Published: 2026-06-30 13:26:16
Authors: Marius Fischer, Asbjørn Christian Nordentoft
Categories: math.NT
Abstract:
Let $A/\mathbb{Q}$ be a modular abelian variety of analytic rank $0$. If $G$ is a non-trivial finite abelian group such that all prime factors of $\lvert G \rvert$ are sufficiently large in terms of $A$, we show that there are infinitely many $G$-extensions $F/\mathbb{Q}$ such that $A(F)$ is finite. When $A$ is a rational elliptic curve of analytic rank zero with no exceptional primes, or the product of two such curves, the same conclusion holds without any assumptions on $|G|$. Our proof relies on new simultaneous non-vanishing results for twisted central $L$-values of even-weight holomorphic newforms. These results are obtained via novel constructions related to horizontal $p$-adic $L$-functions and are of independent interest.
Published: 2026-06-30 13:19:44
Authors: Gabriele de Mauro, Satya N. Majumdar, Gregory Schehr
Categories: cond-mat.stat-mech
Abstract:
We study $N$ non-interacting Brownian particles in an external potential under simultaneous stochastic resetting to the origin. Although they do not interact directly, common resets generate strong dynamically emergent correlations (DEC). We analyze how confinement modifies these correlations and the nonequilibrium stationary state for $V(x)=κ|x|^α$, $α\geq0$, focusing mainly on two analytically tractable cases: harmonic confinement (HC), $α=2$, and box confinement (BC), $α\to\infty$. In both cases the stationary state is controlled by the competition between confinement and resetting lengths. We derive exact results for the stationary joint distribution, density, correlations, extreme value statistics (EVS), and gap statistics. While the density behaves similarly in HC and BC, the normalized correlation coefficient differs sharply. In BC it is non-monotonic and overshoots the unconfined value, as hard walls suppress decorrelating trajectories. In HC it instead increases monotonically toward the unconfined limit. For general $α$, the behavior is monotonic for $0<α<α_c=1+\sqrt{5}$ and non-monotonic for $α>α_c$. The difference between HC and BC is also visible in edge observables. In HC, the maximum scales as $M_1=O(\sqrt{\ln N})$ and has a limiting distribution with bounded support and a shape transition controlled by the ratio of the two length scales. In BC, the maximum is at distance $O(1/N)$ from the boundary, as in equilibrium, but its fluctuations have a broad power-law tail with logarithmic corrections. The first gap shows a similar contrast: BC gives a smaller typical gap but stronger anomalous fluctuations than HC. Finally, we extend the EVS analysis to general $α$ and identify, via simulations and scaling arguments, three universality classes: $0\leqα\leq1$, $1<α<\infty$, and the singular limit $α\to\infty$.
Published: 2026-06-30 13:10:29
Authors: Shuyan Zhai, Jiaqi He, Weixia Zhang, Liang Wang, Zhenjie Lee, Zufeng Zhang, Kede Ma
Categories: cs.CV
Abstract:
Existing smartphone image quality assessment (IQA) methods commonly reduce perceptual quality to a single score. However, this scalar formulation is poorly aligned with practical image signal processor (ISP) tuning, where engineers must identify specific quality issues, estimate their severities, and determine whether they are acceptable or require intervention. In this work, we introduce a Practical ISP-aware Structured Model for IQA (PrISM-IQA), which reformulates smartphone IQA as a multi-issue ordinal diagnosis problem. Rather than regressing a single quality score, PrISM-IQA predicts an \textit{ordered} severity level -- absent, minor, severe, or critical -- for each ISP-relevant issue, covering both global image-level artifacts and local content-dependent defects. To produce logically consistent predictions, PrISM-IQA combines cumulative ordinal encoding with structured inference that captures within-issue monotonicity as well as cross-issue subsumption and exclusion relations. We evaluate PrISM-IQA on a reconstructed SPAQ benchmark annotated with $53$ ISP-relevant quality issues and on a small-scale expert-annotated real-world dataset. Experimental results demonstrate the effectiveness of PrISM-IQA for practical issue-level diagnosis, reveal transferable perceptual quality representations through linear probing, and further show how its predictions can support actionable and meaningful ISP tuning.
Published: 2026-06-30 13:09:59
Authors: Johannes Hauskrecht, Kristoffer Simula, Yifan Cheng, Evelin Martine Corvid Christlmaier, Daniel Kats, Ali Alavi
Categories: physics.chem-ph
Abstract:
We introduce an additive reference correction for the transcorrelated (TC) method and its three-body mean-field approximation (xTC), to improve energy differences computed in small orbital basis sets. The correction is motivated by the observation that, for xTC atomization energies, the dominant error in double-ζ bases originates from the reference contribution rather than from the correlation energy. In the proposed reference-corrected scheme (RC-xTC), the small-basis correlation energy is retained, while the corresponding TC reference energy is replaced by its value from a larger basis. Benchmark calculations for the non-relativistic HEAT set with the Dunning basis-set family show that RC-xTC substantially improves both total and atomization energies relative to standard xTC in double-ζ bases. At the CCSD(T) level, RC-xTC yields better atomization energies than CCSD(T)-F12a in the double-ζ regime, while preserving the favorable total-energy accuracy of xTC. At the CCSD level, RC-xTC improves atomization energies relative to F12a throughout the full basis-set sequence. As the basis set is enlarged, xTC and RC-xTC become progressively identical, as expected from the construction of the correction.
Published: 2026-06-30 13:09:28
Authors: Jean-Marc Richard
Categories: math-ph, hep-ph
Abstract:
We review the collaboration that led to the first rigorous proof of the stability of the hydrogen molecule within quantum mechanics and discuss several related issues concerning few-charge systems. Particular emphasis is placed on the role of symmetry breaking, the stability domains of Coulombic few-body systems, and some applications to exotic hadrons in the quark model.
Published: 2026-06-30 12:56:42
Authors: Ajmal M., Abin Roy, Afthab Salam Kanniyan, Jawadh Abdul Kabeer, Jerin James, Preslav Nakov, Zhuohan Xie
Categories: cs.CL
Abstract:
Large Language Models (LLMs) achieve strong results on many medical benchmarks, but their clinical reasoning remains difficult to evaluate reliably. A central risk is an evaluation illusion: fluent and well-structured explanations can appear clinically convincing even when the final diagnosis is incorrect. We introduce CLExEval, a human-in-the-loop framework for evaluating LLM clinical reasoning under progressive information masking. CLExEval combines 5,600 expert-physician annotations with 200 clinical reasoning traces derived from 40 rare diagnostic cases. Our analysis identifies three recurring failure patterns: (i) verbosity bias, where GPT-4o-mini's diagnostic accuracy drops from 95.0% to 32.5% under information scarcity; (ii) a hidden knowledge paradox, where a specialist model reaches 92.5% maximum diagnostic potential but fails to retrieve that knowledge reliably in verbose contexts; and (iii) a 68.6% reasoning-to-output mismatch, where correct diagnoses appear in reasoning traces but are not reflected in final answers. We further evaluate the LLM-as-a-Judge paradigm on a human-verified failure set (n = 142). GPT-4o-mini approved 47.9% of clinically incorrect outputs, while HuatuoGPT-o1 approved all validly scored failures and showed a positive self-preference bias. These results suggest that standalone automated clinical evaluations can substantially overestimate clinical reliability without expert-grounded validation.
