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01.
medRxiv (Medicine) 2026-06-12

Effect of tenofovir on the outcomes of COVID-19 in persons with chronic hepatitis B: a nationwide cohort study in Sweden.

Background: Patients with chronic hepatitis B (CHB) may have an increased risk of severe COVID-19. Tenofovir has been hypothesized to confer protection against severe disease, but evidence is inconclusive. We evaluated the risk of severe COVID-19 among CHB patients treated with tenofovir compared with other nucleos(t)ide analogues (NAs). Methods and findings: In this nationwide, registry-based cohort study, we included all adults with CHB and laboratory-confirmed COVID-19 in Sweden between February 2020 and July 2022. Data from national health and socioeconomic registers were linked using unique personal identification numbers (PINs). Patients with HIV, hepatitis C, or hepatitis D coinfection were excluded. Exposure was defined as tenofovir versus other NA therapy. The primary outcome was severe COVID-19, defined as hospitalization >2 days or death within 30 days of diagnosis. Logistic regression was used to estimate adjusted odds ratios (aOR) with 95% confidence intervals (CI), controlling for age, sex, comorbidities, vaccination, socioeconomic status, and region of birth. Among 5,877 CHB patients with COVID-19, 672 were receiving NA therapy (437 tenofovir, 235 other NAs). Severe COVID-19 occurred in 8.0% of tenofovir-treated patients and 14.5% of those receiving other NAs (unadjusted OR 0.52; 95% CI, 0.31-0.85). After adjustment, the association was attenuated and no longer significant (aOR 0.72; 95% CI, 0.39-1.31). Older age, comorbidities, and unvaccinated status were strongly associated with severe disease. Conclusions: The apparent protective effect of tenofovir against severe COVID-19 in unadjusted analyses was largely explained by confounding factors. The risk of severe disease was primarily driven by age, comorbidities, and vaccination status. Prevention of severe COVID-19 in patients with CHB should instead focus on vaccination and management of comorbidities.

02.
medRxiv (Medicine) 2026-06-10

Documented clinical genetic testing among carriers of hereditary breast and ovarian cancer variants: Ancestry and socioeconomic disparities in the All of Us research program

Importance: Hereditary breast and ovarian cancer (HBOC) variant carriers benefit from risk-reducing interventions, but only if identified. The extent to which carriers are clinically recognized, and whether recognition is equitable across diverse populations, is poorly characterized in a single large U.S. cohort. Objective: To estimate P/LP HBOC carrier prevalence across genetic ancestry groups, quantify documented clinical genetic testing among carriers, and evaluate ancestry and socioeconomic disparities in testing. Design, Setting, and Participants: Cross-sectional analysis of the All of Us Research Program Controlled Tier (Curated Data Repository v8/C2024Q3R9), comprising participants with short-read whole genome sequencing and linked electronic health record (EHR) and survey data. Carriers were ascertained from research genomic data independent of clinical testing. Exposures: Genetically inferred ancestry (African [AFR], Admixed American [AMR], East Asian [EAS], European [EUR], Middle Eastern [MID], South Asian [SAS]); self-reported household income and educational attainment. Main Outcomes and Measures: (1) Carrier prevalence with Wilson 95% CIs; (2) documented clinical genetic testing (procedure codes) among carriers; (3) adjusted odds of documented testing among women, by ancestry, before and after socioeconomic adjustment, using multivariable logistic regression. Results: Among 414,830 participants, P/LP HBOC carrier prevalence was 1.42% (95% CI, 1.38-1.45) overall and similar across ancestry groups (AFR 1.24%, AMR 1.32%, EAS 1.19%, EUR 1.52%, MID 1.68%, SAS 1.33%; overlapping CIs). Among 250,071 women in the testing analysis, documented clinical genetic testing was rare: only 74 of 5,878 carriers overall (1.3%) and 59 of 3,572 European-ancestry carriers (1.7%) had a documented test, with counts below reportable thresholds in all other ancestry groups. African-ancestry women had lower adjusted odds of documented testing than European-ancestry women (Model 1 adjusted odds ratio [aOR], 0.32; 95% CI, 0.27-0.39), an association that attenuated but persisted after adjustment for income and education (Model 2 aOR, 0.48; 95% CI, 0.40-0.58; P < 0.001); Admixed American women also had reduced adjusted odds (aOR, 0.71; 95% CI, 0.61-0.84). Lower income and lower education were independently and dose-dependently associated with lower testing odds (income

