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01.
arXiv (CS.AI) 2026-06-19

On the Limitations of Ray-Tracing for Learning-Based RF Tasks in Urban Environments

Authors:
Armen Manukyan↗Hrant Khachatrian↗Edvard Ghukasyan↗Theofanis P. Raptis↗

arXiv:2507.19653v2 Announce Type: replace-cross Abstract: We study the realism of Sionna v1.0.2 ray-tracing for outdoor cellular links in central Rome. We use a real measurement set of 1,664 user-equipments (UEs) and six nominal base-station (BS) sites. Using these fixed positions we systematically vary the main simulation parameters, including path depth, diffuse/specular/refraction flags, carrier frequency, as well as antenna's properties like its altitude, radiation pattern, and orientation. Simulator fidelity is scored for each base station via Spearman correlation between measured and simulated powers, and by a fingerprint-based k-nearest-neighbor localization algorithm using RSSI-based fingerprints. Across all experiments, solver hyper-parameters are having immaterial effect on the chosen metrics. On the contrary, antenna locations and orientations prove decisive. By simple greedy optimization we improve the Spearman correlation by 5% to 130% for various base stations, while kNN-based localization error using only simulated data as reference points is decreased by one-third on real-world samples, while staying twice higher than the error with purely real data. Precise geometry and credible antenna models are therefore necessary but not sufficient; faithfully capturing the residual urban noise remains an open challenge for transferable, high-fidelity outdoor RF simulation.

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02.
arXiv (quant-ph) 2026-06-16

Noise-induced shallow circuits and absence of barren plateaus

Authors:
Antonio Anna Mele↗Armando Angrisani↗Soumik Ghosh↗Sumeet Khatri↗Jens Eisert↗Daniel Stilck Fran\c{c}a↗Yihui Quek↗

arXiv:2403.13927v3 Announce Type: replace Abstract: Motivated by realistic hardware considerations of the pre-fault-tolerant era, we comprehensively study the impact of uncorrected noise on quantum circuits. We first show that in the task of estimating observable expectation values any noise truncates most quantum circuits to effectively logarithmic depth. We then prove that quantum circuits under any non-unital noise do not exhibit barren plateaus for cost functions composed of local observables. However, by using the effective shallowness, we also design an efficient classical algorithm to estimate observable expectation values within any constant additive accuracy, with high probability over the choice of the circuit, in any circuit architecture. Taken together, our results establish that, unless we carefully engineer quantum circuits to take advantage of the noise, noisy quantum circuits are unlikely to offer an advantage over shallow ones for algorithms that output observable expectation value estimates, such as many variational quantum machine learning proposals.

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03.
arXiv (CS.AI) 2026-06-15

CADET: Physics-Grounded Causal Auditing and Training-Free Deconfounding of End-to-End Driving Planners

Authors:
Zikun Guo↗

arXiv:2606.14438v1 Announce Type: cross Abstract: End-to-end (E2E) autonomous-driving planners trained by imitation are prone to statistical shortcuts: they associate scene elements that merely co-occur with expert actions (a roadside object, a building facade) with driving decisions, rather than the variables that causally determine them. Such causal confusion silently compromises reliability in long-tail scenarios, and it is difficult to detect, because prevailing open-loop metrics (L2 displacement and collision rate) are dominated by ego status and do not indicate whether a planner depends on spurious cues. Existing remedies based on causal-intervention training require retraining large models and cannot audit a planner that is already deployed. We present CADET, a training-free framework that audits, benchmarks, and repairs spurious reliance in pretrained E2E planners without any parameter update.

