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Search indexed arXiv papers on artificial intelligence, large language models, computer vision and robotics. Read source abstracts and follow links to arXiv.

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arXiv2026

Structural cryptanalysis of classic McEliece: Assessment of claims, costs and evidence

For the last two years, classical structural attacks on McEliece with binary Goppa codes progressed further, at the parameters of Classic McEliece, than in the previous four decades: a subexponential and then a quasi-polynomial distinguisher, key recoveries for public challenges with brute-force labels up to $254$, and conditional key-recovery estimates of about $2^{89}$ to $2^{128}$ per run of a large sparse linear-algebra computation, depending on the parameter set and on whether and how memory access is charged. On September 2026 the German BSI advised against new deployments of the scheme. This paper gives a version-aware critical assessment of these results. It separates proved distinguishers, conditional cost estimates and actual recoveries; analyses in detail Saarinen's HOVER algorithm, his conditional cost analysis and his recovery of the TII-254 challenge, together with Weis's cost study of Holdout/Hermite key recovery; and compares the other key-recovery routes and the objections raised against them. Our own analysis finds that the cost model is now the main quantitative uncertainty, with about thirty bits between the cheapest and the most expensive model for the same computation; that for the four larger parameter sets every model considered leaves the conditional estimates far below the targeted levels, whereas for mceliece348864 the shortfall is of the order of the spread between models; that the structural estimates are lower than Grover-accelerated information-set decoding for the four larger sets in essentially every model and comparable for mceliece348864; and that none of this is a demonstrated attack, since the underlying heuristics have been tested only on instances tens of thousands to hundreds of thousands of times smaller than the target. We close with open questions.

cs.CR↗

arXiv2026

String Amplitudes on Integer Rectangles

Hidden zeros of tree-level string amplitudes of colored scalars lie on loci specified by integer-valued dimensionless momentum contractions arranged in rectangles on the kinematic mesh. We study the same kinematic loci for open-string amplitudes with gluons, fermions and higher-spin states at arbitrary finite mass levels. Disk monodromy relations yield $\textit{zero-locus factorization}$: at regular points where the monodromy system is invertible, the amplitude is a weighted sum of physical three-point amplitudes sewn to complete on-shell $(n-1)$-point amplitudes. Hidden zeros follow when the external wavefunctions annihilate all contributing three-point couplings. In the low-energy limit, the massless zero-integer branch recovers the corresponding field-theory factorization. For closed strings, including gravitons, persistent cubic zeros in both chiral sectors remove the disk poles, allowing the Kawai--Lewellen--Tye momentum-kernel zero to imply amplitude zeros. The operator product expansions of external vertices give a common worldsheet origin for these conditions across spins and mass levels.

hep-th↗

arXiv2026

Different Tasks, Different Timesteps: Temporal Task-Aware Quantization for Diffusion Language Models

Diffusion Language Models (DLMs) introduce unique quantization challenges due to their iterative denoising process. In this work, we reveal that quantization sensitivity is both taskand time-dependent, with different downstream tasks exhibiting distinct precision requirements across denoising steps. We further show that these patterns can be predicted without groundtruth labels or task-specific evaluation metrics. Building on these findings, we introduce T 2Quant, a task and time-aware mixedprecision quantization framework that uses a label-free sensitivity signal to optimally allocate activation precision across denoising steps under a fixed bit-width budget. Extensive experiments across three DLMs and six benchmarks demonstrate that T 2Quant consistently improves the average accuracy of existing quantization methods, achieving gains of up to 6.7 percentage points under the same budget.

cs.AI↗

arXiv2026

Investigating Multi-Cultural Value Selection Biases in LLMs

Large language models (LLMs) serve users whose cultural identities are multi-layered, yet cultural evaluations commonly reduce identity to one label. In this work, we study LLMs under simultaneous identity cues, with a country persona in the system prompt and a second identity signaled in the user turn, either by naming a country (explicit cue) or through the query language (implicit cue). We test World Values Survey (WVS) multiple-choice probing and open-ended sociopolitical generation. In multiple-choice probing, language cues shift answers about three times more than explicit cues but away from the associated country's distribution, and explicit cues do not consistently move answers toward the named country. Across 1.6 million open-ended responses, naming a country closes 30-44% of the gap to the model's own responses for that country, and explicit cues produce more differentiated value changes than language cues. Our results suggest that country and language cues should be evaluated as distinct signals rather than interchangeable proxies for culture.

