final choice can neither pose this long-horizon challenge nor explain which requirement an agent missed. To address this gap, we introduce EComAgentBench, a benchmark of 662 tasks grounded in real Amazon products and reviews. Each task scatters these requirements across a visible query, a tool-gated profile, and scripted clarification; an agent must uncover hidden intent, verify candidates against attributes and review evidence, and commit to a single product within 100 tool calls. Moreover, typed, source-tagged rubrics grade every task, attributing each failure to a requirement and its source. Construction is automated yet reliable, with every answer fixed in code before any text is generated and every sample validated. Our evaluation of seven models reveals that even the strongest attains only 57.1% overall accuracy, and rubric satisfaction degrades from visible to hidden sources. Overall, we believe EComAgentBench will serve as a reproducible foundation for moving shopping agents from single-query search toward dependable assistance over long horizons.

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16.
arXiv (CS.AI) 2026-06-12

Counterfactual Credit Policy Optimization for Multi-Agent Collaboration

Authors:
Zhongyi Li↗Wan Tian↗Jinju Chen↗Huiming Zhang↗Yang Liu↗Yikun Ban↗Fuzhen Zhuang↗

arXiv:2603.21563v5 Announce Type: replace Abstract: Collaborative multi-agent large language models (LLMs) can solve complex reasoning tasks by decomposing roles, but reinforcement learning for such systems is limited by credit assignment: shared terminal rewards obscure individual contributions and can encourage free-riding. We introduce two optimizer-agnostic credit assignment methods for converting joint outcomes into agent-specific learning signals. Counterfactual Credit for Policy Optimization (CCPO) estimates an agent's marginal contribution by comparing the realized joint outcome with a counterfactual outcome where that agent is removed. Self-Evaluated Credit for Policy Optimization (SEPO) uses constrained self- and peer-evaluations as a verifier-anchored credit signal while keeping the external task outcome dominant. Both operate at the reward-construction layer rather than as policy optimizers, producing role-specific rewards or advantages for GRPO, GSPO, or REINFORCE++. We instantiate these credit signals in a sequential Think–Solve setting and evaluate them on mathematical reasoning benchmarks. Results show that explicit credit assignment often improves dual-agent reasoning, especially on MATH500 and several out-of-distribution settings, while gains vary across models and datasets. Our code is available at: https://github.com/bhai114/ccpo.

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17.
bioRxiv (Bioinfo) 2026-06-11

OCOO-T : A SIMPLE AND SCALABLE VIRTUAL CELL MODEL FOR TRANSCRIPTIONAL PERTURBATION RESPONSE PREDICTION

Authors:
Zhao↗Lai↗Jiang↗An↗

Predicting single-cell transcriptional responses to genetic, chemical and cytokine perturbations is a fundamental challenge in computational biology and AI Virtual Cell (AIVC) modeling, with direct implications for drug discovery and the elucidation of gene regulatory networks. Existing approaches often rely on auxiliary cell-state encoders, hierarchical variational autoencoders, dedicated Transformer encoder-decoder modules, or gene-interaction priors to compress high-dimensional expression profiles into latent representations. While effective, these designs increase architectural complexity and may limit scalability and generalizability. This paper introduces OCOO-T, a minimalist flow-matching-based AIVC model for transcriptional perturbation response prediction. OCOO-T utilizes a vanilla Transformer stack that operates directly on continuous gene expression profiles and formulates perturbation response prediction as a continuous-time denoising process. Perturbation embeddings, dosage information, and cell-line/cell-type specificity are integrated through adaptive layer normalization and in-context tokens. Comprehensive evaluations on Tahoe100M, Replogle, and PBMC benchmarks demonstrate that OCOO-T achieves state-of-the-art performance across diverse perturbations and cell types while effectively scaling to long transcriptional profiles through patching and depatching of cellular contexts. By leveraging the simplicity of Transformer-based denoising for single-cell omics, OCOO-T provides an effective and scalable framework for in-silico cellular simulation.