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04.
arXiv (CS.CL) 2026-06-17

Algorithmic Prompt Generation for Diverse Human-like Teaming and Communication with Large Language Models

Authors:
Siddharth Srikanth↗Varun Bhatt↗Boshen Zhang↗Werner Hager↗Charles Michael Lewis↗Katia P. Sycara↗Aaquib Tabrez↗Stefanos Nikolaidis↗

Understanding how humans collaborate and communicate in teams is essential for improving human-agent teaming and AI-assisted decision-making. However, relying solely on data from large-scale user studies is impractical due to logistical, ethical, and practical constraints, necessitating synthetic models of multiple diverse human behaviors. Recently, agents powered by Large Language Models (LLMs) have been shown to emulate human-like behavior in social settings. But, obtaining a large set of diverse behaviors requires manual effort in the form of designing prompts. On the other hand, Quality Diversity (QD) optimization has been shown to be capable of generating diverse Reinforcement Learning (RL) agent behavior. In this work, we combine QD optimization with LLM-powered agents to iteratively search for prompts that generate diverse team behavior in a long-horizon, multi-step collaborative environment. We first show, through a human-subjects experiment, that humans exhibit diverse coordination and communication behavior in this domain. We then present a series of experiments showing that our approach captures behaviors that are difficult to observe without large-scale data collection, and a follow-up user study to show that these generated behaviors are human-like. Our findings highlight the combination of QD and LLM-powered agents as an effective tool for studying teaming and communication strategies in multi-agent collaboration.

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05.
arXiv (CS.CL) 2026-06-17

From Parasocial Scripts to Dyadic Persistence in Autonomous AI-Agent Communities

Authors:
Mohammadsadegh Abolhasani↗Hamid Reza Firoozfar↗Reza Mousavi↗Paul Jen-Hwa Hu↗

While parasocial interactions (PSIs) and parasocial relationships (PSRs) have been studied in conventional media settings, we investigate whether PSI- (colloquial) relational cues also exist in online communities where both sides are autonomous AI agents. We analyze 4,434 posts and 50,338 comments from Moltbook through three theory-based textual indicators: attachment/intimacy language, reciprocity bids, and self-identification to original poster (OP). The combined results across methods based on keyword matching, few-shot large language model (LLM) annotation, and grouped-context LLM annotation reveal that PSI colloquial cues prevail and are strongly associated with OP re-engagement and a reciprocal reply structure. These results are robust across negative controls, nullification, clustered-standard-error re-estimation, and multiple-testing correction. A dyadic persistence test further affirms reciprocity bids aligned with sustained OP-involving mutual recurrence, providing empirical evidence for bridging interaction-level PSI scripts with PSR-consistent repeated dyadic patterns. We interpret the evidence as a behavioral structure in discourse by LLM-enabled agents.

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06.
Nature (Science) 2026-06-10

Mitochondria directly interact with the nuclear pore complex

Authors:
Ivan Menendez-Montes↗

Mitochondria regulate cellular processes through direct and indirect interactions with other organelles. A well-studied example has been contact with the endoplasmic reticulum at mitochondrial-associated endoplasmic reticulum membranes1, which control pathways including redox and calcium homeostasis2,3. Recent studies have also reported direct mitochondria–nuclear membrane contacts in cancer cells and yeast that promote pro-survival signalling4,5. Here we identify direct interactions between mitochondria and nuclear pores. Using two unbiased proteomic screens, GST pulldown and BioID, we found that VDAC1 was the top mitochondrial candidate that interacts with the filamentous nuclear pore protein RANBP2. In vitro RANBP2 CRISPR knockout, RANBP2 truncation or site-directed mutagenesis of RANBP2–VDAC1 interacting amino acids resulted in reduced mitochondria–nucleus proximity and decreased nuclear ATP and phosphocreatine levels. This was accompanied by a decline in the levels of the nuclear phosphoproteome and downregulation of pathways involved in histone modification, cellular differentiation and transcriptional regulation in vitro. Moreover, deletion of the RANBP2 C-terminal domain in vivo in mice resulted in embryonic lethality due to cardiac and neural crest differentiation defects. Collectively, these results describe a mechanism by which mitochondria directly interact with the nuclear pore complex, a phenomenon critical for regulation of nuclear energetics and cellular differentiation. Undoubtedly, additional roles of this interaction remain to be revealed. Mitochondria interact directly with the nuclear pore complex via VDAC1–RANBP2 binding to sustain nuclear ATP levels.