cs.CL↗

arXiv2026

Eight new, triply eclipsing triple star systems

We have identified numerous triply eclipsing hierarchical triple star systems from observations made with the space telescope TESS. For many of these systems we can potentially determine their dynamical and astrophysical parameters with high precision, in some cases even without the use of radial velocity data. In the present paper we report the comprehensive study of eight new, triply eclipsing triple star systems, taken from this larger sample: TICs 77177148, 220456364, 232874003, 306690194, 343763382, 358795247, 379084660, 385933895. Besides the photometric data from the TESS observations, we obtained supplementary ground-based photometric measurements, as well as radial velocity measurements for some of the systems. The eclipse timing variation curves extracted from the TESS data and from the ground-based follow up data, the photometric light curves, radial velocity data, and the spectral energy distribution are combined in a complex photodynamical analysis to yield the stellar and orbital parameters for all of the investigated systems. The outer periods are in the range of 63-294 days, including the currently sixth tightest known triple system, TI 385933895 with an outer to inner period ratio of 9.82. We find that all of the triples are nearly coplanar, with mutual inclination angles of <=2.5deg. Despite such flatness, we still detect eclipse depth variations in TIC 220456364 that are induced by orbital plane precession. Moreover, we find that TIC 358795247 is likely a (2+1)+1-type hierarchical quadruple system. Finally we make some statistical assessments of a number of the orbital elements, stellar parameters, and distances of these systems.

astro-ph.SR↗

arXiv2026

The Reversible Intermediate Language L4, its Type System, and its Bidirectional Small-Step Semantics

L4 is a generic, reversible, linearly-typed, low-level language intended as an intermediate language for compiling reversible programming languages to reversible machines. L4 is intended to replace and generalise RSSA, a reversible SSA form. Inspiration is drawn from other low-level reversible languages such as HSSA. In this paper, we define L4 first informally, and then give a formal type system and a bidirectional small-step semantics for L4: The rules have direction indicators to determine forwards or backwards execution, and the direction can be changed during execution.

cs.PL↗

arXiv2026

Predicting Music Valence and Arousal via Textual and Multimodal In-Context Learning in Large Language Models

While arousal can be reliably predicted from musical audio, emotional valence remains difficult to model due to its subjective and context-dependent nature. In this work, we investigate the potential of in-context learning (ICL) with large language models to advance continuous valence and arousal prediction. Rather than predicting emotion scores directly, our framework leverages contextual demonstrations from an auxiliary dataset to refine the initial predictions produced by a small, task-specific base model. We systematically evaluate a text-based ICL variant using lyrics and generated audio captions, alongside a multimodal variant that incorporates raw audio signals. Using the entire MER60 dataset strictly as a held-out test set, our experiments demonstrate substantial performance gains, particularly for valence. Text-based ICL improves the valence $R^2$ from 0.110 to 0.427 with 80 demonstration shots, while the multimodal variant achieves 0.346 with only 10 shots. These findings suggest that training-free ICL offers an effective inference-time strategy for current language models to predict continuous music emotion.

cs.SD↗

arXiv2026

Almost tight distributed approximation for domination in sparse graph classes

We study distributed approximation of Minimum Dominating Set in a very general class of sparse graphs, extending near-optimal approximation results from the minor-free setting to bounded expansion. In more detail, for fixed $d$ and $\varepsilon>0$, we give a new deterministic constant-round LOCAL algorithm for Minimum Dominating Set on graphs of maximum average degree at most $d$ and bounded depth-one shallow-minor density. Its approximation ratio is $d+1+\varepsilon$, which is optimal up to $\varepsilon$ for every even integer $d\geq 2$. This improves on the recent results of Amiri and Siebertz by extending beyond proper minor-closed classes. From this algorithm, we derive almost-tight constant-round approximations for a number of graph classes. We get $(7+\varepsilon)$-approximations for planar graphs and graphs of bounded Euler genus, and $(5+\varepsilon)$-approximations for their triangle-free subclasses. This answers the $7+\varepsilon$ question of Wawrzyniak positively, and matches the recent results of Amiri and Siebertz. We also apply our algorithm to classes of bounded queue-number or stack-number, obtaining bounds that are tight up to $\varepsilon$. Additionally, we answer questions of Amiri and Siebertz about improving the dependence of the round complexity on $1/\varepsilon$ and obtaining polynomial local computation for fixed parameters, when comparing the output to the usual domination number.

