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01.
arXiv (quant-ph) 2026-06-12

Global Control with the Tavis-Cummings Interaction

arXiv:2606.12906v1 Announce Type: new Abstract: We study the controllability of a system of qubits under global control, where control pulses act identically on all qubits. Specifically, we consider a collection of qubits identically coupled to a single bosonic mode, or harmonic oscillator, via the Jaynes-Cummings interaction. This collective coupling, known as the Tavis-Cummings (TC) interaction, has been realized in several quantum computing platforms, including superconducting and atomic qubit systems. Although the qubits do not interact directly with one another, they can become entangled through their common coupling to the bosonic mode. We characterize the group of unitaries that can be implemented on the joint Hilbert space of the qubits and bosonic mode using the TC interaction together with a global $z$ field $J_z$, corresponding to identical z rotations on all qubits. We show that for n>2 qubits the set of realizable unitaries is restricted by an "accidental" symmetry of the TC Hamiltonian, distinct from its "standard" U(1) and permutational symmetries. On the other hand, we find that the Hamiltonian $J_z^2$ breaks this accidental symmetry and, together with the TC interaction and $J_z$, achieves semi-universality: it allows the implementation of arbitrary unitaries that respect permutational and U(1) symmetry, up to certain constraints on the center of the group. In a companion paper, we further analyze this remarkable accidental symmetry and show that it can be understood through Schwinger's bosonic model of angular momentum.

02.
bioRxiv (Bioinfo) 2026-06-20

Ribosomes are covered by a coat of flexible protein fragments

Ribosomal proteins contain flexible terminal regions that are averaged out during electron density reconstructions, rendering them absent from experimental models derived by X-ray crystallography or cryogenic electron microscopy. These flexible protein fragments (FPFs) collectively form an invisible coat on the ribosome surface whose presence has been systematically overlooked. Here we analysed FPFs from 36 ribosomes spanning bacteria, eukaryotes, and mitochondria. We found that mitoribosomes harbour the most numerous and longest FPFs. Structural predictions confirmed that FPFs are predominantly disordered across all ribosome classes. Comparison of FPF amino acid composition against proteome-wide background frequencies revealed strong and domain-specific compositional biases. The balance between arginine and lysine content tracks the cardiolipin content of the membrane each ribosome class contacts. The arginine enrichment in mitoribosomal FPFs may additionally reflect selection arising from the RNA-rich environment of mitochondrial RNA granules, membraneless condensates where mitoribosomes are assembled. FPFs are uniformly depleted in aromatic residues, arguing against protein-driven liquid–liquid phase separation propensity. Our findings suggest that the flexibly tethered coat is a highly functional intrinsic part of all ribosomes.

03.
arXiv (CS.AI) 2026-06-19

Dual-Agent Framework for Cross-Model Verified Translation of Natural-Language Protocols into Robotic Laboratory Platform

arXiv:2606.20120v1 Announce Type: cross Abstract: Biological experiment protocols are written in natural language, whereas automation systems rely on predefined control commands, creating a semantic gap that limits autonomous execution. Microplate-based automatic experiments are particularly challenging due to the need to simultaneously control well mapping, sample-reagent combinations, replicate placement, and parallel dispensing. This study proposes an agent-based protocol translation framework that converts natural-language microplate-based protocols into executable control commands for a robotic laboratory platform. A Parser Agent formalizes the natural-language protocol into a structured representation, and a rule-based mapping engine deterministically incorporates the operational constraints of the robotic laboratory platform to generate device-level control commands. A heterogeneous LLM Validation Agent verifies completeness, parameter accuracy, and execution order, and triggers a self-correction loop with structured feedback when errors are detected. A sweep involving 7 Parsers and 3 Validators on randomly selected ELISA protocols evaluates how model scale and Validator type affect translation accuracy and pass rates under cross-model verification. The accuracy-latency trade-off is further verified by comparing the rule-based mapping of the proposed framework with LLM end-to-end direct mapping. Finally, Bradford assay-based protein quantification using a microplate was demonstrated on a robotic laboratory platform, validating end-to-end autonomous execution from natural-language protocols to real-world experiments. The proposed framework provides a flexible approach to narrowing the semantic gap between natural-language protocols and microplate-based self-driving laboratories.