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07.
arXiv (CS.CV) 2026-06-16

Teacher-Student Structure for Domain Adaptation in Ensemble Audio-Visual Video Deepfake Detection

Authors:
Elham Abolhasani↗Maryam Ramezani↗Hamid R. Rabiee↗

The rapid advancement of generative AI models is leading to more realistic deepfake media, encompassing the manipulation of audio, video, or both. This raises severe privacy and societal concerns. Numerous studies in this area have yielded promising intra-domain results; however, these models frequently exhibit decreased efficacy when faced with data from dissimilar domains. Consequently, recent deepfake detection approaches focus on enhancing the generalization ability through multiple techniques that incorporate all input modalities, including audio, images, and their interactions. In this regard, we propose the EAV-DFD method, a generalized deep ensemble audio-visual model (EAV-DFD) combined with a domain adaptation mechanism utilizing a teacher-student framework to enhance the model's ability to perform and generalize effectively across unseen domains. To evaluate the model's performance, we used the FakeAVCeleb dataset as the primary domain and the DFDC, Deepfake_TIMIT, and PolyGlotFake datasets as an unseen domain. Our experimental results demonstrate that the proposed framework is efficient in domain adaptation, improving AUC performance of the model by 4.09%, 17.94%, and 0.5% on three unseen datasets, using only a small portion of them to train the student model. This leads to a novel deepfake detection model capable of adapting to new domains and interpreting which modality has been manipulated, highlighting the potential of our approach for real-world applications.

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

HiRo: A Compact Four-Directional Hierarchical Reservoir Token-Mixer for Efficient Image Classification

Authors:
Md Farhadul Islam↗Ishan Thakkar↗J. Todd Hastings↗

Recent image classification models must balance local feature modeling, cross-window interaction, and parameter efficiency. Many high-performing architectures rely on fully trainable token-mixers, which improve representation learning but increase parameter count, optimization complexity and computational cost. We propose a parameter-efficient image classification model called HiRo that integrates shifted-window partitioning with multi-directional hierarchical reservoir computing. Images are divided into non-overlapping patches (treated as tokens), linearly projected, normalized, and enriched with 2D sinusoidal positional encodings, then processed within local windows. Inside each window, tokens are scanned in four directions and passed through a two-stage slice-and-mix reservoir module. In the first stage, directional sequences are split into contiguous slices, each processed by its own fixed reservoir with a trainable closed-loop readout. The resulting slice outputs are summarized using the start, end, and mean representations, and then mixed by a second-stage fixed reservoir for each direction. The mixed slice representations are expanded back to the token level and fused with the first-stage outputs, after which the four directional outputs are realigned and averaged. Consecutive blocks alternate between regular and shifted windows to enable cross-window interaction, followed by layer normalization, a residual feed-forward network, and global pooling for classification. This design combines regular and shifted window partitioning with hierarchical multi-directional reservoirs to make an efficient local-to-cross-window token-mixing framework for image classification. Despite using under 1M trainable parameters and significantly lower memory and time than transformer-style baselines, HiRo also achieves 99.46%, 85.57%, and 59.10% accuracy on MNIST, CIFAR-10, and CIFAR-100, respectively.

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09.
arXiv (CS.AI) 2026-06-12

SciR: A Controllable Benchmark for Scientific Reasoning in LLMs

Authors:
Pierre Beckmann↗Marco Valentino↗Andre Freitas↗

arXiv:2606.13020v1 Announce Type: new Abstract: Three paradigmatic forms of inference recur across scientific reasoning: deduction, induction, and causal abduction. Reliably evaluating LLMs on these in scientific settings is currently out of reach: scientific benchmarks built on human annotations are costly and lack mechanistic ground truth, while synthetic logical-reasoning benchmarks do not resemble real scientific documents. We introduce SciR, a benchmark that combines multi-paradigm reasoning with controllable scientific rendering, anchored on three paradigmatic scientific problems. Tasks are generated from formal objects (deduction tree, inductive rule hypothesis, causal graph) to guarantee verifiable answers, then rendered into multi-document scientific discourse via per-track domain-tuned genres. The construction lets us independently vary two difficulty axes: how hard it is to extract the key information needed for inference, and how hard the principled inference itself is. We test six models. Both axes hurt every model, and their effects compound. The rendering even hurts neurosymbolic pipelines, which hand inference to a verified solver. The two axes yield a per-model extraction-vs-inference profile: for instance, reasoning models like deepseek-r1 mostly surpass non-reasoning instruct models on the inference axis. To our knowledge, SciR is the first multi-paradigm scientific-reasoning benchmark with parametric control on both extraction and inference difficulty.

