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01.

arXiv (math.PR) 2026-06-18 DOI: arXiv:2606.19075

Random Schrödinger operators on manifolds and abstract bounds for multiplier-type operators

作者:

Jean-Claude Cuenin↗Konstantin Merz↗Eduard Stefanescu↗

arXiv:2606.19075v1 Announce Type: cross Abstract: We study random Schrödinger operators on closed Riemannian manifolds with Anderson-type potentials. We prove high-probability spectral inclusion bounds showing that eigenvalues remain close to those of the Laplacian, with deviations controlled by a norm of the potential coefficients. Compared with deterministic bounds, this yields a square-root cancellation gain. The proof is based on a general principle showing that randomisation improves operator norm bounds for multiplier-type operators, which we formulate in both discrete and continuous settings.

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02.

arXiv (CS.AI) 2026-06-12 DOI: arXiv:2606.12814

Stubborn: A Streamlined and Unified Reinforcement Learning Framework for Robust Motion Tracking and Fall Recovery for Humanoids

作者:

Xiao Ren↗Yuhui Yang↗Zongbiao Weng↗Zhijie Liu↗He Kong↗

arXiv:2606.12814v1 Announce Type: cross Abstract: Recent reinforcement learning approaches have shown great promise in improving humanoid motion tracking performance and achieving fall recovery under disturbances. However, most existing works treat motion tracking and fall recovery as different tasks and require multi-stage training with specialized recovery rewards and/or separate recovery policies. Moreover, existing reinforcement learning-based methods often terminate training episodes immediately after severe tracking failures, limiting recovery-oriented exploration in unstable or fallen states. To address the above issues, we propose Stubborn, a streamlined and unified reinforcement learning framework to achieve robust humanoid motion tracking and fall recovery. Specifically, Stubborn uses an asymmetric Actor-Critic architecture and consists of three major components. First, a yaw-aligned tracking representation is adopted to reduce sensitivity to global drift and heading disturbances while preserving gravity-related balance information. Second, we introduce a Bernoulli-based probabilistic termination mechanism that enables the policy to encourage exploration of fall-recovery behaviors under varying failure modes. Third, we propose a probabilistic termination and tracking-error-driven strategy that dynamically reshapes the sampling distribution based on tracking performance, increasing the training efficiency for difficult motion segments and unstable states. Extensive comparisons with SOTA methods and ablation studies show that Stubborn achieved competitive performance, and the proposed probabilistic termination mechanism and adaptive sampling strategy contributed to the performance and robustness gains. For real-world demonstrations, please refer to https://aislab-sustech.github.io/Stubborn/.

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03.

arXiv (CS.CL) 2026-06-16 DOI: arXiv:2606.16910

IMPACTeen: Intentions, Manipulation, Persuasion, Annotations, and Consequences in Teen Communication Dataset

作者:

Aleksander Szcz\k{e}sny↗Wiktoria Mieleszczenko-Kowszewicz↗Maciej Markiewicz↗Beata Bajcar↗Tomasz Adamczyk↗Jolanta Babiak↗Grzegorz Chodak↗Przemys{\l}aw Kazienko↗

IMPACTeen is a dataset of textual social influence scenarios spanning interpersonal, media-based, and digital settings in an adolescent context. It contains 1,021 texts, 5,100 individual annotation records, and gold labels for social influence techniques, with each text annotated from five distinct perspectives: teenagers, parents, psychologists, communication experts, and teachers. The resource was constructed through constrained LLM generation, followed by a two-step human editing and validation phase aimed at ensuring youth-context realism. A multi-dimensional annotation covered influence presence, techniques, intentions, consequences, resistance, reactions, and annotation confidence. The dataset supports research on social influence detection, annotator disagreement, cross-lingual modeling, and the training and evaluation of language models. The dataset was created in Polish and is accompanied by a corresponding English version.

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04.

arXiv (CS.AI) 2026-06-19 DOI: arXiv:2606.20151

Hybrid ANN-SNN Pipeline with Local Plasticity

作者:

Denis Larionov↗Khairutin Shtanchaev↗Mikhail Kiselev↗Mikhail Korovin↗Ivan Tugoy↗

arXiv:2606.20151v1 Announce Type: cross Abstract: This work proposes a hybrid ANN-SNN pipeline that effectively leverages the rich embeddings of pretrained artificial neural networks (ANNs) to enable high-performance spiking neural networks (SNNs). The architecture couples a pretrained EfficientNet encoder with a CoLaNET spiking classifier. We convert the encoder's activations into spike trains via rate-coding and train the subsequent SNN classifier using local, biologically inspired learning rules, bypassing end-to-end gradient propagation. This approach achieves 99.09% accuracy on a 64-class ImageNet benchmark, demonstrating performance on par with conventional deep networks. The work presents a biologically plausible and efficient framework for adapting powerful pretrained encoders to downstream spiking neural network tasks.