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18.
arXiv (CS.CL) 2026-06-15

AgentSpec: Understanding Embodied Agent Scaffolds Through Controlled Composition

Authors:
Jixuan Chen↗Jianzhi Shen↗Haoqiang Kang↗Zhi Hong↗Qingyi Jiang↗Soham Bose↗Yiming Zhang↗Leon Leng↗Amit Vyas↗Lingjun Mao↗Siru Ouyang↗Kun Zhou↗…

LLM agents are increasingly built not as single model calls, but as scaffolded systems that combine reasoning, memory, reflection, action execution, and learning. While such scaffolds often improve performance, they are often embedded in tightly coupled pipelines, making it difficult to isolate component contributions, compare alternative designs, or understand how module interactions shape agent behavior. We introduce AgentSpec, a modular specification framework that represents embodied agents as typed compositions of reusable policy components with standardized interfaces. AgentSpec standardizes the interfaces among perception, memory, reasoning, reflection, action, and optional learning, enabling components to be swapped and recombined under controlled conditions. We instantiate this framework across DeliveryBench, ALFRED, MiniGrid, and RoboTHOR, and analyze reasoning, memory, reflection, and reinforcement-learning modules across model backbones. Our results show that agent performance is governed by scaffold compatibility and interaction effects rather than isolated module strength. In particular, structured multi-granularity memory improves long-horizon state tracking, reasoning and memory interact non-uniformly across environments, reflection trades off correction and cost, and RL-trained policies compose best when optimized with deployment-time scaffold structure. AgentSpec provides a controlled foundation for studying, comparing, and designing composable LLM agents. Our code, baselines and interactive playground are publicly available at https://agentspec-embodied.github.io.

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19.
medRxiv (Medicine) 2026-06-22

Age-related changes in acoustic cue use for speech-in-speech perception

Authors:
Fish↗DiNino↗

Acoustic cues such as pitch and spatial location allow listeners to attend to a target speaker and ignore competing talkers, aiding speech recognition in background noise. Diminished ability to utilize acoustic cues for speech stream segregation may thus contribute to older adults' challenges hearing in noise. Adults aged 18-74 completed a speech-in-speech identification task with three conditions containing 1) only pitch cues (fundamental frequency), 2) only spatial cues (interaural time differences; ITDs), and 3) both pitch and spatial cues for segregating a target talker from competing talkers. Hearing thresholds at standard and extended high frequencies (EHFs), auditory brainstem responses (ABRs), and digit span scores were acquired to examine the influence of sensory and cognitive factors on use of each acoustic cue for speech-in-speech recognition. Significant differences were observed between cue condition scores indicating that use of the available cue(s) drove performance. ABR metrics were not a significant predictor but digit span scores significantly predicted scores on all three cue conditions. Working memory abilities therefore set a baseline for participants' speech-in-speech recognition regardless of the acoustic content. Hearing thresholds at standard frequencies significantly predicted scores on the Pitch condition. EHF hearing thresholds better predicted Spatial and Both Cue condition performance, suggesting that EHF thresholds represent auditory processing important for coding ITDs. Age group analysis revealed that older adults (aged 40+) performed significantly more poorly on all cue conditions of the speech-in-speech recognition task relative to younger adults. Age-related changes in auditory sensory processing may therefore impair older adults' speech-in-noise perception by reducing their ability to use acoustic cues for segregating target and competing speech.

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20.
arXiv (quant-ph) 2026-06-15

The Magic Barrier before Thermalization

Authors:
Lukas Ebner↗Berndt M\"uller↗Andreas Sch\"afer↗Leonhard Schmotzer↗Clemens Seidl↗Xiaojun Yao↗

arXiv:2510.11681v2 Announce Type: replace Abstract: We investigate the time dependence of anti-flatness in the entanglement spectrum, a measure for non-stabilizerness and lower bound for non-local quantum magic resource, on a subsystem of a linear SU(2) plaquette chain during thermalization. Tracing the time evolution of a large number of initial states, we find that the anti-flatness exhibits a barrier-like maximum during the time period when the entanglement entropy of the subsystem grows rapidly from the initial value to the microcanonical entropy. The location of the peak is strongly correlated with the time when the entanglement exhibits the strongest growth. This behavior is found for generic highly excited initial computational basis states and persists for coupling constants across the ergodic regime, revealing a universal structure of the entanglement spectrum during thermalization. We conclude that quantitative simulations of thermalization for nonabelian gauge theories require quantum computing. We speculate that this property generalizes to other quantum chaotic systems, a conjecture supported by analogous behavior observed in real-time simulations of the mixed-field Ising model.