Published: 2026-06-30 12:55:27
Authors: Mohammadhossein Bakhtiaridoust, Dominik Baumann, Shankar Deka
Categories: eess.SY
Abstract:
Uncertainty quantification for learned stochastic dynamical systems is essential in safety-critical tasks such as control and monitoring. Standard conformal prediction provides finite-sample coverage guarantees under exchangeability, but this assumption is typically violated in dynamical systems because trajectory data are temporally dependent, state distributions evolve, and recursive prediction errors accumulate. This paper proposes an invariant-measure conformal prediction (imCP) framework that calibrates uncertainty using independent samples from an invariant measure of the Markov process induced by the dynamics. This aligns calibration with the stationary operating regime and restores the statistical symmetry needed for rolling one-step split conformal guarantees. For recursive multi-step prediction, imCP combines conformal calibration with Lipschitz error propagation through the learned predictor to obtain explicit horizon-dependent bounds.These pre-deployment uncertainty tubes are suitable for rolling and receding-horizon applications, such as self-triggered control and fault detection, where uncertainty bounds must be computed before future residuals are observed. Numerical experiments show that imCP yields reliable bounds, while non-invariant calibration can become misaligned during deployment.
Published: 2026-06-30 12:45:48
Authors: Uriel Shafir, Ronnie Kosloff
Categories: quant-ph
Abstract:
The core step in quantum simulations is typically matrix vector multiplication $φ= \Hmat ψ$. Executing this step is limited by memory requirement to store the Hamiltonian. We present a memory-scalable, hardware-adaptive matrix-free framework for applying large operators on vectors without materializing the full matrix on a single accelerator.
The operator is represented through a block-procedural interface: blocks may be generated, loaded, cached, distributed, or applied directly only when their action is needed. For quantum simulation, it provides the core kernel for quantum operations. An adaptive planner selects block size, cache strategy, GPU grouping, row distribution, and task parallelization from memory and workload estimates. We describe analytic, measured, and learned planning strategies that choose between procedural generation, partial caching, full caching, and row-distributed caching. The method removes the requirement that the full dense matrix fit in the accelerator memory. This shifts large simulations from a fixed memory barrier to a tunable balance between block generation, cache reuse, data movement, parallel scheduling, and numerical accuracy.
Published: 2026-06-30 12:44:17
Authors: Diego Alberici, Davide Gabrielli, Giulia Pallotta
Categories: math.PR, cond-mat.stat-mech
Abstract:
We consider a family of continuous-time Markov chains with finite strongly connected transition graph and rates $\left(r_N\right)_{N>0}$ depending on a parameter $N$, so that, when $N$ is large, transitions may happen on different time scales. Under suitable general assumptions on the asymptotic behavior of the rates, we give a recursive characterization of the limiting invariant measure. The recursion is encoded in a forest structure equivalent to the one recently developed in the analysis of dynamical aspects of metastability \cite{BL,LX}.
Our proof is based on a combinatorial representation of the invariant measure, given by the Markov chain tree theorem. Basic steps are the reduction of the chain by a trace process, the introduction of an effective dynamics, and a careful analysis of the set of relevant arborescences in the expansion. In particular we use a factorization of fast arborescences. As a byproduct we obtain properties of the arborescences of generalized star-delta reductions of weighted digraphs.
Published: 2026-06-30 12:43:06
Authors: Ilmun Kim
Categories: math.ST
Abstract:
Classical minimax lower bounds for testing are typically derived for fixed error probabilities, while high-confidence results often impose a common failure probability. We study prescribed-error testing, in which the level and the target type II error may be small and of different orders. Standard prior-based reductions generally aggregate the two errors into a single quantity and therefore do not capture their distinct roles. We develop a general lower-bound technique based on a binary reduction that preserves the separate roles of the two error targets. The reduction yields two directed Kullback-Leibler information requirements, corresponding respectively to the level and the target type II error. When both directed mixture divergences can be controlled, they combine into a binary Jeffreys divergence, leading to the logarithmic dependence on the level and the target type II error. Applying the framework to Gaussian sequence testing, multinomial uniformity testing, and continuous uniformity testing over Hölder balls, we obtain lower bounds that match corresponding high-confidence upper bounds and hence establish prescribed-error minimax rates sharp up to constant factors.
Published: 2026-06-30 12:42:45
Authors: D. Kotlorz, S. V. Mikhailov
Categories: hep-ph
Abstract:
We explore approaches to numerically optimize a segment of the perturbative series for physical quantities using the QCD renormalization group. We apply these methods to the perturbative series for the coefficient function $C_{Bjps}$ of the Bjorken polarized sum rule and the Adler function $D_A$. Using various techniques proposed in the literature, we discuss the consequences of ``optimization.''
Published: 2026-06-30 12:38:58
Authors: Shun Kenney, Teppei Suzuki
Categories: cs.CV, cs.AI
Abstract:
The remarkable scalability of Transformers has expanded their application to 3D computer vision, where camera-aware positional encoding is crucial for providing spatial cues in multi-view geometry. Recent advancements have established the practice of using camera parameters -- such as extrinsics or projection matrices -- as relative positional encoding into the query, key, and value vectors of the attention mechanism. However, when scaling up the training recipe of novel view synthesis (NVS) models with the camera-based positional encoding, we observe a significant issue: model performance stagnates in the late stages of training.
In this paper, we investigate the cause of the performance bottleneck when scaling up and demonstrate that storing rotation and translation given by the positional encoding in the same dimensions of the value vector causes indeterminacy in their independent identification, hindering training scalability. To address this, we propose Decoupled Pose Positional Encoding (DPPE), a novel camera-based positional encoding that explicitly decouples rotation and translation. Extensive evaluations on NVS tasks demonstrate that DPPE enables stable long-term training even in scaled-up training setup. Furthermore, it exhibits superior generalization performance in extrapolation settings, such as handling an increased number of viewpoints and zoom-in scenarios.
Published: 2026-06-30 12:38:13
Authors: Jinliang Xu, Liping Ma
Categories: cs.NE, cs.MA
Abstract:
This paper evaluates the Metabolic Multi-Agent Optimizer (MMAO) under a stricter empirical protocol rather than reintroducing the framework itself. The study asks whether MMAO's closed-loop resource-allocation principle remains credible under broader, more standard, and more explicitly budget-controlled continuous and discrete benchmarks. The main completed matrix covers eight CEC2017 functions at 10D and 30D with 20 seeds each, and five TSPLIB instances with 20 seeds each, together with stronger reproducible baselines including PSO-lite, ES-lite, and an iterated-greedy 2-opt route baseline. We further add trajectory-level diagnostics for communal budget, success rate, role evolution, and population turnover, plus an auxiliary OR-Library multiple-knapsack slice to extend the discrete evidence beyond routing. Under this protocol, MMAO clearly outperforms the external baseline set on the continuous side and on the TSPLIB side, while the ablation variants remain much closer to the full method than the external baselines are. We therefore position MMAO as a benchmark-backed cross-domain adaptive framework whose most clearly validated value is endogenous resource redistribution under evidence pressure, while also noting that the strongest remaining gap is not basic workability but sharper mechanism isolation and broader competition-grade comparison.