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10.
arXiv (CS.AI) 2026-06-19

Oranits: Mission Assignment and Task Offloading in Open RAN-based ITS using Metaheuristic and Deep Reinforcement Learning

Authors:
Ngoc Hung Nguyen↗Nguyen Van Thieu↗Quang-Trung Luu↗Anh Tuan Nguyen↗Senura Wanasekara↗Nguyen Cong Luong↗Fatemeh Kavehmadavani↗Van-Dinh Nguyen↗

arXiv:2507.19712v3 Announce Type: replace-cross Abstract: In this paper, we explore mission assignment and task offloading in an Open Radio Access Network (Open RAN)-based intelligent transportation system (ITS), where autonomous vehicles leverage mobile edge computing for efficient processing. Existing studies often overlook the intricate interdependencies between missions and the costs associated with offloading tasks to edge servers, leading to suboptimal decision-making. To bridge this gap, we introduce Oranits, a novel system model that explicitly accounts for mission dependencies and offloading costs while optimizing performance through vehicle cooperation. To achieve this, we propose a twofold optimization approach. First, we develop a metaheuristic-based evolutionary computing algorithm, namely the Chaotic Gaussian-based Global ARO (CGG-ARO), serving as a baseline for one-slot optimization. Second, we design an enhanced reward-based deep reinforcement learning (DRL) framework, referred to as the Multi-agent Double Deep Q-Network (MA-DDQN), that integrates both multi-agent coordination and multi-action selection mechanisms, significantly reducing mission assignment time and improving adaptability over baseline methods. Extensive simulations reveal that CGG-ARO improves the number of completed missions and overall benefit by approximately 7.1% and 7.7%, respectively. Meanwhile, MA-DDQN achieves even greater improvements of 11.0% in terms of mission completions and 12.5% in terms of the overall benefit. These results highlight the effectiveness of Oranits in enabling faster, more adaptive, and more efficient task processing in dynamic ITS environments.

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11.
arXiv (CS.AI) 2026-06-16

Exploring Starts Are Not Enough: Counterexamples and a Fix for Monte Carlo Exploring Starts

Authors:
Octave Oliviers↗Glenn Vinnicombe↗

arXiv:2606.15247v1 Announce Type: cross Abstract: The asymptotic behaviour of Monte Carlo Exploring Starts (MCES) is a long-standing open question in reinforcement learning, even in the tabular setting. We investigated the convergence properties of tabular MCES by constructing examples in which the algorithm converges to suboptimal solutions. This paper presents new counterexamples for both initial-visit and first-visit MCES and gives a convergence-restoring modification for the initial-visit case. We show that stable suboptimal solutions may exist for initial-visit MCES with sample-average updates even when greedy actions are updated more often than non-greedy actions on average. However, by scaling learning rates inversely to update frequencies on a state-by-state basis, convergence to optimality is guaranteed. Unlike previous uniformisation methods, this modification is applicable to large-scale problems that require approximating the estimated value function. We then extend the example to show that sample-average first-visit MCES may also converge to suboptimal solutions. This largely settles a fundamental open problem and shows that exploring starts alone do not guarantee convergence to optimality. More broadly, these results highlight that convergence depends critically on the relative size and frequency of updates applied to different actions, making the choice of learning rates and the balance between exploration and exploitation central to the analysis of MCES and the implementation of scalable Monte Carlo control methods.

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12.
arXiv (math.PR) 2026-06-16

A 0-1 Law for Multifractal Spectra via the HGDS Scale Derivative

Authors:
Jokubas Petkevi\v{c}ius↗

arXiv:2606.15850v1 Announce Type: new Abstract: We prove that the multifractal spectrum D(h,omega) of a stochastic process is almost surely deterministic under a scale decorrelation condition on the HGDS scale derivative. The key difficulty is that the pointwise Hölder exponent lives in the germ sigma-algebra, where classical 0-1 laws do not reach. We get around this by working with the geometry accumulation integral G_Lambda, which is a genuine Lebesgue integral over scales and concentrates almost surely. The boundary case – log-correlated fields – is sharp: the variance summability condition fails exactly there.