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21.
arXiv (CS.CV) 2026-06-16

R2RDreamer: 3D-aware Data Augmentation for Spatially-generalized 2D Manipulation Policies

Authors:
Xiuwei Xu↗Haowen Sun↗Angyuan Ma↗Yiwei Zhang↗Zhenyu Wu↗Xiaofeng Wang↗Bingyao Yu↗Zheng Zhu↗Jie Zhou↗Jiwen Lu↗

Spatial generalization is critical for imitation-learned manipulation policies, but achieving it typically requires scaling demonstrations across diverse object poses, robot configurations, and camera viewpoints. Data augmentation from a few source demonstrations offers a practical alternative to costly real-world collection. Simulation-based augmentation can create controllable variation, but requires complex environment and object setup and may introduce a sim-to-real gap. Recent real-to-real methods avoid these issues by jointly editing 3D observations and action trajectories from real demonstrations, yet they still rely on strong 3D scene parsing and geometry completion, and often produce observations tailored to 3D pointcloud policies rather than RGB-based 2D policies. We propose R2RDreamer, a real-to-real demonstration augmentation framework that preserves the geometric consistency of 3D action-observation editing while moving visual completion to 2D video space. Specifically, R2RDreamer first performs lightweight 3D augmentation by editing incomplete object pointclouds and end-effector trajectories in a shared 3D frame; it then projects the edited scene into masked image-space control videos with occlusion-aware reasoning and uses a dense-control image-to-video model to complete temporally coherent RGB observations. Experiments on spatially shifted manipulation tasks with both 2D diffusion-style policies and vision-language-action policies show that R2RDreamer improves spatial generalization from limited source demonstrations, with analyses validating the contributions of 3D editing, occlusion-aware projection, and video completion.

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22.
medRxiv (Medicine) 2026-06-15

Unveiling the Awareness of Private Health Insurance Coverage among Healthcare Professionals in Freetown, Sierra Leone: Insights Extracted from Their Perspectives.

Authors:
Gary↗L. P↗Kamara↗A. N↗Jimmy↗A. I↗Lebbie↗A. P↗

Our study is an assessment of the knowledge, personal coverage, and related determinants of private health insurance as revealed by healthcare professionals in Freetown, the urban capital of Sierra Leone. This study stands as a precursor for Low- and Middle-Income Countries (LMICs), like Sierra Leone, seeking to establish Universal Health Coverage (UHC) to provide healthcare access and coverage through publicly arranged risk pooling, designed to help protect against unmanageable medical costs. In parallel, such countries face significant challenges with achieving sustainable universal coverage due to limited public resources, inefficient allocation systems, uneasy reliance on out-of-pocket payments, and large struggling populations. Our research sheds particular light on how healthcare professionals view their own participation with private healthcare options. A cross-sectional, analytical study was conducted, openly recruiting individuals from various facilities in Freetown. Using the Yamane Formula, a sample size of 109 participants was calculated. STATA 14.0 was used for data analysis. Our findings revealed that 96 (88.9%) participants did not have private health insurance, while 12 (11.1%) did have private coverage. However, 105 (97.2%) reported other modes of health insurance, with only 3 (2.8%) uninsured. Notably, 97.2% expressed willingness to join a private health insurance scheme. Our study found no statistically significant associations between selected indicators (demographic or socioeconomic fac tors) and current insurance coverage among study participants. These results highlight a low prevalence and understanding of private health insurance among healthcare professionals in a representative urban center in Sub-Saharan Africa (SSA), while acknowledging high willingness to enroll. The lack of any significant determinants suggests other unexamined factors, such as cost, accessibility, or awareness, capable of influencing the adoption and implementation of a universal health program.