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

DPRM: A Plug-in Doob h transform-induced Token-Ordering Module for Diffusion Language Models

arXiv:2604.24357v2 Announce Type: replace-cross Abstract: Diffusion language models generate without a fixed left-to-right order, leaving token ordering as a central algorithmic choice. Existing systems mainly use random masking or confidence-driven ordering, which respectively suffer from train–test mismatch and myopic exploration. We introduce DPRM (Doob -transform Process Reward Model), a plug-in token-ordering module that keeps the host architecture, denoising objective and supervision unchanged, and modifies only the ordering policy. DPRM starts from confidence-driven ordering and gradually shifts to process-reward-guided ordering through online estimates. We characterize the exact DPRM policy as a reward-tilted Gibbs reveal law, prove convergence of its stagewise Soft-BoN approximation, show that the online bucketized controller tracks the exact DPRM score at empirical-Bernstein rates, and establish a sample-complexity advantage under tractable optimization assumptions. Across nine hosts covering language reasoning, test-time scaling, protein, single-cell, molecular, DNA, text-to-image generation, and VQA, DPRM order variants improve several language, DNA, and multimodal settings while also identifying boundary cases where confidence-only ordering or task-specific utilities are preferable. Code is available at: https://github.com/DakeBU/DPRM-DLLM

05.
arXiv (CS.AI) 2026-06-19

SafeSpec: Fast and Safe LLM via Dynamic Reflective Sampling

arXiv:2606.19755v1 Announce Type: cross Abstract: Speculative inference accelerates large language model (LLM) decoding but provides no inherent safety guarantees. Existing safety defenses are largely incompatible with speculative inference: they either introduce additional computation or disrupt the draft-verify mechanism, negating acceleration benefits. This reveals a fundamental incompatibility between current safety methods and speculative decoding. We propose SafeSpec, a safety-aware speculative inference framework that integrates risk estimation directly into the verification process. SafeSpec attaches a lightweight latent safety head to the target model to jointly evaluate semantic validity and safety in a single forward pass. When unsafe generations are detected, SafeSpec applies rollback and safety-guided reflective multi-sampling to recover safe continuations rather than terminating generation. We model jailbreak attacks as distributional shifts over generative trajectories, where adversarial prompts increase the probability of harmful continuations without eliminating safe ones. Under this model, SafeSpec performs risk-aware trajectory recovery within the speculative decoding process. Across multiple models and adversarial benchmarks, SafeSpec achieves a substantially improved safety-efficiency trade-off. On Qwen3-32B, SafeSpec reduces attack success rates by 15% while preserving a 2.06x inference speedup on benign workloads, demonstrating that speculative acceleration and inference-time safety can be jointly optimized.

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

CoRA: Confidence-Rationale Alignment for Reliable Chain-of-Thought Reasoning

Chain-of-thought (CoT) reasoning can improve LLM performance, but high answer confidence may be misleading when the accompanying CoT rationale is plausible yet incomplete or poorly supported. We study confidence–rationale alignment: whether a model's confidence in its committed answer is justified by its generated rationale. We introduce a GRPO-based reinforcement learning framework that jointly rewards answer correctness, committed-answer probability, and rubric-based rationale support, where the rubric assesses grounding, coherence, task match, and connection to the selected answer without revealing the gold answer to the judge. Across MedQA, MathQA, and OpenBookQA using three open-weight LLMs, our method reduces the confidence–rationale alignment error by up to 26.51% compared with untuned checkpoints, SFT, and correctness-only GRPO, while maintaining competitive accuracy and often improving calibration. These results show that reliable CoT reasoning requires not only confident answers, but rationales that substantively support them.