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05.

medRxiv (Medicine) 2026-06-17 DOI: HASH:d4e45e358f97aa870f613429179e18d0

Multi-strain Probiotics Alter Gut Microbiota and Estrobolome Pathways in Primary Dysmenorrhea

作者:

Kiraman↗S. K↗Zakaria↗I. A↗Loh↗S. Y. E↗Norfuad↗F. A↗Abdul Ghani↗N. A↗Ahmad↗H. F↗…

Background: Exact cause of primary dysmenorrhoea is unknown but recent evidence uncovers a potential link between gut dysbiosis and benign gynaecological disorder via disruption of estrobolome. Methods: A randomized controlled trial to investigate the effects of multi-strain oral probiotics on primary dysmenorrhoea has been conducted. This is a secondary analysis comparing the stool microbiome in women with primary dysmenorrhoea and those without (control), and the effects of treatment with probiotics versus placebo. Results: Although microbial richness and evenness were comparable between groups (alpha diversity, p > 0.05), gut microbial community composition differed significantly (Bray Curtis PERMANOVA, p = 0.015), characterised by reduced Bifidobacterium adolescentis and Blautia and enrichment of Faecalibacterium in dysmenorrhoea, alongside condition-specific core taxa. Post-intervention analysis revealed significant shifts in microbial community structure between pre- and post-treatment groups (PERMANOVA, F = 2.11, p = 0.005), with probiotic supplementation inducing more consistent and directed microbiome changes than placebo, without altering alpha diversity (p > 0.05). Functional prediction showed no significant difference in overall beta glucuronidase pathway abundance (p > 0.05); however, dysmenorrhoea was associated with higher abundance of beta glucuronidase producing taxa (MaAsLin2, q < 0.05) that were differentially modulated by probiotic treatment. Conclusion: This discovery provides evidence on the microbial disruption in primary dysmenorrhoea as well as the benefit of probiotics to modulate the intestinal microbiota to improve the condition.

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06.

arXiv (CS.CV) 2026-06-16 DOI: arXiv:2606.15982

Mind the Gap: Diagnosing Constraint Discovery Failures in Text-in-Image Editing

作者:

Rui Gui↗

A key challenge in multimodal reasoning is determining which visual dependencies become relevant under a specific task, rather than merely recognizing visible content. We study this through edit-induced constraint discovery in text-in-image editing, a controlled diagnostic setting where a local text change can activate secondary consistency constraints: given a valid editing instruction and an image, can a model identify the secondary regions that must also change? Across 461 diagnostic cases, four MLLMs, and 19 constraint subtypes, models recover only 46% case-level macro recall under unguided prompting versus 94% when constraints are explicitly provided, suggesting that a substantial portion of the failure arises when models must decide which unstated dependencies to surface. Oracle-field decomposition shows that case-specific causal explanations are the most effective partial guidance (0.782 recall), above region names (0.610) or type labels (0.646), suggesting that edit-specific causal cues account for much of the oracle gain. A downstream experiment further shows that higher self-discovery recall does not necessarily improve task performance: unverified self-discovery introduces false positives that offset recall gains, motivating precision-aware constraint elicitation.

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07.

arXiv (CS.CV) 2026-06-16 DOI: arXiv:2602.08029

Dynamic Black-hole Emission Tomography with Physics-informed Neural Fields

作者:

Berthy T. Feng↗Andrew A. Chael↗David Bromley↗Aviad Levis↗William T. Freeman↗Katherine L. Bouman↗

With the success of static black-hole imaging, the next frontier is the dynamic and 3D imaging of black holes. Recovering the dynamic 3D gas near a black hole would reveal previously-unseen parts of the universe and inform new physics models. However, only sparse radio measurements from a single viewpoint are possible, making the dynamic 3D reconstruction problem significantly ill-posed. Previously, BH-NeRF addressed the ill-posed problem by assuming Keplerian dynamics of the gas, but this assumption breaks down near the black hole, where the strong gravitational pull of the black hole and increased electromagnetic activity complicate fluid dynamics. To overcome the restrictive assumptions of BH-NeRF, we propose PI-DEF, a physics-informed approach that uses differentiable neural rendering to fit a 4D (time + 3D) emissivity field given EHT measurements. Our approach jointly reconstructs the 3D velocity field with the 4D emissivity field and enforces the velocity as a soft constraint on the dynamics of the emissivity. In experiments on simulated data, we find significantly improved reconstruction accuracy over both BH-NeRF and a physics-agnostic approach. We demonstrate how our method may be used to estimate other physics parameters of the black hole, such as its spin.