03.
arXiv (CS.AI) 2026-06-18

Space Is Intelligence: Neural Semigroup Superposition for Riemannian Metric Generation

作者:

arXiv:2606.18828v1 Announce Type: cross Abstract: Traditional approaches place intelligence in the agent, whether as a learned policy or a search procedure. We instead place intelligence in the space itself: a scene induces a Riemannian metric on the configuration manifold, and action reduces to following the geodesics of that metric rather than invoking a separate planner or collision checker. A single Encoder-Router network realizes this idea through three complementary parameter groups – frame parameters that orient the generators, modulation parameters that govern their spatial propagation, and basic coefficients that determine their strength. These groups combine through a shared semigroup-superposition mechanism to produce a single Riemannian metric field, yielding a compact architecture whose geometry scales naturally with scene complexity. Trained on a single two-obstacle scene, the model demonstrates robust zero-shot generalization across unseen obstacle configurations, with orders-of-magnitude separation between collision-free and obstacle-penetrating path costs.

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

Top-Theta Attention: Sparsifying Transformers by Compensated Thresholding

We present Top-Theta (Top-$\theta$) Attention, a training-free method for sparsifying transformer attention during inference. Our key insight is that static, per-head thresholds can be calibrated to retain the desired constant number of significant elements per attention row. This approach enables content-based sparsity without retraining, and it remains robust across data domains. We further introduce compensation techniques to preserve accuracy under aggressive sparsification, establishing attention thresholding as a practical and principled alternative to top-k attention. We provide extensive evaluation on natural language processing tasks, showing that Top-$\theta$ achieves 3-10x reduction in V-cache usage and up to 10x fewer attention elements during inference while degrading no more than 1% in accuracy.

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

Performance-Driven Environment Abstraction with Multi-Timescale Learning

arXiv:2606.17377v1 Announce Type: new Abstract: We study performance-driven environment abstraction for decision-making in large Markov decision processes. Rather than preserving geometric or topological structure, we seek abstractions that directly optimize decision quality. We model abstraction as a controlled approximation obtained by aggregating the state space and enforcing a shared action distribution within each aggregated state. For a fixed partition, we establish a performance guarantee that separates value-function approximation error from the loss introduced by action sharing. Guided by this analysis, we develop a multi-timescale reinforcement learning framework that jointly adapts the policy and a tree-structured environment abstraction. The resulting algorithm refines and coarsens regions of the state space based on Q-value discrepancies, balancing performance against abstraction size and complexity. Empirical results demonstrate substantial state compression, improved sample efficiency, and faster replanning compared to actor-critic baselines.

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

Beyond the GUI Paradigm: Do Mobile Agents Need the Phone Screen?

Recent advances in mobile agents are dominated by the GUI paradigm, in which agents perceive UI information and emit screen interactions. However, mobile platforms also expose a command-line interface (CLI) that provides direct access to device services and data. We argue CLI deserves first-class consideration alongside GUI. We evaluate three coding agents (Claude Code, Terminus-2, mini-swe-agent) across four model APIs on AndroidWorld and MobileWorld without any mobile-specific post-training, comparing against three reproducible GUI baselines (GUI-Owl-1.5-32B, MAI-UI, Qwen3-VL-32B). Claude Code (Opus 4.7) reaches 71.8\% and 51.9\%, outperforming every reproducible GUI baseline (69.3/68.1/57.8\% on AndroidWorld; 43.2/26.3/13.3\% on MobileWorld), while every other CLI configuration remains competitive. To establish the paradigm's ceiling, we provide oracle CLI solutions that reach 88.8\% on AndroidWorld (103/116 tasks CLI-solvable) and 86.3\% on MobileWorld (101/117 tasks CLI-solvable), indicating substantial room for future improvement. To cover everyday user intents beyond the GUI scope, we introduce the CLI-Advantage Task Suite, comprising 45 templates across five categories: bulk operations, multi-condition filtering, aggregation, cross-app workflows, and hidden device state. Every CLI agent outperforms every GUI baseline in all five categories, with substantially fewer steps per task (10.7 vs.\ 18.6). To support future research on mobile CLI agents, we will open-source agent implementations, oracle solutions, the CLI-Advantage suite, and evaluation infrastructure.