07.
medRxiv (Medicine) 2026-06-22

Early-life nutritional environment is associated with late-life cognition in the Health and Retirement Study, a pellagra epidemic natural experiment

Early-life exposures are important to several late-life health outcomes. We sought to study the effect of an in utero nutritional environment and its interaction with Alzheimer's disease (AD) genetic risk on late-life cognitive function. We used a natural experiment created by the pellagra epidemic, a nutritional disease caused by a vitamin B3 deficiency, to evaluate the association between in utero pellagra epidemic exposure and late-life cognitive function in the Health and Retirement Study (N = 18,285). We also evaluated whether the in utero exposure could modify the AD polygenic score's (PGS) effect on cognition. In utero pellagra epidemic exposure was significantly associated with cognition ({beta} = -0.025). However, these effects were not isolated to the prenatal period as exposure during childhood periods also had an effect. The interaction between the in utero exposure and the AD PGS was significant, where the genetic effect on cognition was amplified with increasing (progressively worse) in utero exposure levels. These associations imply that the early-life nutritional environment affects late-life cognitive function and that these effects can modify genetic risk.

08.
arXiv (CS.AI) 2026-06-17

DeepInsight: A Unified Evaluation Infrastructure Across the Physical AI Stack

arXiv:2606.17574v1 Announce Type: new Abstract: Evaluating a Physical AI stack spans operators that differ by more than three orders of magnitude – from a single foundation-model decoding step to thousands of physics ticks of whole-body control – varying orthogonally in modality, reward semantics, and resource profile. No existing framework spans this range, so the stack is evaluated today by stitching together separate harnesses that share neither runtime nor scoring, preserving each segment's local validity but losing the shared identity needed to diagnose cross-layer regressions. We present DeepInsight, an evaluation infrastructure that serves this full spectrum on a single runtime. Rather than homogenize the regimes, it preserves their heterogeneity behind three narrow abstractions – task, resource, and result – each realized as one invariant shared by every subsystem: one episode driver, one resource-handle protocol implemented by every expensive backend (LLM inference and sandboxed runtimes alike), and one trace identity scheme under which every event is written. Deployed in production across all three layers of an embodied humanoid stack, this single set of invariants onboards new benchmarks largely by configuration. Where mature peer orchestrators exist – at the foundation-model end – it reproduces published references and peer-framework readings within their own spread, runs the same suites faster on a single node, and scales near-linearly across nodes. Its distinctive return is diagnostic: because every layer writes into one shared trace, a regression that begins in one layer and surfaces in another stays localizable on that trace – a cross-layer payoff no federation of per-segment harnesses can reproduce.

09.
arXiv (CS.AI) 2026-06-18

Code-Augur: Agentic Vulnerability Detection via Specification Inference

arXiv:2606.18619v1 Announce Type: cross Abstract: The advent of agentic vulnerability detection is already becoming a watershed moment for software security. Audits conducted entirely by autonomous LLM agents are uncovering critical vulnerabilities in fundamental software underpinning digital society. Many of these vulnerabilities remained masked for years, surfacing only now with AI agents. Yet the reasoning behind these discoveries remains alarmingly opaque and unvalidated. What assumptions did the agent make about a function's inputs when it deemed that function to be secure? Failures in reasoning and incorrect assumptions can lead to missed vulnerabilities and reduce trust in agentic analysis. We propose a security-specification-first paradigm that (1) exposes the agent's tacit assumptions explicitly as security specifications and (2) continuously refines those specifications via runtime falsification. We realize our approach in Code-Augur, a novel harness for agentic vulnerability detection. Given a codebase, Code-Augur analyzes each component of the system for vulnerable code. When it deems a component to be secure, it commits the local invariants behind that judgment as in-source assertions. In parallel, Code-Augur leverages a guided fuzzer to attempt to falsify those assumptions. When the fuzzer triggers an assertion, this either reveals a genuine vulnerability or a flawed specification to refine. In both cases, this process grounds the agent's understanding, aligning its view of code intent with how the code actually behaves. On real-world subjects, Code-Augur effectively leverages security specifications to detect more vulnerabilities than other state-of-the-art agents. Additionally, Code-Augur found 22 new vulnerabilities in key open-source projects. Compared to curated specialized models like Claude Mythos, Code-Augur offers effective agentic vulnerability detection built on widely available LLMs like Sonnet and DeepSeek.

