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arXiv2026

Spin-Dependent Transport in One-Dimensional Mercury- and Silver-Mediated DNA Nanowires

Metallo-DNA nanowires serve as a versatile, programmable scaffold for molecular-scale electronics. In this work, we demonstrate that periodic heavy-metal coordination fundamentally reshapes the electronic landscape of DNA, generating dispersive, metal-centered conduction bands rather than isolated localized states. Driven by enhanced inter-layer coupling, where second nearest-neighbor hopping plays a crucial role: the conduction bandwidth can approach 1 eV, more than double that of canonical DNA. These broad bands suggest enhanced robustness of transport against structural fluctuations. As a result of relativistic effects, in these quasi-one-dimensional materials, spin-orbit (SO) coupling enhances band dispersion and governs spin-dependent transport. We predict that although ligand hybridization reduces atomic SO splitting, the effective SO splitting in these nanowires is comparable to the next-nearest-neighbor hopping strength, which results in considerable spin mixing. We find that Hg2+-coordinated nanowire exhibit pronounced spin-flip conductance comparable to spin-conserving conductance across the LUMO manifold. This efficient spin conversion remains robust over extended molecular lengths and is accompanied by a set of highly delocalized unoccupied states with oscillating nodal structures, readily accessible under low-bias transport measurements. In contrast, the Ag+-coordinated nanowires exhibit weak spin-flip scattering that is more than an order of magnitude smaller than spin-preserving transport. This duality establishes the choice of coordination metal as a decisive parameter to govern metal-metal interaction, electron delocalization, and spin-dependent transport. Overall, our findings demonstrate how relativistic effects and quantum confinement intersect in these bio-templated, quasi-one-dimensional molecular conductors.

cond-mat.mes-hall↗
arXiv2026

SR-ANM: A Square-Root Atomic Norm Minimization Estimator for Noise-Robust Direction-of-Arrival Estimation

This paper proposes a Square-root Atomic Norm Minimization (SR-ANM) estimator for direction-of-arrival (DoA) estimation in the single measurement vector (SMV) setting. This new framework, inspired by the Square-root Least Absolute Shrinkage and Selection Operator (Square-root LASSO), relies on a residual term based on the $L_{2}$-norm rather than the usual squared $L_{2}$-norm so as to obtain an estimator whereby the hyperparameter of the atomic norm in the unconstrained convex optimization problem exhibits robustness to the noise level compared to classical ANM. Experimental verification across a wide signal-to-noise ratio (SNR) range from 0 dB to 40 dB using the the probability of detection and the angular root-mean-squared error (RMSE) confirms the increased noise robustness of the proposed framework compared to ANM.

eess.SP↗
arXiv2026

Piano2Pop: Expanding Piano Demos into Pop Productions Without Aligned Supervision

We introduce Piano2Pop, a music-expansion task that transforms piano MIDI into instrumental pop productions. Enabled by recent advances in music generation and polyphonic music transcription, our pseudo-supervised approach conditions a pretrained flow-matching model on piano rolls without aligned piano-pop supervision. We train on synthetic piano-instrumental pairs derived from pop recordings, comparing pair-free transcription, including function-selective (FST) and register-density (RDA) augmentations, with a pair-supervised piano-cover generator as pseudo-demo sources. On 476 testing real-world piano-cover/pop pairs, the pair-supervised approach achieves the best Frechet Audio Distance, Accompaniment Prompt Adherence, and spectral shape on real-piano inputs, while FST mitigates piano-heavy outputs in pair-free training and remains a promising alternative. All fine-tuned systems significantly outperform the zero-shot baselines, which suggests a role for task-specific modeling.

cs.LG↗
arXiv2026

DuoCook: An Overcooked-Inspired Benchmark for Visual Planning and Coordination

Coordinating two players in a cooking task requires a vision-language agent to track changing scene states, sequence recipe steps, and allocate shared resources under a time budget. We introduce an Overcooked-inspired benchmark for evaluating these capabilities in Unreal Engine environments constructed using coding agents. A single vision-language model controls both players from top-down visual observations, selecting high-level actions while the engine executes navigation. The benchmark contains 20 maps in five categories. These categories support evaluation of centralized coordination under equipment contention, changing terrain, and handoff dependencies. A prop legend and pot ingredient-count indicators provide explicit visual cues, while grid-based destinations support spatial grounding. The evaluation compares four VLMs on all 20 maps, measuring task score, order completion, delivery precision, action acceptance rate, and model API expenditure. A memory ablation on the same 20 maps examines the effect of a shared, self-authored notebook while retaining six-response action history in both conditions. The strongest evaluated model, \texttt{gpt-6-astra}, achieves 26.2\% mean order completion. Notebook memory has mixed score effects across models, with positive mean differences for Claude and Qwen and negative differences for GPT and Gemini.