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

HRIR-Former: Grid-Free Time-Domain Reconstruction of Head-Related Impulse Responses with a Spatially Encoded Transformer

arXiv:2603.27998v2 Announce Type: replace-cross Abstract: Individualized head-related impulse responses (HRIRs) enable binaural rendering, but dense per-listener measurements are costly. We address HRIR spatial up-sampling from sparse per-listener measurements: given a few measured HRIRs for a listener, predict HRIRs at unmeasured target directions. Prior learning methods often work in the frequency domain, rely on minimum-phase assumptions or separate timing models, and use a fixed direction grid, which can degrade temporal fidelity and spatial continuity. We propose HRIR-Former, a time-domain, grid-free binaural Transformer for reconstructing HRIRs at arbitrary directions from sparse inputs. It uses sinusoidal spatial features, a Conv1D refinement module, and auxiliary interaural time difference (ITD) and interaural level difference (ILD) heads. On SONICOM, it improves normalized mean squared error (NMSE), cosine distance, and ITD/ILD errors over prior methods; ablations validate modules and show minimum-phase preprocessing is unnecessary.

08.
Science (Express) 2026-05-21

DNA polymerization activates RNA cleavage of a reverse transcriptase–like antiviral enzyme | Science

作者: 未知作者

Defense-associated reverse transcriptases (DRTs) transcribe noncoding RNAs (ncRNAs) for antiviral defense, but the mechanisms of ncRNA-independent DRTs remain unclear. In this work, we show that a single DRT4 mediates RNA-targeting antiphage defense by integrating DNA polymerase, exonuclease, and RNA endonuclease activities. First, through an equilibrium between its DNA polymerase and exonuclease activities, DRT4 senses phage infection, as elevated dNTP levels shift the equilibrium toward polymerase activity, thereby promoting protein-primed single-stranded DNA (ssDNA) synthesis. Second, ssDNA of sufficient length, phage DNA-binding proteins, and deoxyguanosine triphosphate collectively activate an unusual RNA endonuclease activity of DRT4, excising 3′–guanosine monophosphate from both phage and host RNA to terminate infection. These findings reveal a distinctive immune strategy combining nucleic acid synthesis and degradation, expanding the functional landscape of DRTs for new DNA- and RNA-processing technologies.

09.
bioRxiv (Bioinfo) 2026-06-21

SPA-C: an hybrid tool to accurately scaffold genomes using Hi-C and Deep-Learning

Genome assembly is a computational pipeline designed to reconstruct chromosomes from small sequencing reads. Following their assembly, contiguous sequences (contigs) are arranged into chromosome-long sequences during scaffolding. Hi-C, a long-range linkage information between regions of the genome widely used in recent large sequencing projects, is often required to correctly order contigs. Several tools have been developed to automate this task following either statistical or deep-learning approaches. Statistical approaches summarise 2D Hi-C matrices into contact densities across sequences, thus ignoring informative visual patterns. The sole existing deep-learning tool uses a transformer-based computer vision model to correct the assembly. It has been trained on several species and uses Hi-C matrices directly. Yet it comes as a supplementary step in the scaffolding process, introducing extra computation time, and has been trained on a dataset that might contain labelling errors, which could provide sub-optimal results. We propose SPA-C, an hybrid pipeline combining the strengths of both approaches. Linkage prediction is handled with a frugal CNN-based model and a graph-solving algorithm is used to generate the scaffolds. Through our input's design, the model is able to both correct errors within assemblies and link contigs, leveraging small, local Hi-C contact matrices. We handled low-complexity regions that might induce erroneous predictions using an external tool, improving the overall accuracy of generated assemblies. On a benchmark of six various genomes and four standard metrics, SPA-C outperformed four out of four state-of-the-art methods while achieving comparable start-to-end computation time.Python and Bash scripts are available on GitHub (https://github.com/SPA-C/SPA-C.git) and Zenodo (https://doi.org/10.5281/zenodo.19000361).