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

Communication-Efficient Neural Tangent Kernels for Heterogeneous Decentralized Federated Learning

Authors:

arXiv:2512.12737v2 Announce Type: replace Abstract: Decentralized federated learning (DFL) enables collaborative model training without a central server, but converges slowly under statistical heterogeneity. Recent work has shown that neural tangent kernel (NTK) methods achieve faster convergence than gradient-based updates in DFL, while momentum has proven effective for accelerating gradient-based FL. However, applying momentum to NTK updates can destabilize training under heterogeneous data. We propose SPARK, which addresses this instability with a stage-wise annealed soft-label regularizer evaluated on neighborhood-aggregated data, so that momentum can accelerate NTK updates stably. Under high heterogeneity, SPARK converges about 3$\times$ faster than baselines and lowers the total communication to a target accuracy by up to about 70\%, and it attains higher accuracy across heterogeneity levels. We further study random projection as an optional Jacobian-compression strategy for bandwidth-constrained settings. We validate the approach across multiple datasets, network topologies, and heterogeneity levels.

11.
medRxiv (Medicine) 2026-06-22

A Plasmodium vivax controlled human infection and transmission model to evaluate interventions across the life cycle

Background Plasmodium vivax is an underappreciated cause of malaria disease burden. No reproducible and standardized full life-cycle controlled human malaria infection (CHMI) model to accelerate development of novel interventions is available. Methods This transmission-CHMI trial was conducted in Nijmegen, Netherlands. Healthy, malaria-naive adults were sequentially enrolled into three cohorts of four and inoculated with the asexual blood-stage isolate PvW1. Primary endpoint was proportion of oocyst-positive laboratory-reared Anopheles stephensi mosquitoes. The sequential design allowed for adaptations between cohorts. At parasitemia >10 parasites/microL or symptom onset, participants received oral gametocyte-sparing treatment (GST): mepacrine (Cohort 1 and 3; 100 mg at 0, 8 16 hours, then once daily for 3 days) or piperaquine (Cohort 3; 480 mg single-dose). Transmission was assessed by direct skin feeding (DSF) and membrane feeding assay (DMFA) with and without enrichment of gametocytes. End-of-study treatment was atovaquone-proguanil (1000/400 mg once daily for 3 days). The trial was registered: NL-OMON57011. Findings Participants were enrolled between September 17, 2024 and March 25, 2025, all (12/12) developed parasitemia and transmitted PvW1 to mosquitoes. No serious adverse events occurred. Most adverse reactions were related to malaria. Mepacrine and piperaquine reduced asexual parasitemia while preserving gametocytemia and transmission. Peak transmission occurred within 3 days after GST and depended on the parasite developmental cycle, with highest gametocyte-infectivity ~48 h post ring-stage. In Cohort 3, mosquito infection reached 100% in all transmission assays. Median peak oocyst counts were 24 (IQR: 14-31) for DSF, 17 (12-19) for DMFA, and 150 (116-199) for enriched DMFA. A two-fold increase in pre-GST maximal parasitemia was associated with 20 additional oocysts (95% CI 8,6-32) in enriched DMFA. Sporozoites were viable in primary human hepatocytes. Interpretation A PvW1 transmission-CHMI is reproducible and safe, enabling P. vivax sporozoite production, relapse models and evaluation of transmission-blocking interventions.