cs.DC↗

arXiv2026

Feeder-Aware Service-Conditioned Flexibility of Grid-Interactive Buildings

Grid-interactive buildings can adjust demand, but building-feasible power budgets may differ from accepted feeder-head offers. This study addresses a practical question: to what extent can the adjustable electricity consumption of buildings be translated into deliverable flexibility services while satisfying feeder-head operating requirements? To enable a fair comparison, we adopt the same baseline operating schedule, equipment conditions, and post-service recovery requirements for indoor temperature and battery state of charge (SOC). Flexibility is then evaluated under two settings: one considering only building-level constraints, and the other additionally accounting for feeder-head constraints. We first use a simplified network model with explicit security margins to determine a preliminary amount of flexibility that can be committed. The resulting schedule is then validated using a more detailed network simulation via Modelica. If network losses cause the actual response to deviate from the committed response, the electricity consumption of individual buildings is rescheduled. If the required response still cannot be achieved, the committed flexibility level is reduced. The experiments cover multiple service activation times, regulation directions, and service durations. For comparative purposes, we retain the initial optimization result as the reference estimate of the maximum potentially available flexibility, rather than interpreting the failure of a particular subsequent realization attempt as evidence that a larger flexibility level is inherently infeasible. The results demonstrated that 24.51\% of nonzero flexibility contracts were rejected due to feeder-head constraints, 67.04\% of contracts were accepted with some rescheduling and building redispatch, and only 8.09\% of contracts were accepted without any changes.

eess.SY↗

arXiv2026

A Visual Grounding Benchmark for Retinal OCT with Multimodal Large Language Models

Multimodal large language models (MLLMs) can answer clinical questions about medical images, but trustworthy use requires visual grounding: models should localize the pathology they report rather than rely on spurious shortcuts. Whether MLLMs can visually ground clinical findings in retinal optical coherence tomography (OCT), the most widely used imaging modality in ophthalmology, remains largely unexplored. To address this gap, we introduce RetinaGroundBench, the first benchmark for biomarker visual grounding in retinal OCT. It comprises 815 B-scans exhaustively annotated by ophthalmologists with 4,537 bounding boxes covering 13 biomarkers across 8 diseases. Because MLLMs often fragment a single lesion into multiple partial predictions, we propose AggIoU, a metric that evaluates whether the union of overlapping predicted boxes covers the annotated region. Across 27 open-source MLLMs (0.8-38B parameters), we find that visual grounding remains largely unsolved: the best general-purpose model achieves a macro F1 of just 23.7%, with consistently low recall, despite reaching up to 73% balanced accuracy on biomarker presence questions. To narrow this gap, we developed RetGrounder, an MLLM fine-tuned using an OCT-specific strategy integrating grounding and textual information. Despite its relatively small size (8B), RetGrounder improves grounding F1 to 33.9%, a 43% relative improvement. Our benchmark provides a reference point for measuring progress in the field of OCT diagnosis with MLLMs.

cs.CV↗

arXiv2026

Mn-deficient Ni-Mn-Ga alloys: phase transitions, magnetic properties, and disappearance of martensitic transformation