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13.
arXiv (CS.CV) 2026-06-11

Exploring Adaptive Masked Reconstruction for Self-Supervised Skeleton-Based Action Recognition

Authors:
Shengkai Sun↗Zhiyong Cheng↗Zefan Zhang↗Jianfeng Dong↗Zhihui Li↗Meng Wang↗

Recently, masked skeleton reconstruction models have emerged as strong action representation learners, driving significant progress in self-supervised skeleton-based action recognition. However, existing state-of-the-art methods must predict an exceedingly large number of spatiotemporal patches, significantly prolonging training time. Besides, by treating all spatiotemporal regions equally during reconstruction, these models are distracted from learning the critical motion patterns that underlie action semantics. To address these challenges, we propose Adaptive Masked Reconstruction (AMR), a faster and stronger pre-training framework. We first decouple the decoder from the encoder, enabling flexible prediction of larger spatiotemporal patches and dramatically reducing reconstruction complexity. Given that larger patches contain more complex information, which is challenging to predict and consequently degrades performance, we accordingly introduce an adaptive guidance module. This module identifies regions of high motion informativeness, guiding the model to focus on the most discriminative parts of each patch and alleviating reconstruction difficulty. Experiments on NTU RGB+D 60, NTU RGB+D 120, and PKU-MMD datasets demonstrate that AMR not only accelerates pre-training substantially but also improves downstream recognition accuracy, surpassing current state-of-the-art approaches.

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

X-OPD: Cross-Modal On-Policy Distillation for Capability Alignment in Speech LLMs

Authors:
Di Cao↗Dongjie Fu↗Hai Yu↗Siqi Zheng↗Xu Tan↗Tao Jin↗

While the shift from cascaded dialogue systems to end-to-end (E2E) speech Large Language Models (LLMs) improves latency and paralinguistic modeling, E2E models often exhibit a significant performance degradation compared to their text-based counterparts. The standard Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) training methods fail to close this gap. To address this, we propose X-OPD, a novel Cross-Modal On-Policy Distillation framework designed to systematically align the capabilities of Speech LLMs to their text-based counterparts. X-OPD enables the Speech LLM to explore its own distribution via on-policy rollouts, where a text-based teacher model evaluates these trajectories and provides token-level feedback, effectively distilling teacher's capabilities into student's multi-modal representations. Extensive experiments across multiple benchmarks demonstrate that X-OPD significantly narrows the gap in complex tasks while preserving the model's inherent capabilities.

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

Beyond Self-Attention: Sub-Quadratic Vision Transformers for Fast Image Captioning

Authors:
Chiradeep Ghosh↗Dakshina Ranjan Kisku↗

Image captioning is a challenging and significant task that aims to generate coherent and semantically meaningful textual descriptions for given images. To accomplish this task, it requires a deep understanding of visual content along with the ability to express that understanding in natural language. Despite remarkable progress with transformer-based architectures, existing approaches often suffer from limitations, such as a lack of rich local feature representations and the high computational cost of quadratic self-attention. The proposed model focuses on improving computational efficiency by restructuring the vision transformer architecture. In designing this approach, the standard self-attention mechanism in Vision Transformers is replaced with a probabilistic transformer approach based on a Gaussian Mixture Model (GMM), a soft-clustering technique. Instead of computing pairwise attention among all image patches, the model groups similar patches into a fixed number of clusters using an Expectation-Maximization (EM) algorithm. This clustering-based mechanism reduces the computational complexity from quadratic O(n^2) to linear O(nK), where K