cs.AI↗
arXiv2026

Towards User Simulators That Stay On Task And Still Sound Natural

Users increasingly rely on AI systems for extended, multi-turn tasks, yet evidence indicates that LLM-based system performance degrades over prolonged interaction. To evaluate and improve AI assistants in such settings, we need user simulators that faithfully follow complex user intents while exhibiting realistic conversational behavior. Existing approaches manifest a trade-off: prompted assistants can follow complex user intents but lack behavioral fidelity, while specialized user simulators better approximate conversational behavior but struggle to remain faithful to complex intents. In this paper, we propose a reinforcement learning (RL) recipe that jointly rewards task intent adherence and behavioral naturalness. A crucial contribution of the method is operationalizing naturalness with a lightweight discriminator trained in an alternating, adversarial loop with the user simulator. We release the resulting model, UserLM-2.0, which produces valid simulated conversations (i.e., meeting both task-faithfulness and naturalness criteria) 46.2% of the time, over three times the rate of the strongest prior user simulator (14.1%).

cs.CL↗
arXiv2026

Closing All-Exp Gaps in Local Search for the Traveling Salesman Problem and Maximum Constraint Assignment

We prove the all-exp property for the Traveling Salesman Problem with the $2$-opt neighborhood, and for $(3,2,2)$- and $(2,3,2)$-Maximum Constraint Assignment with the flip neighborhood. The all-exp property states that infinitely many instances have an initial solution from which every sequence of improving moves to a local optimum, and hence every run of local search, has exponential length. For the Traveling Salesman Problem, the all-exp property was previously known for every $k$-opt neighborhood with $k \ge 3$, so our result for $2$-opt completes the picture for all $k$-kopt neighborhoods. In $(p,q,r)$-Maximum Constraint Assignment, each constraint involves at most $p$ variables, each variable occurs in at most $q$ constraints, and each domain has size $r$. Decreasing any of the parameters of $(3,2,2)$ or $(2,3,2)$ yields a problem in which every sequence of improving flips has polynomial length, so both Maximum Constraint Assignment results are tight. All three results settle long-standing open problems. Our proofs build on a common construction idea, which reveals a close connection between the local-search dynamics of the three problems.

cs.DS↗
arXiv2026

Can Coulomb breakup of $N=Z$ clustered nuclei constrain capture cross sections? Analyzing the $α(d,γ){}^{6}\mathrm{Li}$ case

In nuclear astrophysics, it is fundamental to measure the cross section of radiative-capture reactions involving charged particles at low energy. However, this endeavor is difficult. Coulomb breakup of nuclei on heavy targets has been proposed as an indirect method to infer the low-energy cross sections, because this reaction corresponds to the time reversed process of radiative capture. Two Coulomb breakup experiments aiming to infer the $α(d,γ){}^{6}\mathrm{Li}$ radiative-capture cross section have been performed at 26 MeV/nucleon and 150 MeV/nucleon. Within a fully dynamical reaction model, we report here on a new theoretical analysis of these reactions. Our analysis indicates that the breakup of ${}^6\mathrm{Li}$ onto ${}^{208}\mathrm{Pb}$ is nuclear dominated at forward angles because Coulomb breakup is suppressed due to the $N=Z$ cluster structure of ${}^6\mathrm{Li}$. Our analysis also suggests that the reaction is characterized by marked Coulomb-nuclear interferences. Moreover, we find that higher-order effects considerably increase the breakup cross section at low energies. Consequently, extracting radiative capture cross sections from the breakup reaction data in this case is extremely challenging. We suggest that the forward Coulomb breakup suppression is a general feature of collision processes involving $N=Z$ clustered nuclei. Our findings underscore the importance of advancing alternative indirect methods for nuclear reactions in astrophysics such as electron-induced dissociation.

nucl-th↗
arXiv2026

Problem-aware sampling for bias and variance reduction in randomised Hamiltonian simulation