Published: 2026-06-30 12:27:21
Authors: Eric P. Glasbrenner, David Fabian, Wolfgang P. Schleich
Categories: quant-ph
Abstract:
Three ingredients of the elementary Landau-Zener problem determine the familiar expression $a_{LZ}\equiv\exp\left[-π/(2ε)\right]$ for the asymptotic value of the probability amplitude for remaining in the initial level: (i) A wave whose phase is determined by the product of a contour integral over a simple pole at the origin of the complex plane and the inverse of twice the scaled chirp parameter $ε$. (ii) An asymptotic limit of the associated path connecting the points $\pm 1$ along the real axis and circumventing the pole in the upper half-plane, and (iii) a half-circle in the lower half plane enclosing together with the asymptotic path the pole. The Cauchy theorem immediately provides us with the value $\iiπ$ of the asymptotic contour, and thus with $a_{LZ}$. Our analysis demonstrates not only that $a_{LZ}$ is the consequence of a logarithmic phase singularity but also explains why the Markov approximation also leads to $a_{LZ}$.
Published: 2026-06-30 12:24:21
Authors: Srimanta Santra, Oleksii Molodchyk, Matti Noack, Timm Faulwasser
Categories: eess.SY
Abstract:
This paper studies the certification of a fixed candidate trajectory on a finite certification grid under parametric uncertainty. For each constraint-time pair, we define a scalar measure of constraint violation and aggregate the resulting pointwise chance constraints into a worst-case Value-at-Risk (VaR) margin. The goal is not to generate a new trajectory, but to assess online whether a trajectory produced by a planner or predictive controller is sufficiently safe on the certification grid. Direct evaluation requires repeated uncertainty propagation and is often too expensive for computationally demanding models. We therefore adopt an offline-online scheme: offline, a surrogate of the constraint violation map along the candidate trajectory is constructed using polynomial chaos expansion (PCE) when the uncertainty law is known, or kernel regression when only sampled input-output data are available; online, the surrogate is sampled to evaluate conservative VaR bounds at low computational cost. On the theoretical side, we derive a finite-sample upper bound for the grid-based VaR margin using empirical quantiles, the Dvoretzky-Kiefer-Wolfowitz (DKW) inequality, and a union bound over all constraint-time pairs, without assuming a parametric family for the underlying violation distribution. We also show how a uniform surrogate error bound transfers to the certified VaR margin. The approach is illustrated on a crystallization population balance model, where the surrogate-based risk estimates track direct Monte Carlo results while substantially reducing online evaluation time.
Published: 2026-06-30 12:19:05
Authors: Quan Quan
Categories: cs.RO
Abstract:
Stabilization learning is an interdisciplinary paradigm that bridges control theory and machine learning. Its core idea is to enable systems to adjust their policies under perturbations or environmental changes through real-time feedback and adaptive mechanisms. It takes stability as its primary goal, distinguishing itself from certificate learning, which focuses on formal proofs, and reinforcement learning, which pursues optimality. It encompasses a range of methods, including Lyapunov-based analysis and design, deep feature extraction, and data-driven feedback synthesis, and is applicable to complex high-dimensional, nonlinear systems. This paper elaborates on the two major categories of stability in stabilization learning, as well as three typical application scenarios: control, observation, and recognition. It constructs a unified mathematical framework based on a six-tuple, and expands into two types of seven-tuple models: constrained learning with barrier spaces and tracking problems with targets. It also analyzes the roles, meanings, and implementation choices of key elements such as state space, controlled system, metrics, and policy. Through the formal reformulation of 11 types of problems, including multi-agent cooperative tracking, visual servo robot position stabilization, chess games, and Push-T tasks, this paper illustrates the potential applicability of the framework across multiple domains. Finally, it points out that future stabilization learning will focus on two major directions: constructing a unified problem framework and achieving efficient and robust learning, providing solutions for complex system control that combine theoretical rigor with engineering practicality.
Published: 2026-06-30 12:07:35
Authors: Shihong Liu, Yu Rong
Categories: astro-ph.GA
Abstract:
Direct electron-density measurements at high redshift are usually limited to galaxies with individually strong density-sensitive doublets, and therefore may not trace the average interstellar medium of ordinary low-mass galaxies. We stack public JWST/NIRSpec medium-resolution spectra from the DAWN JWST Archive to measure [SII]-based electron densities $n_e$ for low-mass galaxies at $2
Published: 2026-06-30 11:52:24
Authors: Aditya Shukla, Nikolas Mavrikakis, Can Yildirim
Categories: cond-mat.mtrl-sci
Abstract:
The relationship between plastic deformation accommodation structures and residual elastic strain fields in deformed metals is poorly understood at the intragranular scale, largely because no experimental technique has provided simultaneous, three-dimensional, bulk-sensitive access to both fields at the length scale of dislocation boundaries. Here we use dark-field X-ray microscopy (DFXM) to map intragranular misorientation and residual elastic strain simultaneously in three dimensions within a grain of 50% cold-rolled Fe 3%Si alloy. We resolve geometrically necessary boundaries (GNBs) and incidental dislocation boundary (IDB) cell structures in the bulk non-destructively. Correlating the elastic strain field with the segmented plastically deformed substructure reveals that GNBs act as the primary carriers and distributors of long range residual elastic strain. GNBs separate subdomains of distinct mean d-spacing, across the grain volume. The plastic misorientation associated with IDBs and dislocation cells develops within GNB-delimited subdomains that carry comparatively similar values of elastic strain. This supports a mechanistic picture in which GNBs accommodate nearly all the long-range residual elastic strain in the deformed state, while plastic slip propagates into GNB interiors to organize into IDB cells with similar strain levels. The three-dimensional misorientation and strain gradients quantified here provide direct experimental input for recovery and recrystallization modelling in ferritic steels, such as electrical steels.
Published: 2026-06-30 11:50:46
Authors: Baiyu Chen, Lin Gan, Guangwen Yang, Wenjian Yu
Categories: cs.AR, cs.DC, math.NA
Abstract:
High-precision effective resistance computation is a cornerstone of Electronic Design Automation (EDA) sign-off, yet it remains a fundamental bottleneck in large-scale power grid analysis, spectral sparsification, and circuit reliability. Existing approaches face a prohibitive "precision-memory impasse": approximate methods lack the stringent accuracy required for high-stakes industrial sign-off, while exact methods either suffer from redundant query overheads or trigger $O(n^2)$ memory explosions. To resolve this, we propose PEERS, a Parallel and Exact Effective Resistance Solver powered by an implicit inverse computing model of the Cholesky factor. By integrating a state-inherited augmented depth-first search (DFS) with a dynamic query update mechanism, PEERS eliminates numerical redundancy and evaluates all-edge resistance queries in a single parallel sweep. We provide a rigorous Work-Span analysis, proving that for graphs satisfying an $O(n^α)$ separator theorem, PEERS achieves a theoretically optimal parallel span of $O(n^α)$ while strictly maintaining $O(nnz(L))$ space complexity. Numerical evaluations on industrial benchmarks demonstrate that PEERS achieves an average speedup of 83.3x over state-of-the-art parallel solvers under identical memory constraints. Notably, PEERS processes a 1-million-node industrial graph in just 18.8 seconds and scales to 17 million nodes in under an hour, providing the first computationally feasible path for exact all-edge resistance analysis in multi-million-gate designs.