12.
arXiv (quant-ph) 2026-06-17

Quantum Chip Paradigm Framework

arXiv:2606.17899v1 Announce Type: new Abstract: Quantum Electronic Design Automation (Q-EDA) is emerging as quantum chips move from laboratory prototypes to scalable engineering systems. This paper argues that superconducting quantum chip design is approaching a "SPICE moment" similar to early classical EDA, where growing qubit scale, control complexity, frequency planning, packaging, process variation, and cryogenic measurement feedback require a shift from experience-based design to model-driven engineering. We propose a Quantum Chip Paradigm Framework that treats Q-EDA not only as software, but as part of the quantum chip development paradigm. Unlike classical HDL-first design, quantum chip design must begin with physical structures such as Josephson junctions, resonators, couplers, readout elements, control lines, and packaging environments. The framework emphasizes PCell-based modeling, SPICE-Q simulation, Quantum PDKs, and design-technology-measurement co-optimization. We further outline a hierarchical Q-EDA system spanning physical structures, qubit PCells, logical qubits, quantum arithmetic, functional quantum IP, and Quantum SoC systems. The key goal is to turn physical models, layout rules, simulation results, fabrication data, and measurement feedback into reusable and auditable engineering objects for large-scale quantum processors and fault-tolerant quantum computing.

13.
arXiv (math.PR) 2026-06-16

Well-posedness of stochastic parabolic equations with gradient nonlinearities and applications to phase-field models

Authors:

arXiv:2606.15425v1 Announce Type: new Abstract: We study well-posedness of stochastic parabolic equations with gradient nonlinearities. Our analysis is based on recent maximal-regularity frameworks for nonlinear stochastic parabolic equations in critical spaces. We extend the existing results by controlling drift and noise coefficient separately. This way we can allow for less regular driving noise in case of subcritical dispersion coefficients. Our approach, based on gluings of local solutions, moreover implies new continuation criteria. We then apply our existence result and the continuation criteria to show global well-posedness of phase-field models of moving boundary problems.

14.
arXiv (CS.AI) 2026-06-16

TechRAG: Evidence-Gated Multimodal Agentic RAG for Technical Literature Reasoning

arXiv:2606.01613v2 Announce Type: replace-cross Abstract: This paper presents an agentic multimodal retrieval-augmented generation (RAG) framework for domain-specific literature reasoning, instantiated on a curated corpus of several thousand papers in intelligent tires, vehicle dynamics, vehicle control, sensing, estimation, and machine learning. Unlike conventional single-pass RAG systems, the proposed architecture uses an autonomous, evidence-gated pipeline that classifies query intent, generates separate text and visual query rewrites, performs hybrid text retrieval with FAISS and BM25 followed by cross-encoder reranking, expands evidence through graph-guided chunk traversal over a Neo4j knowledge graph, and retrieves visual document evidence using ColSmol late-interaction embeddings with MUVERA fixed-dimensional encoding, approximate nearest-neighbor search, and MaxSim reranking. The framework scores evidence sufficiency using a 100-point rubric with hybrid rule-based/LLM review, retries retrieval through drift-guarded reformulation, searches external academic databases through optimize–search–vet loops, merges and deduplicates multimodal evidence, verifies citation integrity, and generates cited answers through Planner, Researcher, Writer, and Critic agents with self-correcting revision. Key contributions include: (i) a scalable multimodal retrieval architecture combining text, graph, and visual evidence over 40,000 document pages; (ii) an interpretable evidence sufficiency and retry mechanism; (iii) a multi-agent generation pipeline with evidence mapping and critic-driven revision; (iv) a domain knowledge graph with LLM-based entity extraction, OpenAlex author validation, and intra-corpus citation resolution; and (v) a route-dependent external search architecture for targeted literature expansion. The result is a practical, evidence-gated, multimodal agentic RAG architecture for technical reasoning over specialized research corpora.