Randomised quantum simulation algorithms such as qDRIFT reduce circuit depth by replacing deterministic product formulas with stochastic compilations, but achieving high accuracy can require many steps. In vanilla qDRIFT, Hamiltonian terms are sampled with probabilities proportional to their coefficient magnitudes. We propose StateDRIFT, a variant of qDRIFT which optimises its sampling distribution to estimate time-evolved quantities using knowledge of the initial state and target observable, with a provable truncation bound that keeps qDRIFT's guarantees. The distribution at each depth is chosen by solving an optimisation problem targeting bias and/or variance. StateDRIFT can significantly reduce the bias and variance of estimators, and hence circuit depth, which we test on short-time dynamics of lattice models and molecules under Jordan-Wigner up to 18 qubits. For example, with BeH2 StateDRIFT can reduce the number of two-qubit gates required to achieve a given accuracy from 11,201 to 983. StateDRIFT involves measuring observables and commutators on the initial state, so we propose two practical ways to estimate these: classical surrogates such as tensor networks, and classical shadows, which recover most or all of the saving from exact oracles in the systems tested.

quant-ph↗
arXiv2026

Functions determining $(q+3)/2$ directions over the finite field with $q$ elements

Let $q=p^n$, where $p>2$ is prime. We prove that every $q$-set $U\subseteq\mathrm{AG}(2,q)$ determining exactly $(q+3)/2$ directions is affinely equivalent to the graph of $x^{(q+1)/2}$. For $n\ge2$, we also prove that a $q$-set determining at most $(q+p)/2$ directions is either an affine $\mathbb{F}_p$-linear set or affinely equivalent to this graph. The classification for $n=1$ is due to Lovász and Schrijver. For $n=2$, the classification and the stated stability result follow from results of Gács, Lovász, and Sz\H onyi and the direction theorems of Blokhuis, Ball, Brouwer, Storme, and Sz\H onyi, completed by Ball. These direction theorems establish affine $\mathbb{F}_p$-linearity for functions with fewer than $(q+3)/2$ determined directions. Our proof uses Rédei polynomials, results of Gács, Lovász, and Sz\H onyi on their coefficients, and an earlier polynomial comparison lemma of the author.

math.CO↗
arXiv2026

Liouville-type theorems and perturbation uniqueness for stationary incompressible MHD with directional fractional dissipation

We study Liouville-type theorems for stationary solutions of the incompressible magnetohydrodynamics equations in $\mathbb{R}^n$, with directional fractional dissipation $(-\partial_i^2)^αu$ and $(-\partial_j^2)^βb$, where $n\ge 2$ and $0<α,β\le 1$. We prove that $u\equiv b\equiv 0$ by assuming that $u\in L^{p,\infty}(\mathbb{R}^n)$ and $b\in L^{q,\infty}(\mathbb{R}^n)$, with suitable conditions on $p$ and $q$. The proof uses a localized fractional energy identity and does not assume finiteness of the global homogeneous Sobolev energy. The method also applies to the case of full fractional dissipation. For the system with common-directional fractional dissipation, we prove stationary uniqueness for compactly supported perturbations of arbitrary smooth transverse shear backgrounds when $0<α,β<1$. We also establish a quantitative uniqueness criterion for perturbations of backgrounds with bounded gradients that are not necessarily compactly supported, for all $0<α,β\le 1$, assuming directional localization.

math.AP↗
arXiv2026

SCOPE: Scene-graph Construction Optimized by Plan Error-correction for Robot Long-term Planning Datasets

Task and Motion Planning (TAMP) stands as a fundamental challenge in robotics, essential for enabling autonomous agents to execute complex, long-horizon tasks in diverse environments. While Large Language Models (LLMs) introduce a new paradigm for TAMP by leveraging extensive common sense, their inherent reasoning limitations hinder direct application, and fine-tuning strategies to enhance these capabilities are currently bottlenecked by the scarcity of high-quality datasets. Existing automated generation methods often suffer from homogeneity, limiting model generalization, and unsolvability, where hallucinated scene graphs create physical mismatches with task goals. To address these issues, we propose Scene-graph Construction Optimized by Plan Error-correction (SCOPE), an automated framework designed to generate diverse and executable TAMP datasets. SCOPE incorporates multi-level diversity metrics to maximize the information entropy of scenarios. Crucially, it introduces a Plan Feedback Error-correction pipeline that utilizes planner execution failures as feedback signals to retroactively rectify logical inconsistencies in scene graphs, ensuring physical feasibility. Experimental results demonstrate that SCOPE exhibits greater robustness to erroneous scene graphs and generates higher-quality data. When utilized for LLM fine-tuning, SCOPE-generated data yield a downstream TAMP model that achieves an absolute 12.33% gain in success rate over its counterpart fine-tuned with data generated by DELTA, a state-of-the-art PDDL-based planner.