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

Revisiting the Systematicity in Negation in the Era of In-Context Learning

Understanding the meaning of negated sentences remains one of the challenges for language models, even in the era of large language models (LLMs). We analyze systematicity regarding LLM understanding of negation from two perspectives: behavioral systematicity and representational systematicity. For behavioral systematicity, we confirm that through demonstrations and in-context learning, LLMs can recognize negation expressions and scope within sentences to some extent, but they fail to achieve perfect performance. In particular, the difficulty of the negation scope recognition for models varies depending on the output format. For representational systematicity, we analyze the extent to which function vectors can be robustly constructed from in-context examples for tasks that are essential to understanding negation. The experiments suggest that while function vectors can be composed for negation cue extraction tasks, extracting function vectors for recognizing scope is more challenging.

11.
PLOS Computational Biology 2026-06-11

MicroRNA target gene prediction model based on input-feature dependency and sample data expansion technique

作者:

by Yan Shao, Yazhou Li, Hexin Zhai, Shimin Dong Predicting microRNA target genes is essential for understanding their biological functions. This study developed a miRNA target gene prediction model based on input-feature dependency. Features were treated as multiple random variables, with marginal densities estimated using Gaussian mixture models (GMM) and dependencies captured by regular vine (R-vine) copula to derive joint probability density functions. We constructed class-conditional joint densities for positive and negative samples separately using GMM and R-vine copula, then combined these with prior probabilities using Bayes’ rule to obtain posterior probabilities of positive interactions, using a standard 0.5 probability threshold for deterministic prediction. To address insufficient data and class imbalance, hybrid distribution mega-trend diffusion was used to generate virtual samples for data augmentation. Computational validation showed high predictive performance even when only 30% of the training data were used. As proof-of-concept, we experimentally validated one predicted interaction (miR-8485 targeting JAK2) using dual-luciferase, cellular, and animal experiments, confirming the biological relevance of this specific model-generated prediction. These findings provide a valuable tool for understanding miRNA functions and disease mechanisms.

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

Rethinking Structural Anomaly Detection: From Decision Boundaries to Projection Operators

arXiv:2606.15280v1 Announce Type: new Abstract: Most existing anomaly detection methods rely on estimating a probability density or learning an enclosing decision boundary, implicitly assuming that normal data occupies a region of non-zero volume in the ambient space. In contrast, structural anomaly detection considers data that lies near a low-dimensional manifold, creating a mismatch between the inductive bias of existing methods and the structure of the data, often resulting in degraded performance. To address this mismatch, we introduce a geometric perspective. Specifically, we learn a projection operator onto the manifold of normal samples and define a sample as anomalous if it is altered by this projection. This formulation naturally integrates the inductive bias of manifold-supported data and reframes anomaly detection in terms of a projection residual, thereby resolving issues arising from modeling degenerate distributions. Notably, it provides a unifying interpretation of reconstruction-based methods by explaining their success and failure in terms of projection quality. In particular, it explains the strong generalization ability of projection-aligned models as a consequence of contraction behavior toward the manifold. Moreover, by decoupling anomaly detection from probabilistic modeling, it reduces the tendency to misclassify rare but normal samples, a widely recognized limitation of existing approaches. Empirically, we demonstrate that projection-aligned methods achieve strong performance, outperforming boundary-based methods while improving upon existing reconstruction-based approaches.