15.
arXiv (CS.CL) 2026-06-12

PRISM: Prosody-Integrated Multi-Agent Reasoning Framework for Empathetic Spoken Dialogue

Empathetic spoken dialogue systems require not only semantically appropriate responses but also emotionally aligned prosodic expression. However, cascade pipelines often discard acoustic cues during speech-to-text conversion, while end-to-end speech models lack interpretable control over emotion and knowledge integration. To address these challenges, we propose PRISM, a multi-agent framework for empathetic spoken dialogue that decouples speech perception, response generation, and speech synthesis into coordinated components. PRISM introduces a prosody-to-language translation mechanism to stabilize large language model reasoning and enables on-demand invocation of external knowledge tools for empathetic dialogue generation. Experimental results demonstrate that PRISM achieves consistent improvements in empathy, prosodic appropriateness, and text response generation quality across objective and subjective metrics. Our code is available at: https://github.com/Bxzfrm/PRISM.

16.
arXiv (CS.AI) 2026-06-17

No-Free-Fairness: Fundamental Limits and Trade-offs in Learning Systems

Authors:

arXiv:2606.17810v1 Announce Type: cross Abstract: In this paper, we establish a set of theoretical impossibility results, termed the No-Free-Fairness theorems, that identify three fundamental sources of disparity in learning systems. First, we show that when a task exhibits irreducible cost on a subgroup, any decision rule must trade off overall performance with disparity, yielding an inherent fairness–cost frontier. Second, we prove that even in ideal, noise-free settings where a perfectly fair and accurate solution exists, finite-sample learning alone induces nontrivial subgroup disparity, ruling out distribution-free fairness guarantees. More seriously, enforcing strict relative fairness creates a statistical bottleneck: achieving low cost may require exponentially many samples. Third, we show that limitations of the model class can independently induce disparity: if the model cannot represent accurate solutions for a subgroup, fairness remains unattainable regardless of data or training procedure. Overall, these results demonstrate that unfairness is not solely a consequence of biased data or suboptimal optimization, but arises from the intrinsic structure of decision problems, the constraints of finite data, and the expressivity of models. Our framework applies broadly beyond standard supervised learning, and suggests that achieving fairness requires explicit trade-offs and should be treated as a core design consideration.

17.
medRxiv (Medicine) 2026-06-11

A global cross-sectional survey of health professionals' interest-confidence gaps in value-based health care implementation: a learning needs assessment

Abstract Objectives Value-Based Health Care (VBHC) increasingly guides health system redesign internationally. Despite the increasing availability of VBHC education, gaps remain between health professionals' conceptual understanding of VBHC and their confidence to implement it in practice. This study assessed perceived learning needs and preferences of healthcare professionals across foundational topics essential to VBHC implementation. Design Cross-sectional online survey study Setting and participants The survey was distributed to the global VBHC community and yielded 518 responses. Most respondents were based in the UK and Ireland (51%) and 65% had more than 10 years of experience in the health sector. Participants represented a variety of professional backgrounds, including clinicians (34%), operational or executive managers and leaders (22%), and life sciences or procurement professionals (13%). Primary and secondary outcome measures Primary outcome measures included self-reported interest and confidence across 15 VBHC domains and the magnitude of the gap between them. Secondary outcomes included perceived implementation challenges and preferred VBHC learning approaches, including prior engagement with VBHC-related learning. Results Respondents identified substantial VBHC implementation challenges, including implementing outcome measurement (62.4%), conflicting priorities (57.7%), and resistance to change (56.8%). Interest in all VBHC domains was high (median >= 80/10), while confidence to implement remained substantially lower across most domains (median