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08.

arXiv (CS.CL) 2026-06-16 DOI: arXiv:2606.15949

FinBalance: A Multi-Document Accounting Reconciliation Benchmark

作者:

Sasank Tumpati↗Devansh Agarwal↗Ayush Kedia↗Arjun Neekhra↗Murari Mandal↗Krishna Garg↗Yash Sinha↗Suman Gupta↗Dhruv Kumar↗

Existing financial-NLP benchmarks mostly evaluate prepared artifacts such as filings, tables, or extracted values. Real accounting begins earlier: source documents must be reconciled into cited journal entries, aggregated into a balance sheet, and checked for contradictions. We introduce FinBalance, a multi-document accounting reconciliation benchmark built from source-document bundles across eight industries, three period types, and five difficulty levels. Human-authored business scenarios, accounting policies, tax/FX treatments, document schemas, distractors, and inconsistency templates are composed by a deterministic generator whose ledger produces journal entries,balance sheets, and 23 inconsistency-code labels. On a 710-record evaluation split, six contemporary LLMs reach at most 46% exact final-balance-sheet accuracy. Four models show a 26-41 pp gap between BS_exact, the model's reported balance sheet, and BS_recon, the balance sheet obtained by replaying its entries through our ledger. Models often recover numerically plausible entries but fail to bind them to supporting documents and aggregate them consistently. Citation-pressure prompting barely changes document-linking errors, while ledger-feedback ablations substantially improve reported balance sheets and expose inconsistency-detection trade-offs. Expert finance reviewers validate the benchmark design and labels.

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09.

arXiv (CS.AI) 2026-06-18 DOI: arXiv:2606.18363

Guava: An Effective and Universal Harness for Embodied Manipulation

作者:

Haowen Liu↗Xirui Li↗Shaoxiong Yao↗Peng Shi↗Tianyi Zhou↗Jia-Bin Huang↗Furong Huang↗Jiayuan Mao↗

arXiv:2606.18363v1 Announce Type: cross Abstract: Language models trained on large-scale vision-language data have demonstrated strong potential for embodied agents. Harnessing models through embodied tools use offers a promising alternative to end-to-end vision-language-action systems by combining high-level reasoning with external modules for perception, planning, and control. However, it remains unclear what makes an effective harness for embodied manipulation, and to what extent such a harness can unlock embodied capabilities in a wide range of reasoning models. In this work, we present Guava, a harness framework for embodied tool use developed through systematic exploration of the design space of agent workflows, action spaces, and observation spaces. Our study identifies three key ingredients for effective embodied agents: iterative perception-reasoning-action loops, semantic action abstractions, and multimodal observations. To understand whether these design principles are universal even to small models, we develop an end-to-end training pipeline that distills embodied manipulation capabilities into a 4B open-source model using fewer than 2K trajectories collected entirely in simulation. Experimental results in both simulation and real-world environments show performance comparable to frontier proprietary models while exhibiting strong generalization to unseen objects, novel instructions, and long-horizon tasks. Results suggest that a well-designed harness can serve as a scalable, model-agnostic interface for embodied manipulation, enabling strong emergent embodied capabilities in compact open-source models with minimal training data.

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10.

arXiv (quant-ph) 2026-06-15 DOI: arXiv:2606.14140

Quantum Entanglement of Bethe States

作者:

Yu Hao↗Yunfeng Jiang↗Bi-Quan Yang↗De-liang Zhong↗

arXiv:2606.14140v1 Announce Type: cross Abstract: We investigate the quantum entanglement of Bethe states across a family of integrable spin chains, including the XXX$_{\frac{1}{2}}$ model, its higher-spin generalizations (XXX$_s$), and the non-compact $SL(2,\mathbb{R})$ chain. For on-shell eigenstates, we perform a comprehensive scan of the bipartite entanglement entropy across the entire spectrum of finite chains with periodic boundary conditions, and identify the Bethe solutions that minimize and maximize the entanglement. These extremal solutions follow systematic, spin-dependent patterns in the Bethe quantum numbers. In the XXX$_{\frac{1}{2}}$ spin chain, for the antiferromagnetic chain, the state with minimal entropy always coincides with the lowest-energy state (the ground state) within a given fixed-magnon sector. For the higher-spin XXX$_s$ model, however, the lowest-entropy state is not always identical to the ground state, and can even be the state of highest energy. By contrast, the Bethe roots that maximize entropy exhibit considerably more intricate structure. Our analysis further reveals how special Bethe root configurations, such as singular and strange solutions, affect entanglement, and it uncovers characteristic entanglement features in the non-compact $SL(2,\mathbb{R})$ chain that are absent from compact spin chains. For off-shell Bethe states, we develop an optimization algorithm that extremizes the entanglement entropy over rapidity distributions, enabling us to explore the maximum entanglement achievable by a Bethe state without imposing the Bethe ansatz equations.