13.
arXiv (CS.CV) 2026-06-16

Disagreement-Based Cross-Model Routing for Implicit Video Question Answering

We study multiple-choice video question answering on the ImplicitQA benchmark, where the correct answer is never explicitly shown but must be inferred from off-screen events, line-of-sight cues, causal structure, and cross-shot spatial layout. On this benchmark a single frontier video LLM already operates near its accuracy ceiling, and we observe that conventional self-consistency strategies – majority voting across repeated samples of the same model – can hurt rather than help, because the model's errors on hard questions are correlated. We propose disagreement-based cross-model routing, a pure inference-time procedure that requires no labels and no training. We triple-sample a native-video model (Gemini 3.1 Pro Preview) at temperature zero, exploit the genuine sample-to-sample variance of its video-processing pipeline to identify the roughly 20% subset of questions where the three samples disagree, and route only that subset to a second model from a different family (Claude Opus 4.8) that consumes uniformly sampled frames with adaptive thinking. On the 1001-question validation set with public ground truth – our main evaluation – the method improves AvgAcc by +1.43 over the best single sample of the primary model, with per-category gains concentrated on Motion & Trajectory (+5.49), Inferred Counting (+3.45), and Vertical Spatial Reasoning (+1.82) – the categories most dependent on cross-shot reference resolution. The same pipeline applied to the held-out 172-question CVPR 2026 ImplicitQA challenge test set achieves 82.03 AvgAcc / 79.71 MacroAvgAcc (+1.81 over the best single sample of the primary model), confirming the validation result on an independent split.

14.
medRxiv (Medicine) 2026-06-22

Why drinking episodes escalate differently: Event-level pathways linking hazardous alcohol consumption and sexual risk

Background: Alcohol-involved drinking episodes vary in whether they involve hazardous alcohol consumption alone, near-miss sexual risk, or sexual risk behavior, but the within-event mechanisms underlying this variability remain unclear. Methods: Guided by syndemic theory, we conducted a qualitative event-level analysis using modified grounded theory among adults in the San Francisco Bay Area who reported hazardous alcohol consumption, defined as an Alcohol Use Disorder Identification Test score [&ge;]16. In-depth interviews elicited narratives of recent heavy drinking episodes and yielded 64 discrete drinking events across 22 participants. We focused on 35 events with evidence of within-event interaction between biopsychosocial and contextual factors. Using constant comparison, we identified escalation pathways, characterized interruption, and examined how events diverge into three outcomes: hazardous alcohol consumption only, hazardous alcohol consumption with near-miss sexual risk (when risk was plausible but not enacted), and hazardous alcohol consumption with sexual risk behavior. Results: Two primary escalation pathways emerged. Dose-driven escalation involved cumulative alcohol or substance exposure that progressively impaired awareness and self-regulation. Meaning-driven escalation involved prioritizing connection, intimacy, or belonging despite awareness of risk. Time-driven continuation extended exposure across contexts and amplified both pathways. Hazardous alcohol consumption-only events more often followed dose-driven pathways, whereas events involving sexual risk behavior more often followed meaning-driven pathways. Near-miss events occurred across both pathways and illustrated how interruption before the escalation constraint point, when the capacity to modify behavior became reduced, could redirect escalation before sexual risk behavior occurred. Across events with similar levels of intoxication narratives, outcomes diverged according to when the interruption occurred and whether it altered escalation. Conclusion: Hazardous drinking episodes diverge into different outcomes based on escalation pathways and the timing and effectiveness of interruption. Early and effective interruption before the escalation constraint point may represent a key target for harm-reduction strategies to prevent progression to sexual risk behavior.

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

Ensembling Sparse Autoencoders

arXiv:2505.16077v2 Announce Type: replace Abstract: Sparse autoencoders (SAEs) are used to decompose neural network activations into human-interpretable features. Typically, features learned by a single SAE are used for downstream applications. However, it has recently been shown that a single SAE captures only a limited subset of features that can be extracted from the activation space. Motivated by this limitation, we introduce and formalize SAE ensembles. Furthermore, we propose to ensemble multiple SAEs through naive bagging and boosting. In naive bagging, SAEs trained with different weight initializations are ensembled, whereas in boosting SAEs sequentially trained to minimize the residual error are ensembled. Theoretically, naive bagging and boosting are justified as approaches to reduce reconstruction error. Empirically, we evaluate our ensemble approaches with three settings of language models and SAE architectures. Our empirical results demonstrate that, compared to an expanded SAE that matches the number of features in the ensemble, ensembling SAEs improves the reconstruction of language model activations along with SAE stability. Additionally, on downstream tasks such as concept detection and spurious correlation removal, SAE ensembles achieve better performance, showing improved practical utility.