18.
arXiv (quant-ph) 2026-06-11

Single Photon Cross-Phase Shifts Can Be Enhanced by Localization in both Frequency and Time

arXiv:2606.11516v1 Announce Type: new Abstract: Single-photon optical nonlinearities face a fundamental trade-off: maximum nonlinearity requires both spectral resonance (narrow bandwidth) and high peak intensity (short duration), constraints that are incompatible due to the time-energy uncertainty relation. We demonstrate experimentally that this limitation does not need to exist in cases involving post-selection. We measure a cross-phase shift (XPS) produced by a resonant photon from a narrow-band source that is first transmitted through a cold atomic cloud and then localized in time through detection. The peak size of this XPS is greatly enhanced compared to that of Gaussian single-photon-level pulses without post-selection, benefiting from the narrow bandwidth of the resonant prepared state and the high intensity of the post-selected state simultaneously. We measure enhancements in the peak XPS of 6$\pm$1 at an optical depth (OD) of 2.4$\pm$0.1, and our results are in qualitative agreement across a range of optical depths with the recently developed weak value theory of atomic excitation [Thompson et al., APL Quantum 2, 036108 (2025)] for such post-selected photons. This work uncovers new consequences of having simultaneous knowledge of frequency and time, raising new foundational questions about how a particle behaves, and interacts with other systems, when its preparation and post-selection are non-commuting.

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

Score Approximation for Diffusion Models on Arbitrary Low-Dimensional Structures

arXiv:2606.19894v1 Announce Type: new Abstract: The remarkable success of score-based diffusion models has spurred significant efforts to establish their theoretical foundations. However, existing complexity bounds for score approximation rely heavily on restrictive assumptions like Lipschitz continuous densities or smooth manifold supports, which are routinely violated by the singularities, sharp boundaries, and disjoint clusters inherent to real-world perceptual data. This work establishes a universal score approximation theorem that works for any distribution supported on any compact set of upper Minkowski dimension $d$. Using a novel discrete-mixture formulation, we prove that the score function can be approximated with a ReLU network whose complexity grows exponentially only with $d$, thus breaking the exponential curse of ambient dimensionality. Combined with existing theories on accurately solving the backward diffusion SDE for arbitrary compact distributions, our work shows that diffusion models readily adapt to irregular, non-smooth data structures, explaining their competence in real-world generative tasks.

20.
medRxiv (Medicine) 2026-06-16

A Poisson Process Life Expectancy framework for optimising patient lifetime during chemotherapy

Cancer therapy balances between two competing objectives - treatment efficacy against the tumour and the risk of treatment related severe adverse events, including patient death. Most existing optimal control theory (OCT) formulations rely on optimising heuristic cost functionals that lack direct clinical interpretability. In clinical practice treatment efficacy and patient tolerability are primarily assessed through survival metrics and adverse event rates. Here we introduce the Continuous Lifetime Payoff (CLP), a novel OCT objective functional that directly links treatment decisions to patient survival. It explicitly incorporates tumour dynamics, tumour eradication, and patient mortality from tumour progression, drug-related toxicity and age. We fit age-related mortality from life tables and infer parameters from simulated survival data. The CLP provides a clinically grounded framework for optimising chemotherapy regimens.

21.
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.

22.
arXiv (CS.CL) 2026-06-17

Olmo Hybrid: From Theory to Practice and Back

Recent work has demonstrated the potential of non-transformer language models, especially linear recurrent neural networks (RNNs) and hybrid models that mix recurrence and attention. Yet there is no consensus on whether the potential benefits of these new architectures justify the risk and effort of scaling them up. To address this, we provide evidence for the advantages of hybrid models over pure transformers on several fronts. First, theoretically, we show that hybrid models do not merely inherit the expressivity of transformers and linear RNNs, but can express tasks beyond both, such as code execution. Putting this theory to practice, we train Olmo Hybrid, a 7B-parameter model largely comparable to Olmo 3 7B but with the sliding window layers replaced by Gated DeltaNet layers. We show that Olmo Hybrid outperforms Olmo 3 across standard pretraining and mid-training evaluations, demonstrating the benefit of hybrid models in a controlled, large-scale setting. We find that the hybrid model scales significantly more efficiently than the transformer, explaining its higher performance. However, its unclear why greater expressivity on specific formal problems should result in better scaling or superior performance on downstream tasks unrelated to those problems. To explain this apparent gap, we return to theory and argue why increased expressivity should translate to better scaling efficiency, completing the loop. Overall, our results suggest that hybrid models mixing attention and recurrent layers are a powerful extension to the language modeling paradigm: not merely to reduce memory during inference, but as a fundamental way to obtain more expressive models that scale better during pretraining.