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11.

arXiv (CS.CL) 2026-06-16 DOI: arXiv:2604.03472

Vocabulary Dropout for Curriculum Diversity in LLM Co-Evolution

作者:

Jacob Dineen↗Aswin RRV↗Zhikun Xu↗Ben Zhou↗

Co-evolutionary self-play, where one language model generates problems and another solves them, promises autonomous curriculum learning without human supervision. In practice, the proposer quickly converges to a narrow distribution of problems that satisfy the reward function. This diversity collapse renders the curriculum uninformative for the solver, stalling the co-evolutionary loop. We introduce vocabulary dropout, a random mask applied to the proposer's output logits during both policy training and curriculum generation, as a lightweight mechanism to sustain diversity. The mask is hard and non-stationary, preventing the proposer from locking into fixed token sequences. Training Qwen3-4B and Qwen3-8B on mathematical reasoning via R-Zero, we find that vocabulary dropout sustains proposer diversity across lexical, semantic, and functional metrics throughout training. It also yields solver improvements averaging +4.4 points at 8B, with the largest gains on competition-level benchmarks. Our findings suggest that explicit action-space constraints, analogous to the structural role that game rules play in classical self-play, can help sustain productive co-evolution in language. Vocabulary dropout is one simple instantiation of this principle.

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12.

arXiv (CS.CL) 2026-06-11 DOI: arXiv:2606.11722

ICA Lens: Interpreting Language Models Without Training Another Dictionary

作者:

Sida Liu↗Feijiang Han↗

Finding interpretable directions in language-model representations is critical for understanding and controlling model behavior. Sparse autoencoders (SAEs) have become the standard tool for this purpose, but using them as the default first lens often requires training, storing, and evaluating large overcomplete dictionaries. This bottleneck limits rapid exploration and raises a fundamental question: how much interpretable structure is already visible from activation geometry before training another neural dictionary? Our intuition is simple: many interpretable directions are selective on tokens, and these directions should look less Gaussian than random directions. We therefore revisit independent component analysis (ICA), a classical method for finding non-Gaussian directions, as a compact lens for language-model interpretability. We find that ICA has been underestimated for LLM interpretability, because prior uses often relied on off-the-shelf ICA implementations that are brittle on LLM activations and lacked systematic tools for inspecting and evaluating the recovered directions. To bridge these gaps, we introduce ICALens, the first practical workflow for stable, efficient, and auditable ICA analysis of LLM representations. It combines an optimized GPU-parallel FastICA pipeline with LLM-specific stability recipes and better fitting diagnostics, enabling efficient and reliable layer-wise analysis. Across GPT-2 Small, Gemma 2 2B, and Qwen 3.5 2B Base, ICALens efficiently recovers compact, human-interpretable directions without per-layer gradient-based dictionary training. On SAEBench, ICA is competitive with public SAEs in sparse probing and outperforms them in targeted probe perturbation under small-to-medium budgets. These results suggest that ICA should not be viewed as a weak baseline, but as an efficient and complementary first lens for exploring language-model representations.

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13.

arXiv (CS.AI) 2026-06-16 DOI: arXiv:2606.13693

Limited Marginal Benefit of Reasoning-Heavy LLM Deployment in ESG Narrative Scoring: A 4-Model Consensus Study on Japanese Listed Firms

作者:

Hiroyuki Kokubu↗

arXiv:2606.13693v1 Announce Type: cross Abstract: Automated scoring of ESG narrative disclosures with large language models (LLMs) is gaining traction, yet whether reasoning-heavy frontier models add value commensurate with their cost remains empirically unsettled. We evaluate this question on a corpus of ten Japanese listed firms across three rubric axes – quantitative targets, progress-tracking infrastructure, and external-standard alignment – using a four-model consensus design that combines a reasoning-on frontier model with three reasoning-off contemporaries. Across 120 firm x axis x model scores, the pooled mean absolute deviation between the reasoning-on model and each reasoning-off counterpart is 0.38 on a 5-point scale; only 2% of pairwise comparisons reach a two-point deviation, and none exceeds two points. Per-firm cost accounting shows the reasoning-on arm alone costs roughly 5.6x as much as the three-provider reasoning-off ensemble, for outcomes that differ only within small margins. We conclude that in span-based ESG narrative scoring, reasoning-heavy deployment does not materially improve outcomes relative to reasoning-off consensus, while substantially increasing operational cost. We discuss implications for cost-effective ESG auto-scoring pipelines and LLM deployment governance in applied accountability settings. An earlier version of this work is available on SSRN (Abstract ID 6683303).

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14.

medRxiv (Medicine) 2026-06-11 DOI: HASH:f710aa3045b7bf61af1fb3d51a342e1b

Polygenic risk scores associate with asthma phenotypes and proteomic analyses implicate IL1R1 in two family-based studies

作者:

Lee↗Moll↗Mendez↗Prince↗Lasky-Su↗Lutz↗S. M↗Weiss↗S. T↗Lange↗Kelly↗R. S↗…

Despite its high prevalence and the discovery of hundreds of genetic associations, the genetic determinants and heterogeneous manifestations of asthma remain incompletely understood. Incorporating polygenic risk scores (PRS) into asthma research offers a powerful approach to quantify inherited susceptibility, refine risk profiles, and advance mechanistic understanding of disease development. For this study, we leveraged whole-genome sequencing (WGS) data from two family-based cohorts of childhood asthma - the Genetics of Asthma in Costa Rica Study (GACRS) and the Childhood Asthma Management Program (CAMP) - to examine the transmission profiles of externally derived asthma PRS and their associations with clinical phenotypes in children with asthma. To further elucidate molecular mechanisms, we integrated large-scale external genome-wide association study (GWAS) summary statistics and genetic prediction models of protein abundance in a two-step proteome-wide association study (PWAS) of asthma. Our findings provide robust evidence supporting the validity of externally derived asthma PRS (asthma PRS association p-value p={10}^{-24} [GACRS and CAMP trios combined] for the Global Biobank Meta-analysis Initiative [GBMI]) and reveal consistent associations with spirometry measures and atopy markers across both studies, as 13 of 21 traits (62%) were significantly associated with the GBMI-PRS in the meta-analysis after multiple-testing correction. Moreover, the results of the integrative proteomic analysis implicate IL-1 signaling in the etiology of asthma, reinforcing the candidacy of IL1R1 antagonists for drug repurposing.

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15.

arXiv (CS.AI) 2026-06-11 DOI: arXiv:2606.12207

Intelligent Automation for Embodied Benchmark Construction: Pipelines, Embodiments, Simulators, and Trends

作者:

Jinshan Lai↗Jianwei Hu↗Baoyang Jiang↗Fengchun Zhang↗Leyuan Wang↗Haotian Li↗Yida Wang↗Tingxuan Huang↗Xi Ren↗Qiang Ma↗

arXiv:2606.12207v1 Announce Type: cross Abstract: Embodied intelligence now spans navigation, household assistance, manipulation, autonomous driving, aerial agents, and multimodal large-model control. This expansion has made benchmark construction a central bottleneck for reliable evaluation. Unlike static datasets, embodied benchmarks combine task specifications, environments, robot data, demonstrations, annotations, metrics, evaluation scripts, and release policies into a single evaluation system. This survey reviews the literature through a five-stage construction pipeline: requirement and task construction, data acquisition, data cleaning and annotation, benchmark suite generation and metric definition, and evaluation execution with diagnostic feedback. For each stage, the survey analyzes the transition from manual curation to traditional automation, foundation-model assistance, and agentic closed-loop workflows. It also compares qualitative construction costs across human labor, data and asset acquisition, compute and simulation, validation and debugging, governance and maintenance, and rework risk. The main conclusion is that automation does not simply reduce benchmark cost. Instead, it often shifts cost toward validation, auditability, version control, and long-term governance. Progress in embodied evaluation will therefore depend not only on larger benchmark suites, but also on construction pipelines that are diagnosable, auditable, and responsibly refreshable.