cs.RO↗
arXiv2026

From Prompt Rules to Golden Data Model: Production-Scale Generation of Personalized, Compliant Pharmaceutical Claims

Large language models (LLMs) are increasingly used to generate domain-specific commercial & medical content (e.g., claims, statements, and promotional phrases) in regulated industries such as pharmaceuticals. However, ensuring its regulatory compliance, factual accuracy, and audience-specific personalization remains a major challenge. We report a production case study asking whether moving compliance and personalization signal from prompt-level rules into retrieval time structured context improves pharmaceutical claims generation, using Composer, a deployed internal tool built on a Golden Data Model (GDM) of 1,867 pharmaceutical claims spanning four products. The GDM integrates claim metadata, references, audience labels, material type, usage history, Match Text Variant (MTV) links, and a six dimension quality score (6C). Across six product-audience segments, the context pipeline improves intended indication (+29%), intended content type (+62%) and intended target audience (+67%), 6C quality score (+86%), and claim reuse (+34%). Production log analysis over the deployment period shows a 29.5% reduction in weekly API cost. We report this as descriptive deployment evidence. This work is a systems and data architecture contribution: it shows that in a regulated, high-stakes domain, restructuring claim data for retrieval-time filtering is a more scalable lever than expanding prompt-level rules, and documents the engineering path data cleaning, graph linking, and quality scoring.

cs.AI↗
arXiv2026

The Geometry of Metaphor: Probing and Steering Metaphor Representations in Large Language Models

Metaphor is a crucial aspect of human cognition. As such, we would like to understand how it is represented in large language models (LLMs) and how it might be controlled. We use mechanistic interpretability techniques to identify causal subspaces in a range of LLMs covering different architectures and sizes. We show that we are able to steer output on classification tasks across different parts of speech. We are also able to steer output on generation tasks, indicating that these techniques can be used to control the metaphoricity of LLM output.

cs.CL↗
arXiv2026

Jev-DLC: Decision-Level Continual Learning from Typed Feedback

Individual preferences often become visible only after deployment, when a user accepts one decision or revokes it in favor of another. Jev-style typed interfaces make this feedback explicit by identifying the selected action and, under revocation, the decision it replaces. We study how these consequences persist across requests as Decision-Level Continual Learning from Typed Feedback (Jev-DLC). Jev-DLC treats each correction as both evidence about a recurring action identity and a directed relation between the preferred and rejected choices. It preserves these signals in complementary bounded channels above a frozen scorer and combines their scores to shape the next decision. The method retains no raw interaction history, adds no scorer call, and performs no runtime language-model backward pass. We also introduce TCPA-Bench, a chronological suite that credits a correction only through its effect on later decisions. On TCPA-Repeat, Jev-DLC reaches 44.83% user-macro accuracy overall, 2.43 points above Recent-4 history replay. After at least two earlier feedback events, it reaches 56.07%, a 7.54-point margin over the strongest reported comparison at this depth. These results show how explicit typed corrections can become compact causal state for continual personalization.

cs.AI↗
arXiv2026

Beyond Causal Masks: Auditing and Localizing Hidden Future-Token Leakage in Adaptive Sequence Models

Hidden future-token leakage can create false architectural gains: an autoregressive model may appear substantially better under teacher-forced evaluation while its free-running generation deteriorates. We investigate this failure in an adaptive residual-routing decoder whose sequence-level routing summary exposes prefix positions to future tokens despite a correct causal attention mask. An anomalous 24-layer checkpoint reduces validation loss from the baseline's 3.5958 to 1.4259, yet it fails a prefix-invariance audit and has substantially higher reference-model perplexity during generation. Layer-wise diagnostics and implementation inspection trace the violation to a full-sequence summary broadcast through the dynamic routing-query path. Two independently trained 12-layer causal repairs eliminate detectable leakage and return validation loss and reference perplexity to the control scale, removing the large apparent advantage. As diagnostic tools, we use the established prefix-invariance contract, maximum prefix-output discrepancy (LeakScore), complementary behavioral metrics, and layer-wise profiles. Supporting experiments cover six synthetic implementation pairs spanning five module families, clean pretrained controls, controlled fault injection, and cache consistency across numerical precisions. These results illustrate how hidden future-token access can produce misleading claims of architectural progress and why lower teacher-forced loss should be interpreted together with whole-model causal checks and free-running evaluation.