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

Escaping the Cognitive Well: Efficient Competition Math with Off-the-Shelf Models

arXiv:2602.16793v2 Announce Type: replace Abstract: In the past year, custom and unreleased math reasoning models reached gold medal performance on the International Mathematical Olympiad (IMO). Similar performance was then reported using large-scale inference on publicly available models but at prohibitive costs (e.g., 3000 USD per problem). In this work, we present an inference pipeline that attains best-in-class performance on IMO-style math problems at an average inference cost orders of magnitude below competing methods while using only general-purpose off-the-shelf models. Our method relies on insights about grader failure in solver-grader pipelines, which we call the Cognitive Well (iterative refinement converging to a wrong solution that the solver as well as the pipeline's internal grader consider to be basically correct). Our pipeline addresses these failure modes through conjecture extraction, wherein candidate lemmas are isolated from generated solutions and independently verified alongside their negations in a fresh environment (context detachment). On IMO-ProofBench Advanced (PB-Adv), our pipeline achieves 67.1 percent performance using Gemini 3.0 Pro with an average cost per question of approximately 31 USD. At the time of evaluation, this represented the state-of-the-art on PB-Adv among both public and unreleased models, and more than doubles the success rate of the next best publicly accessible pipeline, all at a fraction of the cost.

17.
arXiv (quant-ph) 2026-06-16

Bath memory as a precision resource in quantum transport

arXiv:2606.17026v1 Announce Type: new Abstract: Structured baths can reshape transport fluctuations in mesoscopic quantum devices, yet a predictive criterion for when this enhances precision has been lacking. We propose a route towards such precision advantages by utilizing bath memory in coherent fermionic transport through a noninteracting quantum-dot chain. Using the Landauer-Büttiker formalism, we derive a dual impedance-matching condition that synchronizes the conductor mode splitting, boundary dissipation, and bath bandwidth, and sustains constructive multimode interference across the transmission window. The analytical predictions for the optimal bath bandwidths show excellent agreement with exact nonequilibrium Green's function calculations of the transport for Lorentzian, Gaussian, and Newns spectral densities. The prescription yields an optimal bath bandwidth at which the current Fano factor is minimized and the thermodynamic and kinetic precision coefficients are simultaneously enhanced beyond their Markovian limits. The alignment of the optimal precision regime with the experimentally accessible current Fano factor minimum thus provides a practical strategy for designing precision-enhanced transport in mesoscopic platforms such as semiconductor quantum-dot arrays and ultracold fermionic channels.

18.
arXiv (quant-ph) 2026-06-15

Quantifying and detecting quantum-state texture

arXiv:2604.07257v2 Announce Type: replace Abstract: Quantum-state texture is a recently proposed quantum resource that characterizes the inhomogeneity of a quantum state's matrix element distribution in the computational basis, enriching our understanding of quantum state structure. To expand its quantification toolkit and establish detection methods, in this article, we investigate the resource theory of texture from both quantitative and detection perspectives. First, we construct a texture measure $\mathcal{T}^{GR}_{\alpha,z}(\rho)$ based on the $\alpha$-$z$ Rényi relative entropy and present some of its inherent properties. Second, we analyze the mathematical relationships between several existing texture measures, revealing connections among different quantifiers. Finally, drawing on the witness concept from other resource theories, we systematically introduce texture witnesses into the texture theory and provide examples of texture witnesses with special properties.

19.
arXiv (CS.CL) 2026-06-17

Precision Is Not Faithfulness: Coverage-Aware Evaluation of Grounded Generation with a Complete Oracle