23.
arXiv (CS.CL) 2026-06-16

A large-scale pipeline for LLM-assisted corpus annotation: variation and change in the English consider construction

As natural language corpora expand at an unprecedented rate, manual annotation remains a significant methodological bottleneck in corpus linguistic work. We address this challenge by presenting a scalable pipeline for automating grammatical annotation in voluminous corpora using large language models (LLMs). Unlike previous supervised and iterative approaches, our method employs a four-phase workflow: prompt engineering, pre-hoc evaluation, automated batch processing, and post-hoc validation. We demonstrate the pipeline's accessibility and effectiveness through a diachronic case study of variation in the English evaluative consider construction (consider X as/to be/{\O} Y). We annotate 143,933 'consider' concordance lines from the Corpus of Historical American English (COHA) via the OpenAI API in under 60 hours, achieving 98%+ accuracy on two sophisticated annotation procedures. A Bayesian multinomial GAM fitted to 44,527 true positives of the evaluative construction reveals previously undocumented genre-specific trajectories of change, enabling us to advance new hypotheses about the relationship between register formality and competing pressures of morphosyntactic reduction and enhancement. Our results suggest that LLMs can perform a range of data preparation tasks at scale with minimal human intervention, unlocking substantive research questions previously beyond practical reach, though implementation requires attention to costs, licensing, and other ethical considerations.

24.
bioRxiv (Bioinfo) 2026-06-22

EMAlign: accurate alignment of cryo-EM maps through main-chain probability using deep learning

Accurate alignment of cryo-EM density maps is essential for comparing conformational states, searching map libraries, and guiding atomic model building, but remains challenging for noisy experimental maps and partially overlapping structures. Existing alignment methods are often based on raw maps, which may result in reduced accuracy due to the density noise, or require manual intervention for local alignment, which suffers from limited general applicability. Addressing the limitations, we present EMAlign, an automatic global and local cryo-EM map alignment with predicted main-chain probability using deep learning. First, EMAlign predicts main-chain prob ability maps from raw cryo-EM density maps using a BiMCUNet network. Then, a fast Fourier transform (FFT)-based search strategy is used to globally search the accurate alignment between cryo-EM maps based on predicted main-chain probability maps. As such, the main-chain prob ability map overcomes the noisy raw map problem, and the FFT-based exhaustive global search ensures the general applicability of alignment. EMAlign is evaluated on 64 global map pairs, 195 local map pairs, and 60 structure-to-map pairs at 3-10 [A] resolution and compared with gmfit, fitmap, VESPER, and CryoAlign. It is shown that EMAlign outperforms the other methods in both global and local alignment, achieving mean RMSDs of 1.03 [A] (global), 2.56 [A] (local), and 0.82 [A] (structure-to-map), with success rates of 100.0%, 100.0%, and 98.3% under the criterion of RMSD < 10 [A]. The EMAlign package is freely available at https://github.com/huang-laboratory/EMAlign/.

25.
PLOS Medicine 2026-06-12

Placenta accreta spectrum in the 21st century: Challenging dogma and redefining disorder

Authors:

by Eric Jauniaux, Helena C. Bartels, Yalda Afshar Placenta accreta spectrum (PAS) is a serious pregnancy complication caused by abnormal placental attachment to the uterus. In this Perspective, Eric Jauniaux and colleagues discuss emerging evidence that challenges our long-held pathophysiological understanding of PAS, and argue that a critical reassessment of definition, diagnosis, and management is overdue. In this Perspective, Jonathan Evans and colleagues discuss why restricting access to joint replacement surgery based on BMI alone is not supported by evidence, and highlight how such rest rictions risk exacerbating stigma, inequity and avoidable harm to those who would benefit from surgery.