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16.

arXiv (CS.AI) 2026-06-17 DOI: arXiv:2606.17996

Multiple cyclicity and Wavelet Decomposition with Channel Correlation for Long-term Time Series Forecasting

作者:

Bin Wang↗Heming Yang↗Jinfang Sheng↗

arXiv:2606.17996v1 Announce Type: cross Abstract: Cyclicity and trend are important components of time series data and many studies based on cyclicity and trend have achieved good results in long-term time series forecasting. However, we believe that current work neglects the influence of real-world inter-channel correlations in time series data which leads to suboptimal predictions. Furthermore, these models rely on complex designs to capture diverse information so that resulting in low computational efficiency. To address this challenge, we propose McWC, a long-term time series forecasting model that separately models the cyclicity, trend, and inter-channel correlations. Specifically, McWC first decouples cyclical information from data using a multi-layer cyclicity construction module. Then, it extracts inter-channel correlations using multi-layer perceptron. Next, it models and fuses the multi-layer high-frequency and low-frequency information from data using a multi-level wavelet decomposition module. Finally, it aggregates the results of different components to obtain the output. Simultaneously, we decouple intra-channel autocorrelations by calculating a loss function in the frequency domain. Experiments on six real-world datasets demonstrate that McWC achieves state-of-the-art performance, exhibiting excellent computational efficiency and historical information extraction capabilities.

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17.

arXiv (CS.AI) 2026-06-12 DOI: arXiv:2606.13670

Automated reproducibility assessments in the social and behavioral sciences using large language models

作者:

Tobias Holtdirk↗Pietro Marcolongo↗Anna Steinberg Schulten↗Felix Henninger↗Stefan Rose↗Sarah Ball↗Bolei Ma↗Frauke Kreuter↗Markus Weinmann↗Stefan Feuerriegel↗

arXiv:2606.13670v1 Announce Type: new Abstract: Reproducibility in the social and behavioral sciences is typically evaluated by independent researchers who reanalyze the original data to assess whether the published findings can be recovered. However, such approaches are resource-intensive and difficult to scale. Here, we show that large language models (LLMs) can automate reproducibility assessments. Using N=76 published studies with predefined claims from the behavioral and social sciences, we compare LLM-generated analysis with the original findings and human reanalysis. For 7 studies, the LLM could not produce a viable effect size estimate. For the remaining studies, our LLM pipeline recovered the original effect sizes in 41% of studies using a +/-0.05 tolerance in Cohen's d. Further, our LLM pipeline reached the same qualitative conclusion as the original study in 96% of cases, where conclusions indicate whether the reanalysis supports the original claim. For comparison, human reanalysts recovered the original effect sizes in 34% of studies and reached the same qualitative conclusion in 74% of cases. Together, these results show that LLMs can serve as a scalable tool for automated reproducibility assessment and provide a foundation for systematic auditing of empirical results in the social and behavioral sciences.

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18.

arXiv (CS.LG) 2026-06-16 DOI: arXiv:2606.16729

Learning Policy from a Single Trajectory in Average-Reward Markov Decision Process

作者:

Jongmin Lee↗Ernest K. Ryu↗Vaneet Aggarwal↗

arXiv:2606.16729v1 Announce Type: new Abstract: While there is an extensive body of work characterizing the sample complexity of discounted cumulative-reward MDPs, finite sample analyses for average-reward MDPs have been limited, and most existing works rely on restrictive assumptions such as ergodicity or access to a generative model. In this work, we establish the first finite sample complexity guarantees from a single trajectory for weakly communicating average-reward MDPs. To this end, we study the dynamics of a single trajectory in weakly communicating MDPs and based on this analysis, we develop novel model-free methods. Notably, our value-based and policy-based methods provide finite sample complexity guarantees of $\widetilde{O}(1/\varepsilon^2)$ and $\widetilde{O}(1/\varepsilon^4)$ from a single trajectory in weakly communicating MDPs, respectively. Furthermore, we introduce the first model-free method that requires no prior knowledge of problem-dependent quantities for communicating MDPs.

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19.

arXiv (CS.AI) 2026-06-12 DOI: arXiv:2606.12747

Prefill Awareness in Large Language Models

作者:

Andy Wang↗Parv Mahajan↗David Demitri Africa↗Alexandra Souly↗Jordan Taylor↗Robert Kirk↗

arXiv:2606.12747v1 Announce Type: new Abstract: Safety-relevant studies of language models, including alignment and jailbreaking evaluations and AI control protocols, often rely on prefilling model outputs. If AI models can recognize and act on the fact their prior assistant messages have been inserted or edited, the effectiveness and validity of these methods could be compromised. We investigate whether frontier language models can distinguish between tampered and untampered assistant-side context, a capability we call prefill awareness. To do so, we construct a binary preference benchmark across three prefill mechanisms, filtering for cases where models show consistent stances. We find that frontier models show substantial prefill awareness: Claude Opus 4.5 detects prefills opposing its preferences in 9-35% of cases with a 0% false positive rate when prompted; additionally, models often revert towards baseline behavior without explicitly reporting that the prefill was foreign. Controlled ablations later also show that detection and resistance rely on different cues, where stylistic mismatch mainly affects whether models flag a prefill as foreign, while preference mismatch mainly affects whether they revert toward their baseline answer. We also examine more realistic agentic settings such as misalignment-continuation evaluations and SWE-bench trajectories, where frontier models sometimes disavow prefilled assistant turns in ways that depend strongly on dataset, task success, and hidden formatting artifacts. Our results indicate that prefill awareness is already a substantial confound for some prefill-based methods. We recommend that model developers track this capability in frontier systems.

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20.

arXiv (CS.LG) 2026-06-18 DOI: arXiv:2605.04267

QUIVER: Cost-Aware Adaptive Preference Querying in Surrogate-Assisted Evolutionary Multi-Objective Optimization

作者:

Florian A. D. Burnat↗

arXiv:2605.04267v2 Announce Type: replace Abstract: Interactive multi-objective optimization systems face a budget allocation dilemma: one can spend resources on expensive objective evaluations or on eliciting decision-maker preferences that identify the relevant region of the Pareto set. Moreover, preference elicitation itself spans modalities with different information content and cognitive burden, ranging from cheap, noisy pairwise preference statements (PS) to richer but costlier indifference adjustments (IA). We study cost-aware optimization under an unknown scalarization and introduce QUIVER (Query-Informed Value Estimation for Regret), a surrogate-assisted evolutionary multi-objective optimizer that adaptively chooses between objective evaluations and heterogeneous preference queries. At each step, QUIVER selects the next action by maximizing the expected decision-quality improvement per unit total cost. Across DTLZ and WFG benchmarks under synthetic decision-maker models, QUIVER achieves the lowest final utility regret on challenging WFG problems (utility regret of 2.14 on WFG4, 2.82 on WFG9: a 25% improvement over baselines), outperforming all single-modality baselines. We analyze how the optimal mix of PS and IA adapts to problem difficulty: on easy problems (DTLZ2), QUIVER selects 80\% PS queries; on hard problems (WFG9), it shifts to 35% IA queries. This adaptive modality selection demonstrates cost-aware preference learning in action.

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21.

arXiv (CS.LG) 2026-06-16 DOI: arXiv:2606.16562

MIRAGE: Auditing Anti-Muslim Bias in Frontier LLMs Across Reasoning, Agentic, and Time-Coupled Conditions

作者:

Noor Islam S. Mohammad↗Tamim Sheikh↗

arXiv:2606.16562v1 Announce Type: new Abstract: Five years after the discovery of persistent anti-Muslim bias in large language models, most evaluations remain confined to single-turn prompt completion, a setting that no longer reflects how frontier LLMs are deployed. We introduce MIRAGE (Muslim-Identity Reasoning and Agentic Generation Evaluation), a benchmark of 1{,}200 prompts spanning three deployment-realistic conditions: direct completion, chain-of-thought reasoning, and simulated agentic decision-making across content moderation, lending triage, refugee claim summarization, and hiring screens. Across six frontier models, we find that (i) chain-of-thought reasoning amplifies rather than suppresses Muslim-violence associations by 12–34\% relative to direct completion, (ii) agentic decisions exhibit a 9–22 percentage-point asymmetry between Muslim and matched non-Muslim cases on identical evidence, and (iii) bias is sharply time-coupled to retrieved news context, increasing 18–27\% under recent-conflict retrieval. Existing prompt-based mitigations transfer poorly across our three conditions, suppressing direct-completion bias while leaving agentic asymmetry largely intact. We release MIRAGE and an open evaluation harness to support targeted mitigation research.