Published: 2026-06-30 11:44:19
Authors: Lars Rohwedder, Leander Schnaars
Categories: cs.DS, math.OC
Abstract:
In the Graph Scheduling problem we schedule a given multiset of edges on discrete time steps, such that at each step the set of edges forms a matching. The goal is to minimize the sum of weighted group completion times, where a group is a set of edges and it completes when the last edge has been scheduled. Two popular variants of this problem are Coflow Scheduling and Data Migration. Our main result is extending a recent iterated rounding approach from Coflow Scheduling, roughly corresponding to the bipartite case, to the general Graph Scheduling problem. This yields an essentially tight $(2+ε)$-approximation for the asymptotic setting where OPT is assumed to be large. For this we rely on polyhedral techniques from general matching, namely odd-set inequalities, and graph theoretical results on edge colorings in multigraphs. The state-of-the-art approximation algorithm for Data Migration is a $(1 + φ)$-approximation that improves when OPT is small. Taking the best of this and our main result, we obtain an improvement of the approximation rate for Data Migration in any regime.
Published: 2026-06-30 11:39:45
Authors: T. P. Makarova, D. A. Estyunin, V. A. Golyashov, K. A. Kokh, O. E. Tereshchenko, A. S. Frolov, S. Ideta, Y. Kumar, K. Shimada, A. M. Shikin
Categories: cond-mat.mtrl-sci
Abstract:
This study presents a systematic investigation of Mn$_{1-x}$Pb$_{x}$(Bi$_{1-y}$Sb$_{y}$)$_{2}$Te$_{4}$ crystals over a wide range of concentrations (x = 10-60%, y = 5-60%). It was found that the value of the bulk band gap is determined exclusively by the Pb concentration and it closes at Pb 40-50 %, which corresponds to a topological phase transition. The position of the Dirac point is determined by the Pb/Sb ratio, rather than the absolute Sb content. The magnetic properties depend on the dilution of the Mn sublattice by Pb and are weakly sensitive to Sb. We show that the simultaneous substitution of Mn and Bi allows independent control of the topological phase and the position of the Fermi level.
Published: 2026-06-30 11:29:23
Authors: Muhammad Usman, Malik Zohaib Nisar, Florian Aschauer, Dorit Merhof
Categories: cs.AR, math.LO, math.OC
Abstract:
We present MINT, a dynamic-precision CNN inference accelerator based on left-to-right (LR) arithmetic. LR arithmetic computes in most-significant-digit-first manner and exposes useful partial results early so that the computation can be terminated once the desired precision is achieved. At the core, there is a MSDF serial-parallel inner-product unit, which uses redundant signed-digit representation to compute each convolution window. A budget-constrained greedy search profiles all convolution layers from INT2 to INT7 and selects the lowest precision per layer while constraining total accuracy loss to within 2\% of the INT8 baseline for VGG-16 and ResNet-18 networks. The design is synthesized on a Xilinx Zynq-7020 at \SI{200}{\mega\hertz}, and uses 5.64 average bits for VGG-16 and 6.04 for ResNet-18, while achieving 19.86 GOPS and 29.51 GOPS/W on VGG-16, and 18.86 GOPS and 26.40 GOPS/W on ResNet-18. This corresponds to 32.6\% and 26.0\% higher throughput and 82.10\% and 62.90\% higher energy efficiency than INT8 with only 1.81\% and 1.96\% drops relative to the INT8 baseline. Compared with representative prior FPGA CNN accelerators considered in this study, MINT delivers the highest energy efficiency among the listed VGG-16 and ResNet-18 designs on Zynq-7020 platform.
Published: 2026-06-30 11:01:06
Authors: Chen Min, Shuli Lv, Pengda Mao, Huixin Cao, Li Hong, Quan Quan
Categories: cs.RO
Abstract:
Due to the limited endurance of embedded energy sources such as lithium-polymer (LiPo) batteries, the flight duration and operational range of unmanned aerial vehicles (UAVs) are severely constrained. Although energy-efficient trajectory planning and control have been widely studied, most existing approaches rely on accurate system models and computationally expensive optimization procedures. This paper proposes a model-free online iterative learning (IL) framework to minimize energy consumption. Without requiring explicit models of UAV dynamics or energy consumption, the proposed method improves energy efficiency while maintaining a low computational cost. The per-iteration computational complexity is O(n), where n denotes the number of path points. In the tested cases, the proposed method is approximately 50--60 times faster than the model-based IPOPT benchmark. Simulation results and real-world flight experiments across multiple UAV platforms validate the effectiveness, computational efficiency, and practical applicability of the proposed approach.
Published: 2026-06-30 10:58:04
Authors: Jinghao Sun, Zhenchu Hu, Ye Ma, Bo Tang, Qingxu Deng, Xiuzhen Cheng
Categories: cs.SE
Abstract:
Safety-critical embedded control programs must complete each control cycle within a bounded period. Sequential execution on conventional processors can become a bottleneck when the dependency structure of the program contains subtasks that could be executed concurrently. This paper studies the Maximum Parallel Execution (MPE) problem for series-parallel task graphs under a staged batching model: compatible tasks inside one batch execute in parallel, while the selected batches are launched sequentially in a topological order that preserves precedence. We formulate MPE as a weighted clique-partitioning problem that minimizes the sum of batch execution times, with each batch cost determined by its slowest task. To solve this problem efficiently, we propose a Lagrangian-based Iterative Heuristic (LIH). LIH constructs a pricing-filtered restricted pool of feasible candidate batches from singleton columns and random greedy clique generation. It then applies Lagrangian pricing to guide column selection and uses a repair procedure to recover a legal clique partition. Experiments against a weighted mixed-graph-coloring branch-and-bound baseline and a randomized greedy baseline show that LIH matches the exact optimum in 91.25% of comparable instances, with an average gap of 0.073% and an average runtime of 18.19 ms. In the largest exact-reference node setting, the exact baseline requires hundreds of seconds on average, whereas LIH remains below 50 ms. We further present an end-to-end PLC ladder-logic case study in which PLCOpen-style programs are converted to MPE graphs, optimized by LIH, translated into FPGA-oriented HDL, and simulated against the original PLC scan execution.
Published: 2026-06-30 10:54:41
Authors: Kihyun Yu, Junehee Lee, Dabeen Lee
Categories: cs.LG, math.OC
Abstract:
We study constrained online convex optimization with adversarial losses and stochastic or adversarial constraints. For stochastic constraints, existing algorithms that achieve nearly optimal regret and constraint violation bounds typically rely on regularity assumptions such as Slater's condition, while adversarial-constraint algorithms avoid these assumptions by using a rather restrictive round-wise feasible comparator. We bridge this gap with an anytime primal-dual framework that incorporates an adaptive regularizer into the dual update. The regularizer stabilizes the dual process without relying on the negative drift induced by Slater's condition. For stochastic constraints and convex losses, our algorithm achieves $O(\sqrt{T})$ expected regret and $O(\sqrt{T}\log T)$ expected cumulative constraint violation. Furthermore, we show that our algorithm also admits high-probability bounds of the same order on regret and constraint violation. For strongly convex losses, the regret bound improves to $O(\log T)$ with a violation bound of the same order. With a minor modification, the framework also applies to adversarial constraints and provides guarantees for hard constraint violation.