Composition gradients formed during directional solidification are generally considered undesirable in Ni-Mn-Ga magnetic shape-memory alloys. However, these gradients offer a highly effective platform for systematically investigating composition-property relationships under identical growth and heat-treatment conditions. To exploit this approach, one Mn-deficient and two near-stoichiometric Ni-Mn-Ga oligocrystals were grown by optical zone remelting and characterized by X-ray fluorescence mapping, optical microscopy, X-ray diffraction, low-field magnetic measurements, and vibrating-sample magnetometry. The ingots contained elongated grains and axial compositional gradients, primarily associated with the redistribution of Mn and Ga, whereas Ni remained relatively uniformly distributed. The martensitic and premartensitic transformation temperatures generally increased with increasing Mn content. Neither transformation was detected between 10 and 400 K in specimens containing 20.5-22.0 at.% Mn, all of which had $e/a < 7.40$. In contrast, both transformations were observed in the investigated specimens containing at least 22.1 at.% ($e/a \geq 7.46$). This abrupt change may indicate a possible electron-concentration threshold for the occurrence of these transformations. Saturation magnetization increased with Mn content up to 25.8 at.%, reaching 96.9 A m$^2$/kg at 10 K and 71.2 A m$^2$/kg at 300 K. Post-growth annealing increased the transition temperatures and changed the low-field magnetic response. The results indicate that composition-gradient oligocrystals serve as an effective platform for investigating the relationships between composition, structure, and magnetic properties in Ni-Mn-Ga alloys with the same processing history.

cond-mat.mtrl-sci↗

arXiv2026

Shaping Near Fields of Photonic Modes into Topological Textures

Topological textures of light, such as optical vortices and skyrmions, are attractive for high-density information encoding and light-matter interactions. In the near field, topologies have primarily been realized using polaritonic platforms that inherently tie the platform to the material dispersion, limiting their footprint due to propagation losses and restricting their operating spectral region. Here, we introduce a route to shape near-field topologies through the geometric engineering of a dielectric metasurface supporting a quasi-bound state in the continuum resonance, without the need for polaritonic surface waves. In-plane deformation of our metasurface bars conserves their resonance-driven near-fields locally, so that the metasurface geometry and the polarization of the illuminating light dictate the generated topology. By shaping silicon unit cells into geometries with rotational symmetry, we create optical topologies with controllable topological charge. Using phase-resolved scattering-type near-field optical microscopy, we experimentally characterize high-purity resonance-locked topologies such as vortices in concentric rings and topological charge doublets in Archimedean spirals. In contrast to conventional topological textures, our purely photonic mode-based field textures are stationary standing waves in the radial direction, without relying on surface-wave propagation or polaritonic dispersion, establishing a spectrally and spatially scalable platform for near-field topological photonics.

physics.optics↗

arXiv2026

Functional Inference for the Mean Curve of Intraday Spot Correlation

In this paper, we propose estimators for the mean of the daily spot-correlation curves, which are robust to the presence of finite-variation jumps in the price processes. The related feasible functional central limit theorems for the proposed estimators have been established under a joint infill and long-span setting. Based on the asymptotic theory, we develop a test procedure for flatness of the curves. Monte Carlo simulations confirm the strong finite-sample performance of the estimators. In an empirical application to 30 U.S. stocks from 2010 to 2019, we find a pronounced end-of-day surge in the mean of correlation curves across diverse stock groupings, alongside substantial calendar-time non-flatness in the curves.

math.ST↗

arXiv2026

Moving Regions, Not Moving Objects: Segmenting What Actually Moves under Camera Motion

Moving object segmentation is conventionally formulated at the sequence and object level, marking an entire instance as moving once it moves anywhere in a video. Recent instantaneous formulations distinguish moving from stationary intervals, but still generate masks at the object level through frozen segmentation foundation models. Because the mask is derived from an instance-level proposal, motion is typically attributed to the entire instance rather than localized to the regions that actually move. Moreover, such instance-level predictions can produce masks even when no part of the instance is actually moving, revealing a more fundamental issue: motion is not an intrinsic property of an instance; it can only be determined by comparing at least two frames. Building on this premise, we introduce MoRSe, which jointly interprets two frames to predict a moving-region mask, distinguishing actual scene motion from apparent motion induced by the camera. A single RGB encoder replaces multi-stage foundation stacks, so no optical flow, point tracks, or separate mask generator is needed at inference. We release MoRSe-5K, a benchmark of 42 sequences with Static, Dynamic-Static, and Dynamic settings designed to evaluate the model's ability to distinguish and localize moving and stationary regions under different camera and scene motion conditions. MoRSe outperforms both specialized moving object segmentation methods and dense 4D reconstruction methods with motion-thresholded predictions, while producing the fewest false positives on sequences where nothing moves.