cs.AI↗
arXiv2026

Diffusion-Based Synthesis of Complete 3D Biparametric Prostate MRI

Synthetic biparametric MRI (bpMRI) may help alleviate the limited availability of large and representative prostate MRI datasets for deep learning applications. In this work, we present a conditional 3D diffusion framework for anatomy-conditioned synthesis of complete prostate bpMRI from anatomical masks. The framework supports sequential synthesis, in which T2w and diffusion modalities are generated by separately trained models, and joint synthesis, in which all bpMRI modalities are generated simultaneously within a single model. Five configurations were trained using PI-CAI, FastMRI Prostate, and PROSTATEx datasets to investigate different conditioning schemes, target modalities, and synthesis strategies. Evaluation using distribution-based metrics, paired-image similarity, and qualitative assessment demonstrated the feasibility of both synthesis pathways. Joint synthesis achieved competitive distributional fidelity while generating complete bpMRI, whereas direct synthesis of ADC and computed high-b-value DWI outperformed their derivation from synthetic diffusion-weighted images.

cs.CV↗
arXiv2026

Nonparametric autoregressive estimation of probability distributions with parallel gradient boosting

Nonparametric estimation of joint probability distributions is a core problem in statistics, which is notoriously challenging with high-dimensional data. Such challenges also arise with conditional distribution estimation and have led to flexible algorithms that aim to tackle the ''curse of dimensionality'' by exploiting predictive structures of lower dimension than the ambient space. In this paper, we study the use of such conditional distribution estimators to estimate joint distributions by factorizing the latter in an ''autoregressive'' way. We show that for loss functions satisfying a subadditivity property, bounds on the convergence rate of an autoregressive estimator can be derived from bounds on the convergence rate of the conditional distribution estimators. These bounds relate the difficulty of estimating the joint distribution to that of estimating the most challenging factor in the autoregressive factorization. When the conditional estimators adapt to sparsity in their conditioning sets, we characterize this difficulty through variable elimination in an undirected dependence graph. This allows bounding the rates of convergence by quantities related to the sparsity of the graph, and provides an additional motivation for the autoregressive approach. We then evaluate the practical performance of an autoregressive estimator based on parallel gradient boosting, a flexible nonparametric method for estimating conditional distributions. The estimator demonstrates competitive estimation performance on various simulated and real datasets, even without optimizing the factorization order. Together, these results support nonparametric autoregressive estimation as a practical approach to flexible joint distribution estimation.

stat.ML↗
arXiv2026

History-Aware Control Barrier Functionals with Nonsingular Fading-Memory Kernels

Safety-critical protection often depends not only on the state and physical limits of the system, but on how that state evolved over time. Here, the safety margin measures the remaining allowance before a physical limit is reached. In this context, the objective is to enforce both physical safety and history-dependent retention of this margin for integer-order control-affine systems. To this end, changes in this margin are converted into memory through fading-memory kernels with finite nonnegative weights. A history-aware control barrier functional (HA-CBFal) is constructed explicitly, with nonnegativity equivalent to retaining a prescribed fraction of this margin's weighted historical value. This condition is enforced by an affine input constraint and proved to imply physical safety under stated kernel, initialization, and feasibility conditions, without finite-dimensional memory realization. In 36 calibration cases on the ADMIRE flight-control benchmark, mean per-case tracking-RMSE reductions of 27.2\% and 7.0\% are obtained relative to calibrated state-only CBF and moving-average control barrier functional (MA-CBFal), respectively, with both constraints preserved to numerical tolerance. In a shorter-recovery maneuver outside calibration with frozen gains, both constraints are preserved to tolerance by HA-CBFal and state-only CBF, but the retention requirement is violated despite MA-CBFal nonnegativity.

eess.SY↗