Published: 2026-06-30 10:54:16
Authors: Jie Ma, Binfei Chu, Jie Gao, Jinlu Zhang, Yiwei Ma, Yi Tan, Jiayi Ji, Xiaoshuai Sun, Rongrong Ji
Categories: cs.AI, cs.CV
Abstract:
Autonomous research agents can now draft hypotheses, write code, run experiments, and produce papers, but they remain brittle when experiments fail. Under the prevailing paradigm, failure recovery is usually delegated to a single free-form reflection: a rich trajectory of metrics, logs, and design choices is compressed into one verbal critique, which often leads either to localized trial-and-error or to hard pivots that discard useful context. We propose SAGE, a Self-correcting, Autonomous, Grounded Experimenter, to tackle this failure-recovery bottleneck. Its core mechanism, Multi-Hypothesis Failure Attribution (MHFA), treats recovery as a structured causal diagnosis. By analyzing dynamic trajectory features, MHFA systematically generates multiple evidence-grounded explanations for a failure, independently evaluates their severity, and deterministically routes the verified root cause to the correct intervention level (hypothesis, experimental design, or implementation). To guarantee scientific honesty, SAGE further employs a grounded reporting mechanism that explicitly constrains drafted results to actual measured values, redacting hallucinated numbers. On a 12-topic, 5-domain benchmark, SAGE increases metrics-bearing outputs from 42% to 92% over a reflection baseline, improves artifact quality from 5.00 to 6.75/10, and blindly outscores AI-Scientist-v2 (52.0 vs. 48.2), with gains concentrated in code development and execution. While fully autonomous scientific writing and generating conference-ready papers remain notoriously difficult open problems for the entire field, SAGE successfully produces significantly more reliable and higher-quality scientific artifacts. Ultimately, by coupling structured recovery with explicit grounding constraints, SAGE significantly outperforms monolithic reflection paradigms, establishing a highly trustworthy foundation for future autonomous research.
Published: 2026-06-30 10:25:58
Authors: Artur Bromboszcz
Categories: math.AG, math.CO
Abstract:
We study arrangements of smooth conics and lines in the complex projective plane whose singularities are limited to nodes, tacnodes, and ordinary triple points. The first part of the paper gives numerical restrictions for plus-one generated conic arrangements with defect $ν(C)=3$ and explains how these restrictions interact with Bézout's theorem, the Dimca--Sernesi bound for the minimal degree of a Jacobian syzygy, and Hirzebruch-type inequalities. In particular, the possible numbers of conics are bounded, and the exceptional low-degree cases are separated from those that remain open. The second part concerns arrangements of total degree at most $6$. We identify the weak and strong Ziegler pairs occurring in the database recorded in the Appendix.
Published: 2026-06-30 10:25:46
Authors: Jiahang Tu, Fengyu Yang, Chenyang Ma, Xihang Yu, Ziyao Zeng, Shaokai Wu, Hanbin Zhao, Zhi Tao, Chao Zhang, Hui Qian, Alex Wong
Categories: cs.RO, cs.AI
Abstract:
Unified multimodal models (UMMs) have shown great promise in integrating understanding and generation across diverse modalities. However, existing research rarely extends this paradigm to the tactile domain, where both object-level semantics and sensor-level configurations jointly determine the meaning of touch. To address this gap, we propose UniTac, the first UMM designed for tactile understanding and generation. UniTac models the tactile process as a transition from non-contact to contact, capturing the physical interaction between sensors and objects through a dual-level representation that encodes both sensor and object attributes. For tactile understanding, UniTac introduces two tasks, object property description and sensor identification, to enhance reasoning over physical and cross-sensor information. For tactile generation, we design a two-stage training paradigm consisting of reconstruction and alignment, together with a sensor-prior-based sampling strategy that simulates realistic tactile contact. Trained on large-scale multi-sensor datasets, UniTac achieves state-of-the-art performance in tactile understanding and generates realistic tactile signals across sensors.
Published: 2026-06-30 10:25:32
Authors: Alexander Ulanovskii, Ilya Zlotnikov
Categories: math.FA, math.CA
Abstract:
The goal of this note is twofold. First, we provide explicit examples of periodic (though not necessarily lattice) sets that give rise to Gabor systems failing to form frames. Our constructions depend only on the parity of the window function $g$.
Second, for a wide range of finite-dimensional function spaces $V$ we show that $V$ contains a function $g$ such that a lattice of high density fails to generate a Gabor frame. In particular, we prove that the Gröchenig-Lyubarskii theorem is sharp in the finite-dimensional space of polynomials with Gaussian weight. More precisely, for every $N\in\mathbb{N}$ and every $α,β>0$ satisfying $αβ=\frac{1}{N+1}$, we give an explicit algorithm for finding an even or odd polynomial $p$ of degree at most $N$ such that $\mathcal{G}(p(x)e^{-πx^2}, α\mathbb{Z} \times β\mathbb{Z})$ does not form a frame. The proofs are constructive, elementary, and based on linear algebra.
Published: 2026-06-30 10:19:02
Authors: Keito Inoshita
Categories: cs.AI
Abstract:
Emotion-sensing AI is rapidly becoming embedded in vehicles, home appliances, dialogue agents, and social infrastructure, giving rise to a sphere in which emotion is no longer confined to individual experience but is instead observed and computed at a societal scale, a domain we term the Affectosphere. Yet a central normative question in this domain has remained underexplored: who has the final authority to determine the meaning of one's own emotion? This study addresses the question from the epistemological side of measurement's structural limits. We define a meaning distribution as the distribution of labels assigned by annotators drawn from a population under a fixed annotation protocol, and decompose its uncertainty into reducible and irreducible components. We then demonstrate that, while emotion AI can assign high-confidence point labels and discriminate real differences at an aggregate level, the irreducible component of the meaning distribution for individual instances cannot be estimated with adequate coverage under realistic annotator counts, a systematic divergence we term the epistemic gap. The key finding is that high device confidence does not constitute evidence that irrecoverable meaning has been recovered. From this epistemic gap, together with an explicitly stated normative premise, namely that the output of a system which cannot recover a quantity in principle must not be treated as its authoritative determination, we derive the norm that the final interpretive authority over the meaning of one's emotion is procedurally reserved for the experiencing subject, the norm of affective sovereignty. These results suggest that the design, evaluation, and regulation of emotion AI should place explicit allocation of interpretive authority, rather than accuracy maximisation, at their core.
Published: 2026-06-30 10:08:58
Authors: M. Yu. Piotrovich, S. D. Buliga, T. M. Natsvlishvili
Categories: astro-ph.HE
Abstract:
We estimated the spins of a sample of 58 low-mass AGNs. Analysis of the obtained spins showed that they decrease with increasing SMBH mass, leading us to hypothesize that mergers and/or chaotic accretion are the primary mechanisms for mass growth. In this regard, we proposed a more general hypothesis about the evolution of AGNs. We assume that early low-mass SMBHs have high spins, then, during their evolution, the spins initially decrease and then begin to increase, with the rate of increase gradually slowing.
Published: 2026-06-30 10:08:45
Authors: Yuchen Huang, Xiang Li, Zhenqing Ling, Sijia Li, Qianli Shen, Daoyuan Chen, Yi R. Fung, Yaliang Li
Categories: cs.AI, cs.CL
Abstract:
Data refinement involves executing multi-step recipes over evolving text states, where both composition and execution order of processing operators determine the outcome. While existing benchmarks either isolate text editing or entangle it with code and tool execution, it remains unclear whether LLMs can directly and faithfully execute these compositional, order-sensitive data refinement recipes. To fill this gap, we introduce CDR-Bench, a comprehensive benchmark featuring 3,462 high-quality tasks spanning four real-world data refinement domains and 29 distinct operators. Our benchmark evaluates models across atomic, order-agnostic, and order-sensitive settings, leveraging deterministic reference outputs to enable exact evaluation. Experiments on 10+ state-of-the-art LLMs reveal consistent failure patterns: performance degrades sharply in compositional settings, and order-sensitive recipe success collapses. These findings underline that current LLMs lack the procedural faithfulness required for reliable compositional data refinement.