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23.
arXiv (CS.CV) 2026-06-11

STEAM: Squeeze and Transform Enhanced Attention Module

Authors:
Rishabh Sabharwal↗Ram Samarth B B↗Parikshit Singh Rathore↗Punit Rathore↗

Channel and spatial attention mechanisms introduced in earlier work enhance the representational capabilities of deep convolutional neural networks (CNNs) but often increase parameter and computational costs. While recent approaches focus solely on efficient feature context modeling for channel attention, we aim to model both channel and spatial attention comprehensively with minimal parameters and reduced computation. Leveraging the principles of relational modeling in graphs, we introduce a constant-parameter module, STEAM: Squeeze and Transform Enhanced Attention Module, which integrates channel and spatial attention to enhance the representation power of CNNs. To our knowledge, we are the first to propose a graph-based approach for modeling both channel and spatial attention, utilizing concepts from multi-head graph transformers. Additionally, we introduce Output Guided Pooling (OGP), which efficiently captures spatial context to further enhance spatial attention. We extensively evaluate STEAM for large-scale image classification, object detection and instance segmentation on standard benchmark datasets. STEAM achieves a \(2\%\) increase in accuracy over the standard ResNet-50 model with only a meager increase in GFLOPs. Furthermore, STEAM outperforms the leading modules, ECA and GCT, in terms of accuracy while achieving a threefold reduction in GFLOPs. The code will be made available upon acceptance.

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24.
arXiv (CS.CV) 2026-06-17

Two-Stage Fine-Tuning of ResNet50 for High-Sensitivity Melanoma Detection on Dermoscopic Images

Authors:
Aryan Bhagat↗

Melanoma is the most dangerous form of skin cancer with five-year survival rates exceeding 99% when detected early but falling sharply once the disease spreads. This paper proposes and evaluates a two-stage fine-tuning approach for ResNet50 applied to binary melanoma classification on dermoscopic images. The core challenges addressed are class imbalance and suboptimal transfer learning from single-stage fine-tuning. After stratified train/validation/test splitting, random oversampling was applied exclusively to the training set to achieve a 1:1 class balance. Stage 1 trained only the classification head with the ResNet50 base frozen, while Stage 2 fine-tuned all layers jointly at a low learning rate of 1e-5 to prevent catastrophic forgetting of learned visual features. On an independent test set of 3,826 images, the model achieved an AUC-ROC of 0.9559, accuracy of 88.34%, sensitivity of 87.56%, specificity of 89.13%, and F1-score of 88.29%. An ablation study confirms the two-stage protocol significantly outperforms single-stage fine-tuning, with sensitivity gains of over 4%. Grad-CAM visualizations demonstrate correct lesion localization. A fully deployable Streamlit detection application is provided alongside all training code.

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25.
arXiv (CS.LG) 2026-06-18

Riemannian MeanFlow for One-Step Generation on Manifolds

Authors:
Zichen Zhong↗Haoliang Sun↗Yukun Zhao↗Yongshun Gong↗Yilong Yin↗

arXiv:2603.10718v3 Announce Type: replace Abstract: Flow Matching enables simulation-free training of generative models on Riemannian manifolds, yet sampling typically still relies on numerically integrating a probability-flow ODE. We propose Riemannian MeanFlow (RMF), extending MeanFlow to manifold-valued generation where velocities lie in location-dependent tangent spaces. RMF defines an average-velocity field via parallel transport and derives a Riemannian MeanFlow identity that links average and instantaneous velocities for intrinsic supervision. We make this identity practical in a log-map tangent representation, avoiding trajectory simulation and heavy geometric computations. For stable optimization, we decompose the RMF objective into two terms and apply conflict-aware multi-task learning to mitigate gradient interference. RMF also supports conditional generation via classifier-free guidance. Experiments on spheres, tori, SO(3), and SE(3) demonstrate competitive one-step sampling with improved quality-efficiency trade-offs and substantially reduced sampling cost.

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