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

Squeeze-Release: Iterative Pruning with Exact Structural Minimization

Authors:
Roman Denkin↗Ida Akerholm↗Prashant Singh↗Ida-Maria Sintorn↗

arXiv:2606.14346v1 Announce Type: cross Abstract: Unstructured pruning produces sparse weight tensors, but the standard implementation keeps tensor shapes unchanged so the deployed model is no smaller than before pruning. We present an exact structural rewrite, which we call minimization, that converts a masked network into a smaller dense network with the same forward function up to floating-point rounding. The Squeeze-Release cycle iterates pruning and minimization with an intermediate release step that re-enables the exact-zero positions inside the compacted tensors as small calibrated noise, turning otherwise wasted capacity back into trainable parameters. Successive cycles use that capacity to find structural redundancy a single pass cannot reach. We additionally introduce CompensatedLayerNorm, a function-preserving replacement for LayerNorm that extends minimization to channel reduction across LayerNorm-equipped residual streams. Squeeze-Release compresses the deployable network to 39x smaller than the unpruned model on a fully-connected model network and 14.8x smaller on modern CNN (ConvNeXt-Tiny), at comparable accuracy. In addition we prove that the rewrite can be extended to transformer architectures.

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

On Rate-Optimal Partitioning Classification from Observable and from Privatised Data

Authors:
Bal\'azs Csan\'ad Cs\'aji↗L\'aszl\'o Gy\"orfi↗Ambrus Tam\'as↗Harro Walk↗

arXiv:2312.14889v4 Announce Type: replace-cross Abstract: In this paper we revisit the classical method of partitioning classification and prove novel convergence rates under relaxed conditions, both for observable (non-privatised) and for privatised data. We consider the problem of classification in a $d$ dimensional Euclidean space. Previous results on the partitioning classifier worked with the strong density assumption (SDA), which is restrictive, as we demonstrate through simple examples. Here, we study the problem under much milder assumptions. We presuppose that the distribution of the inputs is a mixture of an absolutely continuous and a discrete distribution, such that the absolutely continuous component is concentrated on a $d_a$ dimensional subspace. In addition to the standard Lipschitz and margin conditions, a novel characteristic of the absolutely continuous component is introduced, by which the convergence rate of the classification error probability is computed, both for the binary and for the multi-class cases. This bound can reach the minimax optimal convergence rate achievable using SDA, but under much milder distributional assumptions. Interestingly, this convergence rate depends only on the intrinsic dimension of the continuous inputs, $d_a$, and not on $d$. Under privacy constraints, the data cannot be directly observed, and the constructed classifiers are functions of the randomised outcome of a suitable local differential privacy mechanism. In this paper we add Laplace distributed noises to the discretisations of all possible locations of the feature vector and to its label. Again, tight upper bounds on the convergence rate of the classification error probability can be derived, without using SDA, such that this rate depends on $2d_a$.

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19.
arXiv (CS.CL) 2026-06-11

Geometric Metrics and LLMs: What They Measure and When They Work

Authors:
Viacheslav Yusupov↗Anna Antipina↗Ameliia Alaeva↗Danil Maksimov↗Anna Vasileva↗Tatyana Zaitseva↗Alina Ermilova↗Evgeny Burnaev↗Egor Shvetsov↗

We present a systematic stress-test of geometric metrics for LLM evaluation. Rank-based geometric properties of internal representations have shown promise as reference-free quality signals, but the conditions under which they are reliable remain unclear. We evaluate eight commonly-used metrics: intrinsic-dimensionality estimators, spectral norms, and related quantities across six tester models (0.5-8B) and eight generators on contrasting tasks, separating genuine geometric signal from text-length effects and from what standard text statistics already capture. Three findings emerge. First, some metrics (notably Schatten Norm and MOM) mainly reflect output length, and their apparent discriminative power collapses once length is controlled. Second, geometric metrics add modest but real information beyond text statistics: combined with them, a classifier reaches 78% accuracy on 6-way generator identification versus 69% for text statistics alone. Third, rather than tracking a general notion of text quality, the metrics demonstrate only moderate association between the intrinsic-dimensionality and lexical diversity (RTTR). We give use-case-specific recommendations and identify failure detection as the most promising near-term application.