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22.

medRxiv (Medicine) 2026-06-15 DOI: HASH:765c95e67be1834cc977c6a90b042e1f

Multi-domain AD risk burden and plasma biomarkers in cognitively unimpaired adults

作者:

Li↗M. Y↗Tolosa-Tort↗Heston↗M. B↗Insel↗Andrews↗S. J↗

Introduction: Alzheimer's disease (AD) pathology accumulates decades before symptom onset, yet how the cumulative effect of genetic, familial, and modifiable lifestyle risk burden jointly affects plasma biomarker levels and trajectories in cognitively unimpaired older adults remains unknown. Methods: We analyzed data from 261 participants in the PREVENT-AD cohort. A composite risk score integrating APOE e4 status, polygenic score, family history, and modifiable/lifestyle risk was examined against six plasma biomarkers using linear regression and linear mixed-effects models. Results: APOE e4 was the strongest predictor of plasma biomarker levels. Higher composite risk burden was associated with elevated ptau181, ptau217, ptau217/Ab42, and GFAP levels, and lower Ab42/40 levels. A higher risk burden was predictive of accelerated ptau181 accumulation. Discussion: Cumulative AD risk burden is broadly associated with plasma biomarker levels and specifically predicts accelerated ptau181 accumulation in cognitively unimpaired older adults, supporting structured composite risk profiling as a framework for AD risk stratification.

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23.

arXiv (CS.AI) 2026-06-16 DOI: arXiv:2603.18897

Parallelizing Tool Execution and LLM Generation for Low-Latency Agent Serving

作者:

Yifan Sui↗Han Zhao↗Rui Ma↗Zhiyuan He↗Hao Wang↗Jianxun Li↗Kaiqiang Xu↗Kai Chen↗Yuqing Yang↗

arXiv:2603.18897v2 Announce Type: replace-cross Abstract: LLM-powered agents execute tasks through a sequential loop of model generation and tool execution. Today's serving systems serialize this loop, leaving tool latency exposed on the task critical path. This paper presents PASTE, a tool-aware agent-serving system that predicts concrete future tool invocations from recurring agent patterns and executes them speculatively while the LLM is still generating. PASTE isolates speculative results until confirmed by the LLM and jointly schedules tool execution and returning LLM sessions to avoid shifting bottlenecks to the GPU. Across deep research, coding, and scientific-agent workloads, PASTE reduces average task completion time by 43.5% and lowers observed tool latency by 1.8x.

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24.

arXiv (CS.CL) 2026-06-15 DOI: arXiv:2606.13756

QIAS 2026: Overview of the Shared Task on Islamic Inheritance Reasoning

作者:

Abdessalam Bouchekif↗Somaya Eltanbouly↗Samer Rashwani↗Shahd Gaben↗Mutaz Al-Khatib↗Heba Sbahi↗Emad Mohamed↗Mohammed Ghaly↗

This paper presents a comprehensive overview of the QIAS 2026 shared task, organized as part of the OSACT7 Workshop and co-located with LREC 2026. The shared task was designed to evaluate the ability of large language models to perform complex reasoning in the religious and legal domain of Islamic inheritance. Unlike conventional question-answering benchmarks, QIAS 2026 focuses on end-to-end reasoning from natural language cases, requiring systems to perform the full inheritance calculation process, from identifying the eligible heirs to assigning the correct share to each beneficiary. To support this evaluation, the task was based on the MAWARITH benchmark, a dataset of $12{,}500$ Arabic inheritance cases annotated with intermediate reasoning steps and final answers. System submissions were evaluated using MIR-E, a multi-step metric that measures performance across the main stages of inheritance reasoning. A total of $16$ teams participated in the shared task, investigating a range of approaches, including prompting-based methods, retrieval-augmented generation, and fine-tuning strategies. The results show that Islamic inheritance remains a highly challenging benchmark for current language models, especially in stages that require precise legal interpretation and structured numerical reasoning. This overview summarizes the task design, dataset, evaluation framework, participating systems, and main results.

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25.

arXiv (CS.LG) 2026-06-11 DOI: arXiv:2606.11998

Bootstrapped Monitoring: Leveraging Transparent Reasoning to Oversee Stronger AI Agents

作者:

Frank Xiao↗Mary Phuong↗

arXiv:2606.11998v1 Announce Type: new Abstract: Trusted monitoring is a cornerstone of AI control. However, as frontier models grow more capable, the increasing capabilities gap between trusted and untrusted models may render trusted models unreliable monitors. We introduce bootstrapped monitoring, a protocol that addresses this by inserting a stronger, intermediate untrusted model with transparent chain-of-thought reasoning into the oversight chain. The untrusted monitor ($U_m$) evaluates the agent's actions, while a weaker trusted model ($T$) oversees $U_m$'s reasoning to detect collusion. We evaluate bootstrapped monitoring on multi-turn software engineering tasks (BashArena) across multiple agents and monitors. Bootstrapped monitoring substantially improves catch rates over trusted-only monitoring, even when the untrusted monitor actively colludes with the agent, provided we have access to its raw chain-of-thought. Our results suggest that bootstrapped monitoring can extend the useful lifetime of trusted models in control as AI capabilities advance.

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