Reference-free faithfulness metrics verify each atomic claim a model makes against ground truth, and are increasingly used to evaluate grounded generation. We show they share a blind spot: they measure only precision – are the stated claims supported? – and therefore reward abstention, since a model can score near-perfect faithfulness by saying almost nothing. We make this measurable using Formula 1 telemetry, a domain where strategic ground truth is derived deterministically and, crucially, completely: for each decision we know the full set of facts that mattered. This completeness – absent in open-domain faithfulness benchmarks – lets us measure recall (coverage of the relevant facts) exactly, alongside precision. On a multilingual (EN/ES/PT) benchmark of 7,253 decision instances spanning 157 races, the most precise frontier model covers under half of the relevant facts and ranks last by F1, so requiring coverage reorders the systems; the same effect reappears in a second complete-oracle domain (NOAA weather forecasts). Fine-tuning small models (1B-7B) on the complete oracle closes the precision-recall gap entirely (F1 ~0.98), beating every zero-shot frontier system regardless of scale. We pair faithfulness with coverage into a single score, validate the metric (controlled perturbation; agreement across a model-free regex extractor and a cross-family LLM extractor, system-level Spearman 1.0), and give a verifier-guided generation method that improves precision and recall without references. We release the benchmark, structured annotations, metric, baselines, and an interactive demo.

20.
arXiv (quant-ph) 2026-06-12

Non-invertible symmetries out of equilibrium: Eigenstate order and Floquet physics

arXiv:2508.14213v2 Announce Type: replace-cross Abstract: Through the study of the Rep($D_8$) non-invertible symmetry, we show how non-invertible symmetries manifest in dynamics. Results are presented for dynamics generated by Hamiltonians as well as Floquet unitaries. For both examples, the role of the non-invertible symmetry is studied through the appearance of non-invertible symmetry protected edge modes. In addition, the role of the non-invertible symmetry for the Hamiltonian is studied through eigenstate order. In particular, by considering the effect of symmetry preserving disorder, the non-invertible symmetry is shown to give rise to degeneracies in the spectra of the Hamiltonian that can only be completely lifted at orders of perturbation that scale with system size. The eigenstates of disordered Hamiltonians, whose ground state correspond to non-trivial symmetry protected topological (SPT) states, are shown to have either trivial or non-trivial SPT order that are detected as non-zero expectation value of string order-parameters. In contrast, non-trivial SPT order is absent in the eigenstates of trivial SPT Hamiltonians with disorder. The interface between two different SPT phases host edge modes whose dynamics is studied numerically and analytically. The edge mode is shown to oscillate at frequencies related to different effective chain lengths that are weighted by the temperature, becoming an exact zero mode in the limit of zero temperature. A Floquet model with the non-invertible symmetry is constructed whose edge mode is shown to exhibit period-doubled dynamics at low effective-temperatures. The zero and period-doubled edge modes differ from those in conventional SPTs by being symmetric under the invertible symmetry, while being charged under the non-invertible symmetry.

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

FastMix: Fast Data Mixture Optimization via Gradient Descent

arXiv:2606.14971v1 Announce Type: cross Abstract: While large and diverse datasets have driven recent advances in large models, identifying the optimal data mixture for pre-training and post-training remains a significant open problem. We address this challenge with FASTMIX, a novel framework that automates data mixture discovery while training only a single proxy model. Instead of relying on predefined heuristics or resource-intensive simulations, FASTMIX jointly optimizes mixture coefficients and model parameters, substantially improving efficiency and scalability over prior approaches. At the core of FASTMIX is a reformulation of mixture selection as a bilevel optimization problem. Under this reformulation, we show that optimizing mixture ratios is mathematically equivalent to assigning per-source loss weights under uniform source sampling. This embeds the mixture coefficients directly into the differentiable iterative optimization objective, enabling efficient, gradient-based optimization of both mixture and model. To solve the optimization problem, FASTMIX implements an approximate iterative optimization procedure, alternating between (i) updating model parameters on data sampled according to current mixture ratios (inner loop) and (ii) updating mixture ratios based on validation feedback (outer loop). Across pre- and post-training, FASTMIX outperforms baselines while drastically reducing search cost. Code (https://github.com/hrtan/fastmix)