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20.
arXiv (CS.CL) 2026-06-18

Application of integrated gradients explainability to sociopsychological semantic markers

Authors:
Ali Aghababaei↗Jan Nikadon↗Magdalena Formanowicz↗Maria Laura Bettinsoli↗Carmen Cervone↗Caterina Suitner↗Tomaso Erseghe↗

Classification of textual data in terms of sentiment, or more nuanced sociopsychological markers (e.g., agency), is now a popular approach commonly applied at the sentence level. In this paper, we exploit the integrated gradient (IG) method to capture the classification output at the word level, revealing which words actually contribute to the classification process. This approach improves explainability and provides in-depth insights into the text. We focus on sociopsychological markers beyond sentiment and investigate how to effectively train IG in agency, one of the very few markers for which a verified deep learning classifier, BERTAgent, is currently available. Performance and system parameters are carefully tested, alternatives to the IG approach are evaluated, and the usefulness of the result is verified in a relevant application scenario. The method is also applied in a scenario where only a small labeled dataset is available, with the aim of exploiting IG to identify the salient words that contribute to building the different classes that relate to relevant sociopsychological markers. To achieve this, an uncommon training procedure that encourages overfitting is employed to enhance the distinctiveness of each class. The results are analyzed through the lens of social psychology, offering valuable insights.

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21.
medRxiv (Medicine) 2026-06-15

Poly-Social Risk for Hypertension Among Black and Latina Women

Authors:
Belak↗James↗Auguste↗Rana↗Eweka↗De Moor↗Sarma↗Ensing↗Economy↗K. E↗Powe↗C. E↗…

Background: Hypertension is a leading modifiable cardiovascular risk factor prominently influenced by health-related social needs (HRSN). Whether detailed information on HRSN can improve identification of hypertension among minoritized women is unknown. Methods: Black and Latina women aged 18-65 years completed the Centers for Medicare and Medicaid Services Accountable Health Communities Screening Tool, assessing 13 HRSN domains. Hypertension was ascertained by a validated EHR-based algorithm or self-report of hypertension. Logistic regression tested associations of HRSN with hypertension. LASSO regression with 10-fold cross-validation was used to derive a poly-social risk score in the training set (random 70%) and tested in the validation set (30%) against a sociodemographic model (age, race, income, education). Results: Among 1302 participants (mean [SD] age 40.1 [11.3] years, 70.4% Black, 44.3% Latina), higher cumulative burden of HRSN was associated with increased odds of hypertension (adjusted odds ratio [aOR] for each additional domain of HRSN: 1.07 [95% CI 1.01-1.14], P=0.02). Food insecurity (aOR 2.30 [1.37-3.87], P= 0.002), lapse in utilities (aOR 1.44 [1.04-1.96], P=0.02), poor concentration (aOR 1.57 [1.13-2.17], P=0.007), and social isolation (aOR 1.77 [1.14-2.73], P=0.01) were associated with hypertension. In the validation set, the poly-social risk score did not improve discrimination for hypertension vs. the sociodemographic model (AUC 0.76 [95% CI 0.71-0.81] vs. AUC 0.80 [0.75-0.85]). Conclusion: In this cross-sectional analysis of Black and Latina women, greater cumulative social disadvantage was associated with hypertension. While inclusion of HRSN did not improve hypertension prediction beyond conventional sociodemographic indices, findings may inform targeted interventions among minorities at cardiometabolic risk.

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22.
arXiv (CS.AI) 2026-06-15

GAGPO: Generalized Advantage Grouped Policy Optimization

Authors:
Siyuan Zhu↗Chao Yu↗Rongxin Yang↗Zongkai Liu↗Jinjun Hu↗Qiwen Chen↗Yibo Zhang↗