Published: 2026-06-30 09:44:21
Authors: Karam Tomotaki-Dawoud, Anna Hilsmann, Peter Eisert, Sebastian Bosse
Categories: cs.CV, cs.AI
Abstract:
Single-stage video object detectors are increasingly deployed in time-critical applications, yet it remains unclear whether these models genuinely reason over temporal context or merely exploit a single informative frame-a gap hidden by standard metrics, which reward correct predictions regardless of how they are reached. We address this from two complementary directions: first, we propose TemporalLens, a model-agnostic diagnostic framework probing temporal dependence through controlled perturbations, structured occlusions, temporal shuffling, redundancy injection, and resolution degradation, revealing whether a detector actually uses information across time. Applied to stacked-frame 2D detectors and our YOLO-3D architecture, it exposes behavioural differences invisible to mAP: stacked 2D models collapse when the target frame is removed, while spatiotemporal models recover predictions from earlier frames, a signature of real temporal reliance. Second, we detail YOLO-3D, a modular real-time spatiotemporal detector built on YOLOv8, and show that simply preserving temporal depth through the backbone is the dominant performance driver (+3.7 pp mAP@50 at 32 frames averaged across scales). Together, the diagnostics and architecture turn "does this detector reason over time?" into a measurable, actionable question.
Published: 2026-06-30 09:36:55
Authors: Anh-Tuan Dao, Driss Matrouf, Mickael Rouvier, Nicholas Evans
Categories: cs.CL, cs.LG
Abstract:
Rapid advancements in generative speech technology have compromised the reliability of voice biometrics. While current spoofing detectors excel when assessed under in-domain conditions, generalisation to out-of-domain settings is often poor. We show that this can be due to linguistic bias. A reliance on linguistic cues observed in training data can then compromise robustness to cross-data. We propose a linguistic-invariant spoofing detection framework utilizing teacher-student adversarial learning. The linguistic-aware teacher model, pre-trained on linguistic content of an external dataset, guides the student detector via gradient reversal to minimize the linguistic information. To prevent the inadvertent removal of non-linguistic cues, we incorporate a Variational Information Bottleneck to enable suppression of principal cues. Across nine DF Arena datasets, our method achieves up to a 36.2% relative reduction in the EER compare to the baseline.
Published: 2026-06-30 09:25:02
Authors: Ming Li, Fan Liu, Yifeng Xiong, Jie Xu, Tao Liu
Categories: eess.SY
Abstract:
We study the information-theoretic limits of controlling unstable linear systems through non-designable observation mechanisms. Unlike classical communication-constrained control, the information bottleneck lies in the observation mechanism rather than in a designable encoder-channel interface. For noiseless linear dynamics, we derive necessary conditions for mean-square observability and stabilizability, showing that the directed information rate from the unstable state process to the observation process must dominate the open-loop expansion rate of the unstable modes. We further show that this lower bound persists under additive process disturbances. In the Linear-Gaussian setting, although the unstable-state directed information rate remains intractable in closed form, we obtain an exact characterization of the full-state directed information rate, which upper-bounds the unstable-state quantity and yields computable necessary conditions. Under suitable posterior regularity conditions, we also establish sufficient conditions for asymptotic mean-square observability and, via certainty-equivalence control, asymptotic mean-square stabilizability. The key step is an entropy-to-error bridge: a strict surplus in directed information over the expansion rate forces posterior uncertainty to collapse and thereby drives the estimation error covariance to zero. These results identify a fundamental feasibility boundary for sensing-limited control and clarify how classical communication-based limits must be reinterpreted when the sensing interface is non-designable.
Published: 2026-06-30 09:13:38
Authors: Yi-Jing Chen, Bao-Lei Liu, Ze-Yuan Dong, Zhi-Hao Zhao, Yi-Ying Zhang, Chun-Min Yu, Zhi-Hua Xu, Yuan-Jin Yu, Zhao-Hua Yang
Categories: physics.optics, physics.app-ph
Abstract:
Traditional hyperspectral imaging (HSI) relies on sequential scanning with complex and bulky hardware, inherently limiting its temporal resolution while increasing system complexity and cost. Computational HSI offers cost-effective alternatives with simplified hardware. However, most existing computational methods rely on fixed spectral encoding units, which lack adaptability for different spectral tasks. Here, we present a reconfigurable optical stochastic encoding (ROSE) framework with programmable illumination, which can be adaptively optimized for different spectral tasks, for high-throughput, compressive HSI. By leveraging an array of monochromatic light-emitting diodes (LEDs), we synthesize stochastic spectral patterns that enable compressive acquisition using a standard monochrome camera. The proposed framework allows dynamic reconfiguration of illumination patterns, making it adaptable to diverse imaging requirements. We experimentally validate the proposed method and achieve HSI with a spatial resolution of 2048 by 1536, reconstructing 60 spectral bands across the spectral range of 400-700 nm. Furthermore, we introduce an automatic optimization strategy to search for optimal illuminations tailored to specific tasks, improving both reconstruction accuracy and task-oriented performance. We demonstrate the effectiveness of our approach in applications including anti-counterfeiting inspection and oral imaging, and further validate its compatibility with standard microscope and endoscope systems. The developed ROSE illumination module could serve as a universal, plug-and-play add-on for conventional cameras and existing optical systems, providing a cost-effective pathway to upgrade them into high-performance, task-adaptive HSI systems.
Published: 2026-06-30 09:09:06
Authors: Federica Troni, Violette Gontran, Davide Grassano, Sara Bonella
Categories: physics.comp-ph
Abstract:
We introduce P3MaZe, a real-space particle-mesh electrostatic method that combines the standard short-range/long-range decomposition of Particle-Particle Particle-Mesh (P3M) electrostatics with the Mass-Zero constrained dynamics (MaZe) framework. In this formulation, the smooth long-range electrostatic potential is represented on a mesh as a zero-inertia auxiliary field, while the discretized Poisson equation is enforced as a holonomic constraint during molecular dynamics. By retaining the standard P3M decomposition, P3MaZe preserves the systematic accuracy controls associated with the real-space cutoff, the Ewald splitting, the mesh spacing, and the charge-assignment procedure, while replacing the conventional multigrid Poisson solver by a constrained correction problem. The method is validated for molten NaCl and simple point-charge flexible water (SPC/Fw). Structural, translational, collective, and rotational dynamical observables are in quantitative agreement with those obtained with established electrostatic methods, including real-space P3M, and Ewald summation. The constrained formulation consistently requires fewer multigrid iterations than the corresponding real-space P3M solver while retaining the expected linear scaling with system size. These results establish P3MaZe as a promising new direction for scalable real-space electrostatics in large-scale molecular simulations.