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

From Chatbot to Digital Colleague: The Paradigm Shift Toward Persistent Autonomous AI

arXiv:2606.14502v1 Announce Type: new Abstract: Large Language Models (LLMs) are undergoing a fundamental transformation from conversational generators into integrated AI systems capable of reasoning, action, memory, and self-improvement. We conceptualize this transition as a shift from Chatbot to Digital Colleague: from conversational answers to persistent work. We organize this transition along two tightly coupled dimensions. First, at the cognitive core level, LLMs are advancing from Chatbot-era "fast thinking" systems driven by next-token prediction toward Thinking LLMs that leverage inference-time computation, Chain-of-Thought reasoning, reflection, process supervision, and reinforcement learning to support more deliberate and reliable cognition. Second, at the tool-augmented task execution level, LLMs are progressing from tool-calling Agents that invoke external resources in an ad hoc manner toward OpenClaw-style workstation systems (OpenClaw) equipped with persistent Workspaces, skills, verification loops, and governance. The "Workspace + Skill" paradigm makes episodic tool use colleague-like via state persistence, reusable procedures, task closure, and experience reuse. We examine data construction shifts from instruction-response pairs to State-Action-Observation trajectories and evaluation from static benchmarks to sandboxed, auditable, self-evolving AI ecosystems.

23.
arXiv (quant-ph) 2026-06-16

Electronic Band Structure of Silicon Determined via a Variational Adiabatic Eigensolver: Theory and Experiment

arXiv:2606.16604v1 Announce Type: new Abstract: This work addresses the critical challenge of excited-state preparation for semiconductor band structure calculations. We introduce a variational adiabatic eigensolver (VAE) protocol that combines adiabatic evolution with variational optimization to prepare high-fidelity eigenstates on noisy intermediate-scale quantum (NISQ) devices. Applying a momentum-space truncation, we accurately compute the electronic band structure of silicon – an idealized infinite periodic system – using only a modest number of qubits. Our approach employs multi-qubit parameterized circuits and a phase-based loss function, overcoming limitations of conventional methods. These limitations include the circuit-construction difficulty in traditional adiabatic approaches and the reduced accuracy of variational quantum eigensolvers for excited states. Through rigorous numerical simulation and experimental implementation on a superconducting quantum processor, we successfully prepare silicon's valence-band and conduction-band eigenstates. Single-shot readout yields state fidelities exceeding 96%, and the measured energy expectations agree with theoretical band energies within 0.5 eV. Further refinement via single-frequency oscillation fitting reduces the energy deviation to below 0.01 eV. This framework provides a robust and practical pathway for precisely determining electronic structures in quantum materials.

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

Pretraining A Large Language Model using Distributed GPUs: A Memory-Efficient Decentralized Paradigm

Pretraining large language models (LLMs) typically requires centralized clusters with thousands of high-memory GPUs (e.g., H100/A100). Recent decentralized training methods reduce communication overhead by employing federated optimization; however, they still need to train the entire model on each node, remaining constrained by GPU memory limitations. In this work, we propose SParse Expert Synchronization (SPES), a memory-efficient decentralized framework for pretraining mixture-of-experts (MoE) LLMs. SPES trains only a subset of experts per node, substantially lowering the memory footprint. Each node updates its local experts and periodically synchronizes with other nodes, eliminating full-parameter transmission while ensuring efficient knowledge sharing. To mitigate limited per-expert data utilization under sparse expert updates, we introduce an expert-merging warm-up strategy, where experts exchange knowledge early in training, to rapidly establish foundational capabilities. With SPES, we train a 2B-parameter MoE LLM using 16 standalone 48GB GPUs over internet connections, which achieves competitive performance with centrally trained LLMs under similar computational budgets. We further demonstrate scalability by training a 7B model from scratch and a 9B model upcycled from a dense checkpoint, both of which match prior centralized baselines. Our code is available at https://github.com/zjr2000/SPES.

25.
arXiv (CS.LG) 2026-06-11

Trajectory Geometry of Transformer Representations Across Layers

arXiv:2606.09287v2 Announce Type: replace Abstract: Understanding how transformer representations evolve across layers, not merely what they encode, remains an open problem in mechanistic interpretability. We recast the transformer forward pass as a discrete population trajectory through a high-dimensional representation manifold, drawing on geometric tools from computational neuroscience. Rather than probing for pre-specified features, we characterize trajectory geometry using five metrics computed directly in the ambient space: trajectory length, curvature, a semantic convergence index, layerwise cosine similarity, and representational stability. Across three model families (GPT-2, TinyLlama, Qwen2.5) and five controlled prompt families, we report four findings. First, semantically related prompts converge significantly in middle-to-late layers (peak CI 0.41–0.58, p