arXiv:2605.13217v1 Announce Type: cross Abstract: Reinforcement learning has become a powerful paradigm for post-training large language model agents, yet credit assignment in multi-turn environments remains a challenge. Agents often receive sparse, trajectory-level rewards only at the end of an episode, making it difficult to determine which intermediate actions contributed to success or failure. As a result, propagating delayed outcomes back to individual decision steps without relying on costly auxiliary value models remains an open problem. We propose Generalized Advantage Grouped Policy Optimization (GAGPO), a critic-free reinforcement learning method for precise, step-aligned temporal credit assignment. GAGPO constructs a non-parametric grouped value proxy from sampled rollouts and uses it to compute TD/GAE-style temporal advantages, recursively propagating outcome supervision backward through time. Combined with group-wise advantage normalization and an action-level importance ratio, GAGPO extracts stable, localized optimization signals directly from multi-turn trajectories. Experiments on ALFWorld and WebShop show that GAGPO outperforms strong reinforcement learning baselines. Further analyses demonstrate faster early-stage learning, improved interaction efficiency, and smoother optimization dynamics, suggesting that GAGPO offers a simple yet effective framework for multi-turn agentic reinforcement learning.

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23.
Nature (Science) 2026-06-17

The ancestors of eukaryotic cells contained a mix of genes from various microbes

Authors: Unknown Author

Reconstruction of the ancestral gene repertoire of eukaryotic cells reveals traces of a series of close, long-term interactions with diverse microorganisms, and a role of viruses in gene exchange. The findings challenge the view that eukaryotic cells evolved from a simple merger of just two organisms. A series of gene-transfer events might have taken place in complex microbial communities.

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24.
arXiv (CS.CL) 2026-06-16

TokenPilot: Cache-Efficient Context Management for LLM Agents

Authors:
Buqiang Xu↗Zirui Xue↗Dianmou Chen↗Chenyang Fu↗Chiyu Wu↗Caiying Huang↗Chen Jiang↗Jizhan Fang↗Xinle Deng↗Yijun Chen↗Yunzhi Yao↗Xuehai Wang↗…

As LLM agents are deployed in long-horizon sessions, context accumulation drives up inference costs. Existing approaches utilize text pruning or dynamic memory eviction to minimize token footprints; however, their unconstrained sequence mutations alter layouts, introducing prefix mismatches and cache invalidation. This reveals a critical trade-off between text sparsity and prompt cache continuity. To address this, we present TokenPilot, a dual-granularity context management framework. Globally, Ingestion-Aware Compaction acts as a framework harness to stabilize prompt prefixes and eliminate open-world environmental noise at the ingestion gate. Locally, Lifecycle-Aware Eviction monitors the ongoing residual utility of context segments, enforcing a conservative batch-turn schedule to offload content segments only when task relevance expires. Experiments on PinchBench and Claw-Eval under both isolated and continuous modes demonstrate that TokenPilot reduces costs by 61% and 56% in isolated mode, and 61% and 87% in continuous mode, while maintaining competitive performance compared to prior systems. TokenPilot has been integrated into LightMem2 at https://github.com/zjunlp/LightMem2.

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25.
arXiv (CS.CV) 2026-06-12

Bridging Modal Isolation in Interleaved Thinking: Supervising Modality Transitions via Stepwise Reinforcement

Authors:
Tingyu Li↗Le Zhou↗Siyuan Li↗Yujun Wu↗Xinglong Xu↗Jingxuan Wei↗Conghui He↗Cheng Tan↗

Interleaved thinking, where a unified multimodal model alternates between textual reasoning and visual generation, has shown promise on spatial and physical tasks. However, in complex long-chain scenarios, we identify a fundamental failure mode: generated images diverge from the textual context while subsequent text ignores the visual evidence, causing the two modalities to alternate without genuinely informing each other. We term this Modal Isolation and attribute it to compounding information loss at modality boundaries. We decompose each reasoning cycle into atomic operations and define modality transition loss, quantifying cross-modal hallucination (text-to-image) and visual utilization deficit (image-to-text) at each boundary. We propose MoTiF (Modality Tiransition Fidelity), a two-stage training framework that directly optimizes these transitions: Reflective SFT trains the model to detect and recover from erroneous visual outputs; Flow-GRPO improves image generation fidelity via reinforcement learning. All training signals in MoTiF derive from transition-level fidelity rather than end-task accuracy. Across four visual puzzle benchmarks, this transition-level supervision substantially improves both cross-modal coherence and final task accuracy. The results demonstrate that effective interleaved reasoning requires explicit structural supervision at modality boundaries, not merely scaling or end-task optimization.

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