cs.CV↗

arXiv2026

Telling Neural Network What We Know: Weight Configuration for RNN and Its Application to NextG Real-Time Neural Receiver

AI-enabled receivers offer a promising approach to adaptive physical-layer processing, but conventional neural receivers often require extensive offline training and computationally expensive backpropagation, limiting their suitability for low-latency cellular systems. Moreover, classical MIMO-OFDM receivers rely on accurate channel estimation, leading to substantial pilot overhead. This paper introduces skip-RNN, a lightweight skip-augmented recurrent neural receiver that performs online time-domain receive processing using fixed recurrent and skip branches with an adaptive linear readout. We show that the skip-RNN can be interpreted as a structured deconvolution filter whose recurrent and skip branches realize IIR and FIR basis functions, respectively. We develop a domain-knowledge informed weight-configuration framework for the skip-RNN by exploiting its structural correspondence with the target stable and causal inverse filter of mixed-phase channels. The dominant IIR structure extracted from estimated channel statistics is used to configure the recurrent dynamics, while the non-minimum-phase component is addressed through the skip branch and a statistical FIR readout prior incorporated into prior-centered regularized online readout adaptation. We further derive an approximation-error bound that characterizes the resulting configured neural-receiver representation. Simulations over standardized 3GPP channels demonstrate the effectiveness of the configured neural receiver under extremely low pilot overhead.

eess.SP↗

arXiv2026

An algebraic proof of 1-extension

We give a new proof that 1-extension preserves generic local rigidity of bar-joint frameworks in all dimensions. The proof does not use a special position argument, which makes the approach potentially useful for X-replacement in 3-space. Instead, the balancing conditions recently introduced by the first author use directed paths in the line graph, and by modifying the end of these paths, we transfer a self-stress on the 1-extended graph back to the original. Treating the new vertex as variable allows us to extract the balancing condition of the smaller graph as one coefficient of a homogeneous quadratic. A Lean 4 formalization of the main results accompanies the paper.

math.CO↗

arXiv2026

Exact value functions and sensitivities for energy storage by fast Legendre transforms

Long chains of decisions coupled only through a bounded cumulative state, such as the operation of an energy-storage device, form convex programs that generic solvers treat as arbitrary sparse models. We present an exact solver for this class that propagates the complete convex cost-to-reach function through restrictions and infimal convolutions, each evaluated by fast Legendre-Fenchel conjugation as an ordered merge of slopes: no state grid, no value approximation, and a function rather than a single primal-dual pair as output. Price-taking storage instances of 5,000 to 15,000 hourly steps solve 35-93 times faster than Gurobi. The value function is what pays: exact capacity marginals in one differentiated pass, recourse assembled rather than sampled on scenario trees, and a certified sizing loop whose cut count does not grow with the number of weather years. We then widen the carried state. Two storages at one node can be eliminated rather than coordinated by carrying the value function of the pair, and we prove that this object is combinatorially rigid: every breakline the recursion realizes is vertical, horizontal, or borne by a line through the origin, with no genericity assumption. For d co-located stores sharing one charge-discharge coefficient, every realized wall is a position hyperplane or an indifference hyperplane {s_i = s_j}. A catalogue law maps where speed holds: one-dimensional chains beat the frontal solve by 15-30x and growing, while the planar block loses by 13.5-107x on rich network curves. Those curves are themselves exact, traced by a parametric dual simplex. The open-source package FLToptim, every benchmark driver and all data accompany the paper.

math.OC↗

arXiv2026

Hadamard states for linearized gravity on asymptotically flat spacetimes

We consider the quantization of linearized Einstein equations on 4-dimensional asymptotically flat spacetimes. We prove the existence of fully gauge invariant Hadamard states in the harmonic gauge for any Einstein metric asymptotic to the Minkowski metric in a conic neighborhood of spatial infinity.

math-ph↗