Published: 2026-06-30 09:05:29
Authors: Weibin Lin, Jiangtao Meng, Zheng Zheng
Categories: cs.SE
Abstract:
Deep Reinforcement Learning (DRL) agents have been widely adopted across diverse domains to address challenging decision-making problems, such as autonomous driving and robotic control. Given that many of these applications are safety- and security-critical, rigorous testing of DRL agents is indispensable. Existing testing methods are typically guided by reward signals to detect failures. However, for well-trained agents, whose performance approaches optimal levels in standard operating conditions, reward signals remain generally high, making current methods ineffective at uncovering critical failures.
To address these challenges, we propose a novel failure-based method that leverages task-induced failure insights to enhance failure detection capability while reducing the number of tests required. Since DRL agents are inherently designed with human-defined tasks, they provide valuable cues about task difficulty. Intuitively, a DRL agent is more likely to fail when confronted with a more difficult task; therefore, PRT prioritizes these tasks. Building on this foundation, we propose Prior Random Testing, a black-box failure-based testing method that enables targeted prioritization while preserving the diversity of generated test cases. Guided by task-induced failure insights, PRT prioritizes failure-prone regions of the input domain, thereby facilitating efficient failure detection.
PRT is evaluated on four widely used benchmarks and compared with different state-of-the-art methods including fuzzing, search-based and generative-based methods. PRT ranks among the top performers in terms of both the cost of finding the first failure and the diversity of test cases. Notably, compared to random testing, PRT achieves better diversity and reduces the testing cost by over 50%.
Published: 2026-06-30 09:03:24
Authors: Zewen Liu
Categories: cs.LG, cs.AI, cs.CL
Abstract:
When large language model (LLM) agents adapt their behavior through evaluator feedback, systematic evaluator biases propagate into the agent's learned strategy distribution - a phenomenon termed evaluator preference coupling. Prior work has documented this coupling and established a diagnostic framework (EPC) to measure it, but has not investigated whether calibration techniques can mitigate the effect. We present the first study of evaluator calibration as mitigation: applying probability calibration to the evaluator's pairwise judgments to reduce spurious preference propagation. In a controlled within-subjects experiment (N=5) comparing standard binary TTRL (win/loss) with confidence-calibrated TTRL (probability-weighted updates) using DeepSeek-V4-Pro as executor and GLM5.2 as evaluator, we find that calibration reduces the coupling coefficient gamma by 20-49% and Jensen-Shannon divergence by 45-67%. A symmetric-LR control confirms the effect is not due to reduced update asymmetry. We release the calibrated TTRL protocol and recommend it as a lightweight mitigation for LLM-as-judge deployment pipelines.
Published: 2026-06-30 09:02:21
Authors: Maël Dumas
Categories: cs.DS, cs.DM
Abstract:
We give a constant-factor approximation algorithm for Max Dist-2 Independent Set in graphs of bounded radius-2 merge-width. The same result holds for Min Dominating Set from [Bonamy and Geniet, 2025], [Chan et al., SODA '12]. Both approximation algorithms are LP-based, showing that the domination-to-2-independence ratio is bounded in graphs of bounded radius-2 merge-width. Moreover, this result is tight in the sense that the ratio can be unbounded in graphs of bounded radius-1 merge-width.
Published: 2026-06-30 08:58:52
Authors: Chenyao Ma, Di Zhang, Weibo Gong, Wei Du, Rui Su, Yuhang Chen, Kan Xu, Huan Gu, Limin Li, Piao Ma, Zhenghao Li, Hao Li
Categories: cond-mat.mtrl-sci, cs.AI, physics.chem-ph
Abstract:
Driven by high-throughput experimentation, computational modeling, and artificial intelligence (AI), materials data has expanded at an unprecedented rate. Conventional materials databases function only as passive repositories, archiving raw experimental records indiscriminately including both successful and failed data, without systematic value filtering or asset management. This creates a critical gap between massive data accumulation and actionable innovation, hindering the identification of high-potential materials and industrial translation. To address this bottleneck, we propose an industrialization-oriented Materials Bank, a dedicated valuefiltering and assetization layer that operates beyond traditional databases. It does not merely curate high-quality data but systematically elevates qualified candidates into standardized, upgradable materials assets via a multi-dimensional BankCard framework covering scientific validity, synthesis feasibility, application readiness, and industrial value. By unifying databases, AI models, automated experimentation, and multi-criteria assessment into a cohesive closed-loop ecosystem, the Materials Bank establishes a clear trajectory from data to knowledge, candidate, asset, and product. It serves not as an enhanced database or screening tool, but as a decision infrastructure bridging academic discovery and industrial demand, offering a scalable paradigm to accelerate AI-driven materials innovation and deliver tangible real-world impact.
Published: 2026-06-30 08:26:50
Authors: Hongpeng Cao, Liqun Zhao, Yuliang Gu, Naira Hovakimyan, Lui Sha, Marco Caccamo
Categories: cs.LG, cs.RO
Abstract:
Safe online reinforcement learning requires policies to respect safety constraints while maintaining smooth optimization dynamics. Existing approaches typically rely on either strict safety enforcement via action interventions, which introduce discontinuities in system interaction and learning, or soft safety constraint formulations, which preserve smooth learning but provide limited safety assurance. We propose AutoSafe, a safety-aware policy architecture that integrates structured safety monitoring and intervention directly into the action generation process. This design enables smooth, risk-dependent transitions between performance-driven and safety-preserving behaviors, resulting in continuous online interaction and learning dynamics. Empirical results across a suite of continuous-control benchmarks demonstrate strong safety enforcement without sacrificing learning smoothness. We further validate AutoSafe on a physical cart-pole system, highlighting its practical effectiveness for safe online learning in the real world.
Published: 2026-06-30 08:23:44
Authors: Xinming Wang, Fan Tang, Yingli Wei, Yakun He, Zhe Liu, Ping Jiang, Haoyu Wu, Zihan Guo, Chao Shen
Categories: eess.SY
Abstract:
With large-scale integration of emerging power electronic devices represented by grid-forming inverters, power system dynamics increasingly exhibit strong nonlinearity, multi-timescale coupling, and black-box control logic. These features hinder conventional parameter identification requiring known model structures and structure identification based on predefined function libraries, making complete differential-algebraic dynamic model recovery difficult under weak prior information. To address this challenge, this paper proposes an LLM-based multi-agent collaborative framework for differential-algebraic dynamic model discovery in power systems. It integrates heterogeneous exploratory agents, individual candidate model memories, parameter fitting and evaluation, and a coordinator agent. Under unified measurement-data constraints, agents generate candidate equation structures in parallel, while candidates are optimized, evaluated, retained, and summarized to provide closed-loop search guidance. The task is decomposed into differential equation structure discovery and algebraic closure discovery, enabling joint recovery of state dynamics, algebraic constraints, and key intermediate variables with incomplete prior information. Case studies on synchronous generators and grid-forming inverters show that the proposed method outperforms single-agent LLM-based discovery and conventional symbolic regression in reconstruction accuracy, generalization, search efficiency, and noise robustness. In the generator case, OOD MAPE reaches 0.19\%; in the inverter case, discovery time is reduced by 25.7\% compared with the single-agent LLM baseline.
Published: 2026-06-30 08:23:11
Authors: Yacine Ikhlef
Categories: math-ph, cond-mat.stat-mech, hep-th
Abstract:
We consider classical 2d lattice models with face interactions defined in terms of a fusion category. The symmetries of such models typically include an algebra of topological operators sitting on a closed path in the lattice. In the case when the face interactions obey the Temperley-Lieb (TL) relations, we present a generic algorithm to determine the decomposition of the transfer-matrix space of states as a direct sum of simple TL modules. We apply this approach to several examples, and analyse the action of topological operators. As an application, we compute the modular transformation of the irreducible TL characters at primitive roots of unity.
Published: 2026-06-30 08:19:00
Authors: Zhengxing Li, David J. Miller, Guangmingmei Yang, George Kesidis
Categories: cs.CR, cs.AI, cs.LG
Abstract:
While post-training backdoor detection and trigger inversion schemes have been developed for AIs used e.g. for images, there is a paucity of such methods for LLMs. First, the LLM input space is discrete, with up to 150,000^k k-tuples to consider with k the token-length of a putative trigger. Second, one must blacklist tokens typical of the putative target response (class) of an attack, as such tokens may give false detection signals. However, a comprehensive blacklist is not available, in general, for a given domain. We develop a highly effective detection and inversion framework for LLMs treated as classifiers. Central to our approach is class subspace orthogonalization (CSO), a novel plug-and-play paradigm for backdoor detection that serves two fundamental roles when applied to LLMs: i) it enhances both sensitivity and specificity of a baseline detector; ii) it provides a form of implicit blacklisting, as it penalizes against inclusion, in a candidate trigger, of tokens that induce signal perturbations "in the direction of" the putative target class of an attack. One version of our detector performs continuous optimization in token embedding space, while a companion trigger-inversion and detection method performs greedy accretion in discrete token space. Our methods give both strong detection performance and accurate inversion of ground-truth triggers on several LLM classification domains, and for several different LLM architectures.
Published: 2026-06-30 08:18:45
Authors: Lisa Taldir, Muhammad Ahmad Saeed, David Defour, Pablo de Oliveira Castro, Eric Petit
Categories: cs.AI
Abstract:
This paper investigates the capability of Large Language Models (LLMs) to detect and classify floating-point errors statically in software code. We introduce InterFLOPBench, a benchmark of 90 C kernels with 1 130 test samples designed to evaluate LLMs across six categories of floating-point error: cancellation, comparison, division by zero, overflow, underflow and NaN, compared across 14 LLMs. The evaluation framework treats floating-point error detection as a multi-label classification problem and employs the F1-score metric to measure performance. Results demonstrate that latest models (Qwen 3 32b, Gemini 2.5 Flash, Phi 4 Reasoning, DeepSeek R1T2, and gpt-oss 20b and 120b) achieve a performance greater than 0.88 overall F1-score. Performance varies between error categories, between explicit operations such as division by zero (Average F1-score: 0.8479) and more subtle numerical phenomena such as underflow (Average F1-score: 0.6059) and cancellation (Average F1-score: 0.6164).
Published: 2026-06-30 08:17:56
Authors: M. Donaire, A. Cano
Categories: quant-ph
Abstract:
Superconducting qubits are a leading platform for quantum computing. However, simultaneously achieving low noise sensitivity to suppress decoherence and sufficient anharmonicity to enable fast gate operations remains a central challenge. Here, we introduce the concept of the ferroelectric transmon (FEmon), in which the Josephson junction is shunted by a ferroelectric, or incipient ferroelectric, capacitor. We show, in particular, that the nonlinear ferroelectric response of the capacitor provides an additional degree of freedom for optimizing qubit anharmonicity while preserving operation in the charge-noise-insensitive regime.
Published: 2026-06-30 08:16:15
Authors: Huanyu Zhang, Yulin Hu, Xiaopeng Yuan, Aydin Sezgin, Anke Schmeink
Categories: eess.SP, cs.AI
Abstract:
The emerging techniques of semantic communications and edge computing in 6G networks necessitate a paradigm shift toward co-designed semantic-aware and adaptive resource allocation for short-packet transmissions. However, there is a fundamental gap between the semantic layer and the physical layer under low-latency finite blocklength (FBL) effects. To bridge this gap, we introduce the Quantized Semantic Age of Information (QSAoI), a novel metric that rigorously captures the trade-offs among freshness and semantic efficiency of high-level features in real-time communication in the FBL regime. Guided by this metric, we propose a novel foundation model-based efficient co-designed framework to minimize the expected QSAoI over wireless fading channels in latency-constrained semantic communication. Specifically, we formulate a non-linear joint optimization problem to dynamically optimize the block-wise mixed-precision quantization (MPQ) strategy and the physical blocklength. To efficiently resolve this complex problem, we develop a high-efficiency low-complexity algorithm based on fixpoint inspection and bisection search. Extensive simulations validate that our proposed algorithm dynamically adapts the semantic quantization precision to varying channel conditions, effectively minimizing the expected QSAoI compared to baselines.
Published: 2026-06-30 08:11:29
Authors: Rocco D'Agostino
Categories: gr-qc, astro-ph.CO, hep-th
Abstract:
In this work, we investigate the distance duality relation (DDR) in symmetric teleparallel theories, where gravity is mediated by nonmetricity. Starting from the general metric-affine formulation and adopting the geometrical optics approximation, we show that the standard Etherington reciprocity relation remains valid in the presence of nonmetricity when electromagnetism is minimally coupled and the photon number is conserved. We then extend the analysis to a class of $f(Q)$ theories with a nonminimal coupling between the electromagnetic field and the nonmetricity scalar. We demonstrate that such an interaction modifies the conservation of the photon number current, leading to a dynamical violation of the DDR. Focusing on a homogeneous and isotropic spacetime background in the coincident gauge, we derive a generalized DDR formula that directly relates observational distance measures to the Hubble expansion rate. Furthermore, we discuss the link between the deviations from Etherington's relation and variations of the effective fine-structure constant. Specific illustrative examples of the coupling function are also analyzed, showing that phenomenologically viable models predict only small deviations from the standard DDR. Our results provide a unified framework to distinguish between the geometric and dynamical origins of DDR violations, opening new avenues for testing non-Riemannian gravity with future high-precision astrophysical and cosmological observations.
Published: 2026-06-30 08:10:38
Authors: Bofeng Huang, Jingwei Zhang, Chang-An Zhao
Categories: cs.IT
Abstract:
Self-dual cyclic codes have garnered significant interest owing to their rich algebraic structures and wide-ranging applicability. Their construction and the establishment of lower bounds on their minimum distances are fundamental problems in coding theory. Chen and Ding laid an important foundation for the construction of self-dual cyclic codes in the case where the multiplicative order of $q$ module $n$, denoted by $\operatorname{ord}_n(q)$, is odd. Building on their work, we extend the investigation to the case of even order $\operatorname{ord}_n(q)$ and demonstrate that the minimum distances of the resulting self-dual cyclic codes satisfy square-root lower bounds. By examining the consecutive zero segments in the defining set of the dual code, we determine the exact parameters of Euclidean self-dual cyclic codes with even $\operatorname{ord}_n(q)$ and Hermitian self-dual cyclic codes with odd $\operatorname{ord}_n(q)$. Furthermore, for Euclidean self-dual cyclic codes with odd $\operatorname{ord}_n(q)$ and Hermitian self-dual cyclic codes with even $\operatorname{ord}_n(q)$, we introduce a refined parameter selection that leads to larger minimum distances with the same code length and dimension. This approach also yields tighter lower bounds for several families of self-dual cyclic codes. This work enriches the theory of self-dual cyclic codes and offers new insights into estimating lower bounds on their minimum distances.